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Andrej Karpathy Blog 2016-09-07 11:00 UTC Score 36.0 USR-0115-20160907-ai-specialis-85602144 Full article

A Survival Guide to a PhD

This guide is patterned after my “Doing well in your courses” , a post I wrote a long time ago on some of the tips/tricks I’ve developed during my undergrad. I’ve received nice comments about that guide, so in the same spirit, now that my PhD has come to an end I wanted to compile a similar retrospective document in hopes that it might be helpful to some. Unlike the undergraduate guide, this one was much more difficult to write because there is significantly more variation in how one can traverse the PhD experience. Therefore, many things are likely contentious and a good fraction will be specific to what I’m familiar with (Computer Science / Machine Learning / Computer Vision research). But disclaimers are boring, lets get to it! Preliminaries First, should you want to get a PhD? I was in a fortunate position of knowing since young age that I really wanted a PhD. Unfortunately it wasn’t for any very well-thought-through considerations: First, I really liked school and learning things and I wanted to learn as much as possible, and second, I really wanted to be like Gordon Freeman from the game Half-Life (who has a PhD from MIT in theoretical physics). I loved that game. But what if you’re more sensible in making your life’s decisions? Should you want to do a PhD? There’s a very nice Quora thread and in the summary of considerations that follows I’ll borrow/restate several from Justin/Ben/others there. I’ll assume that the second option you are considering is joining a medium…

Andrej Karpathy Blog 2016-05-31 11:00 UTC Score 59.0 USR-0115-20160531-ai-specialis-fd04d0db Full article

Deep Reinforcement Learning: Pong from Pixels

--> This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go , simulated quadrupeds are learning to run and leap , and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over the last ~year: I worked through Richard Sutton’s book , read through David Silver’s course , watched John Schulmann’s lectures , wrote an RL library in Javascript , over the summer interned at DeepMind working in the DeepRL group, and most recently pitched in a little with the design/development of OpenAI Gym , a new RL benchmarking toolkit. So I’ve certainly been on this funwagon for at least a year but until now I haven’t gotten around to writing up a short post on why RL is a big deal, what it’s about, how it all developed and where it might be going. Examples of RL in the wild. From left to right : Deep Q Learning network playing ATARI, AlphaGo, Berkeley robot stacking Legos, physically-simulated quadruped leaping over terrain. It’s interesting to reflect on the nature of recent progress in RL. I broadly like to think about four separate factors that hold back AI: Compute (the obvious one: Moore’s Law, GPUs, ASICs), Data (in a nice form, not just out there somewhere on the int…

Oxford Machine Learning Research Group 2016-01-11 17:55 UTC Score 39.0 USR-0027-20160111-research-aca-96a14953 Full article

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Autonomous Intelligent Systems This project intertwines Bayesian inference, model-predictive control, distributed information networks, human-in-the-loop and multi-agent systems. The project focuses on the principled handling of uncertainty for distributed modelling in complex environments which are highly dynamic, communication poor, observation costly and time-sensitive. We aim to develop robust, stable, computationally practical and principled approaches which naturally accommodate these rea…

Disrupt Africa 2015-12-22 13:06 UTC Score 17.0 USR-0197-20151222-regional-new-b9f45806 Full article

Comment on Banks face extinction if they don’t find ways to work with fintech startups by Dejene Mulugeta

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Disrupt Africa 2015-12-22 12:59 UTC Score 17.0 USR-0197-20151222-regional-new-2ab39ff2 Full article

Comment on Impact Hub’s Resilience Africa to launch new hubs, incubators by Dejene Mulugeta

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Andrej Karpathy Blog 2015-11-14 11:00 UTC Score 27.0 USR-0115-20151114-ai-specialis-46526a97 Full article

Short Story on AI: A Cognitive Discontinuity.

The idea of writing a collection of short stories has been on my mind for a while. This post is my first ever half-serious attempt at a story, and what better way to kick things off than with a story on AI and what that might look like if you extrapolate our current technology and make the (sensible) assumption that we might achieve much more progress with scaling up supervised learning than any other more exotic approach. A slow morning Merus sank into his chair with relief. He listened for the satisfying crackling sound of sinking into the chair’s soft material. If there was one piece of hardware that his employer was not afraid to invest a lot of money into, it was the chairs. With his eyes closed, his mind still dazed, and nothing but the background hum of the office, he became aware of his heart pounding against his chest- an effect caused by running up the stairs and his morning dose of caffeine and taurine slowly engulfing his brain. Several strong beats passed by as he found his mind wandering again to Licia - did she already come in? A sudden beep from his station distracted him - the system finished booting up. A last deep sigh. A stretch. A last sip of his coffee. He opened his eyes, rubbed them into focus and reached for his hardware. “Thank god it’s Friday”, he muttered. It was time to clock in. Fully suited up, he began scrolling past a seemingly endless list of options. Filtering, searching, trying to determine what he was in the mood for. He had worked hard a…

Oxford Machine Learning Research Group 2015-11-05 09:55 UTC Score 36.0 USR-0027-20151105-research-aca-e6c30d21 Full article

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Applications Current Projects Human Agent Collectives - ORCHID As computation increasingly pervades the world around us, we will increasingly work in partnership with highly inter-connected computational agents that are able to act autonomously and intelligently. Humans and software agents will continually and flexibly establish a range of collaborative relationships with one another, forming human-agent collectives (HACs) to meet their individual and collective goals.

TechCabal 2015-06-14 07:37 UTC Score 12.0 USR-0196-20150614-regional-new-311b6c7b Full article

Comment on Seven Top Nigerian Universities Where Nigerian Developers Come From by Seven Top Nigerian Universities Where Nigerian Developers Come From | WAKAPOST.

[…] Still, we were curious about what universities Nigerian coders come from. So we decided to make a list, based on an informal survey of tech companies and developers who we are familiar with. Some are no surprise, while others that made the list areSeven Top Nigerian Universities Where Nigerian Developers Come From […]

TechCabal 2015-06-11 16:16 UTC Score 12.0 USR-0196-20150611-regional-new-9922f87f Full article

Comment on European Business Angel Network is Ready to Partner with the African Angel Community by Thursday News Roundup: European Business Angel Network ready to support African Angel Community | techcabal.com

[…] European Angel Network Ready to Partner with African Angel Community The European Business Angel Network (EBAN) is ready to the support of the development of the African angel community. […]

Oxford Machine Learning Research Group 2015-06-11 16:04 UTC Score 20.0 USR-0027-20150611-research-aca-ad8775d9 Full article

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TechCabal 2015-06-11 11:31 UTC Score 17.0 USR-0196-20150611-regional-new-fa6d55f6 Full article

Comment on 88mph halts investments in African startups by Africa’s best-known tech funder is taking a break from investing in startups - Quartz

[…] better known locally based pan-African funders, and any sign that it is halting its operations is bad for sentiment in Africa. Barnwell, who stresses that 88mph is merely taking a break, says there is no reason for […]

Oxford Machine Learning Research Group 2013-10-14 16:55 UTC Score 20.0 USR-0027-20131014-research-aca-a9aa633f Full article

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Oxford Machine Learning Research Group 2012-12-11 12:48 UTC Score 20.0 USR-0027-20121211-research-aca-65fe38dc Full article

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Oxford Machine Learning Research Group 2012-12-11 12:34 UTC Score 20.0 USR-0027-20121211-research-aca-4e4b2d6a Full article

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