Tesla says that all the cars it produces will now be capable of driving completely autonomously. A video released on Thursday shows a demo of a car driving itself – with a human in the seat "only there for legal reasons". The car appear to pull out of a garage before a man gets in. The vehicle then sets off, the steering wheel moving while the driver's hands remain hovering just above it. The video ends with the car pulling off a manoeuvre tricky to many human drivers: parallel parking. The video – which is admittedly rather stylised – follows Tesla's announcement on Wednesday that all cars produced in its factories from now will be equipped with "the hardware needed for full self-driving capability at a safety level substantially greater than that of a human driver".

The term "robo-advisor" was essentially unheard-of just five years ago, but it is now commonplace in the financial landscape. The term is misleading and doesn't involve robots at all. Rather, robo-advisors (companies such as Betterment, Wealthfront, and others) are algorithms built to calibrate a financial portfolio to the goals and risk tolerance of the user. Users enter their goals (for example, retiring at age 65 with 250,000.00 in savings), age, income, and current financial assets. The advisor (which would more accurately be referred to as an "allocator") then spreads investments across asset classes and financial instruments in order to reach the user's goals. The system then calibrates to changes in the user's goals and to real-time changes in the market, aiming always to find the best fit for the user's original goals.

This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations. Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py).

DeepMind is now capable of teaching itself based on information it already possesses. In a significant step forward for artificial intelligence, Alphabet's hybrid system -- called a Differential Neural Computer (DNC) -- uses the existing data storage capacity of conventional computers while pairing it with smart AI and a neural net capable of quickly parsing it. TNW NYC is our New York technology event for anyone interested in helping their company grow. "These models can learn from examples like neural networks, but they can also store complex data like computers," wrote DeepMind researchers Alexander Graves and Greg Wayne. Much like the brain, the neural network uses an interconnected series of nodes to stimulate specific centers needed to complete a task.

Two years ago Stephen Hawking told the BBC that the development of full artificial intelligence, could spell the end of the human race. His was not the only voice warning of the dangers of AI - Elon Musk, Bill Gates and Steve Wozniak also expressed their concerns about where the technology was heading - though Professor Hawking's was the most apocalyptic vision of a world where robots decide they don't need us any more. What all of these prophets of AI doom wanted to do was to get the world thinking about where the science was heading - and make sure other voices joined the scientists in that debate. That they have achieved that aim was evident on Wednesday night at an event in Cambridge marking the opening of the Centre for the Future of Intelligence, designed to do some of that thinking about the implications of AI. And Professor Hawking was there to help launch the centre.

This is the fourth installment in a series that aims to explain and peer inside the black-box algorithms that increasingly dominate our lives. Early computers were mostly just big calculators, helping us process large numbers. Now, however, computers are so powerful that they are learning how to make decisions on their own in the rapidly growing field of artificial intelligence. But AI-enabled machines are only as smart as the knowledge they have been fed. Microsoft learned that lesson the hard way earlier this year when it released an AI Twitter bot called Tay that had been trained to talk like a Millennial teen.

Microsoft's speech-recognition AI could eventually be used to enhance Cortana's accessibility features, say, for deaf people. Microsoft researchers have developed a system that recognizes speech as accurately as a professional human transcriptionist. Researchers and engineers from Microsoft's Artificial Intelligence and Research group have set a new record in speech recognition, achieving a word error rate of 5.9 percent, down from the 6.3 percent reported a month ago. The word error rate is the percentage of times in a conversation that a system, in this case a combination of neural networks, mishears different words. Microsoft's system performed as well as humans who were asked to listen to the same conversations.

Looking back on Dennis Mortensen and x.ai's update at our PSFK conference and the future of artificial intelligence At PSFK 2015, Dennis Mortensen presented x.ai, the AI email assistant affectionately named Amy that facilitates email scheduling with the ebullient personality of a human personal assistant. Over the last couple years, the conversation around artificial intelligence has become more nuanced working its way into everything from care taking, to finance tools, to customer service. Along with this, there has been a huge proliferation of branded bots, finding their place in retail and service, and helping customers get what they need more efficiently. Amid the increasing awareness and interest in what artificial intelligence means for us, we want to look back at where this is coming from. Check out this talk from 2015 highlighting some of the challenges x.ai's assistant Amy is able to tackle and the promise AI holds for the workforce of the future.

AI imbued throughout the enterprise is the major technology trend for 2017, according industry watcher Gartner Group. The company announced its prognostications today at the annual Gartner Symposium/ITxpo in Orlando. "Gartner's top 10 strategic technology trends for 2017 set the stage for the Intelligent Digital Mesh," said David Cearley, vice president and Gartner Fellow. "The first three embrace'Intelligence Everywhere,' how data science technologies and approaches are evolving to include advanced machine learning and artificial intelligence allowing the creation of intelligent physical and software-based systems that are programmed to learn and adapt. The next three trends focus on the digital world and how the physical and digital worlds are becoming more intertwined. The last four trends focus on the mesh of platforms and services needed to deliver the intelligent digital mesh."

"Although remarkable progress has been made in the field of computer vision, the vast majority of both these theories and technologies have yet to transition to the real automotive world, where you have a huge variety of road infrastructure, side buildings, road signs, vehicles, and most importantly, human driving behaviors," stated Nexar co-founder and CTO, Bruno Fernandez-Ruiz. "At Nexar, we've assembled a first class technical team dedicated to propelling the automotive industry into the future using deep learning. Alongside fellow industry leaders participating in the BDD Consortium, the Nexar team will apply its rapidly expanding data network and industry know-how to infuse state-of-the-art deep learning techniques for the optimal and safest driving experience." Since its launch in February 2016, Nexar has tracked upwards of 20 million miles and recorded more than a half-million instances of driving incidents worldwide. This has provided the company with a large and diverse panel of data of real-world driving conditions, a databank that continues to expand every day and serves as a vital resource for the future success of the autonomous car industry.