Gift Ideas For Everyone On Your List Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Playbook Mashable Selects In My Bag Say More AI at School Safety Net Versus Trending Now Back to School All Series Who are we expecting to save us from AI? An industry consumed with profit and a government hobbled by firings does not offer much reassurance. President Trump met with tech leaders, including Elon Musk and Mark Zuckerberg, on Wednesday. On Wednesday, the New York Times reported that Meta has been treating its AI data centers as experimental facilities to claim billions of dollars in federal research tax credits, a strategy the company's own accountants reportedly flagged as legally risky. The savings reportedly grew from $700 million in 2023 to $3.9 billion in 2025.

Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility. Existing methods rely on generating multiple responses from the base policy for search using a reward model, which can be considered as searching in a discrete response space. However, these methods struggle to explore informative candidates when the base policy is weak or the candidate set is small, resulting in limited effectiveness. In this paper, to address this problem, we propose Simple Energy Adaptation (SEA), a simple yet effective algorithm for inference-time alignment.

Regularization is one of the most fundamental topics in optimization, statistics and machine learning. To get sparsity in estimating a parameter u Rd, an ℓq penalty term, u q, is usually added to the objective function. What is the probabilistic distribution corresponding to such ℓq penalty? What is the correct stochastic process corresponding to u q when we model functions u Lq? This is important for statistically modeling high-dimensional objects such as images, with penalty to preserve certain properties, e.g.

Jack Dorsey leaves the Élysée Palace in Paris, France, on 7 June 2019. Jack Dorsey leaves the Élysée Palace in Paris, France, on 7 June 2019. What was really behind Jack Dorsey laying off nearly half of Block's staff? Jack Dorsey cited AI as the driving force behind cutting 40% of his company's employees, but other factors such as a weak crypto market, overstaffing and a declining stock price may also have motivated the move. Last week, the financial technology company Block announced that it would lay off 4,000 of its 10,000 workers.

Regularization is one of the most fundamental topics in optimization, statistics and machine learning.

As the housing market stalls, Zillow's CEO sees AI as "an ingredient rather than a threat" that can both help the company protect its turf and reinvent how people search for homes. This will not be a banner year for the real estate app Zillow. "We describe the home market as bouncing along the bottom," CEO Jeremy Wacksman said in our conversation this week. Last year was dismal for the real estate market, and he expects things to improve only marginally in 2026. "The way to think about it is that there were 4.1 million existing homes sold last year--a normal market is 5.5 to 6 million," Wacksman says.

Stock market prediction is a long-standing challenge in finance, as accurate forecasts support informed investment decisions. Traditional models rely mainly on historical prices, but recent work shows that financial news can provide useful external signals. This paper investigates a multimodal approach that integrates companies' news articles with their historical stock data to improve prediction performance. We compare a Graph Neural Network (GNN) model with a baseline LSTM model. Historical data for each company is encoded using an LSTM, while news titles are embedded with a language model. These embeddings form nodes in a heterogeneous graph, and GraphSAGE is used to capture interactions between articles, companies, and industries. We evaluate two targets: a binary direction-of-change label and a significance-based label. Experiments on the US equities and Bloomberg datasets show that the GNN outperforms the LSTM baseline, achieving 53% accuracy on the first target and a 4% precision gain on the second. Results also indicate that companies with more associated news yield higher prediction accuracy. Moreover, headlines contain stronger predictive signals than full articles, suggesting that concise news summaries play an important role in short-term market reactions.