Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach is through counterfactual explanations, which provide suggestions on what a user can do to alter an outcome. Not only must a counterfactual example counter the original prediction from the black-box classifier but it should also satisfy various constraints for practical applications. Diversity is one of the critical constraints that however remains less discussed. While diverse counterfactuals are ideal, it is computationally challenging to simultaneously address some other constraints. Furthermore, there is a growing privacy concern over the released counterfactual data. To this end, we propose a feature-based learning framework that effectively handles the counterfactual constraints and contributes itself to the limited pool of private explanation models. We demonstrate the flexibility and effectiveness of our method in generating diverse counterfactuals of actionability and plausibility. Our counterfactual engine is more efficient than counterparts of the same capacity while yielding the lowest re-identification risks.

In February, Meta released its large language model: LLaMA. Unlike OpenAI and its ChatGPT, Meta didn't just give the world a chat window to play with. Instead, it released the code into the open-source community, and shortly thereafter the model itself was leaked. Researchers and programmers immediately started modifying it, improving it, and getting it to do things no one else anticipated. And their results have been immediate, innovative, and an indication of how the future of this technology is going to play out.

A lawyer who relied on ChatGPT to prepare a court filing for his client is finding out the hard way that the artificial intelligence tool has a tendency to fabricate information. Steven Schwartz, a lawyer for a man suing the Colombian airline Avianca over a metal beverage cart allegedly injuring his knee, is facing a sanctions hearing on June 8 after admitting last week that several of the cases he supplied the court as evidence of precedent were invented by ChatGPT, a large language model created by OpenAI. Lawyers for Avianca first brought the concerns to the judge overseeing the case. "Six of the submitted cases appear to be bogus judicial decisions with bogus quotes and bogus internal citations," U.S. District Judge P. Kevin Castel said earlier this month after reviewing Avianca's complaint, calling the situation an "unprecedented circumstance." The invented cases included decisions titled "Varghese v. Schwartz ― an attorney with Levidow, Levidow & Oberman who's been licensed in New York for more than 30 years ― then confessed in an affidavit that he'd used ChatGPT to produce the cases in support of his client and was "unaware of the possibility that its content could be false."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Michigan Supreme Court will hear a dispute over the legality of using a drone to take pictures of a salvage yard near Traverse City. Aerial photos were used as evidence in a lawsuit against Todd and Heather Maxon, who were accused of violating a zoning ordinance and creating a nuisance with cars and other salvaged material in Long Lake Township. The Maxons argue that aerial photos violated their constitutional right against unreasonable searches.

If you want to really hurt someone's feelings in the year 2023, just call them an AI. An all-star cast of celebrities and public figures have recently been the victim of such jokes: the NBA player Jordan Poole ("AI Steph Curry"), Raquel Leviss from the reality-TV show Vanderpump Rules ("what would happen if you asked chat GBT [sic] to create an American girl"), Transportation Secretary Pete Buttigieg ("our first A.I. cabinet member?"). That these slights span the three pillars of American life--sports, politics, Bravo--suggests that no one, or rather nothing, is safe. Such digs have popped up all over social media; on Twitter alone, insults like these have been levied against TV shows, songs, sports uniforms, commencement speeches, White House press releases, proposed legislation, and lots of news articles. That AI has become an attack is a result of the huge moment for AI we're in.

Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology'to mitigate' its risks. A stranger in a coffee shop can watch you and learn virtually everything about you, where you've been and even predict your movements "with greater ease and precision than ever before," experts say. All the user would need is a photo and advanced artificial intelligence technology that already exists, said Kevin Baragona, a founder of DeepAI.org. "There are services online that can use a photo of you, and I can find everything. Every instance of your face on the internet, every place you've been and use that for stalker-type purposes," Baragona told Fox News Digital.

Steve Wozniak is no fan of Elon Musk. In February, the Apple co-founder described the Tesla, SpaceX and Twitter owner as a "cult leader" and called him dishonest. Yet, in late March, the tech titans came together, joining dozens of high-profile academics, researchers and entrepreneurs in calling for a six-month pause in training artificial intelligence systems more powerful than GPT-4, the latest version of Chat GPT, the chatbot that has taken the world by storm. Their letter, penned by the United States-based Future of Life Institute, said the current rate of AI progress was becoming a "dangerous race to ever-larger unpredictable black-box models". The "emergent capabilities" of these models, the letter said, should be "refocused on making today's powerful, state-of-the-art systems more accurate, safe, interpretable, transparent, robust, aligned, trustworthy and loyal".

Despite a growing interest in diffusion-based language models, existing work has not shown that these models can attain nontrivial likelihoods on standard language modeling benchmarks. In this work, we take the first steps towards closing the likelihood gap between autoregressive and diffusion-based language models, with the goal of building and releasing a diffusion model which outperforms a small but widely-known autoregressive model. We pursue this goal through algorithmic improvements, scaling laws, and increased compute. On the algorithmic front, we introduce several methodological improvements for the maximum-likelihood training of diffusion language models. We then study scaling laws for our diffusion models and find compute-optimal training regimes which differ substantially from autoregressive models. Using our methods and scaling analysis, we train and release Plaid 1B, a large diffusion language model which outperforms GPT-2 124M in likelihood on benchmark datasets and generates fluent samples in unconditional and zero-shot control settings.

As advanced machine learning systems' capabilities begin to play a significant role in geopolitics and societal order, it may become imperative that (1) governments be able to enforce rules on the development of advanced ML systems within their borders, and (2) countries be able to verify each other's compliance with potential future international agreements on advanced ML development. This work analyzes one mechanism to achieve this, by monitoring the computing hardware used for large-scale NN training. The framework's primary goal is to provide governments high confidence that no actor uses large quantities of specialized ML chips to execute a training run in violation of agreed rules. At the same time, the system does not curtail the use of consumer computing devices, and maintains the privacy and confidentiality of ML practitioners' models, data, and hyperparameters. The system consists of interventions at three stages: (1) using on-chip firmware to occasionally save snapshots of the the neural network weights stored in device memory, in a form that an inspector could later retrieve; (2) saving sufficient information about each training run to prove to inspectors the details of the training run that had resulted in the snapshotted weights; and (3) monitoring the chip supply chain to ensure that no actor can avoid discovery by amassing a large quantity of un-tracked chips. The proposed design decomposes the ML training rule verification problem into a series of narrow technical challenges, including a new variant of the Proof-of-Learning problem [Jia et al. '21].

As pointed out by several scholars, current research on hate speech (HS) recognition is characterized by unsystematic data creation strategies and diverging annotation schemata. Subsequently, supervised-learning models tend to generalize poorly to datasets they were not trained on, and the performance of the models trained on datasets labeled using different HS taxonomies cannot be compared. To ease this problem, we propose applying extremely weak supervision that only relies on the class name rather than on class samples from the annotated data. We demonstrate the effectiveness of a state-of-the-art weakly-supervised text classification model in various in-dataset and cross-dataset settings. Furthermore, we conduct an in-depth quantitative and qualitative analysis of the source of poor generalizability of HS classification models.