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Workera recently published a report on AI career pathways. It doesn't mention hardware. I also don't see the difference b/w SWE-ML & ML Engineer. But it highlights some important distinctions. I also like
@josh_tobin_ talk on the structure of AI teams https://www.youtube.com/watch?v=Qb3RhwNb4EM …pic.twitter.com/KysDChOjgR
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Some of the keywords in paper titles that have seen the most change from NeurIPS 2018 to 2019. - meta-learning, kernel methods, reinforcement learning are
- more hardware-aware, more theory-driven
- recurrent & convolutional get little love
Full NeurIPS recap coming soon!pic.twitter.com/vHYoW3LBR7
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I had a chance to discuss research w/ these amazing researchers. Takeaways: 1. Too many ML papers & most are bad. 2. For PhD apps,
@lawrennd &@tdietterich look at blog & OSS too. 3. Avoid trendy topics. Work on what you believe in. 4. Research may not be applicable & it's ok.pic.twitter.com/nk5nayp0fv
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When everyone is a Dr and you're just you
Humbled to speak alongside these wonderful researchers and industry leaders at the annual @vietaiorg summit. Excited about the progress my country is making in AI. If you're working on AI in Vietnam, I'd love to chat!pic.twitter.com/kLEZGVb9e2
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Non-CS people: "How many lines of code do you write a day?" Me: "Um idk minus 200?" Can we take a moment to appreciate all the relentless deleters who are willing to dig into the mess that is our code and make it concise and readable?pic.twitter.com/0EWW7kv23R
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The numbers of accepted papers from universities are more evenly distributed. Stanford, CMU, and MIT surprisingly overtook UC Berkeley by a large margin. Non-US institutions such as Oxford, Tsinghua, INRIA, and Peking University are rapidly catching up.pic.twitter.com/G0MEQh5uyu
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Stuck at the airport so I browsed
#neurips2019 papers. Out of 1429 accepted papers, 167 (~12%) have at least one author from Google/DeepMind, same as Microsoft, Facebook, IBM, & Amazon combined. Is there any stats on the % of reviewers who are Google/DeepMind affiliated?pic.twitter.com/bXPoB135PA
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Okay GPT-2 I made him eat a sandwich now what (if you haven't tried it out already GPT-2 gives fabulous dating advice https://talktotransformer.com/ )pic.twitter.com/hHt4xDYybR
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Companies with the hardest interviews (as perceived by candidates) are Google, Airbnb, and Amazon.pic.twitter.com/3HZ4NWckHb
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Senior candidates are harder to please than junior candidates. This might explain the abysmal Netflix interview experience. While all other companies keep their shares of senior interviews to under one third, Netflix exclusively hire senior positions.pic.twitter.com/eJuD0IbCDE
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The more negative experience a candidate has, the less likely they are to accept the offer. If a candidate who receives an offer has a positive experience, they accept the offer with probability 87%. If that candidate has a negative interview experience, the yield rate is 1/3.pic.twitter.com/43u0IFbCU7
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Candidates with offers are more likely to have a positive experience (correlation 0.75). Companies that give the best candidate experiences are Salesforce, Intel, and Adobe.pic.twitter.com/xxY4IPwYT6
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Everyone complains that the interview process is broken. It’s not entirely true, at least from the perspective of the candidates who get interviews. 60% of candidates report a positive interview experience.pic.twitter.com/pj910Hoakj
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Referrals matter, a lot. For junior roles, about 10 - 20% of candidates that get to onsites are referred, with Uber leading the chart with almost 30%. For senior roles, that numbers are higher. Salesforce, Uber, and Cisco all have ~30% of their senior onsite candidates referred.pic.twitter.com/HvFqXe0ZV1
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Strong correlation bw onsite-to-offer rate and offer yield rate (% of candidates who accept their offers). The more selective the company is, the less likely a candidate is to accept their offer. Candidates that pass interviews at FAANG are likely to have other attractive offers.pic.twitter.com/rWe6jxjJKX
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Each review consists of: - result (no offer/accept offer/decline offer) - difficulty (easy/medium/hard) - experience (positive/neutral/negative) - review (application/process/questions) The largest SWE employers are Google, Amazon, Facebook, and Microsoft.pic.twitter.com/fEXYRDYZD2
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You know your friend works at OpenAI when his phone autocorrects "ago" to "agi"
pic.twitter.com/EwHscYbFiE
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8. Introduction to Reinforcement Learning by DeepMind RL is hard, but David Silver is here to the rescue. This course provides a great introduction to RL with intuitive explanations and fun examples, taught by one of the world’s leading experts.https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ …
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Okay people it's "banh mi" as in the Vietnamese food not "bahn mi" as in the autobahn that takes you away from unhappy food.pic.twitter.com/vxJ7U3VKQ8
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What's the worst/best email you've received from a recruiter? Mine.pic.twitter.com/cAWht1fVM7
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