Fostering AI Common Sense: The need for Critical Thinking and Healthy Skepticism

Very important,   SAS 

Fostering AI common sense: The need for critical thinking and healthy skepticism

by REGGIE TOWNSEND on MAY 25, 2023 

As AI rapidly advances over the next several years, I’ve been fortunate to have an active role in helping to guide a responsible path forward when it comes to technology’s impact on our daily lives. Currently, this role includes serving as Vice President for the SAS Data Ethics Practice, as an EqualAI board member and as a member of the National Artificial Intelligence Advisory Committee (NAIAC).

The acceleration of AI development and application has incredible potential for supercharging our decision making and democratizing access to technology. However, it also carries the risks of widespread misinformation, fomenting division and perpetuating historical injustices. Because of these pitfalls, promoting “AI common sense” among the public is essential, encouraging a basic understanding of AI benefits, limits, and vulnerabilities it might exploit or create. In other words, an understanding of how AI affects one’s well-being.

I like to compare AI to electricity. Most of us don’t have a detailed understanding of how electrons, transformers and grounding wires work, but we all get the basics: We plug something into an outlet, and it powers our devices, appliances, etc. We have a common understanding of basic electrical safety as well. We keep implements and hands away from outlets, and we don’t let electric devices or wires touch the water.  Though we likely came to a more advanced understanding of these rules in science class, they comprise a general electricity “common sense” most of us learn prior to any formal schooling.

AI common sense would include a general understanding of AI's functions and risks at a basic level, especially as AI capabilities multiply. It’s easy to get lost in conversations around machine learning, neural networks and large language models. Still, everyday users don’t need to be familiar with these terms to be aware of AI’s impact on their daily lives, including the potential dangers.

Here are some ways we can foster AI common sense as the technology becomes more prevalent in our lives:

Recognizing human nature and AI

In today's fast-moving tech landscape, it's easy to be swept away by the allure of AI's capabilities. However, we must recognize that AI systems are created by humans, which means they can carry human biases and limitations with them. These biases can manifest in the data used to train AI, leading to potential discrimination or unfair treatment. For example, AI algorithms used in hiring processes may inadvertently favor certain demographics over others if trained on biased data.

Though learned bias can be pervasive in AI implementations, it isn’t unsolvable. Responsible developers and innovators are working to mitigate inequity in AI systems by approaching the issue from all directions: training models with broad, inclusive and diverse data; testing models for disparate impact across different groups and regularly monitoring them for drift over time; instituting skills-based “blind hiring” for development teams; and combining humans and technology to form a system of checks and balances that can override unintended bias.

While these efforts are being made to reduce bias, acknowledging the potential for imperfect judgment in AI systems remains critical to fostering AI common sense and helping users understand the potential for risks and inaccuracies.

Combating automation bias

Automation bias occurs when people trust automated systems, like AI, over their judgment, even when the system is wrong. There is a common assumption that machines don’t make careless errors as humans do. We’re inclined to trust a calculator's results, because it’s an objective machine. But AI tools go far beyond addition and subtraction. In fact, AI purists would argue that addition and subtraction is prescriptive or rules-based, whereas AI is predictive in nature. Though it seems minor, the distinction is important because it increases the probability that AI can replicate biases from past data, make false connections, or “hallucinate” information that doesn’t exist but seems reasonable to a reader.

This overreliance on AI can have severe consequences. In health care, a doctor might rely on an AI system to diagnose a patient, despite evidence contradicting the AI's recommendation. By recognizing this bias, we can encourage individuals to question AI systems and seek alternative perspectives, thus reducing the risk of harmful outcomes. Some trustworthy AI platforms have “explainability” features to help mitigate this challenge by providing additional reasons and context for why an AI model produced what it did.

Promoting critical thinking

Encouraging a culture of inquiry and curiosity can help individuals better understand the real-world impact of AI technologies. Enhancing our critical thinking skills and maintaining a healthy skepticism about AI systems is crucial to promoting AI common sense. This means questioning AI-generated results, recognizing possible limitations in the underlying data and being aware of potential biases in the algorithms. The axiom “trust but verify” should guide AI interactions until they are repeatedly proven accurate and effective, especially in high-risk scenarios.

This critical thinking approach can empower individuals to make informed decisions and better understand the limitations of AI systems. For example, users of AI-generated news should be aware of the potential for inaccuracies or misleading information and should verify claims from multiple sources. With generative applications like Dall-E and Midjourney already capable of photorealistic images virtually indistinguishable from reality, we should all be inclined to question incendiary or controversial pictures until we can confirm their veracity with corroborating evidence, like consistent images from multiple angles and trustworthy first-person reporting.   ... ' 

Made Me Think: About SynthAI

Still thinking this, Like to test its usefulness, efficiency.  Join me.

For B2B Generative AI Apps, Is Less More?   by Zeya Yang and Kristina Shen in Andreessen Horowitz

AI, machine & deep learning  enterprise & SaaS  Generative AI

Table of contents

Wave 1: Crossing the bridge from consumer to enterprise

What’s the cost (or benefit) of disrupting the workflow?

Wave 2: Converging information for improved decision making

Implementing SynthAI

A battle to own the workflow

We’ve watched large language models (LLMs) become mainstream over the past few years and have studied the implementations in the context of B2B applications. Despite some enormous technological advances and the presence of LLMs in the general zeitgeist, we believe we’re still only in the first wave of generative AI applications for B2B use cases. As companies nail down use cases and seek to build moats around their products, we expect a shift in approach and objectives from the current “Wave 1”  to a more focused “Wave 2.”

Here’s what we mean: To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information. That is, they will show us less content by synthesizing the information available. Aptly, we refer to Wave 2 as synthesis AI (“SynthAI”) to contrast with Wave 1. While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change.

Ultimately, as we explain below, the battle among B2B solutions will be less focused on dazzling AI capabilities, and more focused on how these capabilities will help companies own (or redefine) valuable enterprise workflows. ... '      (charts at the link at Andreessen)

Do We Need a National Algorithms Safety Board?

 Safety is yes, means of determining that is the issue.    Especially with regard to transportation and related supply chain decisions and risks. 

Do We Need a National Algorithms Safety Board?

By The Hill, February 28, 2023,     Opinion Piece

A close-up of hands on a laptop keyboard, with image overlays of various tech-based iconography.

Perfectly safe algorithmic systems are not possible, but safer systems are.

In the U.S., the National Transportation Safety Board is widely respected for its prompt investigations of plane, train, and boat accidents. Could a National Algorithms Safety Board have a similar impact in increasing safety for algorithmic systems, especially the rapidly proliferating artificial intelligence applications based on unpredictable machine learning? Alternatively, could agencies such as the Food and Drug Administration, Securities and Exchange Commission, or Federal Communications Commission take on the task of increasing safety of algorithmic systems?

From The Hill   View Full Article

Data and Analytics in Soccer, Rise of Deeper Decision Manking

The Rise of Deeper Decision Making, Money and data driving next steps. 

Data and Analytics in Soccer

As the 2022 FIFA World Cup gets underway in Qatar on November 20, some of the most important action will be taking place off the field. Most teams will be furiously crunching data on goalies’ tendencies to try to determine how to win a penalty shoot-out if there’s a draw at the game’s final whistle. But this type of single-instance analysis is only a small part of the revolution taking place in the boardrooms at some of soccer’s biggest clubs. Today, the most important hire is no longer the 30-goal-a-season striker or an imposing brick wall of a defender. Instead, there’s an arms race for the person who identifies that talent.

Barcelona players in a tight huddle as they celebrate a win during the 2012 UEFA Champions League

How the best soccer team in the world lost its luster,  BY SIMON KUPER

Members of the Italian national soccer team celebrate scoring a goal during a qualifying match for the UEFA European Championship.

Successful teams: Superstars need not apply, BY BEN LYTTLETON

Sports Industry Outlook 2022 

The research department at Liverpool FC, the team that won England’s Premier League in 2020, for example, is now led by a Cambridge University–trained polymer physicist. Arsenal FC recently hired a former Facebook software engineer as a data scientist, and current Premier League champion Manchester City hired a leading AI scientist with a PhD in computational astrophysics to their research department. Chelsea FC’s new American owner, Todd Boehly, spent his summer trying and failing to hire a new sporting director with a data background. These are all examples from England, where the sport’s richest clubs are investing to gain an edge—and often recruiting from ahead-of-the-curve clubs with proven track records, like Monaco, in the French League, and the German club RB Leipzig.

Soccer has a rich history of this sort of analysis. Charles Reep, a military accountant, became soccer’s first data analyst in the 1950s, predating personal computers, Billy Beane, and the Moneyball moment in baseball, in 2003. That was followed up, in 2009, by the soccer equivalent, Soccernomics, by Simon Kuper and Stefan Szymanski, and data-driven sports analysis entered a new era. Among Kuper and Szymanski’s findings: goalkeepers are undervalued in the transfer market, and players from Brazil are overvalued.

I cofounded a football consultancy ten years ago with the authors of the book. One of our first clients was the Netherlands national team. We’ve been applying data to soccer for a while—but a lot of it is backward-looking, trying to mine past performance to account for what could happen on the field. We provided the Dutch team with a penalty-kick dossier before the 2010 World Cup final against Spain, in which Professor Ignacio Palacios-Huerta, an expert in game theory, showed penalty trends and patterns of Spain’s kickers. Spain scored four minutes before the end of the game to win, but the Dutch were confident they would have won on penalties.  .... ' 

Experts Making Complex Decisions

 Note also my mention of 'process mining' which can lead to and simplify useful decision making. 

How Experts Make Complex Decisions

By studying 200 million chess moves, researchers shed light on what gives players an advantage—and what trips them up.

BASED ON THE RESEARCH OF: Yuval Salant, Yevgenia Nayberg  in https://insight.kellogg.northwestern.edu 

Making a simple decision is akin to ordering off a restaurant menu: you evaluate the available options one by one and choose whichever alternative promises to make you happiest or deliver the greatest payoff. But when it comes to more complicated choices—say, shopping for a house, devising a business plan, or evaluating insurance policies—identifying the objective “best” option is impractical, and often impossible.

Choosing a health-insurance plan, for example, requires estimating the likelihood that you’ll need a biopsy or an appendectomy—a multilayered guessing game sure to be fraught with error. Selecting a marketing strategy can be similarly knotty, as every potential move opens the door to myriad reactions from customers and competitors, leading to millions of possible scenarios, any of which the decision-maker can only imperfectly foresee.

“There’s a different dimension to decision-making when the available alternatives are so complex that you can’t even figure out what a given option is worth to you,” says Jörg L. Spenkuch, an associate professor of managerial economics and decision sciences at the Kellogg School.

So what does it take to make a good choice when facing this kind of complexity? Does slowing down or having more experience help—or do these convoluted decisions simply leave everyone grasping at straws, regardless of their expertise or how long they spend pondering their options?

These questions have received limited attention from social scientists, says Yuval Salant, a professor of managerial economics and decision sciences. So he and Spenkuch recently teamed up to shed new light on the dynamics of decision-making in complicated scenarios. In a new study, they derive first-of-their-kind predictions about how people behave when making complex choices by using an unusual laboratory: the chess board. “Complexity and chess go hand-in-hand,” says Salant.

Using an immense dataset of more than 200 million moves from an online chess platform, the researchers draw novel conclusions about how chess players find their way through the fog of complexity. They find that slowing down helps everyone, but that masters of the game benefit considerably more from extra decision time than less-expert players. And counterintuitively, they show that adding a mediocre option into the mix can actually be worse than adding a bad one.

How Chess Players Make Decisions

Chess has several features that make it perfect for studying byzantine decisions.

First, the quality of each move can be objectively ranked. Unlike picking an insurance plan or a marketing strategy, where accurately measuring and ranking alternatives is possible only with a well-functioning crystal ball, certain chess moves can be clearly identified as part of a winning strategy: these moves will (if followed up by subsequent optimal play) guarantee a win, no matter what one’s opponent does. Other moves can similarly guarantee a draw or would result in a loss.

(It may seem bizarre that the famously cerebral game can be boiled down to predefined winning and losing moves. But this was proved more than a century ago by the German mathematician Ernst Zermelo. “He basically said, ‘Chess isn’t an interesting game,’” Salant explains, “‘because either white has a winning strategy no matter what black is doing, or black has a winning strategy no matter what white is doing, or both of them can force a draw.’”)  ... ' 

Imaginable

 Now reading game designers Jane McGonigal's just published:  " Imaginable: How to see the Future Coming and feel ready for anything - even things that seem impossible today"    Had met her at a meeting some time ago and was impressed.   We were looking at how to integrate games with company process decisions.  I see she works with IFTF  , which we also worked with at the time.

Amazon's description:

  An innovative guide to living gamefully, based on the program that has already helped nearly half a million people achieve remarkable personal growth.

In 2009, internationally renowned game designer Jane McGonigal suffered a severe concussion. Unable to think clearly or work or even get out of bed, she became anxious and depressed, even suicidal. But rather than let herself sink further, she decided to get better by doing what she does best: She turned her recovery process into a resilience-building game. What started as a simple motivational exercise quickly became a set of rules for “post-traumatic growth” that she shared on her blog. These rules led to a digital game and a major research study with the National Institutes of Health. Today nearly half a million people have played SuperBetter to get stronger, happier, and healthier.

But the life-changing ideas behind SuperBetter are much bigger than just one game. In this book, McGonigal reveals a decade’s worth of scientific research into the ways all games - including videogames, sports, and puzzles - change how we respond to stress, challenge, and pain. She explains how we can cultivate new powers of recovery and resilience in everyday life simply by adopting a more “gameful” mind-set. Being gameful means bringing the same psychological strengths we naturally display when we play games - such as optimism, creativity, courage, and determination - to real-world goals.

Drawing on hundreds of studies, McGonigal shows that getting superbetter is as simple as tapping into the three core psychological strengths that games help you build:

Your ability to control your attention, and therefore your thoughts and feelings

Your power to turn anyone into a potential ally, and to strengthen your existing relationships

Your natural capacity to motivate yourself and super-charge your heroic qualities, like willpower, compassion, and determination

SuperBetter contains nearly 100 playful challenges anyone can undertake in order to build these gameful strengths. It includes stories and data from people who have used the SuperBetter method to get stronger in the face of illness, injury, and other major setbacks, as well as to achieve goals like losing weight, running a marathon, and finding a new job.

As inspiring as it is down to earth, and grounded in rigorous research, SuperBetter is a proven game plan for a better life. You’ll never say that something is “just a game” again.  ... ' 

LPA VisiRule AutoAudit Announcement

Visirule is an interesting way to introduce process rules for integrated decisions into AI applications.

LPA announce VisiRule AutoAudit automated testing for its No-Code Low-Code visual rules tool

LPA announce a new automated testing component for VisiRule which allows VisiRule authors to test and validate their charts at a single click. VisiRule AutoAudit tests for logical consistency and completeness as well as support rule maintenance and updates.

LONDON (PRWEB) FEBRUARY 24, 2022

LPA announce a new release of VisiRule which includes VisiRule AutoAudit, an automated testing component for checking the logic of the chart.

VisiRule AutoAudit allows authors to monitor the behaviour and calculated logic contained within their charts This enables business users to validate the underlying decision logic empirically.

In its simplest mode, the module offers a single-click way to generate and execute a test suite which invokes the chart multiple times and produces an output table containing all the results. For each individual data row the table contains the conclusion reached, a success/failure indicator and, optionally, insight into internal calculations and outputs.

As test suites are generated as readable data files, they can be hand-edited by authors to target specific combinations of data values. The result is that authors can produce tables of results which show the answers the system computes for various combinations of input values in a format that can be verified and validated by external reviewers. Switches are available for authors who want to exploit VisiRule's unique backtracking mechanism to compute alternate solution sets.

In its more advanced mode the module allow authors to make changes to charts and see what effect, if any, the changes have on the resulting computations using the nominated test suite. This is aimed at projects where logic is subject to continual refinement and authors need to know that previously correct computations have not been affected by any proposed updates.  .... 

Poker and Game Theory

Re Game-theory Optimal Poker

How Game Theory Changed Poker

The Wall Street Journal, Oliver Roeder, January 13, 2022

Researchers at the University of Alberta's Computer Poker Research Group in Canada pioneered game theory mathematics that has transformed how professional poker players approach the game. Poker's mathematical complexity rivals or surpasses that of chess while adding randomness and hidden data, bringing it closer to the "real world" that artificial intelligence scientists want to control. Many poker-playing algorithms incorporate the minimization of regret, a mathematical concept for decision-making in uncertain environments. Game-theory optimal poker players hire programmers to analyze their game data, finding "leaks" or errors in strategy, and to conduct game-theoretical analyses, calculating optimal plays in any of the innumerable situations that can confront a player.  ... ' 

Data for Better Business Decision?

Ultimately this is the 'thing', how do we do it effectively?  Some starting thoughts.

SmartData Collective > Big Data > How To Use Data For Smarter Business Decisions

BIG DATA

How To Use Data For Smarter Business Decisions

Big data technology is of the upmost importance for any company trying to meet its growth targets in 2022.

BY Sean Mallon

Big Data Technology Has Become a Nontrivial Element of Modern Business

If you intend to start resting your case with investing in data, analytics and more insightful business forecasts, stop. Instead, shift your focus toward prioritizing the business investment categories that would bring you the biggest bang for the buck in terms of both revenue and bottom line.

Most of your competitors are probably relying on data to run their businesses for a while now. They use data to automate their processes by turning some of their operational and transactional data into alerts that help them make better business decisions in the quest for income. While this is an intelligent thing to do, that’s where most of these efforts to use data in the process of running a business come to an end. The so-called insights-driven business transformation is the next level of making the most out of data. This is the ability to morph enterprise data into insights and then use these insights to spark actions that directly impact the outcome of a business. The evolution process then loops over and over again, in a continuous stream of learning and improving. This is how customer-centric companies operate. Also, this has become the top priority for many CIOs and business analysts. You should know that almost 70% of CIOs consider their company has changed or is currently changing its management culture to make quantitative decisions one of their highest priorities.  ... ' 

We Need AI Literacy

 Yes, indeed, and how current capabilities might likey change from future developments.

America Needs AI Literacy Now

By pnw.ai, December 9, 2021

Can artificial intelligence (AI) replace a doctor in the operating room? Are some AI algorithms inherently biased, or are they merely trained on biased data? If you're not sure about the answers to these questions, you are not alone. We recently conducted a national survey with Echelon Insights of 1,547 US adults, including a twenty-question 'True/False/Don't Know' quiz, and found that most Americans are remarkably ill-informed about AI. Only 16% of participants "passed" the test (scoring above 60%) indicating that the majority of Americans are AI illiterate.

Perhaps AI illiteracy shouldn't surprise us. AI is not part of our schools' curricula, and the main source of information about it today, according to our survey, is YouTube and social media. Yet AI is transforming the world around us at an alarming pace; AI literacy (a basic understanding of what it can do and what it cannot do) is critical for informing everyday decisions, adopting appropriate economic policies, and maintaining our national security. We are not advocating that everyone become adept at creating AI software, but rather that people should clearly understand AI's capabilities, limitations, and trajectory and how it affects their daily lives.

From pnw.ai  

View Full Article  

Making Decision Makers Use and Understand the Value of Models

Many times had to consider how to get key decision makers to use the results of analytical models.  This article touches on that in some ways. Like to consider further how this could be done consistently. 

Making machine learning more useful to high-stakes decision makers

A visual analytics tool helps child welfare specialists understand machine learning predictions that can assist them in screening cases.

Adam Zewe | MIT News Office

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation.

But these models don’t do any good if the humans they are intended to help don’t understand or trust their outputs.

Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening. In collaboration with a child welfare department in Colorado, the researchers studied how call screeners assess cases, with and without the help of machine learning predictions. Based on feedback from the call screeners, they designed a visual analytics tool that uses bar graphs to show how specific factors of a case contribute to the predicted risk that a child will be removed from their home within two years.... ' 

Stuck in a Logic Box?

Can think of a number of times. Can see how this could be added to ML delivered choices.

Are you stuck in a “logic box”?

Beware the trap of making a smart choice among flawed options.

In Strategy+Business by Adam Bryant

Many years ago, as a young business reporter at the New York Times, I learned about the pernicious concept of institutional imperative. The phrase was coined by Warren Buffett, who first wrote about it in his 1989 letter to shareholders, to help explain why organizations that are run by generally smart leaders often make misguided decisions. Though the term institutional imperative sounds like a good thing, Buffett characterized it as a sheeplike response to power and the status quo that can derail critical thinking.

“In business school,” the sage of Omaha wrote, “I was given no hint of the imperative’s existence and I did not intuitively understand it when I entered the business world. I thought then that decent, intelligent, and experienced managers would automatically make rational business decisions. But I learned over time that isn’t so. Instead, rationality frequently wilts when the institutional imperative comes into play.”

Two of Buffett’s examples: “Any business craving of the leader, however foolish, will be quickly supported by detailed rate-of-return and strategic studies prepared by his troops,” and “the behavior of peer companies, whether they are expanding, acquiring, setting executive compensation or whatever, will be mindlessly imitated.”

This powerful insight helped me understand the ways in which CEOs explained the rationale for deals that seemed puzzling in the moment, such as Time Warner’s merger with AOL in 2000. The US$350 billion deal, which was largely unwound ten years later, has been studied endlessly as one of the worst business transactions in history. But in the heat of the moment, once the leaders of each company had convinced themselves that the combination made sense, the institutional imperative kicked in to build unstoppable momentum and make the deal happen. ... ' 

Vint Cerf Examines Googles Misinformation

Short intro and link forward below. 

Via ACM NEWS

How Internet Pioneer Vint Cerf Illuminated Google's Misinformation Mess

By Fast Company,September 27, 2021

In June 2020, the Parliament of the U.K. published a policy report with numerous recommendations aimed at helping the government fight against the "pandemic of misinformation" powered by internet technology. The report is rather forceful on the conclusions it reaches: "Platforms like Facebook and Google seek to hide behind 'black box' algorithms which choose what content users are shown. They take the position that their decisions are not responsible for harms that may result from online activity. This is plain wrong."  ... .

Google chief Internet evangelist Vint Cerf testified that Google's evaluation of Websites includes "a manual process to establish criteria and a good-quality training set, and then a machine-learning system to scale up to the size of the World Wide Web, which we index. ... 

Full article. in FastCompany

Robot Swarms Chatting Less Means Better Decisions

General statement is interesting as as give decisions over to groups of robots

Less Chat Can Help Robots Make Better Decisions

By University of Sheffield (U.K.), July 30, 2021

Robot swarms could cooperate more effectively if communication among members of the swarm were curtailed, according to research by an international team led by engineers at the U.K.'s University of Sheffield.

The research team analyzed how a swarm moved around and came to internal agreement on the best area to concentrate in and explore.  Each robot evaluated the environment individually, made its own decision, and informed the rest of the swarm of its opinion; each unit then chose a random assessment that had been broadcast by another in the swarm to update its opinion on the best location, eventually reaching a consensus.

The team found the swarm's environmental adaptation accelerated significantly when robots communicated only to other robots within a 10-centimeter range, rather than broadcasting to the entire group.

From University of Sheffield (U.K.)

Pitfalls of Binary Decisions

Good thoughts, but simple is also good. 

Five ways to avoid the pitfalls of binary decisions

Before you decide, check how the question is framed to ensure you have all the information you need and have considered all your options.

by Eric J. McNulty

Deciding is easy: true or false? The first challenge in answering this question is that it’s impossible to know without more information. Which decisions, with what stakes, and on what timeline—these are just a few of the contextual factors most of us would want to consider before answering. The second challenge is that it’s probably not a true-or-false proposition.  ... '

AI Talks Decisions and their Completeness.

Good approach.   There is I recall a way to rate how good an explanation of a decision is in healthcare.  How well was this done? Also, since should be a complete conversation, a give and take set of statements and questions, how well was that done?   Further, its important to note which questions were not asked, and how they relate to that complete 'explanation'.    Overall,  conversation correctness and completeness and how they relate to future decisions.  

AI Matches Cardiologists' Expertise, While Explaining Its Decisions

University of California, San Francisco News, Elizabeth Fernandez, August 26, 2021

Scientists at the University of California, San Francisco and the University of California, Berkeley designed an artificial intelligence (AI) algorithm that diagnosed cardiovascular ailments as well as expert cardiologists, while explaining its reasoning. The researchers trained the convolutional neural network on commonly accessible electrocardiogram (ECG) data. The researchers said the algorithm performed strongly across 38 different diagnoses in five broad diagnostic categories. Because the researchers incorporated "explainability" into the algorithm, it highlighted ECG segments critical for each diagnosis, which may boost physicians' confidence in using it. The researchers said their results “offer strong support for AI algorithms like neural networks to be incorporated into existing commercial ECG algorithms, since they perform better for many diagnoses, can improve over time and provide additional insights through explainability.”

Algorithm Helping Demystifying Networks

Interesting, but don't understand it directly.  But thinking it. Boolean models are easy.

Algorithm May Help Scientists Demystify Complex Networks  By Penn State News,  July 21, 2021

( note the PSU article does it better)

A new algorithm capable of analyzing models of biological systems can lead to greater understanding of their underlying decision-making mechanisms, with implications for studying how complex behaviors are rooted in relatively simple actions.

Pennsylvania State University (Penn State)'s Jordan Rozum said the modeling framework includes Boolean networks.

Said Penn State's Reka Albert, "Boolean models describe how information propagates through the network," and the nodes' on/off states eventually slip into repeating patterns that correspond to the system's stable long-term behaviors.

Complexity can scale up dramatically as the system incorporates more nodes, particularly when events in the system are asynchronous. The researchers used parity and time-reversal transformations to boost the efficiency of the Boolean network analysis.

Full PSU article.

Data and Analytics for Better Decisions

From MIT Sloan, SAS, some thoughts on decisions for analytics.  I link to items I have read and liked.  All accessible from top link.

Data and Analytics for Better Decisions

Stepping up to business challenges and opportunities means knowing how to find relevant data — and put it to work. Free access to these four MIT Sloan Management Review articles is provided courtesy of SAS   

1. To Succeed With Data Science, First Build the ‘Bridge’  

2. Demystifying Data Monetization 

3. The Recession’s Impact on Analytics and Data Science 

4. Data Science, Quarantined 

...'  

AI and Decisions

Good, colorful piece on the who idea of designing decisions.  I like the point made that 'most decisions are not binary',  noting that it is rarely just finding the best decision, at minimum such an endeavor should include a risk analysis for that decision, and typically more. 

AI Designs Decisions  in Towardsdatascience

Dissection of survey evidence on AI-powered decision-making

Ian Domowitz

Havelock Ellis said it is not the attainment of the goal that matters, it is the things met with by the way. He was speaking of philosophy. In business AI is all about goal attainment. The things met along the way are decisions.

Decisions constitute a focus of the recent survey by Signal AI of 1,000 C-suite executives in an attempt to estimate the impact of AI on the U.S. economy. According to the survey, 96 percent of business leaders believe AI will transform decision making and 92 percent agree companies should leverage AI to augment decision-making processes.

AI is not so sure.

Most decisions are not binary

Neither survey nor business directors are informative with respect to the types of decisions involved. Most respondents say they spend upwards of 40 hours a week on the process. No surprise: that is presumably why they are paid, but with 80 percent of leaders claiming there are too much data to evaluate, senior management is looking for relief. Where does AI fit in the picture?

AI aspires to set and achieve goals by motivating and guiding the organization through phases of decision making. Four kinds of decisions are relevant.

Policy decisions involve choosing what goals to pursue and how they will be attained. Proper adaptation of the technology to the company ought to define these objectives. AI risks failure at this step by falling in love with creative fire and failing to recognize practical guidelines.  .... 

Excel as a Programming Language

Intriguing Podcast.  The mere notion will  get considerable disdain from coders.   But an interesting point is made about the idea. There is power here.    Podcast and text transcript:  

Advancing Excel as a programming language with Andy Gordon and Simon Peyton Jones

Episode 120 | May 5, 2021   from Microsoft Research. 

Today, people around the globe—from teachers to small-business owners to finance executives—use Microsoft Excel to make sense of the information that occupies their respective worlds, and whether they realize it or not, in doing so, they’re taking on the role of programmer. 

In this episode, Senior Principal Research Manager Andy Gordon, who leads the Calc Intelligence team at Microsoft Research, and Senior Principal Researcher Simon Peyton Jones provide an inside account of the journey Excel has taken as a programming language, including the expansion of data types that has unlocked greater functionality and the release of the LAMBDA function, which makes the Excel formula language Turing-complete. They’ll talk specifically about how research has influenced Excel and vice versa, programming as a human-computer interaction challenge, and a future in which Excel is the first language for budding programmers and a tool for incorporating probabilistic reasoning into our decision-making.  

Learn more: 

Excel Blog: “Announcing LAMBDA: Turn Excel formulas into custom functions” 

Microsoft Research Blog: “LAMBDA: The ultimate Excel worksheet function” 

Research Collection: “Innovation by (and beyond) the numbers: A history of research collaborations in Excel”    ... "