January 1, 2020, organizations that employ individuals based in Illinois will need to keep in mind the Artificial Intelligence Video Interview Act.

AWS introduced new services that use AI to allow more developers to apply machine learning to create better end user experiences, including new machine learning-powered enterprise search, code reviews and profiling, fraud detection, medical transcription, and human review of AI predictions. Machine learning continues to grow at a rapid clip, and today there are tens of thousands of customers doing machine learning on AWS (twice as many as the next largest cloud provider), including many customers that opt to use AWS's fully managed AI Services like Alfresco, Bayer Crop Science, Cerner, CJ Cox Automotive, C-SPAN, Deloitte, Domino's, Emirates NBD, Fred Hutchinson Cancer Research Center, FICO, FINRA, Gallup, Kelley Blue Book, Kia, Mainichi Newspapers Co, NASA, PricewaterhouseCoopers, White House Historical Association, and Zola. In the past year, AWS has introduced several new fully managed AI Services like Amazon Personalize and Amazon Forecast that allow customers to benefit from the same personalization and forecasting machine learning technology used by Amazon's consumer business to power its award-winning customer experiences. AWS customers are interested in learning from Amazon's vast experience using machine learning at scale to improve operations and deliver better customer experiences, without needing to train, tune, and deploy their own custom machine learning models. Today, AWS is announcing five new AI services that build upon Amazon's rich experience with machine learning, and allow organizations of all sizes across all industries to adopt machine learning in their enterprises – with no machine learning experience required. Despite many attempts over many years, internal search remains a vexing problem for today's enterprises, and most employees still frequently struggle to find the information they need. Organizations have vast amounts of unstructured text data, much of it incredibly useful if it can be discovered, stored in many formats and spread across different data sources (e.g.

One of China's tech titans, Baidu, is pursuing a new global business twist that takes the Beijing-based search company far from its roots in search to Hollywood movie promotion, Madison Avenue advertising and social media. It sounds far fetched, and the timing might be better given US-China tensions. But Baidu is making a play beyond China and is gaining traction with its latest outreach that blends search, artificial intelligence, advertising, and media overseas. Baidu has recently aligned with social networks Snap, Reddit and Pinterest, and movie producers Sony and Paramount, among others, in about a dozen sales partnerships. Baidu's distinct strategy for its global business unit is part of a trend of Chinese technology companies going outside China and transitioning from an original, core line of business.

Each month, Future Tense Fiction--a series of short stories from Future Tense and ASU's Center for Science and the Imagination about how technology and science will change our lives--publishes a story on a theme. Stop me if you've heard this one before." David had heard this one before, but he needed a job. He folded his hands in his lap and summoned the patience he'd learned sitting through Talmudic debates. He waved for Aiden Shure, Town of Our Own's CEO, to continue. "It's a dive bar, lots of rough language from the other patrons, but the bartender says, 'Father, what can I get you?' The priest says: 'Well, I have to lead Mass in the morning, but a wee nip can't hurt. Gimme three fingers of Irish whiskey and cut it with holy water.' So the bartender runs over to the church next door, borrows a bit of holy water, and makes the drink. The priest is satisfied, so the bartender moves on: 'Rabbi, what can I get you?' The rabbi says, 'Well it is the Sabbath day, but if it's not too much work I wouldn't say no to a glass of kosher wine from the vineyards of the Holy Land.' So the bartender finds a bottle of sweet Israeli red, and the rabbi thanks him." Aiden told the joke like he'd practiced it a lot while stuck in traffic. David braced for the punchline he knew was coming. "So the bartender turns to the robot, which has been quietly listening to the other patrons.

To many, artificial intelligence (AI) presents an almost endless sea of opportunity. But if AI scares you, you're not alone. As consumers, as employers, and simply as people, the concept of replacing humans with robots in any capacity can feel uncomfortable. Steven Spielberg signed all of us up for some deep soul-searching in 2001's AI. The film questions just what distinguishes humans from machines and if we can ever bridge that gap.

Getting its world premiere at documentary festival IDFA in Amsterdam, Tonje Hessen Schei's gripping AI doc "iHuman" drew an audience of more than 700 to a 10 a.m. Many had their curiosity piqued by the film's timely subject matter--the erosion of privacy in the age of new media, and the terrifying leaps being made in the field of machine intelligence--but it's fair to say that quite a few were drawn by the promise of a Skype Q&A with National Security Agency whistleblower Edward Snowden, who made headlines in 2013 by leaking confidential U.S. intelligence to the U.K.'s Guardian newspaper. Snowden doesn't feature in the film, but it couldn't exist without him: "iHuman" is an almost exhausting journey through all the issues that Snowden was trying to warn us about, starting with our civil liberties. Speaking after the film--which he "very much enjoyed"--Snowden admitted that the subject was still raw for him, and that the writing of his autobiography (this year's "Permanent Record"), had not been easy. "It was actually quite a struggle," he revealed.

While several methods for predicting uncertainty on deep networks have been recently proposed, they do not readily translate to large and complex datasets. In this paper we utilize a simplified form of the Mixture Density Networks (MDNs) to produce a one-shot approach to quantify uncertainty in regression problems. We show that our uncertainty bounds are on-par or better than other reported existing methods. When applied to standard regression benchmark datasets, we show an improvement in predictive log-likelihood and root-mean-square-error when compared to existing state-of-the-art methods. We also demonstrate this method's efficacy on stochastic, highly volatile time-series data where stock prices are predicted for the next time interval. The resulting uncertainty graph summarizes significant anomalies in the stock price chart. Furthermore, we apply this method to the task of age estimation from the challenging IMDb-Wiki dataset of half a million face images. We successfully predict the uncertainties associated with the prediction and empirically analyze the underlying causes of the uncertainties. This uncertainty quantification can be used to pre-process low quality datasets and further enable learning.

After learning topic vectors from an auxiliary text corpus via NMF, the decoder is trained so that it is more likely to sample response words from the most correlated topic vectors. One of the main advantages in our architecture is that the user can easily switch the NMF-learned topic vectors so that the chatbot obtains desired topic-awareness. We demonstrate our model by training on a single conversational data set which is then augmented with topic matrices learned from different auxiliary data sets. We show that our topic-aware chatbot not only outperforms the non-topic counterpart, but also that each topic-aware model qualitatively and contextually gives the most relevant answer depending on the topic of question. Another area where deep learning algorithms have been successfully applied is sequence learning, which aims at understanding the structure of sequential data such as language, musical notes, and videos. One example of an application of deep learning in language modeling is conversational chatbots . A chatbot is a program that conducts a conversation with a user by simulating one side of it. Chatbots receive inputs from a user one message, or question, at a time, and then form a response that is sent back to the user. One of the most widely used machine learning techniques for sequence learning is Recurrent Neural Networks (RNN).