MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by "seeing" through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot's ability to reliably grasp and manipulate objects that are blocked from view. This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.

Japanet is expanding its venture capital fund with Pegasus Tech Ventures, after early investments in firms like SpaceX, OpenAI, Anthropic and xAI showed strong growth. Japanese home shopping company Japanet is expanding its venture capital fund with San Jose-based Pegasus Tech Ventures, following the success of early bets in SpaceX, OpenAI, Anthropic and xAI. The Nagasaki-based retailer known for infomercials targeting seniors in aging Japan will allocate $200 million to the fund, up from an initial $50 million in 2021, following significant growth" in investments so far, the companies said in a statement. The fund, of which Pegasus is general partner, will focus on areas such as generative AI, robotics and space technology. Its Japan portfolio includes startup Aillis, which seeks to use artificial intelligence to analyze medical scans. Asian companies have struggled to win stakes in promising startups in Silicon Valley, hampered by a lack of personal connections and reputation for slow decision-making. Pegasus also manages startup investments on behalf of Toyota Motor-affiliate Aisin, Japanese chemical maker Denka, Taiwan's Asustek Computer and Acer and Indonesia's pharma company Kalbe Farma. Everybody wants a piece of the Silicon Valley AI action," Pegasus Chief Executive Officer Anis Uzzaman said on a video call.

LinkedIn's new Crosscheck feature lets premium subscribers test competing AI models for free The feature is a blind taste test for AI models from Anthropic, Google, OpenAI and other companies. You can now use LinkedIn to test out some of the latest AI models from OpenAI, Anthropic, Google, Microsoft and other companies without having to worry about token limits or paying for an extra subscription. The professional network is experimenting with a new feature that allows people to test AI platforms' latest offerings within LinkedIn. It's called Crosscheck, and it's rolling out now to anyone with a LinkedIn Premium subscription in the United States. The feature is meant to be a kind of blind taste test for AI models, according to the company's Chief Product Officer Hari Srinivasan.

Sam Altman speaks during the BlackRock infrastructure summit on 11 March in Washington DC. Sam Altman speaks during the BlackRock infrastructure summit on 11 March in Washington DC. How a fiery attack on Sam Altman's home unfolded Molotov cocktail attack on OpenAI CEO's home comes amid growing discontent against artificial intelligence I n the early hours of 10 April, a man approached the gate of OpenAI CEO Sam Altman's house in San Francisco and hurled a molotov cocktail at the building before fleeing. Federal and California state authorities have charged Moreno-Gama with a range of crimes including attempted arson and attempted murder. His parents issued a statement this week saying that their son had recently suffered a mental health crisis.

Panic says the Playdate Catalog won't accept games made with generative AI Using it for coding assistance is still OK, but you can't generate art, music, text or story elements. Panic's Playdate console displaying Season Two games. Panic, the company behind the tiny and excellent Playdate console, is taking a stand on generative AI. The company has published an AI disclosure that says as of this month, the Playdate Catalog "will no longer accept titles that use'Generative AI' for art, audio, music, text, or dialog." Panic does allow for developers to use AI assistance for coding, but also says that "we will flag any title as such and specify the extent that it was used (for example, "Lua debugging") so the customer can decide whether to support it or not."

OpenAI logo is seen in this illustration taken on 20 May 2024. OpenAI logo is seen in this illustration taken on 20 May 2024. AI companies know they have an image problem. OpenAI made a surprise announcement this week - not an update to ChatGPT or another multibillion-dollar datacenter - but a policy paper that called for a reimagining of the social contract based around "a slate of people-first ideas". It's the latest move in an aggressive effort by the major AI players to reshape the narrative around their industry, as polls show public disapproval of AI increasing.

Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS). Nowadays, in a pivotal time for artificial intelligence (AI), with even more sophisticated attacks that utilize advanced techniques, such as generative artificial intelligence (GenAI) and reinforcement learning, it has become a vital component if we wish to protect our personal data, which are scattered across the web. In this paper, we address two tasks, in the first unified multi-modal NIDS dataset, which incorporates flow-level data, packet payload information and temporal contextual features, from the reprocessed CIC-IDS-2017, CIC-IoT-2023, UNSW-NB15 and CIC-DDoS-2019, with the same feature space. In the first task we use machine learning (ML) algorithms, with stratified cross validation, in order to prevent network attacks, with stability and reliability. In the second task we use adversarial learning algorithms to generate synthetic data, compare them with the real ones and evaluate their fidelity, utility and privacy using the SDV framework, f-divergences, distinguishability and non-parametric statistical tests. The findings provide stable ML models for intrusion detection and generative models with high fidelity and utility, by combining the Synthetic Data Vault framework, the TRTS and TSTR tests, with non-parametric statistical tests and f-divergence measures.

Deep generative models for tabular data (GANs, diffusion models, and LLM-based generators) exhibit highly non-uniform behavior across datasets; the best-performing synthesizer family depends strongly on distributional stressors such as long-tailed marginals, high-cardinality categorical, Zipfian imbalance, and small-sample regimes. This brittleness makes practical deployment challenging, especially when users must balance competing objectives of fidelity, privacy, and utility. We study {intent-conditioned tabular synthesis selection}: given a dataset and a user intent expressed as a preference over evaluation metrics, the goal is to select a synthesizer that minimizes regret relative to an intent-specific oracle. We propose {stress profiling}, a synthesis-specific meta-feature representation that quantifies dataset difficulty along four interpretable stress dimensions, and integrate it into {SYNTHONY}, a selection framework that matches stress profiles against a calibrated capability registry of synthesizer families. Across a benchmark of 7 datasets, 10 synthesizers, and 3 intents, we demonstrate that stress-based meta-features are highly predictive of synthesizer performance: a $k$NN selector using these features achieves strong Top-1 selection accuracy, substantially outperforming zero-shot LLM selectors and random baselines. We analyze the gap between meta-feature-based and capability-based selection, identifying the hand-crafted capability registry as the primary bottleneck and motivating learned capability representations as a direction for future work.

The poster child of the AI boom, valued at $850bn, needs to show strategic discipline after'casting its net too wide' If OpenAI is going to float this year, it has to get serious about its business model. The wow factor around the US company - the poster child of an AI industry boom that has stoked fears of a stock market bubble - has been long established, but when will the profits come? The developer of ChatGPT is one of the biggest startups in the world and is now valued at $850bn (£645bn). Meanwhile, it is reportedly spending $600bn on infrastructure (the amount it invests in datacentres and chips to power its AI models) by 2030. At least this is a reduction on an initial estimate of $1.4tn .

Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with complex, multimodal data such as images and text. Current approaches often face notable limitations, including rigid assumptions and limited generalizability, constraining their effectiveness across diverse supervised learning tasks. To overcome these limitations, we introduce Generative Score Inference (GSI), a flexible inference framework capable of constructing statistically valid and informative prediction and confidence sets across a wide range of multimodal learning problems. GSI utilizes synthetic samples generated by deep generative models to approximate conditional score distributions, facilitating precise uncertainty quantification without imposing restrictive assumptions about the data or tasks. We empirically validate GSI's capabilities through two representative scenarios: hallucination detection in large language models and uncertainty estimation in image captioning. Our method achieves state-of-the-art performance in hallucination detection and robust predictive uncertainty in image captioning, and its performance is positively influenced by the quality of the underlying generative model. These findings underscore the potential of GSI as a versatile inference framework, significantly enhancing uncertainty quantification and trustworthiness in multimodal learning.