The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-i.i.d testing data. Recently, invariant learning methods for out-of-distribution (OOD) generalization propose to find causally invariant relationships with multienvironments. However, modern datasets are frequently multi-sourced without explicit source labels, rendering many invariant learning methods inapplicable. In this paper, we propose Kernelized Heterogeneous Risk Minimization (KerHRM) algorithm, which achieves both the latent heterogeneity exploration and invariant learning in kernel space, and then gives feedback to the original neural network by appointing invariant gradient direction. We theoretically justify our algorithm and empirically validate the effectiveness of our algorithm with extensive experiments.

OpenAI and Jony Ive's vision for its AI device is a screenless companion that knows everything about you. Details leaked to the Wall Street Journal give us a clearer picture of OpenAI's acquisition of io, cofounded by Ive, the iconic iPhone designer. The ChatGPT maker reportedly plans to ship 100 million AI devices designed to fit in with users' everyday life. "The product will be capable of being fully aware of a user's surroundings and life, will be unobtrusive, able to rest in one's pocket or on one's desk," according to a recording of an OpenAI staff meeting reviewed by the Journal. The device "will be a third core device a person would put on a desk after a MacBook Pro and an iPhone," per the meeting which occurred the same day (Wednesday) that OpenAI announced its acquisition of Ive's company.

Is Google's new AI Mode feature theft? The News/Media Alliance, trade association representing news media organizations in the U.S. and Canada, certainly thinks so. At Google's I/O showcase earlier this week, the tech company announced the public release of AI Mode in Google Search. AI Mode expands AI Overviews in search and signifies a pivot away from Google's traditional search. Users will see a tab at the top of their Google Search page that takes them to a chatbot interface much like, say, ChatGPT, instead of your typical Google Search results.

In learning theory, a standard assumption is that the data is generated from a finite mixture model. But what happens when the number of components is not known in advance? The problem of estimating the number of components, also called model selection, is important in its own right but there are essentially no known efficient algorithms with provable guarantees let alone ones that can tolerate adversarial corruptions. In this work, we study the problem of robust model selection for univariate Gaussian mixture models (GMMs). Given poly(k/ϵ) samples from a distribution that is ϵ-close in TV distance to a GMM with k components, we can construct a GMM with Õ(k) components that approximates the distribution to within Õ(ϵ) in poly(k/ϵ) time. Thus we are able to approximately determine the minimum number of components needed to fit the distribution within a logarithmic factor. Prior to our work, the only known algorithms for learning arbitrary univariate GMMs either output significantly more than k components (e.g.

Artificial intelligence systems could account for nearly half of datacentre power consumption by the end of this year, analysis has revealed. The estimates by Alex de Vries-Gao, the founder of the Digiconomist tech sustainability website, came as the International Energy Agency forecast that AI would require almost as much energy by the end of this decade as Japan uses today. De Vries-Gao's calculations, to be published in the sustainable energy journal Joule, are based on the power consumed by chips made by Nvidia and Advanced Micro Devices that are used to train and operate AI models. The paper also takes into account the energy consumption of chips used by other companies, such as Broadcom. The IEA estimates that all data centres – excluding mining for cryptocurrencies – consumed 415 terawatt hours (TWh) of electricity last year.

Breakthroughs, discoveries, and DIY tips sent every weekday. Drones can be a divisive subject, but they do have their uses (beyond causing mass panic). Professional unpiloted aerial vehicles (UAVs) are already instrumental in conservation efforts and engineering projects, but even personal use drones do more than recording cool aerial shots of your vacation. In the case of a recent emergency in Florida, one man's drone helped save a teenager's life. Earlier this month, amateur shark fisherman Andrew Smith was convinced by a friend to come with them to Pensacola Beach after getting off from work.

Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem. However, the criteria by which the prediction model is trained are often inconsistent with the goal of the downstream optimization problem. Recently, decision-focused prediction approaches, such as SPO+ and direct optimization, have been proposed to fill this gap. However, they cannot directly handle the soft constraints with the max operator required in many real-world objectives. This paper proposes a novel analytically differentiable surrogate objective framework for real-world linear and semi-definite negative quadratic programming problems with soft linear and non-negative hard constraints. This framework gives the theoretical bounds on constraints' multipliers, and derives the closed-form solution with respect to predictive parameters and thus gradients for any variable in the problem. We evaluate our method in three applications extended with soft constraints: synthetic linear programming, portfolio optimization, and resource provisioning, demonstrating that our method outperforms traditional two-staged methods and other decision-focused approaches.

Just a few years ago, getting a security camera to keep an eye on your kids or pets while you aren't home would've been pretty expensive. This tiny little thing can be placed anywhere inside your home, as long as it's close enough to an outlet for plugging in. Whether you're placing it on a bookcase shelf, near your TV, or on a nightstand, the Arlo Essential camera can capture most of any room thanks to its large 130-degree field of view and high-def 2560 1440 resolution. Even during the night, this camera will capture great-quality video, making it ideal for keeping an eye on your sleeping baby or watching out for burglars. Since it works with Alexa, Google Home, Apple Home, and IFTTT, you can integrate the camera with your local smart home setup and do things like pull up the video feed on your smart screen.