Humanity continues to embark on a period of unparalleled technological advancement. The next 5, 10 and 20 years will present both significant challenges and opportunities. Private sectors, governments, academics and entrepreneurs are all seeking the roadmap for navigating these profound changes in the world of work. Such a road map must be created collaboratively by all stakeholders. At its core, an industrial revolution can be characterized by advancements in technology that humanity applies to improve the process of production.

This talk was recorded in London on October 30th, 2018. Slides from the talk can be viewed here: https://www.slideshare.net/0xdata/nea... Session: Travelport is a leading travel commerce platform that has truly huge data and many complex needs in terms of processing, performance and latency. This talk will demonstrate how we were able to harness big data technologies, H2O and cloud integration to deploy AI at scale and at low latency. The talk to cover practical advice taken from our AI journey; you will learn the successful strategies and the pitfalls of near real-time retraining ML models with streaming data and using all opensource technologies. Bio: As principal data scientist at Travelport, Levi Brackman leads a team of data scientists that are putting ML model into production.

Amino acid sequence portrays most intrinsic form of a protein and expresses primary structure of protein. The order of amino acids in a sequence enables a protein to acquire a particular stable conformation that is responsible for the functions of the protein. This relationship between a sequence and its function motivates the need to analyse the sequences for predicting protein functions. Early generation computational methods using BLAST, FASTA, etc. perform function transfer based on sequence similarity with existing databases and are computationally slow. Although machine learning based approaches are fast, they fail to perform well for long protein sequences (i.e., protein sequences with more than 300 amino acid residues). In this paper, we introduce a novel method for construction of two separate feature sets for protein sequences based on analysis of 1) single fixed-sized segments and 2) multi-sized segments, using bi-directional long short-term memory network. Further, model based on proposed feature set is combined with the state of the art Multi-lable Linear Discriminant Analysis (MLDA) features based model to improve the accuracy. Extensive evaluations using separate datasets for biological processes and molecular functions demonstrate promising results for both single-sized and multi-sized segments based feature sets. While former showed an improvement of +3.37% and +5.48%, the latter produces an improvement of +5.38% and +8.00% respectively for two datasets over the state of the art MLDA based classifier. After combining two models, there is a significant improvement of +7.41% and +9.21% respectively for two datasets compared to MLDA based classifier. Specifically, the proposed approach performed well for the long protein sequences and superior overall performance.

We present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise rate and a variable noise rate. Our main results are tight regret bounds for learning with noise in the adversarial online learning model.

Robin E. was folding laundry when she heard her son talking to Alexa downstairs in a soft, hopeful voice. The 5-year-old was asking, "Alexa, will you be my friend?" Robin held her breath, waiting tensely for Alexa's response. Finally, she heard the assistant say, brightly, "I'm happy to be your friend." Robin and her husband have an Echo Spot in their bedroom and an Echo Show on their kitchen counter.

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We introduce SimplerVoice: a key message and visual description generator system to help low-literate adults navigate the information-dense world with confidence, on their own. SimplerVoice can automatically generate sensible sentences describing an unknown object, extract semantic meanings of the object usage in the form of a query string, then, represent the string as multiple types of visual guidance (pictures, pictographs, etc.). We demonstrate SimplerVoice system in a case study of generating grocery products' manuals through a mobile application. To evaluate, we conducted a user study on SimplerVoice's generated description in comparison to the information interpreted by users from other methods: the original product package and search engines' top result, in which SimplerVoice achieved the highest performance score: 4.82 on 5-point mean opinion score scale. Our result shows that SimplerVoice is able to provide low-literate end-users with simple yet informative components to help them understand how to use the grocery products, and that the system may potentially provide benefits in other real-world use cases.

Pascal Weinberger in conversation with his team at Telefonica's "moonshots" division Alpha, where he heads AI research and development. Flying cars, augmented reality glasses and contact lenses that can detect diabetes: They're all innovations born out of Google X, the skunkworks division of Alphabet. Three years ago Spanish telco giant Telefónica established Alpha, a lab in Barcelona staffed by around 100 people, working in stealth on innovative technology that holds the promise of a potential new revenue streams. The person running all things AI at the lab is Pascal Weinberger, 21. Weinberger is originally from Germany and like many other computer programmers is self-taught.

DALLAS--(BUSINESS WIRE)--To solve humanity's most complex and demanding problems ranging from creating sustainable global food production and preventing infectious disease epidemics to ensuring the safety of our planet and natural resources is the HPC community's next grand challenge. HPC and AI are revolutionizing how we untangle and solve global threats and humanitarian crises. The SC18 plenary session will examine the potential for advanced computing to help mitigate human suffering and elevate our capacity to protect the most vulnerable. This plenary session will hear from innovators who are redefining how we predict and prevent humanitarian crises by leveraging advanced computing. The session is the kick-off event, which immediately precedes the Exhibitor Opening Gala.

Facebook is making some of the artificial intelligence it uses to prod people to chat and post more available for free. People can now use the social networking giant's Horizon coding tools to create their own software that can learn to do tasks in the most efficient way possible by trial-and-error. An outside developer working in her garage, for example, could use the sophisticated technology to build the next addictive app. "A hobbyist or high school student can run it on their laptop or you can run it on thousands of machines in the cloud," said Jason Gauci, Facebook's lead engineer for Horizon. Facebook, which announced the availability of the tool on Thursday, has used the technology to teach its computers to figure out which notifications users are most likely to respond to.