The emergence of cognitive systems signals a breakthrough in the relationship between humans and technology. First of all in the way systems interact with people, showing more ‘human-like’ ways of communicating – for example in using powerful natural language processing capabilities. But also in the way systems seemingly adapt and learn ‘fuzzy’ human ways of absorbing and interpreting information, particularly when it’s very unstructured and very complex. When applied well, it eredicates the usual friction between the individual seeking insight and the technology providing it. Cognitive systems thus can augment people at the point of action in the most fluent way, without imposing an all too obvious algorithmic perspective.

Ray Kurzweil famously predicted that by 2029, computers would pass the ‘Turing Test’ – the moment at which intelligent machine behavior would be indistinguishable from that of a human. This prediction came before the arrival of the fax machine. He also predicted that by 2045, computers would be a billion times more powerful than all of the human brains on earth.

Now whether we like Kurzweil’s singularity vision or not, that would be truly deep learning, wouldn’t it?

Cognitive systems like IBM’s Watson – but also simply Apple’s Siri and Microsoft’s Cortana – learn, interpret and use natural language to communicate in increasingly more powerful ways. In some cases, these abilities extend existing applications, for example in the way Watson Analytics uses natural language to make it easier for business users to articulate their analytical questions.

But there is much more: already in 2011, IBM’s Watson performed as a contestant on the US game show ‘Jeopardy’ and won. What’s astounding is that its exploits weren’t coded by human engineers, but self-taught (with a little help) by reading and interpreting Wikipedia – all of it. In such a case, the cognitive technology not only uses natural language to understand and respond to the Jeopardy challenge, it also dives in the content itself – applying guided machine learning or even unguided, self-optimizing deep learning – to find complex patterns and associations and, ultimately, answers and new insight.

Often, cognitive systems are positioned as being able to explore and interpret massive amounts of unstructured data (not only text, but also for example audio, images and video) as opposed to the much better known analytics systems that particularly focus on structured data.

But a better look at typical cognitive toolkits shows all sorts of different ways of creating a compelling cognitive illusion, which – in its results and experience – all have in common that they come closer to the way humans communicate and interpret information. A service that analyzes tweets, blog posts, e-mails and articles to create insight around somebody’s character and social orientation, might thus be based on very straightforward, structured algorithms: it may appear humanlike and fuzzy, but it’s definitely not in the inside.

 next evolution in computing will create systems that know the answer to our questions before we ask them. This will be based on an insatiable ability to process enormous amounts of human and machine-generated log data in its raw and unstructured form to derive context, meaning, and perhaps most importantly of all, dynamic underlying relationships.

A combination of cognitive and analytical systems will facilitate strategic, economic, and political decisions, accelerating creation, ideation, and rationalization. And the more systems show their cognitive side – by using seemingly human-like ways of communicating and reasoning – the more they will be a natural augmentation to humans, thus improving the acceptance of technology to support – or even replace – their decisions and actions.

An innovative HR solution, based on IBM Watson Explorer and BigInsights, supports companies in the optimal management of challenges like recruitment, internal mobility and career development. Natural language processing, machine learning, predictive analytics, and advanced data visualization are all combined to enable this approach. With it, people can for example be much better matched to capability requests – if needed almost complete automatically - thus optimizing the allocation of often scarce resources while improving the career development of individuals.

Cognitive systems have the potential to automate patient diagnosis, research and development activities, product ideation cycles, financial and risk-decisioning, and supply-chain optimization to name but a few potential areas. And the overarching domain of machine intelligence - including robotics, autonomous systems, gestural computing, emotional recognition and A.I. - will only further shape the vision (here's a colourful, elaborate overview of technologies and topics).

There are tangible real-world examples in place today from cognitive cooking through to health advice and improved heart disease diagnosis to reducing crisis response times and from biodiversity and conservation through to faster recruitment screening; the possibilities are endless.

Conscious technology, even more conscious people and enterprises: it may make the difference between just being there and being a true digital existence.

(About the author: Ron Tolido is an analyst with Capgemini)