Data Science and AI into 2018 - A PoV

As we leave past memories of 2017 and embark onto 2018, we look forward to more interesting and mature aspects of Data Science and AI. While last few years has noticed lot of interesting ideas, innovations around Machine Learning(ML), Deep Learning(DL) etc. were getting into mainstream, 2018 will probably look at how automated ML can play a role, how DL can become better, faster and will be able to solve more complex business problems.

We knew "Data is the new oil". Andrew Ng has mentioned "AI is the new electricity".

1. Automated Machine Learning will be the "new normal": With more focus around data preparations, data transformations and data management, the automation of Machine Learning and democratization of it will start getting mainstream. The framework and life-cycle of Machine Learning are anyways being defined as part of the transformation journey earlier.

2. Practical real life use cases increase and go in-depth: According to Gartner, 59 percent of organizations are still building their enterprise AI strategies while the remaining 41 percent of the organizations have already made the plunge. Organizations are getting "mature" to solve their real life problems.

3. Innovation around Machine Learning / Data Science will accelerate: There is no disagreement that innovation in the world of Machine Learning, Deep Learning is accelerating from last few years and not new. However, at the same time, despite all new research, acceleration of innovation, the fact remains that most of the firms struggle to use these cutting-edge solutions and approaches in real life applications. This will probably change in 2018 as innovation around Machine Learning catches up with the theoretical research and approaches.

4. Hardware will continue to augment: With focus in Deep Learning getting more important, availability of requisite hardware to augment the power of analytics will be felt. Since availability and analysis of unstructured content, behavioral content etc. are getting more important; Machine Learning will take a step forward from its contemporary scope of implementation to find additional capabilities for mainstream business needs.

5. Chatbots / Digital Assistants getting smarter: Chatbots are already creating a lot of impact. Whether it's Amazon's Alexa, or Google Assistant, or Microsoft's Cortana or others; conversational systems are having some sort of purposeful interactions with humans through text or speech. As plethora of data being used to train these interfaces, it is getting more important to learn from these experiences by these systems. This is getting smarter as we move into 2018.

Some of the stats from different sources indicate - StackOverflow, a site which helps developers for coding related questions/issues, is observing more visits to its site for R and Python related questions than Java etc in 2017. During the year, languages such as R and Python maintained it's popularity and of course more adoption and usage around open source is getting mature as well.

AI and its sub-components such as Machine Learning, Deep Learning, Natural Language Processing are being used heavily around various analytics strategies for functions involving Sales, Marketing, Operations and thus enabling the decision making process better and faster. This will continue to refine into 2018.

Disclaimer: "The postings on this site are my own from my experiences, thoughts, readings from various sources and don't necessarily represent any firm's positions, strategies or opinions.”