As artificial intelligence (AI) begins to power more technology across industries, it's been truly exciting to see what our community of developers can create with Watson. Developers are inspiring us to advance the technology that is transforming society, and they are the reason why such a wide variety of businesses are bringing cognitive solutions to market. With AI becoming more ubiquitous in the technology we use every day, developers need to continue to sharpen their cognitive computing skills. They are seeking ways to gain a competitive edge in a workforce that increasingly needs professionals who understand how to build AI solutions. It is for this reason that today at World of Watson in Las Vegas we announced with Udacity the introduction of a Nanodegree program that incorporates expertise from IBM Watson and covers the basics of artificial intelligence. The "AI Nanodegree" program will be helpful for those looking to establish a foundational understanding of artificial intelligence.

AI has a long way to go before people can or should worry about turning the world over to machines. Elon Musk's new plan to go all-in on self-driving vehicles puts a lot of faith in the artificial intelligence needed to ensure his Teslas can read and react to different driving situations in real time. AI is doing some impressive things--last week, for example, makers of the AlphaGo computer program reported that their software has learned to navigate the intricate London subway system like a native. Even the White House has jumped on the bandwagon, releasing a report days ago to help prepare the U.S. for a future when machines can think like humans. But AI has a long way to go before people can or should worry about turning the world over to machines, says Oren Etzioni, a computer scientist who has spent the past few decades studying and trying to solve fundamental problems in AI.

Apple CEO Tim Cook doesn't think the future of artificial intelligence will infringe on users' privacy, the way it does now. Responding to a question about Apple's virtual assistant Siri on a call to discuss the company's latest quarterly earnings, Cook said: "In terms of the balance of privacy and AI, this is a long conversation, but at a high level, this is a false tradeoff. People would like you to believe you have to give up privacy to have AI do something for you, but we don't buy that. It might take more work, it might take more thinking, but I don't think we should throw our privacy away. You shouldn't have to make a choice."

Artificial intelligence is currently making a resurgence since the 1990s. Today, the focus is on machine learning and statistical algorithms. This shift has served AI well. Machine learning and statistics provide effective algorithm solutions to certain kinds of problems, such as board games, spam detection, voice and image recognition, etc. How is AI different today from 20 years ago?

SAN FRANCISCO (Reuters) - General Motors and IBM on Tuesday said a jointly created platform would combine the artificial intelligence system Watson with the carmaker's OnStar connectivity service in an effort to market new services to drivers. "OnStar Go," which will debut early next year in more than two million GM vehicles with 4G, is billed as a way to optimize time spent in the car by enhancing in-vehicle experiences, the companies said.

AI and machine learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing or application. Creating intelligent systems that learn, adapt and potentially act autonomously rather than simply execute predefined instructions is primary battleground for technology vendors through at least 2020. AI and machine learning (ML), which include technologies such as deep learning, neural networks and natural-language processing, can also encompass more advanced systems that understand, learn, predict, adapt and potentially operate autonomously. Systems can learn and change future behavior, leading to the creation of more intelligent devices and programs. The combination of extensive parallel processing power, advanced algorithms and massive data sets to feed the algorithms has unleashed this new era.

Artificial intelligence, the machine learning technology that allows "smart" machines to take over human tasks like driving cars or ordering pizza, is quickly becoming the go-to technology for many industries to hire talent for, including health care, auto, and finance. Research firm Markets and Markets estimates the AI market will grow to more than $5 billion by 2020, given the rising adoption of AI across these industries. That's why online education company Udacity is debuting a new way for workers to learn skills needed to be experts in developing artificial intelligence for the likes of IBM and others. Udacity originally launched "Nanodegrees" to train people hoping to land technical jobs, such as software developing. Nanodegrees also aim to teach people about the advanced and emerging technologies like self-driving cars or Android development for mobile phones.

Samsung Electronics has introduced two new modules in its ARTIK IoT platform and updated cloud capabilities. The new ARTIK 0 family is low-power, lightweight and cost-optimized, while the higher-spec ARTIK 7 is intended for high-end gateways and multimedia applications. ARTIK 0 is for end-devices such as HVAC, lighting, industrial sensors, and personal health monitoring. It has a power-efficient ARM Cortex-M microcontrollers and supports lower power wireless standards: ZigBee, Thread, or Bluetooth Smart. It comes with networking stacks, RF, and precertification for regulatory approval.

Using deep learning to harness and explore large datasets has become increasingly important for businesses in every industry. There are many companies and services trying to make this a tenable problem, and yet, more people are still required to munge together home-grown solutions to meet their specific needs. Fortunately, there are many tools and resources in the market today that make building cognitive apps more doable. Here are nine interesting tools and resources I've seen and/or worked with recently to build cognitive apps: 1. Deeplearning.net: Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

Robotic process automation (RPA) can be the true antidote to manual, rote work, or it can be our worst nightmare if you listen to all the drama or the hype. RPA centers on the use of artificial intelligence (AI) to apply human-like thinking to streamline a typically manually intensive process or activity; and whether we like it or not, it's here to stay. Take, for instance, the process of data extraction from documents such as invoices. Application of advanced optical character recognition (OCR) and intelligent document recognition can automate a significant amount of the job of data entry typically performed by clerks or specialized data entry staff. Interestingly, human effort is still involved with attaining the ability to hand off a process or task to a machine.