The Information Technology world has been blown away by the Artificial Intelligence (AI) storm. AI is now considered the go-to-technology when it comes to harnessing data and gaining insights to power strategic business decisions. And, this is supposedly only the tip of the AI iceberg. Some of the developments to watch out for in the AI space are:
Artificial intelligence is becoming more accessible with every passing day. What was the privilege of a few tech giants a couple of years ago is now easily available to all organizations, irrespective of their size.
Numerous open source software such as TensorFlow are making AI more affordable for the digital businesses of today. Some of the biggest work on AI is being undertaken in China with initiatives to increase the use of artificial intelligence by building an AI-skilled pool of talent.
Much has been said about AI gobbling up jobs and rendering millions of people around the globe jobless. The truth of the matter, however, is that AI will replace the more mundane and routine jobs that are not only monotonous but may also be hazardous for human lives. A PwC report on job automation has suggested that by 2020, only 3% jobs will be replaced by automation. That said, automation will create the need for professionals with expert digital skills in order to stay relevant for the changing job scenario, as the same study suggests that 44% low-skill jobs will be at risk by 2030.
Google AlphaGo’s victory over Lee Sedol, has strengthened the case for reinforcement learning where machine learns from ‘experience’ without the need for instruction. Reinforcement learning will also eliminate the need for large data sets currently required to train machine learning algorithms, as machines will learn by responding to situations and actions that provide positive and negative results.
Duelling Neural Networks
Since machines do not enjoy the power of imagination the way humans do, they cannot ‘create’ anything. The idea of Generative Adversarial Network (GAN) is a possible solution in this direction. Two different neural networks are trained on the same data sets through a ‘real or fake’ game. The aim being to train a neural network to create near-real images that the other network cannot distinguish as fake. Research is currently underway on GAN and preliminary results have been encouraging.
The Article has been Written by Neetu Katyal, Content and Marketing Consultant
She can be reached here.