Harnessing artificial intelligence and machine learning technologies has become the new arms race among the great powers, a Hudson Institute panel on handling big data in military operations said Monday. Speaking at the online forum, Richard Schultz, director of the international security program in the Fletcher School at Tufts University, said, "that's the way [Russian President Vladimir] Putin looks at it. I don't think we have a choice" but to view it the same way. He added in answer to a question that "the data in information space is enormous," so finding tools to filter out what's not necessary is critical. U.S. Special Operations Command is already using AI to do what in the old days was called political or psychological warfare, in addition to targeting, he added.

Deepfakes (aka synthetic media) can spread misinformation and disinformation very effectively. The 2020 US election is just one example, but the use of deepfakes isn't limited to politics. In fact, representatives from a big brand company recently asked Avivah Litan, vice president and distinguished analyst at Gartner Research, what they could do if deepfakes were used to undermine the reputation of the brand or CEO. Unfortunately, her reply was "nothing," because there's no way they can stop the social sharing of content. "[T]he companies that have to solve this problem are the social media networks in terms of spreading deepfakes around the world," said Litan.

Today, AI is used with increasing regularity across nearly every industry, with AI-based systems and technologies introducing new efficiencies, unlocking extraordinary opportunities and delivering powerful new insights and capabilities that were previously unattainable -- perhaps even unthinkable. Not only are health care and pharmaceuticals no exception to that rule, but life sciences actually represents one of the most innovative and exciting new frontiers for AI technology and machine learning. In recent years, AI usage has exploded in pharma, health care and biotech. Life sciences companies and institutions have used AI to develop and test new drugs, advance new therapeutics and treatment protocols, and, in some cases, completely transform the drug development and distribution process. The power and potential of AI-based technology in life sciences has arguably never been more important.

Google's dismissal of a top artificial intelligence researcher vocal about the company's failures to address the lack of diversity in its workforce has drawn sharp new scrutiny of its treatment of Black employees, particularly women. Timnit Gebru says she was fired via email after refusing to retract a research paper that asked tough questions about a type of artificial intelligence, including Google's use of it. Jeff Dean, Google's executive in charge of AI, told employees in an email that Gebru's paper did not follow the rules for work published externally. Some 2,000 Google employees signed a petition protesting the company's handling of the situation. Academic researchers called out Google on social media in a rare and widespread rebuke.

Since the pandemic, the World has been accelerating at an unprecedented rate. And, yet, there's irony in that it has entirely slowed down, altogether. We have been presented with a whole new dichotomy and are experiencing things we had never known before. For some, the pandemic has brought about a sense of relief. It has given them time to reflect and time to catch up.

Data science has taken hold at many enterprises, and data scientist is quickly becoming one of the most sought-after roles for data-centric organizations. Data science applications utilize technologies such as machine learning and the power of big data to develop deep insights and new capabilities, from predictive analytics to image and object recognition, conversational AI systems and beyond. Indeed, organizations that aren't adequately investing in data science likely will soon be left in the dust by competitors that are gaining significant competitive advantages by doing so. What exactly are data scientists doing that provides such transformative business benefits? The field of data science is a collection of a few key components: statistical and mathematical approaches for accurately extracting quantifiable data; technical and algorithmic approaches that facilitate working with large data sets, using advanced analytics techniques and methodologies that tackle data analysis from a scientific perspective; and engineering tools and methods that can help wrangle large amounts of data into the formats needed to derive high-quality insights.

AI is, loosely speaking, a form of machine learning and Wang believes it needs to be a part of education in schools.

A new device created by researchers at The University of Texas at Austin can overcome challenges like bad weather to deliver more secure, reliable communications. This could aid military communications in challenging areas, improve the ability of self-driving cars to see the environment around them and speed up wireless data for potential 6G networks. Ray Chen, professor in the Cockrell School of Engineering's Department of Electrical and Computer Engineering and leader of the project, made a comparison to TV satellite dishes that go out or become fuzzy during poor weather. The same thing can happen with communications technology, and that's the problem Chen wants to fix. Chen's device operates in an area of the light spectrum -- mid infrared -- that allows signal to penetrate through clouds, rain and other weather to get to their intended target without shedding significant amounts of light.

Transparency, explainability, and trust are big and pressing topics in AI/ML today. Nobody wants to find themselves at the receiving end of a black box system that makes consequential decisions (e.g., about jobs, healthcare, citizenship, etc.), especially if those decisions are unfair, biased, or just plainly not in our favor. And most organizations agree that consumer trust and confidence that AI is being used ethically and transparently are keys to unlocking its true potential. And while there are literally hundreds of documents describing and prescribing AI principles, frameworks, and other good things, there haven't been any practical tools that could help with implementing transparency. On September 28, 2020, the Cities of Helsinki and Amsterdam jointly announced the launch of their public AI registers.

Machine learning algorithms have been successfully used to approximate nonlinear maps under weak assumptions on the structure and properties of the maps. We present deep neural networks using dense and convolutional layers to solve an inverse problem, where we seek to estimate parameters in a FitzHugh-Nagumo model, which consists of a nonlinear system of ordinary differential equations (ODEs). We employ the neural networks to approximate reconstruction maps for model parameter estimation from observational data, where the data comes from the solution of the ODE and takes the form of a time series representing dynamically spiking membrane potential of a (biological) neuron. We target this dynamical model because of the computational challenges it poses in an inference setting, namely, having a highly nonlinear and nonconvex data misfit term and permitting only weakly informative priors on parameters. These challenges cause traditional optimization to fail and alternative algorithms to exhibit large computational costs. We quantify the predictability of model parameters obtained from the neural networks with statistical metrics and investigate the effects of network architectures and presence of noise in observational data. Our results demonstrate that deep neural networks are capable of very accurately estimating parameters in dynamical models from observational data.