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AI Philosophy

The AI model was trained using answers from Dennett on a range of questions about free will, whether animals feel pain and even favorite bits of other philosophers. The researchers then asked different groups of people to compare the AI’s responses and Dennett’s real answers and see if they could tell them apart. They used responses from 302 random people online who followed a link from Schwitzgebel’s blog, 98 confirmed college graduates from the online research platform Prolific, and 25 noted Dennett experts. Immersion in Dennett’s philosophy and work didn’t prevent anyone from struggling to identify the source of the answers, however.

The research platform participants only managed an average success rate of 1.2 out of 5 questions. The blog readers and experts answered ten questions, with the readers hitting an average score of 4.8 out of 10. That said, not a single Dennett expert was 100% correct, with only one answering nine correctly and an average of 5.1 out of 10, barely higher than the blog readers. Interestingly, the question whose responses most confused the Dennett experts was actually about AI sentience, specifically if people could “ever build a robot that has beliefs?” Despite the impressive performance by the GPT-3 version of Dennett, the point of the experiment wasn’t to demonstrate that the AI is self-aware, only that it can mimic a real person to an increasingly sophisticated degree and that OpenAI and its rivals are continuing to refine the models so that similar quizzes will likely get harder to pass.

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Multivariable calculus, differential equations, linear algebra—topics that many MIT students can ace without breaking a sweat—have consistently stumped machine learning models. The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.

Now, a multidisciplinary team of researchers from MIT and elsewhere, led by Iddo Drori, a lecturer in the MIT Department of Electrical Engineering and Computer Science (EECS), has used a to solve university-level math problems in a few seconds at a human level.

The model also automatically explains solutions and rapidly generates new problems in university math subjects. When the researchers showed these machine-generated questions to , the students were unable to tell whether the questions were generated by an algorithm or a human.

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Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum.

Often, an expert operator must use manual trial-and-error—possibly making thousands of prints—to determine ideal parameters that consistently print a new material effectively. These parameters include speed and how much material the printer deposits.

MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses to watch the and then correct errors in how it handles the material in real-time.

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This post is also available in: he עברית (Hebrew)

Imagine knowing the future. Being able to predict what’s going to happen next. It feels that this concept is merely a dream, but in reality, this dream is underway. Modeling and simulation, data analytics, AI and machine learning, distributed systems, social dynamics and human behavior simulation are fast becoming the go-to tools, and their qualities could offer significant advantages for the battlespace of tomorrow.

According to army-technology.com, London-based technology provider Improbable has been working closely with the UK Ministry of Defense (MoD) since 2018 to explore the utility of synthetic environments (SEs) for tactical training and operational and strategic planning. At the core of this work is Skyral, a platform that supports an ecosystem of industry and academia enabling the fast construction of new SEs for almost any scenario using digital entities, algorithms, AI, historic and real-time data.

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Got a protein? This AI will tell you what it looks like.

AlphaFold was recognized by the journal Science as 2021’s Breakthrough of the Year, beating out candidates like Covid-19 antiviral pills and the application of CRISPR gene editing in the human body. One expert even wondered if AlphaFold would become the first AI to win a Nobel Prize.

The breakthroughs have kept coming.

Last week, DeepMind announced that researchers from around the world have used AlphaFold to predict the structures of some 200 million proteins from 1 million species, covering just about every protein known to human beings. All of that data is being made freely available on a database set up by DeepMind and its partner, the European Molecular Biology Laboratory’s European Bioinformatics Institute.

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View insights.

The University of Innsbruck, Austria, realized a quantum computer that breaks out of this paradigm and unlocks additional computational resources, hidden in almost all of today’s quantum devices. Computers are well-known for operating with binary information, or zeros and ones, which has led to computers powering so much. This new approach results in more computational power with fewer quantum particles.

Quantum computers work with more than zero and one and digital computers work with zeros and ones, also called binary information. Quantum computers are also designed with binary information processing in mind. In fact, it was so successful that computers now power everything from coffee makers to self-driving cars, and it’s hard to imagine life without them. Restricting researchers to binary systems prevent these devices from living up to their true potential.

The research team succeeded in developing a quantum computer that can perform arbitrary calculations with so-called quantum digits, thereby unlocking more computational power with fewer quantum particles. Unlike the classical method, the new method that utilizes more states does not negatively impact the reliability of the computer. The researchers have developed a quantum computer that can make use of the full potential of these atoms.

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Constructing a tiny robot from DNA and using it to study cell processes invisible to the naked eye… You would be forgiven for thinking it is science fiction, but it is in fact the subject of serious research by scientists from Inserm, CNRS and Université de Montpellier at the Structural Biology Center in Montpellier. This highly innovative “nano-robot” should enable closer study of the mechanical forces applied at microscopic levels, which are crucial for many biological and pathological processes. It is described in a new study published in Nature Communications.

Our are subject to exerted on a microscopic scale, triggering biological signals essential to many involved in the normal functioning of our body or in the development of diseases.

For example, the feeling of touch is partly conditional on the application of mechanical forces on specific cell receptors (the discovery of which was this year rewarded by the Nobel Prize in Physiology or Medicine). In addition to touch, these receptors that are sensitive to mechanical forces (known as mechanoreceptors) enable the regulation of other key biological processes such as blood vessel constriction, pain perception, breathing or even the detection of sound waves in the ear, etc.

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Physicists are (temporarily) augmenting reality to crack the code of quantum systems.

Predicting the properties of a molecule or material requires calculating the collective behavior of its . Such predictions could one day help researchers develop new pharmaceuticals or design materials with sought-after properties such as superconductivity. The problem is that electrons can become “quantum mechanically” entangled with one another, meaning they can no longer be treated individually. The entangled web of connections becomes absurdly tricky for even the most powerful computers to unravel directly for any system with more than a handful of particles.

Now, at the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) in New York City and the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have sidestepped the problem. They created a way to simulate entanglement by adding to their computations extra “ghost” electrons that interact with the system’s actual electrons.

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