Big Data Lab (BDL) BDL is led by Dr. Tong Zhang. BDL focuses on large-scale machine learning algorithms and applications in areas such as predictive analytics, large data structure algorithms, and intelligent systems research. BDL's mission is to make people's lives better through big data. Institute of Deep Learning (IDL) Baidu launched the Institute of Deep Learning in 2013. The team's focus areas include image recognition, machine learning, robotics, human-computer interaction, 3D vision and heterogeneous computing.

AI is a powerful force and a reality for everybody. It improves our healthcare, shopping and travel experiences and is starting to make inroads into the workplace and government. With that, comes a responsibility to make sure AI is a force for good, and that its tremendous power does not create a new chasm in our society. Many areas of public policy, from education and the economic safety net, to defense, environmental preservation, and criminal justice, will see new opportunities and new challenges driven by the continued progress of AI. Government must continue to build its capacity to understand and adapt to these changes.

Robotic cars are great at monitoring other cars, and they're getting better at noticing pedestrians, squirrels, and birds. The main challenge, though, is posed by the lightest, quietest, swerviest vehicles on the road. "Bicycles are probably the most difficult detection problem that autonomous vehicle systems face," says UC Berkeley research engineer Steven Shladover. Nuno Vasconcelos, a visual computing expert at the University of California, San Diego, says bikes pose a complex detection problem because they are relatively small, fast and heterogenous. "A car is basically a big block of stuff. A bicycle has much less mass and also there can be more variation in appearance -- there are more shapes and colors and people hang stuff on them."

Artificial intelligence has reached a new milestone, with a program beating four professional players in a poker tournament lasting 20 days. Libratus, an AI program developed by a team of researchers at Carnegie Mellon University, took on Dong Kim, Jimmy Chou, Daniel McAulay and Jason Les at no-limit Texas Hold'em in a Pittsburgh casino, eventually taking $1.76 million (£1.4 million) in chips. It's been hailed as a milestone for AI, with Libratus co-creator Tuomas Sandholm declaring, "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans." Boston Dynamics describes itself as'building dynamic robots and software for human simulation'. It has created robots for DARPA, the US' military research company Deep Blue, a computer created by IBM, won a match against world champion Garry Kasparov in 1997. Apple's virtual assistant for iPhone, Siri, uses artificial intelligence technology to anticipate users' needs and give cheeky reactions Xbox's Kinect uses artificial intelligence to predict where players are likely to go, an track their movement more accurately Its human opponents had been sharing notes in an effort to expose Libratus' weaknesses, but the AI grew stronger as the tournament went on.

Researchers are taking inspiration from the group hunting tactics of penguins to test how car software is organized. The tool looks for ways to organize smart car code in the same way that penguins look for ways to obtain food. As cars become more complex and more connected, researchers say the penguin-inspired testing system could be key in finding optimum software's for smart cars to prevent them from being hacked. Penguins work together to match shoals of fish they hunt, helping them minimize the overall energy they need to catch fish - and researchers say their tactics could help catch hackers trying to take over smart cars. Engineers and researchers often look to plants and wildlife for technical solutions to problems.

The history of Artificial Intelligence is long, but it's only been recently that technology companies and markets have begun to get excited about it… Why? After a few decades of exploration of symbolic AI methods, the field shifted toward statistical approaches, that have as of late started working in a broad array of tasks due to the explosion of data and computing power, this in turn has led to machine learning and, most importantly, enabled deep learning. This is great news for the tech industry. The downside is that there aren't enough data scientists that understand deep learning. For those who do, there is a huge demand for their services.

When you are working, you have browsed information that is not relevant to your work, haven't you? I feel awkward when my boss is creeping behind. Of course, I can switch the screen in a hurry, but such behavior is suspicious, and sometimes I don't notice him. So, in order to switch the screen without being suspected, I create a system that automatically recognizes that he is approaching to me and hides the screen. Specifically, Keras is used to implement neural network for learning his face, a web camera is used to recognize that he is approaching, and switching the screen.

Rest assured that despite the constant Matrix-like scenarios, we're actually big fans of AI technology. But we're also sci-fi geeks, so we have to get it out somewhere. Earlier this year, I wrote about the basics of how machine learning works, and how we've been using it to train computer programs to beat us at the Chinese strategy game, Go. You'd think that teaching a computer how to think strategically and crush their opponents beneath their cybernetic heel would be enough for researchers, but they've decided to raise the bar again. Now, they want to teach computers just what it is that humans fear.

What is intelligent automation and what does it mean for the financial services industry? Banks currently face a number of specific problems for which intelligent automation - and within this the capability of AI - may provide solutions. Far from becoming our new masters, our prediction is that thinking machines could become our most trusted assistants, enhancing our productivity through providing us deeper and more timely information, and perhaps even automating the business of generating insights and making considered decisions. We believe that intelligent automation will, above all, allow us to free people from routine work and so empower them to concentrate on more creative, value-added services. The overall benefits to the economy from such enhancements could be large, as could the benefits in terms of enjoyment of work and quality of life.

The World Health Organization has estimated that there is a global shortfall of approximately 4.3 million doctors and nurses, with poorer countries disproportionally impacted. In the U.S., these shortages are less acute; instead, the country struggles with ever-increasing heath care costs, which often translate into limits on the time a patient is able to spend with a doctor. One study estimated that U.S. doctors spend on average just 13 to 16 minutes with each patient. Developments in the use of artificial intelligence technologies in medical care are beginning to demonstrate AI's potential to improve the quality of patient care. Around the globe, researchers are studying how AI might become a new tool in the caregiver tool kit.