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That's unlikely. There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. The wors
by superbcarrot 6y ago
That's unlikely. There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. The worst case for the field is that progress slows down, people realise that their expectations were unrealistic and the hype inevitably dies down. Which has to happen eventually. So even if ML isn't the hottest thing or a massively growing field, it will still be used for a long time.
- otabdeveloper4 6y ago> There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. If you could name three of them I'd be really grateful. Serious question; everything surrounding ML seems to be only good for (non-monetizable) art projects. As art it is amazing, not going to lie, but "commercial use" seems like a huge stretch.
- Tenoke 6y agoHere's 3 off the top of my head, but there's more especially when you get into less flashy territory. Translation (Google translate, DeepL) Automatically generated product descriptions, sometimes also edited by humans (Alibaba) Image Tagging (Facebook photos)
- otabdeveloper4 6y agoOkay, I should have worded my comment more carefully. These applications seem to firmly fall into the "I'm willing to compromise on quality if I don't have to pay a living person a wage" niche, so they're value-destroying, not value-creating. Are there examples of value-creating applications for ML? (From a business point of view; obviously the "shitty translations but at no cost" proposition creates value for the average Internet user.)
- Tenoke 6y agoI don't understand this. Do you think that e.g. the average engineer is value destroying because if the business hires a more expensive and experienced one they will do a better job? In either case it is only 'value-destroying' if the business has unlimited resources.
- rolisz 6y agoImage tagging is not in that category. Google Photos, Synology Moments and other similar projects are valuable for end users. Nobody had time to manually tag hundreds of gigabytes of photos with even 10% of the items in them.
- otabdeveloper4 6y agoLike I said, I'm not talking about end users. (Obviously for end users ML enables doing lots of things "for free" that used to be paid services before.) I'm talking about commercial applications, like the OP said. That is, things I could potentially pitch to management and substantiate with something concrete that isn't "you can fire your lowest-paid contractors now".
- sanxiyn 6y agoSome things are worth doing but not worth paying people. Google is using ML to read house numbers from Google Street View photos. Why is that not a good commercial application? Google gets its map updated faster and cheaper with ML.
- Voloskaya 6y agoYour qualification of value destroying/value-creating makes little sense to me. If I need a news article or some other webpage translated I can do it thanks to Google Translate. If Google Translate was not here, I would simply not get it translated and lose the information, there is no way I would contact a translation firm for that kind of stuff. To me it is firmly value creating. At any rate here are way more than 3 other uses of DL today off the top of my head: * Autocompletion (be it in search engines toolbars or in Gmail/Word/...) * Superresolution GAN, the most interesting example to me being NVIDIA DLSS, you render a game at ~720p or less and then upscale it to the target resolution of 1080p or 4k, allowing to get quality that the machine would not have been able to support at the target resolution directly. * Image recognition/tagging: Most of this is used in the security domain, but there is also a lot of stuff around inventory management, safety etc. * Semantic search * Protein folding (AlphaFold) * In astrophysics: detection of supernovaes, FRBs and probably a bunch of other stuff I'm not aware of * Self-driving cars: Even assuming self-driving technology does not evolve anymore from now on, the current state of the art is still a selling point. * Predictive maintenance: Used for plane engines and other things
- funcDropShadow 6y agoGoogle translate, good enough? Today, I've received a package from Amazon containing router bits (for wood working not IT). It contains a so called "User Manual" which is obviously so badly translated, I assume automatically, that it will only fool a spell checker, that it is actually written in German. I often hear and read good things about Google Translate but every time I read something from it, e.g. when a browser or webpage helpfully decides that I would prefer a butchered salad of German words instead of an English web page, I am repulsed.
- Tenoke 6y agoI don't know but I live in Berlin, dont know German and use google translate on all sorts of official and non-official stuff (e.g. for my Anmeldung two weeks ago, reading up on Mietendeckel, news reports, random letters and shopping sites etc.) and it works almost always. Obviously not as perfect as a real translator but good enough > 99% of the time.
- mbeex 6y agoIn this case, give DeepL a try. Translations between English and German at least, are magnitudes above Google, IMO. Not without errors, of course.
- superbcarrot 6y agoI think you are underestimating how many of the services you already use incorporate machine learning somewhere. It's not about GANs or deepfakes (right now those are closer to the art projects you are referring to). Some examples for actual applications or classes of applications are image recognition, speech recognition, recommender systems, virtual assistants, fraud detection, financial forecasting.
- belval 6y agohttps://docs.aws.amazon.com/whitepapers/latest/aws-overview/machine-learning.html https://docs.aws.amazon.com/whitepapers/latest/aws-overview/... Most of these were developed woth actual business partners and are being used right now.
- wrinkl3 6y agoDo you consider voice assistants, DLSS and protein folding predictors to be non-monetizable art projects?
- eli_gottlieb 6y ago>If you could name three of them I'd be really grateful. Latent Dirichlet Allocation, message-passing in Hidden Markov models, and Naive Bayes for spam filtering. Outside my subfield, there's always the basic handwriting recognition employed in ATMs.
- Jugurtha 6y agoWe do machine learning for large organizations and business is good. We've been doing it for many years. We're even making our platform (https://iko.ai https://iko.ai) to do it faster and more consistently as it takes care of all the menial tasks. These projects have an impact in the real world [predictive maintenance on infrastructure that serves people, for example].
- bidirectional 6y agoAs far as I'm aware, ML is driving a pretty sizeable amount of new functionality at the big tech companies. How do you think Google does search and translate, Netflix recommends films, Tesla drives your car, Siri recognises your voice, etc. etc. I see the sentiment you've expressed a lot, and feel it speaks to a massive disconnect amongst developers. Most people are interacting with ML systems dozens if not hundreds of times a day.
- otabdeveloper4 6y agoQuestion wasn't if ML is useful. The question is - does ML make money or create (financial) value for the company? The stuff you listed doesn't, it's just part of a moat for already established products that don't depend on ML for their market share. (It's not like Netflix will lose market share if they switch from ML to some other approach for their recommendation system.)
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- dkislyuk 6y agoAny online service with considerable content creation and recommendation that you can think of is using ML to improve every aspect of the system (better embeddings, better retrieval, better ranking, better text understanding, better personalization, etc.) And the commercial usefulness is measurable. Industrial robotics, driver assist, drug discovery, computational photography, speech translation, and so many other examples illustrate a clear commercial applicability of ML methods scaled in the last 10 years specifically.
- benlivengood 6y agoSpeech-to-text. Every voice assistant, many phone trees, assistive software. Facial recognition. iphone face unlock, photo tagging, etc. Behavior prediction for advertising. Ad quality scores basically.
- crypto420 6y agoNot sure what you're talking about. Neural networks are powering a LOT of the internet. > Siri/Cortana/Google Home/Alexa, powered by DeepSpeech+language models > Google Search, powered by BERT > Tesla, powered by variants of YOLO > Facial recognition powered by MTCNN+FaceNet > AirBnB product search+recommendations > Amazon product recommendations GANs are a bit artsy, but JFC they're not even a decade old - we've gone from shitty MNIST clones to fully synthetic faces in the span of 5 years! Predictions: 1. I suspect we're going to see DeepFakes in Hollywood - famous people might license their faces to movies that they might not have the time to star in 2. People are going to start building even more powerful versions of search, like combinations of CLIP 3. Neural networks still aren't optimized for edge devices - we're going to see a deluge of cheap drones with cutting edge computer vision by default
- IdiocyInAction 6y agoSpam detection is the oldest and most well-known one. It is also used quite a bit in graphics and imaging; DLSS is a consumer-facing application, but it is also used in other domains, like OCR. Machine translation is another ubiquitous use case. As is any kind of language processing, like text-to-speech. Also, in the industry, it is heavily used for anomaly and defect detection. Also, Google reportedly uses it for a lot of search/recommendation stuff. ML is definitely monetizable, but not every company needs it, by a long shot. It is not "AI" and seems to often resemble a complex DSP step when used in practice. I think it's overhyped, but it is far, far more useful than anything blockchain will ever be.
- zffr 6y agoNot even remotely an ML expert. Just some ideas I had: Speech recognition + basic NLP for automatic customer support triage. None of these are great to use as a customer, but they seem to be effective enough to continue using and save companies lots of money. Automatic "offensive" content detection for social media. I'd bet they use ML to do a first-pass on uploaded content to make sure it doesn't contain porn, gore, etc. Probably lets these companies save money. Automatic defect detection in factories. Instead of training humans to detect subtle issues in manufacturing issues. I think companies like Samsara are experimenting with offering this tech as a service. Facial recognition/tracking for law enforcement/defense. Ignoring the ethics of it for a moment, it seems like governments would be willing to pay a good amount of money for this tech. Could be used to automatically search through hours of footage to find which frames (if any) contain a target.