"What use is a machine learning model if you don't deploy to production " -- Anonymous You have done a great work building that awesome 99% accurate machine learning model but your work most of the time is not done without deploying. Most times our models will be integrated with existing web apps, mobile apps or other systems. How then do we make this happen? I said a thousand, I guess I have just a few. I am guessing you would have found the right one for you before you get past the first two or three.

Artificial Intelligence (AI) is a computer or electronic device performing actions as if it were a human – it would apply some sort of intelligence factor or representation to accomplish the task. Some of these human services these electronic devices are performing include different planning methods and actions that include learning, problem solving, motion, thought manipulation, social response and intelligence, creativity, knowledge representation, and imitation. These electronic manipulations happening occur simultaneously with our daily lives, most of the time without us even realizing. Different examples of frequently used AI programming include virtual assistants (like Amazon's Alexa or Apple's Siri) photo recognition (like on social platforms and personal devices), and spam and credit card fraud testing; as well as more in-depth projects, like self-driving cars, check-out kiosks, and recommendation engines that frequent your past purchases to create their own ads. As consumers and participants in a fast-paced electronically changing world, we have not only let these AI infiltrations become a part of our daily lives, but we also have not educated ourselves on their pros and cons.

As an activist, Nandini Jammi has become accustomed to getting harassed online, often by faceless social media accounts. But this time was different: a menacing tweet was sent her way from an account with a profile picture of a woman with blonde hair and a beaming smile. The woman went only by a first name, "Jessica," and her short Twitter biography read: "If you are a bully I will fight you." In her tweet sent to Jammi last July, she said: "why haven't you cleaned your info from Adult Friend Finder? It's only been three years."

A scientist and a researcher, Brian Roemelle, once said that artificial intelligence is the electricity of the future. And it is difficult to disagree, for AI has a huge impact on many industries right now -- from banking to auto. But have you ever thought how AI works for a sextech? Great changes are happening right now, and although you may not even notice it, your sex experience is getting better. The sex industry is booming -- people accept themselves and their bodies, some of them open out, some start experimenting, and some identify themselves as digisexuals (people whose primary sexual identity comes through the use of technology -- they don't need other people to have sex to).

The first season of Westworld focused on the idea of artificial intelligence and humans co-mingling in one epic theme park. The second season saw those same artificially intelligent beings lead a revolution against their human counterparts. In Westworld's third season, Dolores has escaped from the Delos-owned park she called home -- or perhaps prison is the more correct word choice. She teams up with Aaron Paul's character, who she tasks with taking down a mysterious person who took his future away. Maeve, who also escaped, is given a pretty hefty task of her own: kill Dolores.

We aim to improve question answering (QA) by decomposing hard questions into easier sub-questions that existing QA systems can answer. Since collecting labeled decompositions is cumbersome, we propose an unsupervised approach to produce sub-questions. Specifically, by leveraging >10M questions from Common Crawl, we learn to map from the distribution of multi-hop questions to the distribution of single-hop sub-questions. We answer sub-questions with an off-the-shelf QA model and incorporate the resulting answers in a downstream, multi-hop QA system. On a popular multi-hop QA dataset, HotpotQA, we show large improvements over a strong baseline, especially on adversarial and out-of-domain questions. Our method is generally applicable and automatically learns to decompose questions of different classes, while matching the performance of decomposition methods that rely heavily on hand-engineering and annotation.

These days almost every journalism conference has at least one session on the role of Artificial Intelligence (AI) in modern journalism and, interesting, it is always been asked: "will AI replace journalists and writers?". Last week I had an opportunity to visit the technology center of America's top news agency in Washington. There were using many tools and techniques to generate quick, accurate and foolproof contents using Artificial Intelligence (AI). These tools had multiple layers of data-centric AI wrappers to ensure the filtration of Fake News. During my visit, I was able to produce 550 words article, based on a press release, with a single click and amazingly this article had many relevant references from the past.

Abstract: The key distinguishing property of a Bayesian approach is marginalization, rather than using a single setting of weights. Bayesian marginalization can particularly improve the accuracy and calibration of modern deep neural networks, which are typically underspecified by the data, and can represent many compelling but different solutions. We show that deep ensembles provide an effective mechanism for approximate Bayesian marginalization, and propose a related approach that further improves the predictive distribution by marginalizing within basins of attraction, without significant overhead. We also investigate the prior over functions implied by a vague distribution over neural network weights, explaining the generalization properties of such models from a probabilistic perspective. From this perspective, we explain results that have been presented as mysterious and distinct to neural network generalization, such as the ability to fit images with random labels, and show that these results can be reproduced with Gaussian processes.

"With today's advancements, Cisco is further enabling our service provider partners with machine learning to simplify and accelerate cellular IoT …

Baker Hughes is building on its relationship with California, US-based C3.ai to ramp up the development of artificial intelligence applications for the …