Crowdsourcing has become a popular method for collecting labeled training data. However, in many practical scenarios traditional labeling can be difficult for crowdworkers (for example, if the data is high-dimensional or unintuitive, or the labels are continuous). In this work, we develop a novel model for crowdsourcing that can complement standard practices by exploiting people's intuitions about groups and relations between them. We employ a recent machine learning setting, called Ballpark Learning, that can estimate individual labels given only coarse, aggregated signal over groups of data points. To address the important case of continuous labels, we extend the Ballpark setting (which focused on classification) to regression problems. We formulate the problem as a convex optimization problem and propose fast, simple methods with an innate robustness to outliers. We evaluate our methods on real-world datasets, demonstrating how useful constraints about groups can be harnessed from a crowd of non-experts. Our methods can rival supervised models trained on many true labels, and can obtain considerably better results from the crowd than a standard label-collection process (for a lower price). By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine learning models in a cost-effective way.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative filtering methods to make unfair predictions against minority groups of users. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Be sure to also wrap up some extra rotors, a spare battery or two, and get drone registration through the Federal Aviation Association (FAA), because the drone registration requirements that were declared dead earlier this year were just revived by the Trump administration. A relative footnote in the National Defense Authorization Act, which was signed into law today, the new regulation requires that drone owners register their unmanned aerial vehicles before taking to the skies. You can register you new drone on the FAA's drone Unmanned Aircraft System website. But for longtime drone pilots, this requirement is nothing new. In December 2015, regulators began requiring drone registration, and the program took off, with 300,000 drone owners signing up within the first month.

WASHINGTON (Sinclair Broadcast Group) - Lawmakers on Capitol Hill met Tuesday to discuss advancements in the field of artificial intelligence (AI) and machine learning. The Senate Committee on Commerce, Science, & Transportation's Subcommittee on Communications, Technology, Innovation, and the Internet, wanted to discuss the new and emerging role of AI in the nation's growing digital environment. Artificial intelligence is defined as "a branch of computer science dealing with the simulation of intelligent behavior in computers," and "the capability of a machine to imitate intelligent human behavior," according to Merriam-Webster's. But it was also evident during Tuesday's hearing that the definition and uses for AI are still evolving. Subcommittee Chairman Sen. Roger Wicker, R-Miss., said the increase in data collected from Americans through the use of the internet and mobile devices has contributed to the advances in the industry.

We have an answer for you, but apparently it wasn't good enough for the United States Congress. A new bill (pdf) drafted by senator Maria Cantwell asks the Department of Commerce to establish a committee on artificial intelligence to advise the federal government on how AI should be implemented and regulated. Passing of the bill would trigger a process in which the secretary of commerce would be required to release guidelines for legislation of AI within a year and a half. We expect #AI will be an incredibly transformative force for growth and productivity. We need to be ready for it.

The 100 startups on our list have raised $11.7B in aggregate funding across 367 deals since 2012. Today, CB Insights unveiled the second annual AI 100 -- a list of 100 of the most promising private companies applying artificial intelligence algorithms across 25 industries, from healthcare to cybersecurity -- at the A-Ha! conference in San Francisco. The companies were selected from a pool of 2,000 startups based on several criteria, including investor profile, tech innovation, team strength, patent activity, mosaic score, funding history, valuation, and business model. The market map below categorizes the AI 100 companies based on their industry focus. Please click on the image to enlarge.

Cyberwarfare has become a very real part of our business, our government, our technology and our culture. We've seen terms like hacking, cyber-security and DDoS explode into our popular vernacular and for good reason. Real cyber-attacks have increased exponentially in the past 12 months and the growing fear of them has gone off the charts. Still, many of us do not incorporate simple, best practices when it comes to things like creating passwords and engaging with social media.

Loaded with facial recognition technology and artificial intelligence that can keep tabs on people and their activities, the cameras represent a blessing for the security state and a nightmare for privacy advocates, dissidents and anyone else the Chinese government deems a threat.

Artificial intelligence is already reshaping the labor market. Its impact will likely become even more disruptive. But experts have historically been bad at predicting which jobs and tasks will be lost to automation, and public officials have historically been slow to respond to technological advances with smart, effective regulations. That's the nutshell of a RAND Corporation report on "The Risks of Artificial Intelligence to Security and the Future of Work," released earlier this week. What can K-12 educators and policymakers take away from the work?

Expert system can be made use of for photo recognition, like in this screen at a current modern technology conference. Researchers are rushing to recognize the prospective ramifications of A.I. Credit scores Saul Loeb/Agence France-Presse– Getty Images There are basically 3 huge inquiries about artificial intelligence and its impact on the economic climate: Just what can it do? As well as just how quickly will it spread out? Three new records incorporate to suggest these answers: It can possibly do less today compared to you believe. However it will ultimately do more than you possibly believe, in even more locations than you possibly assume, as well as will possibly evolve faster than powerful modern technologies have in the past.