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I very strongly disagree with #3. Machine learning has not yet proved its value outside of a handful of key areas (image recognition, voice recognition/synthesi

I very strongly disagree with #3. Machine learning has not yet proved its value outside of a handful of key areas (image recognition, voice recognition/synthesizing, etc). I think it's entirely possible we'll look back on today as a kind of machine learning mania. I certainly believe there is plenty of room to build sustainable businesses without a machine learning asset (and the associated data collection), and it's in no way "table stakes for every tech company, large and small".

chubot 7y ago
Yup, I came here to question that as well. What are some examples of companies that hinged on machine learning? DATA is crucial, but that's very different than "sophistiated machine learning models". For example, do Uber and Lyft need machine learning? As far as I can tell, they need data about drivers, users, ride completion, maps, and a lot of people doing simple statistics on that data and improving the product. Machine learning might give them a few percentage points in some specific subsystem, but it isn't going to make or break the product as a whole. It's not "table stakes". Pick another industry and do the same thought experiment. What are some other breakout companies? Did snapchat succeed (in its day) because of machine learning? What about AirBNB? I think they are optimizing the product with "data science", not machine learning. Certainly not deep learning. Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small. Accumulating a data asset around your product and service and using sophisticated machine learning models to personalize and improve your product is not a nice to have.
- AndrewKemendo 7y ago
  You would be astonished at some of the non-public (not that they are secret) machine learning things that Uber does that you wouldn't even be aware of. My favorite is that they do pothole detection, prediction and routing around based on what they classified based on driver accelerometer data. They've also reduced traffic incidents with insights from the same dataset that drove product changes.
  - chubot 7y ago
    That's interesting, and in line with what I perceived as Uber's extreme focus on operations. To me, the accelerometer DATA is the new and important thing -- it's the thing that taxi companies don't have. Whether the machine learning model is "sophisticated" doesn't seem critical on the face of it. I guess it depends whether there are a lot of non-pothole events that look like pothole events to "naive" signal processing. Do you need deep learning for this problem? I don't know but I would be skeptical without a citation. But I am interested in more info and examples. If someone can give a bunch of examples I would adjust my view.
    - m_ke 7y ago
      Amazon uses machine learning to forecast demand and ship products to warehouses before you order something. It's part of what makes their 1 day deliveries work. https://www.npr.org/2018/11/21/660168325/optimized-prime-how-ai-and-anticipation-power-amazons-1-hour-deliveries https://www.npr.org/2018/11/21/660168325/optimized-prime-how... Data is obviously crucial, but it's useless if it's just sitting on hard drive. Machine learning allows them to squeeze out as much juice from it as possible, and a few % point lift makes a huge difference at scale, especially in high volume businesses with low margins per transaction.
    - AndrewKemendo 7y ago
      This is just nitpicking phrases at this point, a popular past time for us nerds, so I get it but it misses the point. "Sophisticated" is enough of a suitcase term that it's effectively worthless. More importantly it's worth understanding that ML allows us to do things we couldn't do before, because it's cheaper and scales better than previous optimization methods, when you have the data. So yes, the data infrastructure is the critical part, however the ML pipelines and Data Science work is what gives the massive multiplier at a cost/scale you can't get with traditional optimization methods.
- m_ke 7y ago
  Machine Learning is a technology the same way blockchain is. Customers buy products and solutions, not your tech stack so it doesn't make sense for any company to sell themselves as a machine learning company, unless you're selling APIs. Alexa / Siri / Google Assistant would not be possible without machine learning. Same goes for Snapchat's filters and any other form of AR. Google's Pixel phones wouldn't have such a great camera without computational photography / machine learning.
- 6gvONxR4sf7o 7y ago
  Those of us doing it mostly roll our eyes at how VCs and (especially) marketers describe it, so I agree with you, but the "is it ML or stats?" conversation has been going nowhere for as long as this round of AI hype has been here. That said, uber and lyft certainly benefit from much more interesting ML and stats than just "simple statistics." For example, setting prices and incentives to properly balance the driver side against the rider side of the marketplace.
  - chubot 7y ago
    Yeah I agree the argument is mostly about terminology. Anyway you slice it you have a bunch of data and you have a team that's improving the product with the data. That is for sure "table stakes". And yes I probably should have said "data science" or "statistics" rather than "simple statistics", definitely for Uber and Lyft (less so for smaller companies). I worked on a data science team 10 years ago when it was called "statistics". I even remember a 2009 NYTimes article that talked about data science without using the word "data science", because it hadn't come into use yet. If people want to redefine "machine learning" to mean "data science", OK fine. I guess that's what's happening now. The term "data science" already annoyed some people, but I guess now it's "machine learning" and "deep learning".
- morganherlocker 7y ago
  > For example, do Uber and Lyft need machine learning? Anyone doing large scale ETA prediction and routing these days is using ML models at various phases in the pipeline, from GPS noise filtering to road network data generation. You might get past the early stages of a rideshare or delivery service with more traditional naive models, but there are still significant efficiency gains to be made in even the most cutting edge systems today. For maps and navigation, this is especially true when you go global and need models that adapt to subtle cultural and behavioral changes across markets.
m_ke 7y ago
You're wrong. Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google, translation, speech recognition, finance, fraud detection. In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives. EDIT: for people who are downvoting me, here are some examples of how machine learning is and will transform our lives: 1. 3D graphics: https://www.youtube.com/watch?v=FlgLxSLsYWQ https://www.youtube.com/watch?v=FlgLxSLsYWQ Andrew Price doesn't have a technical background but does a decent job summarizing some of the ways deep learning will transform computer graphics. Soon all of the mocap, character design and animation will be driven by deep learning systems. 2. Computer Architecture and Traditional Software: https://www.youtube.com/watch?v=TTpKWOuzOxc https://www.youtube.com/watch?v=TTpKWOuzOxc There's a ton of recent research showing that you can use machine learning to beat human crafted heuristics in hardware, scheduling, compiler design and query planning.
- chubot 7y ago
  Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small. I would say it's table stakes for large companies in certain areas, but not small (see my other reply). Machine learning seems to be good at squeezing percentage points out of monopoly with a business that already works. That's very different than "table stakes for small companies". Small companies need optimize their product with fundamental changes. For example, in the early days of AirBNB there was a more inconsistent user experience, which you heard about in the media. I think they addressed those problems with policies, incentives, and some elbow grease (kicking bad users off the platform, etc.). Not machine learning. Machine learning doesn't help you expand into Europe or Asia, etc.
- cm2012 7y ago
  This guy is downvoted but he's right. A huge amount of Facebook's and Google's value growth comes from machine learning driving their various algorithms.
- eitland 7y ago
  > Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google, Yep, and the results are hilarious or tragic depending on how you look at it: We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.) [0]: some people will always claim it is because black hat SEO is so much worse, but that doesn't explain why Google sometimes can neither understand doublequotes nor the verbatim option anymore. That is not the result of black hat SEO but of sloppy maintenance, and I guess so is a number of other problems.
CapmCrackaWaka 7y ago
I think machine learning masquerading as AI is definitely over-hyped. The real revolution is open source predictive modeling and statistical techniques becoming commonplace. Creating statistical models is much more useful and applicable to 99% of the businesses that have a need for analytics, but it seems to be overshadowed by the AI hype.
- m_ke 7y ago
  AI and machine learning have no business being mentioned in the same sentence. The AI hype is pushed by marketers and journalists because it gets clicks and invites the futurist crowd to chime in. We're decades away from anything that will resemble an AI system and any talk of it now is a waste of time. Machine Learning has made amazing strides in the past decade and will change all aspects of our lives. There will be a ton of startups in the next decade using speech recognition, image recognition, natural language processing and etc to automate all of our repetitive tasks. If hardware keeps up we'll probably see a boom in robotics in the next 5-15 years that will allow us to automate most manufacturing and food production.
  - falcor84 7y ago
    This is just semantics. What you're referring to as AI is often given the name AGI (Artificial General Intelligence), while many (most?) would agree that AI includes nowadays common technologies such as maps pathfinding and grammar checking.
    - m_ke 7y ago
      I've been doing machine learning for almost 10 years now and can tell you from experience that when a layman hears AI they think AGI, a magic pixie dust that can solve any problem that they throw it at. AI is a branch of computer science, but outside of A* search it's practically all machine learning.
  - CapmCrackaWaka 7y ago
    Sure there will be a (ton??) of startups using these things, but my point is that it will not be nearly as prevalent as general statistics and predictive modeling. Which one do you think will be more common: 1) companies using speech recognition, NLP, Image recognition, etc. 2) companies using R/Python to help guide their business decisions through predictive models / simpler statistics? I believe the demand for companies to actually use the fancier parts of machine learning are going to be much fewer than people think. Number 2, on the other hand, is much more generalizable, and is having a much larger effect on the 'average' business than NLP and image recognition.
CalChris 7y ago
Strongly dis-disagree. Google's AlphaZero and AlphaGo look awfully fearsome. Generalizing from that to other expert endeavors seems inevitable rather than some sort of mania. Moreover, saying that chess and go aren't benchmark hard problems strikes me as a technical No True Scotsman because they were always seen as that before Alpha obliterated them.
- hakfoo 7y ago
  Chess and Go are constrained problem spaces. The space may be huge, but it's still finite. The win condition is unambiguous. The rules can fit on a postcard. There are only a finite number of possible board configurations. Real-world win conditions tend to be more complex. For non-general AI tasks, this is much less of a problem. "Identify hot dog vs. not-hot-dog accurately 99% of the time" is measurable and verifiable. But a simple goal for a complex general-AI task is likely to build a Paperclip Optimizer. I suspect we don't even have the right language to build a good win condition for a lot of real world problems-- we may end up ignoring "insignificant" factors only to discover they're huge over tine or at scale. Games also tend to provide near-perfect or perfect information. At a minimum, there's constrained state transitions. I can't pull out my Oyster card in the middle of a poker game, add it to my hand, and declare it's actually a four of clubs. Real problems can have surprise off-the-wall, or outright externally triggered transitions, that completely throw the model off. "AI, build me a market-beating stock portfolio to cash out 2040-01-01" is a lot harder than "AI, checkmate this king."
  - CalChris 7y ago
    I was replying to a comment about ML, not AI.
eanzenberg 7y ago
What an interesting thing to say when practically all user interactions in apps and devices are now driven by data science and machine learning. In fact you’d have to work real hard to use a device or service that hasn’t been tuned or driven by ml.
Psyladine 7y ago
The giveaway is in the text; "Machine Learning" is being propped up to support cloud architecture by the big 3.

chubot 7y ago

Yup, I came here to question that as well. What are some examples of companies that hinged on machine learning? DATA is crucial, but that's very different than "sophistiated machine learning models". For example, do Uber and Lyft need machine learning? As far as I can tell, they need data about drivers, users, ride completion, maps, and a lot of people doing simple statistics on that data and improving the product. Machine learning might give them a few percentage points in some specific subsystem, but it isn't going to make or break the product as a whole. It's not "table stakes". Pick another industry and do the same thought experiment. What are some other breakout companies? Did snapchat succeed (in its day) because of machine learning? What about AirBNB? I think they are optimizing the product with "data science", not machine learning. Certainly not deep learning. Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small. Accumulating a data asset around your product and service and using sophisticated machine learning models to personalize and improve your product is not a nice to have.

AndrewKemendo 7y ago
You would be astonished at some of the non-public (not that they are secret) machine learning things that Uber does that you wouldn't even be aware of. My favorite is that they do pothole detection, prediction and routing around based on what they classified based on driver accelerometer data. They've also reduced traffic incidents with insights from the same dataset that drove product changes.
- chubot 7y ago
  That's interesting, and in line with what I perceived as Uber's extreme focus on operations. To me, the accelerometer DATA is the new and important thing -- it's the thing that taxi companies don't have. Whether the machine learning model is "sophisticated" doesn't seem critical on the face of it. I guess it depends whether there are a lot of non-pothole events that look like pothole events to "naive" signal processing. Do you need deep learning for this problem? I don't know but I would be skeptical without a citation. But I am interested in more info and examples. If someone can give a bunch of examples I would adjust my view.
  - m_ke 7y ago
    Amazon uses machine learning to forecast demand and ship products to warehouses before you order something. It's part of what makes their 1 day deliveries work. https://www.npr.org/2018/11/21/660168325/optimized-prime-how-ai-and-anticipation-power-amazons-1-hour-deliveries https://www.npr.org/2018/11/21/660168325/optimized-prime-how... Data is obviously crucial, but it's useless if it's just sitting on hard drive. Machine learning allows them to squeeze out as much juice from it as possible, and a few % point lift makes a huge difference at scale, especially in high volume businesses with low margins per transaction.
  - AndrewKemendo 7y ago
    This is just nitpicking phrases at this point, a popular past time for us nerds, so I get it but it misses the point. "Sophisticated" is enough of a suitcase term that it's effectively worthless. More importantly it's worth understanding that ML allows us to do things we couldn't do before, because it's cheaper and scales better than previous optimization methods, when you have the data. So yes, the data infrastructure is the critical part, however the ML pipelines and Data Science work is what gives the massive multiplier at a cost/scale you can't get with traditional optimization methods.
m_ke 7y ago
Machine Learning is a technology the same way blockchain is. Customers buy products and solutions, not your tech stack so it doesn't make sense for any company to sell themselves as a machine learning company, unless you're selling APIs. Alexa / Siri / Google Assistant would not be possible without machine learning. Same goes for Snapchat's filters and any other form of AR. Google's Pixel phones wouldn't have such a great camera without computational photography / machine learning.
6gvONxR4sf7o 7y ago
Those of us doing it mostly roll our eyes at how VCs and (especially) marketers describe it, so I agree with you, but the "is it ML or stats?" conversation has been going nowhere for as long as this round of AI hype has been here. That said, uber and lyft certainly benefit from much more interesting ML and stats than just "simple statistics." For example, setting prices and incentives to properly balance the driver side against the rider side of the marketplace.
- chubot 7y ago
  Yeah I agree the argument is mostly about terminology. Anyway you slice it you have a bunch of data and you have a team that's improving the product with the data. That is for sure "table stakes". And yes I probably should have said "data science" or "statistics" rather than "simple statistics", definitely for Uber and Lyft (less so for smaller companies). I worked on a data science team 10 years ago when it was called "statistics". I even remember a 2009 NYTimes article that talked about data science without using the word "data science", because it hadn't come into use yet. If people want to redefine "machine learning" to mean "data science", OK fine. I guess that's what's happening now. The term "data science" already annoyed some people, but I guess now it's "machine learning" and "deep learning".
morganherlocker 7y ago
> For example, do Uber and Lyft need machine learning? Anyone doing large scale ETA prediction and routing these days is using ML models at various phases in the pipeline, from GPS noise filtering to road network data generation. You might get past the early stages of a rideshare or delivery service with more traditional naive models, but there are still significant efficiency gains to be made in even the most cutting edge systems today. For maps and navigation, this is especially true when you go global and need models that adapt to subtle cultural and behavioral changes across markets.

m_ke 7y ago

You're wrong. Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google, translation, speech recognition, finance, fraud detection. In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives. EDIT: for people who are downvoting me, here are some examples of how machine learning is and will transform our lives: 1. 3D graphics: https://www.youtube.com/watch?v=FlgLxSLsYWQ https://www.youtube.com/watch?v=FlgLxSLsYWQ Andrew Price doesn't have a technical background but does a decent job summarizing some of the ways deep learning will transform computer graphics. Soon all of the mocap, character design and animation will be driven by deep learning systems. 2. Computer Architecture and Traditional Software: https://www.youtube.com/watch?v=TTpKWOuzOxc https://www.youtube.com/watch?v=TTpKWOuzOxc There's a ton of recent research showing that you can use machine learning to beat human crafted heuristics in hardware, scheduling, compiler design and query planning.

chubot 7y ago
Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small. I would say it's table stakes for large companies in certain areas, but not small (see my other reply). Machine learning seems to be good at squeezing percentage points out of monopoly with a business that already works. That's very different than "table stakes for small companies". Small companies need optimize their product with fundamental changes. For example, in the early days of AirBNB there was a more inconsistent user experience, which you heard about in the media. I think they addressed those problems with policies, incentives, and some elbow grease (kicking bad users off the platform, etc.). Not machine learning. Machine learning doesn't help you expand into Europe or Asia, etc.
cm2012 7y ago
This guy is downvoted but he's right. A huge amount of Facebook's and Google's value growth comes from machine learning driving their various algorithms.
eitland 7y ago
> Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google, Yep, and the results are hilarious or tragic depending on how you look at it: We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.) [0]: some people will always claim it is because black hat SEO is so much worse, but that doesn't explain why Google sometimes can neither understand doublequotes nor the verbatim option anymore. That is not the result of black hat SEO but of sloppy maintenance, and I guess so is a number of other problems.

CapmCrackaWaka 7y ago

I think machine learning masquerading as AI is definitely over-hyped. The real revolution is open source predictive modeling and statistical techniques becoming commonplace. Creating statistical models is much more useful and applicable to 99% of the businesses that have a need for analytics, but it seems to be overshadowed by the AI hype.

m_ke 7y ago
AI and machine learning have no business being mentioned in the same sentence. The AI hype is pushed by marketers and journalists because it gets clicks and invites the futurist crowd to chime in. We're decades away from anything that will resemble an AI system and any talk of it now is a waste of time. Machine Learning has made amazing strides in the past decade and will change all aspects of our lives. There will be a ton of startups in the next decade using speech recognition, image recognition, natural language processing and etc to automate all of our repetitive tasks. If hardware keeps up we'll probably see a boom in robotics in the next 5-15 years that will allow us to automate most manufacturing and food production.
- falcor84 7y ago
  This is just semantics. What you're referring to as AI is often given the name AGI (Artificial General Intelligence), while many (most?) would agree that AI includes nowadays common technologies such as maps pathfinding and grammar checking.
  - m_ke 7y ago
    I've been doing machine learning for almost 10 years now and can tell you from experience that when a layman hears AI they think AGI, a magic pixie dust that can solve any problem that they throw it at. AI is a branch of computer science, but outside of A* search it's practically all machine learning.
- CapmCrackaWaka 7y ago
  Sure there will be a (ton??) of startups using these things, but my point is that it will not be nearly as prevalent as general statistics and predictive modeling. Which one do you think will be more common: 1) companies using speech recognition, NLP, Image recognition, etc. 2) companies using R/Python to help guide their business decisions through predictive models / simpler statistics? I believe the demand for companies to actually use the fancier parts of machine learning are going to be much fewer than people think. Number 2, on the other hand, is much more generalizable, and is having a much larger effect on the 'average' business than NLP and image recognition.

CalChris 7y ago

Strongly dis-disagree. Google's AlphaZero and AlphaGo look awfully fearsome. Generalizing from that to other expert endeavors seems inevitable rather than some sort of mania. Moreover, saying that chess and go aren't benchmark hard problems strikes me as a technical No True Scotsman because they were always seen as that before Alpha obliterated them.

hakfoo 7y ago
Chess and Go are constrained problem spaces. The space may be huge, but it's still finite. The win condition is unambiguous. The rules can fit on a postcard. There are only a finite number of possible board configurations. Real-world win conditions tend to be more complex. For non-general AI tasks, this is much less of a problem. "Identify hot dog vs. not-hot-dog accurately 99% of the time" is measurable and verifiable. But a simple goal for a complex general-AI task is likely to build a Paperclip Optimizer. I suspect we don't even have the right language to build a good win condition for a lot of real world problems-- we may end up ignoring "insignificant" factors only to discover they're huge over tine or at scale. Games also tend to provide near-perfect or perfect information. At a minimum, there's constrained state transitions. I can't pull out my Oyster card in the middle of a poker game, add it to my hand, and declare it's actually a four of clubs. Real problems can have surprise off-the-wall, or outright externally triggered transitions, that completely throw the model off. "AI, build me a market-beating stock portfolio to cash out 2040-01-01" is a lot harder than "AI, checkmate this king."
- CalChris 7y ago
  I was replying to a comment about ML, not AI.

eanzenberg 7y ago

What an interesting thing to say when practically all user interactions in apps and devices are now driven by data science and machine learning. In fact you’d have to work real hard to use a device or service that hasn’t been tuned or driven by ml.

Psyladine 7y ago

The giveaway is in the text; "Machine Learning" is being propped up to support cloud architecture by the big 3.