Sure, Apache Spark looks cool, but does it live up to the hype? Is there anything you can actually do with it? Actually, there are some pretty cool use cases going on right now. One of the best features of modern programming languages is that many of them offer interactive shells, from Bash to Python to Scala. Instead of a time-consuming write/compile/test/debug cycle, you can try out your ideas in the shell immediately.

There are some smartphone apps that will allow you to point your camera at an object and receive information about it… but typically those apps have to connect to the internet. But Google and chip maker Movidius want to bring that sort of functionality to mobile devices without requiring an internet connection. The company's have announced a deal which will have Google licensing the latest Movidius chip and software for mobile devices. The low-power chips are said to be far more energy efficient than most processors used for "neural network" or "machine learning" systems. A neural Network allows computers to "learn" things that they might not have been explicitly taught.

RMIT's Centre for Innovative Justice, Victoria Legal Aid and the National Directors of Legal Aid Commissions are holding a forum to showcase new approaches and provide a glimpse of current work in design thinking, artificial intelligence and the law, including a demonstration of the new online dispute resolution platform Rechtwijzer 2.0. We aim to cultivate a community of practice and spark collaboration between disciplines to improve access to justice.

Andy Rubin, who co-founded Android and jump-started Google's robotics efforts, imagines a future where artificial intelligence is so powerful that it powers every connected device. Speaking at Bloomberg's Tech Conference in San Francisco today, Rubin said a combination of quantum computing and AI advancements could yield a conscious intelligence that would underpin every piece of technology. "If you have computing that is as powerful as this could be, you might only need one," Rubin says. "It might not be something you carry around; it just has to be conscious." It sounds outlandish and theoretical, and it is.

In fact, unit shipments of artificial intelligence (AI) systems used in infotainment and ADAS systems are expected to rise from just 7 million in 2015 to 122 million by 2025, according to IHS Inc. (NYSE: IHS), the leading global source of critical information and insight. The attach rate of AI-based systems in new vehicles was 8 percent in 2015, and the vast majority were focused on speech recognition. However, that number is forecast to rise to 109 percent in 2025, as there will be multiple AI systems of various types installed in many cars. "An artificial-intelligence system continuously learns from experience and by its ability to discern and recognize its surroundings," said Luca De Ambroggi, principal analyst-automotive semiconductors, IHS Technology. "It learns, as human beings do, from real sounds, images, and other sensory inputs. The system recognizes the car's environment and evaluates the contextual implications for the moving car." Specifically in ADAS, deep learning -- which mimics human neural networks -- presents several advantages over traditional algorithms; it is also a key milestone on the road to fully autonomous vehicles.

We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure. Finally, we note the relevance of the theory to the design of artificial intelligent agents, highlighting connections between neuroscience and machine learning.

In the age-old tech struggle between open and controlled systems, Apple has realized that when it comes to artificial intelligence, it needs to edge toward open. The computer giant has announced it will be opening up its digital assistant Siri to third-party apps and at the same time has put out an API to its artificial intelligence technology. Realistically, the company has been given little choice: Amazon's Alexa has taken off, in large part due to it opening up to other companies, and Google's artificial intelligence systems have streaked ahead of Apple and Siri because when it comes to such a complex and wide-ranging interplay of information and action, broader is better. Where Siri was once a wonder – it worked where other systems didn't – it risks becoming an also-ran, with only Apple fanbois crowding round it in excitement at the latest nerd joke. Not that Apple is taking the news well.

The White House and New York University's Information Law Institute, with support from Google Open Research, Microsoft Research and the MacArthur Foundation will host a major public symposium to address the near-term impacts of AI technologies across social and economic systems. The focus will be the challenges of the next 5-10 years, specifically addressing four themes: social inequality, labor, healthcare, and ethics. Leaders from industry, academia, and civil society will share ideas for technical design, research and policy directions.

Filmmaker Oscar Sharp and AI expert Ross Goodwin created a neural network named Benjamin (its own choice!) over the course of a year. Once the network was up and running, Benjamin wrote the script based on just a few prompts. Then, they made it into a movie. And yes, that is the guy from Silicon Valley.

A team of researchers led by MIT's Katie Bouman have developed a new computer algorithm that could help astronomers generate the first true image of a black hole. At present, astronomers rely of imaginative minds of artists to create a clear image of a black hole. Black holes are very compact and are very far away from Earth, making it harder for astronomers to create a high-quality photo. "Taking a picture of the black hole in the center of the Milky Way galaxy is equivalent to taking an image of a grapefruit on the moon, but with a radio telescope," Bouman explained in a press release. For years, researchers have been using radio wavelengths to detect and explore distant objects due to the ability of radio frequencies to penetrate through galactic dust.