Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively 6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer. The human brain is capable of remarkable acts of perception while consuming very little energy.

The client required a solution involving Artificial intelligence to disintermediate the process between vehicle buyers/seller and the platform. The client required a solution involving Artificial intelligence to disintermediate the process between vehicle buyers/seller and the platform. Maruti Techlabs designed a solution using SMS-Bot. In the first month of implementation, SMS-Bot dispatched 9793 initial offers and client observed 5.5% increase in the initial offers generated. Maruti Techlabs designed a solution using SMS-Bot.

What do you get if you cross aspirin with ibuprofen? Harvard chemistry professor Alán Aspuru-Guzik isn't sure, but he's trained software that could give him an answer by suggesting a molecular structure that combines properties of both drugs. The AI program could help the search for new drug compounds. Pharmaceutical research tends to rely on software that exhaustively crawls through giant pools of candidate molecules using rules written by chemists, and simulations that try to identify or predict useful structures. The former relies on humans thinking of everything, while the latter is limited by the accuracy of simulations and the computing power required.

They say what happens in Vegas stays in Vegas. But that would be a wild mistake if applied to this circumstance. If you're a business leader or entrepreneur intent on staying competitive in the years to come, you'd best pay close attention. Last month, IBM hosted the World of Watson conference in Las Vegas aimed at raising awareness and educating participants about advances in computing over the last decade. But how did we get to this point?

Level 5 self-driving tech is the highest variety, which aims for fully autonomous vehicles with no need of human steering mechanisms. AdasWorks, a Budapest-based startup launched in 2015, is charing its name to AImotive and expanding to a new U.S.-based Mountain View office to further its efforts in making Level 5 autonomy a lived reality for people and cars. AImotive's goal is to offer a full suite of self-driving enabling technologies to carmakers, via its full-stack aiDrive offering, which includes a number of AI-powered engines for learning to identify different objects organized by class, landmark-based location recognition, real-time object tracking and control of not only gas and brake, but other car features including headlights and horn. AImotive also offers aiKit, which covers collection, simulation and data testing; and aiWare, the hardware designed for use in vehicles, provided embedded computing for advanced neural networks used to drive its AI. AImotive's aim is to provide scalable Level 5 autonomy to carmakers regardless of chips used, with technology that can truly work at the global level, across different geographies, climates, environmental conditions and cultures.

I don't want to tell data scientists and AI developers what to do in any given situation. I want to give scientists and engineers tools for thinking about problems. We surely can't predict all the problems and ethical issues in advance; we need to be the kind of people who can have effective discussions about these issues as we anticipate and discover them.

Experts from across the AI world came together in New York for the O'Reilly AI Conference in New York 2016. Below you'll find links to highlights from the event. Building reliable, robust software is hard, says Peter Norvig. It's even harder when we move from deterministic domains, such as balancing a checkbook, to uncertain domains, such as recognizing speech or objects in an image. Watch "Software engineering of systems that learn in uncertain domains."

Artificial intelligence is learning to take on an increasing number of sophisticated tasks. Google Deepmind's AI is now able to imitate human speech, and just this past August IBM's Watson successfully diagnosed a rare case of leukemia. Rather than viewing these advances as threats to job security, we can look at them as opportunities for AI to fill in critical gaps in existing service providers, such as mental healthcare professionals. In the US alone, nearly eight percent of the population suffers from depression (that's about one in every 13 American adults), and yet about 45 percent of this population does not seek professional care due to the costs. There are many barriers to getting quality mental healthcare, from searching for a provider who's within your insurance network to screening multiple potential therapists in order to find someone you feel comfortable speaking with.

Machine Learning Algorithms From Scratch was designed for you. The book that finally unlocks how machine learning algorithms work. You don't need the math. Everything is explained in simple words, and we work in the language you do know: code. You don't need to be a Python master.

In a 2013 report by IBM, the amount of data created everyday was estimated to be roughly 2,500,000TB. It very likely greatly exceeds this now, as wearables, AI, and connected devices have increasingly embedded themselves into society, gathering a veritable tidal wave of additional information for organisations to interrogate. This data comes in three forms: unstructured, semi-structured, and structured. Since the dawn of IT, structured data has been the main resource of analysts. Even today, this is the case.