Age of computers and Data, where do Humans Stand

One thing that computers can't create is knowledge....and the real question is the human knowledge and discovery at a place where the incremental effort and value is just re-gurgitation, maybe complex combinations that appear unique but are patterns and insights about existing knowledge.

An increase in computing power and understanding of pattern recognition in human communication, be it facial expressions or speech recognition or language translation, is making emotionless machines appear cognitive. Pleasure, pain, memory and experience, combined with the contexts (surroundings, circumstances) in which all those occurred, and intensity of each experience,  along with the DNA of the individual that makes the absorption of the experience unique to the subject, eventually mold the decision making into a personal trait. In short the inputs that go into a human machine with abstractions of data signals is almost infinite. Although many particular behaviors of humans have become more predictable, at individual level maybe it is hard to guess which way a particular individual will swing unless an extensive psycho analysis experiments and results for that individual are evaluated, but at a group level probabilistic behavioral outcomes are being done in many fields.

The point in question is AI, the latest and the greatest of the current buzz words has arrived. Soon it will be difficult to distinguish if you are talking to a man or machine online or on phone (except for the very balanced voice of Karen Jacobson telling you she is recalculating, and you almost feel she is in the car). These new developments have led some very ambitious projects and the latest ones I read about is to develop management decisions by experimentation, recording and then "algorithmising". This is quiet an interesting approach, and probably will work in certain situations. Hedge funds that can crunch 150 million signals into few decisions should embark on a more precarious route of mapping human interactions to simple outcomes. The problem is that those decisions are based on infinite other "nuances" of individuals that Bridgewater probably considers noise that can be suppressed. For one thing, the decision making by computer brains is easy. It is devoid of the emotional manipulations and political consequences and hence really, shall I say boring; easy nonetheless and in some cases where very defined paths must exist, efficient. Humans are also leaving an ever increasing trail of their activities and decisions following those activities, that make defining inputs and outputs of machines more accurately "human". The (people with)machines as it so happens will always carry a temporary advantage because in hyper competitive market nano seconds can switch millions. That being said, what one can "predict" using the same algorithms using similar data points very quickly becomes available to all market participants at some cost. The game ultimately returns to humans to weigh the inputs "calibrate" and then interpret the outputs, and the betting begins again.


The digitopoly blog talks about judgement becoming the currency of the day in the machine age, and what is funny about that is judgement is always the most precious of gifts at the higher echelons of management. The conundrum is judgement skills vs prediction skills; can one really predict without judgement which is a direct result of experience (practice and observation). Prediction in this sense will be basically one more input point to the judgement, the weight of prediction on the eventual judgement in this case will be more, simply because the machine will evaluate far more inputs in a shorter amount of time, more frequently, and arrive at a logical conclusion with consistency, accuracy and speed.

The improvements in AI are an extension of computing power. Computers are leveraged to do great many tasks but depend on humans. A certain type of knowledge is being created by the immensity of data and algorithms. Unique understandings from the cross section of this immense data and algorithms/models are forming the basis of new type of knowledge. This knowledge has its own basic blocks that can form bigger themes and simple conclusions around those themes under known set of conditions, inputs. More and more humans will master this knowledge and enhancements in AI will become extensions of humans just like language. In essence AI will not be AI but EI (enhanced intelligence) for humans.