Google executives talk about the company's future in artificial intelligence. Whenever we talk about artificial intelligence, someone inevitably mentions Skynet, the destructive machine system in the Terminator movies. But we shouldn't be worried about a dystopian rise of the robots. Because we're so far away from anything that would even resemble that scenario, he said Friday at Google's I/O developer conference near the company's HQ in Mountain View, California. "I think researchers in the field don't really put much thought into that," he said.

Using its Xbox Kinect 3D sensor, a camera, a laptop and a laser pointer, Watch-Bot observed a week's worth of human activity in a kitchen and an office. During that time, it collected 458 videos -- about half of which included someone human deliberately "forgetting" to do something. The team then made Watch-Bot analyze the videos and use its unsupervised learning algorithm to determine which human actions were intentional and which ones -- like leaving the milk out on the counter -- were accidental. Using probabilistic learning models, Watch-Bot was able to independently figure out which actions the humans were forgetting. When Watch-Bot does notice the clumsy human in the room forgot to do something, it quietly highlights that item with the laser pointer until it is put away or dealt with.

On Wednesday, Google introduced its new personal assistant, Google Home, which will listen to your voice and provide information on demand, much like the popular Amazon Echo. Apple's Siri and Microsoft's Cortana have been chatting with people for years -- and one expert predicts that voice-driven technology will have startling effects on our social interactions moving forward. "There used to be a disconnect between how we interacted with, say, our desktop computers and our family," Illah Nourbakhsh, a professor of robotics at Carnegie Mellon University, told The Huffington Post. "We interacted with that computer only when we wanted to. Now technology is pervading the home environment. Your machines can interrupt and interact with you day or night, should they choose to."

From movie shoots to search-and-rescue operations to your neighborhood park, drones are everywhere. This week, the Federal Aviation Administration released data revealing the exact whereabouts of the country's registered drones. Among the findings: Los Angeles County is the drone capital of America, with 12,250 registered drones. In second place is Arizona's Maricopa County, home to a number of Phoenix-based aerial photography companies. Looking at the data from a per capita perspective, Hinsdale County, Colorado wins out, with 5.2 drones for every 1,000 people.

ANSYS has released its SeaScape architecture for product developers. SeaScape is claimed to allow organisations to innovate faster than the ever by bringing together the advanced computer science of elastic computing, big data and machine learning and the physics-based world of engineering simulation. Engineering simulation generates huge amounts of data - more than most organisations can effectively leverage for future product designs. At the same time, engineering supercomputing resources are not keeping pace with the demand for higher fidelity simulations needed for increasingly complex products. By leveraging such big data technologies as elastic compute and map reduce, SeaScape is said to provide an infrastructure to address these issues in the context of almost any engineering design objective.

Unsupervisedly learned word embeddings have been exceptionally successful in many NLP tasks and are frequently seen as something akin to a silver bullet. In fact, in many NLP architectures, they have almost completely replaced traditional distributional features such as Brown clusters and LSA features. Proceedings of last year's ACL and EMNLP conferences have been dominated by word embeddings, with some people musing that Embedding Methods in Natural Language Processing was a more fitting name for EMNLP. Semantic relations between word embeddings seem nothing short of magical to the uninitiated and Deep Learning NLP talks frequently prelude with the notorious \(king - man woman \approx queen \) slide, while a recent article in Communications of the ACM hails word embeddings as the primary reason for NLP's breakout. This post will be the first in a series that aims to give an extensive overview of word embeddings showcasing why this hype may or may not be warranted.

We are very excited with all the positive feedback about BigML's latest release. It was a huge milestone to announce WhizzML, the very first domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and sharing them with others is now publicly available. Thanks to everyone who attended. For those who couldn't make it, we'll publish the video recording soon. More that ever, BigML is committed to its mission to make Machine Learning beautifully simple for everyone.

Intelligent Machines and the Future of Recruitment The impact of AI, Big Data and semantic technologies on the labour market, talent acquisition and the way we work. The rise of machine learning, search engines, semantic technologies and the large amounts of available information are changing the labour market as we know it now. What is the state-of-the-art in these technologies and what can we expect in the near future? How will we use it to define policies? How will it change the way we hire and how we work?

These tools allow machines to read and understand English text (such as text you type into a browser to do a Google search). And the Parsey McParseface program implements SyntaxNet in English (it learned from an annotated collection of old newswire stories called The Penn Treebank Project). Here's an example of how it parses and analyzes an English sentence:Using deep neural networks, SyntaxNet is implemented in Google's TensorFlow (see Google open-sources its TensorFlow machine learning system). On a standard benchmark consisting of randomly drawn English newswire sentences ("Penn Treebank"), Parsey McParseface recovers individual dependencies between words with over 94% accuracy, Google says.

Google has just released two powerful natural language understanding tools for free, open-source use by anyone. These tools allow machines to read and understand English text (such as text you type into a browser to do a Google search). SyntaxNet is a "syntactic parser" -- it allows machines to parse, or break down, sentences into their component parts of speech and identify the underlying meaning). And the Parsey McParseface program implements SyntaxNet in English (it learned from an annotated collection of old newswire stories called The Penn Treebank Project). Here's an example of how it parses and analyzes an English sentence:Using deep neural networks, SyntaxNet is implemented in Google's TensorFlow (see Google open-sources its TensorFlow machine learning system).