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From drones with "first-person" visualization to real-time 360-degree replays and Olympics GPT, get ready to immerse yourself in the Winter Games in Milan and Cortina. During the 2024 Summer Olympics in Paris, 5G and 4K were the leading technologies available to many viewers. There was some AI, but it was mostly used for athletes' benefit. For the 2026 Milano Cortina Winter Games there will be more technology than ever, for both athletes and fans. Much of that technology has never been used at the Games before, says Yiannis Exarchos, the managing director of Olympic Broadcasting Services and executive director of Olympic Channel Services.

In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classifier by masking salient parts of the input image. Our model generalises well to unseen images and requires a single forward pass to perform saliency detection, therefore suitable for use in real-time systems. We test our approach on CIFAR-10 and ImageNet datasets and show that the produced saliency maps are easily interpretable, sharp, and free of artifacts. We suggest a new metric for saliency and test our method on the ImageNet object localisation task. We achieve results outperforming other weakly supervised methods.

Mistral's New Ultra-Fast Translation Model Gives Big AI Labs a Run for Their Money "Too many GPUs makes you lazy," says the French startup's vice president of science operations, as the company carves out a different path than the major US AI companies. Mistral AI has released a new family of AI models that it claims will clear the path to seamless conversation between people speaking different languages . On Wednesday, the Paris-based AI lab released two new speech-to-text models: Voxtral Mini Transcribe V2 and Voxtral Realtime. The former is built to transcribe audio files in large batches and the latter for nearly real-time transcription, within 200 milliseconds; both can translate between 13 languages. Voxtral Realtime is freely available under an open source license.

While SpaceX is routinely described as being owned by Elon Musk, he is not the only person in the capsule. While SpaceX is routinely described as being owned by Elon Musk, he is not the only person in the capsule. Elon Musk is taking SpaceX's minority shareholders for a ride T o Elon Musk's fanclub, there is nothing to see apart from more evidence of the great man's visionary genius. SpaceX, the rocket firm, is buying xAI, the artificial intelligence developer, and the combination of these two Musk-controlled entities is being valued at $1.25tn (£910bn). Or, as Musk described it, he is creating "the most ambitious, vertically integrated innovation engine on (and off) Earth, with AI, rockets, space-based internet, direct-to-mobile device communications and the world's foremost real-time information and free-speech platform".

Elon Musk's SpaceX is already one of the world's most valuable private companies. Elon Musk's SpaceX is already one of the world's most valuable private companies. Aerospace business and artificial intelligence firm to unite for IPO as world's most valuable private company Elon Musk's aerospace company SpaceX has acquired his artificial intelligence business xAI, in a $1.25tn (£910bn) merger that consolidates part of Musk's empire as SpaceX prepares to go public later this year. The two companies announced the deal on Monday in a statement on SpaceX's website, saying the merger would form "the most ambitious, vertically-integrated innovation engine on (and off) Earth, with AI, rockets, space-based internet, direct-to-mobile device communications and the world's foremost real-time information and free speech platform". SpaceX, one of the world's most valuable private companies, will gain xAI properties such as its Grok chatbot and the social media platform X.

Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy. When trained on offline datasets, poisoning adversaries have to inject the poisoned data in advance before training, and the order of feeding these poisoned batches into the model is stochastic. In contrast, practical systems are more usually trained/fine-tuned on sequentially captured real-time data, in which case poisoning adversaries could dynamically poison each data batch according to the current model state. In this paper, we focus on the real-time settings and propose a new attacking strategy, which affiliates an accumulative phase with poisoning attacks to secretly (i.e., without affecting accuracy) magnify the destructive effect of a (poisoned) trigger batch. By mimicking online learning and federated learning on MNIST and CIFAR-10, we show that model accuracy significantly drops by a single update step on the trigger batch after the accumulative phase. Our work validates that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects, with no need to explore complex techniques.

Find out more about our session on Wednesday 21 January.

We describe a family of architectures to support transductive inference by allowing memory to grow to a finite but a-priori unknown bound while making efficient use of finite resources for inference. Current architectures use such resources to represent data either eidetically over a finite span ('context' in Transformers), or fading over an infinite span (in State Space Models, or SSMs). Recent hybrid architectures have combined eidetic and fading memory, but with limitations that do not allow the designer or the learning process to seamlessly modulate the two, nor to extend the eidetic memory span. We leverage ideas from Stochastic Realization Theory to develop a class of models called B'MOJO to seamlessly combine eidetic and fading memory within an elementary composable module. The overall architecture can be used to implement models that can access short-term eidetic memory'in-context,' permanent structural memory'in-weights,' fading memory'in-state,' and long-term eidetic memory'in-storage' by natively incorporating retrieval from an asynchronously updated memory. We show that Transformers, existing SSMs such as Mamba, and hybrid architectures such as Jamba are special cases of B'MOJO and describe a basic implementation that can be stacked and scaled efficiently in hardware. We test B'MOJO on transductive inference tasks, such as associative recall, where it outperforms existing SSMs and Hybrid models; as a baseline, we test ordinary language modeling where B'MOJO achieves perplexity comparable to similarly-sized Transformers and SSMs up to 1.4B parameters, while being up to 10% faster to train. Finally, we test whether models trained inductively on a-priori bounded sequences (up to 8K tokens) can still perform transductive inference on sequences many-fold longer. B'MOJO's ability to modulate eidetic and fading memory results in better inference on longer sequences tested up to 32K tokens, four-fold the length of the longest sequences seen during training.