Artificial Intelligence (AI) is no longer a matter of the distant future but it is here and now with us- as machines are learning to reason, sense, learn, adapt and accordingly act in the palpable world. It is causing a transformation in various industries and changing lives in fabulous new ways.
Amplification of human abilities, automation of dangerous and tedious tasks, and solving critical problems are some of the ways by which AI is touching our lives.
This article deals with machine learning that enables AI along with deep learning.
Machine learning is growing the fastest, as a field in AI. Another reason for such expansion is dependent on its usage of key computational methods. At the basic level, machine learning computer algorithms make predictions based on data, allows machines to perform functions without being directed. Machines are trained to identify connections and patterns, relations in complicated data, scoring and classification of incoming and new tasks.
Presently it takes a lot of time to learn the machine learning models thereby slowing down the machine itself from learning information and new data. Explosion of data in today’s connected world, machine learning models, an increase in computing power, are becoming increasingly useful and accurate.
This is a branch of machine learning which is fast growing but nascent. It makes use of neural networks to understand and make use of unstructured and extremely complex to give positive insights in the realm of speech recognition, natural language processing (NLP), image recognition, and other tasks. Deep learning emulates the neurons and various synapses of the brain, learning done through reiteration and forming complex pathways within the neural network. There have been many benefits that have been derived from these algorithms like voice recognition on smartphones, facial recognition/tagging feature on social media, semi-autonomous vehicle control, and many more applications. This is actually how AI becomes all the more enlivening.
For machine learning to act and learn quickly, it requires an incredible computational capability to run complicated mathematical algorithms and process large amounts of data. Time for training machine tools requires to be reduced while the speed with which it can score data has to increase. Reducing the time to train machine models, while becoming fast enough to score data, requires a paradigm shift towards distributed computing, multi-node cluster set-up by using a consistent and robust methodology. There also needs to be a programming model that is consistent and a common architecture that could be used across high-performance computing, data analytics for handling machine learning workloads.
Data scientists require a powered processor family that would enable them to train complicated machine algorithms at a faster pace and also run a variety of workloads in comparison to GPUs. An ideal processor should integrate the enhancements required for high performance machine learning training with a mixed precision performance. This would not only reduce the time for deep learning training but also offer increased memory bandwidth for better performance of the complicated neural data sets.