Artificial intelligence is set to reshape business and society. For AI to yield economic value, however, designing algorithms compatible with human thought processes is critical. The ability of artificial intelligence (AI) applications to automate tasks associated with human knowledge is rapidly progressing. Examples include recognizing faces, sensing emotions, driving cars, interpreting spoken language, reading text, writing reports, grading student papers, and even setting people up on dates. Yet at a business level, AI projects often fail to deliver desired outcomes because they are not designed to promote smart adoption by human users.

At age 25, Anjney Midha has a stronger resume than some people twice his age. Before graduating from Stanford, he joined the venture capital firm Kleiner Perkins Caufield & Byers. He led the firm's investment in Magic Leap, the mysterious and much-hyped augmented reality company. Then he ditched venture capital to pursue a dream that had followed him from a technology-free young adulthood on a bird sanctuary in India, to the hyper-connected streets of Singapore, to his days at Stanford. That dream was to share his world--more than he could show in a photo, better than what he could convey with words--with the family and friends he'd left in India.

GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite(TM), today announced the immediate availability of GridGain Professional Edition 2.4, a fully supported version of Apache Ignite 2.4. GridGain Professional Edition 2.4 now includes a Continuous Learning Framework, which includes machine learning and a multilayer perceptron (MLP) neural network that enable companies to run machine and deep learning algorithms against their petabyte-scale operational datasets in real-time. Companies can now build and continuously update models at in-memory speeds and with massive horizontal scalability. GridGain Professional Edition 2.4 also enhances the performance of Apache Spark(TM) by introducing an API for Apache Spark DataFrames, adding to the existing support for Spark RDDs. GridGain Continuous Learning Framework GridGain Professional Edition 2.4 now includes the first fully supported release of the Apache Ignite integrated machine learning and multilayer perceptron features, making continuous learning using machine learning and deep learning available directly in GridGain.

So you want to get started with AI in your enterprise? Just over half of companies say they are investing in AI deployments or pilots, according to recent Forrester Research survey information. What that means may be different for each individual business. Some may choose to invest in data scientists dedicated to machine learning efforts. Others may choose a more tentative step -- looking at the AI-as-a-service options available from vendors.

In the early days of digital computing, it was not uncommon to find a radio receiver tuned to a particular frequency (I don't recall which one, sigh) so that the RF emitted by the computer could be picked up and played through the radio. You could tell when a program went into a loop and sometimes you could tell roughly where a computation had reached by the sounds coming from the radio monitor. Fast-forward to the 21st century and we are seeking a different kind of sound: the sound of programming. Bootstrap Worlda has developed online courses in programming, among other subjects, but what makes Bootstrap World so memorable for me is that the team has focused heavily on accessibility. The programming environment is extremely friendly to screen readers so that a blind programmer can navigate easily through complex programs using keyboard navigation coupled with oral descriptions/renderings of the program text and structure.b

Lucas asserted "... technology-enhanced teaching and learning can dramatically improve the quality and success of higher education ..." His Figure 1 and Figure 2, in outlining traditional versus technology-enhanced courses, suggested traditional teaching methods deliver a low-quality result, while professional (Hollywood) production methods deliver a high-quality result, with, again, no evidence provided. The idea of universities as "content producers" giving students "content" consisting of "course materials and exercises" gave me an analogous idea. Families give food and clothing to their children, but families are inefficient and can involve bloated administrations (parents). Just as parents do more than feed (they try to create an environment where their children can develop and thrive), universities likewise try to create a learning environment for students. Indispensable elements include laboratory work, fieldwork, real essays marked by real scholars (not against a list of bullet points), and project work.

Machine learning has moved from prototype to production across a wide range of business units at financial services giant Capital One due in part to a centralized approach to evaluating and rolling out new projects. This is no easy task given the scale and scope of the enterprise but according to Zachary Hanif who is director of Capitol One's machine learning "center for excellence", the trick is to define use cases early that touch as broad of a base within the larger organization as possible and build outwards. This is encapsulated in the philosophy Hanif spearheads--locating machine learning talent in one repository that can branch out and work with the experts across the many business divisions. Hanif shared these and other lessons for building a machine learning hub inside a large enterprise where purely machine learning experts work with the different domain and departmental efforts to roll new services into production at the GPU Technology Conference (GTC18). While GPUs were not necessarily the topic of the talk by any means, Hanif did say they have quite a number along with standard CPU based clusters and just like any other enterprise or academic center with a wide range of mission-critical R&D projects on the burner, resource contention is a constant struggle, especially when it comes to the more rare and expensive GPUs they have.

Earlier I wrote about How to Reinvent Yout Career With AI Skills.This isn't a time to relax and think what should you learn next? Build skills around what's one of the most significant technologies of the coming decade –and that is Artificial Intelligence. Despite recent growth in interest, AI is a skill possessed by relatively few people. Many of the roles, needed skills and business titles of the future are unknown to us. Talent is no longer same as it used to be five years before.

Elias, the new language teacher at a Finnish primary school, has endless patience for repetition, never makes a pupil feel embarrassed for asking a question and can even do the'Gangnam Style' dance. Elias is also a robot. The language-teaching machine comprises a humanoid robot and mobile application, one of four robots in a pilot programme at primary schools in the southern city of Tampere. Pictured is Elias, a robot teaching children in a Finnish school. The robot is able to understand and speak 23 languages and is equipped with software that allows it to understand students' requirements and helps it to encourage learning.

Knowledge Transfer (KT) techniques tackle the problem of transferring the knowledge from a large and complex neural network into a smaller and faster one. However, existing KT methods are tailored towards classification tasks and they cannot be used efficiently for other representation learning tasks. In this paper a novel knowledge transfer technique, that is capable of training a student model that maintains the same amount of mutual information between the learned representation and a set of (possible unknown) labels as the teacher model, is proposed. Apart from outperforming existing KT techniques, the proposed method allows for overcoming several limitations of existing methods providing new insight into KT as well as novel KT applications, ranging from knowledge transfer from handcrafted feature extractors to {cross-modal} KT from the textual modality into the representation extracted from the visual modality of the data.