News from the AI & ML world
DeeperML - #deeplearning
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@analyticsindiamag.com
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the-decoder.com
, AI News | VentureBeat
François Chollet, the creator of the Keras AI framework and the ARC benchmark, has launched a new AI research lab named Ndea, with the aim of developing artificial general intelligence (AGI). Ndea is taking an approach that combines deep learning with program synthesis to create AI systems that learn more like humans. Program synthesis allows computers to generate programs to solve problems, which could potentially enable AI to adapt and innovate beyond current task-specific applications. Ndea aims to achieve a breakthrough in creating AI that can learn efficiently and improve over time without bottlenecks, this differs from other approaches such as NVIDIA's hardware acceleration focus.
Ndea is assembling a globally distributed team of remote researchers to accelerate scientific progress through AI. The company's name is derived from combining the Greek words for intuitive understanding and logical thinking, reflecting their goal of merging these two approaches to create more general AI. Ndea sees program synthesis as a critical component of achieving AGI, not simply an addition to deep learning which is how most other labs currently view it. The team hopes that their approach will lead to machines that can not only solve existing problems but pose new ones and explore uncharted territories. Recommended read:
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Aswin Ak@MarkTechPost
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www.microsoft.com
, MarkTechPost
Microsoft Research has unveiled BioEmu-1, a deep learning model capable of generating thousands of protein structures per hour on a single GPU. This breakthrough promises to revolutionize protein science, drug discovery, and broader research efforts by providing scientists with an unprecedented glimpse into the various structures each protein can adopt. BioEmu-1 leverages structural changes to understand and predict protein function, offering insights into the dynamic behavior of proteins that traditional methods struggle to capture.
BioEmu-1 combines data from static structural databases, molecular dynamics simulations, and experimental measurements of protein stability, BioEmu-1 offers a new tool to study protein dynamics without overwhelming computational demands. By making BioEmu-1 open-source, Microsoft aims to empower protein scientists to study structural ensembles with greater efficiency compared to classical MD simulations. The technology is featured in Azure AI Foundry Labs. Recommended read:
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staff@insidehpc.com
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BigDATAwire
Nvidia's GTC 2025 event showcased the company's advancements in AI, particularly highlighting the integration of AI into various industries. CEO Jensen Huang emphasized that every industry is adopting AI and it is becoming critical for future revenue. Nvidia also unveiled an open Physical AI dataset to advance robotics and autonomous vehicle development. The dataset is claimed to be the world’s largest unified and open dataset for physical AI development, enabling the pretraining and post-training of AI models.
Central to Nvidia’s ambitions for Physical AI is its Omniverse platform, a digital development platform connecting spatial computing, 3D design, and physics-based workflows. Originally designed as a simulation and visualization tool, Omniverse has evolved significantly and has now become more of an operating system for Physical AI, allowing users to train autonomous systems before physical deployment. In quantum computing, SEEQC and Nvidia announced they have completed an end-to-end fully digital quantum-classical interface protocol demo between a QPU and GPU. Recommended read:
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Alyssa Hughes (2ADAPTIVE LLC dba 2A Consulting)@Microsoft Research
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mappingignorance.org
, www.artificialintelligence-new
Artificial intelligence is making significant strides across various fields, demonstrating its potential to address complex, real-world challenges. Principal Researcher Akshay Nambi is focused on building reliable and robust AI systems to benefit large populations. His work includes AI-powered tools to enhance road safety, agriculture, and energy infrastructure, alongside efforts to improve education through digital assistants that aid teachers in creating effective lesson plans. These advancements aim to translate AI's capabilities into tangible, positive impacts.
A new development in AI has also revealed previously hidden aspects of cellular organization. A deep-learning model can now predict how proteins sort themselves inside the cell, uncovering a layer of molecular code that shapes biological processes. This discovery has implications for our understanding of life's complexity and presents a powerful biotechnology tool for drug design and discovery, offering new avenues for addressing medical challenges. Recommended read:
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