We present a novel algorithm that synthesizes imperative programs for introductory programming courses. Given a set of input-output examples and a partial program, our algorithm generates a complete program that is consistent with every example. Our key idea is to combine enumerative program synthesis and static analysis, which aggressively prunes out a large search space while guaranteeing to find, if any, a correct solution. We have implemented our algorithm in a tool, called SIMPL, and evaluated it on 30 problems used in introductory programming courses. The results show that SIMPL is able to solve the benchmark problems in 6.6 seconds on average.

Computational results demonstrate that posterior sampling for reinforcement learning (PSRL) dramatically outperforms algorithms driven by optimism, such as UCRL2. We provide insight into the extent of this performance boost and the phenomenon that drives it. We leverage this insight to establish an $\tilde{O}(H\sqrt{SAT})$ Bayesian expected regret bound for PSRL in finite-horizon episodic Markov decision processes, where $H$ is the horizon, $S$ is the number of states, $A$ is the number of actions and $T$ is the time elapsed. This improves upon the best previous bound of $\tilde{O}(H S \sqrt{AT})$ for any reinforcement learning algorithm.

Don't know where or how to start learning? But learning more about artificial intelligence, and the myriad overlapping and related fields and application domains does not require a PhD. Getting started can be intimidating, but don't be discouraged; check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year. With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere. Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.

Those findings from DeepMind, the pioneering London-based AI lab, also suggest the motive behind why DeepMind has created a huge new dataset of YouTube clips to help train AI on identifying human actions in videos that go well beyond "Mmm, doughnuts" or "Doh!" To help improve AI's capability to recognize human actions in motion, DeepMind has unveiled its Kinetics dataset consisting of 300,000 video clips and 400 human action classes. Past cases have shown how imbalanced training datasets can lead to deep learning algorithms performing worse at recognizing the faces of certain ethnic groups. This means that even the Kinetics action classes featuring mostly male participants--such as "playing poker" or "hammer throw"--did not seem to bias AI to the point where the deep learning algorithms had trouble recognizing female participants performing the same actions.

Low-Power Wide Area Networks (LP-WANs) are emerging as a promising solution for connecting Internet of Things and Machine Type Communication devices. If ultra-narrowband (UNB) networks, a subclass of LP-WANs, reach predicted deployment numbers and densities, they will face two challenges: inter-technology and intra-technology interference. This paper proposes the first experimental architecture designed for the optimization of UNB networks. We illustrate its implementation on a case study of a SIGFOX network and the resulting extension of the existing LOG-a-TEC testbed. The proposed architecture enables context data collection, context model development, optimization and transmission control using rapid experimentation cycle approach enabled by flow based programming using Node-RED.

One of the most enjoyable aspects of my role as CTO for Capgemini's Business Services is being able to work with technology companies that are pioneering breakthrough innovations – and this is one of the reasons we created our global partnership with Celaton in 2016. Their work around AI and machine learning is impressive to say the least, and the industry is taking notice – Celaton recently received the 2017 Queen's Award for Enterprise in Innovation for the development of their inSTREAM offering. I've had the pleasure of collaborating with Celaton's CEO, Andrew Anderson, applying inSTREAM to improve business process transformation for our clients. In this blog, Andrew shares some of his insights on how machine learning can help manage unpredictable data volume and deliver significant benefits in finance and accounting. According to a recent study, we create a staggering 2.5 quintillion bytes of data every day.

SYS-CON Events announced today that MobiDev, a client-oriented software development company, will exhibit at SYS-CON's 21st International Cloud Expo, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. MobiDev is a software company that develops and delivers turn-key mobile apps, websites, web services, and complex software systems for startups and enterprises. Since 2009 it has grown from a small group of passionate engineers and business managers to a full-scale mobile software company with over 200 developers, designers, quality assurance engineers, project managers in house, specializing in the world-class mobile and web development. The company's main focus is saving the time and efforts of clients by providing full-cycle product development, offering dedicated project teams for long-term collaborations, making quality assurance an integral part of development, and regular product deliveries. Another major work principle embraced by MobiDev is flexibility: the company adjusts its activities to clients' business processes, tools, and environments; enables transparency of actions and quick problem-solving; and establishes effective communication with other involved teams, if required.

There's a 50% chance that artificial intelligence (AI) systems will be able to perform every single task better than their human counterparts in the next 45 years, according to researchers from Oxford and Yale University. The research also gave the same odds that AI automate all human jobs in the next 122 years as well. The research, titled When Will AI Exceed Human Performance? Evidence from AI Experts, was submitted and later revised in late May 2017. Its purpose is to help anticipate the innovation in AI and machine learning for the purpose of developing public policy.

Created by a team of Stanford psychologists and AI experts, Woebot uses brief daily chat conversations, mood tracking, curated videos, and word games to help people manage mental health. Scientists who recently looked at text-chat as a supplement to videoconferencing therapy sessions observed that the texting option actually reduced interpersonal anxiety, allowing patients to more fully disclose and discuss issues shrouded in shame, guilt, and embarrassment. Yesterday, Darcy and a team of co-authors at Stanford published a peer-reviewed study in the Journal of Medical Internet Research, Mental Health that randomized 70 college students and asked them to engage with Woebot or a self-help e-book for two weeks. But using those results to claim it can significantly reduce depression may expose Woebot to legal liabilities that bots in supporting roles have managed to avoid.

Honest question, and I'm really not trying to be adversarial, but what has Francois actually done that would merit him writing a book on DL? Keras is good for the community because it's accessible, even people who don't like it for research generally agree on that, and he has a high kaggle score, and a paper or two that look like a promising start to a research career (though Xception is IMO incremental it's still a decent paper). To me, this honestly seems like him riding the popularity of Keras for a moneygrab, practically on par with that PyImageSearch dude. Counterpoint: the Goodfellow DL book is a regular ole textbook; Maybe the point of this is that it abstracts most of the details and gives a higher level overview that's targeted at laymen? Briefly browsing the table of contents, it looks like a list of "topics that have recently been popular and that you might like to play with or build a neat applet with." Counter counterpoint: To me that doesn't merit wasting ink and paper, especially when there are so many solid resources and tutorials people have put out on the internet.