This was my first day at Strata. Here’s what I found.
I made the very mistake I warned against yesterday: I went to sessions based on the topic, and not the quality of the speaker.
I’ll be more selective about my sessions for the next couple days.
I asked a dozen people, from a variety of industries, what they did for a living. I also asked how ensured their work wasn’t being used to make more profit in an unethical way.
Nobody had an answer to the latter question. I’m fervently hoping this is due to my low sample size and not broadly representative of the data analytics community.
In addition to my ethical survey I had the chance to talk to people from a D.C. startup, the Lawrence Berkeley Lab, Microsoft Research, Netflix, Etsy, Vertafore, the Department of Defense, and Sage Bionetworks. Everyone was ridiculously smart, and most of them were data scientists.
I came prepared with a list of questions:
I found some common elements:
The range of subject areas covered was immense.
There were some boring problems discussed…
Luckily, I was saved by the amount of discussion on data-intensive genomics…
The societal benefit from this work could be immense. I understand why he was so cheerful when he talked.
I was impressed by the quality of thought put into the project:
Deep neural networks have gotten a lot of press lately, mostly because they can work well on problems most ML algorithms struggle with (image recognition, speech recognition, machine translation).
Ilya Sutskever gave a good, useful intro into deep neural networks. ‘Deep’ in this case refers to 8-10 layer of neurons ‘hidden’ between the input and output layers. A traditional neural net has 1-2 hidden layers.
The reasoning to look at 10 layers is great. Humans can do a variety of things in 0.1 seconds. However, neurons are pretty slow; they can only fire about 100/second. Therefore a human task that happens in under 0.1 seconds takes only 10 layers of neurons to do.
One of the big problems behind neural networks is that they require a lot of data to train at this depth. They are also not intuitive to tune; Ilya didn’t go over that at all in his session. It was a good 101-level talk.
“Give me explainability or give me depth”
For more, I’d recommend the Neural Networks Blog.
The reception afterwards was mostly dull. The food was good, and free. The vendors, however, were spreading their own particular flavors of FUD.
I asked 11 different vendors for the data to back up claims behind their value propositions. The responses were a comic mix of dumbfounded expressions, misdirection, and spin. It’s hilarious that companies selling to data and analysis professionals don’t use data to back up their marketing claims.
I find myself excited about the potential to meet awesome people and learn amazing things.
I’m looking forward to tomorrow.Permalink
This week I will be at the Strata conference. It’s the place to be if you’re a data scientist. I shall be there, blogging the whole time.
Register early. Get a hotel early. Book flights early.
I spent about 90 minutes looking at different options and found a price difference of $600+ between my first choice and the cheapest option.
Conferences are expensive and brief. My uber-goal is to be as effective as possible in a limited time. There isn’t time to see everything or meet everyone. I must be selective with my time.
I have been to many technical conferences over the years, and developed tactics that work well.
Conferences last longer than adrenaline. Sleep, nutrition, and hydration are critically important. A brain needs fuel.
Great speakers are good teachers regardless of their session topic. Great topics are only sometimes presented well. I attend sessions based on speaker(s)’ quality.
I learn from the brightest people I can find: engineers, scientists, and researchers. They are always practitioners of some kind.
I contact people before the conference. I say I want to chat, mention some topics we have in common, and ask if they’d like to meet or exchange contact info.
I follow most of the speakers on Twitter, and pay close attention to what sessions they recommend. Those are invariably good.
I prepare a list of questions. What do I want to learn? What are the most useful questions to ask? Which ones minimize my own bias?
I go to as many informal/collaborative events as I can. It’s amazing what I can learn from someone when they let their hair down.
Strata has some good options for this:
I rarely learn anything useful from salespeople, marketers, recruiters, or PMs. I avoid them.
I automatically disqualify anyone who is sexist, racist, or otherwise mean. I try to call them on their behavior, and then avoid them. I have better things to do than deal with their crap.
Great technology sells itself, often by word of mouth. I wonder why companies even do technical marketing. Good engineers have finely honed bullsh*t filters.
A heavily-marketed product is often an inferior product. A company that spends on marketing is choosing to not invest in R&D. I have an anti-marketing bias when making purchasing decisions for this reason.
The best positions often aren’t advertised. Recruited positions are often terrible.
Technical people can spot talent. I network with technical folks. Again, I avoid recruiters.
Very few of my contacts stay in the same job for more than 5 years; 1-2 years is typical. I find it helpful to cultivate useful contacts, especially people who work in healthy companies or ethical industries.
I follow Wheaton’s Law. I network to meet people, learn from them, and lay the groundwork for a potential next gig. The least I can do is return the favor, often. It’s ethical and pays dividends.
I will gladly buy you a drink or a cup of coffee. Let’s chat.Permalink