Meta to capture U.S. employee mouse movements and keystrokes to train AI NEW YORK - Meta is installing new tracking software on U.S.-based employees' computers to capture mouse movements, clicks and keystrokes for use in training its artificial intelligence models, part of a broad initiative to build AI agents that can perform work tasks autonomously, the company told staffers in internal memos. The tool, called Model Capability Initiative (MCI), will run on work-related apps and websites and will also take occasional snapshots of the content on employees' screens, according to one of the memos, posted by a staff AI research scientist on Tuesday in a channel for the company's model-building Meta SuperIntelligence Labs team. The purpose, according to the memo, was to improve the company's AI models in areas where they struggle to replicate how humans interact with computers, like choosing from dropdown menus and using keyboard shortcuts. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Michael Dell and Susan Dell became the first donors to give more than $1 billion to the University of Texas at Austin, funding a massive AI-native hospital and research campus.

We describe Swapout, a new stochastic training method, that outperforms ResNets of identical network structure yielding impressive results on CIFAR-10 and CIFAR100.

Clustering, in particular k-means clustering, is a central topic in data analysis. Clustering with Bregman divergences is a recently proposed generalization of k-means clustering which has already been widely used in applications. In this paper we analyze theoretical properties of Bregman clustering when the number of the clusters k is large. We establish quantization rates and describe the limiting distribution of the centers as k, extending well-known results for k-means clustering.

In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace(λ), with three desired properties: (1) it has low variance; (2) it safely uses samples collected from any behaviour policy, whatever its degree of "off-policyness"; and (3) it is efficient as it makes the best use of samples collected from near on-policy behaviour policies. We analyze the contractive nature of the related operator under both off-policy policy evaluation and control settings and derive online sample-based algorithms. We believe this is the first return-based off-policy control algorithm converging a.s. to Q without the GLIE assumption (Greedy in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins' Q(λ), which was an open problem since 1989. We illustrate the benefits of Retrace(λ) on a standard suite of Atari 2600 games. One fundamental trade-off in reinforcement learning lies in the definition of the update target: should one estimate Monte Carlo returns or bootstrap from an existing Q-function?

Weighted minwise hashing (WMH) is one of the fundamental subroutine, required by many celebrated approximation algorithms, commonly adopted in industrial practice for large -scale search and learning. The resource bottleneck with WMH is the computation of multiple (typically a few hundreds to thousands) independent hashes of the data. We propose a simple rejection type sampling scheme based on a carefully designed red-green map, where we show that the number of rejected sample has exactly the same distribution as weighted minwise sampling. The running time of our method, for many practical datasets, is an order of magnitude smaller than existing methods. Experimental evaluations, on real datasets, show that for computing 500 WMH, our proposal can be 60000x faster than the Ioffe's method without losing any accuracy. Our method is also around 100x faster than approximate heuristics capitalizing on the efficient "densified" one permutation hashing schemes [26, 27]. Given the simplicity of our approach and its significant advantages, we hope that it will replace existing implementations in practice.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Elon Musk speaks at the SpaceX Hyperloop Pod Competition II in Hawthorne, California, in 2017. Elon Musk speaks at the SpaceX Hyperloop Pod Competition II in Hawthorne, California, in 2017. Cursor is a Silicon Valley startup using AI to automate coding as Elon Musk's firm seeks foothold in the AI market SpaceX said it has secured an option to either acquire code-generation startup Cursor for $60bn later this year, or pay $10bn for their new partnership, as it pushes deeper into the lucrative market for AI developer tools. Along with OpenAI and Anthropic, Cursor is one of several Silicon Valley startups that has drawn waves of developers by using artificial intelligence to automate coding, a business where AI companies have found early commercial traction. The deal could give xAI, the Grok chatbot maker that SpaceX merged with in February, a stronger foothold in the AI coding market where it has so far lagged rivals.

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training to the loss related to the task at hand. We empirically show that (i) by modeling uncertainty on the output value, DISCO Nets outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets accurately model the uncertainty of the output, outperforming existing probabilistic models based on deep neural networks.