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Category: robotics/AI – Page 652

DARPA’s 2nd Tools Competition Focuses on AI Tools for Adult STEM, Data Science Learning

The Defense Advanced Research Projects Agency launched a second iteration of its Tools Competition to discover artificial intelligence-enabled technologies that can aid data science and other forms of adult learning.

The agency said Monday that the new program aims to upskill and reskill adults in science, technology, engineering and mathematics and similarly complex areas, preparing them for the 21st century labor landscape.

The opportunity is open to digital learning platform experts, technologists, researchers, students and educators who can propose AI tools that can provide feature tutoring and self-directed learning. The resulting platform may leverage AI or large language models.

The True Story of How GPT-2 Became Maximally Lewd

In this video, we recount an incident that occurred at OpenAI while researchers were trying to finetune GPT-2 to be as helpful and ethical as possible. It’s narrated that inadvertently flipping a single minus sign led GPT-2 to become the embodiment of a well-known cardinal sin.

#ai #aisafety #alignment.

▀▀▀▀▀▀▀▀▀SOURCES \& READINGS▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀

OpenAI blog post: https://openai.com/research/fine-tuni…
OpenAI paper behind the blog post: https://arxiv.org/pdf/1909.08593.pdf.
RLHF explainer on Hugging Face: https://huggingface.co/blog/rlhf.
RLHF explainer on aisafety.info https://aisafety.info/?state=88FN_904…
Concrete Problems in AI Safety, by @RobertMilesAI: • Concrete Problems in AI Safety.

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Brain-based computing chips not just for AI anymore

With the insertion of a little math, Sandia National Laboratories researchers have shown that neuromorphic computers, which synthetically replicate the brain’s logic, can solve more complex problems than those posed by artificial intelligence and may even earn a place in high-performance computing.

The findings, detailed in a recent article in the journal Nature Electronics, show that neuromorphic simulations employing the statistical method called random walks can track X-rays passing through bone and soft tissue, disease passing through a population, information flowing through social networks and the movements of financial markets, among other uses, said Sandia theoretical neuroscientist and lead researcher James Bradley Aimone.

“Basically, we have shown that neuromorphic hardware can yield computational advantages relevant to many applications, not just artificial intelligence to which it’s obviously kin,” said Aimone. “Newly discovered applications range from radiation transport and molecular simulations to computational finance, biology modeling and particle physics.”

Stanford professor on the future of life-saving medicine | Steve Quake

What if AI could tell us we have cancer before we show a single symptom? Steve Quake, head of science at the Chan Zuckerberg Initiative, explains how AI can revolutionize science.

Up next, Harvard professor debunks the biggest exercise myths ► • Harvard professor debunks the biggest…

AI can help us understand complex systems like our cells. better. The Chan Zuckerberg Initiative is committed to building one of the world’s biggest non-profit life science AI computing clusters to help build digital models of what goes wrong in cells when we get diseases like diabetes or cancer and more.

Read the video transcript ► https://bigthink.com/sponsored/future…

We created this video in partnership with the Chan Zuckerberg Initiative.

Go Deeper with Big Think:

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Scientists Coax Bacteria Into Making Exotic Proteins Not Found in Nature

A Whole New World

Scientists have already found hundreds of exotic amino acids. AI models such as AlphaFold or RoseTTAFold, and their variations, are likely to spawn even more. Finding carriers and “glue” proteins that match has always been a roadblock.

The new study establishes a method to speed up the search for new designer proteins with unusual properties. For now, the method can only incorporate four synthetic amino acids. But scientists are already envisioning uses for them.

Performing complex-valued linear transformations using spatially incoherent diffractive optical networks

The bulk of the computing in state-of-the-art neural networks comprises linear operations, e.g., matrix-vector multiplications and convolutions. Linear operations can also play an important role in cryptography. While dedicated processors such as GPUs and TPUs are available for performing highly parallel linear operations, these devices are power-hungry, and the low bandwidth of electronics still limits their operation speed. Optics is better suited for such operations because of its inherent parallelism and large bandwidth and computation speed.

Built from a set of spatially engineered thin surfaces, diffractive deep (D2NN), also known as diffractive networks, form a recently emerging optical computing architecture capable of performing passively at the speed of light propagation through an ultra-thin volume.

These task-specific all-optical computers are designed digitally through learning of the spatial features of their constituent diffractive surfaces. Following this one-time design process, the optimized surfaces are fabricated and assembled to form the physical hardware of the diffractive optical .

Consciousness, AI and the Future of Humanity

In this clip from our 2017 event titled ‘Evolution of the Mind, Consciousness and AI,’ the esteemed philosopher and cognitive scientist, Daniel Dennett is joined by a group of panellists to explore how much we understand about the human mind, and what the creation of artificial consciousness means for our future. Watch and let us know in the comments if you think Dennett’s theories still hold true in light of the rapid developments in AI since he joined us.

See the full session here: • Daniel Dennett on the Evolution of th…

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Rethinking AI’s impact: Study reveals economic limits to job automation

The study, authored by five MIT researchers and titled Beyond AI Exposure, delves deep into the practicalities of replacing human labor with AI in the US, focusing on tasks that lend themselves to computer vision, such as those performed by teachers, property appraisers, and bakers.

Like many of us, you might find yourself nodding to a familiar digital doomsday chorus that vibrates through offices and coffee shops alike: AI will take my job!

Is this looming threat substantiated, or simply a manifestation of our shared anxiety in the wake of constant technological advancement? A new study from MIT CSAIL, MIT Sloan, The Productivity Institute, and IBM’s Institute for Business Value is set to challenge our long-held beliefs.

Their research critically examines the economic practicality of using AI for automating tasks in the workplace, with a specific emphasis on computer vision.

Practical challenges for precision medicine

MachineLearning clinical prediction models fail to generalize across trial data, a new Science study finds. The results “require reexamination of the practical challenges that precision medicine is facing.” Learn more in a new Science Perspective:

The prediction of individual treatment responses with machine learning faces hurdles.

Frederike H. Petzschner [email protected] Authors Info & Affiliations

Science.