The maker of the N95 respirator mask designed it for the mining and construction industry. It filters out 95 percent of the visible airborne dust particles, keeping them from entering the lungs. It has vents so the wearers can exhale. Demolition crews use them, concrete laborers use them, so do workers who sand wood floors, gypsum board walls, and plaster ceilings. Workers in other trades, such as electrical, plumbing, rough carpentry, and wood finishers rarely wear masks.

Last December 10th, I reported on a "land rush" for "deep learning" patents. Before I show the final result from 2019 and indicate how things are going during 2020, I'll begin by describing my USPTO search terms. First, I used the USPTO "advanced" engine. Second, I've memorized only two acronyms: ACLM is the acronym for the Claim(s) field and ISD is the acronym for the Issue Date. The Issue Date portion is simple.

Human fascination with robots has long been fused with fear. The first widespread use of the term came a century ago in a Czech play about robots manufactured to serve and work for people. The bots turn on their masters. That plot has played out in fiction countless times since. Meanwhile, the real world has created ever more advanced versions of mechanical servants.

Human fascination with robots has long been fused with fear. The first widespread use of the term came a century ago in a Czech play about robots manufactured to serve and work for people. The bots turn on their masters. That plot has played out in fiction countless times since. Meanwhile, the real world has created ever more advanced versions of mechanical servants.

Clearly financial services organizations possess the impetus to take advantage of AI and ML capabilities, and yet models still aren't being deployed– which exposes a quagmire in the process of model deployment. Could it be they're focusing too much on the development aspect and ignoring the criticality of ModelOps? Model validation is required across all regulated industries, but FinServ institutions especially face significant regulatory compliance mandates from the federal government – placing yet another roadblock on their path to AI success. Given these same institutions leverage thousands of models per day, they must typically staff large teams across their model risk management program, including spinning up large teams of model validators. ModelOps refers to the process of enabling data scientists, data engineers, and IT operations teams to collaborate and scale models across an organization.

Variational mutual information (MI) estimators are widely used in unsupervised representation learning methods such as contrastive predictive coding (CPC). A lower bound on MI can be obtained from a multi-class classification problem, where a critic attempts to distinguish a positive sample drawn from the underlying joint distribution from $(m-1)$ negative samples drawn from a suitable proposal distribution. Using this approach, MI estimates are bounded above by $\log m$, and could thus severely underestimate unless $m$ is very large. To overcome this limitation, we introduce a novel estimator based on a multi-label classification problem, where the critic needs to jointly identify multiple positive samples at the same time. We show that using the same amount of negative samples, multi-label CPC is able to exceed the $\log m$ bound, while still being a valid lower bound of mutual information. We demonstrate that the proposed approach is able to lead to better mutual information estimation, gain empirical improvements in unsupervised representation learning, and beat a current state-of-the-art knowledge distillation method over 10 out of 13 tasks.

Events unfold and you are dropped into the opening of a long and complex case with 500,000 emails to sift through and you're not even sure what you are looking for, who you are looking for, or when any incidents of interest may have occurred. Currently the review of documents is the most labour-intensive task of an e-discovery investigation often consuming more than 75% of the project budget. This is largely because researchers review the documents manually. To put this into context, to review half a million documents by hand, at 25 documents an hour, would take around 20,000 person-hours. Hence, because it is practically impossible to review all documents in the target corpus by hand, results are too often limited by simple keyword searches.

Joint AI Center (JAIC) acting director Nand Mulchandani said one of JAIC's first lethal AI projects is proceeding into a testing phase now. The JAIC was founded in 2018 to act as the Pentagon's leader in all things AI, and initially focused on non-lethal forms. Mulchandani shared few specifics, but called the project "tactical edge AI" that will involve full human control and likened it to JAIC's "flagship product" for joint warfighting operations. "It is true that many of the products we work on will go into weapons systems. None of them right now are going to be autonomous weapon systems, we're still governed by 3000.09," he said.

This method exposes fake images created by computer algorithms rather than by humans. They look deceptively real, but they are made by computers: so-called deep-fake images are generated by machine learning algorithms, and humans are pretty much unable to distinguish them from real photos. Researchers at the Horst Görtz Institute for IT Security at Ruhr-Universität Bochum and the Cluster of Excellence "Cyber Security in the Age of Large-Scale Adversaries" (Casa) have developed a new method for efficiently identifying deep-fake images. To this end, they analyze the objects in the frequency domain, an established signal processing technique. The team presented their work at the International Conference on Machine Learning (ICML) on 15 July 2020, one of the leading conferences in the field of machine learning.

A cross-disciplinary team of machine learning, security, policy, and law experts say inconsistent court interpretations of an anti-hacking law have a chilling effect on adversarial machine learning security research and cybersecurity. At question is a portion of the Computer Fraud and Abuse Act (CFAA). A ruling to decide how part of the law is interpreted could shape the future of cybersecurity and adversarial machine learning. If the U.S. Supreme Court takes up an appeal case based on CFAA next year, researchers predict that the court will ultimately choose a narrow definition of the clause related to "exceed authorized access" instead of siding with circuit courts who have taken a broad definition of the law. One circuit court ruling on the subject concluded that a broad view would turn millions of people into unsuspecting criminals.