Future of Machine Learning
predictions about Machine Learning, based on current technology trends and ML’s systematic progression toward maturity:
ML will be an integral part of all AI systems, large or small.
As ML assumes increased importance in business applications, there is a strong possibility of this technology being offered as a Cloud-based service known as Machine Learning-as-a-Service (MLaaS).
Connected AI systems will enable ML algorithms to “continuously learn,” based on newly emerging information on the internet.
There will be a big rush among hardware vendors to enhance CPU power to accommodate ML data processing. More accurately, hardware vendors will be pushed to redesign their machines to do justice to the powers of ML.
Machine Learning will help machines to make better sense of context and meaning of data.
Some Predictions about Machine Learning
A seasoned user of ML techniques shares his insights into the world of ML, suggesting these trends are imminent in the field of ML:
Use of Multiple Technologies in ML: The emergence of IoT has benefitted Machine Learning in many ways. The use of multiple technological strategies to achieve better learning is currently is practice in ML; in the future more “collaborative learning” by utilizing multiple technologies is probable.
Personalized Computing Environment: Developers will have access to API kits to design and deliver “more intelligent application.” In a way, this effort is akin to “assisted programming.” Through these API kits, developers will easily embed facial, speech, or vision-recognition features into their systems.
Quantum Computing will greatly enhance the speed of execution of ML algorithms in high-dimensional vector processing. This will be the next conquest in the field of ML research.
Future advancement in “unsupervised ML algorithms” will lead to higher business outcomes.
Tuned Recommendation Engines: ML-enabled services of the future will become more accurate and relevant. For example, the Recommendation Engines of the future will be far more relevant and closer to an individual user’s personal preferences and tastes.