This story was delivered to BI Intelligence Apps and Platforms Briefing subscribers. To learn more and subscribe, please click here. SoundHound, a startup that released its first voice assistant in March 2016, has raised $75 million in new funding, according to a Tuesday announcement. The company's AI platform, called Houndify, was built on top of 10 years of research and development (R&D) and powers SoundHoud's voice assistant app Hound. SoundHound plans to use this round of financing to make headway with its "collective AI" strategy, which enables developers using the Houndify platform to easily leverage information from third-party companies in order to improve their implementations of Houndify.

Having a personal assistant is often a signal that you're a member of the professional elite. The connotation is that only C-level executives and VPs are "worthy" of their own personal assistant handling their schedule.

If you have ever competed in a Kaggle competition, you are probably familiar with the use of combining different predictive models for improved accuracy which will creep your score up in the leader board. While it is widely used, there are only a few resources that I am aware of where a clear description is available (One that I know of is here, and there is also a caret package extension for it). Therefore, I will try to workout a simple example here to illustrate how different models can be combined. The example I have chosen is the House Prices competition from Kaggle. This is a regression problem and given lots of features about houses, one is expected to predict their prices on a test set.

When Tuomas Sandholm began studying poker to research artificial intelligence 12 years ago, he never imagined that a computer would be able to defeat the best human players. "At least not in my lifetime," he says. But Sandholm, a computer science professor at Carnegie Mellon University, along with doctorate student Noam Brown, developed AI software capable of doing just that. The program, called Libratus, successfully defeated four professional poker players in a 20-day competition that ended on Jan. 30. After playing 120,000 hands of heads-up, no-limit Texas Hold'em, Libratus was ahead of its human challengers by more than $1.7 million in chips.

Hackers have stolen the login details of 2.5 million PlayStation and Xbox users. The email addresses and passwords of gamers who had been using the unofficial Xbox360 ISO and PSP ISO forums, which players use to share links to download free and pirated versions of games, were exposed by the cybercriminals behind the hack. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los ...

The machines are here, and they're smarter than ever. Scientists have proven that machine learning and artificial intelligence (AI) can perform some tasks better than humans. Where health and neuroscience are concerned, one doctor says this is "a good thing." By using advanced scanners and AI, a team of scientists from research labs all across the United States created the Human Connectome Project (HCP), the most detailed map of the human brain's circuitry. The AI programs were able to learn 100 new regions of the brain, while monitoring data from 210 adult test subjects.

Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.

If you play video games, imagine how much you sometimes empathize with the character you're controlling. You may even forget the separation between the two of you, experiencing the world as that character. Consider whether your control in these moments is different, both at a high level and in tiny movements, than what the character would do if you had simply written down a set of rules for it to act by. In that difference lies the promise of this method for developing character AI. In psychology research methodology, there's a broad consensus that if you want to know what someone would do in a situation, you don't ask them what they would do.

Over the past year I've reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and TensorFlow. If I had cast my net even wider, I might well have covered a few other popular frameworks, including Theano (a 10-year-old Python deep learning and machine learning framework), Keras (a deep learning front end for Theano and TensorFlow), and DeepLearning4j (deep learning software for Java and Scala on Hadoop and Spark). If you're interested in working with machine learning and neural networks, you've never had a richer array of options. Essentially, a machine learning framework covers a variety of learning methods for classification, regression, clustering, anomaly detection, and data preparation, and it may or may not include neural network methods. A deep learning or deep neural network (DNN) framework covers a variety of neural network topologies with many hidden layers.

As stories of robot teachers (2010), AI game-show winners (2011), Big Data election victories (2012) objects communicating with one another (2013), self-driving cars (2014), robot-run hotels (2015), delivery drones (2016) have become the new normal, the outgoing Obama administration has made policy recommendations premised on a future American economy deeply affected by AI-driven automation, with 47% of US jobs estimated to be at high-risk of computerization in the next 20 years. It's perhaps therefore unsurprising that there is an abundance of words, terminology and concepts floating around the automation and artificial intelligence sphere, often achieving'buzzword' status and cited in isolation – when really they ought to be considered in concert. We can think of automation as being the consolidation of a number of such'concepts', intertwined and often related to one another: this is my take on how some of these fit together and the level of maturity that flows therefrom. We begin by plotting the number and complexity of tasks performed (X-axis) as part of each'concept' against the extent to which the behaviours exhibited are'human-like' (Y-axis). Next we demarcate a zoning of primary "operation modes": concepts relating to direct interaction with humans (mode of interaction in green); gaining and producing knowledge (thinking in purple); and, finally, interaction with other systems (machine-machine interaction in orange).