Tag: AI

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Can A.I. Predict Events in the Lives of Real People?

By Peter Aagaard Brixen

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In a new scientific article, ‘Using sequences of life-events to predict human lives’, published in Nature Computational Science, researchers have analyzed health data and attachment to the labor market for 6 million Danes in a model dubbed life2vec. After the model has been trained in an initial phase, i.e., learned the patterns in the data, it has been shown to outperform other advanced neural networks and predict outcomes such as personality and time of death with high accuracy (https://www.nature.com/articles/s43588-023-00573-5).
The predictions from Life2vec are answers to general questions such as: ‘death within four years’? When the researchers analyze the model’s responses, the results are consistent with existing findings within the social sciences; for example, all things being equal, individuals in a leadership position or with a high income are more likely to survive, while being male, skilled or having a mental diagnosis is associated with a higher risk of dying. Life2vec encodes the data in a large system of vectors, a mathematical structure that organizes the different data. The model decides where to place data on the time of birth, schooling, education, salary, housing and health.
The researchers behind the article point out that ethical questions surround the life2vec model, such as protecting sensitive data, privacy, and the role of bias in data. These challenges must be understood more deeply before the model can be used, for example, to assess an individual’s risk of contracting a disease or other preventable life events.
According to the researchers, the next step would be to incorporate other types of information, such as text and images or information about our social connections. This use of data opens up a whole new interaction between social and health sciences.
A transformer model is a type of AI, deep learning data architecture used to learn about language and other tasks. The models can be trained to understand and generate language. The transformer model is designed to be faster and more efficient than previous models and is often used to train large language models on large datasets.
A neural network is a computer model inspired by the brain and nervous system of humans and animals. There are many different types of neural networks (e.g. transformer models).
Like the brain, a neural network is made up of (artificial) neurons. These neurons are connected and can send signals to each other. Each neuron receives input from other neurons and then calculates an output that is passed on to other neurons.
A neural network can learn to solve tasks by training on large amounts of data. 
Neural networks rely on training data to learn and improve their accuracy over time. But once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence that, according to researchers, allow us to classify and group data at high speed. One of the most well-known neural networks is Google’s search algorithm.

AR #112

The Artificial Intelligence Threat

by Stephen Robbins, Ph.D.

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Can Machines Be Made Self-Aware?

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According to Australian Ph.D. candidate Michael Timothy Bennett, in an online column for The Conversation (www.theconversation.com/us) “To build a machine, one must know what its parts are and how they fit together. To understand the machine, one needs to know what each part does and how it contributes to its function. In other words, one should be able to explain the “mechanics” of how it works. “Human-like intent,” believes Bennett, “would require human-like experiences and feelings, which is a difficult thing to engineer. Furthermore, we can’t easily test for the full richness of human consciousness. Consciousness is a broad and ambiguous concept that encompasses—but should be distinguished from—the more narrow claims.”

According to a philosophical approach called mechanism, humans are arguably a type of machine—and our ability to think, speak and understand the world is the result of a mechanical process we don’t understand.
“Bombs and phones, say other critics, may be getting smarter, but we are getting dumber,” wrote anthropologist Dr. Susan Martinez in A.R. #130, “attention span alarmingly shortened from the New York minute to the Cyber Second. Constant emailing and instant-messaging, says one group of London psychiatrists, “might do more damage to you brain than smoking pot.” Meanwhile, said Martinez, “one Canadian researcher has concluded that our obsessive use of information technology is dumbing us down and encouraging superficial and uncritical thinking … [as well as] leading to compulsive behavior”. Tech addicts are fessing up: Casey F., for one, says she had to ditch her smartphone altogether: “If I have one, I will check it obsessively”. Recovering addict, Blake S. talks about how he finally broke the spell of “days attached to a smart phone from wake until sleep.” The addiction has become so prevalent (many Americans spending one quarter of their time staring at their phones) that we now have books with titles like How to Break Up With Your Phone. College kids, taking out their earbuds long enough to discuss how technology dominates their lives, swarm across campus “like giant schools of cyborg jellyfish” (Gregoire).

You know the blowback is getting serious when major Apple investors call for a probe into iPhone addiction among young users. There is a sense that much of today’s tech is less about “facilitating” the way we live than about shaping and controlling it.

Over a half century ago in his best-seller Future Shock, Alvin Toffler gave a dim prognosis for “goals set without the participation of those affected.” Instability and upheaval, he predicted, will inevitably arise from “top-down technocracy.”

AR #99

The Future of Scientific Genius

by J. Douglas Kenyon, Publisher’s Letter

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Can AI Take a Joke?

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Large neural networks, a form of artificial intelligence, can generate thousands of jokes along the lines of “Why did the chicken cross the road?” But do they understand why they’re funny?

Using hundreds of entries from the New Yorker magazine’s Cartoon Caption Contest as a testbed, researchers challenged AI models and humans with three tasks: matching a joke to a cartoon; identifying a winning caption; and explaining why a winning caption is funny. 

In all tasks, humans performed demonstrably better than machines, even as AI advances such as ChatGPT have closed the performance gap. So are machines beginning to “understand” humor? In short, they’re making some progress, but aren’t quite there yet.

“The way people challenge AI models for understanding is to build t0ests for them – multiple choice tests or other evaluations with an accuracy score,” said Jack Hessel, Ph.D. ’20, research scientist at the Allen Institute for AI (AI2). “And if a model eventually surpasses whatever humans get at this test, you think, ‘OK, does this mean it truly understands?’ It’s a defensible position to say that no machine can truly `understand’ because understanding is a human thing. But, whether the machine understands or not, it’s still impressive how well they do on these tasks.”
Hessel is lead author of “Do Androids Laugh at Electric Sheep? Humor ‘Understanding’ Benchmarks from The New Yorker Caption Contest,” which won a best-paper award at the 61st annual meeting of the Association for Computational Linguistics, held July 9-14 in Toronto.

Lillian Lee ’93, the Charles Roy Davis Professor in the Cornell Ann S. Bowers College of Computing and Information Science, and Yejin Choi, Ph.D. ’10, professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and the senior director of common-sense intelligence research at AI2, are also co-authors on the paper.

For their study, the researchers compiled 14 years’ worth of New Yorker caption contests – more than 700 in all. Each contest included: a captionless cartoon; that week’s entries; the three finalists selected by New Yorker editors; and, for some contests, crowd quality estimates for each submission.  

For each contest, the researchers tested two kinds of AI – “from pixels” (computer vision) and “from description” (analysis of human summaries of cartoons) – for the three tasks.

“There are datasets of photos from Flickr with captions like, ‘This is my dog,’” Hessel said. “The interesting thing about the New Yorker case is that the relationships between the images and the captions are indirect, playful, and reference lots of real-world entities and norms. And so the task of ‘understanding’ the relationship between these things requires a bit more sophistication.”

In the experiment, matching required AI models to select the finalist caption for the given cartoon from among “distractors” that were finalists but for other contests; quality ranking required models to differentiate a finalist caption from a nonfinalist; and explanation required models to generate free text saying how a high-quality caption relates to the cartoon.

Hessel penned the majority of human-generated explanations himself, after crowdsourcing the task proved unsatisfactory. He generated 60-word explanations for more than 650 cartoons.

“A number like 650 doesn’t seem very big in a machine-learning context, where you often have thousands or millions of data points,” Hessel said, “until you start writing them out.”

This study revealed a significant gap between AI- and human-level “understanding” of why a cartoon is funny. The best AI performance in a multiple choice test of matching cartoon to caption was only 62% accuracy, far behind humans’ 94% in the same setting. And when it came to comparing human- vs. AI-generated explanations, humans’ were preferred roughly 2-to-1.

While AI might not be able to “understand” humor yet, the authors wrote, it could be a collaborative tool humorists could use to brainstorm ideas.

Other contributors include Ana Marasovic, assistant professor at the University of Utah School of Ca.

AR #109

Self Fulfilling Skepticism

Brendan D. Murphy

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Ancient-Mysteries Magazine Bucks the Artificial-Intelligence Trend with “AI-FREE” Content

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Forget ChatGPT. At a time when the Internet is ablaze with the wonders of artificial intelligence, at least one content-rich on-line site is challenging conventional wisdom. In its regular blog posts, AtlantisRising.com (formerly Atlantis Rising Magazine, “magazine of record for ancient mysteries, future science, and the unexplained”) is taking the novel and counter-intuitive step of certifying that it publishes only authentic, ‘humanly-written’, content, and is labeling its stories accordingly.

Just as “healthy” food is marketed as, ‘free from artificial ingredients’, Atlantis Rising claims that, no matter how much popular AI-produced content, may resemble the real thing, it remains only a facsimile. Educated readers won’t be fooled. The reasons are complicated but, according to Doug Kenyon, AR’s long-time editor, “notwithstanding advanced machine learning, hyper language modeling, etc., the kind of personal insights that come from someone who still has skin in the game, can’t be duplicated by any mere computer or network.” What passes as the equivalent of genuine human intelligence is as unsatisfactory to the consumer as cheap white wine.

Long before Ancient Apocalypse, and other currently trending media with similar subject matter, there was Atlantis Rising Magazine, distributed for many years on newsstands internationally by Curtis circulation. The Atlantis Rising Research Group web site remains a visible brand on the internet with registered U.S. trademark and a small but loyal band of followers.

 The site’s web-site archives still contain most of the content from 135 published issues, including hundreds of original, well-researched, well-written, authoritative, and profusely illustrated articles, along with many books which the magazine has also produced. PDFs and other related products and videos can be purchased for download on line at AtlantisRising.com.

Ghosts of Atlantis, the recent 436-page book by Atlantis Rising editor J. Douglas Kenyon, is published by Inner Traditions/Simon & Schuster. 


Technologies of the Gods, one of the magazine’s original live action one-hour documentaries has drawn over a million viewings on platforms around the world. (https://www.youtube.com/watch?v=IXZr16VJg1s).

Additional documentaries include: Clash of the Geniuses and English Sacred Sites

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Can Pigeons Compete with Artificial Intelligence?

Psychologists have examined the workings of the pigeon brain and claim the “brute force” of the bird’s learning shares similarities with artificial intelligence. That’s the conclusion of a new paper from University of Iowa scientists.

In their experiment, researchers gave the pigeons complex categorization tests that high-level thinking, such as using logic or reasoning, would not aid in solving. Instead, the pigeons, by virtue of exhaustive trial and error, eventually were able to memorize enough scenarios in the test to reach nearly 70% accuracy.

The researchers equate the pigeons’ repetitive, trial-and-error approach to artificial intelligence. Computers employ the same basic methodology, the researchers contend, being “taught” how to identify patterns and objects easily recognized by humans. Granted, computers, because of their enormous memory and storage power, far surpass anything pigeon brains would be able to conjure.

Still, the basic process of making associations—considered a ‘lower-level’ thinking technique—is the same between the test-taking pigeons and the latest AI advances.

We know that many birds migrate for thousands of miles, but the question of how they navigate so well and on such tight seasonal schedules has baffled science for generations. In recent years, studies, have indicated that the secret may lie in a special sensitivity to Earth’s magnetic field. It has not been entirely clear, however, just how birds are able to read that field, though it has been speculated that it may work for them something like a compass works for humans.

A recent pair of European studies argued that there is a strange protein in the birds’ eyes which makes it possible for them to actually see, at some other level of their vision, the planet’s magnetic field. Biologists at Sweden’s University of Lund, who studied zebra finches, and at Germany’s Carl von Ossietzky University in Oldenburg, who studied Robins, found that a protein called cryptochrome, linked to circadian rhythms, can facilitate something called ‘magnetoreception’.

Alternative science researchers, in one school of thought, have long believed that ‘magnetoreception’ is a faculty available to many species, including humans. Some have even theorized that so-called extra-sensory perception (ESP) is related to magnetic sensitivity. The research of renowned British biologist Rupert Sheldrake, however, suggests that, to explain ESP, something deeper may be needed. Clear evidence has been compiled by Sheldrake suggesting that many domesticated animals, including dogs and cats, know when their owners are coming home, and that many people know who is about to telephone them. These, and many similar phenomena would seem to demand a better explanation than magnetic sensitivity alone.

https://now.uiowa.edu/2023/02/ui-study-pigeons-use-same-basic-learning-process-ai

AR #99

The Future of Scientific Genius

by J. Douglas Kenyon, Publisher’s Letter

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Limits to Artificial Intelligence Revealed

Humans are usually pretty good at recognizing when they get things wrong, but artificial intelligence systems are not. According to a new study, AI generally suffers from inherent limitations due to a century-old mathematical paradox.

Like some people, AI systems often have a degree of confidence that far exceeds their actual abilities. And like an overconfident person, many AI systems don’t know when they’re making mistakes. Sometimes it’s even more difficult for an AI system to realize when it’s making a mistake than to produce a correct result.

Researchers from the University of Cambridge and the University of Oslo say that instability is the Achilles’ heel of modern AI and that a mathematical paradox shows AI’s limitations. Neural networks, the state of the art tool in AI, roughly mimic the links between neurons in the brain. The researchers show that there are problems where stable and accurate neural networks exist, yet no algorithm can produce such a network. Only in specific cases can algorithms compute stable and accurate neural networks.

The researchers propose a classification theory describing when neural networks can be trained to provide a trustworthy AI system under certain specific conditions. Their results are reported in the Proceedings of the National Academy of Sciences. (https://www.pnas.org/doi/full/10.1073/pnas.2107151119)

Deep learning, the leading AI technology for pattern recognition, has been the subject of numerous breathless headlines. Examples include diagnosing disease more accurately than physicians or preventing road accidents through autonomous driving. However, many deep learning systems are untrustworthy and easy to fool.

“Many AI systems are unstable, and it’s becoming a major liability, especially as they are increasingly used in high-risk areas such as disease diagnosis or autonomous vehicles,” said co-author Professor Anders Hansen from Cambridge’s Department of Applied Mathematics and Theoretical Physics. “If AI systems are used in areas where they can do real harm if they go wrong, trust in those systems has got to be the top priority.”

The paradox identified by the researchers traces back to two 20th century mathematical giants: Alan Turing and Kurt Gödel. At the beginning of the 20th century, mathematicians attempted to justify mathematics as the ultimate consistent language of science. However, Turing and Gödel showed a paradox at the heart of mathematics: it is impossible to prove whether certain mathematical statements are true or false, and some computational problems cannot be tackled with algorithms. And, whenever a mathematical system is rich enough to describe the arithmetic we learn at school, it cannot prove its own consistency.

Decades later, the mathematician Steve Smale proposed a list of 18 unsolved mathematical problems for the 21st century. The 18th problem concerned the limits of intelligence for both humans and machines.

“The paradox first identified by Turing and Gödel has now been brought forward into the world of AI by Smale and others,” said co-author Dr Matthew Colbrook from the Department of Applied Mathematics and Theoretical Physics. “There are fundamental limits inherent in mathematics and, similarly, AI algorithms can’t exist for certain problems.”

The researchers say that, because of this paradox, there are cases where good neural networks can exist, yet an inherently trustworthy one cannot be built. “No matter how accurate your data is, you can never get the perfect information to build the required neural network,” said co-author Dr Vegard Antun from the University of Oslo.

The impossibility of computing the good existing neural network is also true regardless of the amount of training data. No matter how much data an algorithm can access, it will not produce the desired network. “This is similar to Turing’s argument: there are computational problems that cannot be solved regardless of computing power and runtime,” said Hansen.

The researchers say that not all AI is inherently flawed, but it’s only reliable in specific areas, using specific methods. “The issue is with areas where you need a guarantee, because many AI systems are a black box,” said Colbrook. “It’s completely fine in some situations for an AI to make mistakes, but it needs to be honest about it. And that’s not what we’re seeing for many systems — there’s no way of knowing when they’re more confident or less confident about a decision.”

“Currently, AI systems can sometimes have a touch of guesswork to them,” said Hansen.”You try something, and if it doesn’t work, you add more stuff, hoping it works. At some point, you’ll get tired of not getting what you want, and you’ll try a different method. It’s important to understand the limitations of different approaches. We are at the stage where the practical successes of AI are far ahead of theory and understanding. A program on understanding the foundations of AI computing is needed to bridge this gap.”

“When 20th-century mathematicians identified different paradoxes, they didn’t stop studying mathematics. They just had to find new paths, because they understood the limitations,” said Colbrook. “For AI, it may be a case of changing paths or developing new ones to build systems that can solve problems in a trustworthy and transparent way, while understanding their limitations.”

The next stage for the researchers is to combine approximation theory, numerical analysis and foundations of computations to determine which neural networks can be computed by algorithms, and which can be made stable and trustworthy. Just as the paradoxes on the limitations of mathematics and computers identified by Gödel and Turing led to rich foundation theories — describing both the limitations and the possibilities of mathematics and computations — perhaps a similar foundations theory may blossom in AI.

AR #112, The Artificial Intelligence Threat
by Stephen Robbins