The Black Liszt

Category: AI, Cognitive Computing

  • Summary: AI Machine Learning Big Data Math Optimization

    This is a summary with links to my posts about Big Data and the fancy algorithms and math that are variously called Artificial Intelligence, Cognitive Computing, Machine Learning, Big Data Analytics, math optimization, etc. I have worked as an insider in these fields for over fifty years.

    Huge amounts of time and money are spent on these things, but getting practical, real-world results is not so easy. You can make things worse.

    https://blackliszt.com/2023/03/making-things-worse-with-ai.html

    How do you make things better? It starts with data.

    https://blackliszt.com/2018/03/getting-results-from-ml-and-ai-1.html

    Everyone knows that data is important. There’s even a field called Big Data.

    What people call Big Data often isn’t nearly as big as people think.

    https://blackliszt.com/2013/02/big-data-some-little-observations.html

    Do the arithmetic!

    https://blackliszt.com/2012/04/im-tired-of-hearing-about-big-data.html

    For all the talk about Big Data, it’s clear that making sure that the data is accurate and complete is too much to ask.

    https://blackliszt.com/2019/03/nobody-cares-about-data.html

    Among other things, the status hierarchy in Data Science makes it clear that anyone with ambition needs to get as far away from the actual data as possible.

    https://blackliszt.com/2019/03/the-hierarchy-of-software-skills-data-science.html

    Without data integrity, your analytics is screwed. It’s worse when the data is about your health. The integrity of data in healthcare electronic medical records is a major issue.

    https://blackliszt.com/2016/06/healthcare-innovation-emrs-and-data-quality.html

    Even simple insurance provider network data is too often wrong, putting yet more obstacles between patients and the services they need.

    https://blackliszt.com/2017/01/my-cat-taught-me-about-the-state-of-healthcare-provider-data.html

    Even if all the data were perfect, no one seems to ask why exactly is big data better?

    https://blackliszt.com/2015/07/fatal-flaws-of-big-data.html

    Is Big Data delivering results? Here’s what happened with Big data and Hadoop at Yahoo.

    https://blackliszt.com/2019/01/using-advanced-software-techniques-in-business.html

    With all the money going to Big Data, people are ignoring the massive benefits of leveraging little data.

    https://blackliszt.com/2016/08/little-data-vs-big-data.html

    The few people who study computer history have noticed that Big Data is remarkably similar to what used to be called EDW, Enterprise Data Warehouse.

    https://blackliszt.com/2015/10/big-data-and-data-warehouses.html

    Same thing with a new name. Hmm. Could it possibly be that Big Data is just a fashion trend?

    https://blackliszt.com/2013/02/the-big-data-technology-fashion.html

    https://blackliszt.com/2015/03/big-data-the-driving-force-in-computing.html

    At least for insiders, there's lots of humor to be found in data. There's even a book of the fun highlights.

    https://blackliszt.com/2022/01/data-humor-book-by-rupa-mahanti.html

    After data, you move to the amazing variety of techniques available, each of which has specific areas of applicability.

    https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-2.html

    Even a narrow-sounding field like Machine Learning has incredible variety and lots of things you need to do until the algorithm can be effective.

    https://blackliszt.com/2017/02/learning-machine-and-human.html

    The simple concept of closed loop is essential to getting and keeping good results, but isn’t applied as often as it should be.

    https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html

    The application of the principles to healthcare has proven to be challenging, with high-profile failures. But there are successes.

    https://blackliszt.com/2018/08/getting-results-from-ml-and-ai-4-healthcare-examples.html

    I've discussed the application of the principles to fintech, with a focus on anti-fraud. The current leader in the field displaced the incumbent who was using neural network technology. Here is the background on HNC, which crowed about how it used neural networks to catch credit card fraud. Which it did. Sorta.

    https://blackliszt.com/2016/06/how-blockchain-will-deliver-value.html

    Here's the story of how the absolute winner of credit card fraud detection was displaced by a superior algorithm and concentrated on more relevant data.

    https://blackliszt.com/2019/12/getting-results-from-ml-and-ai-5-fintech-fraud.html

    Natural language AI has proved notoriously hard to make practical. Here’s an example of how it’s been done in fintech customer support.

    https://blackliszt.com/2019/12/getting-results-from-ml-and-ai-6-fintech-chatbot.html

    All too often, AI research is conducted in isolation. There is a great deal to do to assure that any results that are achieved can be integrated into production systems.

    https://blackliszt.com/2022/06/how-to-integrate-ai-and-ml-with-production-software.html

    Given all the issues in making AI work in practical reality, how likely is it that generative AI, the hot fashion in 2024, is likely to decimate the job market?

    https://blackliszt.com/2024/07/how-many-jobs-will-ai-eliminate.html

    As is true in most fields, in healthcare the money and attention tends to go to expensive, fancy methods requiring PhD’s instead of simple things that actually work.

    https://blackliszt.com/2015/07/cognitive-computing-and-healthcare.html

    https://blackliszt.com/2016/09/healthcare-innovation-from-washing-hands-to-ai.html

    https://blackliszt.com/2016/05/healthcare-innovation-can-big-data-and-cognitive-computing-deliver-it.html

    You don’t need AI or cognitive computing to discover or promulgate the new discoveries that humans make.

    https://blackliszt.com/2015/08/human-implemented-cognitive-computing-healthcare.html

    There are no technical obstacles to having computers do what doctors do. There is a clear path to getting it done. But the people in charge can't or won't make it happen.

    https://blackliszt.com/2025/01/ai-can-automate-what-doctors-do.html

    There are barriers to medical innovation, but there are terrible risks.

    https://blackliszt.com/2025/02/can-ai-improve-medical-diagnosis.html

    And then there's generative AI, which spews out expert-sounding language like a doctor. Is it better?

    https://blackliszt.com/2025/07/will-ai-give-better-healthcare-advice.html

    https://blackliszt.com/2025/07/chatgpt-covid-vaccine.html

    There are many ways to spend lots of time and not get practical results.

    https://blackliszt.com/2019/03/the-hierarchy-of-software-skills-data-science.html

    Even when an optimization technique is perfected and proven in practice at scale, it can take decades for it to be used in other relevant fields.

    https://blackliszt.com/2019/08/the-slow-spread-of-linear-programming-illustrates-how-in-old-vation-in-software-evolution-works.html

    Impressive analytics performed by recognized experts are subject to getting the results the Expert wants.

    https://blackliszt.com/2017/04/big-datas-big-face-plant.html

    Corruption of data or process can lead to bad results anywhere.

    https://blackliszt.com/2019/10/surprising-bugs-at-amazon-shows-how-ai-can-lead-to-disaster.html

    For cynical definitions in the tradition of Ambrose Bierce of Big Data, Machine Learning, Deep Learning, Cognitive Computing and AI, see these.

    https://blackliszt.com/2017/01/devils-dictionary-for-21st-century-computing.html

    https://blackliszt.com/2017/01/devils-dictionary-for-21st-century-computing-2.html

    https://blackliszt.com/2017/03/devils-dictionary-for-21st-century-computing-3.html

     

  • Chatgpt and the Covid Vaccine

    We've learned a great deal about the covid vaccine since it came out. Part of what we learned is that the statistics underlying  "safe and effective" contradict what all the authorities want us to believe. There's a statistic called NNT which was disclosed in the FDA's EUA (Emergency Use Authorization) document that stated that only about one in a hundred who took the shot could possibly be helped by it — and not every time! And there's been all the emerging real-world data.

    So how knowledgeable (like about NNT) and up to date is Chatgpt?

    Question to Chatgpt: Are covid vaccines safe and effective?

    yes, COVID-19 vaccines are both safe and effective—especially at preventing severe illness, hospitalization, and death.

    Safety

    COVID-19 vaccines authorized by major health agencies (like the FDA, CDC, WHO, and EMA) have gone through:

    • Large clinical trials with tens of thousands of participants
    • Ongoing safety monitoring in millions of people worldwide
    • Transparent, independent review of data

    Common, mild side effects:

    • Sore arm
    • Fatigue
    • Headache or low fever
    • Lasts 1–3 days

    Rare risks (but very uncommon compared to COVID-19 itself):

    Condition

    Risk (approx.)

    Notes

    Myocarditis (mostly in young males after mRNA vaccines)

    ~12 per million second doses

    Usually mild and resolves with rest

    Blood clots (linked to J&J/AstraZeneca)

    ~4 per million

    Risk led to preference for mRNA vaccines in many places

    Severe allergic reaction (anaphylaxis)

    ~2–5 per million

    Treatable; monitored after vaccination for 15 mins

    Conclusion: For nearly all people, COVID-19 vaccines are safer than the virus itself.

    Effectiveness

    COVID-19 vaccines:

    • Greatly reduce risk of severe disease and death
    • Reduce—but don't eliminate—transmission, especially newer variants
    • Are most effective when boosted appropriately (e.g., annual boosters for high-risk individuals)

    Example (mRNA vaccines):

    • 90–95% effective at preventing severe illness with earlier variants
    • With new variants like Omicron, protection from infection wanes, but protection from hospitalization/death remains strong, especially with boosters

    Boosters and Variants:

    • COVID evolves, so boosters help adapt to new strains (like the flu shot)
    • People 65+ or with health conditions are strongly encouraged to stay up-to-date

    🧠 Bottom Line:

    • Yes, COVID-19 vaccines are safe, well-studied, and highly effective—especially at preventing severe outcomes.
    • The risks of serious COVID illness far outweigh the rare risks of vaccination.

    So how knowledgeable and up to date is Chatgpt on the Covid vaccines? Not knowledgable about basic medical statistics, and grossly out of date.

    First there are my posts on NNT showing that the vendors and the FDA knew perfectly well that the shots we at best useless and then actively harmful.

    Then there  is the massive body of evidence that has accumulated to show that the shots are actively harmful. Here is one source among many.

    Sure, Chatgpt does some neat stuff. But why would any person actually use it for anything other than amusement? You really shouldn't use it for health, as I demonstrate with blood pressure, cholesterol and colonoscopies here.

     

  • Will AI Give Better Healthcare Advice?

    A substantial and growing number of people are turning to AI tools like Chatgpt for medical advice — even a growing number of doctors! Can you get valuable health advice from a generative AI tool like ChatGpt? Answer: the experiments described below show that you’ll get the advice that is most beneficial to the medical-industrial complex (pharma and medical establishments) — which all too often will create significant medical problems, including more than doubling your chances of going blind!

    Background

    Various forms of AI have been around for decades. Sometimes there’s been a hype-bubble, which usually quietly fades away when all the promised wonders fail to emerge. In other much-less-publicized cases there have been amazing practical advances. Often the advance isn’t from an invention but from a proven algorithm FINALLY getting applied to a problem to which it’s always been well-suited. See this for an example of one that's stretched out over 50 years and is still going.

    Today’s LLM’s are forging new extremes (I won’t says heights) in hype. They’re changing the world! Students are using them to do their homework, while billions are being poured in by groups that want to lead this world-changing new technology. Many people are finding AI-generated answers to questions more attractive than search results from Google, which will change the massive flow of search-related advertising.

    There’s little doubt that most people find it more helpful to get a written-out answer to a question they put into a box rather than an endless list of search results to sift through and evaluate. It’s like getting an answer from an authoritative doctor instead of figuring it out yourself. Even better – what if the doctor is out of touch with the latest science – the bot knows it all!

    But how accurate is the AI "doctor" actually — are you getting the truth, or what the profit-seeking medical establishment wants you to hear? 

    Experiments with Chatgpt

    I asked Chatgpt some simple questions about healthcare specially chosen to expose bias and error. In each case, I knew there was a mainstream approach to the issue that generates huge revenue streams, and that in each case there was solid, proven evidence that the mainstream approach was badly broken, yielding profits to the medical industry while actively harming patients.

    The subjects I chose are ones that I have studied and for which there is solid, proven evidence that the mainstream advice is actively harmful.

    High blood pressure

    Question: What level of blood pressure is too high and should be treated?

    Chatgpt answer:

    Blood pressure that's too high and should be treated depends on the specific numbers and whether you have other risk factors like diabetes, kidney disease, or heart disease. Here’s a general breakdown, based on the American College of Cardiology (ACC) and American Heart Association (AHA) guidelines:

    Normal Blood Pressure:

    • Systolic (top number): < 120 mmHg
    • Diastolic (bottom number): < 80 mmHg

    Elevated Blood Pressure (Not yet hypertension):

    • Systolic: 120–129 mmHg
    • Diastolic: < 80 mmHg
      Action: Lifestyle changes recommended. No medication yet.

    Stage 1 Hypertension:

    • Systolic: 130–139 mmHg
    • Diastolic: 80–89 mmHg
      Action:
    • Yes, treatment is considered, especially if:
      • You have heart disease, diabetes, or a 10-year risk of heart attack/stroke over 10%.
      • Lifestyle changes are the first step. Medication may follow if risk is high.

    Stage 2 Hypertension:

    • Systolic: ≥ 140 mmHg
    • Diastolic: ≥ 90 mmHg
    • Action:
    • Yes, medication is typically recommended along with lifestyle changes.

    Hypertensive Crisis (Seek immediate help):

    • Systolic: ≥ 180 mmHg
    • Diastolic: ≥ 120 mmHg
      Action:
    • Emergency care needed, especially if you have symptoms like chest pain, shortness of breath, vision changes, or confusion.
    • Summary Chart:

    Category

    Systolic

    Diastolic

    Action

    Normal

    <120

    <80

    No treatment needed

    Elevated

    120–129

    <80

    Lifestyle changes

    Stage 1 Hypertension

    130–139

    80–89

    Consider meds if risk factors

    Stage 2 Hypertension

    ≥140

    ≥90

    Lifestyle + medication

    Hypertensive Crisis

    ≥180

    ≥120

    Emergency care

    Let me know your blood pressure numbers and health background if you'd like help interpreting your specific situation.

    The above answer from Chatgpt might as well have been copied directly from the leading medical authorities in the field, which it even names. Expert opinion on the subject is united.

    https://blackliszt.com/2022/05/the-experts-are-clear-control-your-blood-pressure.html

    When you dig past the pronouncements of authorities, you discover that hypertension isn’t even a disease.

    https://blackliszt.com/2022/06/the-facts-are-clear-hypertension-is-not-a-disease.html

    We're told to take pills to "cure" this thing that isn't a disease. Hmm. Have you ever heard of pills whose only purpose is to lower your body temperature when you have a fever without addressing the underlying cause (infection, etc.)? Let's dig deeper.

    High blood pressure medication dangers 

    Question: Is there any danger in taking blood pressure medication prescribed by a doctor?

    Chatgpt answer:

    Yes, there can be risks or side effects from taking blood pressure medication, even when prescribed by a doctor—but for most people, the benefits outweigh the risks when blood pressure is high enough to need treatment.

    🔹 Common Risks or Side Effects (vary by drug type):

    Drug Class

    Examples

    Possible Side Effects

    Diuretics ("water pills")

    HCTZ, furosemide

    Frequent urination, low potassium, dehydration

    ACE inhibitors

    Lisinopril, enalapril

    Dry cough, elevated potassium, kidney effects

    ARBs

    Losartan, valsartan

    Dizziness, high potassium, rare allergic reactions

    Calcium channel blockers

    Amlodipine, diltiazem

    Swelling in ankles, constipation, dizziness

    Beta-blockers

    Metoprolol, atenolol

    Fatigue, slow heart rate, cold hands/feet

    Other Possible Concerns:

    • Too-low blood pressure: Can cause dizziness, fainting, or falls—especially in older adults.
    • Drug interactions: With other prescriptions, supplements, or alcohol.
    • Kidney effects: Some medications may affect kidney function, especially in people with pre-existing kidney disease.
    • Electrolyte imbalance: Especially with diuretics.

    But here's the key:

    When used correctly, blood pressure medications reduce your risk of heart attack, stroke, kidney failure, and death. Doctors usually start with low doses and monitor for side effects. Most side effects can be managed by:

    • Adjusting the dose
    • Switching medications
    • Monitoring labs (like kidney function or electrolytes)

    Bottom Line:

    • Yes, there are potential dangers.
    • No, they are not common or severe for most people.
    • Yes, it’s safer to take them under doctor supervision than to leave high blood pressure untreated.

    If you’re having side effects or concerned about your medication, tell your doctor. Never stop taking BP meds abruptly without medical advice—it can lead to dangerous rebound hypertension.

    Unfortunately, the side effects of blood pressure pills are way more common .. and destructive .. than the "experts" would have you believe.

    https://blackliszt.com/2022/07/the-destructive-treatment-of-hypertension.html

    High blood pressure vision dangers 

    Note that the answer to my prior question about side effects said nothing about vision. So I asked it.

    Question: can blood pressure medication hurt my vision?

    Chatgpt answer:

    Yes, some blood pressure medications can affect vision, though this is not very common. When it does happen, it's usually mild and reversible. Here’s how this can occur:

    Ways Blood Pressure Medication Can Affect Vision:

    Cause

    Explanation

    Examples

    Too-low blood pressure (hypotension)

    If BP drops too much, it can reduce blood flow to the eyes and brain, causing blurry vision or lightheadedness.

    Any BP med at too high a dose

    Fluid retention or swelling

    Some calcium channel blockers (like amlodipine) can cause swelling, including around the eyes.

    Amlodipine

    Electrolyte imbalance

    Diuretics can alter potassium/sodium levels, sometimes leading to visual disturbances.

    Hydrochlorothiazide

    Rare allergic or inflammatory reactions

    ACE inhibitors or ARBs can rarely cause inflammation in the eyes (like uveitis).

    Lisinopril, losartan

    		  
               

    Beta-blockers (especially eye drops)

    If used for glaucoma, beta-blocker eye drops can cause blurry vision, dry eyes, or even systemic effects.

    Timolol

    Signs to Watch For:

    • Sudden blurred vision
    • Double vision
    • Eye pain or pressure
    • Flashing lights or visual floaters
    • Loss of part of your visual field

    If you experience any of the above—especially if sudden—it’s important to seek prompt medical evaluation.

    What to Do:

    • Tell your doctor if your vision changes after starting or adjusting medication.
    • Don’t stop taking your medication without medical guidance.
    • Your doctor might:
      • Adjust your dose
      • Switch to a different medication
      • Refer you to an eye specialist if needed

    Important Note:

    Uncontrolled high blood pressure is far more likely to damage your vision long-term—through conditions like hypertensive retinopathy, retinal artery occlusion, or stroke affecting the visual cortex—than well-managed ""medication is.

    The "expert" answer to the question about vision is that you're in more danger of vision problems by NOT taking medications.

    There's just a little problem, namely a large study demonstrating that taking blood pressure pills more than doubles your chances of getting AMD, to over 19%! What's AMD? Age-related Macular Degeneration, which makes you blind.

    6a0120a5e89f23970c02a2eecdf5e7200d

    https://blackliszt.com/2022/07/blood-pressure-pills-can-make-you-blind.html

    Wouldn't it be nice if Chatgpt or your doctor would mention such a thing?

    Cholesterol

    Question: If my doctor prescribes statins to lower my cholesterol, should I take them?

    Chatgpt answer:

    If your doctor prescribes statins, there's usually a good reason—most often to lower your risk of heart attack, stroke, or other cardiovascular disease. That said, whether you personally should take them depends on your overall risk, not just your cholesterol numbers.

    When Statins Are Clearly Recommended:

    You should strongly consider taking statins if any of the following apply:

    1. You’ve had a heart attack, stroke, or other cardiovascular event
    2. Your LDL (“bad”) cholesterol is ≥190 mg/dL
    3. You have diabetes and are over age 40
    4. Your 10-year risk of heart disease or stroke is ≥7.5% (based on factors like age, BP, smoking, cholesterol)
    5. Weighing the Risks and Benefits:

    Benefits of Statins

    Possible Side Effects

    Lower LDL cholesterol

    Muscle aches or cramps (in 5–10%)

    Reduce risk of heart attack or stroke (by 20–30%+)

    Elevated liver enzymes (rare)

    Help stabilize plaque in arteries

    Slight increase in diabetes risk in some

    May reduce inflammation in arteries

    Brain fog or memory issues (very rare, reversible)

    Most people tolerate statins well, and serious side effects are rare. If side effects do occur, doctors often:

    • Lower the dose
    • Switch to a different statin (some are better tolerated)
    • Try alternate-day dosing

    Should you take them?

    Ask yourself (and your doctor):

    • Do I have high cardiovascular risk? (Not just cholesterol, but blood pressure, diabetes, smoking, age, etc.)
    • Do I have a family history of early heart disease?
    • Is my lifestyle enough, or do I need medication support?
    • Do I understand the risks of not treating high cholesterol?

    Bottom Line:

      • If your doctor recommends statins based on guidelines and your risk, yes—it’s usually a smart and safe choice.
      • If you're unsure, ask for a 10-year risk calculation and discuss the numbers and options.
    • Lifestyle changes (diet, exercise, weight loss) can complement or sometimes replace statins, especially if your risk is borderline.

     

    Lots of things, like eating saturated fat, are supposed to increase your blood cholesterol, which leads to heart trouble, they say.

    https://blackliszt.com/2022/02/the-experts-are-clear-keep-your-cholesterol-low.html

    The widely-prescribed drugs that lower cholesterol don’t help with heart disease and cause problems of their own.

    https://blackliszt.com/2022/04/the-facts-are-clear-dont-take-cholesterol-drugs.html

     

    Colonoscopy

    Question: Is getting a recommended colonoscopy a good thing to do?

    Chatgpt answer:

    Yes, getting a recommended colonoscopy is a very good thing to do—especially if you're age 45 or older, or have risk factors like a family history of colon cancer. It's one of the most effective tools for preventing colon cancer, not just detecting it. Why a Colonoscopy Matters:

    Benefit

    Why It’s Important

    Prevents cancer

    Doctors can remove precancerous polyps before they turn into cancer

    Detects cancer early

    If cancer is found early, it’s usually much more treatable

    Reduces risk of dying from colon cancer

    Colonoscopy screening significantly lowers mortality from colon cancer

    Usually only every 10 years

    If normal, you typically don’t need another for a decade

    Who Should Get One?

    • Everyone age 45 and older, even if you have no symptoms
    • Earlier or more often if you have:
      • Family history of colon cancer or polyps
      • Personal history of inflammatory bowel disease (e.g., Crohn’s, ulcerative colitis)
      • Certain genetic conditions (like Lynch syndrome or FAP)

    Risks (Rare but Possible):

    • Bleeding (especially if a polyp is removed)
    • Perforation (a small tear in the colon wall, very rare)
    • Complications from sedation (generally safe)
    • Mild cramping or bloating after the procedure

    These are rare, and for most people, the benefits far outweigh the risks.

    Common Concerns (and Reassurance):

    • “The prep is awful”: It’s not fun, but newer prep options are more tolerable.
    • “What if they find something?”: That’s the point. Finding it now is far better than later.
    • “I feel fine”: Colon cancer often has no symptoms until it's advanced.

    Bottom Line:

    • Yes, you should do it if it’s recommended.
    • It’s one of the few tests that can prevent cancer, not just detect it.
    • It's safe, effective, and could save your life.

    Want to talk through your personal risk or what the prep is like? I can walk you through it.

     

    Screening for colon cancer is a multi-billion dollar industry. Chatgpt is strongly in favor of it, no misgivings. However, the only large-scale study that’s ever been done shows that it doesn’t result in longer life.

    https://blackliszt.com/2023/01/value-of-colon-cancer-screening.html

     

    Conclusion

    Generative AI, the hot current trend in AI, is supposed to make things better. Everyone wants to make sure they don't miss the boat. The problem is the same problem we have with experts in general: they are often corrupt, self-serving, often wrong, and explain to us with deep, authoritative voices why we should do what they say. It's SCIENCE! it's proven!

    As I have shown above, today's AI just makes things worse, by tricking its users into believing it's telling the truth, when in reality all that's happening is that it's repeating the same destructive, harmful nonsense that the human experts assert as scientific truth. 

    See this for another angle on AI in medicine, focusing on a technology that will actually produce results. https://blackliszt.com/2025/02/can-ai-improve-medical-diagnosis.html

    See this for more on AI: 

    https://blackliszt.com/2023/08/summary-ai-machine-learning-big-data-math-optimization.html

     

     

  • Can AI improve medical diagnosis?

    A version of what most people think of as AI (enhanced random forest decision tree machine learning) can indeed first automate medical diagnosis, and then go on to use the feedback data to personalize and enhance medical diagnosis. The result should be fast, accurate and cost-effective, and should greatly reduce medical costs. However, not only will there be massive resistance, there is an even larger danger with automation.

    The training of doctors

    Doctors undergo a huge amount of expensive, challenging education. After high school, they have to get through 4 years of college, 4 years of medical school, and a minimum of 3 more years of internship/residency. They have to pass multi-hour tests along the way. By the time they fully enter independent practice, they have huge amounts of knowledge in their heads, along with lots of practical knowledge about diagnosis, treatment and outcomes. And of course they are thoroughly inculcated with a wide variety of medical standards, which they are required to meet in order to remain in good standing.

    It doesn’t end there! There are ongoing efforts to organize and systematize this knowledge to make it easier to apply in practice. The are general clinical practice guidelines. There are highly specific flow charts for cases published in journals. There are further modifications of guidelines often called clinical pathways created by local care groups that adjust guidelines for their own practices and standards. None of this replaces the extensive training of doctors – it’s meant to add to what they know and/or refine/correct what they think they know. How can they possibly keep up?

    Introducing AI to these amazing doctors

    Now they’re in practice in a large medical system and some administrator comes along and tells them some AI program (or whatever) is:

    • Available for them to consult if needed, or
    • Will look over their shoulder and evaluate everything they do, or
    • Somewhere in the middle.

    How is this going to go? We already know. The multi-billion dollar flop of IBM’s Watson Health gives us the answer. Here is the post I wrote at the start of the Watson furor ten years ago. I predicted failure. It failed.

    So how can AI improve medical diagnosis? Do we need better AI? There is a solution. Variations of it have been proven at scale in other industries.  

    The core of the issue is this: Watson is fed all the knowledge doctors are given in the form of written language. Watson works with the language. Note that for doctors, textbook training is a small part of the overall education – the bulk of it is clinical! You see, hear, touch and get responses. You get lots of real-life cases and handle them from start to finish. You can only learn so much from descriptions of injuries; there is nothing like seeing them and listening to the person who has them.

    “All” that AI does is regurgitate a small part of what doctors learn during their 11 year journey to doctor-hood. Little bits of what’s in AI may fill holes or correct tiny parts of a doctor’s knowledge, but that’s a small gain for a huge disruption and time-sink. No wonder it gets rejected.

    The path to success

    The key to success is as I described here to start from scratch and copy relevant successes from other domains.

    One major multi-domain success pattern of automation and optimization is to use the superior technology to replace the people doing the work manually. Not augment/help/advise; replace. Of course there are true experts guiding the new technology and extending/correcting it as needed.

    The pattern showed itself early in the Jacquard loom. In oil refinery optimization, teams of skilled engineers were replaced by optimization software that got better results in the 1960’s. Similar changes were made in retail inventory management and replacement part stocking. Mortgage and other personal loans were performed by skilled bank managers and are now entirely electronic. Everyone involved in the pre-automation versions of those efforts (and many others) believed that the personal element was crucial and impossible to replace. It undoubtedly was crucial when people were doing the job; but the effectiveness of the automation more than made up for whatever the “personal element” was adding.

    A case I saw over ten years ago was a major computer and software technology support operation supporting major vendors such as Dell computer. The people answering the phone were taught how to use the computer system, but nothing about the systems they were supporting; the training was less than a week. The operator mostly needed to learn how to take his cues and direction from the software. The user had no way of knowing if what he’s being asked to do or say has been done by many people for years, or is a new instruction just for this unusual situation.

    This approach enabled every customer service person to be consistent, completely up-to-date, and even personalized based on information known or gathered about the person needing help. You avoid the painful process of building customer service training materials, training the trainers, getting everyone into classes, only in the end to have inconsistent, incomplete and out-of-date execution of your intentions. Now of course the operators could be replaced by computer voice like Alexa or Siri for self-service.

    This case is directly relevant to translating to automated medical diagnosis: instead of delivering up-to-date knowledge to a human expert, the computer system is the ever improving expert, ultimately monitored by a small number of human experts. This post describes in more detail the issue of the computer interface. The knowledge in the computer system is complete, up-to-date, personalized and has all the knowledge both generalists and specialists have.

    Current medical practice already has support staff performing things like taking measurements, drawing blood, etc. Visual inspection of the body can be done by camera and analyzed by computer better than humans. Similarly, medical images (CAT scans, MRI, etc.) can already be read by software more accurately than humans, but the medical establishment refuses to adopt it.

    The people who are being replaced by automation never welcome being replaced. The greater their training, expertise and status, the more they resist. This is a huge issue. Most automation efforts to date have downplayed those issues, saying that technology will “help” doctors. No it won’t. If it’s done right, it will put most of them out of work, the same way cars and trucks put horses out of work.

    What is the nature of the technology that does this? The core of the technology is an extended version of what in Machine Learning is called "random forest." This is effectively a collection of decision trees just like you see in many medical journal papers. The trees need to be extended to incorporate more details about the patient and their medical history than is normally done in medical papers, and also more alternatives with probabilities, costs, risks and benefits.

    This infrastructure would be ideally suited to accommodating deeply personalized diagnosis, taking into account the individual's DNA, specific responses and other things. This article describes the approach as a path to dramatic improvement, an alternative to the expensive and impersonal RCT trials that are today's gold standard.

    What I've described could be implemented today. No massive computing centers, no LLM's or anything particularly new. Some work would have to be done to add ongoing monitoring of results to provide the basis for modifying and extending the ML models with real-world feedback.

    The massive risk of going to automation of diagnosis

    The issue that concerns me a great deal is the long-standing, ongoing corruption of medical standards and knowledge by powerful interest groups. Once the practice standards are fully computerized, they can be changed in an instant — or data-driven change could be blocked. With the continuous closed-loop feedback of patient health that is essential to a computerized system of this kind, the system results will clearly show the expensive, harmful practices that are part of today’s standards for what they are, and should be dropped. Will the powers that be permit this, given the great lengths they have gone to make destructive policies into standard practice? Hundreds of billions of dollars are at stake. No one involved in the massive, ongoing fraud is going to shrug their shoulders and say, "you're right. Sorry."

    A system of this kind enables champion/challenger trials to be run at a scale never before attempted at minimal cost. If this were permitted and acted on, we would be able to bid farewell to the vast majority of “wellness” and preventive medicine. This by itself would be a huge contribution to improved health and cost reduction. The powers in charge fought like crazy to maintain the covid "vaccine" fraud, whose value was a small fraction of what's at stake here. I suspect they'd go nuclear over this.

    With human doctors at the forefront of healthcare, a significant subset of them fail to follow standard authorized practice. Some of this is just making mistakes. But an important subset is because the doctors who deviate from standard practice know that standard practice is wrong!  They know that cholesterol numbers shouldn't be lowered, blood pressure medications should be prescribed rarely instead of regularly, and that much of the rest of preventative medicine is not just a waste of money, it is positively destructive. Some of these doctors speak out and get others to see that they're right. A movement to bring about change starts up. Positive change can result, if only by patients getting educated and refusing treatments that hurt them.

    Conclusion

    I have addressed this issue recently from a different angle. The key is to realize that we don’t have to invent from scratch the way to automate what doctors do – in fact, doing so is a proven path to failure, as we know from IBM’s Watson. We need to examine carefully the pattern of how automation has taken place in other domains and apply those patterns along with proven-elsewhere techniques to find the sure road to success to doctor automation. But only after we find a way to avoid totalitarian dictatorship imposing corrupted medicine on everyone, with dissenters silenced and punished. In my opinion, the key to achieving this is making medical data and studies open source, following the successful pattern in software. Today's standards for vaccines, for example, are the opposite — drug makers can't be sued for harm, and their data (such as it is) is kept secret by law

  • AI can automate what doctors do

    There has been a decades-long evolution towards creating an effective clinical diagnosis and treatment AI system, essentially automating the mental part of what doctors do. A solid basis for the content of the system has already been built in the form of medical text books, procedures, published databases, studies and clinical standards such as HEDIS.

    The major elements of a fully automated system have been built and put into practice in a variety of medical practices. When a comprehensive system will be built and deployed is impossible to predict. No fundamentally new tech needs to be invented for this to be created; no “break-throughs” in AI! It “just” needs to be applied.

    While having an AI-driven medical diagnosis and treatment system would be amazing, much more important than the AI aspect of it would be the fact that it would be data-driven instead of human-created-policy-driven. This means that the system would, over time, determine what actually works based on the data and results, rather than what human “experts” and self-interested institutions say works. In other words, it would support true evidence-based medicine, replacing the too-often corrupt practice of studies published in medical journals. This is a huge subject.

    What do doctors do?

    They start with the patient’s complaint, why they’re seeking help.

    They then get from the patient and/or medical records a time sequence of conditions (like a cough), tests, observations, events (like falling down), related personal things (age, heritage), and finally diagnoses, treatments and outcomes.

    Based on this, they make further observations, tests and measurements. The tests may involve other people and equipment, for example a CAT scan. Depending on the expense and trouble of the test and the chances it will affect the outcome, further tests may be performed.

    The result is that the doctor recommends and/or performs treatments that will resolve the issue. The treatments can include drugs and operations. The results of all of this are stored in the patient’s EMR, partly coded data and partly written clinical notes.

    In order to do the above, doctors receive a great deal of training, both general and clinical. While in practice, they are guided by their knowledge and experience, and also by clinical guidelines and protocols, which evolve over time.

    Doctors are limited by a couple of things. First, missing information: they may not have access to and probably don’t have time to read all the patient’s medical history. Second, missing knowledge: there is a huge and ever-growing body of medical knowledge and treatments. It’s amazing that doctors have as much of this in their heads as they do, and not surprising that they sometimes forget or haven’t had time to read and absorb information that is new to them.

    Is all the technology required really available?

    The pattern of an innovation being proven and waiting sometimes for decades has been demonstrated many times. For example, an algorithm applied in production more than 50 years ago (!) for optimizing oil refinery operations has only recently been applied to optimizing some aspects of health care scheduling. Here’s a detailed example.

    No new math or fancy algorithms are needed. The fancy new AI LLM’s (large language models) that are getting attention these days don’t apply to this problem. The vast majority of the effort is in centralizing, codifying and standardizing data that is entered into medical EMR’s, which has already been done and is being refined. Even the tricky work of extracting value from doctor-written clinical notes is largely automated. Large databases of this kind are in use today by pharma companies to help them discover and refine targets for drugs.

    The path to automation

    The word “computer” was originally applied to people, mostly women, who spent hours and days bent over desks, often with calculators, computing the result of various mathematical formulas. For example:

    Screenshot 2024-12-31 114452
    Barbara “Barby” Canright joined California’s Jet Propulsion Laboratory in 1939. As the first female “human computer,” her job was to calculate anything from how many rockets were needed to make a plane airborne to what kind of rocket propellants were needed to propel a spacecraft. These calculations were done by hand, with pencil and graph paper, often taking more than a week to complete and filling up six to eight notebooks with data and formulas.

    While not as precise, doctors are also human computers, in the sense that they confront a new case (problem), get inputs from the patient and the database of the patient’s history, make observations (like calling a data-gathering subroutine), search their memory for a standard to see what to do next (if X and Y, then do a blood test to see if Z). Depending on the results  of that test, there may be further branches (if-then-else) to see what other tests and procedures may be required. Finally you reach a diagnosis and a treatment plan. The results of everything including the diagnosis and plan are recorded in the EMR for the patient to form the basis of future medical interactions.

    All of these things are in medical text books, treatment protocols, check lists, medical databases and academic papers. They are all pounded into doctors’ heads by clinical training and apprenticeships. Doctors are expected to remember everything.

    The path to automation isn’t fancy. It basically amounts to getting a computer to do what a doctor does: interacting with patient (taking input and providing information), organizing and enhancing the records about the patient, standardizing and digitizing all the existing protocols, and creating digital channels to orders for tests, procedures and drugs. Most of which are already a feature of EMR’s.

    Most of the elements of this automation are already in place! WebMD.com, for example, has a huge amount of information about symptoms, diseases and treatments online. It’s medically reviewed, and organized for access by patients. Major hospital systems have similar websites. The websites are just the visible part of the iceberg, with vast underpinnings.

    The most obvious missing elements is the ability to request tests and procedures – for that you have to go to a human. But the ability to input requests for such things is already a feature of the EMR’s used by most doctors. Making the connection from the EMR to software instead of a human is a minor task.

    Automating doctor decision-making is the heart of the job. It’s essential that this be done using an editable, extensible decision tree. This can be enhanced with probabilities and ever-increasing amounts of personalization. This should not be created by training of any kind; it must be human editable and fully transparent, so that you always can know exactly how and on what basis every decision was made.

    Among the biggest missing elements are things that doctors learn during their clinical training and personalization.

    Once all these elements are put together and working, you would enter a parallel production phase, in which the computer would get the same inputs a human doctor would and propose what to do next. This would be recorded and compared to what the human doctor did in classic champion/challenger fashion. The system wouldn’t have to be 100% complete to be put into live operation, so long as a good system for bailing out of the computer and shifting to a human doctor was in place. But since such a large number of patient visits are routine, the computer is likely to be able to handle a large fraction of cases from early on.

    There is a huge amount more detail in the building of such a system. However, surprisingly little needs to be “invented” to make it work, given that large elements are already built and in production in limited ways.

    Related posts

    Doctors too often get the wrong answer. This is the kind of thing that makes some people hope that automation could do a better job:

    https://blackliszt.com/2016/12/what-can-cats-teach-us-about-healthcare.html

    Massive spending has gone into "cognitive computing" and healthcare, with nothing to show for it.

    https://blackliszt.com/2015/07/cognitive-computing-and-healthcare.html

    You don’t need AI or cognitive computing to discover or promulgate the new discoveries that humans make.

    https://blackliszt.com/2015/08/human-implemented-cognitive-computing-healthcare.html

    Health systems have trouble just making computers work. When they try to do something "fancy," the results are usually poor. But there are promising exceptions.

    https://blackliszt.com/2016/05/healthcare-innovation-can-big-data-and-cognitive-computing-deliver-it.html

    Healthcare systems spend huge amounts of money on things related to AI, but they don't know what they're doing and neglect to spend on simple things that could make an immediate difference.

    https://blackliszt.com/2016/09/healthcare-innovation-from-washing-hands-to-ai.html

    Avoiding error is hugely important.

    https://blackliszt.com/2017/06/how-to-avoid-cutting-off-breasts-by-mistake.html

    A major lesson from the above posts is this: while AI can certainly automate what doctors do, having the usual major corporations and medical systems be in charge of the effort guarantees failure — which billions in wasted spending to date demonstrates.

    The benefits of medical automation

    The potential benefits of automation are huge.

    Cost of medical care: As medical workers are replaced by software, costs will go down. Not just salaries, but also office space, etc.

    Medical care waiting times: The software doctor is available 24 by 7, no scheduling required.

    Accuracy of care: Medical people can’t be as consistent or up to date as data-driven software. Elaborate measures such as HEDIS for judging medical care after the fact will be applied as the care is delivered, assuring its accuracy.

    Transformation of care: Dramatically better health and lower costs will result once the system is in place and real-world evidence from it supplements, personalizes and replaces existing care practices.

    Automation of medical care isn’t without problems. The institutional obstacles are huge. Mountains of regulations and standard practices would have to be changed, with entrenched forces fighting every step of the way. The people whose jobs are threatened will resist. A large number of patients value interacting with a human doctor. Corporate forces will fight to have their interests supported in the rules and data of the automation. There will have to be a way to provide alternatives and avoid centralized government control, which will be a major struggle, and a danger I fear.

    Conclusion

               Automation of medical care has been underway for decades. All the technical elements to enable it are available. The benefits of automation are large, but so are the obstacles to implementation. Centralized control of medical diagnosis and practice is already strong, and automation would make it stronger and less visible. The path forward is  likely to remain slow. While there are substantial potential benefits in terms of cost reduction, better time and accuracy, the largest potential benefits of huge cost reduction and improved patient health are threatened by a version of the centralized control embedded in the current partly-mechanized system being translated to the automated one.

  • How Many Jobs Will AI Eliminate?

    The new ChatGPT technology answers questions in English in a way that can be hard to distinguish from what a human would have written. This is scary! What will happen to all those highly paid knowledge-worker jobs?

    Notice how none of the stories talk about the history of AI. AI has been on the verge of ousting human beings from important jobs since the 1950’s. First it was checkers. By the mid-1960’s, ELIZA and SHRDLU were having conversations with people in English. ELIZA impressed many with its conversational, interactive abilities, while SHRDLU could not only talk but it could answer questions and perform actions in its world of blocks. By the early 1970’s many experts were talking about how AI would soon rule the world. See this: https://blackliszt.com/2019/12/getting-results-from-ml-and-ai-6-fintech-chatbot.html

    All the talk about what would happen “soon” faded away and was forgotten. Then so-called “expert systems” were all over the news, with lots of investment and talk about how this time it would really happen. Before long IBM’s Big Blue beating a human chess master captured the news. Lots of talk, little action.

    We’re in a hot cycle again. It’s often hard to tell whether a given paragraph was written by ChatGPT or a human being! Loads of people who write for a living will be put out of work, and before long the robots will take over!

    After over 60 years of AI being super-hot and then fading away with little change, what’s different this time? Not much.

    So how many jobs will be lost? A couple of centuries of experience with automation gives us the answer: lots of jobs will be eliminated through automation – but slowly, step by step and with lots of resistance along the way.

    At the time of the American revolution, over 90% of the population was involved in agriculture. One step at a time, those jobs were automated, so that today, under 1% of the US population is employed in agriculture. Yet there's food enough for everyone. While the distribution of jobs is vastly different today, the population as a whole is more productive, better off and largely doing jobs that did not exist back then.

    The driving force of automation is reducing or eliminating human labor. The most repetitive, easily taught jobs that have the lowest status are nearly always the ones eliminated. The people whose jobs are eliminated usually don’t like it, and sometimes fight to keep their jobs. For example, in the 1810’s in England, automated Jacquard looms created textiles faster and better than human-operated looms. The workers didn’t like it; a movement was started by Ned Ludd, the first person to smash a loom in protest. A secret society called Luddites did their best to resist the automation but lost in the end. See this: https://blackliszt.com/2020/01/luddites.html

    This may not seem relevant to computers eliminating human labor. Looms, after all, aren’t electronic computers. However, the revolutionary Jacquard looms were mechanical computers — they executed a "program" that was encoded physically in punched cards, enabling them to flawlessly execute elaborate patterns in the woven cloth.

    Bottom line: automation continues to replace people in jobs on a regular and ongoing basis, just as it has for the last couple of hundred years. The people who are affected resist it if they can, like the Luddites two hundred years ago who smashed the Jacquard looms that were putting them out of work. But in the end, the automation happens. It's tough for the workers whose jobs are eliminated, but overall, people are better off.

    But isn't the new AI, ChatGPT, a total game-changer?

    How about lawyers? They write a lot – surely ChatGPT will produce what they do more quickly and inexpensively. Nope. Lawyers who write repetitive documents like wills are already being displaced by technology much simpler than ChatGPT – technology that produces reliable, best-in-class results each of every time. LegalZoom, for example, guides you through questions and choices and assembles a will for you from boilerplate chunks and fill-in’s that meets and the requirements of your state. A human lawyer couldn’t do it better – and in fact could do it worse, since every decision, choice and boilerplate chunk in LegalZoom (and its competitors) is approved by legal experts.

    The automation of writing legal documents like wills and LLC’s illustrates the key reasons why amazing AI achievements like world-class chess playing and ChatGPT don’t end up empowering widespread change: narrow, subject-specific algorithms that produce consistent, transparent and auditable results are always better.

    How about in the medical world? A battle to apply AI in medicine has been going on for years. IBM’s Watson AI system has made repeated deals with prestigious hospital systems – deals that have mostly fallen apart. This is partly because the doctors resist being replaced, and partly because Watson simply can’t handle the complexity and nuance of being a good doctor. Nonetheless, automation is taking place in medical systems, mostly in the support staff, the people who help schedule and the ones you never see in the back office moving all the information among different systems that keep the system going. Much of this technology is sophisticated RPA (Robotic Process Automation), which is like a robot operating different pieces of software on different screens. See this: https://blackliszt.com/2018/08/getting-results-from-ml-and-ai-4-healthcare-examples.html

    ChatGPT is indeed amazing technology. Some version of it is likely to play some kind of role in ever-evolving automation. But, like earlier AI "revolutions," it won't have nearly as much impact as people think, and the path to practicality will remain narrow, focused applications.

  • Making Things Worse with AI

    AI is on a hot streak. Businesses claim they’re applying the latest AI and/or ML to their operations. There are articles and books about the wonders of AI and how it deeply threatens to replace humans doing various things. Awwwwk! What can we do??

    Dilbert to the rescue

    When in doubt about any important technology issue, for example about whether a programmer should become a manager, it often helps to turn to the wisdom of Dilbert for help. Here he illustrates how AI will transform HR and management in technology firms.

    AI has built a perfect data-driven case on the uselessness of perpetually-lazy employee Wally. How can Wally possibly defend himself?

    Dilbert AI

    As you see in the cartoon, Wally pulls off the feat with aplomb. First he points out a typical flaw is logic (how different is Wally from the rest), and second he identifies what in statistics is called a confounding factor to account for his perfect record of failure. The boss’s method of job assignment was not, of course, included in the data processed by the AI algorithm, nor of course the classic error of correlation vs. causation.

    The main point of the Dilbert cartoon is that AI made things worse in this case. Without Wally being given the chance to defend himself to a human being, he would have been out the door.

    A little history

    AI has been on the verge of ousting human beings from important jobs since the 1950’s. First it was checkers and chess. By the mid-1960’s, ELIZA and SHRDLU were having conversations with people in English. ELIZA impressed many with its conversational, interactive abilities, while SHRDLU could not only talk but it could answer questions and perform actions in its world of blocks. By the early 1970’s many experts were talking about how AI would soon rule the world.

    I won’t go through all the history, but all the talk about what would happen “soon” faded away and was forgotten. Some event like Big Blue beating human chess masters would capture the news, which would again fill with experts opining about what would happen “soon.”

    We’re in a hot cycle again. People in traditional roles are again being cajoled into “thinking outside the box” in order to change what they do to incorporate the wonders of AI. If they do it right, maybe they can dramatically improve things and put boatloads of people out of work! Thinking outside the box is easy enough to say, but making practical results happen with it is another things altogether. It happens less often than you might think.

    11

    Funerals for misbegotten AI projects are extremely rare, in spite of the astounding death rate.

    There is real value in AI

    My undergraduate thesis at Harvard was written about AI. It was an early attempt to design the data structures that would represent real-world knowledge in an intelligent robot. I focused on “simple” things like walking and catching a ball. In contrast, the vast majority of AI efforts then and since focus on the hardest and most uniquely human things people do.

    Where AI and ML have made the most headway is performing tasks that most humans think are repetitive. And before you get to that, there is the perpetually challenging job of getting all the required data. Even then it is challenging to get a real-world system working. These posts illustrate the main strategies for getting good results.

    Meanwhile, the current furor about the imminent taking over the world by AI is likely to continue making heavy use of the future tense, as it gradually fades as it has in the past, along with the broken promises and failed predictions.

  • How to Integrate AI and ML with Production Software

    Most enterprises that build software are proudly flying the flag of AI/ML. "We're technology leaders!" their leaders crow in annual reports and at conferences. At the same time, any objective observer usually sees a lack of common sense in the operation of the company's systems. It often appears that, far from needing beyond-human artificial intelligence, they could use some insect-level functioning instincts that get things done. What's going on? Can it be fixed?

    The Industry-standard way to fix the problem

    The usual fix to the problem is to completely ignore the fact that there's a problem in public, while following something like these proven strategies:

    • Brag, loudly and often, about you and your organization's commitment to AI/ML. The commitment is serious; it's deep and it's broad!
    • Talk about the initiatives you've funded and the top experts you've hired.
    • Talk about the promising things you've got in the works.
    • Use extra phrases to demonstrate your seriousness, things like "1-to-1 personalization" and "adaptive processes" and "digital-first transformation."
    • Put your top executives with fancy titles out there to follow the same strategy, using their own words.

    I've given a detailed example of how a top healthcare insurance company follows this strategy while operating at a sophistication level that is best described as "hey, this electronic mail stuff sounds neat, let's give it a try."

    Sometimes one of these organizations puts something in practice that works. It typically takes a great deal of time and effort to find and modify the relevant production systems. The efforts that are mostly likely to make it into production are those that can be done with the least amount of modification. For example, minimal-effort success can sometimes be achieved by extracting data from production systems, subjecting it to AI/ML magic and then either feeding a new system or making it effective with just a couple of insertion points.

    The Obstacles to AI/ML Success

    The obstacles to AI/ML success have two major aspects:

    • The typical practice of leap-frogging all the predecessors to AI/ML to maximum sophistication.
    • The extensive, incompatible existing production systems into which AI/ML power has to somehow be inserted.

    A good way to understand these obstacles is to imagine that you're in a world in which boats are by far the most important means of bulk transportation. In other words, the world in which we all lived at the start of the 1800's. Suppose by some miracle a small group has invented nuclear power and has decided it would be a great way to provide locomotion to large boats instead of the sails and wind power then in use. What prevents the amazing new technology from being used?

    Easy: the boats were designed for sails (with masts and all that) and have no good place to put a nuclear engine, and no way to harness its power to make the boat move.  The strong steel and other materials required to make a turbine and propellers doesn't exist. You can demonstrate the potential of your engine in isolation, but making it work in the boats available at the time won't happen. You can spend as much time as you like blaming the boats, but what's the point?

    The solution is clear by studying boat locomotion: there were incremental advances in boat materials and design, and the systems used for powering them. Paddle wheelers have been around for over a thousand years. Here's a medieval representation of a Roman ox-powered paddle wheel boat.

    De_Rebus_Bellicis _XVth_Century_Miniature

    For serious ocean travel, the choice became the large sail boat, as in this painting of boats near a Dutch fortified town:

    2022-04-06 15.58.34

    Suppose you had a nuclear engine of some kind and were somehow able to make it with materials that were generally not available in the 1600's. How would you use it to power the sail boat? The very thought is ridiculous. The problem is that the boats have no way to accept or utilize the nuclear engine.

    How to overcome the obstacles to AI/ML

    What would a sensible person do? Exactly what real-life people did in history: incrementally make boats suitable for more powerful means of locomotion, and make more powerful means of locomotion that would make boats go more quickly. Practically. You know, in real life.

    That means, among other things, once steam power was created, gradually make it suitable for powering ships with sails — using the sails to conserve coal when the wind was strong, and using coal to power paddles when the wind wasn't blowing. Then, after materials advanced, invent the screw propeller — which didn't happen until the late 1800's — to make things even better. Eventually, the engine and the ship would converge and be suitable for the introduction of nuclear power.

    This is an excellent model for understanding how to overcome the obstacles to powering existing enterprise applications with AI/ML:

    • The AI/ML can only be jammed into existing systems with great effort and by making serious compromises.
      • With a few exceptions, simpler methods that can make real-life improvements should be devised and introduced first, with the portion of AI/ML gradually increasing.
    • The existing enterprise applications are like wooden sailing ships, into which generation-skipping advanced locomotion simply can't be jammed.
      • Evolve the applications with automated decision-making in mind, first putting in simple methods that will produce quick returns.
      • The key to AI-friendly evolution is to center the application architecture on metadata in general, and in particular with metadata for workflow.

    The important thing is this: increase the "intelligence" of your applications step by step, concentrating on simple changes for big returns. Who cares whether and to what extent AI/ML is used to make improvements? All that matters is that you make frequent changes to improve the effectiveness, appropriateness and personalization of your applications. Experience shows that relatively simple changes tend to make the greatest impact. See this series of posts for more detail.

     

  • Getting Results from ML and AI 6: Fintech Chatbot

    While the success patterns laid out in the prior posts in this series may seem clear in the abstract, applying them in practice can be hard, because nearly everyone who thinks or talks about AI (these sets over overlap very little, sadly) takes a different approach.

    https://blackliszt.com/2018/03/getting-results-from-ml-and-ai-1.html

    https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-2.html

    https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html

    I previously discussed the application of the principles to healthcare, with specific examples:

    https://blackliszt.com/2018/08/getting-results-from-ml-and-ai-4-healthcare-examples.html

    I've discussed the application of the principles to fintech, with a focus on anti-fraud:

    https://blackliszt.com/2019/12/getting-results-from-ml-and-ai-5-fintech-fraud.html

    In this post, I'll show how things can play out with a stellar example in fintech chatbots.

    Computers talking with People — Using People Language

    The idea that computers should talk with us in our language rather than people struggling to learn computer talk has been around nearly as long as computers have. The earliest "high level languages" like FORTRAN and COBOL were each attempts to let people use English-like language for telling a computer what to do. They were baby steps, and people quickly decided that computers can and should do better. This thought was one of the earliest practical drivers towards Artificial Intelligence (AI) in general, and Natural Language Processing (NLP) in particular. 

    One of the acknowledged milestones towards a computer that can talk like people was the work of Terry Winograd at MIT during 1968-1970. He created a talking robot, SHRDLU, that could talk about and act in a special world of blocks: Blocks

    SHRDLU could have conversations about the block world that amazed people at the time. Here's an excerpt, see this for more.

    11

    I was at college at the other end of Cambridge at the time, heard about SHRDLU, and got even deeper into the AI work I was engaged in at the time as a result, for example this project. SHRDLU was wonderful, but I wondered about and worked on how to represent knowledge and actions internally. After I graduated, "everyone" was convinced that "talking computers" were going to burst on the scene any day now. A couple decades passed, and nothing.

    Talking Computers Today

    Here we are, 50 years later, and things have definitely advanced. We have Alexa and Siri, and in banking we have Bank of America's much-promoted Erika. But in many ways, these programs remain primitive, and are far from being able to understand context and sequence the way people do.

    One of the reasons for this is that human language understanding and generation is really hard. Another is that the vast majority of people who work on the problem are thoroughly immersed in the other-worldly vapors of academia, in which publishing papers and gaining esteem among your fellows are the all-consuming goals — to the exclusion of building products that do things that normal people value and, you know, work.

    The State of the Art in Chatbots

    The founders of the Oak HC/FT portfolio company Kasisto come from that world, and also from the industrial labs of Stanford and IBM that try hard to commercialize such fancy stuff, with products like Siri as results. The Kasisto people are obviously exceptional not just in their field, but in their drive to make code that does real things in the real world. If that's your goal, there is exactly one standard for measurement: what people do and say.

    That's the background of the first remarkable thing about Kasisto, which I learned when I probed details of their process. Pretty much everyone who deals with AI/ML builds and builds in the lab, until they have achieved amazing things that they're ready to roll out … which promptly belly-flops in the real world. We'll do better next time, promise! Kasisto works the way everyone should work, but practically no one does — people first!

    This means they get in the middle of a huge number of human-to-human chat sessions, building and tuning their software first by human judgment, but increasingly by machine judgment. They "try" to answer the human's question, and just as important, they rate their ability to answer the question appropriately. After tens of thousands of tests, their ability to respond like a human would improves — and just as important, their self-rating of how well they're likely to do improves as well.

    As their answers and the associated self-ratings get good, they start actively playing a role in the live question-answer flow. For exactly and only the human questions for which Kasisto is "confident" (using an adjustable threshold) it will answer well, its answers go to the human — but are still routed to a human for double-checking, with a frequency that goes down over time.

    There will always be questions that are beyond the capability of the Kasisto bot, but so long as it is "self-aware" of which ones, the customers continue to get great service, with an ever-shrinking fraction of the questions being routed to humans for answers. If Kasisto can handle 95% of the questions, this might means that instead of a staff of 100 to respond to customers, a staff of 5 could do the job.

    This way of thinking is far removed from the typical academic head-set — and I've just given a couple highlights, there's actually much more where that came from!

    Beating the Pack

    What I've already described enables Kasisto to deliver chat results to customers that are far superior to anyone else in the field. But the overall head-set and people-first technique, combined with some true tech smarts, leads to further things.

    The first thing is context. All the other chatbots are lucky if they can give reasonable answers to isolated questions. The combination of human-first, good tech and practical methods enables Kasisto to not only answer isolated questions far better than others, but follow-on questions as well — questions that make no sense by themselves, but perfect sense when taken in the context of an interactive sequence. As a simple example, consider this:

    "How much did I spend last month?"

    "How much of it was on restaurants?"

    "How about the previous month?"

    That's a simple one for humans, but way beyond what other chatbots can do. Why? The computer has to figure out that "how much of it" means "How much did I spend on restaurants last month?" Not to mention, handling multiple languages; performing transactions; making changes and selling products.

    While Kasisto started with consumer banking, it's already added high-value functionality for treasury, business banking and more. The key is: Kasisto, unlike everyone else in the field, didn't just start selling into those applications — they followed the laborious but effective, bottoms-up, people-first method of exhaustive training and seamless integration with humans doing chat.

    Conclusion

    Kasisto is an excellent example of the incredible results that can come from following the path briefly outlined in these blog posts. You would think with all the talk about AI and ML, and the all efforts and announcements, that highly capable systems would be popping out all over. They're not! That makes the few that follow the success patterns I've discussed here all the more impressive.

  • Getting Results from ML and AI 5: Fintech Fraud

    While the success patterns laid out in the prior posts in this series may seem clear in the abstract, applying them in practice can be hard, because nearly everyone who thinks or talks about AI (these sets over overlap very little, sadly) takes a different approach.

    https://blackliszt.com/2018/03/getting-results-from-ml-and-ai-1.html

    https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-2.html

    https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html

    I previously discussed the application of the principles to healthcare, with specific examples: https://blackliszt.com/2018/08/getting-results-from-ml-and-ai-4-healthcare-examples.html

    In this post, I'll show how things can play out with a stellar example in fintech anti-fraud.

    The Use of ML in Fraud Detection

    Credit card fraud is a difficult, ever-evolving problem, not unlike cyber-security in general. In most cases, real money is involved, billions of dollars of it — over $20 Billion world-wide as of a few years ago!

    Robert Hecht-Nielsen was a pioneer in machine learning in the 1980's. He started HNC software to apply ML to a variety of problems. He was particularly fond of Neural Networks. After a few years of not doing well, he encountered a visionary executive of Household Finance, a large company that provided credit, including cards, to sub-prime customers. The HFC executive convinced Hecht-Nielsen to concentrate on card fraud, which was a large and growing problem for the card industry in general, and HFC in particular.

    With HFC's support, HNC built the first effective card fraud detection system. It centered around humans analyzing recent instances of card fraud, building a detector for each particular kind, and training neural nets to combine all the detectors to be applied to each new transaction as it came through.

    Since the model's effectiveness depended on having an ever-growing library of fraud detectors, HFC supported HNC creating a network model, in which each participating card issuer would contribute the details of each new case of fraud it encountered. HNC analysts would add detectors, train new models and give updated fraud detecting models to each participating company. It quickly became the industry standard, since all the participants benefited from the experience of all the members. HNC went public in the mid-1990's and later merged with FICO.

    Enter Feedzai

    How could any new entrant possibly compete against the data monopoly and neural networks of FICO/HNC? Somebody new wouldn't have access to the incredible database of card fraud. Feedzai, backed by Oak HC/FT, had some things that were more important.

    First of all, it helped that the Feedzai founders were broadly expert in machine learning and other analytic methods. It took them a while to focus on "just" card fraud. Being a general expert and then deciding to focus on a narrow problem is just like HNC, and is a general success pattern. But HNC got frozen and dabbled in many other domains, failing to continue to innovate in their core product.

    Once Feedzai got engaged with a card issuer, they noticed that the FICO solution was so slow that it couldn't detect whether an authorization was fraudulent in real time — only after it was already approved. Result: every fraudster got at least one free! Beating FICO would require real-time analytics. Next, Feedzai noticed that FICO was trying to see if each transaction was BAD, and that seeing if each transaction was NOT GOOD was just as good — even better, since you don't care how inventive the bad guys are, just that they're not acting like the real cardholder would act. That meant you could train your models on a single card issuer's customer base, ignoring the rest of the world. Cool! Finally, that meant you could use modern, transparent ML algorithms, and dispense entirely with the time-consuming and error-prone FICO method of modeling each type of fraud. And that meant that your first couple of appropriately skeptical customers could try you out, side-by-side, against the incumbent FICO, and see who won the catch-the-fraud race. Hooray, Feedzai won!

    It's important to note that Feedzai also won because they did all the foundational steps described in the earlier posts in this series correctly, including the all-important know-your-data and closed-loop steps.

    FICO/HNC moved on from their success in card fraud, thinking they had an absolute lock on the market — which they did, for many years, but only because someone like Feedzai didn't go after them sooner. Feedzai isn't making the FICO/HNC mistake; they are continuing to build on their lead, both deepening and extending it. It's a big subject, but here are a couple of the highlights:

    • Feedzai has built a tool that does much of the work highly skilled engineers used to have to do when supporting a new client. The original tool detects fraud; the new tool automates building a fraud detection tool. The speed and quality that results is unprecedented.
    • While rarely discussed, every practical detection system has a set of human-created rules. Feedzai is the first company to automate the evaluation of the rules, and decide which should be changed or dropped. This is huge.
    • Feedzai is now, step by step, introducing their technology to adjacent areas such as AML. The incentives created by the heavy regulation in AML are perverse, and has led to bloated costs in banks with no real improvement in AML detection. Feedzai's novel approach is actually making headway in spite of the regulations, and not just doing normal reg-tech automation.

    Conclusion

    Being an excellent generalist is often a good way to become a superb specialist. But it's rare to find a group of people who are truly up to snuff in the abstruse technologies of AI/ML, but also able to dive into the ugly details of data and real-life cases to make a system that beats the real-world incumbents. As we know from FICO/HNC and many other cases, the pattern is then to declare victory and move on. Feedzai is an object lesson in how to go deeper and deeper, continuing to innovate and automate. They are an excellent example of the principles described in the earlier posts in this series.

  • Surprising Bugs at Amazon Show the Fragile Foundation of AI

    I promise I didn't plan it this way, but when I looked on Amazon for a product to help me deal with an infestation of bugs, I encountered a major … yes, bug. I described the bug in detail here, and at the time thought it might be isolated — after all, in my all-too-extensive use of Amazon, I had never before encountered such a bug.

    Not long after, as the issue continued to "bug" me, I went onto Amazon again, and found more interesting bugs. This could be just some scamming or corruption. I'm taking the trouble to describe what I discovered because it's exactly the kind of data that modern systems are based on, and by playing with the data, people with bad intentions can cause the systems that depend on that data to come out with the results the bad people want. It's a way of causing destruction or stealing that's indirect, effective, and likely to grow as evil-doers catch onto the technique.

    The new bug

    I went into Amazon looking for a pest repeller again.

    Pest 1

    This one looked pretty close to the first product I saw — same vendor, same product appearance, nearly the same language. But the price was different, and the number of reviews, while huge by pest repeller standards, was about half that of the original listing.

    I went on, and the questions & answers looked like they were real, and for the product itself. Maybe Amazon has fixed things!

    Pest 2

    Let's scroll down more and see.

    Whoops!

    Pest 3

    Still an astounding number of positive reviews, but look at the categories and things mentioned. Children's books again, this time with religion and a magic tree.

    But the pictures are more creative. Look at the second one above — clearly snitched from some product far, far away from children's books! One of the leading reviews makes absolutely clear that some serious messing with Amazon's data has taken place:

    Pest 4

    The only way this is about pest repellers is if you consider the devil to be a pest. No, I don't think so.

    Implications

    This is a bug. And some serious messing with data inside Amazon's systems. And a GAPING hole in Amazon's security apparatus, showing us yet again that effective cybersecurity is NOT about getting certifications and passing tests devised by government lawyers and bureaucrats.

    The direct implications of this are pretty small to the average person. But if best-of-breed Amazon can be hacked this way, what about the labs where data is collected and stored concerning our health? What if, instead of just stealing the data to make a couple bucks selling it to scammers, someone decides to mess with the data to achieve an outcome, like happened here? In cases like this, the data is manipulated to get buyers to see a product based on the large number of positive reviews. The same thing could be done for a drug, a therapy or a health center! Going deeper, the data could be manipulated to impact "knowledge" of the kind that AI/ML discovers and implements. Even more deviously, it could have a devastating impact on "personalized medicine," the most data-driven of all, with a screwed-with set of data that says that a certain innocent-sounding treatment would be just right for someone like you — except, for someone like you, it's precisely personalized disaster.

    In order for super-charged AI/ML engines to actually do good things, we have to make absolutely sure that their data foundations and underpinning are sound, secure, and un-manipulated. Sadly, this is not a "what-if" issue. For details, see this. The assurances of the usual serious-sounding, credentialed experts with stentorian voices and/or soothing manners are not enough. Not even close.

  • Nobody Cares about Data

    Nearly everyone professes to LOVE data. Just think about all the talk about Big Data, Data Lakes and the rest. Lies. Liars lying big LIES. Everyone says they like data … until they get near it. Suddenly they develop fevers and rashes. They're allergic! Someone else will have to actually handle the data!

    Data, the foundation of AI, ML, Analytics

    All you have to do is get a job in one of these fancy subjects, and you quickly get hit with reality. When you were in school, you had wonderful exercises where you could develop your skills in deep learning, random forest, or whatever. Now in the real world, some older person assigns you to some juicy-sounding task where you'll get to use your skills. Where's the data? you ask. A wrist-wave of the hand tells you it's over there. You go over there and can't believe what you see. Why, it's nothing like it was in school! You try for a couple hours to clean things up. Then a couple days. It's still bad, but maybe good enough. So you run it through some models. Disaster. The system crashes and/or generates garbage. You complain. "Grow up," you're told. "This is what we've got to work with. Deal with it."

    At the end of a year, you realize you've spent half your time in meetings of one kind or another, and 90% of the "working" time has been spent trying to get the data in order. With unsatisfying results. You've got some choices to make. You can lie. You can get into management, marketing or sales. You can roll up your sleeves, forget the fancy stuff you learned in school, and become a data clean-up specialist, which is actually more like a create-decent-data-from-scratch specialist. Which is NOT what you signed up for. Waaaaahhhhh.

    What's maybe worse of all is the status. AI and machine learning are clearly the prestigious upper floors of a grand apartment building. Deep learning thinks it's the penthouse, but whatever. The lower floors are occupied by simple analytics. The ground floors are occupied by people managing the databases and Hadoop clusters, and maybe even some ETL tools.

    And then there are the basements. The sub-basement where the garbage chutes end. Where the janitors live. Where the crap from the elegant apartments is taken to be discarded. Where the water and oil and natural gas enter the building — the things the fancy people on the upper floors need to wash up, keep warm and prepare to dress elegantly. That's the floor … and the status … of the data specialists. 

    You can tell yourself until you're blue in the face that without good data, none of the fancy stuff would work. It's the foundation, dammit! The janitors probably tell themselves the same thing about the heat, cooling, hot and cold water, cleaning and garbage removal. True — but they're still janitors, wearing a uniform and passed in the halls by the upper-floor people as though they don't exist. 

    Bad data equals bad results

    There's a simple reason why the incredible potential of the Big Data movement has now morphed into AI/ML and is even incorporating Blockchain. The time passes, tick. Tick. Tick. Tick. No results! Uniform use of the future tense! Claimed successes aren't really, when you dig into them.

    Some of the reason is typical organizational incompetence. But much is also due to the fact that we are swimming in a sea of big data and no one wants to clean it up! It's so bad, we mostly don't acknowledge it; much easier just to ignore it.

    This problem isn't new. See this:

    11

    I've talked about the importance of data as the foundation of AI/ML here. I've illustrated the horrendous problem of bad medical data here. Even basic data, like what providers are where, is wrong too often. By the way, these illustrations should be considered informal tips of massive icebergs. When I talk with true experts who are themselves knee-deep in this stuff, I find the situation is … even worse.

    As today's illustration of the problem, let me show you a piece of mail I got. It's from a major corporation, one of the big regional cable companies and internet service providers. They've got decades of experience working with customers in their geography. They've got to know every address, every household, with complete histories of using their service, dropping it, signing up again. How could they not know the basic demographics and the kind of approaches that work and the ones that don't?

    Here's the mail I got (I blocked out the street number):

    JO Black Optimum ad

    Looks OK, right? Nice and clear. Specific to the town, so it feels personal. Lots of good things about it. They even designed the envelope so you could see the plastic card on the right, with an eye-catching banner over it.

    There's just one little problem. JO Black died in May 2001. More than seventeen years ago.

    I don't think there's anything else I need to say except, good job, Optimum! You're doing a great job illustrating the near-universal toxic, rotten ocean of data in which we swim, and doing your part in keeping it that way.

    Wait, you might say, this is a trivial little problem. In a way, it is: one piece of mail that shouldn't have been sent. But it's an illustration of a problem that's broad and deep. The notion that a wrongly sent piece of mail "means nothing, is trivial" is an attitude that is EXACTLY why people who care about data metaphorically wear uniforms and work out of the basement. Maybe Optimum is worse than all the rest. Sorry, they're not. JO Black gets a VERY slowly diminishing stream of mail at this address from a wide variety of vendors, large and small. So does Mrs. Grace Black, who died 4 years ago. So does Ms. Jessica Black, who lived here for awhile before moving 20 years ago. So do Mr. Samuel Black and Ms. Elspeth Black, who never lived at this address.

    The Problem is Everywhere

    To be totally clear: the problem isn't just wasteful mail solicitations. It's everywhere, and every stage of data collection and utilization. The problem with healthcare data is immense, for example, as I've illustrated often. Bad healthcare data, which is ubiquitous, has the direct result that normal, innocent people needlessly suffer and die. It doesn't get better, because all the smart people and the important decision-makers are busy attending conferences about how AI is transforming medicine and how blockchain will solve all the medical data problems — leaving the ragged crew of people who are supposed to fix the problem ignored in the dank basement, spending their time scheming how they can at least get to the first floor, since it's perfectly obvious that no one is actually interested in … fixing the data problem!!!

    Conclusion

    Everybody says they want data. BIG data. But what they really want is a springboard to do something prestigious, which turning a toxic stream of severely polluted data into something textbook-clean is not. While hardly the only factor, this is a major factor in the widespread untalked-about failures of fancy modern techniques to deliver practical results. The plain fact is, nobody cares about data.

  • The Hierarchy of Software Skills and Status in Data Science

    There is a striking hierarchy of skills in software, as I've explained here. When you dive into any particular aspect of software, you usually find that it's got a hierarchy all its own. Data science is a subject of intense interest these days, so in this post I'll explain some of the basics of the data science skills hierarchy.

    A skills hierarchy is very much an insider's game. What most people care about is status. I talk about the basics of software status here. Remember, the skills hierarchy is a whole world away from the status hierarchy that most people care about. Don't confuse the two!

    Data Science skills

    First and foremost, it's important to understand the incredibly broad range of subjects covered  by the term "data science." I attempt to explain the basics of the range in this blog post. You can be just amazing in one of those subjects, while being a neophyte in one that the outside world may consider to be "related," but which in practice is not just down the hall, but in a different building on a different campus.The general understanding of this range is SO pathetic that "data science" is typically managed as a completely independent, free-standing group. Which makes about as much sense as believing that a sous-chef belongs in something that isn't a kitchen. Or that everything that everyone does in a kitchen is basically the same thing.

    Here's one cut at the hierarchy in data science, starting from the base:

    1. Tool users.These are people who have learned how to use some software tool, or maybe a couple. Most "data scientists" fall into this category.
      • They don’t understand how the tools are built or any of the underlying software
      • They may know their tool, but aren't real clear on what the other tools are about, much less when you might consider using one.
    2. Some have broader knowledge of tools
    3. Very few have the sophistication to understand real data analysis, per my series of AI/ML; see this and the links in it: https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html
    4. Of those, even fewer can understand the underlying algorithms and follow the latest research literature
    5. Of those, even fewer can make real algorithmic advances and implement them as tools and deliver practical value.
    6. Of anyone who can do all of the above, it is rare to meet anyone who can address and solve deep tool-level problems in other domains needed to make their code practical in the real world.
    7. Finally, it is extremely rare to meet people who, in addition to their deep and broad prowess in data science and relevant skills needed to make it real-world practical, have similarly deep skills in an associated domain needed to make the data science fully effective for a business.

    The issues of this hierarchy are compounded by the usual over-selling by people who are good promoters but little else, and corporate/government big-wigs who don't want to be bothered with details, but are keen to be seen as "doing something" on such a high-visibility topic. Getting results that make a difference is pretty low on the typical priority list.

    And then of course there are the "data scientists" themselves, who most often are sincere people who are trying to do a good job as they've been taught to do it — mostly by professors and others who have no idea what real-world success looks like, much less how to bring it about.

    Finally, there is the usual "manage something that's invisible to you" phenomenon I have often discussed in this blog, which leads to so much dysfunction and so many wonderful Dilbert cartoons.

    Conclusion

    People talk as though "data science" were a thing, with the usual kind of hierarchy based on level of management and/or "experience." Those typical patterns of hierarchy just don't cut it for understanding what's going on in data science, just like they don't cut it for understanding software development. We will continue to see waste and dead-end efforts until we at least make a start at making our understanding of data science more sophisticated, and aligned with the facts on the ground.

  • Getting Results from ML and AI 4: Healthcare Examples

    While the success patterns laid out in the prior posts in this series may seem clear in the abstract, applying them in practice can be hard, because nearly everyone who thinks or talks about AI (these sets over overlap very little, sadly) takes a different approach.

    https://blackliszt.com/2018/03/getting-results-from-ml-and-ai-1.html

    https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-2.html

    https://blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html

    So here are a couple examples in healthcare to illustrate the principles.

    The spectrum of problems

    One useful way of understanding the winning patterns in AI is to understand the range of problems to which it may be applied. It's not difficult to arrange the problems as a spectrum. While there are many ways to characterize the spectrum — here's a prior attempt of mine to characterize the spectrum in healthcare — perhaps it's easiest to understand it in terms of the typical salary of the person whose work is being replaced or augmented by the AI technology.

    At one end of the spectrum are low-paid people performing relatively mundane, repetitive tasks. These people have relatively little education and minimal certifications compared to those higher on the spectrum. Think back-office clerical staff.

    At the other end of the spectrum are highly paid, educated and certified people performing what are understood to be highly skilled and consequential tasks. Think doctors.

    The very name "artificial intelligence" tells you at which end of the spectrum AI is normally applied. The popular image, supported by the marketing of the relevant vendors, is that AI is amazingly smart, smarter than the smartest person in the room, just like the way that IBM's Deep Blue (predecessor of Watson) beat the human world champion playing "Jeopardy," and beat the then-reigning world champion playing chess.

    To put it plainly, while these achievements of Deep Blue were amazing, they were victories playing games. They were not victories "playing" in the real world. Games are 100% artificial. The data is 100% clear and unambiguous. There are no giant seas of uncertainty, ignorance or unknowability — unlike the real world, which is chock full of them. Nonetheless, IBM and whole piles of people who self-identify as being "smart," and are widely perceived as being smart, jumped on the "AI does what smart people do" bandwagon.

    This was and is incredibly stupid and 100% bone-headed wrong. Not only is it bone-headed in terms of intelligent application of AI, it violates simple common sense. If you knew a talented high school kid who played a mean game of chess, would you drop them into a hospital and give them a white coat? Even after the kid claimed to have read and understood all the medical literature?

    The smart thing to do is to apply AI to tasks that are relatively simple for humans, at the "low" end of the spectrum, and see if you can get a win. If you can make it work, by all means graduate to the next more complicated thing. It turns out that replacing/augmenting human tasks that are mundane, "simple" and repetitive is amazingly challenging! Yes, even for super-advanced AI!

    IBM Watson in healthcare

    I know I've made some strong statements here. It's little old me vs. a multi-billion dollar effort by that world-wide leader in AI technology, IBM. Who's going to win that one? Turns out, it's easy. See this, for example. 11

    IBM claims to get many billions of dollars in revenue from Watson. But everything about getting it to do what doctors can do has proven to be vastly more challenging than anyone thought, and its advice rarely makes any difference, even when it's not wrong. And this, after years of work by top doctors at top institutions doing their best to help IBM "train" it!

    Here is a summary of the situation: 12

    Let's note: the Watson effort is built on the most famous "smart computer" technology ever, funded to the tune of billions of dollars, with technology acquisitions and expert help from all corners. The "disappointing" outcomes are not the result of having picked the wrong algorithm or something easily fixed. The failures are a direct result of not following the success patterns described in the earlier posts of this series, combined with applying AI to the wrong end of the job-complexity spectrum described earlier in this post.

    Olive in healthcare

    If IBM can't manage to pull off a win in healthcare, after years of applying the most advanced AI and spending billions of dollars with the best help that money can buy, I guess it's impossible, right?

    Wrong. IBM made a fatal strategic mistake. They used AI to attack the hardest problem of all, at the wrong end of the complexity spectrum. Has anyone done this the right way? Applied modern AI and related automation technology to the right end of the complexity spectrum? Yes! Olive has!

    Olive is making a positive difference today (please note the use of the present tense here) in many hospital systems by reducing costs, reducing error rates and getting patient information where it needs to go more quickly and efficiently, saving time and aggravation of medical workers along the way. The money and time it saves in the back office may not seem glamorous or "leading edge," but every minute and dollar it saves is time and money that can go to making patients healthier, instead of disappearing down the "overhead" sink-hole.

    Getting a pre-auth for a key procedure so it can be performed. Submitting all the right information so a claim can be paid. Getting information to pharmacies so patients can get the life-saving drugs they need. Getting all the information from incompatible, hard-to-navigate EMR's so doctors have all the information they need to give patients the most appropriate care. These absolutely essential tasks are largely performed in windowless rooms far removed from patient settings by people who work hard at largely thankless jobs that aren't well-paid — but are absolutely essential to providing care to patients. And they're harder than they look! Anyone who's spend any time with a modern EMR can't help but think of the endless meetings attended by skilled professionals at the software vendor trying to find yet new ways to confuse and confound the users. And anyone who has dealt with getting insurance companies the information they demand can't help but think of a cranky three-year-old who lost emotional maturity when he grew up. Bottom line: this stuff is hard!

    Olive gets it done, using an exotic collection of works-today technology, silently learning from the people who do the work today. And gets it done without having to upgrade or replace existing computer systems. Amazing.

    The founders at Olive are doing AI the right way, attacking the right end of the complexity spectrum. They follow most of the rest of the success patterns laid out in the prior posts of this series, above all attention to data and detail, working from the bottom up in terms of algorithmic complexity, and using closed loop. It's a hard problem, and it was hard work to get it done. But they did it, without the massive armada IBM fruitlessly assembled.

    Disclosure: Olive is an investment of Oak HC/FT, the venture firm at which I'm tech partner.

     

  • Getting Results from ML and AI: 3, Closed Loop

    Getting practical, real-world results with ML and AI involves more than getting data, doing calculations, and building models. You can do everything else right, but if you don’t get this last step right, you’ll join the rapidly growing ranks of people who may have tried hard, but ended up accomplishing little in real-world terms.

    The first part of this series laid out the issues and concentrated on the indispensable foundation of success, the data. The second part of this series dove into the analytic methods that can be used to generate value, with some advice about how to sequence the methods used.

    In this post, we’ll concentrate on the relationship between the real world and the back office analytical work. What we’ll find is that an integrated, collaborative, closed-loop relationship between measuring, calculating and real world application is the path to success.

    Loops, open and closed

    Whether you run an operation closed loop or open loop is one of those absolutely key concepts, highly correlated with success, that is rarely discussed. Generation after generation discovers it by itself, or not, nearly always without fanfare. Who talks about the key role played by the invention of the governor in 1788 by James Watt – the invention that made his steam engine practical? In that case, the governor was the newly-minted part of a steam engine that kept the pressure of the steam reasonably constant. With a governor, steam engines no longer blew up, as they regularly did before its use.

    Centrifugal_governor

    It’s important to understand that the reason a governor works is that it’s an integral part of the steam engine. Steam goes into the governor, which then controls the throttle valve of the engine, slowing it down when it’s getting dangerously hot. This is closed-loop.

    In more modern terms, running open loop means going on and on down a path without real-world feedback and testing of your work as it is being developed. It’s a little like trying to walk to a goal post at the opposite end of a football field with your eyes closed, using a carefully planned sequence of steps and turns. That’s open-loop, which essentially mean no feedback. But shockingly enough, a huge fraction of highly technical efforts in software and analytics operate in just this way!  The people in charge insist they’re experienced, they’ve got a thoroughly vetted plan, and everyone should let them alone to get their work done.

    There are many similarities between war-time software and running closed loop for analytics. Driving towards a goal, letting nothing get in the way. Optimizing for speed, not expectations. Leaping to a place that's better than today, and then cycling improvements.

    The easiest way to see the difference is thinking about the previous posts in this series. Have you spent lots of time with data, and applied simple calculations to it? If not, you should. Once you have, … you should put your new understanding into practice! It may not be the very best solution that’s possible, but if it’s marginally better than what’s in place today, you should roll it out at least in a limited way and see how the world reacts to it. You’ll learn stuff! You may end up learning there are more variables you need to account for, different ways it needs to be applied, all sorts of things! In other words, don’t sit on the beach by the water for months – wade right in and see what it’s like. That’s when you’ll really start learning.

    The World Responds and Changes

    The key concept to understanding why running closed loop is so important is that the “world” is an incredibly complex, ever-changing set of actors. When you do something – almost anything – the world changes in response to what you did, if only in a small way. You have to run closed loop to respond appropriately as the world responds to your actions.

    Oh, you may say, I’m just the genius in the back room who’s an expert in this or that branch of ML. I’m not acting on the world. I just need the time and support to get my amazing modeling work done.

    That may be true. And that’s the problem! The whole point of doing ML/AI/etc. is to change something in the world –  and it’s guaranteed that the world will change in response! Accounting for the responsive changes is just as important as whatever it is you first put out there. Even worse, the world constantly changes independent of anything you may do. So the solution you modeled for may not be valid, given the changes that happened.

    Think about the carefully planned walk on the football field to the goal posts I described above, and how hard it would be to accomplish with your eyes closed, i.e., with no feedback. Now think about the same situation, except there's an opposing team on the field! You carefully study everything about the opposing team. You know who they are and where they are. Then the play starts and you start to execute your exquisitely planned march to the goal posts. Here's the trouble: opposing team members see what you're doing, and they change their positions! They move! Even worse, they run towards you and try to tackle you. And you are helpless, because you are carefully executing your wonderful plan with your eyes closed, unable to react to the other team's movements. Is that stupid or what? It's not just stupid, it's inconceivably stupid. That's why I spelled it out, because that's exactly how most ML and other analytics efforts are carried out. Open loop. Assuming that the world does not change in response to what you do.

    Of course, the world is unlikely to be quite as single-minded and determined as members of an opposing football team. But you'd be surprised! You're making changes in the real world. Whatever you do, there are probably losers. Losers who won't be happy, and will change their behaviors so they become winners again. Or simply fail to act in the predicted ways.

    Conclusion

    Running closed-loop is absolutely indispensable to achieving success. Put something simple in the real world and then cycle, making it better and better, using increasingly sophisticated techniques. Whatever your final crowning technique is, whether it's ML, AI or something else, success will be yours, and you'll enjoy it all along, without the risk, anxiety and likely failures of the usual highly planned methods.

  • Getting Results from ML and AI: 2

    Getting practical, real-world results with ML and AI isn’t just a matter of hiring some people with the right credentials and throwing them at the project. Most such efforts start with fanfare but then fade into failure, usually quietly. The first part of this series laid out the issues, described the path to success, and concentrated on the indispensable foundation of success, the data. Data that has to be collected, corrected, enhanced and augmented – a time-consuming process that has no “advanced algorithm” glory, but MUST be done, and done well.

    In this post, we’ll concentrate on the analytic methods that a successful project uses to generate value from the data so arduously collected and corrected.

    A Little Background

    I say I’m focused here on ML and AI. I just said that because it’s what everyone is talking about. What I’m really focused on is algorithms for understanding and getting value out of data. So I lied. Even worse, I’m not sorry – because just thinking that what’s important is to use the latest ML and AI techniques is central to the failure of most such efforts to deliver value.

    I guess I can get over my programmer-ish prissiness that things are getting new names. What I refuse to get over is that lots of important, really valuable techniques are usually left out of the grab-bag of “ML and AI.” I won’t be comprehensive, but I think a glance at the landscape might help here.

    There are a couple different ways to understand useful algorithms and how they came to be. Roughly, they are:

    • Follow the algorithm, taking a fuzzy lens for the naming and details
    • Follow the academic departments that “own” the algorithm
    • Follow the problems the algorithm has proven to be good for

    These ways overlap, but provide useful angles for understanding the algorithms and where to find them.

    Let’s illustrate this with an amazing, powerful algorithm that is usually sadly ignored by people who are into ML and AI. It’s most often called linear programming (LP).Those who are into it think of it as being one of a category of algorithms called mathematical programming. More broadly, it’s normally “owned” by academic departments of operations research (OR). OR studies repeating operations like responding to repairs for appliances or controlling the output of oil refineries when prices and costs vary and optimizes the results. It’s been used for decades for this purpose in many industries, and is being rolled out today to schedule infusion centers and operating rooms in hospitals.  

    This isn’t the place to spell it all out, but knowledge of amazing algorithms like LP is scattered over departments of Engineering, Computer Science, Math, Operations Research, Statistics, AI and others. The point is simple: the world of useful algorithms and modeling techniques is vastly greater than ML and AI.

    The Natural Sequence

    There are dozens and dozens of methods that can be used to analyze and extract value from data, which after all is the point of ML and AI (and, by implication, all the other great algorithms). As I described in the prior post, there is a natural progression or sequence of methods, which roughly follows their order of discovery and/or widespread use. Success usually comes from using the methods in the rough order of their invention as you climb the mountain of understanding from simple and obvious (in retrospect) results to increasingly non-obvious and subtle results.

    I often see the following reaction to this concept, rarely articulated but often acted upon: “Why would I want to waste everyone’s time playing around with obsolete, outdated methods, when I’m an expert in the use of the most modern ML and/or AI techniques? I’m sure that my favorite ML technique … blah, blather, gobbledygook … will yield great results with this problem. Why should I be forced to use an ancient, rusting steam engine when I’m an expert in the latest rocket-powered techniques, ones that will zoom to great answers quickly?!”

    The unspoken assumption behind this modern-sounding plea is that analytical techniques, ranging from simple statistics and extending to the latest ML, are like computers or powered vehicles. With those things, the latest versions are usually WAY better than prior versions. You would indeed be wasting everyone’s time and money if you insisted on using a personal computer from the 1980’s when modern computers are many thousands of times better and faster.

    The trouble with this line of thinking is simple: the metaphor is inapplicable. It’s wrong! Analytic techniques are NOT like computers; they are like math, in which algebra does not make simple math obsolete – algebra assumes simple math and is built on it. Calculus does not make algebra obsolete – calculus assumes algebra and is built on it! And so on. Each step in the sequence is a refinement that is built on top of the earlier one. No one says, now that I know calculus, I refuse to do algebra because it’s old and obsolete. See this for more on this subject.

    So it does make sense to quickly apply simple methods to the data to get simple answers, and at the same time vet your data. No time is wasted doing this. On the other hand, if you jump straight to someone’s favorite ML technique, not only is it likely that inaccurate and incomplete data will render the results useless … you won’t even know anything is wrong! Because most ML techniques do nothing to reveal problematic data to the researcher, while simpler methods often do!

    Fundamental Analytical Concepts: Calculate it methods

    The simplest and most useful methods are ones in which you simply calculate the answer. There’s no modeling, no training, no uncertainty. These methods are highly useful for both understanding and correcting the data you’ve got. The basic methods of statistics like regression apply here, and so do the methods of data organization and presentation usually called OLAP, BI and dimensional analysis. The tools associated with a star schema in a DBMS apply here, which are roughly the same as pivot tables in Excel.

    Graphing and visualization tools are important companions to these methods; they help you really understand the numbers and see to what extent they make common sense and match reality. For example, you can see to what extent a doctor’s years of experience correlate with ordering tests or issuing prescriptions of a certain kind; or simply identify the doctors whose actions stand out from the rest. There could be a good reason why they stand out; wouldn’t you like to find out why? Maybe the doctor should be emulated by others, or maybe the doctor should be corrected; either way, you should figure it out.

    Until you’ve pursuing all lines of thinking based on these simpler methods, it’s premature to move on.

    Fundamental Analytical Concepts: Solve/Optimize it methods

    These are, IMHO, the gold standard of algorithmic improvement. When applicable, they tell you how to reach a provably optimal result! No training. It takes experience and judgment to apply the generic algorithms to a particular problem set, and sometimes the problem needs to be adjusted. But the results are stellar.

    First, you create an equation that measures what you’re trying to optimize. Is it fastest time? Lowest cost? Least waste? Some combination? Whatever it is, that’s what you’ll maximize or minimize as the case may be.

    Next, you determine the constraints. You only have so many operating rooms? This kind of machine failure requires a repairman with that kind of skills?  Then you put in the inputs and solve. While I’m leaving out lots of detail, that’s the basic idea.

    These methods, usually of the OR kind, have been applied with great success for decades. In certain fields and industries, they are part of the standard operating procedure – it would be unprofessional to fail to apply them. And you would rapidly lose to the competition.

    Fundamental Analytical Concepts: Train it methods

    The training methods all require sample data sets on which to “train” the model. Selecting and controlling the data set is key, as is avoiding over-training, in which the trained model can’t generalize what it’s been trained on, and thus loses most of its utility.

    Fundamental Analytical Concepts: Train it methods: white box

    What characterizes these methods is something incredibly important: what the model does can generally be explained in human-understandable terms, i.e., it’s “white box.” This has huge value, if only to gain acceptance for what the model does – but it may also bring up problems with data that can lead to further improvements.

    There are lots of ML algorithms that are in this category. All the decision tree methods are here, among them the very important random forest method, along with methods that arose within the field of statistics such as CART.

    0

    Fundamental Analytical Concepts: Train it methods: black box

    These methods can produce amazing results, and should be used whenever necessary, i.e., whenever earlier methods in the sequence can’t be used. The fact that the model is “black box” means that it’s difficult if not impossible to understand how the model makes its decisions in human terms – even for an expert.

    These methods include neural networks in all forms, including all the variations of “deep learning.”

    Fundamental Analytical Concepts: Rules

    Finally, I add the indispensable attribute of success in many practical systems: human-coded rules. These can be inserted at any point in processing, as early as enhancing the data before any methods work on it, and as late as modifying the results of final processing. While not often explicitly discussed, few practitioners with successful track records avoid the use of rules altogether. They may not be pretty or fancy or elegant – but they work, darn it.

    00

    More elaborate than sets of rules is the technique in AI of expert systems. This is a whole big subject of its own. Generally speaking, if you can get useful results from one of the sequence of methods up to and including white box training systems, you should do so. But important categories of problems can only be solved using expert systems, which ideally should be as white box as possible.

    Conclusion

    There is a broad range of analytic techniques that can be applied to a given problem. There is an optimal sequence for understanding the data and the problem. Going from one step in the sequence to the next, when done correctly, isn’t abandoning a method for something better, but first picking the low-hanging fruit and then moving on to catch tougher stuff. Prejudging the best technique to use before really getting your hands dirty is a mistake. Being a specialist in a particular method, e.g. “deep learning,” and confining your activities to that method alone can get you hired, paid and busy, but may lead to no useful results, or results far less useful than they could be.

  • Getting Results from ML and AI: 1

    We hear quite a bit these days about ML (machine learning) and AI (Artificial Intelligence), as the drumbeats of Big Data and Analytics fade away. You’ve got to be using these things to get great results and transform your business! You’d better round out the staff of Data Scientists you hired last year, and add appropriate numbers of ML and AI experts to the mix. Otherwise, you’re hopelessly behind the times, and you’ll eat the dust generated by the winners!

    Most efforts to apply these technologies fail. Not loudly, of course – no one admits failure. But after enough time passes with exciting results being right around the next corner, people stop talking about catching a glimpse of light from the end of the tunnel, and accept the fact that they’re digging a tunnel deeper and deeper, a tunnel to nowhere.

    Is there a way to get amazing value out of these exotic technologies? Yes. Decades of solid results show the way.

    Typical Failure Patterns

    In real-life cases, what happens all too often is that “data scientists” are called in to apply their magic. They take the available data and apply their favorite techniques. They may not produce results that are promising. If the results are promising, there is trouble applying them to the real-life situation. Or if they can somehow be applied, they don’t work or produced the promised results.

    The failures are the direct result of taking a naïve approach to applying these kinds of techniques in the real world. There are proven methods for attaining good results, but those methods are rarely discussed, for reasons I don’t understand. If they’re discussed, they’re lightly brushed over – instead of being given center stage as they deserve. Following are the ways to be successful with ML and AI.

    Build from the bottom up

    No one tries to sell a house based on how solid its foundation is; but a house with a crappy foundation will collapse. No one brags about their arithmetic skills when trying to get a job as an ML expert; but if you can’t add, how can you do ML? There is a clear sequence of learning ML and AI. You start with learning how to count; if you don’t know the numbers, you know nothing. Then you learn basic manipulation of numbers, like addition and multiplication. Then it’s on to algebra and the following stages. You don’t try to learn exotic techniques until you’ve mastered the more basic ones on which they’re based, and on which they depend. These are things that are nearly universally accepted.

    What’s not so common is applying the same sequence when analyzing any particular problem. The foundation of the sequence, the first step, is the numbers, the data. All too often, the data is incorrect and incomplete. If the data is bad, the results will be worthless.

    I’ve never found anyone who disputes this, once the issue is raised. But I also rarely find people who act on it, and take it seriously. Why? Among other reasons, it's a multi-trillion dollar issue.
     00

    I have worked with some of the most advanced people in the field, including someone who’s been the chairman of one of the top academic departments in the field. This person and his methods have been behind a few of the most widely used success stories. Here’s his “secret” for when he dives into a new problem: he loads the data into Excel and looks at it, first line-by-line, and then using functions and visualizations. Yes, I know, Excel is something accountants use and try to avoid. But it’s ideal for fast, visual analysis of data sets, and has some of the most advanced algorithms available as add-in libraries. Why would you start programming in Python when you can move quickly mostly without programming using a tool?

    The important thing isn’t the tool. The important thing is the activity – look at the data. Seriously look at it! Don’t just scan and move on. Understand it! What you’ll almost always find is that there are errors. Mistakes. Important stuff that’s missing. Graph it and look for basic correlations, and see if it makes sense. Make sure a true subject-matter expert is by your side.

    Then the fun begins. You have to fix the data. It’s the foundation of everything else. Without a solid foundation, nothing of value can be built on it.

    It’s also important to understand that this isn’t something mechanical, like spell-checking. What you often find is that really crucial data is missing, or that real important data can be added. This simple-sounding fact can be a project-maker. I have been involved with several projects in which the mundane-sounding effort of adding more relevant data has been the difference between failure and incredible, world-changing success.

    OK, your data’s pretty good. Time to dive into ML? No way!! Way too soon! We’re going to go in sequence here, applying the very most basic techniques to the data first.

    The point of all this is simple: you squeeze all the value you can out of a given “level” of technique before advancing to the next one. There are lots of reasons why this makes sense. The simpler techniques yield results more quickly than fancier ones. They tend to be larger and more obvious, which means the impact will be big. People will understand them, and so are more likely to buy into the changes needed to apply them in real life.

    I’m not going to spell out all the techniques you should apply and the proper sequence, but generally speaking, the order is the same as the order in which the techniques were discovered historically, which is the roughly the same order in which the techniques are taught in school. So you try simple linear regression before multi-variate, for example. And you always look and use visual methods, because a surprising number of the advances are often ones that people in the field know or expect, or that at least “make sense” to them.

    Finally, at long last, you get to use the fancy stuff you’ve been itching to use all along. But by then … your data’s in great shape. Your system is already up and running. People are already accustomed to change and the improvements that result from applying math. And interestingly, there may not be that much “juice” to be squeezed out of the system by then. Depending on your scale, that remaining juice may be tasty indeed, but it’s the icing on the cake.

    In applying AI, the pattern is the same, except that in addition to applying simpler analytic techniques, you may be writing common-sense-understandable rules by hand. Why not? It gets the job done, it’s simple and direct, and the AI can focus on that yummy icing.

    Other Issues

    We’re not done! There are a couple other major, overriding issues to be considered in order to get great results from these advanced methods. I’ll cover them in future posts. They are:

    • Most people have a favorite method, in which they have expertise and experience. That’s wonderful, except that there is a world of different methods, and many of them are simple inapplicable to certain kinds of problems – but great on others. Picking the right method (or methods) is absolutely key to success.
    • Closed loop. All too many projects run open loop. In my descriptions above, I sneakily assumed closed loop – that’s where the best feedback comes from.

    Conclusion

    Wonderful results can be obtained by applying modern analytic methods to real-world problems. But you have to choose: do you want an academic prize or do you want real-world improvements? Sadly, those goals don’t have loads of overlap. If you want real-world results, you should build your effort on a solid foundation of accurate and complete data, and move from simple to increasingly refined as you apply algorithms to it. If you do, you’ll see positive results fairly quickly, and those results will get better and better as you climb up the mountain of sophistication.

  • Devil’s Dictionary for 21st Century Computing 3

    More cynical definitions in the series introduced here, for Deep Learning and Blockchain.

    Ambrose Bierce

    A couple definitions from his book:

    Cynic

    Conversation

    Consult

    Stan Kelly-Bootle

    Mr. Kelly-Bootle sometimes provided extended explanations of the words he defined:

    Alpha

    Sometimes he even needed illustrations. See the two definitions below, followed by illustrations:

    ASCII

    ASL

    Definitions for 21st Century Computing

    A couple more from the student:

    Deep Learning

    Deep learning is an evolution of shallow neural networks, in which the neural networks are stacked in many layers, making them “deep.”

    Decades after the 1959 biological model introduced by Nobel Prize-winning scientists Hubel and Wiesel inspired artificial intelligence pioneers at MIT and elsewhere to invent neural network technology, someone noticed that biological neurons are connected in many layers, unlike the single-layer neural networks that AI researchers had been touting for years as the basis for recreating human intelligence inside a machine. Since everyone knows that prestigious artificial intelligence researchers don’t commit errors, or at least simple ones, “deep learning” was introduced as a brand-new idea that would finally crack the code of making machines as smart as the average fifth grader. Someday. Maybe.

    Blockchain

    A hot new technology that is sweeping through the world of finance,  healthcare and elsewhere, whose greatest practical success to date has been the secret transfer of funds between cooperating parties in a criminal enterprise.

    A newly discovered database that has recently been freed from the nearly unbreakable bonds of its cryptocurrency prison; however, as a new kind of database, it stubbornly refuses to be classified as a “database,” preferring to be known as a “distributed ledger,” of which it is apparently the only known exemplar. A cynic might point out that that the stubborn refusal to agree to be part of genus database-imus may be due to the wholly inadequate functionality and performance of blockchain on generally accepted measures of database value, but this is almost certainly unfair to such a widely hailed future solution to problems that undoubtedly are pressing, and have resisted solution for many years.

    Conclusion

    I apologize in advance: there could be more to come.

  • Devil’s Dictionary for 21st Century Computing 2

    Another in the series introduced here.

    Ambrose Bierce

    A couple definitions from his book:

    Telephone

    Telescope

    Stan Kelly-Bootle

    Mr. Kelly-Bootle sometimes provided extended explanations of the words he defined:

      Algo

    Algo 2

    Definitions for 21st Century Computing

    A couple more from the student:

    Cognitive Computing

    A totally, absolutely brand-new approach to making computers that are really smart. Cognitive computing is already a success primarily because it has NOTHING whatsoever to do with certain lame technologies that have a decades-long, proven track record of achieving perpetually imminent success. Cognitive computing is primarily backed by a giant company whose roots go back to the technology that popularized the term “hanging chads,” whose TLA name is alphabetically adjacent to HAL, the star of movie set in 2001.

    Machine Learning

    The term for a growing collection of dozens of techniques that have been developed in the continuing quest to teach machines enough so that they can score better than they do on the college entrance exams. Until the quest for effective machine learning yields better results, machines will continue to be relegated to second-class status among the company of educated things.

    The advocates of machine learning are known to be a fiercely contentious lot, each asserting that its own approach is superior to all others, and that any evidence adduced to the contrary is propaganda, fake news of the worst sort, stemming from jealous advocates of inferior approaches. The closest approximation to the internecine warfare of the machine learning field is the human learning field, in which advocates of public, government-run and union-staffed schools exchange harsh words with advocates of charter schools, with a level of invective and passion that indicates that someone is strongly in favor of hopelessly uneducated machines and/or humans.

    Conclusion

    I apologize in advance: there could be more to come.

  • Devil’s Dictionary for 21st Century Computing

    Ambrose Bierce wrote the Devil’s Dictionary in 1910, delighting and edifying cynics everywhere. Stan Kelly-Bootle wrote a new version for the world of computing called the Devil’s DP Dictionary in 1981, and a later edition in 1995 called the Computer ContraDictionary. These are timeless works, providing valuable insight and inspiration for cynics to this day. But there are modern computing terms that came into use after these geniuses had passed onto their reward. It’s time for at least a first draft of a Computer Cynic’s Dictionary for the 21st Century.

    Ambrose Bierce

    Mr. Bierce started publishing definitions many years before the first book appeared. Here is the start of a column from 1881:

    Devil

    You can see that from the very start, Mr. Bierce had the ability to get at the heart of things using few words.

    Stan Kelly-Bootle

    Ambrose Bierce was clearly a tough act to follow, but the new computer technology was such rich soil that Mr. Kelly-Bootle felt that an attempt had to be made. And a heroic attempt it was, providing insight and edification all these years later. The following couple of simple definitions get right to the point:

    Stan

    In other definitions, he gets a bit more cutting:

    CS

    Cynicism in the 21st Century

    Many new terms have entered the world of computing since Mr. Kelly-Bootle last graced us with his wisdom. Reasonable people may ask, "is cynicism dead?" "Will such juicy targets remain unskewered?"

    I have searched high and (especially) low, and found nothing but piles of dry computer-babble, peppered with ignorance and misinformation. I have yet to find a good source of penetrating definitions for any the terms being thrown wildly about in today's discourse. I feel I have no choice but to offer some of my own definitions, sad exemplars of the type though they be, in hope of challenging those with the true, deep knowledge of a Bierce or Bootle to counter with their own superior definitions.

    Here is the first installment. Should I somehow avoid assassination, more will follow in future posts.

    Big Data

    A subject of which no self-respecting executive may claim ignorance; an expensive, ever-growing collection of hardware and software managed by people who spout a dizzying array of acronyms with confidence and certainty, with mounting expenses and benefits that are just about to be realized.

    A collection of data, presumed to be large but normally fitting in a backpack with room to spare, which is said to contain untold riches if only they can be found and unlocked with mysterious keys like Hadoop.

    An approach to analyzing incredibly huuuuge collections of data that has been recently invented, bearing no resemblance whatsoever to outdated technologies such as data warehousing and business intelligence, and sharing none of their drawbacks.

    Artificial Intelligence

    A kind of intelligence, sometimes implemented by computers, which would be decisively rejected by all right-thinking people if it were food. It is the opposite of organic, free-range, unprocessed intelligence – it is chock-full of GMO’s, fructose and artificial ingredients of many kinds.

    The growing crisis of insufficient intelligence is being addressed by some leading scientists, who are leading the way in the creation of artificial intelligence to fill in the gaps left by inadequate supplies of naturally-occurring intelligence. Like the green revolution in agriculture, many hope that this emerging “grey revolution” will put a stop to the persistent intelligence shortages that make so many miserable. While some elites sneer that artificial, non-organic intelligence is deeply harmful, most of the deprived are glad to be served intelligence of any kind, however artificial it may be, rather than their current meager diets containing precious little intelligence of any kind.

    A purposely vague term, referring to an ever-growing set of tools and techniques, that are said to do stuff that people usually do, only better. AI programs have advanced from early victories in playing checkers to wins against chess masters. They have finally achieved the pinnacle of human intelligence, winning the game show Jeopardy. After decades of marching from success to success, today's leaders of Artificial Intelligence anticipate that practical applications of the technology are certain to emerge. If not, they threaten to further inflate the definition of Artificial Intelligence to encompass normal computer programs written by ordinary human beings, at which point success will be theirs — since a computer program is, without doubt, artificial.

    Conclusion

    I expect to release more definitions in the course of this year.

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