Having recently turned a corner in profitability, Nuance (NASDAQ:NUAN) trades at a PEG ratio of 0.0762. For reference, Apple (NASDAQ:AAPL) has a PEG of 0.2012. Nuance is 1/60th the size of Apple. Nuance has an EV/EBITDA TTM of 25.07. Nuance has a Price/Sales ratio of 4.075.

The brightest light in Apple's (NASDAQ:AAPL) quarterly earnings report came from sales of its iPhone 4S, whose most defining feature is the voice recognition assistant Siri. The phone's eye popping growth, particularly in Asia where it launched in January, was likely behind Nuance's (NASDAQ:NUAN) upside guidance issued on April 26th. Of course, consumer sales to OEMs like Apple still account for less revenue than Nuance's healthcare division, which is riding a wave higher in medical transcription. But, voice driven consumer electronics are increasingly important to Nuance's growth story. Following the launch of the 4S last fall, phones at Nuance were likely lit up by manufacturers looking to close the gap with Apple. In Nuance's Q4 earnings call, CEO Paul Ricci described interest in its solutions as "unprecedented".

And because AI students are trained in such a rich multidisciplinary environment, they have excellent career opportunities. To make a thinking machine is one of humanity's oldest dreams. And since Allan Turing?s 1947 lectures on AI, programmable computers seemed to be the best way to go. Expectations were extraordinarily high in the 1950s and '60s, but without major breakthroughs, the whole subject lapsed temporarily into obscurity. Now, advances in the cognitive sciences that are improving our understanding of the nature of intelligence, memory, and perception from the biological perspective, coupled with the ready availability of ever-faster computers, are creating a second spring for AI--and a mecca for multidisciplinary scientists.

Ray Solomonoff, a physicist who was one of the founders of the field of artificial intelligence, died on Dec. 7 in Boston. He was 83 and had homes in New Ipswich, N.H., and Cambridge, Mass. The cause was a ruptured brain aneurysm, said his wife, Grace. As a child Mr. Solomonoff developed what would become a lifelong passion for mathematical theorems, and as a teenager he became captivated with idea of creating machines that could learn and ultimately think. In 1952 he met Marvin Minsky, a cognitive scientist who was also exploring the idea of machine learning, and John McCarthy, a young mathematician.

It is important to divide the overall understanding process into manageable pieces.

In the future according to robotics researchers, robots will likely fight our wars, care for our elderly, babysit our children, and serve and entertain us in a wide variety of situations. But as robotic development continues to grow, one subfield of robotics research is lagging behind other areas: roboethics, or ensuring that robot behavior adheres to certain moral standards. In a new paper that provides a broad overview of ethical behavior in robots, researchers emphasize the importance of being proactive rather than reactive in this area. The authors, Ronald Craig Arkin, Regents' Professor and Director of the Mobile Robot Laboratory at the Georgia Institute of Technology in Atlanta, Georgia, along with researchers Patrick Ulam and Alan R. Wagner, have published their overview of moral decision making in autonomous systems in a recent issue of the Proceedings of the IEEE. "Probably at the highest level, the most important message is that people need to start to think and talk about these issues, and some are more pressing than others," Arkin told PhysOrg.com.

The development of artificial intelligence-based approaches to junction control is one of many new and promising technologies that can make better use of existing urban and road capacity, while reducing the environmental impacts of road traffic. The research carried out by the University of Southampton team has used computer games and simulations to investigate what makes good traffic control. This work has shown that – given the right conditions – humans are excellent at controlling the traffic and can perform significantly better than the existing urban traffic control computers in use today. This was tested for the BBC's'One Show' programme, where presenter Marty Jopson controlled a'real traffic light junction at the InnovITS proving ground using a laptop, while 30 volunteer drivers tried to negotiate the junction. Dr Simon Box of the University of Southampton Transportation Research Group adds: "The demonstration carried out at innovITS Advance indicates that the human brain, carefully employed, can be an extremely effective traffic control computer. In our research we aim to be able to emulate this approach in a new kind of software that can provide significant benefits in improving the efficiency of traffic flow, hence improving road space utilisation, reducing journey times and potentially, improving fuel efficiency."

The C5.0 algorithm uses the Max-Gain method of selecting the best attribute. H measures the information content or entropy in bits (i.e., number of yes/no questions that must be asked) associated with a set S of examples, which consists of the subset P of positive examples and subset N of negative examples. Note: 0 H(P,N) 1, where 0 no information, and 1 maximum information. Half the examples in S are positive and half are negative. Say all of the examples in S are positive and none are negative.

The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

This course presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. The advantages and disadvantages of using these methods in real-world systems are emphasized, while students gain hands-on experience with application specific methods. The technical focus of the course includes decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems.