Researchers built an AI that gives out money based on who started with less resources—and humans preferred it.
Category: robotics/AI – Page 1,188
A new tool can determine whether a collection of building blocks will assemble into a mechanically sound structure.
One small step for a machine… one giant leap for the singularity.
This AI actually improved a key algorithm that makes it run even faster.
In this video I discuss new Deepmind’s AlphaTensor algorithm and why this work is so important for all the fields of Engineering!
Deepmind’s paper “Discovering faster matrix multiplication algorithms with reinforcement learning”:
https://www.nature.com/articles/s41586-022-05172-4
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Dr. Michael Rose is an evolutionary biologist and authority in gerontology. His many years of research and keen insight establish unique methods to frame the problems of aging. Michael made scientific history with experiments manipulating the life spans of fruit flies. As a pragmatist, Michael sees beyond today’s quick fixes to examine what could be the most important changes in the longevity industry to slow down and stop aging. His view is that genomics in conjunction with machine learning is the future of longevity.
Interested in participating? Join an info session. Register here to join us on Thursday, November 10 at 1pm EST. The info session will feature remarks from Joshua Elliott, DARPA AI Tools for Adult Learning Program Manager, as well as a […].
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Many people are scared of artificial intelligence or AI, and it is not hard to see why! The.
advances made in that field of technology are mind-boggling, to say the least! One such scary.
outcome of AI is Google’s AI, which, before it was switched off, ominously revealed one thing.
billions of people have spent a lifetime trying to discover; the purpose of life! What did Google’s.
AI say the purpose of life is? Can AI truly become smarter than us? What does AI becoming.
more intelligent than humans mean? In this video, we dive deep into Google’s Artificial.
Intelligence and what it revealed was the purpose of life before being switched off!
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Robots That Write Their Own Code
Posted in robotics/AI
A common approach used to control robots is to program them with code to detect objects, sequencing commands to move actuators, and feedback loops to specify how the robot should perform a task. While these programs can be expressive, re-programming policies for each new task can be time consuming, and requires domain expertise.
What if when given instructions from people, robots could autonomously write their own code to interact with the world? It turns out that the latest generation of language models, such as PaLM, are capable of complex reasoning and have also been trained on millions of lines of code. Given natural language instructions, current language models are highly proficient at writing not only generic code but, as we’ve discovered, code that can control robot actions as well. When provided with several example instructions (formatted as comments) paired with corresponding code (via in-context learning), language models can take in new instructions and autonomously generate new code that re-composes API calls, synthesizes new functions, and expresses feedback loops to assemble new behaviors at runtime.
Artificial-intelligence systems are increasingly limited by the hardware used to implement them. Now comes a new superconducting photonic circuit that mimics the links between brain cells—burning just 0.3 percent of the energy of its human counterparts while operating some 30,000 times as fast.
In artificial neural networks, components called neurons are fed data and cooperate to solve a problem, such as recognizing faces. The neural net repeatedly adjusts the synapses—the links between its neurons—and determines whether the resulting patterns of behavior are better at finding a solution. Over time, the network discovers which patterns are best at computing results. It then adopts these patterns as defaults, mimicking the process of learning in the human brain.
“Machine learning provides a way of providing almost human-like intuition to huge data sets. One valuable application is for tasks where it’s difficult to write a specific algorithm to search for something—human faces, for instance, or perhaps ” something strange,” wrote astrophysicist and Director of the Penn State University Extraterrestrial Intelligence Center, Jason Wright in an email to The Daily Galaxy. ” In this case, you can train a machine-learning algorithm to recognize certain things you expect to see in a data set,” Wright explains, ” and ask it for things that don’t fit those expectations, or perhaps that match your expectations of a technosignature.
Crowdsourcing Alien Structures
For instance,’ Wright notes, theoretical physicist Paul Davies has suggested crowdsourcing the task of looking for alien structures or artifacts on the Moon by posting imaging data on a site like Zooniverse and looking for anomalies. Some researchers (led by Daniel Angerhausen) have instead trained machine-learning algorithms to recognize common terrain features, and report back things it doesn’t recognize, essentially automating that task. Sure enough, the algorithm can identify real signs of technology on the Moon—like the Apollo landing sites!
What’s your favorite ice cream flavor? You might say vanilla or chocolate, and if I asked why, you’d probably say it’s because it tastes good. But why does it taste good, and why do you still want to try other flavors sometimes? Rarely do we ever question the basic decisions we make in our everyday lives, but if we did, we might realize that we can’t pinpoint the exact reasons for our preferences, emotions, and desires at any given moment.
Most AI systems are black box models, which are systems that are viewed only in terms of their inputs and outputs. Scientists do not attempt to decipher the “black box,” or the opaque processes that the system undertakes, as long as they receive the outputs they are looking for.