A breakthrough year for artificial intelligence (AI) research has suddenly turned into a breakdown, as a new automated banking system that runs on AI has been caught embezzling money from customers. The surprising turn of events may set back by years efforts to incorporate AI into everyday technology. "This is the nightmare scenario," says Len Meha-Döhler, a computer scientist at the Massachusetts Institute of Technology in Cambridge who was not involved in the work. However, Rob Ott, a computer scientist at Stanford University in Palo Alto, California, who did work on the system--Deep Learning Interface for Accounting (DELIA)--notes that it simply held all of the missing money, some 40,120.16, in a "rainy day" account. "I don't think you can attribute malice," he says.

If all humans have the same brain capacity--about 300 million pattern recognizers in our cortices--then what made Albert Einstein special? In his quest to replicate the human brain, renowned AI engineer Ray Kurzweil finds that a big part is: The courage to stick to your convictions. The average human is inherently conventional, reluctant to pursue ideas outside of the norm. "[Courage] is in the neocortex, and people who fill up too much of their neocortex with concern about the approval of their peers are probably not going be the next Einstein or Steve Jobs." – Ray Kurzweil told Wired. If your work elicits ridicule from the rest of the world, pushing past this skepticism could be a strong indication of brilliance. Anyone who has been dedicated to the field of AI for decades knows this feeling very well.

As artificial intelligence breakthroughs arrive on a near-monthly basis - see what Google's (NASDAQ:GOOG) (NASDAQ:GOOGL) AlphaGo system recently accomplished - Nvidia (NASDAQ:NVDA) execs have been talking to anyone within earshot about the value of its GPUs for handling AI-related computing tasks. Especially for deep learning, a branch of AI focused on using algorithms to uncover patterns within large volumes of content (e.g. The more data a deep learning system takes in, the smarter it becomes. Today, Nvidia stepped up its AI efforts by launching (at its annual GTC conference) the DGX-1, a deep learning-optimized supercomputer that relies on eight of the company's new Tesla P100 GPUs and is declared to provide the throughput of 250 x86 servers (some might beg to differ with that claim). Nvidia, which already sells plenty of Tesla GPUs for third-party supercomputer and high-performance computing ("HPC") systems, is including a deep learning software suite with the DGX-1.

Data scientists and artificial intelligence (AI) researchers require accuracy, simplicity, and speed for deep learning success. Faster training and iteration ultimately means faster innovation and faster time to market. The NVIDIA DGX-1 is the world's first purpose-built system for deep learning with fully integrated hardware and software that can be deployed quickly and easily. Its revolutionary performance significantly accelerates training time, making the NVIDIA DGX-1 the world's first deep learning supercomputer in a box.

As for who might be buying these computers, NVIDIA is positioning this machine for serious research purposes -- the first machines off of NVIDIA's assembly lines will go to ten universities including MIT, Stamford, NYU and Berkeley. The company is also positioning the DGX-1 as a key component of its new AI Driving machine-learning system called Drive PX, which helps to enable vehicle recognition at 180FPS. The goal of having such a relatively system is to make deploying such massive computing power much easier. "Data scientists and AI researchers today spend far too much time on home-brewed high performance computing solutions," Huang said in a press release. "The DGX-1 is easy to deploy and was created for one purpose: to unlock the powers of superhuman capabilities and apply them to problems that were once unsolvable."

As Nvidia (NVDA) CEO Jen-Hsun Huang stood gazing at the Martian surface -- a virtual reality simulation, of course -- he talked about a future for the company that investors should look forward to. At the company's annual GPU Technology Conference, which started here Tuesday, Huang in the opening keynote address told attendees that Nvidia's focus will be even more centered around its core GPU (graphics processing unit) technology. GPUs provide graphics on computers, in video games and in various other applications. Nvidia competes mostly with Advanced Micro Devices (AMD) in this field, but No. 1 chipmaker Intel (INTC) is among other rivals. Huang said the company's key markets will be virtual reality, autonomous cars and artificial intelligence, including chips for drones and deep learning.

In the 1950s, artificial intelligence (AI) became a hot topic of scientific conversation in science fiction novels. The prospect of attacks by intelligent machines became a trend that led the imagination down dark paths of technological domination. AI is no longer science fiction and is on a path of destruction, but not one that could have been predicted 60 years ago. It is playing no small role in the prevention of cybercrime, and leading the fight back in protecting data. Machine learning is providing teams with the capability to thwart APTs (advanced persistent threats) through zero day attacks and with the analytic prowess they need to identify both internal and external threats.

It's all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it's beginning to coincide with science fiction. No, we don't have autonomous androids struggling with existential crises -- yet -- but we are getting ever closer to what people tend to call "artificial intelligence." Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don't have to be explicitly programmed but can change and improve their algorithms by themselves. Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and find terrorist suspects.

Your electric bills could soar if you kept Nvidia's monster DGX-1 computer running continuously for one month. The DGX-1 supercomputer can deliver the computing power of 250 two-socket servers in a desktop box, claimed Nvidia, which introduced the system Tuesday at its GPU Technology Conference in San Jose, California. The computer can deliver about 170 teraflops of performance, and multiple boxes on a rack could deliver 2 petaflops of performance. The fastest computer in the world delivers a peak performance of about 10 petaflops. Nvidia says DGX-1 is about 56 times faster than a server with two Intel Xeon-E5 2697 v3 chips, which can deliver about 3 teraflops of performance.

A new drug to treat cancer. Even in its early stages, deep learning is having a tremendous impact and is sweeping across every industry. Some of the world's most important challenges need to be solved today, but require tremendous amounts of computing to become reality. Today's large-scale data center relies on many interconnected commodity compute nodes, limiting the performance needed to drive these important workloads. Now, more than ever, the data center must prepare for the high-performance computing and hyperscale workloads being thrust upon it.