Artificial intelligence (AI) stands to have a transformative impact on international trade. Already, specific applications in areas such as data analytics and translation services are reducing barriers to trade. At the same time, there are challenges in the development of AI that international trade rules could address. General AI refers to systems that can self-learn from experience with "humanlike breadth" and surpass human performance on tasks. General AI raises broad existential concerns, but remains a technology in the distant future.

More and more organizations are beginning to use or expand their use of artificial intelligence (AI) tools and services in the workplace. Despite AI's proven potential for enhancing efficiency and decision-making, it has raised a host of issues in the workplace which, in turn, have prompted an array of federal and state regulatory efforts that are likely to increase in the near future. Artificial intelligence, defined very simply, involves machines performing tasks in a way that is intelligent. The AI field involves a number of subfields or forms of AI that solve complex problems associated with human intelligence--for example, machine learning (computers using data to make predictions), natural-language processing (computers processing and understanding a natural human language like English), and computer vision or image recognition (computers processing, identifying, and categorizing images based on their content). One area where AI is becoming increasingly prevalent is in talent acquisition and recruiting.

Whereas Russia and the United States (U.S.) competed in a traditional arms race through the Cold War, we are now observing a new combative arena where the U.S. government intends to compete with China in the advancement of Industrial Artificial Intelligence (IAI): a new arms race. IAI is defined as a government's motivation to economically invest and advance the commercialization of artificial intelligence within its society. This new arms race may be on the way after U.S. Senators Martin Heinrich (D-NM) and Rob Portman (R-OH) proposed the Artificial Intelligence Act. Believing that Chinese progression in AI technology may soon surpass and threaten American capabilities, the Act calls for a $2.2 billion federal investment strategy over five years in "research, development, demonstration, application to analysis and modeling, and other activities with respect to science and technology in artificial intelligence (AI)." The Act's bi-partisanship nature demonstrates the growing consensus among government officials surrounding the importance of IAI in protecting and bolstering American life against international interference.

Fox News Flash top headlines for June 17 are here. Check out what's clicking on Foxnews.com Pete Buttigieg fears that artificial intelligence may further fuel racial bias in America's already challenging credit scoring system. The 37-year-old Democratic presidential candidate was asked by a black business owner about the denial of funding during the Black Economic Alliance Forum that was held in Charleston, S.C. "I'm very worried, living in an era where more and more of this is going to be done by algorithms and by big data, that we're going to automate inequality by failing to be intentional about how some of these algorithms pick up structures and systems and attitudes and assumptions that were already racist in nature," said Buttigieg, who is the mayor of South Bend, Ind., according to Bloomberg. Several Big Tech firms have been criticized over the potential for bias in AI.

I just briefly wanted to say a little bit about my background. I studied Math and Computer Science in college and then did a Ph.D. in Math. I worked as a quant in Energy Trading and that's where I first started working with data. I was an early data scientist and backend developer at Uber. I taught full stack software development at Hackbright. I really love teaching and I think I'll always return to teaching in some form. And then two years ago, together with Jeremy Howard, I started fast.ai with the goal of making deep learning more accessible and easier to use. I'm on Twitter @math_rachel and, as William said, I blog about diversity on Medium @racheltho, and I blog about data science at fast.ai. I just have one slide about fast.ai. We have this, as William mentioned, a totally free course, "Practical Deep Learning for Coders." The only prerequisite is one year of coding experience. It's distinctive in that there are no advanced math prerequisites, yet it takes you to the state-of-the-art. We've had a lot of success. We've had students get jobs at Google Brain, have their work featured on HBO and in Forbes, launch new companies, get new jobs.

Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught. On the flip side, we're also told that boring and repetitive jobs could become a thing of the past.

Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught. On the flip side, we're also told that boring and repetitive jobs could become a thing of the past.

In this talk I will discuss some of the long-term challenges emerging with the effort of making deep learning a relevant tool for controlled scientific discovery in many-body quantum physics. The current state of the art of deep neural quantum states and learning tools will be discussed in connection with open challenging problems in condensed matter physics, including frustrated magnetism and quantum dynamics. Variational algorithms for a gate-based quantum computer, like the QAOA, prescribe a fixed circuit ansatz --- up to a set of continuous parameters --- that is designed to find a low-energy state of a given target Hamiltonian. After reviewing the relevant aspects of the QAOA, I will describe attempts to make the algorithm more efficient. The strategies I will explore are 1) tuning the variational objective function away from the energy expectation value, 2) analytical estimates that allow elimination of some of the gates in the QAOA circuit, and 3) using methods of machine learning to search the design space of nearby circuits for improvements to the original ansatz.

It is well-known that many machine learning models are susceptible to so-called "adversarial attacks," in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. We discuss a range of copyright detection systems, and why they are particularly vulnerable to attacks. These vulnerabilities are especially apparent for neural network based systems. As a proof of concept, we describe a well-known music identification method, and implement this system in the form of a neural net. We then attack this system using simple gradient methods. Adversarial music created this way successfully fools industrial systems, including the AudioTag copyright detector and YouTube's Content ID system. Our goal is to raise awareness of the threats posed by adversarial examples in this space, and to highlight the importance of hardening copyright detection systems to attacks.

We introduce CheckNet, a method for secure inference with deep neural networks on untrusted devices. CheckNet is like a checksum for neural network inference: it verifies the integrity of the inference computation performed by untrusted devices to 1) ensure the inference has actually been performed, and 2) ensure the inference has not been manipulated by an attacker. CheckNet is completely transparent to the third party running the computation, applicable to all types of neural networks, does not require specialized hardware, adds little overhead, and has negligible impact on model performance. CheckNet can be configured to provide different levels of security depending on application needs and compute/communication budgets. We present both empirical and theoretical validation of CheckNet on multiple popular deep neural network models, showing excellent attack detection (0.88-0.99 AUC) and attack success bounds.