{"id":38870,"date":"2017-06-19T22:13:45","date_gmt":"2017-06-19T22:13:45","guid":{"rendered":"https:\/\/80000hours.org\/?post_type=career_profile&#038;p=38870"},"modified":"2024-11-06T15:52:54","modified_gmt":"2024-11-06T15:52:54","slug":"machine-learning-phd","status":"publish","type":"career_profile","link":"https:\/\/80000hours.org\/career-reviews\/machine-learning-phd\/","title":{"rendered":"Machine Learning&nbsp;PhDs"},"content":{"rendered":"","protected":false},"author":66,"featured_media":74397,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"[fn 1] \"AlphaGo's moves throughout the competition, which it won earlier this month, four games to one, weren't just notable for their effectiveness. The AI also came up with entirely new ways of approaching a game that originated in China two or three millennia ago and has been played obsessively since then. By their fourth game, even Lee was thinking differently about Go and its deceptively simple grid.\" [http:\/\/web.archive.org\/web\/20170105034850\/https:\/\/qz.com\/639952\/googles-ai-won-the-game-go-by-defying-millennia-of-basic-human-instinct\/](http:\/\/web.archive.org\/web\/20170105034850\/https:\/\/qz.com\/639952\/googles-ai-won-the-game-go-by-defying-millennia-of-basic-human-instinct\/) [\/fn]\r\n[fn 2] 'The field of Machine Learning seeks to answer the question \"How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?\"' Mitchell, Tom Michael. *[The discipline of machine learning](https:\/\/www.cs.cmu.edu\/~tom\/pubs\/MachineLearning.pdf).* Vol. 9. Carnegie Mellon University, School of Computer Science, Machine Learning Department, 2006.[\/fn]\r\n\r\n[fn 3] [http:\/\/web.archive.org\/web\/20170313102628\/http:\/\/shikharsharma.com\/publications\/msc-thesis.pdf](http:\/\/web.archive.org\/web\/20170313102628\/http:\/\/shikharsharma.com\/publications\/msc-thesis.pdf) [\/fn]\r\n\r\n[fn 4] [https:\/\/dash.harvard.edu\/bitstream\/handle\/1\/33840728\/KRAKOVNA-DISSERTATION-2016.pdf?sequence=4](https:\/\/dash.harvard.edu\/bitstream\/handle\/1\/33840728\/KRAKOVNA-DISSERTATION-2016.pdf?sequence=4)[\/fn]\r\n\r\n[fn 5] [http:\/\/web.archive.org\/web\/20150906145638\/http:\/\/reports-archive.adm.cs.cmu.edu\/anon\/ml2014\/CMU-ML-14-101.pdf](http:\/\/web.archive.org\/web\/20150906145638\/http:\/\/reports-archive.adm.cs.cmu.edu\/anon\/ml2014\/CMU-ML-14-101.pdf)[\/fn]\r\n\r\n\r\n[fn 6] \"Advancements in sensor technology coupled with breakthroughs in machine learning \u2014 the ability of computers to learn from vast amounts of data and improve over time \u2014 mean driverless cars (essentially supercomputers on wheels) could become a regular sight on the roads over the next few years.\" [http:\/\/web.archive.org\/web\/20170517071920\/https:\/\/www.nytimes.com\/2016\/12\/13\/technology\/google-parent-company-spins-off-waymo-self-driving-car-business.html](http:\/\/web.archive.org\/web\/20170517071920\/https:\/\/www.nytimes.com\/2016\/12\/13\/technology\/google-parent-company-spins-off-waymo-self-driving-car-business.html)  [\/fn]\r\n\r\n[fn 7] \"we've developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them.\" [http:\/\/web.archive.org\/web\/20170606075631\/https:\/\/research.googleblog.com\/2014\/11\/a-picture-is-worth-thousand-coherent.html](http:\/\/web.archive.org\/web\/20170606075631\/https:\/\/research.googleblog.com\/2014\/11\/a-picture-is-worth-thousand-coherent.html) [\/fn]\r\n\r\n[fn 8] \"We present the first deep learning model [a machine learning technique] to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.\" [http:\/\/web.archive.org\/web\/20170408091305\/https:\/\/deepmind.com\/research\/publications\/playing-atari-deep-reinforcement-learning\/](http:\/\/web.archive.org\/web\/20170408091305\/https:\/\/deepmind.com\/research\/publications\/playing-atari-deep-reinforcement-learning\/) [\/fn]\r\n\r\n[fn 9] \"In this paper, we describe a successful application of reinforcement learning [a machine learning technique] to autonomous helicopter flight.\" Kim, H. Jin, et al. \"[Autonomous helicopter flight via reinforcement learning](http:\/\/web.archive.org\/web\/20160804155229\/http:\/\/people.eecs.berkeley.edu:80\/~jordan\/papers\/ng-etal03.pdf).\" *Advances in neural information processing systems*. 2004. [\/fn]\r\n\r\n[fn 10] \"This paper introduces WaveNet, a deep neural network [a machine learning technique] for generating raw audio waveforms\" van den Oord, A\u00e4ron, et al. \"[Wavenet: A generative model for raw audio](https:\/\/pdfs.semanticscholar.org\/df04\/02517a7338ae28bc54acaac400de6b456a46.pdf).\" *CoRR abs\/1609.03499* (2016). Demo [here](https:\/\/deepmind.com\/blog\/wavenet-generative-model-raw-audio\/). [\/fn]\r\n\r\n[fn 11] The winning solution to the netflix prize for improving movie recommendations went to a team using machine learning techniques. Koren, Yehuda. \"[The bellkor solution to the netflix grand prize](http:\/\/web.archive.org\/web\/20161217122729\/http:\/\/www.stat.osu.edu\/~dmsl\/GrandPrize2009_BPC_BellKor.pdf).\" *Netflix prize documentation* 81 (2009): 1-10. [\/fn]\r\n\r\n[fn 12] According to [CB Insights](http:\/\/web.archive.org\/web\/20170206055852\/https:\/\/www.cbinsights.com\/blog\/top-acquirers-ai-startups-ma-timeline\/), \"Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce are competing in the race to acquire private AI companies, with Ford, Samsung, GE, and Uber emerging as new entrants. Over 200 private companies using AI algorithms across different verticals have been acquired since 2012, with over 30 acquisitions taking place in Q1'17 alone (as of 3\/24\/17). This quarter also saw one of the largest M&A deals: Ford's acquisition of Argo AI for $1B.\" [\/fn]\r\n\r\n[fn 13] \"Seven hundred fifty thousand patients develop severe sepsis and septic shock in the United States each year. More than half of them are admitted to an intensive care unit (ICU), accounting for 10% of all ICU admissions, 20 to 30% of hospital deaths, and $15.4 billion in annual health care costs\" Henry, Katharine E., et al. \"[A targeted real-time early warning score (TREWScore) for septic shock](http:\/\/web.archive.org\/web\/20161130130333\/http:\/\/stm.sciencemag.org\/content\/7\/299\/299ra122).\" *Science Translational Medicine* 7.299 (2015): 299ra122-299ra122.[\/fn]\r\n\r\n[fn 14] \"In summary, our study showed that a TREWScore could predict, many hours before standard screening protocols, patients at high risk of developing septic shock.\" Henry, Katharine E., et al. \"[A targeted real-time early warning score (TREWScore) for septic shock](http:\/\/web.archive.org\/web\/20161130130333\/http:\/\/stm.sciencemag.org\/content\/7\/299\/299ra122).\" *Science Translational Medicine* 7.299 (2015): 299ra122-299ra122.[\/fn]\r\n\r\n[fn 15] According to [paysa.com](https:\/\/web.archive.org\/web\/20170701080258\/https:\/\/www.paysa.com\/salaries\/machine-learning-engineer--t) in June 2017[\/fn]\r\n\r\n[fn 16] \"Twitter just paid $150m for 14-person Magic Pony, a UK-based AI visual search company barely anyone had heard of before the deal. At $10m+ per employee it marks a high water mark in AI for what is essentially a team acquisition. Magister has tracked 26 AI driven deals since 2014 in the US, Europe and Israel, 11 of which involved companies with less than 50 employees which were acquired largely, or entirely, for the team and capability. Across all 11 deals, the median price paid per employee has reached $2.4m, meaning a high quality AI company with 40 employees would be valued at near $100m. Even if it had little or no revenue.\" [https:\/\/www.linkedin.com\/pulse\/artificial-intelligence-teams-being-acquired-employee-victor-basta](https:\/\/www.linkedin.com\/pulse\/artificial-intelligence-teams-being-acquired-employee-victor-basta) [\/fn]\r\n\r\n[fn 17] From [http:\/\/web.archive.org\/web\/20150616151040\/http:\/\/www.cs.cmu.edu\/~harchol\/gradschooltalk.pdf](http:\/\/web.archive.org\/web\/20150616151040\/http:\/\/www.cs.cmu.edu\/~harchol\/gradschooltalk.pdf)[\/fn]\r\n\r\n[fn 18] \"But this semester, about 700 students signed up for the course \u2014 also known as Introduction to Machine Learning \u2014 according to Tommi Jaakkola, the computer science professor who created it. So at the first lecture, more than 100 students watched on a video screen in an overflow room. Not enough students voluntarily dropped the course, so Jaakkola came up with some \"preliminary homework\" to weed more out, by giving them a sense of the level of linear algebra and probability that'd be required of them.\" [http:\/\/web.archive.org\/web\/20170615121557\/https:\/\/www.bostonglobe.com\/business\/2017\/04\/06\/what-makes-this-hottest-class-mit\/wcAVJfK8U9D64hyVSTTbqI\/story.html](http:\/\/web.archive.org\/web\/20170615121557\/https:\/\/www.bostonglobe.com\/business\/2017\/04\/06\/what-makes-this-hottest-class-mit\/wcAVJfK8U9D64hyVSTTbqI\/story.html)  [\/fn]\r\n\r\n[fn 19] \"most companies today have no absolutely no need for machine learning (ML). The majority of problems that companies want to throw at machine learning are fairly straightforward problems be 'solved' with a form of regression.\" [http:\/\/web.archive.org\/web\/20170615121922\/http:\/\/ericbrown.com\/you-probably-dont-need-machine-learning.htm](http:\/\/web.archive.org\/web\/20170615121922\/http:\/\/ericbrown.com\/you-probably-dont-need-machine-learning.htm) [\/fn]\r\n\r\n[fn 20] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. \"[Deep learning](http:\/\/web.archive.org\/web\/20170615122200\/http:\/\/pages.cs.wisc.edu\/~dyer\/cs540\/handouts\/deep-learning-nature2015.pdf).\" *Nature* 521.7553 (2015): 436-444. [\/fn]\r\n\r\n[fn 21] \"Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achieve a similar level of performance and generality. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards. This paradigm of learning by trial-and-error, solely from rewards or punishments, is known as reinforcement learning (RL).\" [http:\/\/web.archive.org\/web\/20170803094013\/https:\/\/deepmind.com\/blog\/deep-reinforcement-learning\/](http:\/\/web.archive.org\/web\/20170803094013\/https:\/\/deepmind.com\/blog\/deep-reinforcement-learning\/) [\/fn]\r\n\r\n[fn 22] \"The reason I focus a lot on reinforcement learning and why OpenAI focuses a lot is that reinforcement learning and things like it, the extended versions of reinforcement learning seems like a better fit for what intelligent agents do in general. \r\n\r\nOften, I have very long range goals, trying to education, trying to get a PhD, trying to have a career, trying to start a family or something. These are all things that unfold over year and involve interacting with my environment in this very complicated way. Our reinforcement learning is the only paradigm we have that even close to capturing this,\" From [our interview with Dario Amodei](https:\/\/80000hours.org\/2017\/07\/podcast-the-world-needs-ai-researchers-heres-how-to-become-one\/) from OpenAI [\/fn]"},"categories":[1182,1181,342,1321,1328],"class_list":["post-38870","career_profile","type-career_profile","status-publish","format-standard","has-post-thumbnail","hentry","category-technical-ai-safety-research","category-artificial-intelligence","category-careers","category-machine-learning","category-machine-learning-phd"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine Learning PhD - Career profile - 80,000 Hours<\/title>\n<meta name=\"description\" content=\"We explain why it\u2019s a high-impact area, how to work out if it\u2019s for you, and exactly how and where to apply.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/80000hours.org\/career-reviews\/machine-learning-phd\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Thinking of doing a machine learning PhD? 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