10 ms·

Adventures in Improving AI Economics

249 points by oliver101 6y ago

tosh 6y ago
this is more related to the previous a16z article on the topic but I found "Data as a Service" by Auren Hoffman a great read for thinking about businesses that sell access to machine learning models https://www.safegraph.com/blog/data-as-a-service-bible-everything-you-wanted-to-know-about-running-daas-companies https://www.safegraph.com/blog/data-as-a-service-bible-every...
mensetmanusman 6y ago
Good analogy about discovery of Pharma molecules. It’s really fun to think about the fact that Tesla has more than enough data to unlock autonomous vehicles, but all that is missing is the correct AI architecture to get it working... Who will figure out how to code that? Will it be a breakthrough, or can sub-optimal architectures eventually reach equilibrium with 10x or 100x the amount of time/data processing.
- antipaul 6y ago
  Perhaps. It seems it’s still an open question whether AI is just about memorizing your data, or can it actually make reliable decisions during previously unseen scenarios. Have we already observed, or collected, all that is possible in the “driving” world?
  - ipsum2 6y ago
    Most self-driving companies use simulations to see how the model performs in unseen scenarios.
    - maxbond 6y ago
      I'm sure that's tremendously helpful but isn't exactly an antidote, is it? You can't really simulate an unforeseen situation. If you can simulate it accurately - then you've foreseen it, and are thus able to account for all relevant variables. If you are randomly generating scenarios, something like fuzz testing or property testing, then I am sure you will discover bugs before they hit production, but you can't be sure you're simulating it accurately. For example, maybe when your car is T-boned while climbing a steep hill, the suspension behaves in a way you didn't expect and which isn't replicated by your simulation. Or maybe you're sent a batch of decals with bad adhesive, and in the hot sun the begin to slip down the windshield until they end up obscuring or otherwise interfering with a sensor. The only simulation that can account for every variable, regardless of whether you've anticipated it, is reality.
  - KorfmannArno 6y ago
    I've yet to see an "AI" that is not just memorizing data.
    - KorfmannArno 6y ago
      But then again, how does something like the following work? https://twitter.com/GoogleAI/status/1293970520753369088 https://twitter.com/GoogleAI/status/1293970520753369088 Any idea how it could be fooled?
      - antipaul 6y ago
        Via a fancy “adversarial” patch https://www.theverge.com/2019/4/23/18512472/fool-ai-surveillance-adversarial-example-yolov2-person-detection https://www.theverge.com/2019/4/23/18512472/fool-ai-surveill...
      - nl 6y ago
        What do you mean by "fooled"? I'm very familiar with the BlazeFace and FaceMesh models (which are related to this in Google's MediaPipe framework). They have weaknesses - they aren't designed for running upside down for example, so if they get data that is that way oriented they will tend to fail. They aren't designed for "life" detection, so you can show printed pictures of a face and it will detected it. But they give you confidence scores etc, so if you give it something like a caricature of a face it will return reasonable confidence numbers indicating it isn't as sure of its predictions.
    - nl 6y ago
      Then you haven't really looked. Most credible machine learning systems work well on unseen data, which by definition isn't memorizing.
      - KorfmannArno 6y ago
        Can you link an example you find to generalize particularly well?
  - nl 6y ago
    > It seems it’s still an open question whether AI is just about memorizing your data, No - it's not at all, and this is a well understood problem in building machine learning systems. There are some cases where this occurs but usually this is just overfitting. Good AI systems generalize well on unseen data.
    - nl 6y ago
      To the downvoters, I give you AlphaZero. Not only is every game of Go it plays and wins brand new (so no memorisation), the same system learnt to play Chess without knowing the rules, and plays in a "style .. unlike any traditional chess engine" https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go https://deepmind.com/blog/article/alphazero-shedding-new-lig...
- rokobobo 6y ago
  I'm sorry, but is that true, that Tesla has enough data to unlock autonomous vehicles? My experience is that until you get an ML model to do X, you never know if you have enough data to train it to do X. Or is that just your opinion, that they don't need more data?
- mdorazio 6y ago
  > Tesla has more than enough data to unlock autonomous vehicles Many people in the automotive industry, myself included, disagree with this statement pretty strongly. Driving data quantity is not equivalent to quality and they are severely lacking in advanced sensor data.
  - mensetmanusman 6y ago
    I don’t know, the existence proof is that it takes a 16 year old a few days of driving before they get it well enough...
  - jointpdf 6y ago
    So is the claim by Elon Musk that current iterations of Tesla vehicles have all of the sensors and compute power needed to be fully autonomous (Level 4+ I guess?) in the future, via software updates only, a specious one?
    - nl 6y ago
      It's hard to be absolutist on the response to that: anything is possible, and humans can drive without LIDAR. But at the moment it seems a strange position to take: we know LIDAR data is useful in many circumstances, and we know it can solve a number of the hard parts of computer vision.
    - sbierwagen 6y ago
      Musk also said he was taking Tesla private at $420 a share, funding secured. He says a lot of things.
  - nmca 6y ago
    I'm likely biased because I spend some of my time doing perception research, but I find the "advanced sensors are necessary" argument so odd. We have clear evidence from humans that you don't need them. I expect we'll be doing this sort of thing [0] in toy dynamic scenes from monocular vision in ~1year, and in real-time on city scenes in ~2. Perception-wise, what more do you need? Planning and control seem much harder, but that's not a sensing problem. [0] https://nerf-w.github.io/ https://nerf-w.github.io/
zamadatix 6y ago
"Andreessen Horowitz (known as "a16z") is a venture capital firm in Silicon Valley, California" In case anyone was as confused as I was about what a16z means - it's just the company not a new abbreviated term related to AI.
- TuringNYC 6y ago
  Also for anyone too young to remember the dot com boom, the founding partners (Marc Andreessen and Ben Horowitz) are some of the legendary techies from that cycle (of Netscape and LoudCloud/Opsware fame, way ahead of their time)
- sabalaba 6y ago
  There are 16 characters between the A of Andreessen and the Z in Horowitz for those that don't get it.
- gilgoomesh 6y ago
  Yeah, I find this kind of abbreviation annoying. But there's a few words that are commonly abbreviated like this: i18n -> internationalization l10n -> localization g11n -> globalization l12y -> localizability a11y -> accessibility It bothers me because my brain does not jump from the abbreviation to the underlying word. I really need to stop and think about each one. And I get the numbers wrong when writing them.
  - makapuf 6y ago
    Also, k8s->kubernetes
  - deleted 6y ago
    [deleted]
  - LifeIsBio 6y ago
    There was a period of a few months when I was first learning about web apps where I saw "i18n" multiple times. The first time I came across it, I tried to sound it out: i18n -> I-one-eight-n -> iwonation I was already a couple of rabbit holes deep at the time and didn't have the mental capacity to look it up and wrap my head around yet another new concept. "Oh boy." I thought to myself, "One more word I've never heard of, probably representing some complicated CS concept." I was so annoyed when I finally found out what was going on.
alextheparrot 6y ago
a16z has a podcast where they explored gross margins a month back. The panel called out AI as an example of a software business that has a high likelihood of not having standard SaaS margins (Most of the panel thought this could be a limitation). The podcast is nice because I think it holistically explores gross margins in a way that you start to understand how it might impact AI as a viable primary business model and valuations related to companies who that is the case for. Quite complementary to the article. Might be interesting to people who are interested in this article: https://open.spotify.com/episode/79lJCrHB3nBn1qXCxKA5s7?si=RFlHPM0_QwWHZsqwf3nbxA https://open.spotify.com/episode/79lJCrHB3nBn1qXCxKA5s7?si=R...
- cinntaile 6y ago
  While I haven't listened to this particular one, I just wanted to say that their podcasts are usually quite interesting if you're interested in new technology/science.
- Jack000 6y ago
  This is a great feature of the AI space for startups - in the short term it reduces competition, in the long term it's not really a problem. If your business is break-even currently, it will be profitable in 2-3 years due to declining cost of compute. In 10 years the compute costs will fall by an order of magnitude and more efficient models will become available, making the economics closer to traditional SaaS. This does disadvantage smaller bootstrapped businesses though.
  - kippinitreal 6y ago
    Not true if there’s enough competition that you need more resources for a bigger model in 2-3 years. Anecdotally, it seems like SOTA model training costs are rising much faster than computer cost is falling.
    - Jack000 6y ago
      maybe, but model quality doesn't scale linearly with model size. The performance/dollar metric is more important, and that definitely will decline over time. Personally, I have a boots trapped business that uses a transformer model. I'm not worried about supermassive models like gpt3 because it would be way too expensive to deploy for my use case, with marginal additional quality.
oliver101 6y ago
> This is the crux of the AI business dilemma. If the economics are a function of the problem – not the technology per se – how can we improve them? The article focusses on the costs of resources to build a model (annotated data + compute) but the economics are also affected by the ongoing cost of making a prediction error. False positives and false negatives usually have a different cost and each user might have their own preferences: e.g. "show me all the content that's a bit relevant" vs "show me just the content that's really relevant". If you can write out the loss function in $$$ terms not just accuracy, then you're closer to either abandoning the problem or finding a profitable AI model.
- mlthoughts2018 6y ago
  Great way of putting it. The trouble there is that it takes an exceptional kind of senior ML person to basically wear a product manager hat all the time and press to translate project success criteria into revenue impact or cost reduction terms. Having these “glue people” that connect ML engineering to product management is probably the most important thing to running an ML organization.
motohagiography 6y ago
Good analysis and great of them to share their thinking. Does feel like this could have been a tweet that said the necessary condition for successful ML solution is applying it to a problem that has asymmetric upside. Great for telling people they should get tested for diseases, terrible for diagnosis. In the alerting first case, consequences of being wrong are no better than base rate as they wouldn't have been tested otherwise, and the upside saves a life. In the latter diagnosis case, the consequences of being wrong are catastrophic, and it is substituting for the best available judgment. Similarly, it's great for fraud detection, terrible for making credit decisions, because the false negative rate is essentially externalized. It's good for finding opportunities, bad for providing services. So funnels and conversion pipelines it's great for. So perhaps there's an ironic Turing test for ML solutions that is related to the relationship between the size of a group of people and the effect of mean reversion of their collective intelligence on their behaviour makes them indifferent to the perceived intelligence of the model, whereas a given individual will find the results of the model unsatisfying. From an indifference perspective, AI can fool some of the people all the time, and all the people some of the time, but no confusion matrix satisfies all the people all the time. Economically, ML will be useful for creating simple and cheap services that people who can't afford better will use, and substitute up from them when they can afford better, known as "inferior goods." There may be a hard limit on ML providing "normal goods," to individuals at scale for this reason. Lots of money to be made, but lots to be wasted tweaking your ROC curve to in the hope of creating a normal good. I yell from the rooftops every chance I get that "the confusion matrix is the product." That is, your FP/FN/TP/TN rate is your product, and you are optimizing your system for the weights your customer assigns to those variables. There is another ML/DL use case I'm hacking on that is about enabling privacy, but even this reduces to the asymmetry of the upside/downside of the confusion matrix. Obviously the article is more nuanced than this, but I think this heuristic is a key tool for reading articles like it.
- jmatthews 6y ago
  I appreciate the thoughtful commentary. I couldn't disagree more with you more of course. There are 2 instances where AI breaks the mold you've cast. Executing rote tasks that no humans need do, and relatedly, while there does seem to be a tough hurdle when it comes to "better than human" execution there is also an inverted survivors bias. Once a technology is production ready it is no longer AI. Cars aren't robots, antilock brakes aren't AI, Once a system outperforms a human it's technology, not intelligence.
eanzenberg 6y ago
In my experience, there are just a lot of "bad" AI/ML engineers who don't fundamentally understand what data can do, what ML algorithms can handle, and how to piece it together to produce something of value to the end user. A couple of these people on a team can torpedo a project. Worse are those who sabotage projects or are general pain points of hindering progress. These may be jaded people who don't believe that ML has any value yet have titles like Data Scientist or ML engineer, and can bring team morale down. The economics are similar to a grad-school research project, yet is infiltrated by all sorts of people with 3 month certificates believing they are the star of the show. The most important element of AI project success is the right people and the right team. Projects are long-term and failure can be often. It's not easy to succeed but cultivating the right people and their mindset is in my opinion a needle mover for AI projects, more-so than what data is available, what algorithms are tried, and what shiny framework people want to use.
- Swizec 6y ago
  Correct me if I’m wrong but isn’t this fundamentally true for any team working on any project?
  - MisterPea 6y ago
    Yeah not sure what they were getting at. There are also a lot of people who fundamentally don't know good software engineering practices and cause a ton of tech debt
  - logicslave12 6y ago
    No, there’s more ambiguity in machine learning projects. When you develop a website, aside from the design, it works or it doesn’t. Whether some kind of ml product can work at all is often team dependent
    - cnasc 6y ago
      > When you develop a website, aside from the design, it works or it doesn’t. This really isn’t true. There are websites and web apps that “work” but are really suboptimal from a performance and UX perspective. It’s possible to do this right, but it’s much easier to do a poor job. You end up with something that kind of works and may even be profitable but which is a boat anchor around your company compared to a better approach.
deleted 6y ago
[deleted]
gk1 6y ago
One way to deal with AI/ML shortcomings I've seen is to require end-user intervention for edge cases; such as a support chatbot that transfers the customer to a human rep if it can't understand the issue. Human intervention isn't mentioned in the article but maybe they'd put that under "narrow the problem," or they may not consider that a solution since human involvement eats into margins. I believe all software companies will be AI companies in <5 years. By then, not having AI/ML would be like not having a database today. There will be no choice but to deal with the long tail, and the competitive advantage will go to the company that does it better. That makes this advice all the more timely and important, and it also means opportunities for startups to innovate in this space. Eg, better model optimization, low-cost operations without regressing to colocated GPUs, etc. "The critical design element is that each model addresses a global slice of data... There is no substitute, it turns out, for deep domain expertise." Totally true for marketing as well. Much more effective to define audience segments and tailor the messaging and marketing for each.
mijail 6y ago
I'm happy a VC is providing these insights. If the economics of AI don't make sense by helping increase profits or cutting costs... it's going to be a long road to reaching the "promised land." I'm biased but in a lot of industries synthetic data has the potential to balance the costs from the perspective of data acquisition and preparation as well as model testing. This article doesn't focus too much on the edge side of things but one pattern I'm seeing is that edge deployment can be notoriously resource intensive and time consuming.
tigerbelt 6y ago
Indubitably
PaulHoule 6y ago
Gr8 article. I'd add that caveat that software dev processes can be well controlled or not well controlled. AIML is not so much a new kind of project but it is a project likely to be poorly controlled. Another thing they don't mention is that AIML projects break the agile assumption that you can manage with only punchclock, not calendar time. Imagine you have a 2 week sprint and it takes 1 week to train a model. You have to get the training started in the first week, and any tasks that need to be done to start training have to start before that. This of course means applying PERT chart thinking even if you don't make PERT charts. It often isn't that hard but in an agile shop that mistakes the map for the territory they will start the 1 week job consistently on the last day of the sprint. The 'containerization' process they describe is close to the methods used by East coast defense contractors (in a band between research triangle park and the applied physics dept at John Hopkins in baltimore) to get high accuracy. Also they were what IBM Watson did as opposed to what people thought they did. It's amazing those methods have remained so obscure, but the mind that is impressed with BERT is going to be impervious to asymtopes. That article should be telling people to run not walk away from those kind of models -- it is how you always be a bridesmaid but never a bride.
known 6y ago
https://yts.mx/movies/robot-frank-2012 https://yts.mx/movies/robot-frank-2012 show subtle issues related to AI in real life;

249 points by oliver101 6y ago

tosh 6y ago

this is more related to the previous a16z article on the topic but I found "Data as a Service" by Auren Hoffman a great read for thinking about businesses that sell access to machine learning models https://www.safegraph.com/blog/data-as-a-service-bible-everything-you-wanted-to-know-about-running-daas-companies https://www.safegraph.com/blog/data-as-a-service-bible-every...

mensetmanusman 6y ago

Good analogy about discovery of Pharma molecules. It’s really fun to think about the fact that Tesla has more than enough data to unlock autonomous vehicles, but all that is missing is the correct AI architecture to get it working... Who will figure out how to code that? Will it be a breakthrough, or can sub-optimal architectures eventually reach equilibrium with 10x or 100x the amount of time/data processing.

antipaul 6y ago
Perhaps. It seems it’s still an open question whether AI is just about memorizing your data, or can it actually make reliable decisions during previously unseen scenarios. Have we already observed, or collected, all that is possible in the “driving” world?
- ipsum2 6y ago
  Most self-driving companies use simulations to see how the model performs in unseen scenarios.
  - maxbond 6y ago
    I'm sure that's tremendously helpful but isn't exactly an antidote, is it? You can't really simulate an unforeseen situation. If you can simulate it accurately - then you've foreseen it, and are thus able to account for all relevant variables. If you are randomly generating scenarios, something like fuzz testing or property testing, then I am sure you will discover bugs before they hit production, but you can't be sure you're simulating it accurately. For example, maybe when your car is T-boned while climbing a steep hill, the suspension behaves in a way you didn't expect and which isn't replicated by your simulation. Or maybe you're sent a batch of decals with bad adhesive, and in the hot sun the begin to slip down the windshield until they end up obscuring or otherwise interfering with a sensor. The only simulation that can account for every variable, regardless of whether you've anticipated it, is reality.
- KorfmannArno 6y ago
  I've yet to see an "AI" that is not just memorizing data.
  - KorfmannArno 6y ago
    But then again, how does something like the following work? https://twitter.com/GoogleAI/status/1293970520753369088 https://twitter.com/GoogleAI/status/1293970520753369088 Any idea how it could be fooled?
    - antipaul 6y ago
      Via a fancy “adversarial” patch https://www.theverge.com/2019/4/23/18512472/fool-ai-surveillance-adversarial-example-yolov2-person-detection https://www.theverge.com/2019/4/23/18512472/fool-ai-surveill...
    - nl 6y ago
      What do you mean by "fooled"? I'm very familiar with the BlazeFace and FaceMesh models (which are related to this in Google's MediaPipe framework). They have weaknesses - they aren't designed for running upside down for example, so if they get data that is that way oriented they will tend to fail. They aren't designed for "life" detection, so you can show printed pictures of a face and it will detected it. But they give you confidence scores etc, so if you give it something like a caricature of a face it will return reasonable confidence numbers indicating it isn't as sure of its predictions.
  - nl 6y ago
    Then you haven't really looked. Most credible machine learning systems work well on unseen data, which by definition isn't memorizing.
    - KorfmannArno 6y ago
      Can you link an example you find to generalize particularly well?
- nl 6y ago
  > It seems it’s still an open question whether AI is just about memorizing your data, No - it's not at all, and this is a well understood problem in building machine learning systems. There are some cases where this occurs but usually this is just overfitting. Good AI systems generalize well on unseen data.
  - nl 6y ago
    To the downvoters, I give you AlphaZero. Not only is every game of Go it plays and wins brand new (so no memorisation), the same system learnt to play Chess without knowing the rules, and plays in a "style .. unlike any traditional chess engine" https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go https://deepmind.com/blog/article/alphazero-shedding-new-lig...
rokobobo 6y ago
I'm sorry, but is that true, that Tesla has enough data to unlock autonomous vehicles? My experience is that until you get an ML model to do X, you never know if you have enough data to train it to do X. Or is that just your opinion, that they don't need more data?
mdorazio 6y ago
> Tesla has more than enough data to unlock autonomous vehicles Many people in the automotive industry, myself included, disagree with this statement pretty strongly. Driving data quantity is not equivalent to quality and they are severely lacking in advanced sensor data.
- mensetmanusman 6y ago
  I don’t know, the existence proof is that it takes a 16 year old a few days of driving before they get it well enough...
- jointpdf 6y ago
  So is the claim by Elon Musk that current iterations of Tesla vehicles have all of the sensors and compute power needed to be fully autonomous (Level 4+ I guess?) in the future, via software updates only, a specious one?
  - nl 6y ago
    It's hard to be absolutist on the response to that: anything is possible, and humans can drive without LIDAR. But at the moment it seems a strange position to take: we know LIDAR data is useful in many circumstances, and we know it can solve a number of the hard parts of computer vision.
  - sbierwagen 6y ago
    Musk also said he was taking Tesla private at $420 a share, funding secured. He says a lot of things.
- nmca 6y ago
  I'm likely biased because I spend some of my time doing perception research, but I find the "advanced sensors are necessary" argument so odd. We have clear evidence from humans that you don't need them. I expect we'll be doing this sort of thing [0] in toy dynamic scenes from monocular vision in ~1year, and in real-time on city scenes in ~2. Perception-wise, what more do you need? Planning and control seem much harder, but that's not a sensing problem. [0] https://nerf-w.github.io/ https://nerf-w.github.io/

zamadatix 6y ago

"Andreessen Horowitz (known as "a16z") is a venture capital firm in Silicon Valley, California" In case anyone was as confused as I was about what a16z means - it's just the company not a new abbreviated term related to AI.

TuringNYC 6y ago
Also for anyone too young to remember the dot com boom, the founding partners (Marc Andreessen and Ben Horowitz) are some of the legendary techies from that cycle (of Netscape and LoudCloud/Opsware fame, way ahead of their time)
sabalaba 6y ago
There are 16 characters between the A of Andreessen and the Z in Horowitz for those that don't get it.
gilgoomesh 6y ago
Yeah, I find this kind of abbreviation annoying. But there's a few words that are commonly abbreviated like this: i18n -> internationalization l10n -> localization g11n -> globalization l12y -> localizability a11y -> accessibility It bothers me because my brain does not jump from the abbreviation to the underlying word. I really need to stop and think about each one. And I get the numbers wrong when writing them.
- makapuf 6y ago
  Also, k8s->kubernetes
- deleted 6y ago
  [deleted]
- LifeIsBio 6y ago
  There was a period of a few months when I was first learning about web apps where I saw "i18n" multiple times. The first time I came across it, I tried to sound it out: i18n -> I-one-eight-n -> iwonation I was already a couple of rabbit holes deep at the time and didn't have the mental capacity to look it up and wrap my head around yet another new concept. "Oh boy." I thought to myself, "One more word I've never heard of, probably representing some complicated CS concept." I was so annoyed when I finally found out what was going on.

alextheparrot 6y ago

a16z has a podcast where they explored gross margins a month back. The panel called out AI as an example of a software business that has a high likelihood of not having standard SaaS margins (Most of the panel thought this could be a limitation). The podcast is nice because I think it holistically explores gross margins in a way that you start to understand how it might impact AI as a viable primary business model and valuations related to companies who that is the case for. Quite complementary to the article. Might be interesting to people who are interested in this article: https://open.spotify.com/episode/79lJCrHB3nBn1qXCxKA5s7?si=RFlHPM0_QwWHZsqwf3nbxA https://open.spotify.com/episode/79lJCrHB3nBn1qXCxKA5s7?si=R...

cinntaile 6y ago
While I haven't listened to this particular one, I just wanted to say that their podcasts are usually quite interesting if you're interested in new technology/science.
Jack000 6y ago
This is a great feature of the AI space for startups - in the short term it reduces competition, in the long term it's not really a problem. If your business is break-even currently, it will be profitable in 2-3 years due to declining cost of compute. In 10 years the compute costs will fall by an order of magnitude and more efficient models will become available, making the economics closer to traditional SaaS. This does disadvantage smaller bootstrapped businesses though.
- kippinitreal 6y ago
  Not true if there’s enough competition that you need more resources for a bigger model in 2-3 years. Anecdotally, it seems like SOTA model training costs are rising much faster than computer cost is falling.
  - Jack000 6y ago
    maybe, but model quality doesn't scale linearly with model size. The performance/dollar metric is more important, and that definitely will decline over time. Personally, I have a boots trapped business that uses a transformer model. I'm not worried about supermassive models like gpt3 because it would be way too expensive to deploy for my use case, with marginal additional quality.

oliver101 6y ago

> This is the crux of the AI business dilemma. If the economics are a function of the problem – not the technology per se – how can we improve them? The article focusses on the costs of resources to build a model (annotated data + compute) but the economics are also affected by the ongoing cost of making a prediction error. False positives and false negatives usually have a different cost and each user might have their own preferences: e.g. "show me all the content that's a bit relevant" vs "show me just the content that's really relevant". If you can write out the loss function in $$$ terms not just accuracy, then you're closer to either abandoning the problem or finding a profitable AI model.

mlthoughts2018 6y ago
Great way of putting it. The trouble there is that it takes an exceptional kind of senior ML person to basically wear a product manager hat all the time and press to translate project success criteria into revenue impact or cost reduction terms. Having these “glue people” that connect ML engineering to product management is probably the most important thing to running an ML organization.

motohagiography 6y ago

Good analysis and great of them to share their thinking. Does feel like this could have been a tweet that said the necessary condition for successful ML solution is applying it to a problem that has asymmetric upside. Great for telling people they should get tested for diseases, terrible for diagnosis. In the alerting first case, consequences of being wrong are no better than base rate as they wouldn't have been tested otherwise, and the upside saves a life. In the latter diagnosis case, the consequences of being wrong are catastrophic, and it is substituting for the best available judgment. Similarly, it's great for fraud detection, terrible for making credit decisions, because the false negative rate is essentially externalized. It's good for finding opportunities, bad for providing services. So funnels and conversion pipelines it's great for. So perhaps there's an ironic Turing test for ML solutions that is related to the relationship between the size of a group of people and the effect of mean reversion of their collective intelligence on their behaviour makes them indifferent to the perceived intelligence of the model, whereas a given individual will find the results of the model unsatisfying. From an indifference perspective, AI can fool some of the people all the time, and all the people some of the time, but no confusion matrix satisfies all the people all the time. Economically, ML will be useful for creating simple and cheap services that people who can't afford better will use, and substitute up from them when they can afford better, known as "inferior goods." There may be a hard limit on ML providing "normal goods," to individuals at scale for this reason. Lots of money to be made, but lots to be wasted tweaking your ROC curve to in the hope of creating a normal good. I yell from the rooftops every chance I get that "the confusion matrix is the product." That is, your FP/FN/TP/TN rate is your product, and you are optimizing your system for the weights your customer assigns to those variables. There is another ML/DL use case I'm hacking on that is about enabling privacy, but even this reduces to the asymmetry of the upside/downside of the confusion matrix. Obviously the article is more nuanced than this, but I think this heuristic is a key tool for reading articles like it.

jmatthews 6y ago
I appreciate the thoughtful commentary. I couldn't disagree more with you more of course. There are 2 instances where AI breaks the mold you've cast. Executing rote tasks that no humans need do, and relatedly, while there does seem to be a tough hurdle when it comes to "better than human" execution there is also an inverted survivors bias. Once a technology is production ready it is no longer AI. Cars aren't robots, antilock brakes aren't AI, Once a system outperforms a human it's technology, not intelligence.

eanzenberg 6y ago

In my experience, there are just a lot of "bad" AI/ML engineers who don't fundamentally understand what data can do, what ML algorithms can handle, and how to piece it together to produce something of value to the end user. A couple of these people on a team can torpedo a project. Worse are those who sabotage projects or are general pain points of hindering progress. These may be jaded people who don't believe that ML has any value yet have titles like Data Scientist or ML engineer, and can bring team morale down. The economics are similar to a grad-school research project, yet is infiltrated by all sorts of people with 3 month certificates believing they are the star of the show. The most important element of AI project success is the right people and the right team. Projects are long-term and failure can be often. It's not easy to succeed but cultivating the right people and their mindset is in my opinion a needle mover for AI projects, more-so than what data is available, what algorithms are tried, and what shiny framework people want to use.

Swizec 6y ago
Correct me if I’m wrong but isn’t this fundamentally true for any team working on any project?
- MisterPea 6y ago
  Yeah not sure what they were getting at. There are also a lot of people who fundamentally don't know good software engineering practices and cause a ton of tech debt
- logicslave12 6y ago
  No, there’s more ambiguity in machine learning projects. When you develop a website, aside from the design, it works or it doesn’t. Whether some kind of ml product can work at all is often team dependent
  - cnasc 6y ago
    > When you develop a website, aside from the design, it works or it doesn’t. This really isn’t true. There are websites and web apps that “work” but are really suboptimal from a performance and UX perspective. It’s possible to do this right, but it’s much easier to do a poor job. You end up with something that kind of works and may even be profitable but which is a boat anchor around your company compared to a better approach.

deleted 6y ago

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gk1 6y ago

One way to deal with AI/ML shortcomings I've seen is to require end-user intervention for edge cases; such as a support chatbot that transfers the customer to a human rep if it can't understand the issue. Human intervention isn't mentioned in the article but maybe they'd put that under "narrow the problem," or they may not consider that a solution since human involvement eats into margins. I believe all software companies will be AI companies in <5 years. By then, not having AI/ML would be like not having a database today. There will be no choice but to deal with the long tail, and the competitive advantage will go to the company that does it better. That makes this advice all the more timely and important, and it also means opportunities for startups to innovate in this space. Eg, better model optimization, low-cost operations without regressing to colocated GPUs, etc. "The critical design element is that each model addresses a global slice of data... There is no substitute, it turns out, for deep domain expertise." Totally true for marketing as well. Much more effective to define audience segments and tailor the messaging and marketing for each.

mijail 6y ago

I'm happy a VC is providing these insights. If the economics of AI don't make sense by helping increase profits or cutting costs... it's going to be a long road to reaching the "promised land." I'm biased but in a lot of industries synthetic data has the potential to balance the costs from the perspective of data acquisition and preparation as well as model testing. This article doesn't focus too much on the edge side of things but one pattern I'm seeing is that edge deployment can be notoriously resource intensive and time consuming.

tigerbelt 6y ago

Indubitably

PaulHoule 6y ago

Gr8 article. I'd add that caveat that software dev processes can be well controlled or not well controlled. AIML is not so much a new kind of project but it is a project likely to be poorly controlled. Another thing they don't mention is that AIML projects break the agile assumption that you can manage with only punchclock, not calendar time. Imagine you have a 2 week sprint and it takes 1 week to train a model. You have to get the training started in the first week, and any tasks that need to be done to start training have to start before that. This of course means applying PERT chart thinking even if you don't make PERT charts. It often isn't that hard but in an agile shop that mistakes the map for the territory they will start the 1 week job consistently on the last day of the sprint. The 'containerization' process they describe is close to the methods used by East coast defense contractors (in a band between research triangle park and the applied physics dept at John Hopkins in baltimore) to get high accuracy. Also they were what IBM Watson did as opposed to what people thought they did. It's amazing those methods have remained so obscure, but the mind that is impressed with BERT is going to be impervious to asymtopes. That article should be telling people to run not walk away from those kind of models -- it is how you always be a bridesmaid but never a bride.

known 6y ago

https://yts.mx/movies/robot-frank-2012 https://yts.mx/movies/robot-frank-2012 show subtle issues related to AI in real life;