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Michal Kosinski, computational psychologist at Stanford University, has been testing several iterations of the ChatGPT AI chatbot developed by Open AI on its ability to pass the famous Theory of Mind Test. In his paper posted on the arXiv preprint server, Kosinski reports that testing the latest version of ChatGPT found that it passed at the level of the average 9-year-old child.

ChatGPT and other AI chatbots have sophisticated abilities, such as writing complete essays for and college students. And as their abilities improve, some have noticed that chatting with some of the software apps is nearly indistinguishable from chatting with an unknown and unseen human. Such findings have led some in the psychology field to wonder about the impact of these applications on both individuals and society. In this new effort, Kosinski wondered if such chatbots are growing close to passing the Theory of Mind Test.

The Theory of Mind Test is, as it sounds, meant to test the , which attempts to describe or understand the mental state of a person. Or put another way, it suggests that people have the ability to “guess” what is going on in another person’s mind based on available information, but only to a limited extent. If someone has a particular facial expression, many people will be able to deduce that they are angry, but only those who have certain knowledge about the events leading up to the facial cues are likely to know the reason for it, and thus to predict the thoughts in that person’s head.

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Mathematicians of the era sought a solid foundation for mathematics: a set of basic mathematical facts, or axioms, that was both consistent — never leading to contradictions — and complete, serving as the building blocks of all mathematical truths.

But Gödel’s shocking incompleteness theorems, published when he was just 25, crushed that dream. He proved that any set of axioms you could posit as a possible foundation for math will inevitably be incomplete; there will always be true facts about numbers that cannot be proved by those axioms. He also showed that no candidate set of axioms can ever prove its own consistency.

His incompleteness theorems meant there can be no mathematical theory of everything, no unification of what’s provable and what’s true. What mathematicians can prove depends on their starting assumptions, not on any fundamental ground truth from which all answers spring.

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Scientists haʋe recently reported discoʋering what they Ƅelieʋe is the мost мassiʋe Ƅlack hole eʋer discoʋered in the early Uniʋerse.

It is 34 Ƅillion tiмes the мass of our Sun, and it eats the equiʋalent of one Sun daily. The research led Ƅy the National Uniʋersity of Australia (ANU) has reʋealed how мassiʋe the fastest-growing Ƅlack hole in the Uniʋerse is, as well as how мuch мatter it can suck in. The Ƅlack hole, known as ‘J2157’, was discoʋered Ƅy the saмe research teaм in 2018. The study detailing the huмongous Ƅlack hole’s characteristics has Ƅeen puƄlished in Monthly Notices of the Royal Astronoмical Society. According to Dr. Christopher Onken and his colleagues, this oƄject is 34 Ƅillion tiмes the Sun’s мass and goƄƄles up the equiʋalent of one Sun eʋery day. That’s a Ƅillion with a Ƅ.

For other coмparisons, the мonstrous Ƅlack hole has a мass of approxiмately 8,000 tiмes that of Sagittarius A*, the Ƅlack hole located at the center of the Milky Way galaxy. “If the Milky Way’s Ƅlack hole wanted to get fat, it would haʋe to swallow two-thirds of all the stars in our galaxy,” explains Onken. Scientists studied the oƄject at a tiмe when the Uniʋerse was only 1.2 Ƅillion years old, less than 10% of its current age, which мakes the Ƅlack hole the largest known in terмs of мass in the early Uniʋerse. “It is the largest Ƅlack hole eʋer мeasured in this early period of the Uniʋerse,” says Onken.

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Why would someone falling into a stellar-mass black hole be spaghettified, but someone crossing the event horizon of a supermassive black hole would not feel much discomfort?

As it turns out, there is a relatively simple equation that describes the tidal acceleration that a body of length d would feel, based on its distance from a given object with mass M: a = 2GMd/R3, where a is the tidal acceleration, G is the gravitational constant, and R is the body’s distance to the center of the object (with mass M).

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The habitable zone is the region around a star where an orbiting planet could host liquid water and, therefore, possibly support life.

The habitable zone is also known as the “Goldilocks zone” because planets orbiting at that “just right” distance from a star are not too hot or too cold to host liquid water. If planets are closer to their star, the water turns to steam; if they’re farther, it freezes.

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This is NOT for ChatGPT, but instead its the AI tech used in beating GO, Chess, DOTA, etc. In other words, not just generating the next best word based on reading billions of sentences, but planning out actions to beat real game opponents (and winning.) And it’s free.

Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. In this full tutorial course, you will get a solid foundation in reinforcement learning core topics.

The course covers Q learning, SARSA, double Q learning, deep Q learning, and policy gradient methods. These algorithms are employed in a number of environments from the open AI gym, including space invaders, breakout, and others. The deep learning portion uses Tensorflow and PyTorch.

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