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There's an arms race among public cloud providers to provide businesses with the best machine learning capabilities. Enterprises are increasingly interested in creating intelligent applications, and companies like Amazon, Microsoft and Google are rushing to help meet their needs. Google fired its latest salvo on Tuesday, announcing a set of enhancements to its existing suite of cloud machine-learning capabilities. The first was a new Jobs API aimed at helping match job applicants with the right openings. In addition, the company is slashing the prices on its Cloud Vision API and launching an enhanced version of its translation API.

That's an interesting question, and I try to answer this is a very general way. The tl;dr version of this is: Deep learning is essentially a set of techniques that help we to parameterize deep neural network structures, neural networks with many, many layers and parameters. And if we are interested, a more concrete example: Let's start with multi-layer perceptrons (MLPs)... On a tangent: The term "perceptron" in MLPs may be a bit confusing since we don't really want only linear neurons in our network. Using MLPs, we want to learn complex functions to solve non-linear problems. Thus, our network is conventionally composed of one or multiple "hidden" layers that connect the input and output layer.

I remember a time, before the Web, when you would look for relevant academic papers by reading large books with tiny fonts that would list all relevant work in a given area published in a given year. Of course, you could have just gone to the shelves and checked the research articles themselves but, by for a slow human being, this would have been just too time consuming. These large volumes contained nothing by "metadata": lists of article titles, authors, keywords… They were tremendously valuable to the researchers. One of the earliest applications of computers was to help manage document collections. In this sense, the Web and Google are very naturally applications for computers.

The next big thing in AI may not be getting a machine to perform a task--it might be requiring the machine to communicate why it took that action. For instance, if a robot decides to take a certain route across a warehouse, or a driverless car turns left instead of right, how do we know why it made that decision? According to Manuela Veloso, professor of computer science at Carnegie Mellon University, explainable AI is essential to building trust in our systems. Veloso, who works with co-bots (collaborative robots), programs the machines to verbalize their decision process. "We need to be able to question why programs are doing what they do," Veloso said.

Research Scientist @ AYLIEN Sebastian is a PhD student in Natural Language Processing at the Insight Research Centre for Data Analytics and a research scientist at AYLIEN. Previously, he worked with Microsoft, IBM Extreme Blue, and Google Summer of Code. His main research interest lies in using Deep Learning for domain adaptation in NLP. In his spare time, he loves to travel, read, blog, and learn new languages.

Some of the world's leading technology professionals have gathered for the third World Internet Conference, being held in Wuzhen, Zhejiang province, and top of everyone's agenda is the booming growth of artificial intelligence (AI), as it continues to penetrate every aspect of business and daily life. From customer services to wealth management, to reshaping the jobs market by replacing low-skilled workers, the sector is growing at an unprecedented pace, said analysts gathered for the event. Big names speaking during the three-day forum include Facebook's vice president Vaughan Smith and smartphone maker Huawei's chief executive Richard Yu. Senior officials from Baidu, Tencent, and Alibaba, which owns the South China Morning Post, are also attending. Hyde Chen, an analyst with UBS' chief investment office, told the Post the biggest issue facing the image of the AI sector is balancing the numbers of those being put out of work by the technology, with the jobs being created in its development. "Jobs with three characteristics are at high risk of being replaced: low-skilled roles, those doing repetitive tasks, and jobs that are predictable. AI [applications] will be doing these jobs in a more effective way in future," Chen said.

I went on Botlist.co and tried every shopping bot there was, including ebay ShopBot. There was nothing personal in the experience they delivered, and I didn't get the help I was looking for. Instead, I found myself browsing the same old products on a new platform. Even Ebay Shopbot -- the best by far -- did barely more than replicate its giant store within Messenger. It's a good chatbot compared to what's on the market.

Google is known for its funky experiments with web technology -- just take a look at its Chrome Experiments page, where the company has accumulated over a thousand creative web apps using its web technology. A lot of Google's products today use machine learning to better serve its users. For example, when searching for'pizza' in Google Photos shows all the pictures in your library of pizza. It knows what the dish looks like by analyzing thousands of pictures of the food and recognizing patterns between them. The technology might be complex, but the company is now making it easy to play around with it.

What Artificial Intelligence Can and Can't Do Right Now By Andrew Ng, Harvard Business Review, November 16, 2016 This post has been viewed 20 times. After understanding what AI can and can't do, the next step for executives is incorporating it into their strategies. That means understanding where value is created and what's hard to copy. The AI community is remarkably open, with most top researchers publishing and sharing ideas and even open-source code. Among leading AI teams, many can likely replicate others' software in, at most, 1–2 years.