Andrew Ng, a leading figure in the world of artificial intelligence, is leaving his post as chief scientist at China's Baidu and says he wants to find ways of advancing AI beyond the technology world. Ng is known for playing a leading role in formulating the AI strategy of both Baidu and Google. He says is leaving the Chinese company on good terms and simply wants to find a new challenge. "I've decided to step away from this role while everything is going well and look at some other things," he told MIT Technology Review. "I don't know precisely what I'll do, but I think AI offers a lot of opportunities, not just at big companies like Baidu but for entrepreneurs, and for advancing basic research," Ng added.

Andrew Ng, one of the world's leading artificial intelligence researchers, said in a Medium post that he is resigning as the head of AI initiatives at Baidu Corp., one of China's largest Internet companies. Ng said he said he will "continue to shepherd" the growth of AI in society, but provided few clues about what might come next. He portrayed his departure from Baidu as amicable, saying: "the team is stacked up and down with talent; I am confident AI at Baidu will continue to flourish." Ng has held a multitude of high-profile positions in Silicon Valley in the past decade, serving as a computer science professor at Stanford University, as head of the Google Brain project, and as chairman of Coursera, an online-education company that he co-founded with Stanford faculty colleague Daphne Koller. I've completed a new book called "You Can Do Anything: The Surprising Power of a Useless Liberal Arts Education."

In recent years, we have become increasingly good at training deep neural networks to learn a very accurate mapping from inputs to outputs, whether they are images, sentences, label predictions, etc. from large amounts of labeled data. What our models still frightfully lack is the ability to generalize to conditions that are different from the ones encountered during training. Every time you apply your model not to a carefully constructed dataset but to the real world. The real world is messy and contains an infinite number of novel scenarios, many of which your model has not encountered during training and for which it is in turn ill-prepared to make predictions. The ability to transfer knowledge to new conditions is generally known as transfer learning and is what we will discuss in the rest of this post. Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. I will then outline reasons why transfer learning warrants our attention. Subsequently, I will give a more technical definition and detail different transfer learning scenarios.

Researchers have some bad news for moms who used DHA supplements while they were pregnant in hopes of boosting their baby's brains: At age 7, kids whose mothers took DHA scored no higher on an IQ test than kids whose moms swallowed capsules that were DHA-free. The results are the latest findings from a study assessing the benefits -- if any -- of giving DHA to babies in utero. They appear in Tuesday's edition of the Journal of the American Medical Assn. DHA, short for docosahexaenoic acid, is an omega-3 fatty acid that plays a key role in brain health. It's essential throughout our lives, and especially during infancy when the brain, eyes and nervous system are developing.

Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) framework, the model learns decomposable document vectors for each individual category and thus overcomes the representational bottleneck in previous methods due to a fixed-length document vector. During prediction, the estimated relevance or saliency weights explicitly capture the contribution of each sentence to the predicted rating, thus offering an explanation of the rating. Our model achieves state-of-the-art performance on multi-aspect sentiment analysis, improving over several baselines. Moreover, the predicted saliency weights are close to human estimates obtained by crowdsourcing, and increase the performance of lexical and topical features for review segmentation and summarization.

Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include online data and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering methods and bootstrapping schemes. Based on decision trees combined with aggregation and bootstrap ideas, random forests were introduced by Breiman in 2001. They are a powerful nonparametric statistical method allowing to consider in a single and versatile framework regression problems, as well as two-class and multi-class classification problems. Focusing on classification problems, this paper proposes a selective review of available proposals that deal with scaling random forests to Big Data problems. These proposals rely on parallel environments or on online adaptations of random forests. We also describe how related quantities -- such as out-of-bag error and variable importance -- are addressed in these methods. Then, we formulate various remarks for random forests in the Big Data context. Finally, we experiment five variants on two massive datasets (15 and 120 millions of observations), a simulated one as well as real world data. One variant relies on subsampling while three others are related to parallel implementations of random forests and involve either various adaptations of bootstrap to Big Data or to "divide-and-conquer" approaches. The fifth variant relates on online learning of random forests. These numerical experiments lead to highlight the relative performance of the different variants, as well as some of their limitations.

Competition amongst universities is set to increase with institutions closely differentiating themselves to attract and retain the best quality students, academics and staff. Key to this differentiation will be an extensive technology adoption and innovation strategy, enhancing the student experience, delivery of learning content, community engagement and campus management. The education technology (Edutech) market in Australia is expected to grow significantly amidst increasing student demand for education services and technology innovation, competition amongst institutions and decreasing acquisition costs. Frost & Sullivan anticipates that as the learning experience becomes increasingly digitised, technologies and solutions incorporating big data and analytics, collaboration, Augmented / Virtual Reality technology, Artificial Intelligence and learning management systems will play a key role within universities in the coming years. Frost & Sullivan's most recent analysis, Australian Edutech Market: Key Trends, Technologies and Opportunities 2016-2022 finds that the Australian Edutech Market is expected to grow to AUD 1.7 Billion by 2022.

United Technology's announcement last November that its Carrier Corp. plant would keep jobs in Indiana rather than move them to Mexico was heralded as a significant victory for American workers. However, the true impact of the deal was hidden below the headlines. United Technologies CEO, Greg Hayes, said the company will invest $16M "to...automate to drive the cost down so that we can continue to be competitive... ultimately...there will be fewer jobs." Carrier's plans underscore a harsh reality: most American jobs aren't going to Mexico, China or another foreign country, they're being automated. The workers left behind often struggle to find employment for comparable pay because they lack the skills required for the jobs that aren't threatened by automation.

Because the AI/VR system is all on the web, it is a 24/7 learning experience. School can be any time. Personalized learning becomes the norm and each student becomes an expert of their interests. From struggling learners to those that are exceptionally intelligent, the AI/VR education system will know their needs, and be able to work with them whenever they wish. Of course there will be required milestones and benchmark requirements.

The 2017 New Rules for the Digital Age report from Deloitte found that only 5 percent of the companies surveyed said they have strong digital leadership development programs and a clear majority (65 percent) said they have no significant program to drive digital leadership skills. Josh Bersin, a principal at the Bersin by Deloitte research group, says the challenge is that companies don't realize how much more complicated digital transformation is than simply acquiring new technology. "Digital technology is easy to buy, but once you turn it on it changes the way you work and how you deliver products and services," Bersin told CIO.com. "From the CIO's perspective, it may seem relatively easy to implement artificial intelligence (AI), social media and other new technology, but these things have a disruptive impact on the workplace." For example, the study found that companies feel 31 percent "less ready" to redesign their organization around digital business models than they did last year.