News from the AI & ML world
DeeperML - #optimization
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@Google DeepMind Blog
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Google DeepMind has unveiled AlphaEvolve, an AI agent powered by Gemini, that is revolutionizing algorithm discovery and scientific optimization. This innovative system combines the creative problem-solving capabilities of large language models (LLMs) with automated evaluators to verify solutions and iteratively improve upon promising ideas. AlphaEvolve represents a significant leap in AI's ability to develop sophisticated algorithms for both scientific challenges and everyday computing problems, expanding upon previous work by evolving entire codebases rather than single functions.
AlphaEvolve has already demonstrated its potential by breaking a 56-year-old mathematical record, discovering a more efficient matrix multiplication algorithm that had eluded human mathematicians. The system leverages an ensemble of state-of-the-art large language models, including Gemini Flash and Gemini Pro, to propose and refine algorithmic solutions as code. These programs are then evaluated using automated metrics, providing an objective assessment of accuracy and quality. This approach makes AlphaEvolve particularly valuable in domains where progress can be clearly and systematically measured, such as math and computer science. The impact of AlphaEvolve extends beyond theoretical breakthroughs, with algorithms discovered by the system already deployed across Google's computing ecosystem. Notably, AlphaEvolve has enhanced the efficiency of Google's data centers, chip design, and AI training processes, including the training of the large language models underlying AlphaEvolve itself. It has also optimized a matrix multiplication kernel used to train Gemini models and found new solutions to open mathematical problems. By optimizing Google’s massive cluster management system, Borg, AlphaEvolve recovers an average of 0.7% of Google’s worldwide computing resources continuously, which translates to substantial cost savings. Recommended read:
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Stephen Ornes@Quanta Magazine
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References:
Quanta Magazine
, medium.com
A novel quantum algorithm has demonstrated a speedup over classical computers for a significant class of optimization problems, according to a recent report. This breakthrough could represent a major advancement in harnessing the potential of quantum computers, which have long promised faster solutions to complex computational challenges. The new algorithm, known as decoded quantum interferometry (DQI), outperforms all known classical algorithms in finding good solutions to a wide range of optimization problems, which involve searching for the best possible solution from a vast number of choices.
Classical researchers have been struggling to keep up with this quantum advancement. Reports of quantum algorithms often spark excitement, partly because they can offer new perspectives on difficult problems. The DQI algorithm is considered a "breakthrough in quantum algorithms" by Gil Kalai, a mathematician at Reichman University. While quantum computers have generated considerable buzz, it has been challenging to identify specific problems where they can significantly outperform classical machines. This new algorithm demonstrates the potential for quantum computers to excel in optimization tasks, a development that could have broad implications across various fields. Recommended read:
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Asif Razzaq@MarkTechPost
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The Quantum Insider
, MarkTechPost
NVIDIA has announced two significant advancements in the fields of AI and quantum computing. The company has open-sourced Dynamo, an inference library designed to accelerate and scale AI reasoning models within AI factories. Dynamo succeeds the NVIDIA Triton Inference Server and offers a modular framework for distributed environments, allowing for the seamless scaling of inference workloads across large GPU fleets. Dynamo incorporates innovations such as disaggregated serving, which separates prefill and decode phases of LLM inference, and a GPU resource planner that dynamically adjusts GPU allocation to prevent over or under-provisioning.
NVIDIA is also launching the NVIDIA Accelerated Quantum Research Center (NVAQC) in Boston. The NVAQC will integrate quantum hardware with AI supercomputers, enabling accelerated quantum supercomputing, and collaborate with industry leaders and top universities to address the hurdles in quantum computing, such as qubit noise and error correction. NVIDIA's GB200 NVL72 systems and CUDA-Q platform will power research on quantum simulations, hybrid quantum algorithms, and AI-driven quantum applications. The NVAQC is expected to begin operations later this year, supporting the broader quantum ecosystem by accelerating the transition from experimental to practical quantum computing. Recommended read:
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@Scientific American
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D-Wave, a quantum computing firm, has asserted that its quantum computers have achieved quantum supremacy by solving a problem of scientific relevance faster than classical computers. Specifically, D-Wave Quantum Inc. claims that its annealing quantum computer outperformed the Frontier supercomputer in simulating complex magnetic materials, a feat published in the journal Science. The company stated that its system completed simulations in minutes that would take Frontier nearly a million years and consume more than the world's annual electricity consumption. The results, according to D-Wave executives, validate the practical advantage of quantum annealing and represent a significant milestone in quantum computational supremacy and materials discovery.
However, the company's claims have been met with scrutiny. Some researchers argue that classical algorithms can still rival or exceed quantum methods in certain cases. For instance, researchers at the Flatiron Institute and EPFL have suggested that classical algorithms, including belief propagation and time-dependent variational Monte Carlo methods, can match or even surpass D-Wave's results in specific scenarios. D-Wave's CEO, Alan Baratz, has responded to these criticisms, arguing that the competing studies tested only a subset of the problems addressed in D-Wave's work and that their simulations covered a broader range of lattice geometries and conditions. Recommended read:
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