Summary: We are swept up by the rapid advances in AI and deep learning, and tend to laugh off AI's failures as good fodder for YouTube videos. But those failures are starting to add up. It's time to take a hard look at the weaknesses in AI and where that's leading us. Trying to spot new themes and directions I was trolling through some data and I found this. This is the Google Trends chart of searches for'Artificial Intelligence' for the last four years.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

In the future imagined by science fiction, artificial intelligence will reign supreme and take over pretty much everything humans can do. Frankly this sci-fi vision isn't helpful when it comes to applying technology because it distracts us from thinking about what people do well and what advanced techniques such as deep learning do well. In the world of data science, great strides are being made in the area of deep learning. We've made so much progress that it is easy to think that instead of having to embrace data science as a discipline, we can somehow wait a little big longer and have a Watson-like box to perform all of these tasks for us. If you think this way, you are going to miss the boat.

Machine-learning experts are in short supply as companies in many industries rush to take advantage of recent strides in the power of artificial intelligence. Google's CEO says one solution to the skills shortage is to have machine-learning software take over some of the work of creating machine-learning software. At Google's annual developer conference today, Pichai introduced a project called AutoML coming out of the company's Google Brain artificial intelligence research group. Researchers there have shown that their learning algorithms can automate one of the trickiest parts of the job of designing machine-learning software to take on a particular task. In some cases, their automated system came up with designs that rivals or beats the best work of human machine-learning experts.

Engineer at @Parietal_INRIA, contributes to scikit-learn.

For the past few days I've been working on how to implement recursive neural networks in TensorFlow. Recursive neural networks (which I'll call TreeNets from now on to avoid confusion with recurrent neural nets) can be used for learning tree-like structures (more generally, directed acyclic graph structures). They are highly useful for parsing natural scenes and language; see the work of Richard Socher (2011) for examples. More recently, in 2014, Ozan İrsoy used a deep variant of TreeNets to obtain some interesting NLP results. In RNNs, at each time step the network takes as input its previous state s(t-1) and its current input x(t) and produces an output y(t) and a new hidden state s(t).

If imitation is the sincerest form of flattery, OpenAI's newest robot system should leave humanity blushing. Not only can it successfully replicate human behaviors, it can do so after just a single demonstration of the task. The research company co-founded and chaired by Elon Musk used two separate neural networks to develop its one-shot imitation learning system. The first, a vision network, analyzes an image from the robot's camera to determine the location of objects in reality (in OpenAI's video example, these objects are blocks of wood on a table). The network is able to do this despite never having seen the actual table or blocks before.

The UK's National Data Guardian (NDG) has criticised the National Health Service (NHS) for its partnership with Google's artificial intelligence company DeepMind. According to a letter published by Sky News yesterday, Google DeepMind has received personal health data from 1.6 million NHS patients on a legally inappropriate basis. An investigation is currently being carried out by the Information Commissioner's Office (ICO), with the ICO telling Sky News that it is "close to conclusion." The letter from Dame Fiona Caldicott of the NDG was sent to NHS Royal Free Trust medical director Stephen Powis on February 20th. DeepMind was given access to NHS records in 2015 to test a smartphone app called Streams on the legal basis that it was offering "direct care."

The Guerrilla Guide to Machine Learning with R; 5 Machine Learning Projects You Can No Longer Overlook, May; The Two Phases of Gradient Descent in Deep Learning; HDFS vs. HBase: All you need to know; Must-Know: What are common data quality issues for Big Data and how to handle them?

Deep learning is becoming an increasingly important part of the artificial intelligence (AI) toolkit, yet it is often misunderstood. Although it supports a developing market and is often touted as an important direction for corporate innovation, it needs to be viewed in context. This is a developing area of technology that is rapidly creating its own domain, becoming richer and increasingly varied. We will soon see new opportunities based specifically on deep learning. The market for deep learning solutions continues to expand.