All you need to know about the Legal and Regulatory Implications of #ArtificialIntelligence! We publish substantive articles as well as news updates on worldwide developments in #AI regulation.

A new course, Artificial Intelligence for Business, will be offered by Wharton to undergraduate and MBA students in 2021. An online version of the course launched on Feb. 20. Artificial Intelligence for Business will be the first course to be fully dedicated to studying AI in a business context, said Kartik Hosanagar, a John C. Hower Professor of Technology and Digital Business. Hosanagar, who will be teaching the course, said he believes the new program will allow students who were not previously experienced with AI to become familiar with the field. He said the curriculum will cover the importance of big data, the use of machine learning, and other forms of AI in business.

Zero-sum games have long guided artificial intelligence research, since they possess both a rich strategy space of best-responses and a clear evaluation metric. What's more, competition is a vital mechanism in many real-world multi-agent systems capable of generating intelligent innovations: Darwinian evolution, the market economy and the AlphaZero algorithm, to name a few. In two-player zero-sum games, the challenge is usually viewed as finding Nash equilibrium strategies, safeguarding against exploitation regardless of the opponent. While this captures the intricacies of chess or Go, it avoids the notion of cooperation with co-players, a hallmark of the major transitions leading from unicellular organisms to human civilization. Beyond two players, alliance formation often confers an advantage; however this requires trust, namely the promise of mutual cooperation in the face of incentives to defect. Successful play therefore requires adaptation to co-players rather than the pursuit of non-exploitability. Here we argue that a systematic study of many-player zero-sum games is a crucial element of artificial intelligence research. Using symmetric zero-sum matrix games, we demonstrate formally that alliance formation may be seen as a social dilemma, and empirically that na\"ive multi-agent reinforcement learning therefore fails to form alliances. We introduce a toy model of economic competition, and show how reinforcement learning may be augmented with a peer-to-peer contract mechanism to discover and enforce alliances. Finally, we generalize our agent model to incorporate temporally-extended contracts, presenting opportunities for further work.

Seyfarth Synopsis: As companies face increasing competition for the best talent within the marketplace, a growing number of businesses are turning to artificial intelligence and data driven strategies to more effectively identify and evaluate potential employees. The first installment of our artificial intelligence series will focus on some of the ways that employers are using these technologies in the area of talent acquisition. Business has always been in a search for "the next big thing." Something to give them an edge over competitors or allow them to anticipate shifts in the marketplace before they happen. Companies who moved from hand production to large-scale manufacturing were able to dominate nascent markets around the turn of the 20th Century.

Certain statements in this press release constitute "forward-looking statements," within the meaning of federal securities laws, including statements related to ICP technology, including financial projections related thereto and potential market opportunity, plans and prospects and other statements containing the words "anticipate," "intend," "may," "plan," "predict," "will," "would," "could," "should," and similar expressions, constitute forward-looking statements within the meaning of The Private Securities Litigation Reform Act of 1995. The Company's actual results could differ materially from those anticipated in these forward-looking statements as a result of various factors. Factors that could cause future results to materially differ from the recent results or those projected in forward-looking statements include the known risks, uncertainties and other factors described in the Company's definitive proxy statement filed on May 29, 2018, the Company's Quarterly Report on Form 10-Q for the quarter ended September 30, 2019 and on the Annual Report on Form 10-K for the year ended December 31, 2018 as well as the Company's prior filings and from time to time in the Company's subsequent filings with the Securities and Exchange Commission. Any change in such factors, risks and uncertainties may cause the actual results, events and performance to differ materially from those referred to in such statements. All information in this press release is as of the date of the release and the Company does not undertake any duty to update this information, including any forward-looking statements, unless required by law.

Computers using artificial intelligence are discovering medicines, designing better golf clubs and creating video games. Patent offices around the world are grappling with the question of who -- if anyone -- owns innovations developed using AI. The answer may upend what's eligible for protection and who profits as AI transforms entire industries. "There are machines right now that are doing far more on their own than to help an engineer or a scientist or an inventor do their jobs," said Andrei Iancu, director of the U.S. Patent and Trademark Office. "We will get to a point where a court or legislature will say the human being is so disengaged, so many levels removed, that the actual human did not contribute to the inventive concept."

Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. Meanwhile, in order to locate essential information for dialogue summarization/extraction, the pretraining process enables external knowledge integration. The proposed fine-tuned pretraining mechanism is comprehensively evaluated via three different dialogue datasets along with a number of downstream dialogue-mining tasks. Result shows that the proposed pretraining mechanism significantly contributes to all the downstream tasks without discrimination to different encoders.

Machine learning models are extensively being used to make decisions that have a significant impact on human life. These models are trained over historical data that may contain information about sensitive attributes such as race, sex, religion, etc. The presence of such sensitive attributes can impact certain population subgroups unfairly. It is straightforward to remove sensitive features from the data; however, a model could pick up prejudice from latent sensitive attributes that may exist in the training data. This has led to the growing apprehension about the fairness of the employed models. In this paper, we propose a novel algorithm that can effectively identify and treat latent discriminating features. The approach is agnostic of the learning algorithm and generalizes well for classification as well as regression tasks. It can also be used as a key aid in proving that the model is free of discrimination towards regulatory compliance if the need arises. The approach helps to collect discrimination-free features that would improve the model performance while ensuring the fairness of the model. The experimental results from our evaluations on publicly available real-world datasets show a near-ideal fairness measurement in comparison to other methods.

Building machine learning models that are fair with respect to an unprivileged group is a topical problem. Modern fairness-aware algorithms often ignore causal effects and enforce fairness through modifications applicable to only a subset of machine learning models. In this work, we propose a new definition of fairness that incorporates causality through the Controlled Direct Effect (CDE). We develop regularizations to tackle classical fairness measures and present a causal regularization that satisfies our new fairness definition by removing the impact of unprivileged group variables on the model outcomes as measured by the CDE. These regularizations are applicable to any model trained using by iteratively minimizing a loss through differentiation. We demonstrate our approaches using both gradient boosting and logistic regression on: a synthetic dataset, the UCI Adult (Census) Dataset, and a real-world credit-risk dataset. Our results were found to mitigate unfairness from the predictions with small reductions in model performance.

A lawyer and hobbyist musician collaborated with a computer programmer to generate every possible 12-note melody in the key of C. The final compilation includes 68.7 billion melodic combinations, which the pair uploaded to the Internet Archive through a Create Commons Zero license, meaning they reserve no rights of ownership to any of them. A lawyer and hobbyist musician partnered with a programmer to create a computer algorithm to generate every 12-note melody possible in the key of C, leading to more than 68.7 billion combinations The project was originally started in 2019 when Damien Riehl, a lawyer and hobbyist musician, and programmer Noah Rubin were having drinks after a cybersecurity event. During the day's presentations Riehl had gotten the idea that it might be possible to'brute force' different combinations of musical notes in the same way that computer hackers brute force different letter and number combinations to crack passwords. At the time, a jury had just ruled against Katie Perry in a lawsuit brough by Flame, a rapper who claimed her chart topper'Dark Horse' had copied a musical fragment from his 2009 song'Joyful Noise.' The team was inspired by computer hackers how use a'brute force' combination technique to crack other people's passwords, and thought a similar approach might be useful for arranging notes into melodic structures Riehl and Rubin originally hoped to have an algorithm come up with every melodic combination of notes possible in western music, using the 88 notes of a standard piano as their starting point.