SANTA CLARA, Jan. 28, 2020 – ServiceNow (NYSE: NOW), the company making work, work better for people, today announced it has signed an agreement to acquire Passage AI, a Mountain View, Calif.–based conversational AI platform company. The transaction will advance ServiceNow's deep learning AI capabilities and will accelerate its vision of supporting all major languages across the company's Now Platform and products, including ServiceNow Virtual Agent, Service Portal, Workspaces and emerging interfaces. "Work flows more smoothly when people can get things done in their native language," said Debu Chatterjee, senior director of AI Engineering at ServiceNow. "Building deep learning, conversational AI capabilities into the Now Platform will enable a work request initiated in German or a customer inquiry initiated in Japanese to be solved by Virtual Agent. Passage AI's technology will enable us to accelerate our vision of empowering great employee and customer experiences by delivering great workflow experiences. ServiceNow believes in making work flow more smoothly across the enterprise, in all major languages."

The global movie industry generated over $43 billion in revenue in 2018, of which the United States' contribution alone topped more than $11 billion. Yet, these seemingly impressive headline figures can obscure the fact that year-on-year growth has been a sluggish 2 per cent over the last several years, with market researchers forecasting further stagnation. Given the inherent financial risk involved in film making, some now believe artificial intelligence, rather than human expertise, is best placed to select which films are most likely to provide suitable returns on investment. In early January 2020, Warner Bros signed a deal with Cinelytic, a Los Angeles-based artificial intelligence company which, according to the press release, aims to help content creators make faster, better-informed decisions through predictive analytics. Belgium's ScriptBook provides a similar service, touted as "artificially intelligent script analysis and box office forecasting". Warner Bros is not the first film studio to pair up with an AI platform of this type, although it is one of the first to disclose its collaboration publicly.

Several years ago, in an effort to initiate dialogue about the moral and legal status of technological artifacts, I posted a photograph of myself holding a sign that read "Robot Rights Now" on Twitter. Responses to the image were, as one might imagine, polarizing, with advocates and critics lining up on opposite sides of the issue. What I didn't fully appreciate at the time is just how divisive an issue it is. For many researchers and developers slaving away at real-world applications and problems, the very notion of "robot rights" produces something of an allergic reaction. Over a decade ago, roboticist Noel Sharkey famously called the very idea "a bit of a fairy tale."

However, according to the resolution, "humans must always be ultimately responsible for, and able to overrule, decisions" that are taken by new technologies, especially in medical, legal and accounting professions. For the banking sector, the committee calls for a regulatory framework that ensures independent supervision of automated decision-making systems by qualified professionals in cases where the public interest is at stake. This framework should also make it possible for consumers to seek human review when mistakes appear as a result of using this type of new technologies. Likewise, automated decision-making systems should only use high-quality and unbiased data sets and "explainable and unbiased algorithms" to guarantee trust and acceptance, the resolution states. "We have to make sure that consumer protection and trust is ensured and that the data sets used in automated decision-making systems are of high-quality and are unbiased," said Belgian MEP Petra De Sutter (Greens/EFA), who chairs the IMCO committee.

Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision.  This article is part of the special track on AI and Society.

Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media. Due to privacy concerns and governmental regulations, such a data stream can only be stored and used for learning purposes in a limited duration. To overcome the noise in this on-line scenario we propose QActor which novel combines: the selection of supposedly clean samples via quality models and actively querying an oracle for the most informative true labels. While the former can suffer from low data volumes of on-line scenarios, the latter is constrained by the availability and costs of human experts. QActor swiftly combines the merits of quality models for data filtering and oracle queries for cleaning the most informative data. The objective of QActor is to leverage the stringent oracle budget to robustly maximize the learning accuracy. QActor explores various strategies combining different query allocations and uncertainty measures. A central feature of QActor is to dynamically adjust the query limit according to the learning loss for each data batch. We extensively evaluate different image datasets fed into the classifier that can be standard machine learning (ML) models or deep neural networks (DNN) with noise label ratios ranging between 30% and 80%. Our results show that QActor can nearly match the optimal accuracy achieved using only clean data at the cost of at most an additional 6% of ground truth data from the oracle.

New Jersey's attorney general, Gurbir S. Grewal, has instructed prosecutors across the state to stop using Clearview AI, a private facial recognition software. Clearview AI's tools allow law enforcement officials to upload a photo of an unknown person they'd like to identify, and see a list of matches culled from a database of over 3 billion photos. The photos are taken from a variety of controversial sources, including Facebook, YouTube, Twitter, and even Venmo. New Jersey attorney general Gurbir S. Grewal told the state's prosecutor's to stop using Clearview AI, private facial recognition software that he worried might compromise the integrity of the state's investigations Grewal decided to issue the ban after seeing Clearview had used footage from a 2019 sting operation in New Jersey promoting its own services, something even he hadn't been aware of at the time. 'I was surprised they used my image and the office to promote the product online," Grewal told the New York Times. 'I was troubled they were sharing information about ongoing criminal prosecutions.' After investigating the issue, Gerwal confirmed one of the 19 people arrested in the sting had been identified using Clearview, but since all the cases were ongoing, he felt uncomfortable with them being used in promotional material. Another report in the Times, on the controversial practices used to generate the company's massive database, further worried Grewal and prompted him to issue the ban. 'Until this week, I had not heard of Clearview AI," he said.

Workplace surveillance sounds like the stuff of nightmares, but we are having to get used to it. In a sign of the times, the European Court of Human Rights has just ruled that a supermarket in Barcelona was entitled to fire employees after catching them stealing on CCTV cameras that they didn't know were installed. This overturned a decision by the court's lower chamber that the cameras had breached the employees' human rights. Yet hidden cameras are almost quaint compared to some of the ways in which employers are now monitoring their staff. They are resorting to everything from software that digitally scans workers' emails to smart name badges that track their whereabouts.

December 30, 2019 Although data science builds on knowledge from computer science, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: challenging scientific questions and pressing questions of societal importance. Is data science a discipline? Data science is a field of study: one can get a degree in data science, get a job as a data scientist, and get funded to do data science research. But is data science a discipline, or will it evolve to be one, distinct from other disciplines? Here are a few meta-questions about data science as a discipline.