Tomorrow we embark on the 20’s! Finally, a decade that’s fun to say. Did anyone ever refer to the last decade as the tens? That’s to say nothing of the “naughts”.
This week, I’d like to look at the direction I think data science is headed in this next year.
Firstly, I foresee that deep learning will continue to dominate natural language processing and image processing. These technologies power self driving cars to all the digital assistants we talk to each day (Siri, Alexa, Google Assistant, to name a few).
Companies will find new and innovative ways to leverage the more and more accurate deep learning models as they’re released. Deep text and visual understanding is now within the reach of anyone with a credit card and the small budget required to rent a cluster of virtual machines for a few hours.
These costs are now too low to be a barrier of entry anymore, and practitioners are getting easier to find all the time. Take a look at these headlines:
- Teen Builds Cloud-Based Neural Network to Diagnose Diseases
- Teen AI Developer Builds Early Detection Tool for Brain Disease
- A West Virginia teen taught himself how to build a rapping AI using Kanye West lyrics
Secondly, as deep learning progresses and becomes more accessible, classical machine learning methods will see a lot less use and adoption. Simultaneously, the big three cloud platforms (Amazon Web Services, Google Cloud Platform and Azure) will continue to make it easier to deploy and monitor neural network models at scale.
The result is that development operations teams will become less of an important group to data scientists. New data science teams will have an internal resource for handling data (the data engineer) and deployment (the machine learning engineer).
Thirdly, reinforcement learning is coming up a lot more lately. Here’s an example to help motivate the need for reinforcement learning: Imagine you’re running an A/B test to determine which product to show to a group of users, and the click through rate for A is 10% better than B.
Well, you should always show A, right? Right! But that assumes that the click is the desired outcome. It might be. But maybe it’s customer lifetime value (LTV). But typical A/B tests don’t look downstream enough to measure LTV. That’s where reinforcement learning comes in. It’s much more challenging to get right, and can be labor intensive, but works well when it does, and it’s getting a lot more attention these days.
Other trends I see getting a lot more attention in the coming year(s):
- A.I. driven Security
- Explainability of A.I. “black box” models
- Better voice assistants (Google, Siri, Alexa)
- Wider adoption of autonomous vehicles through legislature and advances in A.I.
And finally, if you’re worried, here’s an overview of seven jobs that may be gone by 2020: https://fortune.com/2019/11/19/artificial-intelligence-will-obliterate-these-jobs-by-2030/
Happy New Year!!
Safer A.I. – Take a Look at Safety Gym
While much work in data science to date has focused on algorithmic scale and sophistication, safety — that is, safeguards against harm — is a domain no less worth pursuing. This is particularly true with applications like self-driving vehicles, where a machine learning system’s poor judgment might contribute to an accident. https://openai.com/blog/safety-gym/
The 8 Minute Guide to how Your Business can Solve Problems with A.I. and Machine Learning
There are a tremendous number of applications of A.I. to many types of businesses. But what are these applications? How do companies know if they can use their data to impact products and revenue? This easy-to-read article gets at the heart of those questions. https://towardsdatascience.com/the-8-minute-guide-to-how-your-business-can-solve-problems-with-ai-and-machine-learning-b7e66f4b484e
The 6 Research Directions of Deep Recommendation Systems That Will Change the Game
Over the past couple of years, there have been big changes in the recommendation domain, shifting from traditional matrix factorization algorithms to state-of-the-art deep learning-based methods. This post looks at the main reasons why this happened and what the research says about building modern (product) recommenders. https://towardsdatascience.com/recommendation-system-series-part-3-the-6-research-directions-of-deep-recommendation-systems-that-3a328d264fb7