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Microsoft has revealed two new training programs to tackle the shortage of AI-related skills in business and academia. What is AI? Everything you need to know about Artificial Intelligence The first of the two programs, Microsoft AI Academy, will run face-to-face and online training sessions for business and public-sector leaders, IT professionals, developers, and startups. "The academy will be helping to develop practical AI skills, learning, and certification for customers and partners," said Cindy Rose, Microsoft UK CEO, speaking at the Future Decoded event in London today. Rose added that Microsoft will use the academy to train up its own staff, including herself. Microsoft's ambition for the academy, she said, is "to empower you and your organization to do more with AI".

Stochastic gradient based methods are dominant in optimization for most large-scale machine learning problems, due to the simplicity of computation and their compatibility with modern parallel hardware, such as GPU. In most cases these methods use over-parametrized models allowing for interpolation, i.e., perfect fitting of the training data. While we do not yet have a full understanding of why these solutions generalize (as indicated by a wealth of empirical evidence, e.g., [22, 2]) we are beginning to recognize their desirable properties for optimization, particularly in the SGD setting [11]. In this paper, we leverage the power of the interpolated setting to propose MaSS (Momentum-added Stochastic Solver), a stochastic momentum method for efficient training of over-parametrized models. See pseudo code in Appendix A. The algorithm keeps two variables (weights)w andu .

Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or paid subscriptions. In this paper, we study adversarial machine learning in the form of back-box attacks on online classifier APIs. We start with a deep learning based exploratory (inference) attack, which aims to build a classifier that can provide similar classification results (labels) as the target classifier. To minimize the difference between the labels returned by the inferred classifier and the target classifier, we show that the deep learning based exploratory attack requires a large number of labeled training data samples. These labels can be collected by calling the online API, but usually there is some strict rate limitation on the number of allowed API calls. To mitigate the impact of limited training data, we develop an active learning approach that first builds a classifier based on a small number of API calls and uses this classifier to select samples to further collect their labels. Then, a new classifier is built using more training data samples. This updating process can be repeated multiple times. We show that this active learning approach can build an adversarial classifier with a small statistical difference from the target classifier using only a limited number of training data samples. We further consider evasion and causative (poisoning) attacks based on the inferred classifier that is built by the exploratory attack. Evasion attack determines samples that the target classifier is likely to misclassify, whereas causative attack provides erroneous training data samples to reduce the reliability of the re-trained classifier. The success of these attacks show that adversarial machine learning emerges as a feasible threat in the realistic case with limited training data.

Data scientists are expected to know a lot -- machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. How should data scientists who want to be in demand by employers spend their learning budget? I scoured job listing websites to find which skills are most in demand for data scientists. I looked at general data science skills and at specific languages and tools separately. I searched job listings on LinkedIn, Indeed, SimplyHired, Monster, and AngelList on October 10, 2018.

Not too long ago it was often said that computer vision could not compete with the visual abilities of a one-year-old. That is no longer true: computers can now recognize objects in images about as well as most adults can, and there are computerized cars on the road that drive themselves more safely than an average sixteen-year-old could. And rather than being told how to see or drive, computers have learned from experience, following a path that nature took millions of years ago. What is fueling these advances is gushers of data. Data are the new oil. Learning algorithms are refineries that extract information from raw data; information can be used to create knowledge; knowledge leads to understanding; and understanding leads to wisdom. Welcome to the brave new world of deep learning. Deep learning is a branch of machine learning that has its roots in mathematics, computer science, and neuroscience.

Depending on what news headline you have read, you may have perceived an Artificial Intelligence (AI) system as either an Alexa or Siri assistant that understands all your commands, a deep learning system that can recognize dog or a cat from image, a system that recommends personalized medicine, or an intelligent, overpowering machine that can overtake all human tasks and render humans useless. Few of these definitions can be termed as visionary, few fear mongering and rest of them being evolutionary. Last month, I was at Artificial Intelligence (AI) Summit 2018 in San Francisco. The event highlighted the state of AI business as it stands today and real-world examples from enterprises who are using AI to transform their business. I want to give a shout out to organizers of AI Summit – they did a fabulous job in bringing a highly diverse set of speakers across a variety of verticals.

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.