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Category: robotics/AI – Page 1,220
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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.
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.”
Generative AI is trained on sets of images, which are sourced from the internet. The machine can then learn the differences between people, places, and things and generate its own images from any text it receives.
The more data sets the AI can draw from, the more accurate and creative the results.
Corridor Digital experimented with Stable Diffusion a few weeks back and used AI to generate images to tell a story. And the results are downright inspiring.
The AI was able to create images with members of the Corridor Crew as the main characters, placing them in custom settings, complete with costumes, props, and backgrounds based on the input of a few main idea terms.
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.
Before creating OpenAI, Brockman was the CTO of Stripe, which he helped build from four to 250 employees. Watch this talk to learn how foundation models can help businesses benefit from applications that they can create more quickly than with past generations of AI tools.
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.
Researchers at MIT and the MIT-IBM Watson AI Lab are exploring the use of spatial acoustic information to help machines better envision their environments, too. They developed a machine-learning model that can capture how any sound in a room will propagate through the space, enabling the model to simulate what a listener would hear at different locations.