Machine learning is that field of computer science that is involved in enabling the ability to learn from experience with computers without any prior and explicit programming. Its association with data science lies in the use of intelligent algorithms to discover patterns in large data sets.
Some trends to look out for in 2017 are as follows:
This was an earlier trend which made machine learning a hot topic in data science. But the neural networks still needed supervision from humans. The level of supervision of the machine learning in the neural networks is decreasing and one can expect a purely unsupervised neural network or application to emerging soon.
From a conventional machine translation of languages in the digital format which involved the use of statistical programming methods, deep learning application has led to the emergence of neural machine translation (NMT) where large neural networks are able to translate using both supervised and unsupervised machine learning protocols. A prominent example of this shifting trend is Google translate. Google recently announced that their translate service is adopting NMT much more than their previous statistical methods.
The current world is progressing quickly towards the world that is overflowing with data of all sorts. At present, it is calculated that the amount of data in the world doubles while the cost of storing it in cloud form is decreasing at almost the same rate. The high data availability will lead to more machine learning, adaptation by groups that wish to learn from the data and this will lead to more refined data being produced exponentially. This constant explosion of data and consequent adoptions of machine learning constitutes a data flywheel that is expected to expand constantly in 2017. An example is of Tesla, where the data collection rate is at a million miles of driving data every 10 hours.
Instead of a conventional marketplace or even a digital marketplace which are relatively unorganized, an algorithm marketplace enabled by advanced machine learning modules will become a meeting point for researchers, companies, engineers, etc to not just meet and exchange ideas, but also gain useful data patterns that they can use in their own field of expertise.
Cloud-hosted artificial intelligence is expected to reduce costs for companies in need of developing such a system. With machine learning (ML) service providers, enabling cloud hosting, companies need only to rent such services getting accurate and faster results due to greater specialization by the ML service providers.
ML in the healthcare and construction sectors
It is expected that adoption of ML concepts and applications such as deep learning will improve medical imaging, analyzing clinical data, making sense of genomic data of large, diverse populations. IBN’s Watson is a prominent example.
In the construction sector, apart from the obvious application in modeling buildings, ML can be useful for selecting the best materials, the best companies or services according to the region of construction. The autonomous TMA truck is a great example, which intelligently predicts safety for drivers of the truck.
Contrary to doomsday-like predictions that greater the artificial intelligence greater the job loss, it is expected that increased cooperation between humans and machines will lead to better quality outcomes.
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