Why The Time Is right For AI ML Business
Companies are mining online sales data to determine what products to make. As the data shows emerging trends, businesses can switch to manufacturing those products
The footprints of Artificial Intelligence and Machine Learning can be traced back to the 1930s. The pattern continued in the late 1990s, when a confluence of multiple technologies led to the current boom in AI.Alan Turing, a mathematician and computer scientist in the 1950s said that computers could think.He devised a test to determine if a computer could be labelled intelligent.
But during that era, not enough processing power was available for machine learning to take place; therefore the field of AI was only established in 1956 after the inventions of computers.Even then, due to lack of computing power, AI did not deliver on its promises at that time. Also, the issue of language translation is so complex that even today it remains a challenge.
Evolution of Deep Learning
Neural Networks are a form of AI where flow of information resembles the human brain. In 2006, Geoff Hinton suggested stacking of large number of neural networks on top of each other to demonstrate that this technique gave these networks the capacity to learn non-linear relationships and to overcome some of the drawbacks of existing neural networks. Thus, the term deep learning was born.
In 2012, researchers were able to beat top ML models in a famous image recognition challenge called ImageNet.In 2016, Google’s AlphaGo AI program beat the prevailing world champion of Go, Lee Sedol. Go, is a famous Chinese board game that has more possible moves that the number of atoms in the universe.
Today, AI platforms like Tensorflow feature easy-to-follow tutorials on how to design AI models on our laptop.The vulnerability for AI and ML are data availability and processing power. If a scarcity exists of either, then prospects for AI success are low because statistics does not perform well under low data volumes. Also, if you have large amounts of data, the processing requirements rise.
Each of these factors brought large step changes in the amount of data available worldwide. The issue
AI became a rollercoaster ride when Google, IBM and other companies started using it. IBM designed an AI called Deep Blue that successfully beat Garry Kasparov, the world chess champion in the 1990s. This event brought AI to the centre stage, and triggered new research in AI and ML
Once we can have affordable data lakes and processing power available, we should be able to start developing more complex AI algorithms to extract information from raw data
now is how to make sense of all this data, which is where AI comes.AI and machine learning sits at the epicentre of this perfect storm as no human being can process terabytes of data through the normal methods of drawing graphs in visual tools or writing programs in Excel.
The whole product lifecycle is now being impacted by ML. For instance, companies are mining Amazon and eBay sales data to determine what products to make. As the data shows emerging trends, businesses can switch to manufacturing those products. Pricing these products will need machine learning support.Manufacturing processes can use machine learning to improve the usage of plant machinery and avoid down times.
ML: Use Cases for Modern Enterprises
Few common themes will be apparent while considering use cases for ML…
After sale-support now requires machine learning chatbots to support customer all around the clock and using machine learning can reduce the wait times for customers and can be faster than humans support staff.Exceptions are only the handful of industries which are data poor or where the subjective way or flavour of human feelings is involved. For example, people will be hesitant to let a computer cut your hair.