In this paper we present a set of key demarcations, particularly important when discussing ethical and societal issues of current AI research and applications. Properly distinguishing issues and concerns related to Artificial General Intelligence and weak AI, between symbolic and connectionist AI, AI methods, data and applications are prerequisites for an informed debate. Such demarcations would not only facilitate much-needed discussions on ethics on current AI technologies and research. In addition sufficiently establishing such demarcations would also enhance knowledge-sharing and support rigor in interdisciplinary research between technical and social sciences.

The Bessemer Process patented in 1856 by Sir Henry Bessemer is one of the inventions most closely associated with catalyzing the second industrial revolution. By reducing the impurities of iron with an innovative oxidizing air blast, the process ushered in a new wave of inexpensive, high-volume steelmaking. Bessemer decided to license his patent to a handful of steelmakers in an effort to quickly monetize his efforts. But contrary to expectations, technical challenges and monopolistic greed prevented large steelmakers from agreeing to favorable licensing terms. In an effort to drive adoption, Bessemer opened his own steel making plant with the intention of undercutting competitors.

AI is capable of making music, but does that make AI an artist? As AI begins to reshape how music is made, our legal systems are going to be confronted with some messy questions regarding authorship. Do AI algorithms create their own work, or is it the humans behind them? What happens if AI software trained solely on Beyoncé creates a track that sounds just like her? "I won't mince words," says Jonathan Bailey, CTO of iZotope. "This is a total legal clusterfuck."

On Thursday, Attorney General William Barr released a redacted version of the report produced by special counsel Robert Mueller. And strikethroughs look particularly bad!) You can also download the document to search it at your leisure. Disclaimer: This version of the Mueller report was processed using the Recognize Text feature of Adobe Acrobat Pro Version 2019.010.20099 The PDF was then exported into Microsoft Word.

Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks. In real networks, nodes may have multiple tags but existing methods ignore the abundant semantic and hierarchical information of tags. This information is useful to many network applications and usually very stable. In this paper, we propose a tag representation learning model, Tag2Vec, which mixes nodes and tags into a hybrid network. Firstly, for tag networks, we define semantic distance as the proximity between tags and design a novel strategy, parameterized random walk, to generate context with semantic and hierarchical information of tags adaptively. Then, we propose hyperbolic Skip-gram model to express the complex hierarchical structure better with lower output dimensions. We evaluate our model on the NBER U.S. patent dataset and WordNet dataset. The results show that our model can learn tag representations with rich semantic information and it outperforms other baselines.

Microsoft has said it turned down a request from law enforcement in California to use its facial recognition technology in police body cameras and cars, reports Reuters. Speaking at an event at Stanford University, Microsoft president Brad Smith said the company was concerned that the technology would disproportionately affect women and minorities. Past research has shown that because facial recognition technology is trained primarily on white and male faces, it has higher error rates for other individuals. "Anytime they pulled anyone over, they wanted to run a face scan," said Smith of the unnamed law enforcement agency. "We said this technology is not your answer."

The ACLU and other groups urged Amazon to halt selling facial recognition technology to law enforcement departments. Lending tools charge higher interest rates to Hispanics and African Americans. Job hunting tools favor men. Negative emotions are more likely to be assigned to black men's faces than white men. Computer vision systems for self-driving cars have a harder time spotting pedestrians with darker skin tones.

Shares of Qualcomm soared 23% Tuesday – and remained up Wednesday – in the wake of a late-afternoon filing with the Securities and Exchange Commission, wherein the company announced that it reached a "multi-year" "global patent license agreement" and "chipset supply agreement" with Apple that settles the companies' yearslong intellectual property litigation and appears likely to work out to the benefit of both parties. In said filing with the SEC, Qualcomm states that as of April 1, 2019, it has directly licensed its relevant patents to Apple for at least the next six years, with the option to extend the agreement for an additional two years. Moreover, Qualcomm will supply chipsets to Apple for use in the latter's devices for several years at least. In exchange, Apple will make a one-time payment of an unspecified amount to Qualcomm, and pay continuing royalties to boot – also in an amount unspecified. Robotic advances: Mush! Watch a team of Boston Dynamics' SpotMini robot dogs pull a truck down the street Finally, "all worldwide litigation" between the two combatants "will be dismissed and withdrawn," including lawsuits against Apple's contract manufacturers. Qualcomm is expected to report earnings on May 1, at which time the company promises to provide "further updates" on the details of its agreement with Apple.

Apple has settled a major argument with chip maker Qualcomm that could help change the future of the iPhone. The two companies were locked in a bitter and worldwide international dispute about the technology that iPhones use to connect to the internet. The pair had been expected to try and resolve the dispute in legal hearings in San Diego, in a case that involved Apple's key iPhone suppliers. But just as that case began, the surprise truce was announced, with few details of the settlement being revealed. We'll tell you what's true.

This paper presents a taxonomy of explainability in Human-Agent Systems. We consider fundamental questions about the Why, Who, What, When and How of explainability. First, we define explainability, and its relationship to the related terms of interpretability, transparency, explicitness, and faithfulness. These definitions allow us to answer why explainability is needed in the system, whom it is geared to and what explanations can be generated to meet this need. We then consider when the user should be presented with this information. Last, we consider how objective and subjective measures can be used to evaluate the entire system. This last question is the most encompassing as it will need to evaluate all other issues regarding explainability.