Mind Matters Reporting on Natural and Artificial Intelligence

CategoryMachine Learning

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Interview: New Book Outlines the Perils of Big (Meaningless) Data

Gary Smith, co-author with Jay Cordes of Phantom Patterns, shows why human wisdom and common sense are more important than ever now

Economist Gary Smith and statistician Jay Cordes have a new book out, The Phantom Pattern Problem: The mirage of big data, on why we should not trust Big Data over common sense. In their view, it’s a dangerous mix: Humans naturally assume that all patterns are significant. But AI cannot grasp the meaning of any pattern, significant or not. Thus, from massive number crunches, we may “learn” (if that’s the right word) that Stock prices can be predicted from Google searches for the word debt. Stock prices can be predicted from the number of Twitter tweets that use “calm” words. An unborn baby’s sex can be predicted by the amount of breakfast cereal the mother eats. Bitcoin prices can be…

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Entrance gate to Persepolis Persia Iran Gate of All Nations

How Much Can New AI Tell Us About Ancient Times?

An ambitious new project hopes to use the predictive text that cell phones use to unlock their stories

Many researchers hope that AI will leading to a“golden age” of discovery for lost languages, hard to decipher writings, and badly damaged Biblical scrolls. Algorithms can chug through vast numbers of possibilities of interpretation, presenting the scholar with probabilities to choose from. But even powerful algorithms have their work cut out for them. For example, of the hundreds of thousands of clay (cuneiform) tablets that survive from an ancient part of the Near East called Mesopotamia, many are damaged. We may know the language but we don’t know what’s missing from the text and what difference the missing part makes to what is being said. Experts try to fill in the missing parts but guessing at all the possibilities is…

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SwiftKey Co-founder: Computers Can’t Just “Evolve” Intelligence

Can vain hopes for AI spring from a wrong understanding of evolution?
Ben Medlock asks us to look at self-organization as a principle of life, lacking in computers. Read More ›
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Oh no!

Six Limitations of Artificial Intelligence As We Know It

You’d better hope it doesn’t run your life, as Robert J. Marks explains to Larry Linenschmidt

The list is a selection from “Bingecast: Robert J. Marks on the Limitations of Artificial Intelligence,” a discussion between Larry L. Linenschmidt of the Hill Country Institute and Walter Bradley Center director Robert J. Marks. The focus on why we mistakenly attribute understanding and creativity to computers. The interview was originally published by the Hill Country Institute and is reproduced with thanks.  https://episodes.castos.com/mindmatters/Mind-Matters-097-Robert-Marks.mp3 Here is a partial transcript, listing six limits of AI as we know it: (The Show Notes, Additional Resources, and a link to the full transcript are below.) 1. Computers can do a great deal but, by their nature, they are limited to algorithms. Larry L. Linenschmidt: When I read the term “classical computer,” how does a computer function? Let’s build on…

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Real Php code developing screen. Programing workflow abstract algorithm concept. Lines of Php code visible under magnifying lens.

Will Ideas or Algorithms Rule Science Tomorrow?

David Krakauer of the Santa Fe Institute offers an unsettling vision of future science as produced by machines that no one really understands

The basic problem is that accepting on faith what we can’t ever hope to understand is not a traditional stance of science. Thus it’s a good question whether science could survive such a transition and still be recognizable to scientists. But does turning things over to incomprehensible algorithms, as Krakauer proposes, really work anyway? Current results from a variety of areas give pause for thought.

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Robot studies a coronavirus with magnifier,nano robot with bacterium,3d render.

Can AI Save Us from COVID-19? An Expert Is Skeptical

To use AI more successfully next time, we need a clear understanding of its limitations as well as its capabilities

Experts list various problems, including the fact that AI is vulnerable to failure due to unforeseen problems, including problems with data (too sparse, too noisy, too many outliers, etc.). It also doesn’t learn as well from experience as humans do.

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Why Depend on Only One Source for Modeling AI in Healthcare?

We may be missing many of the ways AI can help us

As we struggle with the COVID-19 crisis, many are beginning to ask hard questions about how our system works, its strengths, weaknesses, and vulnerabilities. One vulnerability might be too heavy reliance on a single source for data modeling and predictions. Considering all the uses to which AI may be put in health care, getting our guidance exclusively from the Institute for Health and Metric Evaluation for modeling is reckless.

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Will the COVID-19 Pandemic Promote Mass Automation?

Caution! Robots don’t file for benefits but that’s not all we need to know about them

I understand the panic many business leaders experience as they try to stay solvent while customers evaporate. Panic, however, is a poor teacher: AI-based automation will not only not solve all their problems, it may very well add to them. AI is not a magic box into which we can stuff them and make them disappear.

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AI Is Not Ready to Moderate Content!

In the face of COVID-19 quarantines for human moderators, some look to AI to keep the bad stuff off social media

Big social media companies have long wanted to replace human content moderators with AI. COVID-19 quarantines have only intensified that discussion. But AI is far, far from ready to successfully moderate content in an age of where virtual monopolies make single point failure a frequent risk.

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All AI’s Are Psychopaths

We can use them but we can’t trust them with moral decisions. They don’t care why

Building an AI entails moving parts of our intelligence into a machine. We can do that with rules, (simplified) virtual worlds, statistical learning… We’ll likely create other means as well. But, as long as “no one is home”—that is, the machines lack minds—gaps will remain and those gaps, without human oversight, can put us at risk.

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Citation

Anti-Plagiarism Software Goof: Paper Rejected for Repeat Citations

The scholar was obliged by discipline rules to cite the flagged information repetitively

Not only was Jean-François Bonnefon’s paper rejected by conventional anti-plagiarism software but the rejection didn’t make any sense. Bonnefon, research director at Toulouse School of Economics, was informed of “a high level of textual overlap with previous literature” (plagiarism) when he was citing scientists’ affiliations, standard descriptions, and papers cited by other—information he was obliged to cite accurately, according to a standard format. “It would have taken two [minutes] for a human to realise the bot was acting up,” he wrote on Twitter. “But there is obviously no human in the loop here. We’re letting bots make autonomous decisions to reject scientific papers.” Reaction to the post by Dr Bonnefon, who is currently a visiting scientist at the Massachusetts Institute…

Demographic Change

Can The Machine TELL If You Are Psychotic or Gay?

No, and the hype around what machine learning can do is enough to make old-fashioned tabloids sound dull and respectable

Media often co-operate with researchers’ inflated claims about machine learning’s powers of discovery. An ingenious “creative” approach to accuracy enables the misrepresentation, says data analyst Eric Siegel.

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Will an AI Win a Nobel Prize for Science All by Itself One Day?

No, but Support Vector Machines (SVMs) can allow scientists to frame questions so that a comprehensible answer is more likely

AI can certainly help scientists. But to understand why AI can’t do science on its own, we should take a look at the NP-Hard Problem in computer science. The “Hard” is in the name of the problem for a reason… 

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Machines Can’t Teach Us How To Learn

A recent study used computer simulations to test the “small mistakes” rule in human learning

Machine learning is not at all like human learning. For example, machine learning frequently requires millions of examples. Humans learn from a few examples.

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Archeological site of Phaistos in Crete

Can AI Help Us Decipher Lost Languages?

That depends mainly on the reasons we haven’t yet deciphered ancient texts

AI can speed up translation of ancient documents where only a few scholars know the language. Whether it can help with mysterious unknown languages like Minoan A is another question.

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The difference between right and wrong

Will Self-Driving Cars Change Moral Decision-Making?

It’s time to separate science fact from science fiction about self-driving cars

Irish playwright John Waters warns of a time when we might have to grant moral discretion to computer algorithms, just as Christians now grant to the all-knowing but often inscrutable decrees of God. Not likely.

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Improve Your Job Chances by Scaling the Cloud

WBC Fellow Releases introductory book on Building Scalable PHP web applications using the cloud

One new issue that the cloud creates is that programmers are more often required to be “full stack” developers,” Jonathan Bartlett explains. “Unfortunately, most programmers coming out of college have little to no system administration experience. That’s why this book is based on the ‘full stack’ concept, showing how system administration and programming relate to each other.”

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What Vehicle Would Bob Buy?

Both empirical generalized information (EGI) and the Gini metric can generate useful information

Contrary to traditional Fisherian hypothesis testing, it is possible to create models after viewing the data and still quantify the generality of the model.

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Machine learning: Harnessing the Power of Empirical Generalized Information (EGI)

We can calculate a probability bound from entropy. Entropy also happens to be an upper bound on the binomial distribution

We want our calculation to demonstrate the notion that if we have high accuracy and a small model, then we have high confidence of generalizing. Intuitively, then, we add the model size to the accuracy and subtract this quantity from the entropy of having absolutely no information about the problem.

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Missing piece

Machine Learning: Decision Trees Can Solve Murders

Decision trees increase our chance at a right answer. We can see how that works in a mystery board game like Clue

Entropy is a concept in information theory that characterizes the number of choices available when a probability distribution is involved. Understanding how it works helps us make better guesses.

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