With artificial intelligence becoming all the rage across the HR world, there clearly appears to be rewards from using AI to find and land the best talent. Do AI-based tools in recruiting and hiring really outperform human decision-making? And if they do, could they potentially expose HR and employers to the same types of discrimination issues that can impact hiring driven by people, not algorithms? Right now, the legal landscape in the U.S. has yet to catch up those critical considerations. In Europe, for instance, the U.K.'s Information Commissioner's Office recently released guidance for organizations about transparency within AI decision-making.

As more businesses realize the benefits of artificial intelligence (AI) in their daily operations, the demand for use cases will increase and drive the AI market. The leaps forward in AI will continue, as we see this young technology blossom into wider and more advanced uses. In 2020, companies will keep an eye on proven AI use cases that can help their businesses--they should speed ROI and minimize risk. Look for 2020 to be a year of AI expansion into businesses and out of the proof-of-concept labs. Here are seven leading use-case trends I see for AI in 2020.

It doesn't take a soothsayer to know that artificial intelligence will have a bomber 2020. But getting the details right is important, which is why we turned to industry experts to give us their predictions on exactly how AI will evolve next year. There is widespread fear that AI will replace human workers. But 2020 will be the year when we stop fearing the tech and start realizing how it benefits us, predicts Tim Armandpour, the SVP of Engineering at PagerDuty. "In their next phase, AI and ML will become our allies (not our replacements)," Armandpour says.

Artificial Intelligence (AI) and space are both popular subjects in the current policy climate. AI techniques are being applied to space datasets and accelerating progress in the satellite and space industry through natural language processing, machine vision and advanced analytics. The combination of AI and space could play an integral role in increasing global connectivity and closing the digital divide. AI space services face the same problems as terrestrial AI services. They are exposed to the same policy challenges when delivered through a fibre network as they are when transmitted wirelessly from a satellite.

Researchers said the profiles, linked to the Epoch Media Group, used photos generated by artificial intelligence in a preview of an "eerie, tech-enabled future of disinformation." Facebook said on Friday that it had removed hundreds of accounts with ties to the Epoch Media Group, parent company of the Falun Gong-related publication and conservative news outlet The Epoch Times. The documentary – produced by the actor in partnership with his wife Susan – is one of the platform's highest profile and biggest-budgeted factual commissions to date. The Avengers star is expected to give the Age of AI mass appeal. One AI expert said there was "lots of eye candy for viewers with short attention spans".

Accenture said the acquisition will further equip its clients with capabilities to meet the growing demand for enterprise-scale AI, analytics and automation solutions. On Friday (13 December), professional services company Accenture announced that it has entered into an agreement to acquire Clarity Insights. Clarity Insights, a Chicago-based data consultancy with deep data science, AI and machine learning expertise, will bring its 350 employees to Accenture's Applied Intelligence business in North America. Founded in 2008, Clarity Insights focuses on serving clients' needs from end to end, aiming to transform business processes to embed and scale AI with deeper insights from data. Accenture did not disclose the terms of the deal.

Regulation of Multi-Agent Systems (MAS) was a research topic of the past decade and one of these proposals was Electronic Institutions. However, with the recent reformulation of Artificial Neural Networks (ANN) as Deep Learning (DL), Security, Privacy, Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community. Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of ANN as Agent-based Training of a special type of regulated ANN that we call Institutional Neural Network. This paper introduces the former concept and provides $\mathcal{I}$, a language previously used to model and extend Electronic Institutions, as a means to implement and regulate DL.

Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated. All smell report data are publicly accessible online. These reports are also sent to the local health department and visualized on a map along with air quality data from monitoring stations. This visualization provides a comprehensive overview of the local pollution landscape. Additionally, with these reports and air quality data, we developed a model to predict upcoming smell events and send push notifications to inform communities. We also applied regression analysis to identify statistically significant effects of push notifications on user engagement. Our evaluation of this system demonstrates that engaging residents in documenting their experiences with pollution odors can help identify local air pollution patterns, and can empower communities to advocate for better air quality. All citizen-contributed smell data are publicly accessible and can be downloaded from https://smellpgh.org.

AI is being increasingly used to make important decisions. Many AI experts (including Jeff Dean, head of AI at Google, and Andrew Ng, founder of Coursera and deeplearning.ai) I am an AI researcher, and I'm worried about some of the societal impacts that we're already seeing. At the end, I'll briefly share some positive ways that we can try to address these. Before we dive in, I need to clarify one point that is important to understand: algorithms (and the complex systems they are a part of) can make mistakes. These mistakes come from a variety of sources: bugs in the code, inaccurate or biased data, approximations we have to make (e.g.

The topic du jour for tech regulation is not as you might expect, data, but rather a sexy new topic of interest to policymakers – artificial intelligence (AI). It has all the glamour of Hollywood movies, all the fear that propels despots to power, and it comes complete with simple sentences and graphics that make it a Trumpian communicator's dream. We get tired of hearing it, but it's so true: technology is changing rapidly. The speed of change continues to accelerate and, let's face it, regulators and policymakers do a poor job of understanding technology, much less creating effective regulation for it. In the chaos however, there are repetitive patterns.