Cognitive computing is an outgrowth of artificial intelligence (AI), which originally set out to make computers more useful and more capable of independent reasoning. But where did AI come from? The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.

Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart", "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, medicine/healthcare, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, scientific advances, FinTech, predictive systems and so much more. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms. One major issue is that artificial intelligence – which is really a broad concept/reality, covering many technologies and realities – has become like a thing we talk about and also seem to need to have an opinion/feeling about, with thanks to, among others, popular culture. Hollywood loves AI (or better: superintelligence, not the same).

Like all hardware device makers eager to meet the newest market opportunity, Intel is placing multiple bets on the future of machine learning hardware. The chipmaker has already cast its Xeon Phi and future integrated Nervana Systems chips into the deep learning pool while touting regular Xeons to do the heavy lifting on the inference side. However, a recent conversation we had with Intel turned up a surprising new addition to the machine learning conversation--an emphasis on neuromorphic devices and what Intel is openly calling "cognitive computing" (a term used primarily--and heavily--for IBM's Watson-driven AI technologies). This is the first time to date we've heard the company make any definitive claims about where neuromorphic chips might fit into a strategy to capture machine learning, and marks a bold grab for the term "cognitive computing" which has been an umbrella term for Big Blue's AI business. Intel has been developing neuromorphic devices for some time, with one of the first prototypes that was well known in 2012.

Machine learning, artificial intelligence and augmented reality have been bubbling away in research labs for decades, but in recent years they have burst into business consciousness. For marketers, the rise in big data, programmatic advertising and marketing clouds have all coincided with the commercial emergence of cognitive computing -- the umbrella label for technologies that ingest data and then learn as their knowledge base grows. According to industry analyst Gartner, cognitive computing is a "disruptive platform with a shift more impactful than many other technologies in the past 20 years". For an industry which seems to reinvent itself every three years, this is a suitably bold claim. And yet, clearly, cognitive computing is already having a serious impact across a range of industry sectors.

Although artificial intelligence (as a set of technologies, not in the sense of mimicking human intelligence) is here since a long time in many forms and ways, it's a term that quite some people, certainly IT vendors, don't like to use that much anymore – but artificial intelligence is real, for your business too. Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart", "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, medicine/healthcare, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, scientific advances, FinTech, predictive systems and so much more. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms. One major issue is that artificial intelligence – which is really a broad concept/reality, covering many technologies and realities – has become like a thing we talk about and also seem to need to have an opinion/feeling about, with thanks to, among others, popular culture.

Google shows 44m hits on AI and 9m on Cognitive Computing and the figure below from Google Trends clearly shows that the search term "Artificial Intelligence" is more popular than "Cognitive Computing", however, I'm sure we'll start to see that gap close in 2017. In our white paper "Surviving in the AI hype", we explained some of the fundamental concepts behind AI, as well as touching on Cognitive Science and Computing but in this post we want to focus in more detail on the relationship between AI and Cognitive Computing specifically. To start off, what do Intelligence and Cognition mean if we search for a definition online? Intelligence: "the ability to learn or understand or to deal with new or trying situations: reason; also: the skilled use of reason (2): the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests)." Cognition: "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses."

This includes memory stores and the representations of elements in those memories, but not their contents, Most research in AI is analytic, in that it selects some facet which change as the result of external stimuli and internal of intelligence and attempts to understand it in detail, typically processing. In this sense, a cognitive architecture is analogous in isolation from other elements. This is balanced by to a building architecture, which describes its fixed a smaller movement, synthetic in character, that aims to discover structure (e.g., floors, rooms, and doors), but not its replaceable how different aspects of intelligence interact.

Like all hardware device makers eager to meet the newest market opportunity, Intel is placing multiple bets on the future of machine learning hardware. The chipmaker has already cast its Xeon Phi and future integrated Nervana Systems chips into the deep learning pool while touting regular Xeons to do the heavy lifting on the inference side. However, a recent conversation we had with Intel turned up a surprising new addition to the machine learning conversation--an emphasis on neuromorphic devices and what Intel is openly calling "cognitive computing" (a term used primarily--and heavily--for IBM's Watson-driven AI technologies). This is the first time to date we've heard the company make any definitive claims about where neuromorphic chips might fit into a strategy to capture machine learning, and marks a bold grab for the term "cognitive computing" which has been an umbrella term for Big Blue's AI business. Intel has been developing neuromorphic devices for some time, with one of the first prototypes that was well known in 2012.

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As the sun rises on 2017, a highlight of this New Year will be the gathering strength of the cloud. If the big question posed by businesses and their CIOs in 2016 was whether they should migrate to the cloud; in 2017 the question will be what is the best way to get there. A recent IBM survey of more than 1,000 C-suite executives from 18 industries found that almost every company we surveyed is using cloud, but only in pockets of their business. At the same time, nearly half of workloads - or 45% - are expected to remain on-premises with dedicated servers. As businesses continue to benefit from integrating their on-premises infrastructure with the cloud, they are also increasing their investments in new workloads on public clouds.