When I first heard the term few years ago , I thought “this is somewhere between marketing buzzword and science fiction” . It is now the single most often asked question for me in my work life , and pretty soon this is going to be no different in my social circles too . Rather than provide a definition – I am sure IBM website and Wikipedia will have some articulate definitions – let me try to provide some background on how we make decisions and then try to tie it back to WTH cognitive is .
How do we make important decisions in real life – like say ordering a beverage at Starbucks ?
We use some data (sugar is bad for me) , we use some past experience (the Starbucks in chandler doesn’t make a good capuccino all the time) , we evaluate some options (should I have a frapuccino instead for this one time) and then we pick a path (no I will take a capuccino but with an extra shot) that we think has best odds ( need heavy caffeine dosage to keep up with my calendar) . It’s almost never on the basis of perfect information . Decisions are mostly made on directionally correct information (frapuccino is higher in calories) , not on precise information (it’s exactly 375 calories more ) . Speed , flexibility and agility holds higher value in decision making compared to extreme accuracy and precision . If you are a barrista making a cappuccino – there is probably a perfect ratio of ingredients , temperature etc that you use to make the perfect beverage . But getting it roughly in those ratios in quick time is more useful to you as a barrista (and probably to the customer) , than spending an extra ten minutes in making it perfect . Beyond a point , the strive for precision gets into diminishing marginal returns .
Then there is another important aspect – we have to tune out distractions to get the job done . If you are a barrista at Starbucks at Phoenix airport – at any given moment , there are three people shouting complex orders at you . You need to focus on one , and tune out the rest temporarily , and when done – move on to the next order, rinse and repeat . If you are really good at what you do – you might even be able to take the second order roughly in parallel without making the customer repeat the instructions .
Given how quickly demand can change – getting to an answer usually needs a dialog between two or more parties . If my daughter and I are ordering together our favorite green tea frapuccino , one of us typically will change our mind at the last minute that will need the barrista to engage in a conversation with us (or at least listen to both of us and ask clarifying questions ). This is especially true when my thick Indian accent and my long name makes it harder for them .
Perhaps the most amazing part of how we solve problems is our ability to learn and avoid reinventing the wheel . Over time , Phoenix airport barrista has figured out my preferences and only asks me “capuccino or frapuccino today?”. She knows by now that I will always take an extra espresso shot in mornings and that I don’t like whip cream in my frapuccino in evenings .
Finally , after we take a decision – we are usually able to explain how we arrived at the conclusion we made . And such explanation will need varying amount of detail depending on who you are dealing with ( this is good for you is fine with my daughter , but a longer winded explanation on why I chose the frapuccino is often needed with my wife) .
While it all sounds rather complex – we do this everyday as human beings with no problem at all . Human brain is amazing – way more amazing than any computer . Trying to mimic human thought processes with computers is incredibly complex on many fronts – and that is essentially what cognitive computing tries to do . It tries to solve problems like how we do as human beings – except at a larger scale , using more data and faster .
Most computer programs are built on deterministic (fancy for if-then-else) logic . That is a piece of cake for computers . Cognitive computing can of course reuse deterministic logic , but it primarily needs probabilistic logic ( option A has higher odds than option B type logic ) . Often times the computer might not know the exact answer at all -and then it perhaps needs to figure out what option has lowest risk . If a fellow human being can barely understand my accent, imagine how difficult it will be for a computer to understand the words that are coming out of my mouth !
Now the good news is that the smart women and men working in this field have already done quite a bit of work on making computing. More cognitive . While there are many more miles to go , what is already there can bring immediate value to customers . Siri for example is a starting point on humans talking to computers for simple questions and answers . Siri has limitations – like keeping the context between successive questions asked by a user . IBM Watson can keep context across questions and can have a more meaningful dialog with a human user. But in its current form , it will be hard for Watson to tune out noise (say my wife and I both ask a question each in parallel). But over time ( and I am positive it won’t take that long) – those challenges will go away and technology will get more and more savvy at making computers closer to human way of thinking and solving problems .
While I am all excited about the idea of cognitive computing as the future of this industry ( well, and present too actually given we do multiple projects already using cognitive computing ) – I don’t think it has the potential to replace human information workers in huge numbers . I will explain my rationale in another post .