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Category: robotics/AI – Page 4

In today’s AI news, a majority of senior executives across multiple industries expect AI to fundamentally reshape their businesses in the next 12 to 24 months, according to KPMG’s latest AI Quarterly Pulse Survey. According to the survey, 68% of executives plan to invest between $50M and $250M into GenAI over the next 12 months, marking a substantial increase from 45% in Q1 of 2024.

S chief AI scientist, Yann LeCun, the biggest takeaway from DeepSeek In other advancements, hot healthcare startup Rad AI has raised a Series C funding round. The company, which creates AI-powered tools for radiologists, grabbed $60 million dollars of fresh funding in a Series C round led by Transformation Capital, according to two sources, the new fundraise valued Rad AI at $525 million.

Meanwhile, Alphabet’s Google, already facing an unprecedented regulatory onslaught, is looking to shape public perception and policies on artificial intelligence ahead of a global wave of AI regulation. A key priority comes in building out educational programs to train the workforce on AI. “Getting more people and organizations, including governments, familiar with AI and using AI tools, makes for better AI policy and opens up new opportunities.”

T be fixated on the best big model … + Then, join renowned investor Ray Dalio of Bridgewater Associates, for an engaging fireside chat with Merantix Capital Co-Founder, Rasmus Rothe exploring the enormous potential of artificial intelligence in decision-making, innovation, and global investing.

And, artificial general intelligence could possess the versatility to reason, learn and innovate in any task. But with rising concerns about job losses, surveillance and deepfakes, will AGI be a force for progress or a threat to the very fabric of humanity?

Continue reading “KPMG Survey: 68% Of Execs Plan To Funnel Up To 0 Million Into AI In The Next Year” | >

We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring ~7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human-AI and AI-AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a Principal Component Analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.

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LEV is upon us.

OpenAI chief executive Sam Altman, who provided the initial $180mn to seed the start-up, will put in more money in the series A. The company is in talks with family offices, venture capitalists and sovereign wealth funds, as well as a US “hyperscaler” data centre to provide computing power to run the AI models it uses to create and test its treatments.

In partnership with OpenAI, the start-up has built a bespoke AI model that designs proteins to temporarily turn regular cells into stem cells, which it says can reverse their ageing process.

The San Francisco-based biotech will use the money to fund clinical trials for three drugs, including a potential treatment for Alzheimer’s disease, which will be tested in an early stage study in Australia this year. It is also working on drugs for rejuvenating blood and brain cells.

Continue reading “Sam Altman-backed Retro Biosciences to raise

LEV is upon us.

OpenAI chief executive Sam Altman, who provided the initial $180mn to seed the start-up, will put in more money in the series A. The company is in talks with family offices, venture capitalists and sovereign wealth funds, as well as a US “hyperscaler” data centre to provide computing power to run the AI models it uses to create and test its treatments.

In partnership with OpenAI, the start-up has built a bespoke AI model that designs proteins to temporarily turn regular cells into stem cells, which it says can reverse their ageing process.

The San Francisco-based biotech will use the money to fund clinical trials for three drugs, including a potential treatment for Alzheimer’s disease, which will be tested in an early stage study in Australia this year. It is also working on drugs for rejuvenating blood and brain cells.bn for project to extend human life” | >

When it rains, it pours. OpenAI Operator tested and reviewed, with full paper analysis. Perplexity Assistant is useful. Then Stargate, is it all smoke and mirrors? Strong rumours of an o3+ model from Anthropic. Then a full breakdown of Deepseek R1, and what it’s training method says about the state of AI. It’s not open source BTW. Plus Humanity’s Last Exam, and Hassabis Accelerates his AGI timeline.

https://app.grayswan.ai/arena/chat/ha…
https://app.grayswan.ai/arena.

AI Insiders ($9!): / aiexplained.

Chapters:

Continue reading “Nothing Much Happens in AI, Then Everything Does All At Once” | >

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.”

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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

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