Picking things up is such a fundamental skill for robots, and robots have been picking up things for such a long time, that it's sometimes difficult to understand how challenging grasping still is. Robots that are good at grasping things usually depend on high quality sensor data along with some amount of advance knowledge about the things that they're going to be grasping. Where grasping gets really tricky is when you're trying to design a system that can use standardized (and affordable) grippers and sensors to reliably pick up almost anything, including that infinitely long tail of objects that are, for whatever reason, weird and annoying to grasp. One way around this is to design grasping hardware that uses clever tricks (like enveloping grasps or adhesives) to compensate for not really knowing the best way to pick up a given object, but this may not be a long-term sustainable approach: Solving the problem in software is much more efficient and scalable, if you can pull it off. "I've been studying robot grasping for 30 years and I'm convinced that the key to reliable robot grasping is the perception and control software, not the hardware," Ken Goldberg, a professor of robotics and director of the AUTOLAB at UC Berkeley, told us this week.

Moore's law famously declared that the processing power of microchips will grow exponentially. What today's keyboard warriors will find is that human brainpower and waking hours do not. As computers replace calculators, our tools become more capable, yet from society to software, can our very physical forms possibly keep up? An exhibitor displays Musio, a deep learning based artificial intelligence robot manufactured by AKA Study Limited, during the TechCrunch Disrupt 2015 conference in London, U.K., on Monday, Dec. 7, 2015. The dawn of the new millennium saw a generation raised by spreadsheets and solitaire.

For R users, there hasn't been a production grade solution for deep learning (sorry MXNET). This post introduces the Keras interface for R and how it can be used to perform image classification. The post ends by providing some code snippets that show Keras is intuitive and powerful. Last January, Tensorflow for R was released, which provided access to the Tensorflow API from R. This was signficant, as Tensorflow is the most popular library for deep learning. However, for most R users, the Tensorflow for R interface was not very R like.

Deep-learning systems, which use artificial networks modelled on the human brain, tend to only able to do one task at a time. But now, for the first time, Google has created a system that can do eight tasks at once, including image and speech recognition and language translation. The research suggests that AI could be taught to multitask in the future and become more like humans in the way they think. The network is capable of learning eight different tasks simultaneously. It can detect objects in images, provide captions, recognise speech, translate between four pairs of languages, and do grammatical constituency parsing at the same time.

I've split this post into four sections: Machine Learning, NLP, Python, and Math. For future posts, I may create a similar list of books, online videos, and code repos as I'm compiling a growing collection of those resources too. What's the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?

Which machine learning algorithm should I use?

After just confirming its plans to help Volvo create self-driving cars, NVIDIA has now revealed that it's also working with another leading car manufacturer. Announcing a partnership with Volkswagen, the tech company states its artificial intelligence and deep learning tech will be used to help VW expand its AI business beyond just autonomous vehicles. While this collaboration may sound surprising, the move actually looks to help expand Volkswagen's existing AI-focused research division - The VW Data Lab. The two companies have suggested that this sharing of tech could be used to help the pair optimize traffic flow in cities and even to devise solutions that make human and robot collaboration easier. In a statement, Volkswagen's CIO Dr. Martin Hofmann says that AI is "the key to the digital future of the Volkswagen Group" describing its collaboration with NVIDIA as "a major step" in expanding the company's proficiency in the field.

Lee Se-dol (L), one of the greatest modern players of Go, at a post-match press conference after being beaten by Google's DeepMind AI computer in March. Deep Instinct co-founder and Chief Technology Officer Eli David says the day when a computer is as smart as a human, or smarter, may be a lot closer than many think. AI programs, "are on an accelerating path of growth in their capabilities. In the near future, they will reach near human or superhuman capabilities," David told me in an interview. As AI gets smarter, it's crucial to stay ahead of criminals and terrorists.

Smartphone chipmaker Nvidia has partnered with automotive safety company Autoliv and automotive company Volvo to develop self-driving software and hardware. "This cooperation with NVIDIA places Volvo, Autoliv, and Zenuity at the forefront of the fast-moving market to develop next generation autonomous driving capabilities and will speed up the development of Volvo's own commercially available autonomous drive cars," Håkan Samuelsson, President and Chief Executive of Volvo Cars, stated in the press release Tuesday. The press release further stated the companies will work together to create artificial intelligence-based deep learning solutions for object detection, recognition the anticipation of threats and safe navigation. The self-driven car developed by Nvidia, Volvo, Autoliv, and Zenuity, will use the Nvidia Drive PX2 artificial intelligence based self-driving system which was first showcased at Consumer's Electronic Show (CES) 2016.

When it comes to AI, Sony isn't mentioned in the conversation like Google, Amazon and Apple are. However, let's remember that it was on the forefront of deep learning with products like the Aibo robot dog, and has used it recently in the Echo-like Xperia Agent (above) and Xperia Ear. Sony is finally ready to share its AI technology with developers and engineers to incorporate them into their products and services, it has revealed. Sony joins its rivals Google, Facebook, Microsoft, Apple, Amazon and others in making its AI open source. On one hand, it will help developers build smarts into products, and on the other, Sony is hoping that developers will "further build on the core libraries' programs," it writes.