IT services and consulting company Accenture is launching new services for testing artificial intelligence systems to help companies build own AI-driven products and services based locally or on the cloud. "The adoption of AI is accelerating as businesses see its transformational value to power new innovations and growth," Bhaskar Ghosh, group chief executive, Accenture Technology Services, said in a statement. "As organisations embrace AI, it is critical to find better ways to train and sustain these systems – securely and with quality – to avoid adverse effects on business performance, brand reputation, compliance and humans," Ghosh said. The Dublin-headquartered company said the new testing services works in two phases. While the first phase helps companies focus on choice of data, models and algorithms to teach the machine learning engine, the second phase helps them compare results of the engine with key performance indicators and understand if the engine can explain the decision-making process.

MIT researchers have developed a chip designed to speed up the hard work of running neural networks, while also reducing the power consumed when doing so dramatically – by up to 95 percent, in fact. The basic concept involves simplifying the chip design so that shuttling of data between different processors on the same chip is taken out of the equation. The big advantage of this new method, developed by a team led by MIT graduate student Avishek Biswas, is that it could potentially be used to run neural networks on smartphones, household devices and other portable gadgets, rather than requiring servers drawing constant power from the grid. Because it means that phones of the future using this chip could do things like advanced speech and face recognition using neural nets and deep learning locally, rather than requiring more crude, rule-based algorithms, or routing information to the cloud and back to interpret results. Computing'at the edge,' as its called, or at the site of sensors actually gathering the data, is increasingly something companies are pursuing and implementing, so this new chip design method could have a big impact on that growing opportunity should it become commercialized.

Since the 1980s the number of women completing computer science degrees has plummeted, and in most large tech companies the representation of women in technical roles is below 30%. This lack of diversity prevents us from building products that work for everybody. It can foster toxic "brogrammer" cultures which harm everybody who works within them, and it deprives teams of the well-documented performance boost that women bring. Many of the early superstars in computer science were women -- from Lord Byron's polymath daughter Ada Lovelace, the first person to envisage a general purpose computer, to Rear Admiral Grace Hooper, who pioneered the use of natural language in writing computer programs. Similarly, the post-war computing scene was dominated by women.

Google has been tinkering in the field of medicine over the last few years, including developing a prototype electronic contact lens. The company's latest health project involves detecting cardiovascular conditions by analyzing the vasculature of the retina. The researchers built a deep learning system that processed data from two datasets containing thousands of patients, each of which included images of a patient's retina along with various risk factors and health conditions such smoking and high blood pressure. The system found correlations between various parameters measured within the retinal images and cardiovascular risk factors, as well as disease. For example, the software was able to identify smokers just by looking at the retina 71% of the time.

A standard introduction to online learning might place Online Gradient Descent at its center and then proceed to develop generalizations and extensions like Online Mirror Descent and secondorder methods. Here we explore the alternative approach of putting exponential weights (EW) first. We show that many standard methods and their regret bounds then follow as a special case by plugging in suitable surrogate losses and playing the EW posterior mean. For instance, we easily recover Online Gradient Descent by using EW with a Gaussian prior on linearized losses, and, more generally, all instances of Online Mirror Descent based on regular Bregman divergences also correspond to EW with a prior that depends on the mirror map. Furthermore, appropriate quadratic surrogate losses naturally give rise to Online Gradient Descent for strongly convex losses and to Online Newton Step. We further interpret several recent adaptive methods (iProd, Squint, and a variation of Coin Betting for experts) as a series of closely related reductions to exp-concave surrogate losses that are then handled by Exponential Weights. Finally, a benefit of our EW interpretation is that it opens up the possibility of sampling from the EW posterior distribution instead of playing the mean. As already observed by Bubeck and Eldan (2015), this recovers the best-known rate in Online Bandit Linear Optimization.

Humans and animals have the ability to continually acquire and fine-tune knowledge throughout their lifespan. This ability is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills. Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world. However, continual lifelong learning remains a long-standing challenge for machine learning and neural network models since the incremental acquisition of new skills from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback also for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to continual lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic interference. Although significant advances have been made in domain-specific continual lifelong learning with neural networks, extensive research efforts are required for the development of general-purpose artificial intelligence and autonomous agents. We discuss well-established research and recent methodological trends motivated by experimentally observed lifelong learning factors in biological systems. Such factors include principles of neurosynaptic stability-plasticity, critical developmental stages, intrinsically motivated exploration, transfer learning, and crossmodal integration.

Did you also see the video of the robot opening a door? Did you freak out and think: if they can open doors…what will they learn next? People see a robot do something that looks intelligent and the natural instinct is to extrapolate and assume that there is a general intelligence behind the action. In reality, the set of tasks that robots and Artificial Intelligence (AI) more generally are good at right now is constrained by very narrow parameters. Think about the game of chess (or more recently, the Chinese game of Go).

In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

Emerging technologies like machine learning, deep learning, and, to some extent, artificial intelligence are becoming ripe for adoption. IT professionals need to understand how these technologies affect the businesses they support and begin developing the skills needed to manage and work with these technologies as they evolve. It's only a matter of time before this emerging technology becomes not only commonplace but also essential for IT pros to successfully do their jobs. According to a new Gartner report, by 2022 one in five workers engaged in non-routine tasks will rely on AI to help with their work. The benefits of the analytics and predictive capabilities enabled by ML and AI are far-reaching.

Organizations expend constant effort to deliver information employees need to know for their jobs. You depend on training to help your employees make more sales, provide better customer service, avoid regulatory issues, and make fewer mistakes. But training has no value if we can't retrieve the information we're taught. Training reinforcement is essential to ensure that knowledge and skills learned in training are applied on the job. If you are new to training reinforcement or a bit unfamiliar, here are seven key things to know.