Thoughts From Your Humble Curators

We start to see how machine intelligence can be applied in controversial manner, two related pieces this week:

  • Lyrebird - which astounded us by not only mimicking multiple politicians, but they claim only one minute of training data is enough.
  • Campas - which provides sentence judgement based on software.

We also discuss Recursion Pharmaceuticals and what makes deep learning particularly useful in the company.

As always, if you like our newsletter, remember to subscribe and forward to your colleagues!

Artificial Intelligence and Deep Learning Weekly


Blog Posts

Open Source



Member's Question

Difference Between ML Engineer and Data Scientist?

Q: (From Gautam Karmaker) Guys, what is the difference between ML engineer and a data scientist? How they work together? How their work activity differ? Can you walk through with an use case example?"

A: (From Arthur, redacted)

"Generally, it is hard to decide what a title means unless you know about the nature of the job, usually it is described in the job description. But you can asked what are these terms usually imply. So here is my take:

ML vs data: Usually there is the part of testing/integrating an algorithm and the part of analyzing the data. It's hard to say how much the proportion on both sides for each job. But high dimensional data is more refrained form simple exploratory analysis. So usually people would use the term "ML" more, which mostly means running/tuning an algorithm. But if you are looking at table-based data, then it's like to be "data" type of job. IMO, that means at least 40% of your job would be manually looking at trends yourself.

Engineer vs scientist: In larger organization, there is usually a difference between the one who come up with the mathematical model (scientist) vs the one who control the production platform (engineer). e.g. If you are solving a prediction problem, usually scientist is the one who train, say the regression models, but the engineer is the guy who turn your model to create the production system. So you can think of them as the "R" and the "D" in the organization.

Both scientist and engineer are career tracks, and they are equally important. So you would find a lot of companies would have "junior", "senior", "principal", "director", "VP" prefixed the both track of the titles.

You will sometimes see terms such as programmer or architect replacing "engineer"/"scientist". Programmer implies their job is more coding-related, i.e. the one who actual write code. Architect is rare, they usually oversee big picture issues among programmers, or act as a balance between R&D organizations."

Artificial Intelligence and Deep Learning Weekly

About Us

This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 19,000+ members and host a weekly "office hour" on YouTube.

Share on Twitter | Share on Linkedin

Artificial Intelligence and Deep Learning Weekly