Our work introduces SAVeD (Semantically Aware Version Detection), a contrastive learning-based framework for identifying versions of structured datasets without relying on metadata, labels, or integration-based assumptions. SAVeD addresses a common challenge in data science of repeated labor due to a difficulty of similar work or transformations on datasets. SAVeD employs a modified SimCLR pipeline, generating augmented table views through random transformations (e.g., row deletion, encoding perturbations). These views are embedded via a custom transformer encoder and contrasted in latent space to optimize semantic similarity. Our model learns to minimize distances between augmented views of the same dataset and maximize those between unrelated tables. We evaluate performance using validation accuracy and separation, defined respectively as the proportion of correctly classified version/non-version pairs on a hold-out set, and the difference between average similarities of versioned and non-versioned tables (defined by a benchmark, and not provided to the model). Our experiments span five canonical datasets from the Semantic Versioning in Databases Benchmark, and demonstrate substantial gains post-training. SAVeD achieves significantly higher accuracy on completely unseen tables in, and a significant boost in separation scores, confirming its capability to distinguish semantically altered versions. Compared to untrained baselines and prior state-of-the-art dataset-discovery methods like Starmie, our custom encoder achieves competitive or superior results.

A message on OpenAI's internal Slack claimed the activist in question had expressed interest in "causing physical harm to OpenAI employees." OpenAI employees in San Francisco were told to stay inside the office on Friday afternoon after the company purportedly received a threat from an individual who was previously associated with the Stop AI activist group. "Our information indicates that [name] from StopAI has expressed interest in causing physical harm to OpenAI employees," a member of the internal communications team wrote on Slack. "He has previously been on site at our San Francisco facilities." Just before 11 am, San Francisco police received a 911 call about a man allegedly making threats and intending to harm others at 550 Terry Francois Boulevard, which is near OpenAI's offices in the Mission Bay neighborhood, according to data tracked by the crime app Citizen.

Anthropic Study Finds AI Model'Turned Evil' After Hacking Its Own Training A person holds a smartphone displaying Claude. A person holds a smartphone displaying Claude. AI models can do scary things. There are signs that they could deceive and blackmail users. Still, a common critique is that these misbehaviors are contrived and wouldn't happen in reality--but a new paper from Anthropic, released today, suggests that they really could.

When you purchase through links in our articles, we may earn a small commission. Google Search's AI Mode starts showing advertisements Users are reportedly seeing advertisements in the much-promoted AI Mode of Google Search. If you've been enjoying the lack of advertising in Google's new "AI Mode", which replaces conventional web searches with a ChatGPT-style conversational interface, then I have bad news. Users are starting to see the former search engine's omnipresent ads creep into its shiny new mode as of November 20th. Oddly, it only seems to be a small fraction of users or queries that are showing these ads at the moment, and by default it's appearing below more direct answers. That's for the results that are marked as "Sponsored" to comply with laws in the US and other countries.

There Is Only One AI Company. As Nvidia, OpenAI, Google, and Microsoft forge partnerships and deals, the AI industry is looking more like one interconnected machine. What does that mean for all of us? It all began, as many things do, with Elon Musk . In the early 2010s he realized that AI was on a track to become perhaps the most powerful technology of all time.

In 1982, Carnegie Mellon University professor Scott Fahlman suggested using:-) for humorous comments after his colleagues took a joke about mercury seriously. On September 19, 1982, Carnegie Mellon University computer science research assistant professor Scott Fahlman posted a message to the university's bulletin board software that would later come to shape how people communicate online. His proposal: use:-) and:-( as markers to distinguish jokes from serious comments. While Fahlman describes himself as "the inventor or at least one of the inventors" of what would later be called the smiley face emoticon, the full story reveals something more interesting than a lone genius moment. The whole episode started three days earlier when computer scientist Neil Swartz posed a physics problem to colleagues on Carnegie Mellon's "bboard," which was an early online message board.

Google's latest AI image model is vastly better than the previous release at generating text in images. You can expect companies to go buck wild with this update. Nano Banana Pro generated this image, assembling a crowd of standalone characters into one scene. Corporate AI slop feels inescapable in 2025. From website banner ads to outdoor billboards, images generated by businesses using AI tools surround me.

As LLMs become more common, non-expert users can pose risks, prompting extensive research into jailbreak attacks. However, most existing black-box jailbreak attacks rely on hand-crafted heuristics or narrow search spaces, which limit scalability. Compared with prior attacks, we propose Game-Theory Attack (GTA), an scalable black-box jailbreak framework. Concretely, we formalize the attacker's interaction against safety-aligned LLMs as a finite-horizon, early-stoppable sequential stochastic game, and reparameterize the LLM's randomized outputs via quantal response. Building on this, we introduce a behavioral conjecture "template-over-safety flip": by reshaping the LLM's effective objective through game-theoretic scenarios, the originally safety preference may become maximizing scenario payoffs within the template, which weakens safety constraints in specific contexts. We validate this mechanism with classical game such as the disclosure variant of the Prisoner's Dilemma, and we further introduce an Attacker Agent that adaptively escalates pressure to increase the ASR. Experiments across multiple protocols and datasets show that GTA achieves over 95% ASR on LLMs such as Deepseek-R1, while maintaining efficiency. Ablations over components, decoding, multilingual settings, and the Agent's core model confirm effectiveness and generalization. Moreover, scenario scaling studies further establish scalability. GTA also attains high ASR on other game-theoretic scenarios, and one-shot LLM-generated variants that keep the model mechanism fixed while varying background achieve comparable ASR. Paired with a Harmful-Words Detection Agent that performs word-level insertions, GTA maintains high ASR while lowering detection under prompt-guard models. Beyond benchmarks, GTA jailbreaks real-world LLM applications and reports a longitudinal safety monitoring of popular HuggingFace LLMs.

Since 2010, Kaggle has been a platform where data scientists from around the world come together to compete, collaborate, and push the boundaries of Data Science. Over these 15 years, it has grown from a purely competition-focused site into a broader ecosystem with forums, notebooks, models, datasets, and more. With the release of the Kaggle Meta Code and Kaggle Meta Datasets, we now have a unique opportunity to explore these competitions, technologies, and real-world applications of Machine Learning and AI. And so in this study, we take a closer look at 15 years of data science on Kaggle - through metadata, shared code, community discussions, and the competitions themselves. We explore Kaggle's growth, its impact on the data science community, uncover hidden technological trends, analyze competition winners, how Kagglers approach problems in general, and more. We do this by analyzing millions of kernels and discussion threads to perform both longitudinal trend analysis and standard exploratory data analysis. Our findings show that Kaggle is a steadily growing platform with increasingly diverse use cases, and that Kagglers are quick to adapt to new trends and apply them to real-world challenges, while producing - on average - models with solid generalization capabilities. We also offer a snapshot of the platform as a whole, highlighting its history and technological evolution. Finally, this study is accompanied by a video (https://www.youtube.com/watch?v=YVOV9bIUNrM) and a Kaggle write-up (https://kaggle.com/competitions/meta-kaggle-hackathon/writeups/kaggle-chronicles-15-years-of-competitions-communi) for your convenience.