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Bojan Tunguz

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Mar 17 • 7 tweets • 4 min read

#GTC22 is just around the corner, and it is a fully online and free event. There are numerous incredible and valuable sessions. If you are an academic, researcher, or an early-career ML/AI aspirant, the following sessions might be of particular interest to you. 🧵 👇 1/7

5 Steps to Building a Career in AI: You’ll hear from AI experts as they give you insight into their journey and discuss the top five most practical steps you can take when beginning a career in artificial intelligence.

nvda.ws/3MYkBRU 2/7

Fighting Diseases with High-resolution GPU-accelerated Molecular Dynamics Simulations. The rise of multi-GPU systems allows us to perform long timescale simulations either on supercomputers or in the cloud while enabling increased simulation accuracy. 3/7
nvda.ws/3ie4QYS

Fourier Neural Operators and Transformers for Extreme Weather and Climate Prediction. Deep learning (DL) offers novel methods that are potentially more accurate and orders of magnitude faster than traditional weather and climate model. 4/7
nvda.ws/3thWIwS

Student Sweepstakes. Want a chance to win an NVIDIA GeForce RTX 3090? Simply attend three live conference sessions, share your GTC experience in the form below, and automatically be entered in the sweepstakes. 5/7

nvda.ws/3ie5HJ4

Where to Learn About AI, Machine Learning and Deep Learning. 7 Places to Learn About AI, Fast - a sampling of GTC sessions to help fill your AI toolbox. 6/7

nvda.ws/36o4nAP

@nvidia

For all other @nvidia #GTC22 info, the following link will be useful:

nvidia.com/gtc/ 7/7

#deeplearning #ai #ml #ds #research

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Bojan Tunguz

@tunguz

Mar 10

OK, here is my honest take on when to use which approach/technique with a given dataset. These are my rules of thumb, and caveats could fill out the entire internet.
1. Up to a few hundred datapoint, use stats
2. For few hundred to few thousand use linear/logistic regression 1/

3. Between few thousand to about 10,000 it's anyone's guess. Gradient boosters generally do well here, with other "classical" algorithms.(SVM for instance) sometimes shining. 2/

4. Many thousands to about a billion datapoint is where Gradient Boosted trees rule. If you need just one algorithm, go with this. You'll never go wrong. 3/

Read 4 tweets

Bojan Tunguz

@tunguz

Mar 6

As anyone with an even cursory knowledge of AI history knows, there have been several AI Winters, periods of cooling of interest (and drop in funding) in AI research. All of these came about after the realization that at the time dominant AI paradigms were somehow limited. 1/7

For at least a decade now we have been enjoying an unprecedented AI Springtime. A perfect storm of major advances in algorithms (deep learning), computational architecture (GPUs) and availability of large high quality datasets has enabled the field to grow - exponentially! 2/7

However, in the real world there is no such thing as an endless exponential growth. What may seem like an exponential curve, inevitably turns out to be the fast rising part of a logistic curve. It is hard to speculate when the fastest part of the growth will end though. 3/7

Read 7 tweets

Bojan Tunguz

@tunguz

Feb 20

You gotta have options - that's the line that a jewelry salesman once used on my wife, and has become an inside joke in our family. However, that sales line is a very good consideration to have in all sorts of life situations.

1/

taylorpearson.me/optionality/

I endured some of the biggest setbacks in my life when I found myself in situations where I had just a few bad options, or even worse, just one terrible one. Over the years I found myself unconsciously working to maximize the number of options that I had. 2/

There are many ways that you can increase your optionality, and most of them don't require you to have access to outsize resources. 3/

Read 8 tweets

Bojan Tunguz

@tunguz

Feb 17

A few weeks ago I came across a tweet by a prominent ML/AI developer and researchers that promoted a new post about the use of transformers based neural networks for tabular data classification.

keras.io/examples/struc…

I took a look. Here is what I found. A 🧵 👇 1/27

The post was on Keras’ official site, and it seemed like a good opportunity to learn how to build transfomers with Keras, somethig that I’ve been meaning to do for a while. However, one part of the post and the tweet bothered me. 2/27

However, one part of the post and the tweet bothered me. It claimed that the model mateched “the performance of tree-based ensemble models.” As those who know me well know, I am pretty bullish on the tree-based ensemble models, 3/27

Read 27 tweets

Bojan Tunguz

@tunguz

Jan 20

@Nature

The current issue of @Nature has three articles that show how to make those error-correcting mechanisms achieve over 99% accuracy, which would make silicon-based qubits a viable option for the large-scale quantum computational devices.

#computing #quantumcomputing 1/

Fast universal quantum gate above the fault-tolerance threshold in silicon:
nature.com/articles/s4158…

Precision tomography of a three-qubit donor quantum processor in silicon:
nature.com/articles/s4158…

2/

Quantum logic with spin qubits crossing the surface code threshold:
nature.com/articles/s4158… 3/

Read 8 tweets

Bojan Tunguz

@tunguz

Dec 14, 2021

I posted this back in January:

I've worked for 4 different tech companies in various Data Science roles. For my day job I have never ever had to deal with text, audio, video, or image data. 1/4

Based on the informal conversations I've had with other data scientists, this seems to be the case for the vast majority of them. 2/4

Almost a year later this remains largely true: for the *core job* related DS/ML work, I have still not used any of the aforementioned data. However, for work-related/affiliated *research* I have worked with lots of text data. 3/4

Read 4 tweets

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