When it comes to neural network design, the trend in the past few years has pointed in one direction: deeper. Whereas the state of the art only a few years ago consisted of networks which were roughly twelve layers deep, it is now not surprising to come across networks which are hundreds of layers deep. This move hasn't just consisted of greater depth for depths sake. For many applications, the most prominent of which being object classification, the deeper the neural network, the better the performance. That is, provided they can be properly trained!

Anesthesiologists' jobs look safer than radiologists' jobs. In H. G. Wells's classic The War of the Worlds, the narrator pauses a moment to rue the fact that he didn't react sooner to the arrival of an "intelligence greater than man's"--in his case, Martians landing on earth. Comparing himself to a comfortable dodo in its nest, he imagined those ill-fated birds also dithering as hungry sailors invaded their island: "We will peck them to death tomorrow, my dear." As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? Are you sufficiently aware of the signs that you should?

Apple Inc. hired a prominent artificial intelligence researcher from Carnegie Mellon University as it seeks to regain lost ground against competitors such as Google, Microsoft Corp. and Amazon.com He posted a link to an Apple job application page seeking machine learning specialists. Apple is seeking scientists with "experience in Deep Learning, Computer Vision, Machine Learning, Reinforcement Learning, Optimization, and/or Data Mining," it said in the job listing. Machine learning has gained mounting importance for tech companies to improve their research and enable virtual assistants such as Siri to better anticipate and predict users' needs. Siri is competing with the Google Assistant, Microsoft's Cortana and Amazon's Alexa to become the virtual assistant of choice and the access point for users seeking online services.

Carnegie Mellon University professor Russ Salakhutdinov has been hired by Apple to lead a team focused on artificial intelligence, according to a tweet Salakhutdinov sent out this morning. He will continue to teach at Carnegie Mellon, but will also serve as "Director of AI Research" at Apple. In his tweet, Salakhutdinov says he is seeking additional research scientists with machine learning expertise to join his team. An included job posting asks that candidates have experience with Deep Learning, Computer Vision, Machine Learning, Reinforcement Learning, Optimization, and/or Data Mining. Salakhutdinov specializes in statistical machine learning and has authored many papers on neural networks, deep kernel learning, reinforcement learning, and other related topics.

Apple isn't letting Samsung's acquisition of Viv go unanswered. The Cupertino crew has hired Russ Salakhutdinov, a computer science professor at Carnegie Mellon University, as a director of artificial intelligence research. Interestingly, he isn't giving up his school work -- he may well be publishing research at the same time as he's upgrading your iPhone or Mac. It's not certain what he'll be working on, although Recode observes that his recent studies have involved understanding the context behind questions. We've asked Apple if it can comment.

Machine learning is no longer the stuff of science fiction, nor is it all that new. Its development dates back to the mid-20th century and was defined in 1959 by Arthur Samuel as a "field of study that gives computers the ability to learn without being explicitly programmed". As the modern world becomes increasingly dependent on data-driven technologies, machine learning, along with artificial intelligence (AI), has captured the human imagination. It is clear that businesses are spending huge amounts of time and money scrambling to adopt the latest and greatest technologies in the hope of out-pacing and out-smarting their rivals. However, without a customer-centric, business-relevant big data strategy that is embraced company-wide, all the technology in the world won't sell a thing.

This is a follow up to an article Per wrote last year, Machine Learning in a Week, on how he kickstarted his way into machine learning (ml) by devoting five days to the subject. Follow him on Medium and check out his archive. My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn't even a professional developer, but a hobby coder who'd done a couple of small projects.

Pearson has one defining goal: to help people progress in their lives through learning. We champion innovation and we invest in models for education that deliver on our promise for effective, accessible, and personal learning from early literacy, college and career readiness to professional education, through data informed instruction and inventive applications for mobile and digital learning. Pearson, the world's leading learning company, has global-reach and market leading businesses in education, business, and consumer publishing and is listed on the London and New York stock exchanges (UK: PSON; NYSE: PSO). Pearson is an Equal Opportunity and Affirmative Action Employer, and a member of E-Verify. All qualified applicants, including minorities, women, veterans, and people with disabilities are encouraged to apply.

In this post, we'll show how easy it is to use MLDB to build your own real time image classification service. We will use different brand of cars in this example, but you can adapt what we show to train a model on any image dataset you want. We will be using a TensorFlow deep convolutional neural network, transfer learning, and everything will run off MLDB. At a high level, transfer learning allows us to take a model that was trained on one task and use its learned knowledge on another task. We use the Inception- v3 model, a deep convolutional neural network, that was trained on the ImageNet Large Visual Recognition Challenge dataset.