AI influences nearly 8 billion people and human & earth diverse ecosystems on an unprecedented scale. Startups accelerate to incorporate AI innovation as AI tools proliferate. UNESCO is the United Nations Educational, Scientific and Cultural Organization. The UNESCO recommendations on the ethics of AI recently adopted by member states provides a foundational global agreement on AI Ethics. The objectives ultimately drive emerging AI driven technologies that are trustworthy, safe, human-centered for the benefit of people and humanity.

To have a solid understanding of what is happening, we will examine the old ways first. We won't train a model, but we'll do almost everything else: You set the pretrained flag to True to tell PyTorch that you do not want it to initialize the model's weights randomly. Instead, it should use the weights obtained by training the model on the ImageNet dataset. Then, you define and initialize a composition of data transformations. Next, you are ready to process the image and pass it through the neural network layers to get your output.

This week, hacktivism entered a new phase, as a group known as Cyber Partisans used ransomware to disrupt trains in Belarus. The hackers demanded the release of political prisoners and a promise that Belarus Railways wouldn't transport Russian troops amid mounting tensions in Ukraine. While nation state actors have deployed fake ransomware for political ends before, this appears to be the first large-scale, politically motivated use of an attack method typically reserved for cybercrime. Google this week backed away from FLoC, its controversial system to replace cookies. Instead, the search and advertising giant will use Topics, a way to determine what broad categories you're interested based on your browsing history.

Did you miss a session from the Future of Work Summit? This article was contributed by Loren Goodman, cofounder and CTO at InRule Technology. Traditional machine learning (ML) does only one thing: it makes a prediction based on historical data. Machine learning starts with analyzing a table of historical data and producing what is called a model; this is known as training. After the model is created, a new row of data can be fed into the model and a prediction is returned.

We propose a method for quantifying uncertainty in high-dimensional PDE systems with random parameters, where the number of solution evaluations is small. Parametric PDE solutions are often approximated using a spectral decomposition based on polynomial chaos expansions. For the class of systems we consider (i.e., high dimensional with limited solution evaluations) the coefficients are given by an underdetermined linear system in a regression formulation. This implies additional assumptions, such as sparsity of the coefficient vector, are needed to approximate the solution. Here, we present an approach where we assume the coefficients are close to the range of a generative model that maps from a low to a high dimensional space of coefficients. Our approach is inspired be recent work examining how generative models can be used for compressed sensing in systems with random Gaussian measurement matrices. Using results from PDE theory on coefficient decay rates, we construct an explicit generative model that predicts the polynomial chaos coefficient magnitudes. The algorithm we developed to find the coefficients, which we call GenMod, is composed of two main steps. First, we predict the coefficient signs using Orthogonal Matching Pursuit. Then, we assume the coefficients are within a sparse deviation from the range of a sign-adjusted generative model. This allows us to find the coefficients by solving a nonconvex optimization problem, over the input space of the generative model and the space of sparse vectors. We obtain theoretical recovery results for a Lipschitz continuous generative model and for a more specific generative model, based on coefficient decay rate bounds. We examine three high-dimensional problems and show that, for all three examples, the generative model approach outperforms sparsity promoting methods at small sample sizes.

The Treasury Department is reconsidering the Internal Revenue Service's use of ID.me for access to its website, according to Bloomberg. A department official said the agencies are exploring alternatives to the controversial facial recognition software, though that official didn't specifically cite the privacy concerns around ID.me for the decision. "The IRS is consistently looking for ways to make the filing process more secure," Treasury Department spokesperson Alexandra LaManna told Bloomberg. "We believe in the importance of protecting the privacy of taxpayers, while also ensuring criminals are not able to gain access to taxpayer accounts." Citing a "lack of funding for IRS modernization," LaManna also said it's been "impossible" for the agency to develop its own in-house identification solution, and noted US taxpayers aren't required to file their taxes online.

Headquartered in Palo Alto, California and originally incubated out of IIT-Bombay, Safe Security helps organizations measure and mitigate enterprise-wide cyber risk in real-time using its machine learning-enabled API-First platform. The company claims a 400% business growth in the last one year due to the growing significance of cybersecurity.

The frequency and magnitude of natural disasters such as hurricanes, wildfires and floods has been growing for a number of years. Panelists for the Advanced Technology Academic Research Center's Jan. 26 webinar "Leveraging Predictive Analytics to Address Climate Change Issues" discussed how the use of artificial intelligence and machine learning can provide both short- and long-term guidance for decisionmakers considering how to ameliorate impacts. According to the National Centers for Environmental Information, which is part of the National Oceanic and Atmospheric Administration, in 2021 there were 20 weather and climate disaster events that incurred losses of more than $1 billion each. From 1980 to 2021, the annual average was 7.4 events (adjusted for inflation), but from 2017 to 2021, the most recent five years, the average number of events was 17.2 (adjusted for inflation). In short, the problem is getting worse, and faster.

If you've ever attempted to automate business processes, reduce risk by enforcing regulatory compliance, or ensure physical safety and security in the workplace, you may have run into repetitive tasks that are expensive to scale by using a human workforce. However, when you looked into using AI, you discovered that you'd need a fast internet connection to make the system work. What if you could automatically monitor video streams in real-time? What if you could do this while keeping your video data private? What if you could do all this without even having an internet connection?

ABI Research has shared an infographics explaining what can machine learning do for cybersecurity?