22 ms·

The new business of AI and how it’s different from traditional software

398 points by gk1 7y ago

smoyer 7y ago
My favorite way of describing how AI is different: Traditional software: input + program = output AI: input + output = program
pgcj_poster 7y ago
I think the main difference between AI and traditional software is that the latter is actually useful in the real world. The place where AI has had the most impact is image recognition—and that's cool, but that's a very small part of what we use computers for. As someone who spends almost every waking hour in front a computer, I don't think I use any software that employs AI for any useful purpose except maybe search engines—and I'm pretty sure that they were a lot better before they used AI. GPT-2 is neat, but there's literally no circumstance under which I would want to use GPT-2-generated text for anything except experiments or toys. And while I'm sure Google makes good use of AI for spying on people and recommending YouTube videos to turn my brother into a nazi, I'd much prefer if they didn't do that.
- dsiegel2275 7y ago
  I guess it depends on how narrow one defines "AI". It sounds like you are defining it as "Deep Learning"... (maybe the article did as well, I didn't read it).
- doublekill 7y ago
  Deep learning is mostly focused on image recognition, and classification on tabular data is way more common for smaller businesses. Your activity online triggers 10s of AI models for every hour. AI is just not much of a consumer technology, but it is what the producers increasingly use. GPT-2 is useful way way beyond text generation, it has very powerful embeddings useful for many other tasks. The text generation and cat picture classification is what pulled it into the mainstream: how you know about it, while not familiar with the particulars. Stop putting the sole blame on recommendation algorithms for radicalization. Or paint everything with the same brush and start blaming Youtube too for radical thoughts on climate change, veganism, identity politics, etc. I suspect your brother is not even a Nazi anyway, just has differing views on immigrants and black crime statistics. Let's hope some other Youtuber does not get recommended a video on how to punch a Nazi, as by your reasoning, people could not help themselves.
- dsign 7y ago
  Busted! How did you find out about your brother? I thought we were acting in complete secrecy ...
- thrav 7y ago
  I worked at an AI startup that pioneered modeling over inbound leads and Sales outcomes (most of this data is in Salesforce + Marketo/Pardot/Hubspot) and that application was hugely valuable for a few companies with massive inbound volume. Zendesk and New Relic were the poster child success stories, since they didn’t have to massively scale their sales development teams to call on every mom and pop / student exploring a trial. It highlighted the 5-10% of their volume that could actually yield real money very effectively. Unfortunately, there’s not that many companies with that problem, and the market never materialized to the necessary degree and they sold the existing contracts and pivoted. That said, it was a home run for what it did. If you find the right problem, AI can create massive value. The hard part is never the learning, it’s finding and framing the right problem.
shadowtree 7y ago
"AI" products are consulting heavy as 80% of the effort is spent on data. Real world data inputs are messy, most need human curation up front. Simple example: FMCG/Retail would like to use apps to scan shelves for their own product placement and their competitors. Simple, right? But getting those packshots, account for lighting and placement conditions, ... you need an army of people to do QC. AI really just moves the analytics/BI load from a company to a vendor - hence it is consulting. Good business to have, but human labor always has shittier margins than software.
thulecitizen 7y ago
Are AI startups just a business model built around some proprietary code/software?
specialist 7y ago
I worked on the recommender and personalization stuff for a midsized fashion retailer. It took me about 2 years to figure out most of the work done by our ML & big data teammates was pure fiction. I believe, but have no conceivable way to prove, that all of the easy wins have been won by the first movers. It's weird. I have almost irrational optimism about the unbounded potential for deep learning, ML, big data, and so forth. Amazing, almost magical, stuff like KP figuring out that Vioxx was killing their patients once they had enough data and asked the right questions. But I'm also completely skeptical about using these techniques for culture and entertainment and advertising. Methinks that ceiling has already been hit and there's nothing on the horizon. Too bad. I really wanted our internal version of Stitchfix to actually work. Now I'm not sure it's possible. Or that we (or maybe just me) were asking the wrong questions.
streetcat1 7y ago
Ways to solve some of the issues mentioned: 1) Using containers, the SASS software can be downloaded to the client cluster and managed by Kubernetes operators. Hence the cost of training and storage will be bare by the clients themself and not by the SASS company. 2) Second, the use of AutoML should increase the productivity of the startup employees (especially with the ongoing retraining of models, deployment, monitoring, etc). The one problem that will always be there is new data and edge cases in the data. I do believe that this would be the major obstacle for the next 5 years. Also, I would expect to see the number of models actually explode (assuming that they are trained and deployed by AutoML). Case in point is Uber with models per city/ time of day (with 1000's of models in production).
- jacques_chester 7y ago
  > Case in point is Uber with models per city/ time of day (with 1000's of models in production). That is a good case-in-point, because one of the arguments in the article is that AI is expensive. At what point does 40,000 compute-hours and a few million dollars spent on hundreds of city models become a better use of time and money than an afternoon noodling with ARIMA or some Fourier analysis on a $5,000 workstation? Perhaps -- perhaps -- at Uber's scale, squeaking out a tenth of a percent is worth the time and money. But the rest of us schmoes can do pretty well with an SQL query, some R or Python or Julia and a generous dollop of good old-fashioned all-American hubris.
  - streetcat1 7y ago
    I absolutely agree. I did not say 1000's of deep learning models. There should always be a tradeoff between performance and cost. But it is much better to give customer the option, instead of forcing her to the said SASS architecture.
- ganoushoreilly 7y ago
  This doesn't really work as most AI Modeling is very resource intense (GPU / TPU etc.) that most business aren't going to have.
sjg007 7y ago
Interesting take. Someone who comes up with a good NLP AI though will probably be closer to 80% margins because you’ve got a shared model. Image processing too is usually built on top of pre-trained models and work surprisingly well. This is known as transfer learning. So I think we will see more online AI models.
- deleted 7y ago
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- andrewmutz 7y ago
  I agree that cloud operating costs are lower for NLP AI, but for a different reason than you cite. I think they are lower because the type of data that they operate on is much more compact than AI that is operating on images, video, or audio.
codingslave 7y ago
This is a really good article. One company that I always think of when I think machine learning is the computer vision "startup" Clarifai. At one point in time they were cutting edge, filling the need for large scale image classification that enterprises had. This was when computer vision neural network architectures were rudimentary and hard to train (they still kind of are). Then in-house data science teams sprang up, tooling got better, better network architectures came out, and Clarifai essentially lost all of their edge over night. Machine Learning in itself is basically never the edge, it has to be a unique data set or sticky user base, something else that builds a moat.
- m_ke 7y ago
  I was one of the first few employees at Clarifai so I can add to this. When Matt and Adam started the company there was no Tensorflow and outside of Hinton/Bengio/LeCun triangle nobody was doing deep learning yet. Matt just beat Google on ImageNet (around the time when he was doing an internship at Google Brain under Jeff Dean) and was one of the few experts in the field as he was lucky enough to have Hinton as his masters thesis advisor at UofT and did a PhD at NYU under Rob Fergus and Yann LeCun. We had a clear technological advantage for about 2 years but thanks to the open nature of deep learning research (arxiv and willingness to open source code) the whole field caught up. Google giving out tensorflow and pretrained models for free made a bit of a dent as well. A big problem with the "machine learning model as a service" business model is that each customer has slightly different needs and a different source of data so an off the shelf solution is usually not good enough. Because of that you end up being forced into doing consulting for large companies that don't know how to hire machine learning engineers. You end up spending months building them a model that they won't even know how to use and move on to the next customer. IMHO there are only two viable AI business models right now: 1. Spin out your research lab into a company and keep churning out papers without ever thinking about having real customers and make enough noise to get acquired by FAANG, ala Deepmind, Metamind, etc. 2. Find a problem with a lot of repetitive manual labor and slowly wedge a machine learning model into the process. Design a good feedback loop by first augmenting the workers and use their feedback to keep improving the model until it's good enough to replace them. Doing so requires you to actually build a real product in that domain so you'll need much more diverse team than a paper mill. Most companies doing this shouldn't even call themselves "AI" companies since their customers don't care how the solution works as long as it solves their problem. I believe that companies pursuing option 2 will take over a lot of large established players because building machine learning driven products requires buy in from the whole organization and it's not something that's easy to do at large enterprises. You need to be able to design the product in a way that helps you collect feedback, build data infrastructure to collect it and be willing to accept a solution that won't always be right.
mbesto 7y ago
Ya know what, AI might be the most handy-wavy term ever adopted in the tech world (and this is compared to big data, IoT, blockchain, etc.) No one really can really define what precisely AI is. Just in autonomous driving alone, there are 6 different "levels" of AI. If I write a decision tree algorithm, is that AI? My point is, you can basically use the term AI to justify virtually anything when it comes to the value of software, which just makes articles like this not valuable at all. Why not just call it "this is what software is capable of doing for a business"? One thing is for sure about "AI", and really just "advancements in software", is that repeatable human tasks are and will be replaced by automated systems. The only thing left for non-software business interaction will be to simply deal with other humans beings.
- baylessj 7y ago
  AI is just "if" statements with good marketing.
  - vividmind 7y ago
    AI - amplified "if"s
- Rury 7y ago
  Right, it's a buzzword. Buzzwords are very similar to phatic expressions (aka small talk). They're words that don't convey much information, because they lack stringent definitions. As so, they do little other than feed confirmation biases. Which is why you often find these types of terms effectively used in marketing... and amongst those in management...
- airstrike 7y ago
  Relevant: https://www.reddit.com/r/ProgrammerHumor/comments/f7p3o2 https://www.reddit.com/r/ProgrammerHumor/comments/f7p3o2
- whatshisface 7y ago
  It is said that the true definition of AI is: "Awaiting Implementation."
willart4food 7y ago
Great article, so... the opportunities for innovators and investors are: - new OS paradigm: skunkworks in a garage somewhere in the world - new hardware paradigm: skunkworks in a garage somewhere in the world - new AI business: quietly disrupting a niche of a niche
seibelj 7y ago
I wrote an article I published today about how AI is the biggest misnomer in tech history https://medium.com/@seibelj/the-artificial-intelligence-scam-is-imploding-34b156c3537e https://medium.com/@seibelj/the-artificial-intelligence-scam... I wrote it to be tongue-in-cheek in a ranting style, but essentially "AI" businesses and the technology underpinning it are not the silver bullet the media and marketing hype has made it out to be. The linked a16z article shows how AI is the same story everywhere - enormous capital to get the data and engineers to automate, but even the "good" AI still gets it wrong much of the time, necessitating endless edge-cases, human intervention, and eventually it's a giant ball of poorly-understand and impossible to maintain pipelines that don't even provide a better result than a few humans with a spreadsheet.
- deleted 7y ago
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- blasphemous 7y ago
  Well said, the tech is not ready for what people are trying to use it for (for the most part). The problems people are trying to solve is still best served by clever algorithms + data.
chevman 7y ago
My prediction is that the first, really huge, AI breakout company will be the company that can figure out how to deploy AI to automate and scale data normalization/processing/integration pipelines and the resultant downstream features that drive value.
- adtac 7y ago
  Is this satire? Because I can't tell anymore
  - deleted 7y ago
    [deleted]
  - jmnicolas 7y ago
    I expected to read the keyword 'webscale' at some point but was left disappointed ...
natmaka 7y ago
Isn't 99.99999...% of any currently used AI good ol' statistics or (not XOR) classical operations research stuff or (not XOR) well-known and rather simple NLP?
- jl2718 7y ago
  The more specific the use case, the more that human-engineered tools/rules will dominate. E.g. Regex beats BERT 99% of the time for any specific use case of value to a real customer, but BERT does a decent job in open domains without much effort.
  - natmaka 7y ago
    Where is such 'decent job' part of the wonders touted as "AI", which widely successful product/service sold as AI-powered benefits from it?
- m0zg 7y ago
  >> "rather simple NLP" I'd like to have some of whatever you're smoking. NLP is one of the hardest areas of science with many problems that will not be conclusively solved in my lifetime, both technical and scientific.
  - natmaka 7y ago
    I didn't mean that the field is 'simple' (easy to tackle), but that most tools used on the field does not seem to me "intelligent" in any way. Which one is "intelligent" and why?
    - rytill 7y ago
      What tool would seem to you as intelligent? Can you formalize that in some way?
      - natmaka 7y ago
        Something able to learn or to discover without relying on a 'brute force' (exhaustive exploration of the problem space) approach. Neural networks are in a way pertinent, I reckon, however I'm not enthusiastic about the fact that using it forbids us to 'explain how it solves the problem'. It also apply, albeit somewhat to a lesser extent(?), to bayesians and such.
tehsauce 7y ago
I've revised the naivety out of their 3 points at the beginning of the article -------- In particular, many AI companies have: 1. Lower gross margins due to heavy cloud infrastructure usage and ongoing human support ---> Their product is actually an army of expensive humans 2. Scaling challenges due to the thorny problem of edge cases ---> Their products don't actually work 3. Weaker defensive moats due to the commoditization of AI models and challenges with data network effects ---> No truely valuable technology or systems
- deleted 7y ago
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- ec109685 7y ago
  #1 isn’t saying that at all. Cloud bills are expensive for an early stage startup in the AI space. They later touch on the human angle in the post, but not fair to diminish their cloud bill point. 2. No, they don’t work in all cases. Think Tesla autopilot versus a Level 5 car without a steering wheel. 3. True. I wonder if more ai innovation will start happening behind close doors as a result?
  - mkolodny 7y ago
    Regarding #3, we still have so far to go to get to human-level intelligence that I think companies benefit more from open research than closing their doors. When a company publishes their research, others can find and publish improvements to their research. Then, the company can use the published improvements to improve their product.
    - zitterbewegung 7y ago
      Have you ever tried to reproduce research from academics and or companies ? Some papers take more than a day of work to reproduce . Not only that if you publish the research without the dataset it’s practically impossible or extremely expensive . Doing GPT-2 from scratch is around $50k or compute time and data collection. Even a simple deep learning paper will require you to have at least a NVIDIA 1080ti if you are lucky and for NLP I needed to buy a RTX Titan ($2500 graphics card).
AndrewKemendo 7y ago
"AI companies simply don’t have the same economic construction as software businesses" Last I checked, "AI" is software. Nowhere in here did I read anything about the fundamental truth of modern AI in production: [AI] is a feature inside a successful product. There are no "AI" companies, or "AI" products. There are companies which provide services to do inference on data, or in some cases tools/platforms so you can do it on your own. They also confuse me by using the term services to mean bespoke Non Recurring Engineering efforts, instead of including something like a REST based API service that reflects most "pure" AI companies. Amazon Rekognition or MS Cognitive Services API are perfect examples of SaaS like AI services, but they aren't products exactly because they are used inside some other product as a feature. At the end of the day, if you look at successful uses of AI in products they are one tiny piece of a larger product, that helps the product scale where it couldn't have before. That's pretty much the only place where it is proving very successful. Even then, more and more of that is being done in house with the rise of things like Sagemaker and other turnkey ML inference tools.
- fierarul 7y ago
  > "AI companies simply don’t have the same economic construction as software businesses" > Last I checked, "AI" is software. You might be underestimating the importance of data to apply that software onto. I might have some real estate ideas but unless I get some data about properties and actual prices, etc I am just writing a research paper. Maybe in the US real estate data is more accessible but in other places very much not or it is very expensive. Just an example.
- calebkaiser 7y ago
  I agree that this is a common misconception people have about ML and how it fits into our world. If you want to see successful ML products, you don't have to find some AGI stealth mode startup—just look at your phone: - Gmail's Smart Compose - Netflix's Recommendation Engine - Uber's ETA Prediction ML functionality is becoming a standard feature in software, and in my opinion, that's the real "ML Revolution." As you mention, turnkey inference tools like Cortex (full disclosure/shameless plug: I'm a contributor) are making this accessible to virtually all eng teams.
pcmaffey 7y ago
As they say, ML is written in python. AI is written in power point.
- m463 7y ago
  Sort of like lies, damn lies and spreadsheets?
- philips4350 7y ago
  I mean you could write AI in power point ... https://www.youtube.com/watch?v=uNjxe8ShM-8 https://www.youtube.com/watch?v=uNjxe8ShM-8 /s
fa7pdn 7y ago
data is the new oil!
KKKKkkkk1 7y ago
Over the decades that computers kept getting smaller, there were lots of new businesses, services, and products that were created thanks to this process. Now that we're in a phase where we're making our DNNs deeper and deeper, what are some new successful businesses, services, and products that are springing up as a consequence? I can think of Amazon Echo. Any others?
- tommit 7y ago
  How about the ability to search for objects in your pictures on your phone (granted, this is not a business in itself, but rather a new feature on existing products), use an online translator to translate almost anything in any language (including idioms) using something like google translate or deepL, fun niche products that give us a glimpse into what could be possible in the not so far future like AI Dungeon ... and that's excluding SaaS businesses that sell solutions to other businesses directly, so as a consumer you wouldn't really hear about it. I've worked at two of those companies in the past. Machine Learning techniques have been applied to numerous different fields. Right now, they're often more subsidizing than a product in itself. But they are out there. And while I agree that a lot of companies are just throwing out buzzwords, it's definitely not all snake oil.
  - pingyong 7y ago
    >ability to search for objects in your pictures on your phone This is pretty cool, can save a lot of time in some cases, and doesn't disappoint because object recognition (even by humans) is unreliable by nature. >use an online translator to translate almost anything in any language (including idioms) using something like google translate That's debatable at best. Google Translate at least is still horrible, to the point where it is actually pretty much completely useless, at translating Asian languages to English. For longer texts, even German to English or Spanish to English produces a lot of nonsense, although it is usually at least somewhat intelligible if you're familiar with the sort of mistakes that Google Translate tends to make. Anecdotally, I don't use Google Translate any differently than I did 7 years ago. It's good for single words or expressions, it's okay for single sentences if you are aware of it's pitfalls, it's pretty useless for large articles unless all you want is getting a general idea of what the article is talking about. >AI Dungeon I mean everything we've seen from AI Dungeon was hilarious because of it's nonsensical flaws. If those flaws can somehow be removed in the future we'll be stuck with a version where every individual sentence kind of makes sense but there is no coherent connection or story line, i.e. a product that nobody would use.
allovernow 7y ago
It is increasingly clear to me that vast majority of programmers do not understand the profound difference between modern Machine Learning and software development. AI may just be a buzzword, but neural networks and ML are not. The tech has just arrived, this is fresh, evergreen territory, and the first wave of applied machine learning is quickly approaching, in quiet development across industries. Things are about to get really interesting with the academic explosion of neural network architectures and theory, and I would strongly recommend to any developer to spend a few months getting up to speed!
- jmnicolas 7y ago
  I have yet to find a use case for AI where I work. We just need simple business apps.
  - 0x008 7y ago
    we serve a lot of companies that have actually a use for computer vision and nlp.
- negamax 7y ago
  AI is the new blockchain. The only difference is that Blockchain now beginning to look ready for real world applications having gone through its wild west days. AI is still in that phase. Lots of talk, little to no substance. Yes, Google Maps, Translate, Siri and countless others are great examples of AI. But there's definitely a lot of snake oil being peddled through it. And unless we as an industry get past it, AI is not going to go anywhere as a tool
  - TeMPOraL 7y ago
    > The only difference is that Blockchain now beginning to look ready for real world applications having gone through its wild west days Hold on for a moment, and name three. Because I honestly haven't seen a blockchain that isn't either a) scam, b) naive mistake, or c) an ecological disaster if ever scaled up (therefore unworkable). AI, for all the hype and nonsense around it, isn't even approaching the blockchain levels of bullshit.
monkeydust 7y ago
About 6 years ago I invested in a ML as a startup company as an angel. The proposition was that they had a very nice workflow tool to take data, scrub it, partion it, build models against it, view performance and deploy it through and api. At the time there was nothing else out there really that was as polished and understandable to a business person. Then, very rapidly, the field got democratized. People were firing up notebooks, tensor flow popped into existence and their edge eroded. They were pitching to teams who had just hired data scientists who of course didn't want to buy something that made their job look easier! The other reality, which ties to this article, is that whilst they had to do a lot of hand holding with clients so the service cost became high although this wasn't custom model development but more training on the platform so they eventually worked out a way to package it in the sale as they got established. The company in the end changed course. They built out the product to focus on a single vertical that was getting traction in finance. This was huge contentious change and they lost some people along the way - but - it ultimately saved them from being wiped out. In the end the sold out to a much larger player in that vertical who was had no AI strategy and we all made a decent if not mind blowing return on our investment. Lots of lessons learnt on the way and it was a fun ride overall.
- dhruvkar 7y ago
  was this evolution.ai?
- scottlocklin 7y ago
  Dunno if this is the startup I'm thinking of, but if it is, I'd attribute the exit entirely to the VC dude who took it over who obviously knew what he was doing. The founder-CEO was great at math, but it didn't look so good otherwise.
- ubu7737 7y ago
  Making money on the bubble.
- svij137 7y ago
  Great experience. Would like to understand more from your experience as we build our own venture in the super computing space for data scientists and AI workloads.
denisvlr 7y ago
This article really resonates with me: I'm currently working on bootstrapping an applied AI service and faced most of the challenges mentioned, to the point where I was doubting myself as I was not as efficient or productive as other Saas / Software founders. So this article is kind of reassuring to me. A few more comments: Cloud infra For a traditional web app you can quickly deploy it on a cheap AWS on Heroku VM for a few dollars/mo and later scale as you get more traffic. With AI you now need expensive training VMs. There are free options such as Google Collab but it doesn't scale for anything else than toy projects or prototyping. AWS entry point GPU instance (p2.xlarge) is at $0.900/hour i.e. $648/mo, and a more performant one (p3.2xlarge) at $2160/mo. Yes, you should shut them down when you are done with training but still. You can also use spot instances to reduce cost but it's not straightforward to set up. For inference, you also need a VM with enough memory for your model to fit in, so again an expensive VM from day one. Datasets if you rely on a publicly available dataset, chances are there are already 10 startups doing the same product. In order to have a somewhat unique and differentiated product, you need a way to acquire and label a private dataset. Humans in the loop The labeling part is very tedious and costly both in terms of time and money. You can hire experts or do it yourself at great cost, or you can hire cheap outsourced labor who will deliver low-quality annotations that you will spend a lot of time controlling, filtering, sending back, etc. For inference, depending on your domain, even with state-of-the-art performance you may end up with say 90% accuracy, ie 1 angry customer out of 10. that's probably not acceptable, but it gets worse: chances are you will attract early-adopter customers who are faced with hard cases, whose current solution doesn't work so that's why they want to use your fancy AI in the first place. In that context, your accuracy for this kind of customer might actually be much worse. So again you need significant human resources to control inferences in production. It will be hard to offer real-time results, so you may have to design your product to be async and introduce some delay in responses, which is maybe not the UX you initially had in mind. I still think there are tremendous opportunities in applied AI products and services, but it's important to have these challenges in mind when planning a new product or startup.
- redisman 7y ago
  How does it compare with building your own GPU-heavy computers? I'm not too familiar with it or how consumer-grade GPUs fare in these workloads, but training sounds like it in theory could happen locally easier than any consumer facing parts.
ByronFortescue 7y ago
Just a side note, but please; on-premises, not on-premise.
- natch 7y ago
  Just a side note, but please, use a comma to separate two dependent and incomplete clauses, not a semicolon or colon.
deleted 7y ago
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deepGem 7y ago
My greatest takeaway from fiddling around with machine learning for a past few years is this: Most of the business use cases are deterministic in nature. It is really hard to shoe in a probabilistic response in such use cases, as illustrated in this article. It's even harder to convert this probabilistic response to a deterministic response (humans in the loop), at a cost comparable to just maintaining the deterministic system. There are a few business cases that naturally are probabilistic - ad bidding, stock price prediction, recommendations. The giants have taken over this already. I am still scratching my head to find a use case for a startup to jump in, use machine learning and solve a growing business use case. Probably there are some healthcare related use cases but I have no idea about that domain.
- samcodes 7y ago
  I think this is a good observation. One thing I’ve been thinking about is probabilistically evaluating traditional software. Every un-caught exception, for example, is usually a false negative when evaluating “did the code correctly handle the input?” Not that ML code is bug-free. But it seems like most traditional software is probabilistically correct, despite not being thought of that way.
- calebkaiser 7y ago
  To me, the more interesting question with ML isn't "How can I build an entire business around a model," but rather "How can I exponentially increase a product's value with machine learning?" For example, I don't theoretically need Netflix/Spotify/YouTube to have a great recommendation system. All I need them to do is stream media to my device. However, their recommendation systems add immense value to their product for me. A similar example would be navigation services (Uber/Google Map's ETA prediction, for example). I don't necessarily need those apps to give me predictions about traffic patterns—their core functionality is just giving me directions—but it makes the product much more valuable to me. For those businesses, this increase in value leads to an increase in usage and therefore revenue—it's not simply an abstract "nice to have." I think oftentimes the argument about the value of ML comes down to a binary decision of whether or not ML can 100% replace humans in a given domain, and that this is a flawed model. ML is capable of improving most products, in my opinion, and we're already seeing it happen.
perlgeek 7y ago
I don't think AI is a business. There are lots of business workflows that AI can improve or enable in the first place, and you might be able to make a business out of that, but you should be aware of the difference. If you want to be in the business of enabling / improving business workflows, you should be very explicit about that, and AI could be one of many techniques in your tool belt -- but hopefully not the only one. Starting an "AI business" sounds like "I've got a shiny new hammer, what nails can I hit with it?", which is kinda backwards from my understanding of how businesses should be run. It might work, but it's also easy to fall into the trap thinking AI should be your only tool.
wildermuthn 7y ago
I like the hard-nosed business evaluation of this article. However, the difficulties of a successful “AI” company are the same difficulties a “web” company had in the 90s. How many “World Wide Web” (back when we used that phrase) products survived the bubble? And how many people made a lot of money while creating software of very little value? People understood, correctly, that the “Net” (sorry, can’t help my nostalgia) was a breakthrough technology that would change everything. But very few people understood how to use it effectively in that moment to provide business or consumer value. The same is true today for deep learning models. The tech has advanced to the point where there are clearly products waiting to be built upon advances that are largely open-sourced (if not pretrained). But building a product that is useful hasn’t changed from being a very difficult task to easy simply because a new technology exists. Moreover, as the article points out, costs in terms of dollars and manpower are currently higher than a making another web or mobile app. All this should make hackers salivate. The higher than challenge and the more advanced the stack, the greater an opportunity becomes. A useful way of thinking about this moment is that we have another platform to launch products from: first desktop, then web, then mobile, now “AI”. Each platform enabled a new kind of product. Unlike the other platforms, which were essentially hardware mechanisms of software delivery, ML/DL is a software mechanism of delivering software. It is a fundamentally different way of creating software that produces fundamentally different kinds of software — the kind you couldn’t make any other way. The article is right to throw the cold-water of reality on the hype of AI. But the backdrop is one of breakthrough technologies that are just waiting to be leveraged.
- tkgally 7y ago
  > the difficulties of a successful “AI” company are the same difficulties a “web” company had in the 90s. I had a similar thought reading the comments here about AI hype. In the mid to late nineties, there was a lot of hype about the Internet, and a lot of smart people dismissed the Internet as nothing more than hype. In the end, though, the Internet changed our world. I suspect that, eventually, AI will do the same.

398 points by gk1 7y ago

My favorite way of describing how AI is different: Traditional software: input + program = output AI: input + output = program

pgcj_poster 7y ago

I think the main difference between AI and traditional software is that the latter is actually useful in the real world. The place where AI has had the most impact is image recognition—and that's cool, but that's a very small part of what we use computers for. As someone who spends almost every waking hour in front a computer, I don't think I use any software that employs AI for any useful purpose except maybe search engines—and I'm pretty sure that they were a lot better before they used AI. GPT-2 is neat, but there's literally no circumstance under which I would want to use GPT-2-generated text for anything except experiments or toys. And while I'm sure Google makes good use of AI for spying on people and recommending YouTube videos to turn my brother into a nazi, I'd much prefer if they didn't do that.

dsiegel2275 7y ago
I guess it depends on how narrow one defines "AI". It sounds like you are defining it as "Deep Learning"... (maybe the article did as well, I didn't read it).
doublekill 7y ago
Deep learning is mostly focused on image recognition, and classification on tabular data is way more common for smaller businesses. Your activity online triggers 10s of AI models for every hour. AI is just not much of a consumer technology, but it is what the producers increasingly use. GPT-2 is useful way way beyond text generation, it has very powerful embeddings useful for many other tasks. The text generation and cat picture classification is what pulled it into the mainstream: how you know about it, while not familiar with the particulars. Stop putting the sole blame on recommendation algorithms for radicalization. Or paint everything with the same brush and start blaming Youtube too for radical thoughts on climate change, veganism, identity politics, etc. I suspect your brother is not even a Nazi anyway, just has differing views on immigrants and black crime statistics. Let's hope some other Youtuber does not get recommended a video on how to punch a Nazi, as by your reasoning, people could not help themselves.
dsign 7y ago
Busted! How did you find out about your brother? I thought we were acting in complete secrecy ...
thrav 7y ago
I worked at an AI startup that pioneered modeling over inbound leads and Sales outcomes (most of this data is in Salesforce + Marketo/Pardot/Hubspot) and that application was hugely valuable for a few companies with massive inbound volume. Zendesk and New Relic were the poster child success stories, since they didn’t have to massively scale their sales development teams to call on every mom and pop / student exploring a trial. It highlighted the 5-10% of their volume that could actually yield real money very effectively. Unfortunately, there’s not that many companies with that problem, and the market never materialized to the necessary degree and they sold the existing contracts and pivoted. That said, it was a home run for what it did. If you find the right problem, AI can create massive value. The hard part is never the learning, it’s finding and framing the right problem.

shadowtree 7y ago

"AI" products are consulting heavy as 80% of the effort is spent on data. Real world data inputs are messy, most need human curation up front. Simple example: FMCG/Retail would like to use apps to scan shelves for their own product placement and their competitors. Simple, right? But getting those packshots, account for lighting and placement conditions, ... you need an army of people to do QC. AI really just moves the analytics/BI load from a company to a vendor - hence it is consulting. Good business to have, but human labor always has shittier margins than software.

thulecitizen 7y ago

Are AI startups just a business model built around some proprietary code/software?

specialist 7y ago

I worked on the recommender and personalization stuff for a midsized fashion retailer. It took me about 2 years to figure out most of the work done by our ML & big data teammates was pure fiction. I believe, but have no conceivable way to prove, that all of the easy wins have been won by the first movers. It's weird. I have almost irrational optimism about the unbounded potential for deep learning, ML, big data, and so forth. Amazing, almost magical, stuff like KP figuring out that Vioxx was killing their patients once they had enough data and asked the right questions. But I'm also completely skeptical about using these techniques for culture and entertainment and advertising. Methinks that ceiling has already been hit and there's nothing on the horizon. Too bad. I really wanted our internal version of Stitchfix to actually work. Now I'm not sure it's possible. Or that we (or maybe just me) were asking the wrong questions.

streetcat1 7y ago

Ways to solve some of the issues mentioned: 1) Using containers, the SASS software can be downloaded to the client cluster and managed by Kubernetes operators. Hence the cost of training and storage will be bare by the clients themself and not by the SASS company. 2) Second, the use of AutoML should increase the productivity of the startup employees (especially with the ongoing retraining of models, deployment, monitoring, etc). The one problem that will always be there is new data and edge cases in the data. I do believe that this would be the major obstacle for the next 5 years. Also, I would expect to see the number of models actually explode (assuming that they are trained and deployed by AutoML). Case in point is Uber with models per city/ time of day (with 1000's of models in production).

jacques_chester 7y ago
> Case in point is Uber with models per city/ time of day (with 1000's of models in production). That is a good case-in-point, because one of the arguments in the article is that AI is expensive. At what point does 40,000 compute-hours and a few million dollars spent on hundreds of city models become a better use of time and money than an afternoon noodling with ARIMA or some Fourier analysis on a $5,000 workstation? Perhaps -- perhaps -- at Uber's scale, squeaking out a tenth of a percent is worth the time and money. But the rest of us schmoes can do pretty well with an SQL query, some R or Python or Julia and a generous dollop of good old-fashioned all-American hubris.
- streetcat1 7y ago
  I absolutely agree. I did not say 1000's of deep learning models. There should always be a tradeoff between performance and cost. But it is much better to give customer the option, instead of forcing her to the said SASS architecture.
ganoushoreilly 7y ago
This doesn't really work as most AI Modeling is very resource intense (GPU / TPU etc.) that most business aren't going to have.

sjg007 7y ago

Interesting take. Someone who comes up with a good NLP AI though will probably be closer to 80% margins because you’ve got a shared model. Image processing too is usually built on top of pre-trained models and work surprisingly well. This is known as transfer learning. So I think we will see more online AI models.

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andrewmutz 7y ago
I agree that cloud operating costs are lower for NLP AI, but for a different reason than you cite. I think they are lower because the type of data that they operate on is much more compact than AI that is operating on images, video, or audio.

codingslave 7y ago

This is a really good article. One company that I always think of when I think machine learning is the computer vision "startup" Clarifai. At one point in time they were cutting edge, filling the need for large scale image classification that enterprises had. This was when computer vision neural network architectures were rudimentary and hard to train (they still kind of are). Then in-house data science teams sprang up, tooling got better, better network architectures came out, and Clarifai essentially lost all of their edge over night. Machine Learning in itself is basically never the edge, it has to be a unique data set or sticky user base, something else that builds a moat.

m_ke 7y ago
I was one of the first few employees at Clarifai so I can add to this. When Matt and Adam started the company there was no Tensorflow and outside of Hinton/Bengio/LeCun triangle nobody was doing deep learning yet. Matt just beat Google on ImageNet (around the time when he was doing an internship at Google Brain under Jeff Dean) and was one of the few experts in the field as he was lucky enough to have Hinton as his masters thesis advisor at UofT and did a PhD at NYU under Rob Fergus and Yann LeCun. We had a clear technological advantage for about 2 years but thanks to the open nature of deep learning research (arxiv and willingness to open source code) the whole field caught up. Google giving out tensorflow and pretrained models for free made a bit of a dent as well. A big problem with the "machine learning model as a service" business model is that each customer has slightly different needs and a different source of data so an off the shelf solution is usually not good enough. Because of that you end up being forced into doing consulting for large companies that don't know how to hire machine learning engineers. You end up spending months building them a model that they won't even know how to use and move on to the next customer. IMHO there are only two viable AI business models right now: 1. Spin out your research lab into a company and keep churning out papers without ever thinking about having real customers and make enough noise to get acquired by FAANG, ala Deepmind, Metamind, etc. 2. Find a problem with a lot of repetitive manual labor and slowly wedge a machine learning model into the process. Design a good feedback loop by first augmenting the workers and use their feedback to keep improving the model until it's good enough to replace them. Doing so requires you to actually build a real product in that domain so you'll need much more diverse team than a paper mill. Most companies doing this shouldn't even call themselves "AI" companies since their customers don't care how the solution works as long as it solves their problem. I believe that companies pursuing option 2 will take over a lot of large established players because building machine learning driven products requires buy in from the whole organization and it's not something that's easy to do at large enterprises. You need to be able to design the product in a way that helps you collect feedback, build data infrastructure to collect it and be willing to accept a solution that won't always be right.

mbesto 7y ago

Ya know what, AI might be the most handy-wavy term ever adopted in the tech world (and this is compared to big data, IoT, blockchain, etc.) No one really can really define what precisely AI is. Just in autonomous driving alone, there are 6 different "levels" of AI. If I write a decision tree algorithm, is that AI? My point is, you can basically use the term AI to justify virtually anything when it comes to the value of software, which just makes articles like this not valuable at all. Why not just call it "this is what software is capable of doing for a business"? One thing is for sure about "AI", and really just "advancements in software", is that repeatable human tasks are and will be replaced by automated systems. The only thing left for non-software business interaction will be to simply deal with other humans beings.

baylessj 7y ago
AI is just "if" statements with good marketing.
- vividmind 7y ago
  AI - amplified "if"s
Rury 7y ago
Right, it's a buzzword. Buzzwords are very similar to phatic expressions (aka small talk). They're words that don't convey much information, because they lack stringent definitions. As so, they do little other than feed confirmation biases. Which is why you often find these types of terms effectively used in marketing... and amongst those in management...
airstrike 7y ago
Relevant: https://www.reddit.com/r/ProgrammerHumor/comments/f7p3o2 https://www.reddit.com/r/ProgrammerHumor/comments/f7p3o2
whatshisface 7y ago
It is said that the true definition of AI is: "Awaiting Implementation."

willart4food 7y ago

Great article, so... the opportunities for innovators and investors are: - new OS paradigm: skunkworks in a garage somewhere in the world - new hardware paradigm: skunkworks in a garage somewhere in the world - new AI business: quietly disrupting a niche of a niche

seibelj 7y ago

I wrote an article I published today about how AI is the biggest misnomer in tech history https://medium.com/@seibelj/the-artificial-intelligence-scam-is-imploding-34b156c3537e https://medium.com/@seibelj/the-artificial-intelligence-scam... I wrote it to be tongue-in-cheek in a ranting style, but essentially "AI" businesses and the technology underpinning it are not the silver bullet the media and marketing hype has made it out to be. The linked a16z article shows how AI is the same story everywhere - enormous capital to get the data and engineers to automate, but even the "good" AI still gets it wrong much of the time, necessitating endless edge-cases, human intervention, and eventually it's a giant ball of poorly-understand and impossible to maintain pipelines that don't even provide a better result than a few humans with a spreadsheet.

deleted 7y ago
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blasphemous 7y ago
Well said, the tech is not ready for what people are trying to use it for (for the most part). The problems people are trying to solve is still best served by clever algorithms + data.

chevman 7y ago

My prediction is that the first, really huge, AI breakout company will be the company that can figure out how to deploy AI to automate and scale data normalization/processing/integration pipelines and the resultant downstream features that drive value.

adtac 7y ago
Is this satire? Because I can't tell anymore
- deleted 7y ago
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- jmnicolas 7y ago
  I expected to read the keyword 'webscale' at some point but was left disappointed ...

natmaka 7y ago

Isn't 99.99999...% of any currently used AI good ol' statistics or (not XOR) classical operations research stuff or (not XOR) well-known and rather simple NLP?

jl2718 7y ago
The more specific the use case, the more that human-engineered tools/rules will dominate. E.g. Regex beats BERT 99% of the time for any specific use case of value to a real customer, but BERT does a decent job in open domains without much effort.
- natmaka 7y ago
  Where is such 'decent job' part of the wonders touted as "AI", which widely successful product/service sold as AI-powered benefits from it?
m0zg 7y ago
>> "rather simple NLP" I'd like to have some of whatever you're smoking. NLP is one of the hardest areas of science with many problems that will not be conclusively solved in my lifetime, both technical and scientific.
- natmaka 7y ago
  I didn't mean that the field is 'simple' (easy to tackle), but that most tools used on the field does not seem to me "intelligent" in any way. Which one is "intelligent" and why?
  - rytill 7y ago
    What tool would seem to you as intelligent? Can you formalize that in some way?
    - natmaka 7y ago
      Something able to learn or to discover without relying on a 'brute force' (exhaustive exploration of the problem space) approach. Neural networks are in a way pertinent, I reckon, however I'm not enthusiastic about the fact that using it forbids us to 'explain how it solves the problem'. It also apply, albeit somewhat to a lesser extent(?), to bayesians and such.

tehsauce 7y ago

I've revised the naivety out of their 3 points at the beginning of the article -------- In particular, many AI companies have: 1. Lower gross margins due to heavy cloud infrastructure usage and ongoing human support ---> Their product is actually an army of expensive humans 2. Scaling challenges due to the thorny problem of edge cases ---> Their products don't actually work 3. Weaker defensive moats due to the commoditization of AI models and challenges with data network effects ---> No truely valuable technology or systems

deleted 7y ago
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ec109685 7y ago
#1 isn’t saying that at all. Cloud bills are expensive for an early stage startup in the AI space. They later touch on the human angle in the post, but not fair to diminish their cloud bill point. 2. No, they don’t work in all cases. Think Tesla autopilot versus a Level 5 car without a steering wheel. 3. True. I wonder if more ai innovation will start happening behind close doors as a result?
- mkolodny 7y ago
  Regarding #3, we still have so far to go to get to human-level intelligence that I think companies benefit more from open research than closing their doors. When a company publishes their research, others can find and publish improvements to their research. Then, the company can use the published improvements to improve their product.
  - zitterbewegung 7y ago
    Have you ever tried to reproduce research from academics and or companies ? Some papers take more than a day of work to reproduce . Not only that if you publish the research without the dataset it’s practically impossible or extremely expensive . Doing GPT-2 from scratch is around $50k or compute time and data collection. Even a simple deep learning paper will require you to have at least a NVIDIA 1080ti if you are lucky and for NLP I needed to buy a RTX Titan ($2500 graphics card).

AndrewKemendo 7y ago

"AI companies simply don’t have the same economic construction as software businesses" Last I checked, "AI" is software. Nowhere in here did I read anything about the fundamental truth of modern AI in production: [AI] is a feature inside a successful product. There are no "AI" companies, or "AI" products. There are companies which provide services to do inference on data, or in some cases tools/platforms so you can do it on your own. They also confuse me by using the term services to mean bespoke Non Recurring Engineering efforts, instead of including something like a REST based API service that reflects most "pure" AI companies. Amazon Rekognition or MS Cognitive Services API are perfect examples of SaaS like AI services, but they aren't products exactly because they are used inside some other product as a feature. At the end of the day, if you look at successful uses of AI in products they are one tiny piece of a larger product, that helps the product scale where it couldn't have before. That's pretty much the only place where it is proving very successful. Even then, more and more of that is being done in house with the rise of things like Sagemaker and other turnkey ML inference tools.

fierarul 7y ago
> "AI companies simply don’t have the same economic construction as software businesses" > Last I checked, "AI" is software. You might be underestimating the importance of data to apply that software onto. I might have some real estate ideas but unless I get some data about properties and actual prices, etc I am just writing a research paper. Maybe in the US real estate data is more accessible but in other places very much not or it is very expensive. Just an example.
calebkaiser 7y ago
I agree that this is a common misconception people have about ML and how it fits into our world. If you want to see successful ML products, you don't have to find some AGI stealth mode startup—just look at your phone: - Gmail's Smart Compose - Netflix's Recommendation Engine - Uber's ETA Prediction ML functionality is becoming a standard feature in software, and in my opinion, that's the real "ML Revolution." As you mention, turnkey inference tools like Cortex (full disclosure/shameless plug: I'm a contributor) are making this accessible to virtually all eng teams.

pcmaffey 7y ago

As they say, ML is written in python. AI is written in power point.

m463 7y ago
Sort of like lies, damn lies and spreadsheets?
philips4350 7y ago
I mean you could write AI in power point ... https://www.youtube.com/watch?v=uNjxe8ShM-8 https://www.youtube.com/watch?v=uNjxe8ShM-8 /s

fa7pdn 7y ago

data is the new oil!

KKKKkkkk1 7y ago

Over the decades that computers kept getting smaller, there were lots of new businesses, services, and products that were created thanks to this process. Now that we're in a phase where we're making our DNNs deeper and deeper, what are some new successful businesses, services, and products that are springing up as a consequence? I can think of Amazon Echo. Any others?

tommit 7y ago
How about the ability to search for objects in your pictures on your phone (granted, this is not a business in itself, but rather a new feature on existing products), use an online translator to translate almost anything in any language (including idioms) using something like google translate or deepL, fun niche products that give us a glimpse into what could be possible in the not so far future like AI Dungeon ... and that's excluding SaaS businesses that sell solutions to other businesses directly, so as a consumer you wouldn't really hear about it. I've worked at two of those companies in the past. Machine Learning techniques have been applied to numerous different fields. Right now, they're often more subsidizing than a product in itself. But they are out there. And while I agree that a lot of companies are just throwing out buzzwords, it's definitely not all snake oil.
- pingyong 7y ago
  >ability to search for objects in your pictures on your phone This is pretty cool, can save a lot of time in some cases, and doesn't disappoint because object recognition (even by humans) is unreliable by nature. >use an online translator to translate almost anything in any language (including idioms) using something like google translate That's debatable at best. Google Translate at least is still horrible, to the point where it is actually pretty much completely useless, at translating Asian languages to English. For longer texts, even German to English or Spanish to English produces a lot of nonsense, although it is usually at least somewhat intelligible if you're familiar with the sort of mistakes that Google Translate tends to make. Anecdotally, I don't use Google Translate any differently than I did 7 years ago. It's good for single words or expressions, it's okay for single sentences if you are aware of it's pitfalls, it's pretty useless for large articles unless all you want is getting a general idea of what the article is talking about. >AI Dungeon I mean everything we've seen from AI Dungeon was hilarious because of it's nonsensical flaws. If those flaws can somehow be removed in the future we'll be stuck with a version where every individual sentence kind of makes sense but there is no coherent connection or story line, i.e. a product that nobody would use.

allovernow 7y ago

It is increasingly clear to me that vast majority of programmers do not understand the profound difference between modern Machine Learning and software development. AI may just be a buzzword, but neural networks and ML are not. The tech has just arrived, this is fresh, evergreen territory, and the first wave of applied machine learning is quickly approaching, in quiet development across industries. Things are about to get really interesting with the academic explosion of neural network architectures and theory, and I would strongly recommend to any developer to spend a few months getting up to speed!

jmnicolas 7y ago
I have yet to find a use case for AI where I work. We just need simple business apps.
- 0x008 7y ago
  we serve a lot of companies that have actually a use for computer vision and nlp.
negamax 7y ago
AI is the new blockchain. The only difference is that Blockchain now beginning to look ready for real world applications having gone through its wild west days. AI is still in that phase. Lots of talk, little to no substance. Yes, Google Maps, Translate, Siri and countless others are great examples of AI. But there's definitely a lot of snake oil being peddled through it. And unless we as an industry get past it, AI is not going to go anywhere as a tool
- TeMPOraL 7y ago
  > The only difference is that Blockchain now beginning to look ready for real world applications having gone through its wild west days Hold on for a moment, and name three. Because I honestly haven't seen a blockchain that isn't either a) scam, b) naive mistake, or c) an ecological disaster if ever scaled up (therefore unworkable). AI, for all the hype and nonsense around it, isn't even approaching the blockchain levels of bullshit.

monkeydust 7y ago

About 6 years ago I invested in a ML as a startup company as an angel. The proposition was that they had a very nice workflow tool to take data, scrub it, partion it, build models against it, view performance and deploy it through and api. At the time there was nothing else out there really that was as polished and understandable to a business person. Then, very rapidly, the field got democratized. People were firing up notebooks, tensor flow popped into existence and their edge eroded. They were pitching to teams who had just hired data scientists who of course didn't want to buy something that made their job look easier! The other reality, which ties to this article, is that whilst they had to do a lot of hand holding with clients so the service cost became high although this wasn't custom model development but more training on the platform so they eventually worked out a way to package it in the sale as they got established. The company in the end changed course. They built out the product to focus on a single vertical that was getting traction in finance. This was huge contentious change and they lost some people along the way - but - it ultimately saved them from being wiped out. In the end the sold out to a much larger player in that vertical who was had no AI strategy and we all made a decent if not mind blowing return on our investment. Lots of lessons learnt on the way and it was a fun ride overall.

dhruvkar 7y ago
was this evolution.ai?
scottlocklin 7y ago
Dunno if this is the startup I'm thinking of, but if it is, I'd attribute the exit entirely to the VC dude who took it over who obviously knew what he was doing. The founder-CEO was great at math, but it didn't look so good otherwise.
ubu7737 7y ago
Making money on the bubble.
svij137 7y ago
Great experience. Would like to understand more from your experience as we build our own venture in the super computing space for data scientists and AI workloads.

denisvlr 7y ago

This article really resonates with me: I'm currently working on bootstrapping an applied AI service and faced most of the challenges mentioned, to the point where I was doubting myself as I was not as efficient or productive as other Saas / Software founders. So this article is kind of reassuring to me. A few more comments: Cloud infra For a traditional web app you can quickly deploy it on a cheap AWS on Heroku VM for a few dollars/mo and later scale as you get more traffic. With AI you now need expensive training VMs. There are free options such as Google Collab but it doesn't scale for anything else than toy projects or prototyping. AWS entry point GPU instance (p2.xlarge) is at $0.900/hour i.e. $648/mo, and a more performant one (p3.2xlarge) at $2160/mo. Yes, you should shut them down when you are done with training but still. You can also use spot instances to reduce cost but it's not straightforward to set up. For inference, you also need a VM with enough memory for your model to fit in, so again an expensive VM from day one. Datasets if you rely on a publicly available dataset, chances are there are already 10 startups doing the same product. In order to have a somewhat unique and differentiated product, you need a way to acquire and label a private dataset. Humans in the loop The labeling part is very tedious and costly both in terms of time and money. You can hire experts or do it yourself at great cost, or you can hire cheap outsourced labor who will deliver low-quality annotations that you will spend a lot of time controlling, filtering, sending back, etc. For inference, depending on your domain, even with state-of-the-art performance you may end up with say 90% accuracy, ie 1 angry customer out of 10. that's probably not acceptable, but it gets worse: chances are you will attract early-adopter customers who are faced with hard cases, whose current solution doesn't work so that's why they want to use your fancy AI in the first place. In that context, your accuracy for this kind of customer might actually be much worse. So again you need significant human resources to control inferences in production. It will be hard to offer real-time results, so you may have to design your product to be async and introduce some delay in responses, which is maybe not the UX you initially had in mind. I still think there are tremendous opportunities in applied AI products and services, but it's important to have these challenges in mind when planning a new product or startup.

redisman 7y ago
How does it compare with building your own GPU-heavy computers? I'm not too familiar with it or how consumer-grade GPUs fare in these workloads, but training sounds like it in theory could happen locally easier than any consumer facing parts.

ByronFortescue 7y ago

Just a side note, but please; on-premises, not on-premise.

natch 7y ago
Just a side note, but please, use a comma to separate two dependent and incomplete clauses, not a semicolon or colon.

deleted 7y ago

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deepGem 7y ago

My greatest takeaway from fiddling around with machine learning for a past few years is this: Most of the business use cases are deterministic in nature. It is really hard to shoe in a probabilistic response in such use cases, as illustrated in this article. It's even harder to convert this probabilistic response to a deterministic response (humans in the loop), at a cost comparable to just maintaining the deterministic system. There are a few business cases that naturally are probabilistic - ad bidding, stock price prediction, recommendations. The giants have taken over this already. I am still scratching my head to find a use case for a startup to jump in, use machine learning and solve a growing business use case. Probably there are some healthcare related use cases but I have no idea about that domain.

samcodes 7y ago
I think this is a good observation. One thing I’ve been thinking about is probabilistically evaluating traditional software. Every un-caught exception, for example, is usually a false negative when evaluating “did the code correctly handle the input?” Not that ML code is bug-free. But it seems like most traditional software is probabilistically correct, despite not being thought of that way.
calebkaiser 7y ago
To me, the more interesting question with ML isn't "How can I build an entire business around a model," but rather "How can I exponentially increase a product's value with machine learning?" For example, I don't theoretically need Netflix/Spotify/YouTube to have a great recommendation system. All I need them to do is stream media to my device. However, their recommendation systems add immense value to their product for me. A similar example would be navigation services (Uber/Google Map's ETA prediction, for example). I don't necessarily need those apps to give me predictions about traffic patterns—their core functionality is just giving me directions—but it makes the product much more valuable to me. For those businesses, this increase in value leads to an increase in usage and therefore revenue—it's not simply an abstract "nice to have." I think oftentimes the argument about the value of ML comes down to a binary decision of whether or not ML can 100% replace humans in a given domain, and that this is a flawed model. ML is capable of improving most products, in my opinion, and we're already seeing it happen.

perlgeek 7y ago

I don't think AI is a business. There are lots of business workflows that AI can improve or enable in the first place, and you might be able to make a business out of that, but you should be aware of the difference. If you want to be in the business of enabling / improving business workflows, you should be very explicit about that, and AI could be one of many techniques in your tool belt -- but hopefully not the only one. Starting an "AI business" sounds like "I've got a shiny new hammer, what nails can I hit with it?", which is kinda backwards from my understanding of how businesses should be run. It might work, but it's also easy to fall into the trap thinking AI should be your only tool.

wildermuthn 7y ago

I like the hard-nosed business evaluation of this article. However, the difficulties of a successful “AI” company are the same difficulties a “web” company had in the 90s. How many “World Wide Web” (back when we used that phrase) products survived the bubble? And how many people made a lot of money while creating software of very little value? People understood, correctly, that the “Net” (sorry, can’t help my nostalgia) was a breakthrough technology that would change everything. But very few people understood how to use it effectively in that moment to provide business or consumer value. The same is true today for deep learning models. The tech has advanced to the point where there are clearly products waiting to be built upon advances that are largely open-sourced (if not pretrained). But building a product that is useful hasn’t changed from being a very difficult task to easy simply because a new technology exists. Moreover, as the article points out, costs in terms of dollars and manpower are currently higher than a making another web or mobile app. All this should make hackers salivate. The higher than challenge and the more advanced the stack, the greater an opportunity becomes. A useful way of thinking about this moment is that we have another platform to launch products from: first desktop, then web, then mobile, now “AI”. Each platform enabled a new kind of product. Unlike the other platforms, which were essentially hardware mechanisms of software delivery, ML/DL is a software mechanism of delivering software. It is a fundamentally different way of creating software that produces fundamentally different kinds of software — the kind you couldn’t make any other way. The article is right to throw the cold-water of reality on the hype of AI. But the backdrop is one of breakthrough technologies that are just waiting to be leveraged.

tkgally 7y ago
> the difficulties of a successful “AI” company are the same difficulties a “web” company had in the 90s. I had a similar thought reading the comments here about AI hype. In the mid to late nineties, there was a lot of hype about the Internet, and a lot of smart people dismissed the Internet as nothing more than hype. In the end, though, the Internet changed our world. I suspect that, eventually, AI will do the same.