Research Leaders on Data Mining, Data Science and Big Data key advances, top trends

Continuing our practice of yearly review of Data Science landscape through feedback from research leaders, we reached out to a number of Data Mining, Data Science and KDD research leaders last month with the following two questions:

Q1. What were the most important research advances in Data Science / Data Mining / Machine Learning in 2015?

Q2. Which Data Science / Data Mining / Machine Learning trends do you expect to dominate in 2016?

The most popular research advances in 2015 were: Deep Learning (particularly in speech and vision), distributed machine learning, and commoditization of analytics.

The most popular trends for 2016 were: Deep Learning, self-learning systems, Big Data, and Internet-of-Things (IoT).

Here are the individual answers:

Qiang Yang, Professor, HKUST and the head of Noah's Ark Lab: 

Q1. I think that there are two. 

First, an important development in our field in 2015 is the realization that Deep Learning can be used to scale up the reinforcement learning problems, and beat human performance in Atari games [DeepMind's Nature article, 2015].  This work cleverly bypasses the problem explosive state space and demonstrated the ability to solve the complex real time planning problem.

A second development is the realization that using one example, a learning system can also acquire the ability to generate intelligent behavior that mimics that of a human’s.  This one-shot learning style allows us to get closer to human-like learning, and potentially expands the scope of applications of data mining and machine learning.

Human-level concept learning through probabilistic program induction,
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
Science, 11 Dec 2015, VOL 350 ISSUE 6266


Q2. I think more cognitively motivated learning algorithms and their applications will emerge as we march into 2016.  These abilities include computer-human dialog systems with long and short term memory, ability to understand text and image documents much deeper, and abilities to carry out sophisticated planning and reasoning tasks.

Pedro Domingos, Professor, U. of Washington:

Q1. 2015 was once again dominated by progress in deep learning (e.g., further substantial improvements in object recognition and detection, etc.).

Q2. The main event of 2016 is that progress in deep learning will start to flatten out, barring the emergence of major new directions (i.e., the techniques that have propelled progress in recent years - convnets, backprop, LSTMs, etc. - will start to run out of steam).

Read what other research leaders think in KDnuggets post

Research Leaders on Data Mining, Data Science and Big Data key advances, top trends

https://www.kdnuggets.com/2016/01/research-leaders-data-science-big-data-top-trends.html