Lifeboat Foundation

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.

Continue reading “This New AI is a Game Changer!” »

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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Continue reading “Google Just Shut Down It’s Artificial Intelligence After It Revealed This” »

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

Continue reading “Will AI Reveal Extraterrestrial Intelligence Far Beyond Our Level of Consciousness?” »

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.