A robot that intentionally draws blood can be a real prick. Created by roboticist Alexander Reben, this robot suits this very classification. He's made a robot that he claims to be the first robot to break Isaac Asimov's (an American science fiction author) First Law. The law states that a robot may not harm a human, nor may it be a bystander in any harm brought upon a human. Reben's robot is composed of a uArm where the actuated gripper has been replaced with a small prick.

Romit Shah, of Japanese financial holding company Nomura, has raised his rating on the shares in silicon specialist Nvidia, after spending time with CEO Jen-Hsun Huang and hearing about the company's plans for the future. The Wall Street analyst said that Huang "was very enthusiastic about the prospects for the data center business, as hyperscale companies quickly adopt throughput computing in an effort to accelerate workload performance." Shares jumped three percent on the news. Nvidia's year-on-year data center revenues grew by 63 percent last quarter, mostly due to the "broad adoption" of Tesla M40 GPU accelerator. Nomura believes the company has an 80 percent share of the accelerator market, which accounts for less than one percent of overall data center spending.

In my last post about Bot Framework, I wrote an overview about how you can create a weather bot.. in this post, I'm starting a series where I'll go deep into each part of building a bot using the Bot Framework.. In this post, I'll deep-dive into Language Understanding Intelligence Service (LUIS), and how we can use it to understand the user message. LUIS is an API built by Microsoft and introduced as a part of Microsoft Cognitive Services (A set of intelligence services).. it's a Machine Learning API that allows you to create an app with some intents and entities and then train it on some utterances and then start using it in your apps or websites and most importantly… your bots! In this post, I'll guide you to create a LUIS app focusing on the bots scenarios, let's start! Well, this the most important part of building an intelligent service than can help you understanding the language, what's your intents?

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.