Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the availability of large-scale data from social networks and sensor networks offers an unprecedented opportunity to predict state-changing events at the individual level. Examples of such events include disease transmission, opinion transition in elections, and rumor propagation. Unlike previous research focusing on the collective effects of social systems, this study makes efficient inferences at the individual level. In order to cope with dynamic interactions among a large number of individuals, we introduce the stochastic kinetic model to capture adaptive transition probabilities and propose an efficient variational inference algorithm the complexity of which grows linearly -- rather than exponentially-- with the number of individuals.

Agentic AI is poised to take on a much more central role in the IT industry, according to a new report from Cisco. The report, titled "The Race to an Agentic Future: How Agentic AI Will Transform Customer Experience," surveyed close to 8,000 business leaders across 30 countries, all of whom routinely work closely with customer service professionals from B2B technology services. In broad strokes, it paints a picture of a business landscape eager to embrace the rising wave of AI agents, particularly when it comes to customer service. Also: Can you build a billion-dollar business with only AI agents (yet)? As soon as next year, according to the report, over half (68%) of all customer service and support interactions with tech vendors could become automated, thanks to agentic AI.

When people think about AI, they often think about models that run in the cloud like ChatGPT or Google Gemini. But there's an incredibly simple way of running local AI on your PC that Microsoft just implemented. Microsoft announced Microsoft Foundry Local this past week at its Build conference. It's basically a command line tool that runs LLMs locally on your machine. Although it's initially targeted at developers, it's one of the easiest ways of trying out local AI simply because it does everything for you.

This week in tech: Apple struggles on multiple fronts, OpenAI grows increasingly ambitious, and Trump helps some of his fans lose money on cryptocurrency. Long dominant and unassailable, Apple is showing signs of weakness. The CEO, Tim Cook, can't tame Donald Trump's threats of tariffs that would spike the price of an iPhone; Apple's AI offerings pale against its competitors; and the company can't win a Fortnite match – or a single battle in its legal war with Epic Games – to save its life. On Friday, the president threatened to levy a 25% tariff on any iPhone not made in the US. Trump said in the post: "I have long ago informed Tim Cook of Apple that I expect their iPhones that will be sold in the United States of America will be manufactured and built in the United States, not India, or anyplace else. If that is not the case, a Tariff of at least 25% must be paid by Apple to the US."

Imagine your phone rings and the voice on the other end sounds just like your boss, a close friend, or even a government official. They urgently ask for sensitive information, except it's not really them. It's a deepfake, powered by AI, and you're the target of a sophisticated scam. These kinds of attacks are happening right now, and they're getting more convincing every day. That's the warning sounded by the 2025 AI Security Report, unveiled at the RSA Conference (RSAC), one of the world's biggest gatherings for cybersecurity experts, companies, and law enforcement.

Want to feel like you have a new laptop without shelling out hundreds of dollars? A lifetime license to this latest operating system is now just 14.97 (reg. Whoever said you can't teach an old dog new tricks never saw Windows 11 Pro in action. This operating system was made with the modern professional in mind, so it's ready to give your dusty device a new lease on life with improved productivity features. Take advantage of improved voice typing, seamless redocking, snap layouts, and a more powerful search experience with Windows 11 Pro.

My predictions about achieving full self-driving have been optimistic in the past,

If you're in the market for a new device, you can't really beat the MacBook Air. This sleek and powerful Apple computer is easy to bring anywhere, and right now you can get one for just 199.97-- 800 off the regular price--through July 20. It's hard to find the perfect laptop--they're often packed with serious perks but not very lightweight, or they're slim and portable but lacking in performance. The MacBook Air strikes the perfect balance--it packs a 1.8GHz Intel Core i5 processor and 8GB of RAM in a sleek 2.96-pound design. You can answer emails, stream content, or browse the web on a generous 13.3-inch widescreen display equipped with Intel HD Graphics 6000.

Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a nontrivial certified lower bound of minimum adversarial distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations. However, a generic robustness certification for general activation functions still remains largely unexplored.

This paper develops the FastRNN and FastGRNN algorithms to address the twin RNN limitations of inaccurate training and inefficient prediction. Previous approaches have improved accuracy at the expense of prediction costs making them infeasible for resource-constrained and real-time applications. Unitary RNNs have increased accuracy somewhat by restricting the range of the state transition matrix's singular values but have also increased the model size as they require a larger number of hidden units to make up for the loss in expressive power. Gated RNNs have obtained state-of-the-art accuracies by adding extra parameters thereby resulting in even larger models. FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters. FastGRNN then extends the residual connection to a gate by reusing the RNN matrices to match state-of-the-art gated RNN accuracies but with a 2-4x smaller model. Enforcing FastGRNN's matrices to be low-rank, sparse and quantized resulted in accurate models that could be up to 35x smaller than leading gated and unitary RNNs. This allowed FastGRNN to accurately recognize the "Hey Cortana" wakeword with a 1 KB model and to be deployed on severely resource-constrained IoT microcontrollers too tiny to store other RNN models.