Since IBM's supercomputer Deep Blue became world chess champion in 1997, much has changed in the field of artificial intelligence. In 2011, another supercomputer for the US company, Watson, was named Jeopardy's (a popular TV show in the US) best player . And Lee Sedol, a prodigy of the tough game Go, lost to Google's AlphaGo (a documentary, available on Netflix, was produced about the algorithm). These are amazing facts, no doubt, that show the immense potential of AI. But what about companies? What is the current scenario of artificial intelligence in management? What has really happened? What are the biggest limitations?
As much as we frequently talk about AI news here on the blog, a text with the current map of these innovations was missing. Even so that you understand exactly the extent of impact in your day to day, in a practical way.
Artificial intelligence in management: where we stand
Much has been said about cognitive computing, which is considered the current generation of AI. In other words, systems learning has come to follow the stage of very fast input and output by machines (examples of which are Deep Blue and AlphaGo). This means that new information can be generated from the data with which the systems were fed.
It turns out that so far there was little concrete, even though the IT industry had been talking about neural networks for decades. However, according to this IBM story, the time is now, because both hardware and software are ready for artificial intelligence.
Cognitive computing is really the ball
There is no doubt that computers are learning. After all, back in the early days, processors were able to perform calculations; They then evolved into the use of programmable systems (as we know today), and as we have seen at this time, they are able to process information based on learning from past experiences. It is similar to how our brain functions as we receive information and process it to learn.
An example of the use of cognitive computing, which is based on learning and evolution, is Laura: the first risk-management cognitive robot used in hospitals. The software reads patient information and alerts the medical staff if the patient's condition is, for example, from generalized infection. .
The combination of Cognitive Computing and Machine Learning technologies makes Laura increasingly precise in diagnostics: she identifies patterns, compares with the database, and informs if there is a likelihood of a risk event with the patient. This process is very similar to the deduction process we humans do.
However, when it comes to artificial intelligence in management, there are some limitations. According to this McKinsey text, machines are more “trained” than “programmed”. Because of this, the various processes often require huge amounts of labeled data to perform complex tasks accurately. Hopefully this article can provide an overview of what will happen when AI is applied in management.
