OpenAI denounced Elon Musk's lawsuit against the company in a legal filing on Monday, describing the Tesla CEO's claims as "frivolous" and intended only "to advance his commercial interests". The filing, a response to Musk suing OpenAI earlier this month over allegations that it abandoned its pledge to help humanity, rejects many of the core assertions in Musk's suit. The company denies that it ever broke what Musk calls its "Founding Agreement", stating that no such contract ever existed. "Musk's claims rest on convoluted – often incoherent – factual premises," the filing states. "Musk says his Founding Agreement was'memorialized,' but any actual agreement is conspicuously missing from the pleading."

Earlier this year, sexually explicit images of Taylor Swift were shared repeatedly X. The pictures were almost certainly created with generative-AI tools, demonstrating the ease with which the technology can be put to nefarious ends. This case mirrors many other apparently similar examples, including fake images depicting the arrest of former President Donald Trump, AI-generated images of Black voters who support Trump, and fabricated images of Dr. Anthony Fauci. There is a tendency for media coverage to focus on the source of this imagery, because generative AI is a novel technology that many people are still trying to wrap their head around. But that fact obscures the reason the images are relevant: They spread on social-media networks.

We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy. We enourage readers to consult the more complete manuscript on the arXiv.

We propose Confusions over Time (CoT), a novel generative framework which facilitates a multi-granular analysis of the decision making process. The CoT not only models the confusions or error properties of individual decision makers and their evolution over time, but also allows us to obtain diagnostic insights into the collective decision making process in an interpretable manner.

In October last year Francesa Mani came home from school in the suburbs of New Jersey with devastating news for her mother, Dorota. Earlier in the day the 14-year-old had been called into the vice-principal's office and notified that she and a group of girls at Westfield High had been the victims of targeted abuse by a fellow student. Faked nude images of her and others had been circulating around school. They had been generated by artificial intelligence. Dorota had been tangentially aware of the power of this relatively new technology, but the ease with which the images were generated took her aback.

Many real phenomena, including behaviors, involve strategic interactions that can be learned from data. We focus on learning tree structured potential games where equilibria are represented by local maxima of an underlying potential function. We cast the learning problem within a max margin setting and show that the problem is NP-hard even when the strategic interactions form a tree. We develop a variant of dual decomposition to estimate the underlying game and demonstrate with synthetic and real decision/voting data that the game theoretic perspective (carving out local maxima) enables meaningful recovery.

This time, authors are suing NVIDIA over its AI platform NeMo, a language model that allows businesses to create and train their own chatbots, Ars Technica reported. They claim the company trained it on a controversial dataset that illegally used their books without consent. Authors Abdi Nazemian, Brian Keene and Stewart O'Nan demanded a jury trial and asked Nvidia to pay damages and destroy all copies of the Books3 dataset used to power NeMo large language models (LLMs). They claim that dataset copied a shadow library called Bibliotek consisting of 196,640 pirated books. "In sum, NVIDIA has admitted training its NeMo Megatron models on a copy of The Pile dataset," the claim states.

We consider the Threshold Bandit setting, a variant of the classical multi-armed bandit problem in which the reward on each round depends on a piece of side information known as a threshold value. The learner selects one of K actions (arms), this action generates a random sample from a fixed distribution, and the action then receives a unit payoff in the event that this sample exceeds the threshold value. We consider two versions of this problem, the uncensored and censored case, that determine whether the sample is always observed or only when the threshold is not met. Using new tools to understand the popular UCB algorithm, we show that the uncensored case is essentially no more difficult than the classical multi-armed bandit setting. Finally we show that the censored case exhibits more challenges, but we give guarantees in the event that the sequence of threshold values is generated optimistically.

"There is no Founding Agreement, or any agreement at all with Musk," OpenAI said in a court filing as a defendant in Elon Musk's lawsuit. We're, of course, talking about the lawsuit Musk filed against OpenAI, which accuses it of violating its status as a non-profit, as well as of violating a founding agreement promising the organization would never operate for profit and would release its AI publicly. The company said the billionaire's claims are based on "convoluted -- often incoherent -- factual premises." It called that founding agreement "a fiction Musk has conjured to lay unearned claim to the fruits of an enterprise he initially supported, then abandoned, then watched succeed without him." If the case goes to discovery, there's evidence that would show that Musk supported OpenAI's transition into a for-profit structure, "to be controlled by Musk himself," OpenAI continued.

Although an ever-growing number of applications employ deep learning based systems for prediction, decision-making, or state estimation, almost no certification processes have been established that would allow such systems to be deployed in safety-critical applications. In this work we consider real-world problems arising in aviation and other safety-critical areas, and investigate their requirements for a certified model. To this end, we investigate methodologies from the machine learning research community aimed towards verifying robustness and reliability of deep learning systems, and evaluate these methodologies with regard to their applicability to real-world problems. Then, we establish a new framework towards deep learning certification based on (i) inherently safe design, and (ii) run-time error detection. Using a concrete use case from aviation, we show how deep learning models can recover disentangled variables through the use of weakly-supervised representation learning. We argue that such a system design is inherently less prone to common model failures, and can be verified to encode underlying mechanisms governing the data. Then, we investigate four techniques related to the run-time safety of a model, namely (i) uncertainty quantification, (ii) out-of-distribution detection, (iii) feature collapse, and (iv) adversarial attacks. We evaluate each for their applicability and formulate a set of desiderata that a certified model should fulfill. Finally, we propose a novel model structure that exhibits all desired properties discussed in this work, and is able to make regression and uncertainty predictions, as well as detect out-of-distribution inputs, while requiring no regression labels to train. We conclude with a discussion of the current state and expected future progress of deep learning certification, and its industrial and social implications.