Parliament on Wednesday enacted a bill to establish a new law that will promote the development of artificial intelligence while addressing risks associated with the technology. The bill cleared the House of Councilors, the upper chamber, by a majority vote with support from the Liberal Democratic Party-led ruling bloc and opposition parties including the Constitutional Democratic Party of Japan and Nippon Ishin no Kai. The measure had been adopted by the House of Representatives, the lower chamber, in April. To address mounting concerns over the spread of false and erroneous information generated by AI tools, the new law includes a provision to allow the government to disclose the names of malicious businesses in the event of crime using AI. If a serious incident that infringes on citizens' rights and interests occurs, the government will conduct investigations, advise and instruct related business operators, provide information to the public and take other necessary actions.

We consider a large-scale matrix multiplication problem where the computation is carried out using a distributed system with a master node and multiple worker nodes, where each worker can store parts of the input matrices. We propose a computation strategy that leverages ideas from coding theory to design intermediate computations at the worker nodes, in order to optimally deal with straggling workers. The proposed strategy, named as polynomial codes, achieves the optimum recovery threshold, defined as the minimum number of workers that the master needs to wait for in order to compute the output. This is the first code that achieves the optimal utilization of redundancy for tolerating stragglers or failures in distributed matrix multiplication. Furthermore, by leveraging the algebraic structure of polynomial codes, we can map the reconstruction problem of the final output to a polynomial interpolation problem, which can be solved efficiently. Polynomial codes provide order-wise improvement over the state of the art in terms of recovery threshold, and are also optimal in terms of several other metrics including computation latency and communication load. Moreover, we extend this code to distributed convolution and show its order-wise optimality.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels. We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Intracortical brain-computer interfaces (iBCIs) have allowed people with tetraplegia to control a computer cursor by imagining the movement of their paralyzed arm or hand. State-of-the-art decoders deployed in human iBCIs are derived from a Kalman filter that assumes Markov dynamics on the angle of intended movement, and a unimodal dependence on intended angle for each channel of neural activity. Due to errors made in the decoding of noisy neural data, as a user attempts to move the cursor to a goal, the angle between cursor and goal positions may change rapidly. We propose a dynamic Bayesian network that includes the on-screen goal position as part of its latent state, and thus allows the person's intended angle of movement to be aggregated over a much longer history of neural activity. This multiscale model explicitly captures the relationship between instantaneous angles of motion and long-term goals, and incorporates semi-Markov dynamics for motion trajectories. We also introduce a multimodal likelihood model for recordings of neural populations which can be rapidly calibrated for clinical applications. In offline experiments with recorded neural data, we demonstrate significantly improved prediction of motion directions compared to the Kalman filter. We derive an efficient online inference algorithm, enabling a clinical trial participant with tetraplegia to control a computer cursor with neural activity in real time. The observed kinematics of cursor movement are objectively straighter and smoother than prior iBCI decoding models without loss of responsiveness.

Neural Information Processing Systems http://nips.cc/

Apple shares are coming off their longest sell-off in more than three years as escalating attacks from the White House threaten to further erode the company's profit outlook, suggesting the stock's struggles this year are far from over. U.S. President Donald Trump on Friday threatened to levy a 25% tariff on the company's products if it doesn't shift iPhone production to the U.S. Shares fell 3% to end the week, their eighth straight negative session, the longest such sell-off since January 2022. The stock rose 1.7% on Tuesday. Some analysts are skeptical that the tariffs will come to pass, but any movement in this direction will put the company in a position where it either has to absorb the higher costs, weighing on its earnings and margins, or pass along higher prices to consumers, which could erode demand at a time when Apple is already struggling with tepid growth and difficulties with its artificial intelligence offerings.

Neural Information Processing Systems http://nips.cc/