Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology. Deep learning has achieved some very impressive accomplishments of late. I won't review them here, but chances are you already know about them anyhow. Given these high-profile successes, one could forgive the uninitiated (be they laymen or tech-savvy individuals) for the casual confounding of terms such as "artificial intelligence" and "deep learning," among others.

Volkswagen is working with Nvidia to expand its usage of its artificial intelligence and deep learning technologies beyond autonomous vehicles and into other areas of business, the two companies revealed today. VW set up its Munich-based data lab in 2014. Last year it pushed on with the hiring of Prof. Patrick van der Smagt to lead a dedicated AI team that is tasked with taking the technology into areas such as'robotic enterprise,' or use of the technology in enterprise settings. VW wants to use AI and deep learning to power new opportunities within its corporate business functions and, more widely, "in the field of mobility services." As an example, the German car-maker said it is working on procedures to help optimize traffic flow in cities and urban areas, while it sees the potential for intelligent human-robot collaboration, too.

In my last post, I discussed the state of confusion around deep learning and its abilities. Also, how even software programmers have a hard time understanding how deep learning enables machines to program themselves. In this post, I will try to explain probably the hardest to understand deep learning concept i.e. how deep learning machines program themselves without any human intervention. Since the advent of software programming, humans have been writing code to program the behavior of machines. In other words, the behavior of a machine only changes when the machine is reprogrammed by a human through new lines of code.

The idea of intelligent machines has fascinated people for centuries. And while it may seem like something out of science fiction, people today use artificial intelligence every day in their smartphones, houses, cars and more. At Facebook, AI is used for translation of text between languages, to describe images for visually impaired people, and more. In the below video, Facebook's AI research head, Yann LeCun, explains some of the key concepts that make all of this possible and why you should care. Can you tell the difference between a car and a dog?

Hedge funds seeking artificial intelligence expertise need to cast the net wide these days, due to a shortage of people and a massive uptick in demand over the past 12 months. Citadel has just turned to Microsoft for the new role of chief AI officer. Li Deng, who joined the tech firm straight out of academia 17 years' ago, has just joined Citadel's hedge fund operation in Seattle, but will work also across Chicago and New York. Deng announced his move to Citadel on LinkedIn yesterday, saying that he was "very excited about the opportunities for artificial intelligence innovation here and the firm's passion for growing its leadership in this space." Citadel didn't immediately respond to requests for comment.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming entire industries because of higher performance and faster time to market. Part of the success is due to researchers creating and open sourcing datasets, frameworks, and algorithms (e.g., ImageNet, Caffe). Current leaders are following suit by opening up their own developments (e.g., DeepMind Lab and Sonnet, OpenAI Gym and Universe). Despite this generosity, operating and developing on these components still requires large amounts of expertise, vast computational resources, and lots of money to obtain and maintain. Jack Clark of OpenAI believes that this situation seems to benefit large-scale cloud providers like Amazon, Microsoft, and Google.

Chinese professional Go player Ke Jie preparing to make a move during the second game of a match against Google's AlphaGo in May 2017. Artificial intelligence (AI), once described as a technology with permanent potential, has come of age in the past decade. Propelled by massively parallel computer systems, huge datasets, and better algorithms, AI has brought a number of important applications, such as image- and speech-recognition and autonomous vehicle navigation, to near-human levels of performance. Now, AI experts say, a wave of even newer technology may enable systems to understand and react to the world in ways that traditionally have been seen as the sole province of human beings. These technologies include algorithms that model human intuition and make predictions in the face of incomplete knowledge, systems that learn without being pre-trained with labeled data, systems that transfer knowledge gained in one domain to another, hybrid systems that combine two or more approaches, and more powerful and energy-efficient hardware specialized for AI.

Neural networks have hit the peak of a hype cycle, according to a panel of experts at an event marking the 50th anniversary of the Alan Turing Award. The technology will see broad use and holds much promise, but it is still in its early days and has its limits. Many panelists said that artificial intelligence is a misnomer for neural networks, which do not address fundamental types of human reasoning and understanding. Instead, they are tools to take on a long journey to building AI. The discussion of deep learning was particularly relevant given Turing's vision that machines would someday exceed humans in intelligence.

There are a few moments in the history of artificial intelligence (AI) that are considered major breakthroughs – events that showed the power of machine intelligence in matching or surpassing human performance. Two examples are Deep Blue versus Kasparov in 1997 and Watson versus Jennings in 2008. Most recently, AlphaGo versus Lee Sedol became another major victory, this time driven by a fast developing field known as "deep learning." Deep learning–a machine learning technique based on artificial neural networks–is growing in popularity due to a series of developments in the science and business of data mining. Prior to AlphaGo's victory over the currently best Go player Lee Sedol, computer programs that played Go had only been able to beat average players.

Bitville has launched a major research project to investigate various alternatives about how Artificial Intelligence (AI) can be harnessed to assist in human learning. The research project is funded partly by Tekes, and it belongs to the Team Finland Augmented Intelligence campaign. Tekes is a governmental expert organization for funding research, development and innovation in Finland. The length of the project is six months, and will be conducted by Bitville's AI team. The solutions will be based on deep neural networks, also known as "deep learning".