AI vs IA: At the cutting edge of IT R&D

In truth, I've not paid all that much attention ongoing to developments in the really leading-edge IT R&D space. People have been waxing rhapsodic about the ostensibly ever-incipient promise of "Artificial Intelligence" (AI) since my code-writing days of the 1980's. Significant advances have seemed to always stay just around the corner.

My concerns have been more mundane, mainly commercial software "usability" and RDBMS design efficiency and effectiveness (i.e., "software QA"). As EHRs have evolved into providing at least rudimentary Clinical Decision Support functionality ("CDS"), such capability is really "IA" (Intelligence Augmentation) rather that "AI" (Artificial Intelligence). True, given advances in "neural net" software development, those separate concepts are beginning to blur, but, still, most of the applied work in the area focuses on IA, not AI.

Review my April post section regarding the Weeds' seminal book "Medicine in Denial."

Essential to health care reform are two elements: standards of care for managing clinical information (analogous to accounting standards for managing financial information), and electronic tools designed to implement those standards. Both elements are external to the physician’s mind. Although in large part already developed, these elements are virtually absent from health care. Without these elements, the physician continues to be relied upon as a repository of knowledge and a vehicle for information processing. The resulting disorder blocks health information technology from realizing its enormous potential, and deprives health care reform of an essential foundation. In contrast, standards and tools designed to integrate detailed patient data with comprehensive medical knowledge make it possible to define the data and knowledge taken into account for decision making. Similarly, standards for organizing patient data over time in medical records make it possible to trace connections among the data collected, the patient’s problems, the practitioner’s assessments, the actions taken, the patient’s progress, the patient’s behaviors and ultimate outcomes...

Larry Weed's digital POMR (Problem Oriented Medical Record) focus has clearly been that of applied IA.

My May 22nd post "The Robot will see you now..." cites Martin Ford's bracing new book 'The Rise of the Robots." That book takes us off more in the direction of the implications of increasing (and sometimes troubling) "AI."

Well, my new Harpers issue arrived.

Interesting article therein.

The Transhuman Condition
By John Markoff, from Machines of Loving Grace, out this month from Ecco Books. Markoff has been a technology and business reporter for the New York Times since 1988.

I look forward to getting this book when it's released next month.

Some excerpts from the Harpers piece:

Bill Duvall grew up on the peninsula south of San Francisco. The son of a physicist who was involved in classified research at Stanford Research Institute (SRI), a military-oriented think tank, Duvall attended UC Berkeley in the mid-1960s; he took all the university’s computer-programming courses and dropped out after two years. When he joined the think tank where his father worked, a few miles from the Stanford campus, he was assigned to the team of artificial-intelligence researchers who were building Shakey.

Although Life magazine would later dub Shakey the first “electronic person,” it was basically a six-foot stack of gear, sensors, and motorized wheels that was tethered — and later wirelessly connected — to a nearby mainframe. Shakey wasn’t the world’s first mobile robot, but it was the first that was intended to be truly autonomous. It was designed to reason about the world around it, to plan its own actions, and to perform tasks. It could find and push objects and move in a planned way in its highly structured world.

At both SRI and the nearby Stanford Artificial Intelligence Laboratory (SAIL), which was founded by John McCarthy in 1962, a tightly knit group of researchers was attempting to build machines that mimicked human capabilities. To this group, Shakey was a striking portent of the future; they believed that the scientific breakthrough that would enable machines to act like humans was coming in just a few short years. Indeed, among the small community of AI researchers who were working on both coasts during the mid-Sixties, there was virtually boundless optimism...

Late on the evening of October 29, 1969, Duvall connected the NLS system in Menlo Park, via a data line leased from the phone company, to a computer controlled by another young hacker in Los Angeles. It was the first time that two computers connected over the network that would become the Internet. Duvall’s leap from the Shakey laboratory to Engelbart’s NLS made him one of the earliest people to stand on both sides of a line that even today distinguishes two rival engineering communities. One of these communities has relentlessly pursued the automation of the human experience — artificial intelligence. The other, human-computer interaction — what Engelbart called intelligence augmentation — has concerned itself with “man-machine symbiosis.” What separates AI and IA is partly their technical approaches, but the distinction also implies differing ethical stances toward the relationship of man to machine...

...[T]oday, AI is beginning to meet some of the promises made for it by SAIL and SRI researchers half a century ago, and artificial intelligence is poised to have an impact on society that may be greater than the effect of personal computing and the Internet...

...[T]he falling costs of sensors, computer processing, and information storage, along with the gradual shift away from symbolic logic and toward more pragmatic statistical and machine-learning algorithms, have made it possible for engineers and programmers to create computerized systems that see, speak, listen, and move around in the world.

As a result, AI has been transformed from an academic curiosity into a force that is altering countless aspects of the modern world. This has created an increasingly clear choice for designers — a choice that has become philosophical and ethical, rather than simply technical: will we design humans into or out of the systems that transport us, that grow our food, manufacture our goods, and provide our entertainment?

As computing and robotics systems have grown from laboratory curiosities into the fabric that weaves together modern life, the AI and IA communities have continued to speak past each other. The field of human-computer interface has largely operated within the philosophical framework originally set down by Engelbart — that computers should be used to assist humans. In contrast, the artificial-intelligence community has for the most part remained unconcerned with preserving a role for individual humans in the systems it creates...

...Google mined the wealth of human knowledge and returned it in searchable form to society, while reserving for itself the right to monetize the results.

Since it established its search box as the world’s most powerful information monopoly, Google has yo-yoed between IA and AI applications and services. The ill-fated Google Glass was intended as a “reality-augmentation system,” while the company’s driverless-car project represents a pure AI — replacing human agency and intelligence with a machine. Recently, Google has undertaken what it loosely identifies as “brain” projects, which suggests a new wave of AI...

...[I]t is becoming increasingly possible — and “rational” — to design humans out of systems for both performance and cost reasons. In manufacturing, where robots can directly replace human labor, the impact of artificial intelligence will be easily visible. In other cases the direct effects will be more difficult to discern. Winston Churchill said, “We shape our buildings, and afterwards our buildings shape us.” Today our computational systems have become immense edifices that define the way we interact with our society...

AI and machine-learning algorithms have already led to transformative applications in areas as diverse as science, manufacturing, and entertainment. Machine vision and pattern recognition have been essential to improving quality in semiconductor design. Drug-discovery algorithms have systematized the creation of new pharmaceuticals. The same breakthroughs have also brought us increased government surveillance and social-media companies whose business model depends on invading privacy for profit.

Optimists hope that the potential abuses of our computer systems will be minimized if the application of artificial intelligence, genetic engineering, and robotics remains focused on humans rather than algorithms. But the tech industry has not had a track record that speaks to moral enlightenment. It would be truly remarkable if a Silicon Valley company rejected a profitable technology for ethical reasons. Today, decisions about implementing technology are made largely on the basis of profitability and efficiency. What is needed is a new moral calculus.

'eh?

Any import here with respect to the topic of my prior post "Personalized Medicine" and "Omics" -- HIT and QA considerations? My earlier citations of Nicholas Carr's book "The Glass Cage"? My citations of Morozov's compelling book "To Save Everything, Click HERE"? Peter Thiel's book "Zero to One"? Simon Head's "Mindless"?
__

BACK DOWN TO EARTH

Meanwhile, in the Health IT trenches:

Physicians Vent EHR Frustrations
Lena J. Weiner, for HealthLeaders Media, July 21, 2015

The American Medical Association gives physicians a platform to air their grievances about electronic health records systems, but the technology is here to stay, says an executive with the College of Healthcare Information Management.

Longstanding physician dissatisfaction over electronic health record systems, Meaningful Use, and the federal regulations behind them lit up a town hall-style meeting Monday night, hosted in Atlanta by the American Medical Association and the Medical Association of Georgia and webcast live.

Rep. Tom Price, MD, (R-GA), formerly medical director of the orthopedic clinic at Grady Memorial Hospital in Atlanta and co-host of the town hall kicked things off with one specific complaint of doctors, "inconsistency is a problem." The event was part of the AMA's Break the Red Tape campaign, which aims to postpone the finalization of MU Stage 3 regulations.

AMA President Steven J. Stack, MD, told attendees that the meeting was an opportunity for them to be heard. "This is not for you to hear me talking to you, but for me to hear you talking to me... Has workflow in your office changed?" he goaded the crowd. At least 80% raised their hands. A sole hand remained raised when Stack asked if the change was for the better.

Almost immediately, physicians gave voice to the barriers to care they say are caused by electronic health records systems. Over the course of the 90-minute meeting they raised concerns over reduced productivity, the security of private patient medical records, interoperability, and government regulation.

"We're removing the science from medicine," said one physician who described having to check "yes" and "no" boxes rather than being able to note subtle nuances his patients reported.

"We're removing the science from medicine," said one physician who described having to check "yes" and "no" boxes rather than being able to note subtle nuances his patients reported.

"Thank God I learned to type in high school—I never thought I'd use it," said another, explaining that she now has to make sure every employee she hires can type, regardless of the job for which they are hired.

Some physicians tweeted their frustrations during the meeting, using the hashtag #fixEHR...

"We're removing the science from medicine"

Well, coding and categorical check-box documentation are inescapably what I call "lossy compression." To what extent "nuance loss" is "unscientific" is not all that clear, though. Subjective "nuance impressions" may really come more under the "Art of Medicine."

ERRATUM

THANKS

___

More to come...

"Ethical Artificial Intelligence?"

When we have yet to even get to consistently ethical human intelligence?

Two (of four) of my current book reads. Stay tuned. Timely, important material.

 Note: Henceforth you are able to click on book cover images to go straight to their respective purchase info sites in a new browser window (usually Amazon). 

For openers, succinctly on "ethics."

Ethics

The field of ethics (or moral philosophy) involves systematizing, defending, and recommending concepts of right and wrong behavior. Philosophers today usually divide ethical theories into three general subject areas: metaethics, normative ethics, and applied ethics. Metaethics investigates where our ethical principles come from, and what they mean. Are they merely social inventions? Do they involve more than expressions of our individual emotions? Metaethical answers to these questions focus on the issues of universal truths, the will of God, the role of reason in ethical judgments, and the meaning of ethical terms themselves. Normative ethics takes on a more practical task, which is to arrive at moral standards that regulate right and wrong conduct. This may involve articulating the good habits that we should acquire, the duties that we should follow, or the consequences of our behavior on others. Finally, applied ethics involves examining specific controversial issues, such as abortion, infanticide, animal rights, environmental concerns, homosexuality, capital punishment, or nuclear war.

Notwithstanding my long (albeit late-blooming) white-collar career in a variety of tech disciplines, my field of grad study was squarely in the domain of "applied ethics." My MA is in "Ethics & Policy Studies," an interdisciplinary gumbo of applied ethics ("moral philosophy"), PolySci, Jurisprudence/ConLaw, and Econ, all applied to a policy topic of interest.

So, this kind of stuff is intrinsically of interest to me.

UPDATE

Amazon's AI certainly has my number. Touted just now in my inbox:

“[Coleman] argues that the algorithms of machine learning — if they are instilled with human ethics and values — could bring about a new era of enlightenment.” —San Francisco Chronicle

The Age of Intelligent Machines is upon us, and we are at a reflection point. The proliferation of fast-moving technologies, including forms of artificial intelligence akin to a new species, will cause us to confront profound questions about ourselves. The era of human intellectual superiority is ending, and we need to plan for this monumental shift.

A Human Algorithm: How Artificial Intelligence Is Redefining Who We Are examines the immense impact intelligent technology will have on humanity. These machines, while challenging our personal beliefs and our socioeconomic world order, also have the potential to transform our health and well-being, alleviate poverty and suffering, and reveal the mysteries of intelligence and consciousness. International human rights attorney Flynn Coleman deftly argues that it is critical that we instill values, ethics, and morals into our robots, algorithms, and other forms of AI. Equally important, we need to develop and implement laws, policies, and oversight mechanisms to protect us from tech’s insidious threats.

To realize AI’s transcendent potential, Coleman advocates for inviting a diverse group of voices to participate in designing our intelligent machines and using our moral imagination to ensure that human rights, empathy, and equity are core principles of emerging technologies. Ultimately, A Human Algorithmis a clarion call for building a more humane future and moving conscientiously into a new frontier of our own design.

A groundbreaking narrative on the urgency of ethically designed AI and a guidebook to reimagining life in the era of intelligent technology."

I'm gonna go broke buying books to study. I don't get paid for these rants. Gonna have to find a gig to continue to fund this Jones.

WHO IS FLYNN COLEMAN?

I have a question for Counselor Coleman:

"Assuming / Despite / If / Then / Therefore / Else..." Could AI do "argument analysis?"

e.g., "NLU"--Natural Language Understanding. I remain dubious. But, it's a moving target.

ANOTHER READ TO CONSIDER

...The demonstrated power of artificial intelligence has, in the last few years, led to massive media exposure and commentary. Countless news articles, books, documentary films and television programs breathlessly enumerate AI’s accomplishments and herald the dawn of a new era. The result has been a sometimes incomprehensible mixture of careful, evidence-based analysis, together with hype, speculation and what might be characterized as outright fear-mongering. We are told that fully autonomous self-driving cars will be sharing our roads in just a few years—and that millions of jobs for truck, taxi and Uber drivers are on the verge of vaporizing. Evidence of racial and gender bias has been detected in certain machine learning algorithms, and concerns about how AI-powered technologies such as facial recognition will impact privacy seem well-founded. Warnings that robots will soon be weaponized, or that truly intelligent (or superintelligent) machines might someday represent an existential threat to humanity, are regularly reported in the media. A number of very prominent public figures—none of whom are actual AI experts—have weighed in. Elon Musk has used especially extreme rhetoric, declaring that AI research is “summoning the demon” and that “AI is more dangerous than nuclear weapons.” Even less volatile individuals, including Henry Kissinger and the late Stephen Hawking, have issued dire warnings. 

The purpose of this book is to illuminate the field of artificial intelligence—as well as the opportunities and risks associated with it—by having a series of deep, wide-ranging conversations with some of the world’s most prominent AI research scientists and entrepreneurs. Many of these people have made seminal contributions that directly underlie the transformations we see all around us; others have founded companies that are pushing the frontiers of AI, robotics and machine learning...

Ford, Martin. Architects of Intelligence: The truth about AI from the people building it (p. 2). Packt Publishing. Kindle Edition.

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More to come...

AI: the Possible vs the Probable

Tristan Harris cuts to The Chase

 

"Wisdom Traditions?" "Philosophy?" Define "philosophy.'"

 

So, I punted to Google's new native "AI" jus' fer grins. BTW, some prior riffs on AI.

 

 

I was pleased by that. "Knowledge" and "Wisdom" differ. The former is necessary but insufficient for the latter. Given that my 1998 grad degree is in "Ethics & Policy Studies," I know just a thing or two about the core elements of "applied philosopy."

 

Another material facet of all of this.

 

Click here.

When Jensen Huang, the chief executive of the chipmaker Nvidia, met with Donald Trump in the White House last week, he had reason to be cheerful. Most of Nvidia’s chips, which are widely used to train generative artificial-intelligence models, are manufactured in Asia. Earlier this year, it pledged to increase production in the United States, and on Wednesday Trump announced that chip companies that promise to build products in the United States would be exempt from some hefty new tariffs on semiconductors that his Administration is preparing to impose. The next day, Nvidia’s stock hit a new all-time high, and its market capitalization reached $4.4 trillion, making it the world’s most valuable company, ahead of Microsoft, which is also heavily involved in A.I.

Welcome to the A.I. boom, or should I say the A.I. bubble? It has been more than a quarter of a century since the bursting of the great dot-com bubble, during which hundreds of unprofitable internet startups issued stock on the Nasdaq, and the share prices of many tech companies rose into the stratosphere. In March and April of 2000, tech stocks plummeted; subsequently many, but by no means all, of the internet startups went out of business. There has been some discussion on Wall Street in the past few months about whether the current surge in tech is following a similar trajectory. In a research paper entitled “25 Years On; Lessons from the Bursting of the Technology Bubble,” which was published in March, a team of investment analysts from Goldman Sachs argued that it wasn’t: “While enthusiasm for technology stocks has risen sharply in recent years, this has not represented a bubble because the price appreciation has been justified by strong profit fundamentals.” The analysts pointed to the earnings power of the so-called Magnificent Seven companies: Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla. Between the first quarter of 2022 and the first quarter of this year, Nvidia’s revenues quintupled, and its after-tax profits rose more than tenfold.

The Goldman paper also provided a salutary history lesson...

MORE ON AGI CONCERNS

 

There sre now dozens of these critical AGI videos on YouTube alone. 

 

Briefly back to Econ stuff (pertaining to just OpenAI):

 

OpenAI astounded the tech industry for the second time this week by launching its newest flagship model, GPT-5, just days after releasing two new freely available models under an open source license.

OpenAI CEO Sam Altman went so far as to call GPT-5 “the best model in the world.” That may be pride or hyperbole, as TechCrunch’s Maxwell Zeff reports that GPT-5 only slightly outperforms other leading AI models from Anthropic, Google DeepMind, and xAI on some key benchmarks, and slightly lags on others.

Still, it’s a model that performs well for a wide variety of uses, particularly coding. And, as Altman pointed out, one area where it is undoubtedly competing well is price. “Very happy with the pricing we are able to deliver!” he tweeted.

The top-level GPT-5 API costs $1.25 per 1 million tokens of input, and $10 per 1 million tokens for output (plus $0.125 per 1 million tokens for cached input). This pricing mirrors Google’s Gemini 2.5 Pro basic subscription, which is also popular for coding-related tasks. Google, however, charges more if inputs/outputs cross a heavy threshold of 200,000 prompts, meaning its most consumption-heavy customers end up paying more…

"Tokens?"

 

 Lordy. Wafts of the Crypto bamboozlement ensue.

 

UPDATE

Much of the euphoria and dread swirling around today’s artificial-intelligence technologies can be traced back to January, 2020, when a team of researchers at OpenAI published a thirty-page report titled “Scaling Laws for Neural Language Models.” The team was led by the A.I. researcher Jared Kaplan, and included Dario Amodei, who is now the C.E.O. of Anthropic. They investigated a fairly nerdy question: What happens to the performance of language models when you increase their size and the intensity of their training? ...

From The New Yorker by Cal Newport. Interesting piece. GPT 5 is getting a lot of pushback. 

 

MORE CONSIDERATIONS

 

Chapter 1 
The Artificial Intelligence of the Ethics of Artificial Intelligence  
An Introductory Overview for Law and Regulation  
Joanna J. Bryson 

For many decades, artificial intelligence (AI) has been a schizophrenic field pursuing two different goals: an improved understanding of computer science through the use of the psychological sciences; and an improved understanding of the psychological sciences through the use of computer science. Although apparently orthogonal, these goals have been seen as complementary since progress on one often informs or even advances the other. Indeed, we have found two factors that have proven to unify the two pursuits. First, the costs of computation and indeed what is actually computable are facts of nature that constrain both natural and artificial intelligence. Second, given the constraints of computability and the costs of computation, greater intelligence relies on the reuse of prior computation. Therefore, to the extent that both natural and artificial intelligence are able to reuse the findings of prior computation, both pursuits can be advanced at once.

Neither of the dual pursuits of AI entirely readied researchers for the now glaringly evident ethical importance of the field. Intelligence is a key component of nearly every human social endeavor, and our social endeavors constitute most activities for which we have explicit, conscious awareness. Social endeavors are also the purview of law and, more generally, of politics and diplomacy. In short, everything humans deliberately do has been altered by the digital revolution, as well as much of what we do unthinkingly. Often this alteration is in terms of how we can do what we do—for example, how we check the spelling of a document; book travel; recall when we last contacted a particular employee, client, or politician; plan our budgets; influence voters from other countries; decide what movie to watch; earn money from performing artistically; discover sexual or life partners; and so on. But what makes the impact ubiquitous is that everything we have done, or chosen not to do, is at least in theory knowable. This awareness fundamentally alters our society because it alters not only how we can act directly, but also how and how well we can know and regulate ourselves and each other. 

A great deal has been written about AI ethics recently. But unfortunately many of these discussions have not focused either on the science of what is computable or on the social science of how ready access to more information and more (but mechanical) computational power has altered human lives and behavior. Rather, a great deal of these studies focus on AI as a thought experiment or “intuition pump” through which we can better understand the human condition or the nature of ethical obligation. In this Handbook, the focus is on the law—the day-to-day means by which we regulate our societies and defend our liberties …

Dubber, Markus D.; Pasquale, Frank; Das, Sunit (2020). Oxford Handbook of Ethics of AI (OXFORD HANDBOOKS SERIES) (Function). Kindle Edition.  

Just delving into this. Pretty interesting, right off. 

 

TOBY ORD INTERVIEW

 

 I've cited Toby Ord before.

 

ETHICS OF AI, ANOTHER CITE

Every task we apply our conscious minds to—and a great deal of what we do implicitly—we do using our intelligence. Artificial intelligence therefore can affect everything we are aware of doing and a great deal we have always done without intent. As mentioned earlier, even fairly trivial and ubiquitous AI has recently demonstrated that human language contains our implicit biases, and further that those biases in many cases reflect our lived realities. In reusing and reframing our previous computation, AI allows us to see truths we had not previously known about ourselves, including how we transmit stereotypes, but it does not automatically or magically improve us without effort. Caliskan, Bryson, and Narayanan discuss the outcome of the famous study showing that, given otherwise-identical resumes, individuals with stereotypically African American names were half as likely to be invited to a job interview as individuals with European American names. Smart corporations are now using carefully programmed AI to avoid implicit biases at the early stages of human resources processes so they can select diverse CVs into a short list. This demonstrates that AI can—with explicit care and intention—be used to avoid perpetuating the mistakes of the past. 

The idea of having “autonomous” AI systems “value-aligned” is therefore likely to be misguided. While it is certainly necessary to acknowledge and understand the extent to which implicit values and expectations must be embedded in any artifact, designing for such embedding is not sufficient to create a system that is autonomously moral. Indeed, if a system cannot be made accountable, it may also not in itself be held as a moral agent. The issue should not be embedding our intended (or asserted) values in our machines, but rather ensuring that our machines allow firstly the expression of the mutable intentions of their human operators, and secondly transparency for the accountability of those intentions, in order to ensure or at least govern the operators’ morality. 

Only through correctly expressing our intentions should AI incidentally telegraph our values. Individual liberty, including freedom of opinion and thought, are absolutely critical not only to human well-being but also to a robust and creative society. Allowing values to be enforced by the enfolding curtains of interconnected technology invites gross excesses by powerful actors against those they consider vulnerable, a threat, or just unimportant. Even supposing a power that is demonstrably benign, allowing it the mechanisms for technological autocracy creates a niche that may facilitate a less-benign power—whether through a change of hands, corruption of the original power, or corruption of the systems communicating its will. Finally, who or what is a powerful actor is also altered by ICT, where clandestine networks can assemble—or be assembled—out of small numbers of anonymous individuals acting in a well-coordinated way, even across borders.

Theoretical biology tells us that where there is greater communication, there is a higher probability of cooperation. Cooperation has nearly entirely positive connotations, but it is in many senses almost neutral—nearly all human endeavors involve cooperation, and while these generally benefit many humans, some are destructive to many others. Further, the essence of cooperation is moving some portion of autonomy from the individual to a group. The extent of autonomy an entity has is the extent to which it determines its own actions. Individual and group autonomy must to some extent trade off, though there are means of organizing groups that offer more or less liberty for their constituent parts. [Dubber, et al, Ch 1.]  

A lot to consider in this book.

A.I. for the masses?

What could possibly go wrong?

In my latest snailmail Science Magazine:

Bringing machine learning to the masses

Yang-Hui He, a mathematical physicist at the University of London, is an expert in string theory, one of the most abstruse areas of physics. But when it comes to artificial intelligence (AI) and machine learning, he was naïve. “What is this thing everyone is talking about?” he recalls thinking. Then his go-to software program, Mathematica, added machine learning tools that were ready to use, no expertise required. He began to play around, and realized AI might help him choose the plausible geometries for the countless multidimensional models of the universe that string theory proposes.

In a 2017 paper, He showed that, with just a few extra lines of code, he could enlist the off-the-shelf AI to greatly speed up his calculations. “I don't have to get down to the nitty gritty,” He says. Now, He says he is “on a crusade” to get mathematicians and physicists to use machine learning, and gives about 20 talks a year on the power of these new user-friendly versions.

AI used to be the specialized domain of data scientists and computer programmers. But companies such as Wolfram Research, which makes Mathematica, are trying to democratize the field, so scientists without AI skills can harness the technology for recognizing patterns in big data. In some cases, they don't need to code at all. Insights are just a drag-and-drop away. Computational power is no longer much of a limiting factor in science, says Juliana Freire, a computer scientist at New York University in New York City who is developing a ready-to-use AI tool with funding from the Defense Advanced Research Projects Agency (DARPA). “To a large extent, the bottleneck to scientific discoveries now lies with people.”…

The AI tools are more than mere toys for nonprogrammers, says Tim Kraska, a computer scientist at the Massachusetts Institute of Technology in Cambridge who leads Northstar, a machine learning tool supported by the $80 million DARPA program called Data-Driven Discovery of Models. Wade Shen, who leads the DARPA program, says the tools can outperform data scientists at building models, and they're even better with a subject matter expert in the loop.

In a demo for Science, Kraska showed how easy it was to use Northstar's drag-and-drop interface for a serious problem. He loaded a freely available database of 60,000 critical care patients that includes details on their demographics, lab tests, and medications. In a couple of clicks, Kraska created several heart failure prediction models, which quickly identified risk factors for the condition. One model fingered ischemia—a poor blood supply to the heart—which doctors know is often codiagnosed with heart failure. That was “almost like cheating,” Kraska said, so he dragged ischemia off the list of inputs and the models immediately began to retrain to look for other predictive factors.

Maciej Baranski, a physicist at the Singapore-MIT Alliance for Research & Technology Centre, says the group plans to use Northstar to explore cell therapies for fighting cancer or replacing damaged cartilage. The system will help biologists combine the optical, genetic, and chemical data they've collected from cells to predict their behavior…

The trend toward off-the-shelf AI has risks. Machine learning algorithms are often called black boxes, their inner workings shrouded in mystery, and the prepackaged versions can be even more opaque. Novices who don't bother to look under the hood might not recognize problems with their data sets or models, leading to overconfidence in biased or inaccurate results.
   
But Kraska says Northstar has a safeguard against misuse: more AI. It includes a module that anticipates and counteracts typical rookie mistakes, such as assuming any pattern an algorithm finds is statistically significant. “In the end it actually tries to mimic what a data scientist would do,” he says.

"The trend toward off-the-shelf AI has risks. Machine learning algorithms are often called black boxes, their inner workings shrouded in mystery...Novices who don't bother to look under the hood might not recognize problems with their data sets or models, leading to overconfidence in biased or inaccurate results."

I'll re-post something from last year:
___

Another "Holy Shit" book. Yikes.

ALMOST two decades ago, when I wrote the preface to my book Causality (2000), I made a rather daring remark that friends advised me to tone down. “Causality has undergone a major transformation,” I wrote, “from a concept shrouded in mystery into a mathematical object with well-defined semantics and well-founded logic. Paradoxes and controversies have been resolved, slippery concepts have been explicated, and practical problems relying on causal information that long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Put simply, causality has been mathematized.”

Reading this passage today, I feel I was somewhat shortsighted. What I described as a “transformation” turned out to be a “revolution” that has changed the thinking in many of the sciences. Many now call it “the Causal Revolution,” and the excitement that it has generated in research circles is spilling over to education and applications. I believe the time is ripe to share it with a broader audience.

This book strives to fulfill a three-pronged mission: first, to lay before you in nonmathematical language the intellectual content of the Causal Revolution and how it is affecting our lives as well as our future; second, to share with you some of the heroic journeys, both successful and failed, that scientists have embarked on when confronted by critical cause-effect questions.

Finally, returning the Causal Revolution to its womb in artificial intelligence, I aim to describe to you how robots can be constructed that learn to communicate in our mother tongue— the language of cause and effect. This new generation of robots should explain to us why things happened, why they responded the way they did, and why nature operates one way and not another. More ambitiously, they should also teach us about ourselves: why our mind clicks the way it does and what it means to think rationally about cause and effect, credit and regret, intent and responsibility…

Pearl, Judea; Mackenzie, Dana. The Book of Why: The New Science of Cause and Effect (Kindle Locations 47-61). Basic Books. Kindle Edition.

This one is gonna be fun. Stay tuned. From the Atlantic interview article:

...as Pearl sees it, the field of AI got mired in probabilistic associations. These days, headlines tout the latest breakthroughs in machine learning and neural networks. We read about computers that can master ancient games and drive cars. Pearl is underwhelmed. As he sees it, the state of the art in artificial intelligence today is merely a souped-up version of what machines could already do a generation ago: find hidden regularities in a large set of data. “All the impressive achievements of deep learning amount to just curve fitting,” he said recently...

Yeah.

"If I could sum up the message of this book in one pithy phrase, it would be that you are smarter than your data. Data do not understand causes and effects; humans do."

In short, being unreflectively "data-driven" (that fashionable tech cliche) is a both naive and a cop-out. (Note: some of this will surely go -- at least tangentially --  to the "information ethics" topic of my prior post.)
___

See also my 2018 post "Data Science?"

ERRATA

This is a hoot:

Artificial intelligence is not intelligent enough or, more exactly, not imaginative enough or creative enough to make us resign thinking. Tests for artificial intelligence are not rigorous enough. It does not take intelligence to meet the Turing test – impersonating a human interlocutor – or win a game of chess or general knowledge. You will know that intelligence is artificial only when your sexbot says, ‘No.’

Fernández-Armesto, Felipe. Out of Our Minds. University of California Press. Kindle Edition, location 7720. 

This book, wow!

The speed and reach of the computer revolution raised the question of how much further it could go. Hopes and fears intensified of machines that might emulate human minds. Controversy grew over whether artificial intelligence was a threat or a promise. Smart robots excited boundless expectations. In 1950, Alan Turing, the master cryptographer whom artificial intelligence researchers revere, wrote, ‘I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.’ The conditions Turing predicted have not yet been met, and may be unrealistic. Human intelligence is probably fundamentally unmechanical: there is a ghost in the human machine. But even without replacing human thought, computers can affect and infect it. Do they corrode memory, or extend its access? Do they erode knowledge when they multiply information? Do they expand networks or trap sociopaths? Do they subvert attention spans or enable multi-tasking? Do they encourage new arts or undermine old ones? Do they squeeze sympathies or broaden minds? If they do all these things, where does the balance lie? We have hardly begun to see how cyberspace can change the psyche. [Ibid, location 7441]

ON THE OTHER HAND

Amazon recommended this book to me:

Only $4.99 Kindle price. 5 star reviews. I precipitously did 1-Click.

My Bad. It's awful. Reads like it was written by A.I.

INTRODUCTION 

Machine learning is one in all the quickest growing areas of technology, with far-reaching applications. This textbook is intended to give a proper introduction of machine learning, and all the algorithmic paradigms that machine learning offers, in a principled way. The book provides an intensive hypothesis of the basic concepts underlying machine learning and also the mathematical derivations that remodel these principles into practical algorithms. After a presentation of the basics of the sector, the book covers a wide range of central topics that have never been addressed by previous textbooks. These embody a discussion of the process complexity of learning and also the ideas of convexity and stability; major algorithmic paradigms together with stochastic gradient descent, neural networks, and structured output learning; and rising theoretical ideas like the PAC-Bayes approach and compression-based bounds. Designed for a starting graduate or refined student course, the text makes the elemental and algorithms of machine learning accessible to non-expert readers and pupils of arithmetics, engineering, statistics and computer science.

Samelson, Steven. Machine Learning: The Absolute Complete Beginner’s Guide to Learn and Understand Machine Learning From Beginners, Intermediate, Advanced, To Expert Concepts (pp. 1-2). Kindle Edition.

Seriously? Need I really elaborate? Got played this time.

UPDATE: HEALTH CARE AI ACROSS THE POND

Reported at TechCrunch:

The UK’s National Health Service is launching an AI lab

The UK government has announced it’s rerouting £250M (~$300M) in public funds for the country’s National Health Service (NHS) to set up an artificial intelligence lab that will work to expand the use of AI technologies within the service.

The Lab, which will sit within a new NHS unit tasked with overseeing the digitisation of the health and care system (aka: NHSX), will act as an interface for academic and industry experts, including potentially startups, encouraging research and collaboration with NHS entities (and data) — to drive health-related AI innovation and the uptake of AI-driven healthcare within the NHS.

Last fall the then new in post health secretary, Matt Hancock, set out a tech-first vision of future healthcare provision — saying he wanted to transform NHS IT so it can accommodate “healthtech” to support “preventative, predictive and personalised care”.

In a press release announcing the AI lab, the Department of Health and Social Care suggested it would seek to tackle “some of the biggest challenges in health and care, including earlier cancer detection, new dementia treatments and more personalised care”.

Other suggested areas of focus include:

• improving cancer screening by speeding up the results of tests, including mammograms, brain scans, eye scans and heart monitoring
• using predictive models to better estimate future needs of beds, drugs, devices or surgeries
• identifying which patients could be more easily treated in the community, reducing the pressure on the NHS and helping patients receive treatment closer to home
• identifying patients most at risk of diseases such as heart disease or dementia, allowing for earlier diagnosis and cheaper, more focused, personalised prevention
• building systems to detect people at risk of post-operative complications, infections or requiring follow-up from clinicians, improving patient safety and reducing readmission rates
• upskilling the NHS workforce so they can use AI systems for day-to-day tasks
• inspecting algorithms already used by the NHS to increase the standards of AI safety, making systems fairer, more robust and ensuring patient confidentiality is protected
• automating routine admin tasks to free up clinicians so more time can be spent with patients...

Have to wonder what Seamus O'Mahony would say?
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More to come...

Digitech AI news updates

From NPR's All Things Considered:

Can Computers Learn Like Humans?
The world of artificial intelligence has exploded in recent years. Computers armed with AI do everything from drive cars to pick movies you'll probably like. Some have warned we're putting too much trust in computers that appear to do wondrous things.

But what exactly do people mean when they talk about artificial intelligence?

It's hard to find a universally accepted definition of artificial intelligence. Basically it's about getting a computer to be smart — getting it to do something that in the past only humans could do.
One key to artificial intelligence is machine learning. Instead of telling a computer how to do something, you write a program that lets the computer figure out how to do something all on its own…

From THCB:

Medicine Is a Profession That is Rapidly Losing Control of Its Tools
By ADRIAN GROPPER, MD

Artificial Intelligence hype and reality are everywhere. However, the last month or two has seen some thoughtful reflection. HHS / ONC announced “Hype to Reality: How Artificial Intelligence (AI) Can Transform Health and Healthcare” referencing a major JASON report “Artificial Intelligence for Health and Health Care [PDF -817 KB],”. From a legal and ethical perspective, we have a new multinational program: “PMAIL will provide a comparative analysis of the law and ethics of black-box personalized medicine,…”. Another Harvard affiliate writes “Optimization over Explanation” subtitled “Maximizing the benefits of machine learning without sacrificing its intelligence”. Meanwhile, an investigative journalism report from the UK “Google DeepMind and healthcare in an age of algorithms”, “…draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age”…

Over at Medium.com:

Interesting 4-part (thus far) series:

Living in the Machine
Does technology change the very state of being human?

Artificial intelligence and automation outsources even more of our cognitive functions to machines. What does this mean for art, for relationships — even for our connection to a higher being? What does it mean to be human in the age of the machine?

Comes with audio versions as well. Nice.

From the first post:

From Mining to Meaning 

If you use digital devices, AI is already being sicced on the grotesque bricolage that is your life to eliminate potential sources of “friction” — a tech-speak jargon term that means roughly “whatever grinds your gears.”

Whether it’s by monitoring your calorie intake, presenting you with “optimal” romantic prospects, or making it easier to spend a fortune on Amazon, algorithms are even now insinuating themselves into your every existential crack and crevice like so many squirts of WD-40.

The possibility of using AI to eliminate diseases is undeniably exciting. Excising problems like cancer from society would make for a better future. But is maximizing efficiency the only way to add value to the world?

Most people don’t see the world and its inhabitants simply as a resource to be mined more or less effectively, nor do we tend to think that human value is exhausted by the efficiency or otherwise of this resource mining. Sometimes we just want to make sense of things: to look closely at the world, grasp some pattern in it, and articulate its significance, without some further goal in mind. This desire is what drives people to become scholars, but it’s also why people look at art, listen to music, or strive to build relationships with their grandchildren. If a concern for efficiency is a big part of what makes us human, our desire to grasp significance and share meaningful experiences with others is just as crucial.

Like a world without cancer, a more thoughtful, artistic, and compassionate future strikes us as an unequivocal Good Thing. But adding this kind of value to the world requires something more than maximizing efficiency. Anyone who tries to “hack” being a thoughtful scholar, or a good friend, is kind of missing the point…

Good stuff.

UPDATE

Will robots take your job? Humans ignore the coming AI revolution at their peril.

Artificial intelligence aims to replace the human mind, not simply make industry more efficient.

by Subhash Kak

Robots have transformed industrial manufacturing, and now they are being rolled out for food production and restaurant kitchens. Already, artificial intelligence (AI) machines can do many tasks where learning and judgment is required, including self-driving cars, insurance assessment, stock trading, accounting, HR and many tasks in healthcare. So are we approaching a jobless future, or will new jobs replace the ones that are lost?

According to the optimistic view, our current phase of increasing automation will create new kinds of employment for those who have been made redundant. There is some historical precedent for this: Over a hundred years ago, people feared that the automobile revolution would be bad for workers. But while jobs related to horse-drawn carriages disappeared, the invention of the car lead to a need for automobile mechanics; the internal combustion engine soon found applications in mining, airplanes and other new fields.

The difference, however, is that today’s AI technology aims to replace the human mind, not simply make industry more efficient. This will have unprecedented consequences not predicted by the advent of the car, or the automated knitting machine…

Yeah, this is not a new concern. I've hit on the topic a number of times before. See also here.

BTW, another new read. Just getting started. A lot of technical overlap between AI, IA, AR, and VR.

Strongly recommend you tour his Stanford Virtual Human Interaction Lab website.

From NPR's Science Friday (March 2016):

How advances in virtual reality will change how we work and communicate.

My specific interests go to the potential utility of this technology in health care -- inclusive of clinical pedagogy. I'll reserve judgments until I've finished Jeremy's book.

BTW: Jeff and April, recall, are deploying VR in their startup, NeuroTrainer.com. 

FEB 8TH UPDATE

Any tangential AI/VR connection here? From Medium this morning:

We are our own typos

Everyone seems to be writing about the recently announced effort by Amazon, Berkshire Hathaway, and JP Morgan Chase to attack their employee health costs. It is certainly newsworthy, and I am generally interested in whatever Amazon may do in healthcare.

They may very well have some success with this effort, but until I read a positive story about employee working conditions at Amazon, I’m going to be skeptical that any disruption in healthcare they accomplish with it is something that I shouldn’t be worried about.

So, instead, I’m going write to about why we can’t recognize our own typos, and what that means for our health.

As Wired summarized the problem a few years ago: “The reason we don’t see our own typos is because what we see on the screen is competing with the version that exists in our heads.” They go on to explain that one of the great skills of our big brains is that we build mental maps of the world, but those maps are not always faithful to the actual world.

As psychologist Tom Stafford explained: “We don’t catch every detail, we’re not like computers or NSA databases. Rather, we take in sensory information and combine it with what we expect, and we extract meaning.”

Thus, typos.

Unfortunately, the same is often true with how we view our health. We don’t think we’re as overweight as we are. We think we get more exercise than we do. We think our nutrition is better than it is. Overall, we think we’re in better health than we probably are.

Over the past few decades, the U.S. has been suffering “epidemics” of obesity, diabetes, asthma, and allergies, to name a few. Over half of adults now have one or more chronic conditions. Yet two-thirds of us still report being in good or excellent health, virtually unchanged for at least the last twenty years.

Something doesn’t jibe…

 Hmmm...

I'm reminded of the old QA auditor's saying, "you get what you INspect, not what you EXpect."

More news. Margalit is back with a vengeance:

 

Ambergan Prime

Dear primary care doctor, Jeff Bezos is about to devour your lunch. All of it. And then he’ll eat the table, the plates, the napkins and the utensils too, so you’ll never have lunch ever again. Oh yeah, and they’ll also finally disrupt and fix health care once and for all, because enough is enough already. Mr. Bezos, it seems, got together with two of his innovator buddies, Warren Buffet from Berkshire Hathaway and Jamie Dimon from J.P. Morgan, and they are fixing up to serve us some freshly yummy and healthy concoction.
Let’s call it Ambergan for now.

This is big. This is huge. It comes from outside the sclerotic “industry”. And it’s all about technology. The founders are no doubt well versed in the latest disruption theories and Ambergan will be a classic Christensen stealth destroyer of existing markets. When the greatest investor that ever-lived combines forces with the greatest banker in recent memory and the premier markets slayer of all times, who happens to be the richest man on earth, all to bring good things to life (sorry GE), nothing but goodness will certainly ensue.

Everybody inside and outside the legacy health care industry is going to write volumes about this magnificent new venture in the coming days and months, so I will leave the big picture to my betters. But since our soon to be dead industry has been busy lately bloviating about the importance of good old fashioned, relationship based primary care, perhaps it would be useful to understand that Ambergan is likely to take the entire primary care thing off the table and stash it safely in the bottomless cash vaults of its founders. It’s not personal, dear doctor. It’s business. Ambergan will be your primary care platform and you may even like it…

LOL. Read all of it.

The Cherry on top:

"The Amazon platform IS the network, and there will be terms, conditions, stars and promotions. There certainly are many legacy obstacles to overcome, and perhaps that is why Amazon couldn’t or wouldn’t go it alone. Throwing highly regulated markets wide open requires two strong lobbying arms, and a federal government willing to play fast and loose. The stars are indeed perfectly aligned for the first true disruption of our health care since 1965."

FEB 9TH UPDATE

Happy Birthday to me (72 today). What's the joke? "If I'd known I was gonna live this long, I'd have taken better care of myself."

apropos of the overall topic of this post, another must-read has just come to my attention, via my latest issue of Science Magazine, in a book review entitled "The fetishization of quantification."

From the Amazon blurb:

How the obsession with quantifying human performance threatens our schools, medical care, businesses, and government

Today, organizations of all kinds are ruled by the belief that the path to success is quantifying human performance, publicizing the results, and dividing up the rewards based on the numbers. But in our zeal to instill the evaluation process with scientific rigor, we've gone from measuring performance to fixating on measuring itself. The result is a tyranny of metrics that threatens the quality of our lives and most important institutions. In this timely and powerful book, Jerry Muller uncovers the damage our obsession with metrics is causing--and shows how we can begin to fix the problem.

Filled with examples from education, medicine, business and finance, government, the police and military, and philanthropy and foreign aid, this brief and accessible book explains why the seemingly irresistible pressure to quantify performance distorts and distracts, whether by encouraging "gaming the stats" or "teaching to the test." That's because what can and does get measured is not always worth measuring, may not be what we really want to know, and may draw effort away from the things we care about. Along the way, we learn why paying for measured performance doesn't work, why surgical scorecards may increase deaths, and much more. But metrics can be good when used as a complement to—rather than a replacement for—judgment based on personal experience, and Muller also gives examples of when metrics have been beneficial.

Complete with a checklist of when and how to use metrics, The Tyranny of Metrics is an essential corrective to a rarely questioned trend that increasingly affects us all.

'eh? "Data, Learning, Experience, Perception, Meaning..."

Frrom the Science Magazine (non-paywalled) summary:

Summary
Although the numbers whose "tyranny" forms the subject of Jerry Muller's timely book share some of the attributes of scientific measurement, their purposes are primarily administrative and political. They are designed to be incorporated into systems of what might be called "data-ocracy," often for the sake of public accountability: Schools, hospitals, and corporate divisions whose numbers meet or exceed their goals are to be rewarded, whereas poor numbers, taken to imply underperformance, may bring penalties or even annihilation. In The Tyranny of Metrics, Muller shows how teachers, doctors, researchers, and managers are driven to sacrifice the professional goals they value in order to improve their numbers.

Yeah. While, hey, I'm a long-time "quant guy," a "QI guy," I've had the Brent James training ("if you can't measure it, you can't improve it"), I too have concerns that the phrase "data-driven" can often mean putting your brain in "park." One of the cautions regarding "machine learning" goes to the concern that the machines will "learn" all of our bias errors.

The Science Magazine book review concludes:

In 1975, the American social psychologist Donald Campbell and the British economist C. A. E. Goodhart articulated independently the principle that reliance on measurement to incentivize behaviors leads almost inevitably to a corruption of the measures. Muller explains the logic of this corruption and defends, in place of indiscriminate numbers, an ideal of professional knowledge and experience.

Measurement, he concludes, can contribute to better performance, but only if the measures are designed to function in alliance with professional values rather than as an alternative to them. Good metrics cannot be detached from customs and practices but must depend on a willingness to immerse oneself in the work of these institutions.

Add another book to the stash.

ERRATUM

Speaking of "data," heard this in the car yesterday. The new "Panopticon":

With Closed-Circuit TV, Satellites And Phones, Millions Of Cameras Are Watching
Journalist Robert Draper writes in National Geographic that the proliferation of cameras focused on the public has led "to the point where we're expecting to be voyeur and exhibitionist 24/7."

See my November post "Artificial Intelligence and Ethics." See also my "The old internet of data, the new internet of things and "Big Data," and the evolving internet of YOU."

UPDATE

Interesting long-read article:

The Coming Software Apocalypse
A small group of programmers wants to change how we code — before catastrophe strikes.
By James Somers

--
ANOTHER ERRATUM

Just heard Emily Chang interviewed on MSNBC.

Will have to read this one too. Brings to mind this prior post of mine.

CODA

The ultimate utility of AI/NLP?

BELOW, PURE MARKETING GENIUS

This came across my Twitter feed. My first reaction was "yeah, right, this is straight outa SNL."

Nope. Some "As-Seen-on-TV" vendor fleecing the rubes on cheesy cable channels for months now. Making Bank.

Is this a great country, or what?
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More to come...