Cognitive computing is increasingly becoming a buzzword today. What exactly is cognitive computing? To tell the truth there is no standardized, globally agreed definition yet. All we can say is that it’s an umbrella term used for all the processes and technology which can, together, enable computers to solve complicated problems that have been traditionally solved only by humans and some that were beyond us.
When it started, it was just about making computing user friendly. It was supposed to have an interface that has an incrementally advanced understanding of what the user wants. It was expected to pick up signals about what the user is trying to do and provides an appropriate response. Isn’t this exactly what’s needed even in the most demanding, most complex scientific situations? Cognitive computing is almost there now.
There is hardly a perceived advanced application of technology today where a sprinkling of cognitive computing is not applied. Whenever there is a mention of smart systems, some sort of cognitive computing is touted to be at play.
The seeds for Artificial Intelligence were first said to have conclusively germinated way back in 1997 when IBM’s Deep Blue defeated Gary Kasparov, the defending chess world champion then. It was the first instance when a computer under tournament conditions had defeated a reigning world champion. This was the first example of computers catching up with the human intelligence.
Cognitive computing came of age when IBM’s Cognitive computing system Watson defeated Jeopardy Champions, Brad Rutter and Ken Jennings in a highly publicized and televised challenge in February 2011. What made the feat outstanding was the fact that Jeopardy is not a simple game. It needs a very good understanding of the natural language, including puns, synonyms, homonyms, slang and jargon. Cognitive computing is used heavily in Artificial Intelligence (AI) systems and AI’s progress is directly related to the progress in cognitive computing. AI was said to break new ground when Google Deep Mind’s “AlphaGo” defeated “Lee Sedol”, the undisputed king of “Go” which is considered to be a far more complex game than Chess or Jeopardy. What, however, was really impressive about AlphaGo was that it came up with entirely new ways of approaching the game. In the light of these feats the progress made by Cognitive computing is anybody’s guess.
Cognitive computing is not only confined to capability demonstrators like the AlphaGo but it is already at work for human welfare. In healthcare, IBM’s Watson for Oncology is helping create evidence based treatment for cancer patients. It already has other uses where it is powering apps for speech recognition, sentiment analysis, face detection and even election Insights. In analytics, cognitive computing is already changing the game with its key capabilities like faster understanding of complex data streams and faster delivery to actionable insights. It also has immense potential to change customer experience by helping businesses understand and connect with consumers at the most granular level giving them competitive advantage. Companies like Under Armour, a manufacturer of performance footwear, apparel and equipment are creating apps, which will help people train and maintain fitness by learning how they train. This app will be able to connect with other data sources as well. The UA Record system from the company is combined with a cognitive coaching system that will serve as a personal health consultant and fitness trainer. Google-owned Boston Dynamics has released a video, which shows Atlas, a 6 feet tall, 320-lb humanoid robot running freely in the woods. The military research establishments in advanced economies are already funding research to produce robots that are autonomous and self-aware to diminish the need for human soldiers to risk their lives. Industry leaders like Google, Amazon, Facebook, Microsoft, and Apple look at Cognitive computing as a strategic tool to ensure an edge in business and have invested heavily in this area.om
Cognitive computing is also attracting a lot of attention from the makers of the self-driving cars. This is because true self-driven vehicles will need to be context aware. Braking or turning, are actions that can be the difference between the life and death of the passengers and these are all ultra-low latency endeavors. This implies that the self-driving cars should have the ability to differentiate humans from vehicles, animals and other obstructions along with the most appropriate responses in different scenarios. Anyone who drives a car is aware that every situation we encounter on the road is unique and there is only so much that can be simulated or taught in advance. It has to be a real time thinking and reaction. Real thinking machines are needed. The ability of the car to think and sense its environment will be critical. Automobile companies are already investing big in Artificial intelligence (AI) that helps driving, whether its driver replacement or driver enablement. In late 2015, Toyota announced an investment of US $ 1 Billion in AI ( Read More ) to create a Paolo Alto based research laboratory. This facility will work on AI assisted navigation and factory automation. Artificial intelligence is also working on in transit experiences. IBM’s Watson and Local motors from Arizona have just collaborated and brought out Olli, en electric powered vehicle that can carry 12 passengers. Within Olli, Watson is working on improving the passenger experience along with the car’s self-driving features although not fully ( Read More ).
The global market for Artificial Intelligence is predicted to be US $ 23.4 Billion by 2025 ( Read More ).Companies across the industrial spectrum are investing in Cognitive computing and related AI technologies. Companies are getting acquired at a rapid pace. Google acquired Deep Mind technologies in 2014 for US$ 600 million ( Read More ). It has made 9 acquisitions in the category since then. Although estimates are not known, Twitter has made 4 acquisitions, which includes the image processing company Magic Pony. Facebook has also acquired 2 companies, wit.ai and Face.com. Microsoft acquired Swiftkey, an AI program, which has the capability to predict what a user will type, reportedly for US$ 330.2 million. Nvidia invested US$ 2 billion to develop its Tesla P100 GPU ( Read More ), an ultrafast chip tailor made for deep learning. Even banking giants Goldman Sachs and JP Morgan ( Read More ) are investing heavily in Artificial Intelligence.
Although the total funding for the AI categories is difficult to provide, the top two Artificial intelligence categories by funding have been Machine learning applications which have the funding in excess of US $ 2 Billion and Natural language processing with US$ 662 million ( Read More ). 2015 has been a record year when artificial intelligence and related technologies attracted funding of US $ 1.2 Billion ( Read More ). The top companies in the sector are focusing on AI applications that vary from advanced next gen AI assistants to cutting edge machine learning tools. The startup scene is also heating up in AI. Sentient Technologies, Ayasdi and Vicarious Systems are the top three best-funded start-ups in AI in the period 2010 – 2016 as per CB insights. They attracted an investment of US$ 144 million, 98million and 67 million respectively. ( Read More ).
Cognitive computing promises to open new Vistas for human creativity by taking up all the routine, mundane jobs, where the margin of error by human beings is far higher. It also will assist in the modelling of hypothetical scenarios and the simulative testing of ideas in the virtual domain that will speedup the pace of innovation.
It is an expectation from cognitive computing and systems that they will enable the natural interaction of humans and machines allowing the magnification of human expertise. They are expected to increase the capabilities of our scientists, engineers and doctors by a factor of a million and reduce the response time to a tiny fraction of what it takes today. This is also imperative because of the recent advances in business, society, technology – especially connectivity and big data need new and better computing capabilities. Some may call it epiphany, but it’s quite possible that we might just have to look beyond the traditional rules of science, as we know them. The fundamental building blocks of technology are already under duress and a lot of them are crumbling. An example is that of the silicon based semiconductor chips already reaching their limits. The rate at which the human race is consuming resources, we might have to find solutions to unknown problems like finding new planets, creating new food eco-systems or even designing super systems that create a circular supply chain where there is no wastage at all.
The next stage for cognitive computing is being touted to be Affective computing – systems which can understand human emotions and also simulate them. This might help with a lot of new age special issues that we are coming across – loneliness, fragmented families, child nurturing and grooming, caring for the mentally challenged, caring for the specially abled. The possibilities are almost endless.
Every technology comes with its underbelly, its dark side. The nuclear power which runs the economies of so many countries also created Hiroshima and Nagasaki and now the specter of miniaturized nukes falling into terrorist’s hands is a threat to the whole civilized world. Questions are already being raised about the possibility of cognitive computing powered AI acquiring superhuman capabilities and deciding to take over the human race? Tech visionaries like Elon Musk, Stephen Hawking and Bill Gates are already warning about AI. Mr. Hawking said “Success in creating AI would be the biggest event in human history…unfortunately, it might also be the last, unless we learn how to avoid the risks”. In the shorter term, world militaries are considering autonomous-weapon systems that can choose and eliminate targets. Humans beings are limited by slow biological evolution hence are unable to compete and would be superseded by AI. Elon Musk has called AI “our greatest existential” threat and Mr. Gates also paints an apocalyptic scenario. Bill Gates says, “I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super-intelligent. That should be a positive if we manage it well. A few decades after, the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned”
The purpose of the blog is not to paint an apocalyptic scenario where machines and AI edge the humans out, but to throw light on the prospects of cognitive computing and its immense potential to create a better world. It is however, never wrong to be a bit circumspect and have a look at the flip side. How cognitive computing and AI evolves is totally in human hands right now and we hope that humans will be responsible enough to harvest its colossal capabilities for the collective good. Most big companies today have plans to induct Cognitive computing in a big way. If things work out and Cognitive computing does evolve the way we want it to, we shouldn’t be surprised to see its application as a great business enabler at all the transnational corporations making them billions and saving them millions.
Sethuraman is a former Happiest Mind and this content was created and published during his tenure.
