Shanghai Zhizhen first sued Apple for patent infringement in 2012 regarding its voice recognition technology.

Well, up until recently, you and I didn't know each other, but it turns out we have a mutual friend, Andrew Perry, who's a bit legendary in our industry at the time. And he's my real connection actually, David is my real connection and my real education if I'm being honest on legal technology. And so what I thought we would pick up today is, you know, conversations you and I have been having. So my colleague and I Kashyap, we published a book on AI last year. And honestly, the focus of it was, you know, let's simplify this so that anybody who's interested at least, can get their hands dirty, can figure out what to do without Advanced Math, because AI is a business problem, a business solution.

An artificial-intelligence company recently awarded a Chinese patent for a voice assistant similar to Apple Inc.'s Siri has filed a patent infringement lawsuit against Apple that, if successful, could prevent the American tech giant from selling many of its products in the world's second-largest economy. Shanghai Zhizhen Network Technology Co. said in a statement on Monday it was suing Apple for an estimated 10 billion yuan ($1.43 billion) in damages in a Shanghai court, alleging the iPhone- and iPad-maker's products violated...

Digital discrimination is a form of discrimination whereby users are automatically treated unfairly, unethically or just differently based on their personal data by a machine learning (ML) system. Examples of digital discrimination include low-income neighbourhood's targeted with high-interest loans or low credit scores, and women being undervalued by 21% in online marketing. Recently, different techniques and tools have been proposed to detect biases that may lead to digital discrimination. These tools often require technical expertise to be executed and for their results to be interpreted. To allow non-technical users to benefit from ML, simpler notions and concepts to represent and reason about digital discrimination are needed. In this paper, we use norms as an abstraction to represent different situations that may lead to digital discrimination. In particular, we formalise non-discrimination norms in the context of ML systems and propose an algorithm to check whether ML systems violate these norms.

In the application of machine learning to real-life decision-making systems, e.g., credit scoring and criminal justice, the prediction outcomes might discriminate against people with sensitive attributes, leading to unfairness. The commonly used strategy in fair machine learning is to include fairness as a constraint or a penalization term in the minimization of the prediction loss, which ultimately limits the information given to decision-makers. In this paper, we introduce a new approach to handle fairness by formulating a stochastic multi-objective optimization problem for which the corresponding Pareto fronts uniquely and comprehensively define the accuracy-fairness trade-offs. We have then applied a stochastic approximation-type method to efficiently obtain well-spread and accurate Pareto fronts, and by doing so we can handle training data arriving in a streaming way.

Total conference attendance at the 2020 Design Automation Conference (DAC), the industry's premier event dedicated to the design and design automation of electronic circuits and systems, leapt by 52% compared to DAC 2019, according to the 57th DAC Executive Committee (EC). The intense engagement at the 57th DAC, held for the first time virtually due to the recent pandemic, reflected a voracious appetite among engineers for information and insights to propel design innovation. Submissions to DAC's research track increased by 20% in the past two years, and the Designer, IP and Embedded Tracks submissions increased by 15% compared to 2019, continuing a steady three-year rise. The global reach of DAC, July 19 - 24, soared at the 2020 virtual event with attendance from the following regions: 24% Asia Pac, 11% Europe, 52% United States and 13% a combination of Canada, South America and Middle East. Despite the economic and social disruption caused by the pandemic, design innovation never sleeps," said Zhuo Li, General Chair of the 57th DAC. "We had record attendance viewing each of the four Keynotes, plus attendees globally were able to view the recorded technical sessions at their leisure in their respected time-zones.

Getting work done is a fundamental concern for any business. But today, paradigm-shifting forces seem to be driving significant changes in both work and the workforce. New digital and communications technologies are changing how work gets done. The growth of the gig economy and advances in artificial intelligence are changing who does the work. Even the question of what work looks like is coming under examination as a continually evolving marketplace drives organizations to explore new business models. In the face of these technological and social forces, it could be imperative for businesses to rethink their approaches to the how, who, and what of work in fundamental, perhaps even transformative ways. And as usual, there seem to be no easy answers.

Bradford specializes in matters related to trade secrets and Artificial Intelligence. He is the Chair of the AI Subcommittee of the ABA. Recognized by the Daily Journal in 2019 as one of the Top 20 AI attorneys in California, Bradford has been instrumental in proposing federal AI workplace and IP legislation that in 2018 was turned into a United States House of Representatives Discussion Draft bill. He has also developed AI oversight and corporate governance best practices designed to ensure algorithmic fairness. What was it that initially ignited your interest in artificial intelligence?

Negotiation is the complex social process by which multiple parties come to mutual agreement over a series of issues. As such, it has proven to be a key challenge problem for designing adequately social AIs that can effectively navigate this space. Artificial AI agents that are capable of negotiating must be capable of realizing policies and strategies that govern offer acceptances, offer generation, preference elicitation, and more. But the next generation of agents must also adapt to reflect their users’ experiences.      The best human negotiators tend to have honed their craft through hours of practice and experience. But, not all negotiators agree on which strategic tactics to use, and endorsement of deceptive tactics in particular is a controversial topic for many negotiators. We examine the ways in which deceptive tactics are used and endorsed in non-repeated human negotiation and show that prior experience plays a key role in governing what tactics are seen as acceptable or useful in negotiation. Previous work has indicated that people that negotiate through artificial agent representatives may be more inclined to fairness than those people that negotiate directly. We present a series of three user studies that challenge this initial assumption and expand on this picture by examining the role of past experience.      This work constructs a new scale for measuring endorsement of manipulative negotiation tactics and introduces its use to artificial intelligence research. It continues by presenting the results of a series of three studies that examine how negotiating experience can change what negotiation tactics and strategies human endorse. Study #1 looks at human endorsement of deceptive techniques based on prior negotiating experience as well as representative effects. Study #2 further characterizes the negativity of prior experience in relation to endorsement of deceptive techniques. Finally, in Study #3, we show that the lessons learned from the empirical observations in Study #1 and #2 can in fact be induced—by designing agents that provide a specific type of negative experience, human endorsement of deception can be predictably manipulated.

A new whitepaper coauthored by researchers on the Vector Institute for Synthetic Intelligence examines the ethics of AI in surgery, making the case that surgical procedure and AI carry related expectations however diverge with respect to moral understanding. Surgeons are confronted with ethical and moral dilemmas as a matter in fact, the paper factors out, whereas moral frameworks in AI have arguably solely begun to take form. In surgical procedure, AI purposes are largely confined to machines performing duties managed completely by surgeons. AI may also be utilized in a medical determination help system, and in these circumstances, the burden of accountability falls on the human designers of the machine or AI system, the coauthors argue. Privateness is a foremost moral concern. AI learns to make predictions from giant knowledge units -- particularly affected person knowledge, within the case of surgical programs -- and it's usually described as being at odds with privacy-preserving practices.