Every once in a while a new algorithms comes and makes all others (in the same domain) seems kind of obsolete when it comes to the same domain. Will deep learning make that related algorithms (backpropagation NN, GMM, HMM, ...)? There are several reasons why there will always be a place for other algorithms to be better suited than deep learning in some applications. There are many cases where you need to have an understanding of the domain in order to have optimal results. While some proponents of Deep Learning describe their approach as being general-purpose, I don't think that will ever be true.

The third annual RE•WORK Future of Education workshop will take place in London on 20 June as part of London Technology Week, bringing together education practitioners, technologists, edtech startups, investors and policy leaders to discuss, explore and collaborate to discover how rapidly advancing technology will impact education. Topics explored will include: Wearable Technology, Augmented Reality, Artificial Intelligence, Gamification, Internet of Things, Robotics, Human-Computer Interaction and Facial Recognition. Over the past two years 200 attendees have come together to share their insights into technological advancements, as well as discuss key areas such as: What experience do we want students and teachers to have? How can we make these technologies purposeful? What problem are we trying to solve?

Step by step, rules-based marketing platforms are adding predictive technology and other intelligence on their way to becoming largely self-managing systems. This week, Google Ventures-backed and New York City-based Signpost announced its contribution to that march toward cognitive marketing. Its automated CRM is adding an artificial intelligence agent, dubbed Mia. Signpost's platform already provides a large degree of self-management. It captures data from phone calls, emails, and credit card transactions with a business, and then automatically takes selected marketing actions designed for customer acquisition, customer loyalty, and reviews.

Carrie Anne-Moss (Jessica Jones), Sam Palladio (Nashville), Marshall Allman (Prison Break) Sonya Cassidy (Olympus) and Letitia Wright (Cucumber/Banana) have joined the Season 2 cast of AMC and Channel 4 sci-fi drama series Humans. Moss will play Dr. Athena Morrow, a pre-eminent Artificial Intelligence expert who is driven by her own motives to create a new kind of machine consciousness. Palladio is Ed, a struggling café owner trying to breathe life into his family business; Allman plays Milo Khoury, a young Silicon Valley billionaire, founder and CEO of a leading technology company; intent on changing the world. Humans is produced by Kudos in association with Matador Films, The eight-episode second season is slated to premiere in the UK in late 2016 and 2017 in the U.S.

The other day, the National University of Science and Technology (MISiS) hosted a meeting on the development of robot technologies during the implementation of projects for the National Technology Initiative. Meeting participants watched a presentation of an international project to create an online site for testing unmanned equipment. Russia's KAMAZ Automotive Plant and IT solutions developer Cognitive Technologies have said they are ready to unveil the first Russian-made autonomous truck, an autopilot system that can detect road signs, lane markings and other vehicles. According to developers, the first autonomous commercial trucks could reach production by 2020. Some estimates show that the use of online testing sites will make it possible to save up to two billion rubles that would otherwise be spent on real-life tests and simulated real-life situations.

The impact of Artificial Intelligence (AI) on the world must surely be one of the greatest contemporary puzzles. The spectrum of risk and the gamut of possible applications are significantly complex that almost any scenario can be envisioned, from robot apocalypse to workless utopia. The only assurance is that change is coming, and in my opinion, it is likely to be a revolution of a scale seen only during the onset of history-shifting events such as industrialization or farming. The timing is auspicious: Google AI unit DeepMind's AlphaGo has recently beaten South Korean professional game player Lee Sedol at the ancient Japanese board game Go--a game of huge potential complexity based on simple rules and considered one of the biggest challenges in AI, since it defies brute-force planning. As a milestone in machine intelligence (and good PR), it sits up there with IBM Watson's victory on Jeopardy in 2011 and IBM's earlier AI DeepBlue's victory over chess grandmaster Garry Kasparov in 1997.

Don't miss the livestream on April 21st at 7.45 pm (CET) – right here! Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is an effort to built machines which can learn from their environment, from mistakes and from people. Machines that can learn is the field within aritificial intelligence which is the most observed today. Dr. Werner Huber (Manager Highly Automated Driving BMW Group) "Human or Robot – who will control our future vehicle?"

One of the most useful things to do with machine learning is inform assumptions about customer behaviors. This has a wide variety of applications: everything from helping customers make superior choices (and often, more profitable ones), making them contagiously happy about your business, and building loyalty over time. Increasingly, it's not enough to simply let your customers pick and choose from the products and services options offered. Customers expect intelligent recommendations and for you to chart courses of action with minimal room for ambiguity or misinterpretation. Sounds straightforward enough.. how do you actually make it happen?

Machine learning has come of age in public health reporting according to researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis. They have found that existing algorithms and open source machine learning tools were as good as, or better than, human reviewers in detecting cancer cases using data from free-text pathology reports. The computerized approach was also faster and less resource intensive in comparison to human counterparts. Every state in the United States requires cancer cases to be reported to statewide cancer registries for disease tracking, identification of at-risk populations, and recognition of unusual trends or clusters. Typically, however, busy health care providers submit cancer reports to equally busy public health departments months into the course of a patient's treatment rather than at the time of initial diagnosis.

The increasing number of genome-wide assays of gene expression available from public databases presents opportunities for computational methods that facilitate hypothesis generation and biological interpretation of these data. We present an unsupervised machine learning approach, ADAGE (analysis using denoising autoencoders of gene expression), and apply it to the publicly available gene expression data compendium for Pseudomonas aeruginosa. In this approach, the machine-learned ADAGE model contained 50 nodes which we predicted would correspond to gene expression patterns across the gene expression compendium. While no biological knowledge was used during model construction, cooperonic genes had similar weights across nodes, and genes with similar weights across nodes were significantly more likely to share KEGG pathways. By analyzing newly generated and previously published microarray and transcriptome sequencing data, the ADAGE model identified differences between strains, modeled the cellular response to low oxygen, and predicted the involvement of biological processes based on low-level gene expression differences. ADAGE compared favorably with traditional principal component analysis and independent component analysis approaches in its ability to extract validated patterns, and based on our analyses, we propose that these approaches differ in the types of patterns they preferentially identify. We provide the ADAGE model with analysis of all publicly available P. aeruginosa GeneChip experiments and open source code for use with other species and settings. Extraction of consistent patterns across large-scale collections of genomic data using methods like ADAGE provides the opportunity to identify general principles and biologically important patterns in microbial biology. This approach will be particularly useful in less-well-studied microbial species.