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Engineered enzymes are poised to have transformative impacts across applications in energy, materials, biotechnology, and medicine. Recently, machine learning has emerged as a useful tool for enzyme engineering. Now, a team of bioengineers and synthetic biologists says they have developed a machine-learning guided platform that can design thousands of new enzymes, predict how they will behave in the real world, and test their performance across multiple chemical reactions.

Their results are published in Nature Communications in an article titled, “Accelerated enzyme engineering by machine-learning guided cell-free expression,” and led by researchers at Stanford University and Northwestern University.

“Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design,” the researchers wrote. “To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions.”

Researchers outline a bold strategy to scale neuromorphic computing, aiming to match human brain functionality with minimal energy use.

This involves developing advanced neuromorphic chips and fostering strong industry-academic partnerships, potentially transforming AI and healthcare through improved efficiency and capability.

Scaling Up Neuromorphic Computing

In today’s AI news, Mark Zuckerberg announced a huge leap in Meta Platforms’s capital spending this year to between $60 billion to $65 billion, an increase driven by artificial intelligence and a massive new data center.

Zuckerberg plans to increase the company’s capital expenditures by as much as roughly 70% over 2024.

In other advancements, Hugging Face has achieved a remarkable breakthrough in AI, introducing vision-language models that run on devices as small as smartphones while outperforming their predecessors that require massive data centers. The company’s new SmolVLM-256M model, requiring less than one gigabyte of GPU memory, surpasses the performance of its Idefics 80B model from just 17 months ago — a system 300 times larger.

And, Anthropic has launched a new feature for its “Claude” family of AI models, one that enables the models to cite and link back to sources when answering questions about uploaded documents. The new feature, appropriately dubbed “Citations,” is now available for developers through Anthropic’s API.

Meanwhile, can AI agents reliably click on all images showing motorcycles or traffic lights for us? It might be too early to tell, considering that a robot will essentially have to tell a website that it is not a robot. However, it looks like at least one of OpenAI’s Operator users was able to have the AI agent beat CAPTCHAs for him.

S strategic radar. Disruptions aren Then, In this special episode of Lightcone, the Y Combinator hosts are joined by YC partner and creator of Gmail Paul Buchheit to dig into some of the latest trends in the world of AI startups. They recorded their conversation at a recent retreat where 300 of the top AI founders in the world gathered to share expertise and make predictions about how this technology will shape our future.

And, What new kinds of jobs will AI bring that we never could have imagined before? In this special two-part episode, Reid Hoffman and Aria Finger explore this question and more with Sierra co-founder and OpenAI chairperson Bret Taylor.

Whether we are ready or not, neuro-tech is about to cause a radical social shift that will change our understanding of the mind and our very conception of reality. Telepathy, or even a super humanity based on a symbiotic relationship with artificial intelligence, will no longer be a dream.

This documentary revisits the history of neuroscience and explores the frontiers of this groundbreaking field. It introduces technological advancements that come with catastrophic risks, which is why experts are advocating for the Neuro-Rights — regulations that ensure the privacy of our conscious AND subconscious.
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Chapters.
▷ 00:00 – Intro.
▷ 03:02 – The enigma of human brain.
▷ 07:36 – Why neuroscience.
▷ 10:27 – Merging with the digital.
▷ 12:53 – Neuro-Revolutions: the 90s to today.
▷ 15:50 – From lab to real world (\.

An automated system could potentially monitor real-time images of coronal loop brightness shifts from the Solar Dynamics Observatory, thus enabling scientists to issue timely alerts.

“We could build on this and come up with a well-tested and, ideally, simpler indicator ready for the leap from research to operations,” said Vadim Uritsky, an expert in space physics at NASA’s Goddard Space Flight Center (GSFC) and Catholic University in Washington D.C.

The discovery of flickering coronal loops as a precursor to solar flares opens up transformative possibilities in both research and technology.

While the idea of a digital afterlife is fascinating, it raises some big questions. For example, who owns your online accounts after you die?

This issue is already being discussed in courts and by governments around the world. In the United States, nearly all states have passed laws allowing people to include digital accounts in their wills.

In Germany, courts ruled that Facebook had to give a deceased person’s family access to their account, saying that digital accounts should be treated as inheritable property, like a bank account or house.

Researchers at the University of Toronto’s Faculty of Applied Science & Engineering have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam.

In a new paper published in Advanced Materials, a team led by Professor Tobin Filleter describes how they made nanomaterials with properties that offer a conflicting combination of exceptional strength, light weight and customizability. The approach could benefit a wide range of industries, from automotive to aerospace.

“Nano-architected materials combine high performance shapes, like making a bridge out of triangles, at nanoscale sizes, which takes advantage of the ‘smaller is stronger’ effect, to achieve some of the highest strength-to-weight and stiffness-to-weight ratios, of any material,” says Peter Serles, the first author of the new paper.