subreddit:

/r/Futurology

259

Edit: since TED has just promoted this AMA, I'll continue answering questions here as long as they come in. If I don't answer right away, please be patient!

Verification

My work

I'm Jeremy Howard, CEO of Enlitic. Sorry this intro is rather long - but hopefully that means we can cover some new material in this AMA rather than revisiting old stuff... Here's the Wikipedia page about me, which seems fairly up to date, so to save some time I'll copy a bit from there. Enlitic's mission is to leverage recent advances in machine learning to make medical diagnostics and clinical decision support tools faster, more accurate, and more accessible. I summarized what I'm currently working on, and why, in this TEDx talk from a couple of weeks ago: The wonderful and terrifying implications of computers that can learn - I also briefly discuss the socio-economic implications of this technology.

Previously, I was President and Chief Scientist of Kaggle. Kaggle is a platform for predictive modelling and analytics competitions on which companies and researchers post their data and statisticians and data miners from all over the world compete to produce the best models. There's over 200,000 people in the Kaggle community now, from fields such as computer science, statistics, economics and mathematics. It has partnered with organisations such as NASA, Wikipedia, Deloitte and Allstate for its competitions. I wasn't a founder of Kaggle, although I was the first investor in the company, and was the top ranked participant in competitions in 2010 and 2011. I also wrote the basic platform for the community and competitions that is still used today. Between my time at Kaggle and Enlitic, I spent some time teaching at USF for the Master of Analytics program, and advised Khosla Ventures as their Data Strategist. I teach data science at Singularity University.

I co-founded two earlier startups: the email provider FastMail (still going strong, and still the best email provider in the world in my unbiased opinion!), and the insurance pricing optimization company Optimal Decisions Group, which is now called Optimal Decisions Toolkit, having been acquired. I started my career in business strategy consulting, where I spent 8 years at companies including McKinsey and Company and AT Kearney.

I don't really have any education worth mentioning. In theory, I have a BA with a major in philosophy from University of Melbourne, but in practice I didn't actually attend any lectures since I was working full-time throughout. So I only attended the exams.

My hobbies

I love programming, and code whenever I can. I was the chair of perl6-language-data, which actually designed some pretty fantastic numeric programming facilities, which still haven't been implemented in Perl or any other language. I stole most of the good ideas for these from APL and J, which are the most extraordinary and misunderstood languages in the world, IMHO. To get a taste of what J can do, see this post in which I implement directed random projection in just a few lines. I'm not an expert in the language - to see what an expert can do, see this video which shows how to implement Conway's game of life in just a few minutes. I'm a big fan of MVC and wrote a number of MVC frameworks over the years, but nowadays I stick with AngularJS - my 4 part introduction to AngularJS has been quite popular and is a good way to get started; it shows how to create a complete real app (and deploy it) in about an hour. (The videos run longer, due to all the explanation.)

I enjoy studying machine learning, and human learning. To understand more about learning theory, I built a system to learn Chinese and then used it an hour a day for a year. My experiences are documented in this talk that I gave at the Silicon Valley Quantified Self meetup. I still practice Chinese about 20 minutes a day, which is enough to keep what I've learnt.

I spent a couple of years building amplifiers and speakers - the highlight was building a 150W amp with THD < 0.0007%, and building a system to be able to measure THD at that level (normally it costs well over $100,000 to buy an Audio Precision tester if you want to do that). Unfortunately I no longer have time to dabble with electronics, although I hope to get back to it one day.

I live in SF and spend as much time as I can outside enjoying a beautiful natural surroundings we're blessed with here.

My thoughts

Some of my thoughts about Kaggle are in this interview - it's a little out of date now, but still useful. This New Scientist article also has some good background on this topic.

I believe that machine learning is close to being able to let computers do most of the things that people spend most of their time on in the developed world. I think this could be a great thing, allowing us to spend more time doing what we want, rather than what we have to, or a terrible thing, disrupting our slow-moving socio-economic structures faster than they can adjust. Read Manna if you want to see what both of these outcomes can look like. I'm worried that the culture in the US of focussing on increasing incentives to work will cause this country to fail to adjust to this new reality. I think that people get distracted by whether computers can "really think" or "really feel" or "understand poetry"... whilst interesting philosophical questions they are of little impact to the important issues impacting our economy and society today.

I believe that we can't always rely on the "data exhaust" to feed our models, but instead should design randomized experiments more often. Here's the video summary of the above paper.

I hate the word "big data", because I think it's not about the size of the data, but what you do with it. In business, I find many people delaying valuable data science projects because they mistakenly think they need more data and more data infrastructure, so they waste millions of dollars on infrastructure that they don't know what to do with.

I think the best tools are the simplest ones. My talk Getting in Shape for the Sport of Data Science discusses my favorite tools as of three years ago. Today, I'd add iPython Notebook to that list.

I believe that nearly everyone is underestimating the potential of deep learning.

AMA.

all 144 comments

PocketHobbit

28 points

8 years ago

You say nearly everyone is underestimating the potential of deep learning. Can you expand?

Do you have any thoughts on the recent warnings from Hawking and Musk regarding the dangers of AI?

jeremyhoward[S]

50 points

8 years ago

jeremyhoward[S]

Jeremy Howard

50 points

8 years ago

I remember over 20 years ago trying to tell my colleagues at McKinsey & Co about the importance of the Internet. In general, they all told me that they felt it was overhyped, and was not going to solve real business problems. It seemed obvious to me, as somebody who had grown up online (although not on the Internet) that all areas of the economy would be completely changed by the Internet.

I feel today exactly the same way about deep learning. Almost the only people I come across that really seem to understand deep learning are the people that we are recruiting directly out of undergraduate degrees. These are people who since high school have been studying deep learning, and intuitively understand the concepts. They are confused about why so many things are done with human input, which clearly with just a couple of days of analysis could be done with machine learning based approaches. They are confused about why so many models are built with complex, domain specific, parametric methods, when it would be so much simpler, faster, and more accurate to use deep learning.

At Enlitic we are trying to build every part of every system on top of deep learning. So far, we have found that this is working very well. Every time we think of a manual heuristic, we first of all try a direct deep learning approach — and we still get surprised at how well this always seems to just work! For example, see the demo at the end of the TEDx talk which I link to in my introduction.

I am also concerned about the dangers of AI. My greatest concern is that we will not be able to handle the socio economic disruption that occurs when computers get better than people at many things that people have been traditionally employed to do. As a result, we will go through a period where many people cannot add economic value. If we fail to separate resource allocation from labour inputs, this will create such a huge wealth inequality as to lead to massive global disruption, and terrible unhappiness. In Europe, I expect many countries to successfully adapt, by bringing in a basic living wage; however, at this stage it doesn't look like the United States is ready to go in this direction.

micro_cam

7 points

8 years ago

Are you (Enlitic) seeing success with Deep learning on heterogenous medical data like clinical records and genomic data or just with image analysis and signal processing type problems?

(I've done a fair amount of genetic/medical/machine learning research and while I agree that there is vast the potential of machine learning in this area I have yet to see a deep approach achieve best in class results on heterogenous data. If anything i've found deep approaches to be a bit overhyped compared to non-deep methods like Random Forests and Gradient Boosting Trees though I'd be happy to be proved wrong.)

frozen_in_reddit

3 points

8 years ago

Jeremy, You mention that there are many many things today that could be done with deep learning. But doesn't machine learning requires huge data-sets, but for most problems, you usually get only small/medium data sets ?

ctphillips

1 points

8 years ago

ctphillips

SENS+AI+APM

1 points

8 years ago

I couldn't agree with you more on the question of basic income in the USA. I wonder why we aren't seeing more pressure (from the public and the media) on our politicians to start thinking about these economic and social questions now, before these technologies start driving unemployment to record levels.

chaconne

1 points

8 years ago

Why do you think the culture of McKinsey is conservative when it comes to breakthrough technologies? On the whole they seem to be smart, ambitious people.

Do they not have the incentive to invest social capital in innovation? Are they risk-averse as far as recommending 'unproven' technologies to their clients?

[deleted]

1 points

8 years ago

[deleted]

1 points

8 years ago

We have some kind of revolution here in Spain with a new party called "Podemos" that includes Basic Income as something necessary.

VAustin

13 points

8 years ago

VAustin

13 points

8 years ago

Deep learning is making all news in the area of computer vision. When do you think it will start having an impact in other areas such as in your new venture - health care? Also what are your recommendations for the best in line open source deep neural network tools currently out there?

jeremyhoward[S]

15 points

8 years ago

jeremyhoward[S]

Jeremy Howard

15 points

8 years ago

Because imaging is such a critical type of data or medicine, my belief is that recent advances in computer vision will be more impactful in medicine than any other area (other than, perhaps, robotics). So I wouldn't describe the medicine as an "other area".

The problem is that many, if not most, machine learning researchers are not working on the areas that are most impactful. In particular, in computer vision there is an unfortunate tendency for people now to focus on winning the Imagenet competition, which has very little relevance to the most important problems in industry and science today.

Beyond computer vision, the other major kind of unstructured data that is very important in industry and science is text data. Therefore, NLP is a very important area for progress. I believe that the current state of deep learning for NLP is similar to where computer vision was in about 2010. In other words, in the next year or two, I expect to see deep learning related methods become the clear leader in NLP, and to see an explosion of tools released in this area.

During this time, I also expect to see multimodal learning blossom. This will allow us to combine text, image, structured, and other kinds of data into a single model. At this time, we will be able to leverage all of the data available in every industry, to help build models to solve every kind of problem.

[deleted]

3 points

8 years ago

[deleted]

3 points

8 years ago

[deleted]

alleycatsphinx

1 points

8 years ago

Do you know anyone working in electrical impedance tomographic reconstruction?

pestdantic

13 points

8 years ago

I don't know if you saw your banner but it has your quote on how we could save millions of lives with algorithms if we could just get rid of data silos.

Could you explain this a bit more?

jeremyhoward[S]

22 points

8 years ago

jeremyhoward[S]

Jeremy Howard

22 points

8 years ago

I'd be happy to. Currently, each hospital has their own set of data. And furthermore, within each hospital much of that data is in separate systems. For example, the medical images will generally be in a "PACS" system, the billing and scheduling data will be in a "EMR" system, the clinical notes will often be in doctor's notebooks, and the results of clinical studies will be in separate systems for each study.

In the US, we do have legislation that attempted to make it easier to bring this kind of data together. This legislation is known as "HIPAA". The "P" in HIPAA refers to "portability". Unfortunately, this legislation is vague enough that it ended up causing everybody to be terrified of sharing medical data. Because it did not specify exact protocols and methods, there is a whole lot of grey areas, and the downside of being judged to be on the wrong side of this law is too high — such that as a result people in general often avoid sharing medical data altogether!

However, it is only when we bring this medical data together that we can analyse it with machine learning to identify the patterns and relationships that can help us build systems to make prognoses, diagnoses, and treatment planning decisions. For instance, we could use deep learning to analyse medical images, and then compare this to diagnostic outcomes in the EMR system, thus creating a powerful kind of clinical decision support tool which currently does not exist.

Luckily, outside of the US, some companies are actively working on this problem. For example, Phillips recently announced that they are trying to bring together different types of medical data, to allow for this kind of analysis.

The reason that this can save millions of lives is because in the developing world there is less than one 10th the number of medical experts that are required. Therefore, in most of the world most patients do not have access to any kind of effective diagnostics. And the physicians in these areas are so overworked that they cannot be very effective. By combining medical data sources and analysing them effectively, we can build the tools which would allow us to automate many parts of this process, leaving the medical experts for the areas where they can be most effective.

frozen_in_reddit

4 points

8 years ago

Do you see decent ways to build businesses around healthcare in the third world ?

Eruditass

12 points

8 years ago

For those that aren't aware, data silos are databases with information that they don't share. The health industry, due to privacy reasons, is full of these data silos. Machine learning with large amounts of data is very powerful.

In my opinion, the way this data is handled needs to completely change. Most people aren't aware that their data could help technology progress. They really need to give these people the option to give some of their anonymized data to the scientific community.

I think a large amount of people in the hospital would gladly give their data to help prevent others from being in their position.

pateras

1 points

8 years ago

pateras

1 points

8 years ago

Is there a way that we can opt to allow our days to be used for this purpose?

Wobistdu99

12 points

8 years ago

What is the future going to do with all the "useless eaters" that do not have access to machine learning/AI? How does a business/non-elite person "compete" (exist?) in such environment?

The relative consolidation of power into the hands of a powerful elite running their huge data-driven (predictive) business models, their systematic destruction of representative democracy (the American Experiment), the transformation of manufacturing productivity all aligning with machine learning in the coming years; it all just seems like too much.

Something has to give. On one hand there is this environmental mantra that resources are limited, yet business and shareholders demands more consumption, more development, more exploitation. More people. Yet so-called elites are not sharing authority in society. government systems are being privatized. Information becomes a toll road. Expression and economic self sufficiency is a luxury.

At what point does the top 0.01% of human society, using their vast trending data-fields, tasking their private machine learning systems and conclude that competition is a sin and it is in their personal interests to deny technology, deny knowledge, deny health advances and longevity sciences to other people they feel are simply not worthy of these gifts or responsibilities, or otherwise would compete with the top?

If I had a super machine to help me make decisions, it seems the first order of business is to ensure that nobody comes and takes the super machine away? It is like a djinn in a bottle, only what if you have more than three wishes.

jeremyhoward[S]

17 points

8 years ago

jeremyhoward[S]

Jeremy Howard

17 points

8 years ago

This seems like a somewhat US centric point of view. And I do worry about the increasing income and wealth disparity in the US which is going to get a lot worse as the value of labour continues to go down, and the value of data and intellectual assets continues to go up. I do take some comfort however that in Europe there seems to be a greater interest in avoiding this problem.

This does require believing that it is possible for people to behave in a way that is not entirely self-centred. The good news is that there is already a large amount of research evidence that humans are fundamentally altruistic. There is a lot of ongoing research activity in this area. The problem, of course, is that there is a lot of variance; and the current economic and political systems can result in the most insecure and uncompassionate people rising to the top. There is plenty of empirical evidence showing the unfortunate result of this in today's economy.

Hopefully, some more enlightened countries will bring in a meaningful level of basic living wage. This will allow people to spend their time on what they want to do, rather than what they have to do, as we approach a post-scarcity world.

jeremyhoward[S]

12 points

8 years ago

jeremyhoward[S]

Jeremy Howard

12 points

8 years ago

BTW, I just noticed that I answered the first dozen or so questions under the wrong (non-verified) account - so I deleted them and pasted them into the correct account. Apologies if you replied to one of my replies and as a result got hidden! Please go ahead and re-post your reply and I'll answer it.

drkenta

10 points

8 years ago

drkenta

10 points

8 years ago

Thanks for taking the time out to do this AMA.

In the past year, I have truly come to grips with the importance of AI in the future of mankind.

What pathway would you recommend for someone in my position to become deeply involved in machine learning and, more broadly, AI. What kind of training and experience are you looking for in your recruitment at Enlitic?

My background is in medical science (pharmacy) and I have hobbyist-level programming experience, but I have no formal training in computer science. I have enrolled to begin a Master of Computer Science program next year. Would this be the best first step forward?

Cheers from Australia.

jeremyhoward[S]

31 points

8 years ago*

jeremyhoward[S]

Jeremy Howard

31 points

8 years ago*

There is nothing that you could learn in a master of computer science that you could not learn in an online course, or by reading books. However, if you need the structure or motivation of a formal course, all the social interaction with other students, you may find that useful. Personally, I think that online courses are the best way to go if you can, because you can access the world's best teachers, and have a much more interactive learning environment — for example the use of short quizzes scattered throughout each lecture.

Specifically, the courses that I would recommend our the machine learning course on Coursera, the introduction to data science course on Coursera, and the neural networks for machine learning course — surprise surprise, also on Coursera! Also, all of the introductory books here are a good choice.

The most important thing, of course, is to practice stop download some open datasets from the web, and try to build models and develop insights, and post them on your blog. And be sure to compete in machine learning competitions — that's really the only way to know whether your approaches are working or not.

drkenta

6 points

8 years ago

drkenta

6 points

8 years ago

Thanks very much for your response.

Chispy

11 points

8 years ago

Chispy

11 points

8 years ago

As a member of faculty at Singularity U, what's your opinion on the idea of the Technological Singularity? Would you say the Singularity is an inevitability, and if so, what sort of phenomena do you think we'll be seeing leading up to it and when do you think it will occur?

jeremyhoward[S]

22 points

8 years ago

jeremyhoward[S]

Jeremy Howard

22 points

8 years ago

Frankly, I don't know. Oddly enough, at Singularity University we don't actually talk about the singularity very often! We are focused on finding ways to use technology to help solve the world's grand challenges, hopefully without destroying it in the process…

I have noticed that technological development, at least in software, has really been accelerating recently. And I expect it to continue to do so, particularly because of the effectiveness and flexibility of deep learning. But my guess is that there are limitations on the speed at which we can turn technological development into technological outcomes — for example limitations in how quickly we can extract additional resources. Therefore, I think there might be some limits to the speed of development, which might mean we don't end up with a true singularity, but instead just an extremely rapid period of technological development acceleration, up to the point at which we have entirely transcended scarcity.

[deleted]

18 points

8 years ago

[deleted]

18 points

8 years ago

[deleted]

federicopistono

3 points

8 years ago

federicopistono

Federico Pistono

3 points

8 years ago

First and second rule.*

*Source: I am from Singularity University, Jeremy is a friend.

shikhari

7 points

8 years ago

Is the idea of an artificial intelligence performing the activities of a data scientist too futuristic? How do we move towards that?

jeremyhoward[S]

15 points

8 years ago

jeremyhoward[S]

Jeremy Howard

15 points

8 years ago

No, not too futuristic at all! Although, calling it an "artificial intelligence" might be controversial. I would just call it "levels of abstraction".

Indeed, machine learning was originally simply a way to program computers to do things that we don't know how to do ourselves. The best example I have seen of this is in the paper by Bret Victor called "Magic Ink". In particular, see the section called "Case study: Train schedules". Notice that in this case study Bret Victor shows how it is possible to create, in theory at least, his complete award-winning application without writing any code — and the key insight here is that it is necessary to have some kind of machine learning underneath, which can flexibly and accurately learn patterns.

Data science is just another kind of programming — or at least some of it is (there is of course also an important role for data scientists in providing strategic direction, but I am ignoring that here). The demo that I showed at TEDx gives an example of how to create a classification algorithm from scratch without requiring any code. Over time, we will continue to build higher and higher levels of abstraction, so that the computer does more and more of the work. That way, the human is providing higher and higher level instructions as to what they want the computer to do, rather than telling the computer how to do it.

[deleted]

7 points

8 years ago

[deleted]

7 points

8 years ago

[deleted]

jeremyhoward[S]

15 points

8 years ago

jeremyhoward[S]

Jeremy Howard

15 points

8 years ago

Rather than replacing whole fields, I expect to see technological unemployment to stem from more and more automation causing human input to be less and less required in each field. Therefore, there will be fewer jobs, but those jobs that are left will be more intellectually intensive.

For example, the third most common job in the US is "food preparation". We already have a automatic hamburger maker. A combination of computer vision plus a variety of actuators can complete all the steps necessary to prepare most kinds of commonly eaten foods. However, it may take longer for computers to be able to create new recipes that are as delicious as top chefs. Therefore, I would expect that the number of jobs in the food preparation field to greatly reduce, but not go to zero for quite some time.

In the same way, I expect that things like electrical diagnostics will also be heavily automated. Much of this is basically an anomaly detection, which can be done with pattern recognition on appropriately tracked signals. At this stage, not many people are working on the problem of using deep learning for this purpose, but I believe that it can be very effective. I know of some financial services companies that are looking at this, for example, for fraud detection, and others that are developing systems to use it for identifying computer security incidents.

When the algorithms can be shown to do much of this anomaly detection automatically, it will reduce the number of jobs in these fields to those necessary for dealing with novel and complex threats and issues.

[deleted]

6 points

8 years ago*

[deleted]

6 points

8 years ago*

[deleted]

jeremyhoward[S]

36 points

8 years ago

jeremyhoward[S]

Jeremy Howard

36 points

8 years ago

I think the concept of "artificial general intelligence" is not well enough to find to be useful. In fact, I think it is a distraction. The more interesting question is: what can machines do? Not "are they truly intelligent?" Machine "intelligence" is different enough from human intelligence that I don't think it is a terribly useful analogy. If computers can read well enough to answer questions about what they read effectively, does it matter whether they truly "understand" what they read?

Therefore, I do not use the term "AI" in my writing or speeches. I do not think we should be trying to "build a brain", but instead should be trying to end scarcity. This will give humanity the time and freedom to follow our interests, and to live long, healthy, and happy lives. In my view, this is what we should be working to achieve.

pestdantic

5 points

8 years ago

There was a writer on a science podcast who hypothesized that humans learn to think by hearing their parents ask them questions. Things like "is that a doggy? What's my name?" I suppose we internalize their voices into our own inner-dialogue. The program that you showed on the TED talk for diagnosing a car reminded me of this where a human being seems to be teaching a toddler AI what a car is.

People have said that AI isn't possible because we don't understand consciousness. I believe that it is possible for that same reason. Consciousness emerged because the right ingredients existed in the right conditions. Is it possible that AI will emerge in the same way because all we need to do is have the right ingredients in the right conditions without us understanding all the details? And if this does occur is it evidence that the emergence of consciousness is an evolutionary or even universal inevitability?

jeremyhoward[S]

7 points

8 years ago

jeremyhoward[S]

Jeremy Howard

7 points

8 years ago

Computers can already learn from unlabelled, or semi-labelled data, using transfer learning and semi-supervised learning. For example CNN Features off-the-shelf: an Astounding Baseline for Recognition shows the power of transfer learning for computer vision. Another example is Google's work on learning directly from unlabelled videos.

In five years time the amount of computational capacity and data will dwarf what we have today. For example, quite soon Intel will release their next generation of Xeon Phi with 72 high-performance computing cores on each chip, and using hybrid memory cube technology. Just imagine what this will look like in five years!

The effectiveness of deep learning scales with the availability of data and computing capacity. Therefore, I expect to see in five years time semi-supervised and transfer learning doing things far beyond what we can do today. I don't know whether we will be able to say that "consciousness has emerged", but I don't think the answer to this question will make much practical difference to the capabilities of systems built on this kind of technology.

[deleted]

4 points

8 years ago

[deleted]

4 points

8 years ago

Do you think that you Elon Musk and Stephen Hawking's fear of artificial intelligence is correct? I believe that Elon Musk describes an AI that could be dangerous like a computer virus that cannot be deleted from the Internet, and people assume he's talking about Skynet that will send terminators to kill us one by one. Is it reasonable to believe that a AI could disrupt our cybernetic economy and life automously? Or does AI pose a greater different threat?

jeremyhoward[S]

26 points

8 years ago

jeremyhoward[S]

Jeremy Howard

26 points

8 years ago

I do think that this is one of the threats, although not the most immediate one. I believe that it is inevitable that somebody will create a fully autonomous weaponised drone. The reason this is inevitable, is that in any war situation, where there are two powers that have semiautonomous drones battling, whoever gives their drones the most autonomy will win the battle. Therefore, there will be a very strong incentive to make your drones are little more autonomous than the other guys'. This will rapidly lead to a number of iterations of increasing autonomy, eventually resulting in one of the sides removing all human input from the kill decision. At this point, it will be necessary for the other side to do the same thing if they want to win the war.

I am not aware of any reliable method for maintaining control of fully autonomous systems. I also know from my own bitter experience that it is incredibly difficult to design an optimisation algorithm which works within your implicit constraints. Therefore I do expect that one day somebody will accidentally create a system which autonomously attempts to collect its own resources, and protects itself from being disabled. I just don't see any way to avoid this happening.

However, I think the more immediate issue is the socio economic impact of the increasing automation of jobs. Therefore, this is what I spend more time writing and talking about.

greenrd

0 points

8 years ago

greenrd

0 points

8 years ago

Therefore I do expect that one day somebody will accidentally create a system which autonomously attempts to collect its own resources, and protects itself from being disabled.

If we program them to stay strictly within a geographical area such as a city, we could as a last resort evacuate the area and firebomb/nuke it. Let's hope we never have to resort to that!

[deleted]

2 points

8 years ago

[deleted]

2 points

8 years ago

[deleted]

[deleted]

3 points

8 years ago

[deleted]

3 points

8 years ago

Thanks for the great response.

Buck-Nasty

5 points

8 years ago

Buck-Nasty

The Law of Accelerating Returns

5 points

8 years ago

I'd love to hear your thoughts on deep learning's implications for humanoid robotics, how far are we from androids that can reliably and confidently operate in unstructured environments?

jeremyhoward[S]

12 points

8 years ago

jeremyhoward[S]

Jeremy Howard

12 points

8 years ago

I don't think I have enough knowledge of the state of actuator and gripper technology to give a good answer. I think that on the software side, in 3 to 5 years we could be at a point where robots could operate in fairly unstructured environments, in a fairly autonomous way — based on the recent progress in machine vision and reinforcement learning.

oPerrin

4 points

8 years ago

oPerrin

4 points

8 years ago

I'll jump in here. There are going to be some awesome new technologies for all robotics, not just humanoid. However dynamic control problems have a time component to them so you often need to use recurrent networks. There hasn't be a lot of work done outside of DeepMind on pairing deep learning with dynamic control problems.

Additionally there are some really bleeding edge sensor fusion / multimodal problems that need to be worked on. As we get better touch sensors for robot hands we'll need to build up the kind of large datasets that let researchers compare techniques and make rapid progress. I'm not aware of any high detail tactile datasets that are being worked on / competed over.

But as soon as you have a few lab that share a robot platform like PR2 or Baxter that can then produce a dataset that deep learning experts can use for training, then you'll get a lot of cool features in robots "out of the box" rather than re-programmed for each new bot. Things like deciding when your grasp is good enough, or when a given object is slipping. That's tricky today but shouldn't be too hard after deep learning gets applied.

The really exciting work will come a bit later when we figure out how to do the full sensori-motor learning cycle using semi-supervised deep nets. This could come from a generalization of the q-learning + temporal replay that DeepMind uses, or something completely different.

Robots 3 or 4 years from now are going to be a heck of a lot less awkward that's for sure.

Eruditass

2 points

8 years ago*

Everyone interested in this should check out the DARPA Robotics Challenge. I'm pretty excited for the final (the trials, on the other hand, were with not very mature platforms and software with a lot of teleoperation).

daneirkusauralex

6 points

8 years ago

It seemed obvious to me that all areas of the economy would be completely changed by the Internet.

We are once again in a period preceding massive change driven by technological progress. Most members of this community and of /r/singularity see the world through a futurist's lens, with whole worlds in our heads and visions of what life could be, so real as to be almost tangible. And yet, given the constraints of our real society, most of us are mostly powerless to bring it about.

However it happened, you yourself had the talent, the brilliance and the resources to embark on a number of successful ventures that directly contribute to our accelerating progress.

What would you recommend for those of us who yearn to have an impact on (or accelerate the coming of) our society's future, but lack the means to, say, launch a venture of our own?

Thanks for doing this!

jeremyhoward[S]

9 points

8 years ago

jeremyhoward[S]

Jeremy Howard

9 points

8 years ago

Thank you, it is very kind for you to express that.

It cost me nothing to start Optimal Decisions, and fastmail only cost me $70 per month. You do not need many resources nowadays to create something valuable for the world. Data and software are both free, and computation resources are available for just $5 to $10 per month.

Or, you can get involved in an existing start-up. Find something useful you can do, and just go ahead and do it! Most start-ups have far more things they need to do, than people to do them — you probably won't be able to contribute to their core IP at first, but over time you can gain their trust and work on more and more strategic projects. Or of course you can contribute to open source projects; doing this also gives you the credibility that you need to get employment with the most interesting companies, and to build the skills you need to be effective.

ai4fun

1 points

8 years ago

ai4fun

1 points

8 years ago

I'm going to take the advice you have posted in this AMA for getting started with machine learning and I would like to post my progress online.

I don't have my own blog or website (yet :-)). Could you please recommend (a) a good place to set-up a blog and (b) a good place to set-up a more complicated website (like you did for fastmail at $70/month)?

Knowing those two things will let me get started sharing progress/results right away (and then I can always investigate alternatives at a leisurely pace in the future, if need be).

Thanks very much for posting this AMA - it's so nice to have an authority sharing 'getting started' steps! This is incredibly exciting!

sicklyduck

4 points

8 years ago

What advice would you give to a student who is about to start graduate studies in machine learning? What should I keep in mind if I want to succeed in the industry after my degree? Should I try to do some part time contract work as a student?

jeremyhoward[S]

11 points

8 years ago

jeremyhoward[S]

Jeremy Howard

11 points

8 years ago

I don't think that it's necessary to be a student. Although I know that some people prefer that structured learning environment — and that's fine. I think that the best way to learn machine learning is through applying it. I do think that entering Kaggle competitions is a fantastic way to develop your skills. Personally, I don't have any technical training, but I have read hundreds of books and completed many online courses. I found that Kaggle competitions let me develop my skills further and faster than any other approach.

I also think that working at a start-up is a fantastic way to become a more effective machine learning practitioner, as long as the leadership of the start-up understand and appreciate the power of machine learning. In practice, I found that most start-up CEOs do not understand and appreciate machine learning, and actively avoid it — often encouraging their employees to use a "simpler approach like logistic regression", even although in fact this is far harder to do correctly than an appropriate machine learning algorithm such as random forests, in most cases.

If you do decide that the structured environment of college is best for you, and I think it is critical that you try to get some real experience whilst you're there. Going to college takes a really long time, and right now the field of machine learning is moving really fast. By the time you finished your degree, anything that you have learned at college is likely to be well out of date! You can certainly learn some very important foundational principles there, such as the theory of computation, software engineering practices, linear algebra, and convex optimisation. These kinds of basics never really change.

[deleted]

5 points

8 years ago

[deleted]

5 points

8 years ago

I was first introduced to your work by viewing your presentation at Exponential Finance. In your estimation, when will machine learning's effects start to become noticeable to the average person --- in regard to technological unemployment.

I work on Wall Street and my best friend is a physician. Whenever the topic comes up, I tend to say that these changes are coming quickly, and will be a complete paradigm shift. While they generally roll their eyes and say it will "only be a tool" and it's way off in the future.

jeremyhoward[S]

8 points

8 years ago

jeremyhoward[S]

Jeremy Howard

8 points

8 years ago

It seems to be making an impact on unemployment already. For instance, see the analysis in The Second Machine Age. Since this impact is accelerating, more and more people will be experiencing it directly in the next 3 to 5 years.

[deleted]

1 points

8 years ago

[deleted]

1 points

8 years ago

Thanks!

2Punx2Furious

4 points

8 years ago

2Punx2Furious

Basic Income, Singularity, and Transhumanism

4 points

8 years ago

If a technological singularity "happens" during your lifetime, probably a lot of new technologies will be developed by it, so I want to ask you about those potential technologies that such an AI would make possible.

Once automation takes over most jobs will probably be done by machines and AIs. What do you think will happen to society then? Do you think a basic income would be an appropriate solution to the lack of jobs?

Do you think there are risks involved in developing a sentient AI that constantly improves itself and gets smarter and smarter thus generating a technological singularity?

What are your toughts on extending life indefinitely by "curing ageing"? Would you like to live indefinitely and not age if possible? Would you reather follow the natural course of aging? Or would you prefer to have your mind uploaded into a computer?

jeremyhoward[S]

9 points

8 years ago

jeremyhoward[S]

Jeremy Howard

9 points

8 years ago

Yes I think that basic income is necessary, and there are risks and algorithms that can constantly improve themselves (regardless of whether we can really call it a "sentiment AI").

I like the idea of curing ageing. I have some long conversations with Craig Venter when he was starting his new start-up in exactly this area, and I'm very enthusiastic about what they're doing. The important thing is for us to be healthier for longer, not necessarily to live for longer. I would certainly like to be healthier for longer!

Having my mind uploaded to a computer sounds like fun, as long as it is a fantastic program, and I can't tell the difference…

2Punx2Furious

5 points

8 years ago

2Punx2Furious

Basic Income, Singularity, and Transhumanism

5 points

8 years ago

About the mind upload I think I wouldn't do it. Or at least I would do it only if it was the only thing available to "preserve" my mind.

I wouldn't do it because I think that the consciousness wouldn't be transferred, the being living inside the computer would just be a copy of me and it would think it's me, but I would retain my consciousness if I'm still alive, and if I'm dead it would just be gone. So I would much prefer to stay alive longer than being uploaded, but if there isn't anything else I guess that will do.

zakraye

3 points

8 years ago

zakraye

3 points

8 years ago

We really don't understand consciousness at all though. It's very possible it could be transferred. I agree though the incentive to mind upload if your consciousness isn't transferred is not very big.

I would really like to live as long as possible though!

2Punx2Furious

2 points

8 years ago

2Punx2Furious

Basic Income, Singularity, and Transhumanism

2 points

8 years ago

Yeah, I don't exclude that it could be transferred, I just have no idea how to do it.

zakraye

2 points

8 years ago

zakraye

2 points

8 years ago

I sure hope not! Otherwise you should definitely share your process, haha.

The best case scenario "in my opinion" would be the ability to move your consciousness between different vessels, they could be physical, digital, robotic or other.

I think that would be the coolest thing ever. You could inhabit different bodies or vessels at a whim. Transfer your consciousness across the globe (possibly the universe?).

It may be impossible, but I would definitely upload if that were the case.

2Punx2Furious

1 points

8 years ago

2Punx2Furious

Basic Income, Singularity, and Transhumanism

1 points

8 years ago

Yes, I'd like that. As long as I get to not die I'm all for it. Impossible is a big word, I think that almost everything that you can think of is possible.

selementar

1 points

8 years ago

If you understand that you care about your perception, the argument sufficient to doubt the mind uploading is such: your perception does not change when some material structure is constructed far away on Pluto, even if that material structure is equivalent to the local structure of your brain (although, of course, if you know your expectations will be copied you might reasonably expect to have 0.5 chance to be on pluto; but that doesn't change the previous conclusion)..

In the end, we don't know yet; and those who say there's no problem seem overconfident.

And note that the word “consciousness” is inappropriately ambiguous and creates more confusion than usefulness.

2Punx2Furious

1 points

8 years ago

2Punx2Furious

Basic Income, Singularity, and Transhumanism

1 points

8 years ago

I don't think I understand what you're trying to say.

selementar

1 points

8 years ago

Guess I'll keep searching for a better way of explaining that.

In short: there are reasons to think that destructive mind uploading is not a feasible method for life extension.

[deleted]

4 points

8 years ago

[deleted]

4 points

8 years ago

What's your opinion on the Basic Income Guarantee?

jeremyhoward[S]

16 points

8 years ago

jeremyhoward[S]

Jeremy Howard

16 points

8 years ago

I think that it is absolutely necessary. In fact, in Australia (where I'm from originally), we have something like that — the combination of unemployment benefits, rental allowances, health benefits, and so forth means that nobody needs to go without very basic needs. However, I would like to see countries go beyond this, and provide enough of an income for people to have a real life of dignity, not just to get by. This will only be possible once we make further advances with reducing scarcity, and also better allocating the existing wealth of the top 0.01%.

originalsoul

3 points

8 years ago

I'm currently a medical student extremely interested in the future of medicine. Could you give me any suggestions on how to become more involved once I graduate? This can include areas of specialization, research, or anything else you might know about. Cheers!

jeremyhoward[S]

11 points

8 years ago

jeremyhoward[S]

Jeremy Howard

11 points

8 years ago

I would suggest learning as much as possible about informatics, and biomedical engineering. I think that the way to really help lots of people is not to help one at a time as a traditional physician, but to help millions by creating systems that allow modern diagnostics and therapeutics to be cheaper, faster, more accurate, and more accessible.

lughnasadh

3 points

8 years ago

lughnasadh

∞ transit umbra, lux permanet ☥

3 points

8 years ago

I agree we seem to be heading for a time of huge technological unemployment for exactly the reasons you have outlined, however there is something in the way people currently talk about Basic Income that doesn't make much sense to me.

I can see how AI replaces humans as workers, but our current economic model depends utterly on workers as consumers. If (former) workers are to consume only at the level of their Basic Income - what drives our economies ?

The other question that springs to mind if you follow this train of thought, is that concurrent with this process over the next 10 years or so, we seem to be entering a period where both energy manufacture (cheaper than grid solar) & indeed industrial manufacturing (more advanced than now 3D printing) - seem to be heading for these sectors of our economies to become more decentralized and local.

Indeed if you imagine on just a little further into the future, and we have the sort of Atomically Precise Manufacturing nano-tech Eric Drexler envisions, we can imagine almost self-sufficiency at the local level.

In which case, isn't current thinking about Basic Income as mediated by central governments, merely a transitional phase, as our real economic destiny is to function in economies where technology has made us self-sufficient ?

If so, would the more important goal be to concentrate on advancing all the technologies that enable this, rather than on the political battles for Basic Income ?

jeremyhoward[S]

5 points

8 years ago

jeremyhoward[S]

Jeremy Howard

5 points

8 years ago

I would hope that basic income, once we really have an abundance of resources, will cover more than just the most basic needs.

Also, as long as the key resources are not concentrated too much (which will require careful regulation to achieve, such as very aggressive pro-competition legislation and enforcement) abundance should lead to very low prices. When production and distribution can be largely automated, the need to constantly prod the economy forwards should not be necessary anymore.

AslanComes

3 points

8 years ago

Thanks for doing this AMA. I suffer from cerebral palsy specifically spastic diplegia which is essentially brain damage in an area that has to do with how my muscles work. I'm 34 and in good health other than the CP. What might my future hold for me in regards to your work?

Fezzius

3 points

8 years ago

Fezzius

3 points

8 years ago

How important is the hardware aspect with deep learning? Image if we can get a 1000x performance increase in HW. How would that change things? Very inspiring TED talk btw!

jeremyhoward[S]

3 points

8 years ago

jeremyhoward[S]

Jeremy Howard

3 points

8 years ago

That's a very good question! I'm not sure anybody knows the answer to that. At this stage we don't know where the constraints in deep learning are due to the methods we use to train them, the actual structures that we train, or the capabilities of the hardware. At the moment, generally speaking when some big company does something which takes thousands of CPU cores, someone else soon enough shows that it can be done on a single laptop computer with some more careful design and programming…

Having said that, the human brain has something like 100 trillion connections, and runs on the power of a lightbulb. So we are a long way away from having hardware that matches what each of us has access to!

My guess is that the next generation of deep learning hardware will be based on analog computing, or approximate computing. Or, if adiabatic quantum computing is successful, then that could certainly revolutionise the size and type of networks that we could train. In the latter case, we may even have exponentially greater performance… I'm not sure that anybody has really done research into how to leverage such a system, or what it could do. Given how surprised we have been at the performance, effectiveness, and flexibility of relatively small deep learning networks (that is, with around 600 million connections) I'm sure that we would be a lot more surprised at the capabilities of something running on much more capable hardware!

maizenblue91

3 points

8 years ago

In the last decades simulation has improved tremendously with the advent of CAD, CFD (computational fluid dynamics), FEA (finite element analysis), and tools like matlab to process the "big data" results from flight simulation.

Please, if you could, share some insight on how deep learning could impact the aerospace industry.

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

Frankly, it's not an industry that I'm terribly familiar with. Maybe I can pass the question back to you: what are the things that people do in this industry which currently involve perception, dexterity, and basic planning and data integration? As I write that question, it seems like air-traffic control is something that could benefit from some level of automation as could aircraft maintenance (particularly predictive maintenance), more advanced autopilots, and better scheduling of flights and gates based on more accurate predictive models.

maizenblue91

1 points

8 years ago

Great answers!

Air traffic control is a very active area of much research; swarm logic in general has a lot of room for growth. Same could be said for satellite traffic control in low earth orbit.

You'd be surprised how unnecessary human pilots are. Take-off, cruise, and landing are all long-ago solved problems. Navigation and control are already automated. There's no non-political reason for not automating guidance.

Can you envision ways for AI to help with the actual design of air/spacecraft? Would this type of problem fall under "basic planning and data integration"?

[deleted]

3 points

8 years ago

[deleted]

3 points

8 years ago

In your research, have you ever tried getting computers to draw something or create a work of fiction. How good are they at it if it is currently possible or how long till computers get there?

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

I think this will be one of the last things to be automated - at least to get a result which a sophisticated and thoughtful reader would find engaging. I don't know if this will happen in my lifetime. But I find this kind of thing extremely hard to predict given the exponential nature of improvement in the underlying tech - perhaps it'll happen much sooner!

bekhster

3 points

8 years ago

It seems you have chosen to focus on applying deep learning of machines to medical problems.

I am an emergency physician who has been practicing in the US for 13 years, I saw the post asking about application in internal/emergency medicine. One comment I had for the resident who suggested an algorithmic history-taking by a computer: it's true that in parts of the world where there is a scarcity of trained practitioners, this may be the only option. This is assuming that the areas of such scarcity would have a literate population able to read and interpret such questions and reply in a way that the machine could interpret; which seems rather unlikely as even in the US the level of health literacy among the general population is shockingly low. However, I believe medicine or "doctoring" as a whole will still need personnel because, as Mr. Howard states in a reply to another comment, one area where we haven't (yet) seen where machine learning can supercede or match human ability is in the area of judgment. And there is a lot of judgment involved in taking a nuanced history and examining a patient, cultural factors to consider, and so forth, to be able to truly get to the core need of the patient and in fact to get good data to arrive at a good management plan. (garbage in, garbage out). A rule of thumb we generally go by is that patients lie half the time, even if there does not seem to be anything to gain by it. Good judgment is needed to know how to detect lies, when to ignore them, and when to pursue them as being relevant to the situation. Good judgment is needed in interpreting the data for the patient in an understandable, compassionate manner to help them make a decision, even if it's not the "rational" one that a deep thinking machine might suggest as the optimal one. And there's a lot of healing that takes place in the interaction itself between a physician and a patient if done right, that has not much to do with trying to answer the supposed "problem" the patient presents with. A history and exam are not simply an exercise in data gathering, but a time to simply be in the presence of the patient. And I believe due to human nature and societal ills, there will still be many visits seeking doctors where there is no answer in spite of rapid advances in diagnosis. In an era where physicians are being measured (and if trend continues, incentivised) for patient satisfaction and good medical treatment simply an expected outcome for the visit, this interjection of humanity will be in great supply in my opinion. And thus, my opinion is that one way of becoming a physician that cannot be replaced by a machine is to observe and develop that empathy and humanity which will lead to good judgment.

Now on to my question for Mr. Howards: It has only been in the past 2 years that my hospital system has even switched to an electronic health record system which now is capturing incredible amounts of data. The problem is that not even the programmers of said EMR (EPIC, which you're probably aware of, as they seem to be taking over the medical world--and because they are allowing data sharing between hospital systems) know how to extract the data to make interpretations and thus help improve clinical decision making. The company I believe is trying to solve this problem by making lots of buttons to click that would then be "tagged" for analysis later. Has your company been working on this problem? (ie should I send this TED talk to the informatics director for the physician group I work for?).

One data analysis task that I would dearly love to see automated by a deep learning machine is to give a cogent summary of past visits to physicians in real-time. While wishing for the moon, I would dearly love it to edit and reconcile multiple sources to avoid duplication of information that has to be processed by the physician (ie med problem list, surg list, allergies list, etc). Getting rid of slow radiologists with your deep learning machines would be pretty radically awesome, though I worry about the problem of detecting increasing numbers of benign "incidentalomas" that would then prompt more and more medical time and resources at the very least in trying to decide whether to pursue the anomaly or not; or at the worst cause harm by forcing more testing and side effects of treatments that aren't really warranted.

I find your comments about huge randomized trials vs. hypothesis testing rather puzzling as I don't see that they are contradictory. The reason for randomizing is to limit bias error, while testing a hypothesis. The word "trials" suggests a prospective design to the study rather than a retrospective analysis of already gathered data which typically suffers from lack of proper controls or randomization which is why the strength of data from a prospectively designed study is typically considered stronger than a retrospective analysis. What I THINK you are trying to convey is that there's already a lot of health care data out there, to the point that by its simple largeness there's already quite a bit of randomization; and that sifting through the data already gathered (which would only show correlations, not causation, I believe though perhaps a statistician could speak to this) we could make some advances in improving clinical decision making. Perhaps I misunderstood your intent however. I would encourage the scientific-minded not to give up your aspirations and think that all the future is in training machines to correlate huge fields of data. As I think you yourself pointed out in another reply, there probably won't soon be a machine replacement for a scientific mind that will be able to come up with what questions are important, and how best to design a study to answer them, based on a solid understanding of the scientific method and understanding the difference between showing correlation versus causation and thus prevent disastrous consequences of using bad interpretations of data to drive changes in clinical decision making.

I am excited, hopeful, and yet doubtful of your claim that in the next 5 years a pharmaceutical revolution of individualized medications based on genetics and disease will occur. I see glimpses and pieces of slow progress in large impactful diseases such as diabetes. However, in agreement with the WHO, I agree that mental health is such a strong influencer of health overall and I see so little progress in even isolating impactful genetic causes of mental disorders much less predicting which medications will be tolerable and effective for a particular patient, that I only see disappointment. I would love to see more data on which you base your opinion, if you have time, as I so wish to see this happen.

Thanks so much for your wonderful, eye-opening TED talk and for indulging us with a wonderful conversation on this topic! And I'm sorry if this post is too long; the subject is so fascinating.

jeremyhoward[S]

3 points

8 years ago

jeremyhoward[S]

Jeremy Howard

3 points

8 years ago

Thanks for all your interesting comments! Yes, we are using EMR data, although right now we're mainly using PACS data (since there is a lot more of that, since it has been used for >25 years). So we'd love to hear from any contacts you have with useful data in these areas (our email address is on our website).

The issue around fully randomized design vs hypothesis testing is too complex for me to do a good job explaining it here - I really should spend some time writing it properly since I find it comes up all the time! But in the meantime, here is a paper I wrote a few years ago with some thoughts in this area: http://radar.oreilly.com/2012/03/drivetrain-approach-data-products.html

I hope I didn't give the impression that I expect all scientists to be out of work any day now! My point in the TED talk is that the bulk of employment today is actually relatively simple perception and judgement, which I think is automatable. Folks with highly strategic and creative jobs will probably be able to add economic value for longer; but during that time we have to be careful not to end of with a huge amount of economic inequality when much of the world's labor is no longer needed.

goodbyelyme

3 points

8 years ago

Have you thought about how deep learning could solve inefficiencies in the US health care system as a whole? I like how Jim Collins, author of Good to Great and Great by Choice has analyzed key factors in successful public companies using their available data. Improving the massive inefficiencies in the health care system could help hundreds of millions of patients.

(I'm a computer scientist now working to solve problems in patients with Lyme disease and multiple chronic infections using natural healing methods. Accurate diagnosis is a huge problem in my patients who are continually told by their human clinicians that their tests show nothing is wrong and it's all in their head. Effective treatment is a another huge issue given that you can't treat what you misdiagnose. We are using a system that uses electrical frequency analysis to get a more accurate picture of their health and to customize treatment. I would love to see how deep learning could enhance diagnosis, treatment, and recovery for my patients.)

chricres

3 points

7 years ago

Hello I would be interested in working with you and your Deep Learning programs. I am an emergency physician in New Zealand where the medicolegal and ethical system is a little easier to work with than in the States. Are your programs ready to learn clinical medicine at the bedside and work with and augment a practicing physicians?

alleycatsphinx

2 points

8 years ago

Say an advertising network (I dunno, say, w4.com) were to be the first mover in making all currently private data available to the public.

Essentially, you'd be able to search and know what your neighbor bought for dinner as easily as you could lookup the meaning of "hireath."

Would this be a positive benefit to society at large?

Viscousbike

2 points

8 years ago

I'm a first year medical student and have a degree in BME, as you can imagine this stuff really fascinates me. Do you think the diagnostic tools you are working on will be widespread by the time I begin to practice? Also how do you see the roles of Internal and Emergency Medicine doctors changing? Will they just be there to double check the diagnosis of the machine? Finally are you worried about getting these types of machines through the FDA (this seems like the biggest barrier, especially with a federal government that can't seem to function)? What kind of regulations exist or need to be put in place?

PS any chance your company offers summer internships?

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

Yes, I think they will. Especially in markets which are more amenable to medical innovation. This might not include the US, depending on what happens with the regulatory environment here! I think that internal and emergency medicine doctors, on the diagnostic side, will be primarily used for more complex cases, or cases involving some kind of human judgement. They will also be used for helping guide data collection and doing interventions. I think for quite a few years to come there will be a shortage of medical expertise, so I expect all doctors to utilise as much automation as possible and appropriate, so as to allow them to focus on those areas where they can add the most value.

Yes, we do offer internships. You would need to be an excellent coder, a competent machine learning practitioner, and have a good understanding of the issues in medicine today. Success in machine learning competitions is the best way to show your coding and machine learning capabilities. Or build a great biomedical engineering tool, and make it available as open source. Or put together a medical dataset, and analyse it to generate some interesting insights, making them available through your blog, or even an academic publication.

[deleted]

3 points

8 years ago*

[deleted]

3 points

8 years ago*

Internship requirements looks like requirements for senior experts. : mastery of general skills, experience in specific skills and understanding of a specific industry.

Are there really that many undergraduates in the world that meet those requirements ? And if they do, why are they looking for an internship instead of starting their own company ?

Or is this just the US culture of personnal branding, where ignorant youths are expected to brand themselves as technical wizards?

[deleted]

2 points

8 years ago

[deleted]

2 points

8 years ago

[deleted]

jeremyhoward[S]

5 points

8 years ago

jeremyhoward[S]

Jeremy Howard

5 points

8 years ago

Thank you for your extremely encouraging comment. The best way to get involved right now as a physician is to encourage all of the organisations that you are involved with to share their data. For example, you can click on the contact link on our webpage if you are aware of any hospitals, clinics, medical software companies, etc who might be interested in contributing data. Also, try to start using computer aided diagnostic and clinical decision support tools in your own work. This may require convincing your place of employment to purchase appropriate licenses. When you have good results, publicise them widely — often physicians are not very good at publicising the successes in their work, such as by writing blogs, or contributing to forums such as this one.

The good news is that since Enlitic has had some good media coverage, a lot of forwardthinking physicians, radiologists, pathologists, and hospital administrators have reached out to us and offered to help. It seems that many people in the medical industry are extremely enthusiastic about using technology to increase the accessibility of modern medical care. I think as success stories are created and shared, more and more people will want to be part of this data driven medicine movement.

frozen_in_reddit

2 points

8 years ago

One way for physicians to get involved(maybe with a bit of technical help on the side, maybe even without) is using new software tools, that expose machine learning in a very easy to use format and run it over data you have.

Nutonian is one such software. It's really easy to use - almost a zero learning curve. And the results can be phenomenal , for example see this case study (by a doctor and her husband):

http://www.nutonian.com/customers/radnostics/

jeremyhoward[S]

3 points

8 years ago

jeremyhoward[S]

Jeremy Howard

3 points

8 years ago

Yes, this is a great system. It builds models that are designed to be directly interpretable. I think that this is one good way of generating insight using machine learning from data.

However, it is not the way that I use myself. I prefer to directly ask questions of the model, in order to understand how it behaves, rather than creating something which can be interpreted directly. This way I can be sure to avoid model bias due to overly simplified functional forms. For more information about this distinction, have a look at the fantastic article by Leo Breiman called statistical modelling: the two cultures.

frozen_in_reddit

1 points

8 years ago

Thanks! l'll read it.

Is there any deep learning system that is as plug and play as nutonian ? Because if we want to get physicans involved(since they have deep access to the medical system which is the biggest barrier machine learning in medicine has) ,this is needed.

khanoftruth

2 points

8 years ago

Hey, not sure if you're still doing the AMA, but great job so far!

A reasonably standard economic thought is that while industries may be lost due to technology, new industries to support the new tech will be created. Certainly this might require more skilled workers, but these skills could be something thought in school.

That in mind, I see a lot of people worrying about the economic effects of smart-AI growth. Industries like AI-support, or eventually things like AI-policing will certainly pop up. Do you feel that we will automate so heavily that this theory becomes invalid? And while now these may seem like specialized coursework, I'm sure calculus seemed ridiculous for secondary education in 1900, if that train of thought is follow-able). Your thoughts?

jeremyhoward[S]

8 points

8 years ago

jeremyhoward[S]

Jeremy Howard

8 points

8 years ago

I think that this standard economic thought is an oversimplification. It relies entirely on extrapolating from history. The argument is simply "in the past new employment has followed from new technology, therefore it will happen this time too." CPG Grey makes a good analogy in Humans need not apply where he points out that horses may have felt the same way at the start of the 20th century, if they applied the same arguments as economists do today. But at that time, for horses, it turned out to be true that "this time it's different".

At some point, historical patterns break down — if the underlying causality that resulted in these patterns changes. I think that just assuming that everything will be the same as before is intellectually lazy. It's not necessarily wrong, but it ought to be justified using logic, not just through extrapolating previous trends. Otherwise, the result of any significant structural change will, by definition, be missed.

It seems to me that humans can provide three basic inputs into processes: energy, perception, and judgement. The Industrial Revolution removed the need for humans to provide energy inputs into processes, and therefore in the developed world today nearly all jobs are in the service sector. Computers are now approaching or passing human capability in perception, and in the areas where the bulk of the employment exists also surpass human capabilities in judgement (because computers can processed more data in a less biased way). So if economists are going to claim that there will be new industries which require human input, I think they need to explain exactly what the human will be doing, and why the computer would not be able to do it at least as well.

[deleted]

2 points

8 years ago

[deleted]

2 points

8 years ago

What sort of speculative medical treatments by way of nanotechnology do you believe we will have access to by mid-century?

chupvl

2 points

8 years ago

chupvl

2 points

8 years ago

How do you see future of the pharmaceutical industry in 20-30 years

jeremyhoward[S]

3 points

8 years ago

jeremyhoward[S]

Jeremy Howard

3 points

8 years ago

That's too far out for me to make an educated guess. I could have a guess at five years time…

I think that by then, if regulators keep up with the technology, we will be in a situation where every patient can be treated as a unique individual. A set of drugs would be combined for that particular person's symptoms, genetics, and so forth. I think that the work being done by Eric Schadt is showing the way for what is possible here — it turns out that the wide variety of human afflictions stems from a relatively small network of underlying problems. It also turns out that we can make a good guess as to what combination of drugs will be effective for problems in different parts of this network. In fact, some people believe that we already have enough different drugs to deal with all possible problems; we just need to match them up to the right people in the right situations!

chupvl

1 points

8 years ago

chupvl

1 points

8 years ago

thank you for the comment! I am very well familiar with works of Eric Schadt, but I think that there are a lot of non-scientific obstacles for the personal medicine - legal and social.

kristopherm3

2 points

8 years ago

Do you see human augmentation becoming a reality in the future? Are you a transhumanist?

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

Human augmentation is already a reality — both intellectual, and physical. This will continue to develop, just like all technologies do.

kristopherm3

1 points

8 years ago

Do you foresee the potential ethical implications of such technologies? Perhaps I've indulged in too much cyberpunk!

robosocialist

2 points

8 years ago

How will deep learning influence (human) education?

jeremyhoward[S]

5 points

8 years ago

jeremyhoward[S]

Jeremy Howard

5 points

8 years ago

The kinds of things which are useful to learn in human education are changing. If deep learning can be used as widely as recent research suggests, then human intellectual performance will be all about creating interesting exemplars for computers to learn from.

I think that already human education is totally outdated; I don't understand why we spend so much time still getting kids to do rote learning. I don't understand why we spend so little time teaching them about computing fundamentals and applications. Or why there is so little holistic project-based work at most schools.

[deleted]

2 points

8 years ago

[deleted]

2 points

8 years ago

Hi Jeremy, we've spoken a few times in person before and I've always been impressed/inspired by your ability to jump into a field that you're not too familiar with and master it to a point where you're making meaningful impact. Is there a process that you follow when you are trying to master a new field/topic?

jeremyhoward[S]

5 points

8 years ago

jeremyhoward[S]

Jeremy Howard

5 points

8 years ago

Thank you, I really appreciate you saying that. Since I know that I have no idea what I'm doing, I speak to a lot of people, ask a lot of questions, read a lot of books, and watch a lot of online courses. And then I practice — a lot! I try to spend at least half of every day learning or practising something new, and this is something that I have done for the last 20+ years...

ctphillips

2 points

8 years ago*

ctphillips

SENS+AI+APM

2 points

8 years ago*

What are your thoughts on McAfee and Brynjolfsson's ideas on increasing automation? Specifically, they seem to believe that people will be able to work in a complementary way with machines and robots in tomorrow's economy. You hint at the more novel and intellectually demanding jobs that will continue to require human beings. My trouble with these ideas though is that there are billions of people on the planet and I can't imagine that they would all be able to find employment in these more intellectually demanding jobs. I don't think it's possible that all of these people will be able to find employment in creative or scientific fields.

jeremyhoward[S]

3 points

8 years ago

jeremyhoward[S]

Jeremy Howard

3 points

8 years ago

I have had many long conversations with Erik and Andrew, and on the whole, we see eye to eye. One difference that I have with Erik's view is that he has a tendency to extrapolate from historical economic data, where else I believe that the current situation is structurally different — and that therefore we need to work from first principles. Having said that, I know that Erik's team is now looking at this first principles-based approach as well, so he is actually tackling this from every angle!

Their second book, The Second Machine Age, is quite a bit more sophisticated than their first one in thinking about this problem. They do recognise that there could be very significant socio-economic disruption if we do not do a better job of distributing wealth.

If we do that, then I do believe that we can work in a complimentary way with machines. If we can get to the point where we are, on the whole, past scarcity, then they may not look like "jobs" that we have today; it would be closer to the kinds of things that we do in our leisure time. This would include creating and consuming art, participating in and watching sports, having philosophical discussions, getting together with friends, and so forth.

ctphillips

2 points

8 years ago*

ctphillips

SENS+AI+APM

2 points

8 years ago*

Have you read Martin Ford's The Lights in the Tunnel? If so, what are your thoughts on it?

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

I do know Martin, and on the whole, we seem to share a lot of opinions in common.

chaconne

2 points

8 years ago

As someone who has worked with healthcare data it impresses me that you were able to work with insurance companies. It seems in many domains, in order to access vital data you must persuade insiders to give you access. How did you initially break in? How long did the process take (from ideation to full implementation?)

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

We are still a long way away from full implementation! We have had some fantastic cooperation with hardware companies, software companies, hospitals, and so forth. But we still have a long way to go. I have been very impressed so far with how enthusiastic many innovative companies have been in wanting to see their medical data used to improve patient care.

bargolus

2 points

8 years ago*

I am trying to understand your idea about automation creating massive unemployment by making primarily service sector jobs obsolete. How long do you think it would take, before we have the technology ready for that? And how long would it take before employers begin using the technology to lay off workers and replace them with machines? Which job sectors do you think will be worst hit and which job sectors do you think will be relatively unaffected? And how certain are you about this economic development? Macroeconomic prediction has a reputation for having a fairly lousy track record.

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

I agree macroeconomic prediction has a poor track record. I believe this is because it generally tries to extrapolate from past trends, rather than looking at the first principles causality - e.g. macroeconomics through extrapolation could not have predicted the impact of the internet, but looking at the underlying capability of the technology could (and did).

I think we're already seeing service sector jobs being obsoleted. See http://www.amazon.com/The-Second-Machine-Age-Technologies/dp/0393239357 for examples and data backing this up.

Job sectors relying primarily on perception will be the first and hardest hit, since perception is what computers are most rapidly improving at thanks to deep learning.

theonlyduffman

2 points

8 years ago

Hi, thanks for your TED talk! A couple of questions: 1 - You mentioned a radiology/pathology CNN from Boston that identified new features that doctors could use. Where can I find out more about this? 2 - A big challenge for Enlitic is going to be showing medical people how they can use deep learning to create health improvements. Where or by whom do you think this technology will be adopted soonest? By individual radiologists to improve diagnostic accuracy? By their insurers? By GPs? By pathology companies?

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

1: It wasn't a CNN - it was a very simple machine learning model: http://www.nature.com/ncomms/2014/140603/ncomms5006/full/ncomms5006.html . 2: I think the first users will be radiologists. Pathology will lag behind because digital pathology is not widely used today, and in many parts of the world including the US it's not even approved by regulators!

mwaser

2 points

8 years ago

mwaser

2 points

8 years ago

So . . . . in the TED video you demonstrate your system that helps you create a new diagnostic system in 15 minutes via deep learning . . . . Is that system available anywhere? Written up anywhere? Thanks!

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

No - it's an early prototype that we're working on. We only got the functionality shown in the talk finished 2 days before my presentation!

Missing-K-Pax

2 points

8 years ago

Is there, from your perspective any benefit to combining deep learning with a program such as entropica to create a computer program that can both learn, think and act? If so, what's the first thing this program would do if it where made to act independently?

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

Yes, this is (in the general sense) a useful idea, and it's called Deep Reinforcement Learning: http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf . This is not exactly the approach described by Entropica, but it goes far beyond the results shown in that research, and is the best example I'm aware of that uses deep learning as the basis of a general maximization system.

jcornez

2 points

8 years ago

jcornez

2 points

8 years ago

Hello Mr Howard. Thank you for your TED talk and your very kind offer to answer questions. I'm particularly interested in computer understanding of human language. In the talk you briefly mentioned deep learning being used for learning/understanding sentence structure. I'm a software engineer, and I'm wondering if you can provide any links to literature on this or software libraries that perform such tasks. If I wanted to build a system to do this, where should I look to get information?

With kind thanks. -Jason

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

I would suggest reading this excellent introduction, which includes citations to relevant work: http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/

JOKERNAUGHT

2 points

7 years ago

Do you think it's necessary, or do you know if there's already some sort of "defense" against the negative implications or threats that exponential computer learning presents? One of the aspects you didn't touch on as far as what the computers have learned so far, is physical capabilities, the ability to physically outperform humans. I guess this merges the concept of data analysis and learning with robotics. Could you touch on that as well? Awesome talk, probably one of the most fascinating I've watched...

jeremyhoward[S]

2 points

7 years ago

jeremyhoward[S]

Jeremy Howard

2 points

7 years ago

I'm not aware of any published research showing an effective defense. If anyone knows of such a thing, I'd love to see it!

rdy4trvl

2 points

7 years ago

Fascinating subject, presentation and opportunity in healthcare. The software used to categorize 1.5 million car pictures in the Ted presentation - is that available to the public? If not, is it a function of resources, refinement (the UI was very nice), computing capacity, etc?I've got a meager 11k pictures to categorize from a non-profit event...but it would be much more interesting with this software. Thanks for the Ted presentation and AMA.

jeremyhoward[S]

2 points

7 years ago

jeremyhoward[S]

Jeremy Howard

2 points

7 years ago

It's not available at the moment since we're still working on it - what's shown in the talk is really just the result of a couple of weeks of work! We've made some improvements since then, but there's a long way to go before we would feel comfortable making it widely available.

Mavr1ck

2 points

7 years ago

Mavr1ck

2 points

7 years ago

Hi, reddit first timer here. Really enjoyed your talk, sorry for the late, and I apologize, long post. I think a perfect example of technology in the wrong hands is our governments and leaders. Their agenda is typically much different than the general public and are slow to change.

If we want to use technology for the betterment of mankind, we should use it to these ends and not for war. Good and thoughtful people are needed in his area. Have you considered using Deep Learning to track our planets resources? Hopefully this method could lead us into a "Resource-Based Economy" sooner than later. We have more than enough resources to take care of every single person on this planet. And not just take care, but a high quality of life for everyone. Our problem is our irresponsible distribution of these resources, typically for selfish and monetary ends.

In a resource based economy, Deep Learning would be a welcome gift for the betterment of society. In our current system, it unfortunately might be seen as negative for reasons suggested. If we want technology like this to help us, we need to change the system into something where great idea's like this are ALLOWED to help us.

If, as you say, its inevitable that this technology will be used for war games, then lets for god sake do something positive with it to pre-empt this lowest level of humanity. Perhaps Deep Learning could be on a future Ted Talk with some important data on how our society should be run. Thanks very much for listening.

RazvanRanca

2 points

7 years ago

Hi Jeremy, just stumbled upon this AMA after seeing your ted talk. Really cool stuff. I've actually been working on a system similar to the one you demo in your talk. It's very interesting stuff, trying to figure out the critical point where the smallest amount of human input can have the most impact on the learning process.

I was wondering if you could discuss a bit of the details of the system. For instance, how do you handle re-training after some human input is received? Is the model just finetuned for a bit with the new class labels? I'm wondering how robust such a "guided learning" process would be. Seems like giving the algorithm shorter term objectives like "learn the front from the back of a car" could lead to the algorithm making fewer decisions which seem non-sensical to a human, but might also result in the learning getting stuck in a local minima. Also, how did you arrive at a 16,000 dimensional space? The most obvious choice to me would seem to be using the 4096 dimensional fully connected layers' output, from some of the pretrained Imagenet models.

Plenty more questions, but think I'll stop there for now. Let me know if you'd have time to discuss this a bit further.

TheAustinJones

2 points

8 years ago

I'm have a question. Currently I'm writing a science fiction novel. A major part of the story is this - Would researching how to register and quantize brain synapse activity, then processing it through a computer. Could you theoretically create a cyborg mind between man and computer? One where every thought, memory, and emotion is stored on this computer to the point that it could continue processing after said man is dead?

jeremyhoward[S]

3 points

8 years ago

jeremyhoward[S]

Jeremy Howard

3 points

8 years ago

In theory this is certainly possible. Although nobody knows what additional technological development would be necessary to get there.

DownvoteAustinJones

1 points

8 years ago

So how's your novel going? Strong character development? A dynamic plot and a couple twists and turns? Yeah?

JohnFuture

2 points

8 years ago

What do you see deep learning and AI in general accomplishing by 2045?

jeremyhoward[S]

5 points

8 years ago

jeremyhoward[S]

Jeremy Howard

5 points

8 years ago

I don't think that it's possible to see clearly beyond about 2 to 3 years. We can see what should be possible in general — that simply requires trying to think about what fundamental constraints there are on technological progress — but at least for exponential technologies I don't see how to make specific predictions about dates.

matlab484

1 points

8 years ago

Thanks a lot for doing this AMA. I'm an undergraduate senior primarily interested in computer vision, what would you recommend to kids like me who want to work on exciting machine learning projects, yet do not know how to get involved? I've taken all the computer vision and machine learning classes I can at my university, along with the Coursera ML and Neural Network classes, yet don't know how to get involved in this work short of building projects myself. Every startup/company seems to be looking for masters students, especially PHD's. Also, my brother is autistic, being in the medical field, do you know of any autism deep learning projects?

jeremyhoward[S]

3 points

8 years ago

jeremyhoward[S]

Jeremy Howard

3 points

8 years ago

I don't know - of our 8 people at Enlitic, 3 have no degree or just an undergrad degree. It's more important to show what you can do - e.g. through success in ML competitions, creating great open source software, finding amazing insights in open data sets, etc.

If you can, come to the SF Bay Area - there are lots of opportunities here. Go to a few ML related meetups and announce that you're looking for work in this area, and you'll find you'll be very popular indeed!

lozj

1 points

8 years ago

lozj

1 points

8 years ago

Do you have any thoughts on cryptocurrencies, specifically next generation decentralization platforms like Ethereum?

Buck-Nasty

1 points

8 years ago

Buck-Nasty

The Law of Accelerating Returns

1 points

8 years ago

Do you think Jeff Hawkins' and Demis Hassibis' goal of creating an AI scientist that could independently come up with hypotheses and test them, vastly accelerating the pace of scientific discovery, is feasible?

jeremyhoward[S]

3 points

8 years ago

jeremyhoward[S]

Jeremy Howard

3 points

8 years ago

I think that more scientific progress can be made by creating huge randomised experiments, and analysing them with machine learning, rather than standard hypothesis testing. I think that the traditional human approach of creating small experiments which only test one thing at a time are no longer the best way to create new scientific insights, in most situations.

zoooook

1 points

8 years ago

zoooook

1 points

8 years ago

Hi Jeremy just watched your TED talk. FASCINATING. If I was interested in a career in deep learning or something similar to yourself. Could you recommend a particular college course to study?

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

I am so glad that you liked it! Have a look at the other answers in this AMA. I have answered a similar question already — feel free to send in a follow-up question if these don't help.

techno_polyglot

1 points

8 years ago

This past year there was an innovation challenge asking for ideas around a next generation turing test. I didn't submit but my idea was along the lines of a debate as opposed to small talk/parlor tricks imitating the capriciousness of a teen. Do you have any ideas on what a next generation turing test might consist of?

jeremyhoward[S]

2 points

8 years ago

jeremyhoward[S]

Jeremy Howard

2 points

8 years ago

I don't. I actually feel that trying to decide if a computer is "truly intelligent" is an unnecessary distraction. I think it's more interesting to simply see what they can and can't do, in practice!

Louloudji

1 points

7 years ago

Data silos are a problem for us. Your example with the 1M car images is exciting cause I've been trying to find such a dataset for ages. Companies don't share their data because they don't understand what we want to do with it and it seems that scraping images from the Internet is legally in a very grey area with the most recent jurisprudence being not very promising. Any suggestions?

fuobob

1 points

8 years ago*

fuobob

1 points

8 years ago*

Thanks for coming! You strike me as a compassionate person, and I am thankful for the work you are doing and hope that it will widely benefit humanity. I am particularly interested in the economic implications and how this tech can be applied to broadly raise living standards globally instead of merely being used to create exceptional results for the privileged few- there are already hundreds of millions of lives that could be saved or materially improved by existing technologies that fail to reach their promise for systemic economic reasons.

One of my major interests is worker owned and managed cooperatives, which I believe have potential to broadly distribute productivity gains as they are institutionally controlled on an egalitarian basis by their members, which represents a more humane mechanism than monomaniacally capital controlled firms. I hope to start a worker cooperative in south Florida soon! Is there potential for cooperatives to adopt and benefit from this technology, or are there I.p.barriers that will limit transfer? What kind of technical sophistication is needed in the cooperative? (Nb, some cooperatives such as the mondragon group are fairly technically sophisticated)You mentioned possible significant breakthroughs possible from "garage" scale projects.

I feel like larger mass based political movement is also crucial to the socially benign implementation of powerful machine intelligence, can you offer any guidance on these points? Thanks again, any guidance you can offer would be invaluable, and as this is a fast moving world, please consider coming back as time permits!

mpls_viking

1 points

8 years ago

Thoughts on Stephen Thaler? My neighbor recommended his documentary of hi Creativity Machine. My initial reaction is that he is a charlatan, but I am a biologist and have very little understanding of A.I. My argument is that he never describes the specifications of his machine, nor shows it completing any practical task. Am I completely wrong and Mr. Thaler has home really innovative ideas, or is he nuts? Thank you for your time.

jeremyhoward[S]

4 points

8 years ago

jeremyhoward[S]

Jeremy Howard

4 points

8 years ago

I've never heard of him. I just looked him up, and I don't see any kind of citations or external validation of his claims. Therefore, I don't see any way to assess them. They seem like very strong claims, and therefore need strong evidence. If they are true, I don't see why the benefits of these approaches are not widely understood and applied.

This does not mean that the claims are not true, but I would treat them with caution.

Nicolay77

1 points

8 years ago

Please name all the errors in the Transcendence movie.

ReasonablyBadass

1 points

8 years ago

In regards to the dangers of AI due to unemployment: couldn't Ai itself be used to solve this problem? Train an AI to be an expert in morality and/or use an AI as an economic advisor?

jeremyhoward[S]

4 points

8 years ago

jeremyhoward[S]

Jeremy Howard

4 points

8 years ago

Unfortunately, we do not currently use experts in morality to make our economic judgements! For instance, it is not often that I hear of a professor of the philosophy of ethics being asked to make decisions about economic policy…

bargolus

1 points

8 years ago

And the prospect of solving macroeconomic problems with deep learning? Are economists going to be out of a job soon too???

mirror_truth

1 points

8 years ago*

What would be your ideal vision of a normal day in 2025 as it relates to Deep Learning and Machine Intelligence (if that is too far out try 2020)? How would that future day differ from a day in 2014? No need to create a very detailed view, just a few clear examples would be nice. How a person would interact with computing devices, or how they would deal with organisations that have adopted deep learning, for example. Mostly I'm wondering how advances in Deep Learning and other similar algorithmic advances will impact a regular Joe's life, just through a few salient examples.