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Industry with business model not yet firmly established and investments financed by huge debts is particularly at riskDonald Trump’s most immediate concern in demanding Iran reopen the strait of Hormuz may be rocketing US gasoline prices, but if the conflict drags on, higher energy costs will be felt far beyond the pumps.Systemically higher power prices […]
There’s a particular kind of tedium that every AI engineer knows intimately: the prompt-tuning loop. You write a system prompt, run your agent against a benchmark, read the failure traces, tweak the prompt, add a tool, rerun. Repeat this a few dozen times and you might move the needle. It’s grunt work dressed up in
After forgetting the nibbles, refusing my costume requests and emailing GCHQ, ‘Gaskell’ did at least get us to show upTwo weeks ago, an AI bot invited me to a party it was organising in Manchester. It then promptly lied to dozens of potential sponsors that I’d agreed to cover the event, and misled me into
An AI bot invited me to its party in Manchester. It was a pretty good night Read More »
Video editing has always had a dirty secret: removing an object from footage is easy; making the scene look like it was never there is brutally hard. Take out a person holding a guitar, and you’re left with a floating instrument that defies gravity. Hollywood VFX teams spend weeks fixing exactly this kind of problem.
In this tutorial, we explore the full capabilities of Z.AI’s GLM-5 model and build a complete understanding of how to use it for real-world, agentic applications. We start from the fundamentals by setting up the environment using the Z.AI SDK and its OpenAI-compatible interface, and then progressively move on to advanced features such as streaming
Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information games — scenarios where players act sequentially and cannot see each other’s private information, like poker — has historically relied on manual iteration. Researchers identify weighting schemes, discounting rules, and equilibrium solvers through intuition and trial-and-error. Google DeepMind researchers proposes AlphaEvolve, an LLM-powered evolutionary coding agent
Body that funds Alan Turing Institute says it should offer better strategy and more value for moneyThe UK’s leading AI research institute has been told to make “significant” changes by its main source of taxpayer funding.The warning comes after the Guardian revealed the board of the Alan Turing Institute was reminded of its legal duties
UK’s leading AI research institute told to make ‘significant’ changes Read More »
Human-in-the-Loop AI enables enterprises to scale AI responsibly, enhance decision quality, and strengthen trust across workflows. The post Human-in-the-Loop AI: From Risk Control to Competitive Edge appeared first on Fusemachines.
Human-in-the-Loop AI: From Risk Control to Competitive Edge Read More »
In the current landscape of computer vision, the standard operating procedure involves a modular ‘Lego-brick’ approach: a pre-trained vision encoder for feature extraction paired with a separate decoder for task prediction. While effective, this architectural separation complicates scaling and bottlenecks the interaction between language and vision. The Technology Innovation Institute (TII) research team is challenging
In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer to train, prune, and fine-tune a deep learning model directly in Google Colab. We start by setting up the environment and preparing the CIFAR-10 dataset, then define a ResNet architecture and train it to establish a strong baseline. From there, we apply
