When it comes to generating 3D computer graphics, there's no shortage of software options available. How you decide which software to use is generally priority calculus -- creating meshes for an industrial use case? You may want CAD-specific software like AutoCAD. But what if you want to do everything? And what if you'd prefer to do everything under an open source license (aka free)?

Digital smoking-cessation company Carrot has landed an FDA expanded use indication in a new 510(k) clearance for its connected breath sensor that can detect a user's exposure to cigarette smoke. The new indication allows the tool, called the Pivot Carbon Monoxide Breath Sensor, to be purchased over the counter and used without the oversight of a doctor. Users can blow into the fob-sized sensor to get a reading of their carbon monoxide level. "This is a significant breakthrough in smoking cessation," Dr. David S. Utley, Carrot CEO, said in a statement. "The emergence of an over-the-counter breath sensor that can help people quit tobacco is comparable to when consumer-grade glucose meters became available, empowering people in their own diabetes care."

AI diagnostics startup MediCircle Health has recently introduced in India a rapid spectrometry-based test that employs machine learning and artificial intelligence to detect COVID-19. Spectral Instant Test (SpectraLIT) is a point-of-care diagnostic platform that performs spectral analysis to accurately and instantly determine if a spectral pattern of a virus from a nasal or mouthwash sample resembles SARS-CoV-2, the virus causing COVID-19. The test can deliver results "within seconds of its use", according to a press release by MediCircle. The company shared that the portable solution can be used for entry screening at various airports, malls, schools and other venues. It can also potentially enable secure and real-time reporting to health and other designated authorities.

AI-powered software will soon be powerful enough spearhead advanced cyberattacks, prompting IT teams to deploy smarter security solutions, themselves, new research has said. Polling 300 C-level executives on their views of the future cybersecurity landscape, cybersecurity AI company Darktrace found that almost all respondents (96%) are preparing for an onslaught of AI-powered cyberattacks. Nearly two-thirds (68%) are under the impression that cybercriminals will be deploying AI on impersonation and spear-phishing attacks. To prepare for future attacks, most of the executives polled for the report said they started deploying AI-powered defenses, mostly because they don't believe (60%) humans are a match for automated cyberattacks, even if they could find enough, due to the ever-growing talent drought. They also believe current security solutions are a liability because they're unable to anticipate new attacks.

The purpose of artificial intelligence (AI) is to create intelligent machines. It is used in multiple domains, including finance, manufacturing, logistics, retail, social media, healthcare, and increasingly, cybersecurity. The current discourse about AI and cybersecurity often confuses the different perspectives, as if the intersection of disciplines is monolithic and one-dimensional. Therefore, we need a common language for discussing the various and disparate intersections of AI and cybersecurity that clarifies the differences. I see three parts to the discussion: AI in the hands of defenders, AI in the hands of attackers, and adversarial AI.

As it stands out today,Artificial intelligence elucidates simulation of human intelligence bymachines, particularly computer systems. AI programming focuses on three basiccognitive skills which are learning, reasoning and self-correction. Learning processes is theaspect of AI programming which focuses on acquiring data and creating rules forhow to turn the data into actionable information. These rules are calledalgorithms, and they provide the computing devices stepwise instructions on howto complete a specific task. Reasoning processes is theaspect of AI programming that focuses on choosing the right algorithm to reacha desired outcome. Typically, AI systems demonstrate at least some behaviours which are associated with human intelligence; thesebehaviours are planning,learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesserextent, social intelligence and creativity. The roots of computing dates back to the Logic Theoristprogram which was presented at the Dartmouth Summer scientific research onArtificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in1956.

A population of voters must elect representatives among themselves to decide on a sequence of possibly unforeseen binary issues. Voters care only about the final decision, not the elected representatives. The disutility of a voter is proportional to the fraction of issues, where his preferences disagree with the decision. While an issue-by-issue vote by all voters would maximize social welfare, we are interested in how well the preferences of the population can be approximated by a small committee. We show that a k-sortition (a random committee of k voters with the majority vote within the committee) leads to an outcome within the factor 1+O(1/√ k) of the optimal social cost for any number of voters n, any number of issues m, and any preference profile. For a small number of issues m, the social cost can be made even closer to optimal by delegation procedures that weigh committee members according to their number of followers. However, for large m, we demonstrate that the k-sortition is the worst-case optimal rule within a broad family of committee-based rules that take into account metric information about the preference profile of the whole population.

Availability of challenging benchmarks is the key to advancement of AI in a specific field.Since Legal Text is significantly different than normal English text, there is a need to create separate Natural Language Processing benchmarks for Indian Legal Text which are challenging and focus on tasks specific to Legal Systems. This will spur innovation in applications of Natural language Processing for Indian Legal Text and will benefit AI community and Legal fraternity. We review the existing work in this area and propose ideas to create new benchmarks for Indian Legal Natural Language Processing.

Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al. 2021, by going beyond corruption processes with uniform transition probabilities. This includes corruption with transition matrices that mimic Gaussian kernels in continuous space, matrices based on nearest neighbors in embedding space, and matrices that introduce absorbing states. The third allows us to draw a connection between diffusion models and autoregressive and mask-based generative models. We show that the choice of transition matrix is an important design decision that leads to improved results in image and text domains. We also introduce a new loss function that combines the variational lower bound with an auxiliary cross entropy loss. For text, this model class achieves strong results on character-level text generation while scaling to large vocabularies on LM1B. On the image dataset CIFAR-10, our models approach the sample quality and exceed the log-likelihood of the continuous-space DDPM model.

The cybersecurity industry is rapidly embracing the notion of "zero trust", where architectures, policies, and processes are guided by the principle that no one and nothing should be trusted. However, in the same breath, the cybersecurity industry is incorporating a growing number of AI-driven security solutions that rely on some type of trusted "ground truth" as reference point. This is not a hypothetical discussion. Organizations are introducing AI models into their security practices that impact almost every aspect of their business, and one of the most urgent questions remains whether regulators, compliance officers, security professionals, and employees will be able to trust these security models at all. Because AI models are sophisticated, obscure, automated, and oftentimes evolving, it is difficult to establish trust in an AI-dominant environment.