Lifeboat Foundation

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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.

Up until recently, artificial intelligence was unable to perform such creative-looking tasks.

But all of that is beginning to change thanks to AI Sketch software like DreamStudio, Dall-E 2, and Stable Diffusion, which take a few keywords via a text interface to generate an image in a process known as “generative AI.”

Continue reading “AI Just Got More Creative, and Filmmakers Shouldn’t Be Afraid” »

Today, Replit announced Ghostwriter, an AI-powered programming assistant that can make suggestions to make coding easier. It works within Replit’s online development environment and resembles GitHub Copilot’s ability to recognize and compose code in various programming languages to accelerate the development process.

According to Replit, Ghostwriter works by using a large language model trained on millions of lines of publicly available code. This baked-in data allows Ghostwriter to make suggestions based on what you’ve already typed while programming in Replit’s IDE. When you see a suggestion you like, you can “autocomplete” the code by pressing the Tab key.

Greg Brockman, President and Co-Founder of @OpenAI, joins Alexandr Wang, CEO and Founder of Scale, to discuss the role of foundation models like GPT-3 and DALL·E 2 in research and in the enterprise. Foundation models make it possible to replace task-specific models with those that are generalized in nature and can be used for different tasks with minimal fine-tuning.

In January 2021, OpenAI introduced DALL·E, a text-to-image generation program. One year later, it introduced DALL·E 2, which generates more realistic, accurate, lower-latency images with four times greater resolution than its predecessor. At the same time, it released InstructGPT, a large language model (LLM) explicitly designed to follow instructions. InstructGPT makes it practical to leverage the OpenAI API to revise existing content, such as rewriting a paragraph of text or refactoring code.

Continue reading “OpenAI’s Greg Brockman: The Future of LLMs, Foundation & Generative Models (DALL·E 2 & GPT-3)” »

Imagine the booming chords from a pipe organ echoing through the cavernous sanctuary of a massive, stone cathedral.

The sound a cathedral-goer will hear is affected by many factors, including the location of the organ, where the listener is standing, whether any columns, pews, or other obstacles stand between them, what the walls are made of, the locations of windows or doorways, etc. Hearing a sound can help someone envision their environment.

Continue reading “Using sound to model the world” »