Several Key Trends in 2017

Below are several key trends in the Artificial Intelligence, Big Data, and Data Science fields that I extracted from the links 1, 2, and 3:

  • 2017 will be a big year for both IoT and AI
  • Increasing hybridization of deep learning with other ML/AI techniques, as is typical for a maturing technology
  • More advances in unsupervised learning
  • More complex tasks, more domains, and more acceptance of ML as the way to exploit data everywhere
  • AutoML systems will start replacing human experts for standard machine learning analyses in 2017
  • In 2017, companies will be looking to bring best-of-breed Deep Learning technologies in house to improve the bottom line
  • Automated machine learning will begin to have far-reaching consequences in ML, AI, and data science, and 2017 will likely be the year this becomes apparent
  • This surge of Data volume will continue to increase in 2017. Also in 2017 I believe there will be a surge of projects that will include Machine Learning, Cognitive Computing and predictive analytics, however, data privacy challenges will continue to persist in 2017.

  • Data Scientist and Chief Data Officer/Architect positions will become more utilized and more clearly defined in 2017

  • The dominant 2017 trend will be programmers’ rush to gain data science skills in order to grow their careers. The hottest projects in data science in 2017 will focus on streaming media analytics,  embedded deep learning, cognitive IoT, cognitive chatbots, embodied robotic cognition, autonomous vehicles, computer vision, and autocaptioning. Also, we’re going to see mass deployment of a new generation of optimized neural chipsets, GPUs, and other high-performance cognitive computing architectures in 2017

  • 2017 will be the year of trying to sort-out information rights, privileges, responsibilities, ownership and sovereignty–especially for IoT generated data.

  • Deep learning and artificial intelligence will become smarter and will be applied more often by organizations

  • In 2017, we expect to see greater expansion of edge analytics use cases: machine learning embedded with sensors or close to the point of data collection — the machine learning may be invoked via APIs or in processors close to the data collector or integrated into the sensor chip architecture itself.

  • Questioning of model assumptions: Polling failures in the 2016 presidential election will lead more managers to question the assumptions behind analytical models.

  • Classification of cognitive tools: More organizations will understand and classify the different cognitive tools available to them and apply them to appropriate business problems.

  • As data continues to pervade every aspect of our professional and personal lives, courtesy of the Internet of Things, both the private and public sectors will be pressured to ensure that the collection, use, and analysis of data is safe, secure, and ethical. If a company doesn’t get it right, they will cease to exist.

  • From the conversations we have had with many customers, come 2017 we might witness the explosion of data science as a domain within the Masses.

Let’s keep our fingers crossed and embrace the exciting year of 2017!


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