# Jeff Dean on Reddit (u/jeffatgoogle)

Part of Scientists on Reddit, researched with Autumn (https://www.autumn.ai) from public sources. Updated 2026-11-07. Interactive version: https://www.autumn.ai/resources/scientists-on-reddit/jeff-dean

Jeff Dean: Google's chief scientist, co-founder of Google Brain.

- Reddit account: [u/jeffatgoogle](https://www.reddit.com/user/jeffatgoogle/) (Personal account)
- How we know it's theirs: [The 2016 Google Brain AMA in r/MachineLearning, posted from this account](https://www.reddit.com/r/MachineLearning/comments/4w6tsv/)
- Where they post, in the records we retrieved: r/MachineLearning (86), r/Bard (1), r/IAmA (1)
- Known activity in the posts we found: 2016 to 2025
- Wikipedia: https://en.wikipedia.org/wiki/Jeff_Dean

## Moments

### u/jeffatgoogle in r/MachineLearning, Sep 13, 2017 · Personal story

Replying to a redditor:

> How old is the cutoff for an intern? I'm in my late 40's. :)

> In 2012, I hosted [Geoffrey Hinton](https://scholar.google.com/citations?user=JicYPdAAAAAJ) as a visiting researcher in our group for the summer, but due to a snafu in how it was set up, he was classified as my intern.  We don’t have any age cutoffs for interns.  All we ask is that they be talented and eager to learn, like Geoffrey :).

Full thread: https://www.reddit.com/r/MachineLearning/comments/6z51xb/comment/dmybky4/ · 39 points as of Nov 7, 2026

### u/jeffatgoogle in r/MachineLearning, Aug 11, 2016 · Explains things

Replying to a redditor:

> The energy efficiency of the brain vs. the large amount of power and computing resources used for conventional deep learning models are often used as an argument to do more 'brain-inspired learning': 1. Is this a fair comparison to be made? If yes, what do you believe leads to…

> Regarding energy efficiency, real brains are definitely much more energy efficient and have much more computational ability than current machines.  However, the gap is perhaps not as bad as it might seem, for the reason that real brains take ~20 years to "train", whereas, since we are impatient machine learning researchers, we want to do experiments in a week.  If we were willing to have our experimental cycle time be 20 years instead of 1 week, we could clearly get much better energy efficiency, but we prefer the faster cycle time for experiments, even if it costs us in energy efficiency.

Full thread: https://www.reddit.com/r/MachineLearning/comments/4w6tsv/comment/d6dteht/ · 7 points as of Nov 7, 2026

### u/jeffatgoogle in r/MachineLearning, Aug 11, 2016 · AMA answer

Replying to a redditor:

> On a scale of 1-10, 10 being tomorrow and 1 being 50 years, how far away would you all estimate we are from general AI?

> A 6, but I refuse to be pinned down to whether the scale is linear or logarithmic.

Full thread: https://www.reddit.com/r/MachineLearning/comments/4w6tsv/comment/d6dlrh7/ · 33 points as of Nov 7, 2026

### u/jeffatgoogle in r/MachineLearning, Aug 11, 2016 · Personal story

Replying to a redditor:

> How did you all get into ML ? What were you first projects involving ML?

> I had to write a thesis to graduate with honors at the University of Minnesota, so I worked with my advisor, Vipin Kumar, and settled on the problem of exploring parallel training of neural nets on a 64 processor hypercube machine that the department had. Since neural nets were very computationally intensive, parallel training even back then was an attractive notion to allow scaling the approach to more realistic problems. For the thesis, I was able to get interesting speedups on training models for relatively moderately-sized problems. The computational model of neural nets, of many layers of abstraction, each building on each other, really appealed to me at the time, and I went into grad school with the intention of studying parallel computing, but ended up getting seduced by the appeal of writing compilers for high-level object-oriented languages, and did my Ph.D. research work in that area. However, that little notion that neural nets were interesting never really went away, and about five years ago, it seemed like it was worth exploring again, now that both computational power and interesting datasets had grown so much in the ~2 decades since I last revisited them. This led to the genesis of the Google Brain project (initially started by myself, Andrew Ng, and Greg Corrado).

Full thread: https://www.reddit.com/r/MachineLearning/comments/4w6tsv/comment/d6dgbb1/ · 7 points as of Nov 7, 2026