It looks like fabric, feels like metal, and is as light as rubber. Any watch fan looking to tick all of the above boxes would normally expect to be a dab hand with a spring bar removal tool to experience all the above individually, but a new strap developed by Malaysian independent brand Ming appears to now offer the best of all worlds. The one strap to rule them all has been dubbed the Polymesh, and is 3D-printed from grade five titanium, and comprises 1,693 interconnected pieces (including the buckle) held together without any pins or screws. The only additional parts requiring assembly are the quick-release spring bars at each end that attach it to the watch--the articulated pin buckle is also formed in the same process. Ming says that the strap, which is made up from rows of 15 equilateral triangles, meshed together and bookended by larger end pieces, "has more motion engineered into the radial axis than the lateral one," leading to a supple end result that drapes like fabric yet retains the strength of titanium.

When Ming* found a hidden camera in her bedroom, she prayed for a reasonable explanation, wondering whether her boyfriend had placed it there to record memories of their "happy life" together. But hope quickly turned to horror. Ming's boyfriend had been secretly taking sexually exploitative photos of not just Ming and her female friends, but also of other women in other locations, then using AI technology to generate pornographic images of them. After Ming confronted him, he "begged for mercy" but became angry when she refused to forgive him, Ming reportedly told Chinese news outlet Jimu News. Ming is just one of many women in China who have been covertly photographed or filmed – both in private and public spaces, including toilets – by voyeurs who have then circulated or sold the images online without consent.

Traditional molecule generation methods often rely on sequence or graph-based representations, which can limit their expressive power or require complex permutation-equivariant architectures. This paper introduces a novel paradigm for learning molecule generative models based on functional representations. Specifically, we propose Molecular Implicit Neural Generation (MING), a diffusion-based model that learns molecular distributions in function space. Unlike standard diffusion processes in data space, MING employs a novel functional denoising probabilistic process, which jointly denoises the information in both the function's input and output spaces by leveraging an expectation-maximization procedure for latent implicit neural representations of data. This approach allows for a simple yet effective model design that accurately captures underlying function distributions. Experimental results on molecule-related datasets demonstrate MING's superior performance and ability to generate plausible molecular samples, surpassing state-of-the-art data-space methods while offering a more streamlined architecture and significantly faster generation times.

Methods which utilize the outputs or feature representations of predictive models have emerged as promising approaches for out-of-distribution (OOD) detection of image inputs. However, these methods struggle to detect OOD inputs that share nuisance values (e.g. background) with in-distribution inputs. The detection of shared-nuisance out-of-distribution (SN-OOD) inputs is particularly relevant in real-world applications, as anomalies and in-distribution inputs tend to be captured in the same settings during deployment. In this work, we provide a possible explanation for SN-OOD detection failures and propose nuisance-aware OOD detection to address them. Nuisance-aware OOD detection substitutes a classifier trained via empirical risk minimization and cross-entropy loss with one that 1. is trained under a distribution where the nuisance-label relationship is broken and 2. yields representations that are independent of the nuisance under this distribution, both marginally and conditioned on the label. We can train a classifier to achieve these objectives using Nuisance-Randomized Distillation (NuRD), an algorithm developed for OOD generalization under spurious correlations. Output- and feature-based nuisance-aware OOD detection perform substantially better than their original counterparts, succeeding even when detection based on domain generalization algorithms fails to improve performance.

On Tuesday, CERN will launch a new podcast series on artificial intelligence. The series looks forward to the first edition of the Sparks! Serendipity Forum in September, when over 30 leading thinkers will converge on the laboratory for high-level multidisciplinary discussions designed to spark ethical innovation. To whet your appetite for the forum, the podcasts bring a selection of the Sparks delegates together in pairs. Think of these conversations like collisions in the LHC.

In front of a packed room of MIT students and alumni, Vivienne Ming is holding forth in a style all her own. "Embrace cyborgs," she calls out, as she clicks to a slide that raises eyebrows even in this tech-smitten crowd. Fifteen to 25 years from now, cognitive neuroprosthetics will fundamentally change the definition of what it means to be human." She's referring to the work that interests her most these days, as cofounder of machine learning company Socos and a visiting scholar at UC Berkeley's Center for Theoretical Neuroscience. If you're curious, the answer is unambiguously yes.") But the talk has covered a lot more than this, as Ming has touched on many initiatives and startups she's been involved with, all solving problems at the intersection of advanced technology, learning, and labor economics.

Previously an academic, Vivienne Ming initially regretted her decision to become an entrepreneur. Her first project combined neuroscience and artificial intelligence and education. Thinking she could change the world, she created amazing technologies, that everyone she demonstrated to loved. However, investors didn't see the opportunity and didn't want to work with her. Instead, they wanted to buy the entire product and appoint their own CEO's.

"This is a human problem," said Vivienne Ming, a longtime computer scientist and entrepreneur who calls herself a "professional mad scientist." She was talking about the problem of keeping artificial intelligence ethical. Software that learns or evolves over time while doing tasks–a rough definition of this complex category of technology–is poised to play a greater and greater role in modern society. Techonomy recently brought together a trio of leaders and technologists who are excited by its potential but committed to doing so carefully for a Tech session. These experts all agree that thus far, AI hasn't fulfilled its promise, because it hasn't been designed with sufficient ethical intention. In addition to Ming, we heard from leaders of two major companies that take AI ethics seriously–software giant Salesforce, represented by Paula Goldman, chief ethical and human use officer, and global technology services firm Wipro.

In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a prominent approach. In recent years, RC has gained even more popularity in the context of Description Logics (DLs), the logic underpinning the semantic web standard ontology language OWL 2, whose main ingredients are classes and roles. In this work, we show how to integrate RC within the triple language RDFS, which together with OWL2 are the two major standard semantic web ontology languages. To do so, we start from $\rho df$, which is the logic behind RDFS, and then extend it to $\rho df_\bot$, allowing to state that two entities are incompatible. Eventually, we propose defeasible $\rho df_\bot$ via a typical RC construction. The main features of our approach are: (i) unlike most other approaches that add an extra non-monotone rule layer on top of monotone RDFS, defeasible $\rho df_\bot$ remains syntactically a triple language and is a simple extension of $\rho df_\bot$ by introducing some new predicate symbols with specific semantics. In particular, any RDFS reasoner/store may handle them as ordinary terms if it does not want to take account for the extra semantics of the new predicate symbols; (ii) the defeasible $\rho df_\bot$ entailment decision procedure is build on top of the $\rho df_\bot$ entailment decision procedure, which in turn is an extension of the one for $\rho df$ via some additional inference rules favouring an potential implementation; and (iii) defeasible $\rho df_\bot$ entailment can be decided in polynomial time.

"With AI," Cisco's Joseph Bradley said, "we're at a crossroads for a new kind of moral compass of human equality, at a level literally of the civil rights movement. Because when you think of the number of people that AI can impact and the speed at which it drives decisions, you understand how important it is for us to get it right." That importance will only grow, as artificial intelligence and machine learning take over key decisions in everything from enterprises and public safety to battlefields and operating rooms. Depending on how it's developed and deployed, AI can support a future that's inclusive, sustainable, and rife with opportunity for even for the most disadvantaged in society. Or it can widen the divide, eliminating jobs and basing key decisions on biased programming.