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.

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?

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."

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.

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.

The Machine learning report evaluates technologies and applications in terms of their business impact, adoption rate and maturity level to help users decide where and when to invest. The Predictive Analytics Scores below – ordered on Forecasted Future Needs and Demand from High to Low – shows you Machine learning's Predictive Analysis. The link takes you to a corresponding product in The Art of Service's store to get started. The Art of Service's predictive model results enable businesses to discover and apply the most profitable technologies and applications, attracting the most profitable customers, and therefore helping maximize value from their investments. The Predictive Analytics algorithm evaluates and scores technologies and applications.

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

From all the fields that Artificial Intelligence will disrupt in coming years, HealthCare may see the highest paradigm shift. Artificial Intelligence's influence in HealthCare industry will be wide and immense. Image recognition algorithms already help detect diseases at an astounding rate. This shift should be welcome. Artificial Intelligence at first glance, will bring remarkable well-being to humans.