Tennessee minors sue Musk's xAI, alleging Grok generated sexual images of them Governments and regulators around the world have launched probes into xAI, imposed bans and demanded safeguards in a growing push to curb illegal and offensive material. Three Tennessee plaintiffs, including two minors, sued Elon Musk's xAI on Monday, alleging that it knowingly designed its Grok image generator to let people create sexually explicit content by using real photos of others. The lawsuit, filed in the San Jose, California federal court, is seeking class-action status for people in the United States who were reasonably identifiable in sexualized images or videos generated by Grok based on real images of themselves. The artificial intelligence company did not immediately respond to a request for comment. After an outcry over sexually explicit content generated by the chatbot, xAI said in January that it had blocked all users from editing images of real people in revealing clothing and from generating images of people in revealing clothing in jurisdictions where it's illegal. Governments and regulators around the world have also since launched probes, imposed bans and demanded safeguards in a growing push to curb illegal and offensive material.

A U.S. judge has ordered South Korean game developer Krafton to reinstate the head of one of its video game studios after ruling that he had been improperly removed as part of a takeover plan hatched by ChatGPT. WILMINGTON, DELAWARE - A Delaware judge on Monday ordered that South Korean game developer Krafton reinstate the head of one of its video game studios, ruling he had been improperly removed as part of a takeover plan hatched by ChatGPT. Krafton CEO Changhan Kim had largely followed the advice of artificial intelligence tool ChatGPT during a $250 million dispute with the leaders of the Subnautica game maker Unknown Worlds Entertainment, which Krafton had acquired, according to the ruling by Vice Chancellor Lori Will of the Court of Chancery in Delaware. Businesses and governments are scrambling for new ways to use AI, and the technology has been blamed for mass layoffs, fears of autonomous weapons and concerns about civil rights. Companies caught in takeover-related legal battles often spend millions of dollars on teams of attorneys and advisers from top-flight Wall Street firms. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

In this work, we present new theoretical results on convolutional generative neural networks, in particular their invertibility (i.e., the recovery of input latent code given the network output). The study of network inversion problem is motivated by image inpainting and the mode collapse problem in training GAN. Network inversion is highly non-convex, and thus is typically computationally intractable and without optimality guarantees. However, we rigorously prove that, under some mild technical assumptions, the input of a two-layer convolutional generative network can be deduced from the network output efficiently using simple gradient descent. This new theoretical finding implies that the mapping from the low-dimensional latent space to the high-dimensional image space is bijective (i.e., one-to-one). In addition, the same conclusion holds even when the network output is only partially observed (i.e., with missing pixels). Our theorems hold for 2-layer convolutional generative network with ReLU as the activation function, but we demonstrate empirically that the same conclusion extends to multi-layer networks and networks with other activation functions, including the leaky ReLU, sigmoid and tanh.

This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak]. The proposed algorithm efficiently escapes saddle points and finds approximate local minima for general smooth, nonconvex functions in only $\mathcal{\tilde{O}}(\epsilon^{-3.5})$ stochastic gradient and stochastic Hessian-vector product evaluations. The latter can be computed as efficiently as stochastic gradients. This improves upon the $\mathcal{\tilde{O}}(\epsilon^{-4})$ rate of stochastic gradient descent. Our rate matches the best-known result for finding local minima without requiring any delicate acceleration or variance-reduction techniques.

Population risk is always of primary interest in machine learning; however, learning algorithms only have access to the empirical risk. Even for applications with nonconvex non-smooth losses (such as modern deep networks), the population risk is generally significantly more well behaved from an optimization point of view than the empirical risk. In particular, sampling can create many spurious local minima. We consider a general framework which aims to optimize a smooth nonconvex function $F$ (population risk) given only access to an approximation $f$ (empirical risk) that is pointwise close to $F$ (i.e., $\norm{F-f}_{\infty} \le \nu$). Our objective is to find the $\epsilon$-approximate local minima of the underlying function $F$ while avoiding the shallow local minima---arising because of the tolerance $\nu$---which exist only in $f$. We propose a simple algorithm based on stochastic gradient descent (SGD) on a smoothed version of $f$ that is guaranteed to achieve our goal as long as $\nu \le O(\epsilon^{1.5}/d)$. We also provide an almost matching lower bound showing that our algorithm achieves optimal error tolerance $\nu$ among all algorithms making a polynomial number of queries of $f$. As a concrete example, we show that our results can be directly used to give sample complexities for learning a ReLU unit.

Model-free reinforcement learning (RL) algorithms directly parameterize and update value functions or policies, bypassing the modeling of the environment. They are typically simpler, more flexible to use, and thus more prevalent in modern deep RL than model-based approaches. However, empirical work has suggested that they require large numbers of samples to learn. The theoretical question of whether not model-free algorithms are in fact \emph{sample efficient} is one of the most fundamental questions in RL. The problem is unsolved even in the basic scenario with finitely many states and actions. We prove that, in an episodic MDP setting, Q-learning with UCB exploration achieves regret $\tlO(\sqrt{H^3 SAT})$ where $S$ and $A$ are the numbers of states and actions, $H$ is the number of steps per episode, and $T$ is the total number of steps. Our regret matches the optimal regret up to a single $\sqrt{H}$ factor. Thus we establish the sample efficiency of a classical model-free approach. Moreover, to the best of our knowledge, this is the first model-free analysis to establish $\sqrt{T}$ regret \emph{without} requiring access to a ``simulator.''

AI firm Anthropic seeks weapons expert to stop users from'misuse' The US artificial intelligence (AI) firm Anthropic is looking to hire a chemical weapons and high-yield explosives expert to try to prevent catastrophic misuse of its software. In other words, it fears that its AI tools might tell someone how to make chemical or radioactive weapons, and wants an expert to ensure its guardrails are sufficiently robust. In the LinkedIn recruitment post, the firm says applicants should have a minimum of five years experience in chemical weapons and/or explosives defence as well as knowledge of radiological dispersal devices - also known as dirty bombs. The firm told the BBC the role was similar to jobs in other sensitive areas that it has already created. Anthropic is not the only AI firm adopting this strategy.

Recent years have witnessed substantial progress in understanding the behavior of EM for mixture models that are correctly specified. Given that model misspecification is common in practice, it is important to understand EM in this more general setting. We provide non-asymptotic guarantees for population and sample-based EM for parameter estimation under a few specific univariate settings of misspecified Gaussian mixture models. Due to misspecification, the EM iterates no longer converge to the true model and instead converge to the projection of the true model over the set of models being searched over. We provide two classes of theoretical guarantees: first, we characterize the bias introduced due to the misspecification; and second, we prove that population EM converges at a geometric rate to the model projection under a suitable initialization condition. This geometric convergence rate for population EM imply a statistical complexity of order $1/\sqrt{n}$ when running EM with $n$ samples.

Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation. Existing adversarial domain adaptation methods may struggle to align different domains of multimodal distributions that are native in classification problems. In this paper, we present conditional adversarial domain adaptation, a principled framework that conditions the adversarial adaptation models on discriminative information conveyed in the classifier predictions. Conditional domain adversarial networks (CDANs) are designed with two novel conditioning strategies: multilinear conditioning that captures the cross-covariance between feature representations and classifier predictions to improve the discriminability, and entropy conditioning that controls the uncertainty of classifier predictions to guarantee the transferability. Experiments testify that the proposed approach exceeds the state-of-the-art results on five benchmark datasets.

Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibility, this approach makes it difficult to impose or assess structural constraints such as conditional independence. We propose a framework for learning representations that relies on Auto-Encoding Variational Bayes and whose search space is constrained via kernel-based measures of independence. In particular, our method employs the $d$-variable Hilbert-Schmidt Independence Criterion (dHSIC) to enforce independence between the latent representations and arbitrary nuisance factors. We show how to apply this method to a range of problems, including the problems of learning invariant representations and the learning of interpretable representations. We also present a full-fledged application to single-cell RNA sequencing (scRNA-seq).