In the past week or so, we've had a logo upgrade, a big New York Times profile, and a Moncler outerwear collaboration from LoveFrom, Jony Ive and Marc Newson's San Francisco–headquartered design studio. The real news, though, is confirmation that LoveFrom is working with OpenAI's founder Sam Altman to build a secretive as-yet-unnamed AI device with investors including Laurene Powell Jobs' Emerson Collective, and Ive himself. The former Apple chief design officer is sometimes gently mocked for his obsession with seemingly small details, but when it comes to a potential mainstream human-AI interface, the man who has spent the past five years preoccupied with buttons--going so far as to create a five-volume history of garment fasteners--could be, in a somewhat inevitable way, the exact kind of person required to walk this particular tightrope of ethics and ambition. Details so far are scarce but revealing, at least where intentions are concerned. LoveFrom is designing "a product that uses AI to create a computing experience that is less socially disruptive than the iPhone."

We thank the reviewers for their time and comments. We show similar results on the 3d Chairs dataset, with style as the label and yaw angle as the desired invariance.

Another Amazon Prime Day event is set to run on October 8 and 9, but you don't have to wait another week to bag a bargain. After trawling the world's favorite online store, aisle by digital aisle, we've found the best early Prime Day deals for those looking to get a jump on their shopping. The WIRED Reviews team tests products year-round and uses multiple price-tracking tools to filter the noise. Our deals coverage is different because we begin by cross-referencing our buying guide recommendations. Throughout our Prime Day deals coverage, we only recommend products that someone on our team has personally tested and would recommend buying.

A.1 Multi-Robot Warehouse The multi-robot warehouse environment (Figure 8) simulates a warehouse with robots moving and delivering requested goods. In real-world applications [38], robots pick-up shelves and deliver them to a workstation. Humans assess the content of a shelf, and then robots can return them to empty shelf locations. In this simulation of the environment, agents control robots and the action space for each agent is A = {Turn Left, Turn Right, Forward, Load/Unload Shelf} Agents can move beneath shelves when they do not carry anything, but when carrying a shelf, agents must use the corridors visible in Figure 8. The observation of an agent consists of a 3 3 square centred on the agent.

Most existing animal pose and shape estimation approaches reconstruct animal meshes with a parametric SMAL model. This is because the low-dimensional pose and shape parameters of the SMAL model makes it easier for deep networks to learn the high-dimensional animal meshes. However, the SMAL model is learned from scans of toy animals with limited pose and shape variations, and thus may not be able to represent highly varying real animals well. This may result in poor fittings of the estimated meshes to the 2D evidences, e.g.

Dataset distillation (DD) aims to synthesize a small dataset whose test performance is comparable to a full dataset using the same model. State-of-the-art (SoTA) methods optimize synthetic datasets primarily by matching heuristic indicators extracted from two networks: one from real data and one from synthetic data (see Figure 1, Left), such as gradients and training trajectories. DD is essentially a compression problem that emphasizes maximizing the preservation of information contained in the data. We argue that well-defined metrics which measure the amount of shared information between variables in information theory are necessary for success measurement but are never considered by previous works.

U.S. Secretary of State Antony Blinken underscored strong U.S. concerns about China's support for Russia's defense industrial base in talks Friday with Chinese Foreign Minister Wang Yi, saying Beijing's talk of peace in Ukraine "doesn't add up." In a meeting with Wang on the sidelines of the U.N. General Assembly in New York, Blinken said he also raised China's "dangerous and destabilizing actions" in the South China Sea and discussed improving communication between their militaries. Blinken told a news conference he and Wang also discussed ways to disrupt the flow of drugs into the United States, and the risks posed by artificial intelligence.

All computational procedures were performed either on a desktop workstation running Ubuntu 18.04 By minimising off-target activation, Bayesian target optimisation could enable (e.g.) Here we provide further mathematical details for optimising holographic stimuli. Next we must evaluate the partial derivative on the right-hand side of Equation 13. The covariance between a GP and its derivative is given by [40, Sec 9.4] @) cov g Simulations consisted of both ORF mapping and stimulus optimisation phases. Figure S1: Effect of reducing the number of points at which each ORF is probed. In the low coverage case (right), this reduces to stimulating at just a 3 3 grid (grid points separated by 12 µm) at three powers.

Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision.

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.