In programmatic advertising, ad slots are usually sold using second-price (SP) auctions in real-time. The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price). In SP, for a single item, the dominant strategy of each bidder is to bid the true value from the bidder's perspective. However, in a practical setting, with budget constraints, bidding the true value is a sub-optimal strategy. Hence, to devise an optimal bidding strategy, it is of utmost importance to learn the winning price distribution accurately. Moreover, a demand-side platform (DSP), which bids on behalf of advertisers, observes the winning price if it wins the auction. For losing auctions, DSPs can only treat its bidding price as the lower bound for the unknown winning price. In literature, typically censored regression is used to model such partially observed data. A common assumption in censored regression is that the winning price is drawn from a fixed variance (homoscedastic) uni-modal distribution (most often Gaussian). However, in reality, these assumptions are often violated. We relax these assumptions and propose a heteroscedastic fully parametric censored regression approach, as well as a mixture density censored network. Our approach not only generalizes censored regression but also provides flexibility to model arbitrarily distributed real-world data. Experimental evaluation on the publicly available dataset for winning price estimation demonstrates the effectiveness of our method. Furthermore, we evaluate our algorithm on one of the largest demand-side platforms and significant improvement has been achieved in comparison with the baseline solutions.

The discriminatory biases of the algorithms, the invasion of privacy, the risks of facial recognition and the regulation of human-machine relations are challenges that AI needs to face. However, the interests of governments and large companies often prevail over good practices. Artificial intelligence (AI) is no longer a science fiction thing, it is everywhere. Your bank uses it to know if it is going to give you a credit or not and the ads you see on your social networks come out of a classification carried out by an algorithm, which has microsegmented and'decided' if it shows you offers of wrinkle creams or high-end cars. Facial recognition systems, which use airports and security forces, are also based on this technology.

Last year, communities banded together to prove that they can--and will--defend their privacy rights. As part of ACLU-led campaigns, three California cities--San Francisco, Berkeley, and Oakland--as well as three Massachusetts municipalities--Somerville, Northhampton, and Brookline--banned the government's use of face recognition from their communities. Following another ACLU effort, the state of California blocked police body cam use of the technology, forcing San Diego's police department to shutter its massive face surveillance flop. And in New York City, tenants successfully fended off their landlord's efforts to install face surveillance. Even the private sector demonstrated it had a responsibility to act in the face of the growing threat of face surveillance.

Natural language understanding (NLU) is a subset of natural language processing (NLP), a technology that enables computers to extract information from human speech or text. While NLP can analyse the structure of a set of words, NLU aims to provide insight into what those words actually mean. Of course, doing this well is difficult. There are problems with slang, irony, different syntax and, especially in spoken language, fragmentary phrasing. In fact, it is known as an "AI-hard" problem – one which, if solved by artificial intelligence, would put computers on the way to being as intelligent as people.

Lately, you've probably read more than a few headlines about "robot lawyers," with many of the articles including dire predictions that artificial intelligence (AI) software will soon take over legal jobs, leaving lawyers in the dust. Rest assured, that's not going to happen any time soon. High end legal analytics work will be in high demand for years to come, and AI software won't be able to compete with the knowledge and skill-set of most lawyers. That being said, there's a reason for all the hype. Legal software that takes advantage of AI has a lot of potential.

Factories that produce a high volume of inventory must ensure that defective products are not shipped. This is often accomplished with human workers on the assembly line or through computer vision. You can build an application that uses a custom image classification model to detect and report back any defects in a product, then takes appropriate action. This method provides a powerful, scalable, and simple solution for quality control. It uses Amazon S3, Amazon SQS, AWS Lambda, AWS Step Functions, and Amazon SageMaker.

When we get to a point where literally just about everything can be done more cheaply and more efficiently by robots, the elite won't have any use for the rest of us at all. For most of human history, the wealthy have needed the poor to do the work that is necessary to run their businesses and make them even wealthier. In this day and age we like to call ourselves "employees", but in reality we are their servants. Some of us may be more well paid than others, but the vast majority of us are expending our best years serving their enterprises so that we can pay the bills. Unfortunately, that paradigm is rapidly changing, and many of the jobs that humans are doing today will be done by robots in the not too distant future.

Recently, the emergence of the #MeToo trend on social media has empowered thousands of people to share their own sexual harassment experiences. This viral trend, in conjunction with the massive personal information and content available on Twitter, presents a promising opportunity to extract data driven insights to complement the ongoing survey based studies about sexual harassment in college. In this paper, we analyze the influence of the #MeToo trend on a pool of college followers. The results show that the majority of topics embedded in those #MeToo tweets detail sexual harassment stories, and there exists a significant correlation between the prevalence of this trend and official reports on several major geographical regions. Furthermore, we discover the outstanding sentiments of the #MeToo tweets using deep semantic meaning representations and their implications on the affected users experiencing different types of sexual harassment. We hope this study can raise further awareness regarding sexual misconduct in academia.

New York City is hiring. The city earlier this month unveiled a description of its new Algorithms Management Policy Officer role. But some worry the creation of a procedural position forced to maneuver within an arguably flawed bureaucratic structure only perpetuates the city's imperfect approach to developing policy for government AI use. "It appears this role will simply provide a rubber stamp to current and future use of [Automated Decision Systems] without evaluating or even attempting to address known concerns with ADS currently used by city agencies," Rashida Richardson, director of policy research at the AI Now Institute at NYU and a critic of the city's task force, told RedTail. "This role is unique in urban governance and is intended to help provide protocols and information about the systems and tools City agencies use to make decisions," the city said in a statement.