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It is increasingly clear to me that vast majority of programmers do not understand the profound difference between modern Machine Learning and software developm

• 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.
    • negamax 7y ago
      These are my three favorite use cases (which I use personally) 1. Bitcoin - I have hedged currency successfully using Bitcoin 2. Maker - Check it out. It really does show power of smart contracts 3. Oracles - Although, I am not familiar with the tech but they are beginning to show real world adoption. Maker uses Oracles for e.g. Check Chainlink Edit: Telegram? I use it
      • zelly 7y ago
        Most "AI" is a scam but "blockchain" is far worse of an offender. "Blockchain" has zero utility in the business world. Its only use case is decentralized, censorship-resistant protocols. No one is trying to censor a shipping company's supply chain manifest. No one is trying to censor JP Morgan's bank transactions. The people who need it are dissidents, deplatformed people, people who cannot get bank accounts, or people trading contraband. Not exactly areas to extract an industry from. Half of these altcoins do not have working software. In 2017 the ICOs did not have any software at all, just a promise on a homepage with a button to send money. The other half do not scale or do not meet their own basic requirements. The oldest, most venerable project Bitcoin had an inflation bug a couple years ago that would have let anyone counterfeit an unlimited amount of coins. At least the AI scammers show up with code.
        • redisman 7y ago
          I loved all the hyped Blockchain companies 3 years ago where I would always think "what does this do that a small MySQL instance doesn't already do better for a real business?" Hey - you can save your customer database in a really cool convoluted and slow database. Because bitcoin. Plus your clients can run this fun app that drains their batteries.
      • UweSchmidt 7y ago
        Bitcoin is the ecological desaster that OP talked about. Smart contracts have the fundamental problem of people having to write perfect, error free code (as it can't be changed later), i.e. an impossibility. Often these smart contracts deal with a monetary value (as people strangely assign value to those "coins"), so something like the DAO hack on the Ethereum blockchain is inevitable. Also to do anything on a blockchain, even just add 1+1, on any hardware, no matter how expensive, no matter how far in the future, has to work at full capacity to hack some crypto. Your laptop will heat up and the fans go on full. Sounds trivial but if you've experienced it it's weird. Oracles are a fundamental problem of blockchain: If you trust an outside source of truth, why need the blockchain? Go straight to the oracle.
  • orbifold 7y ago
    In my estimation it is precisely the other way around. There are hardly any applications of blockchains that are not equally well solved by other methods, whereas computer vision alone has a huge number of applications.
  • orbifold 7y ago
    In my estimation it is precisely the other way around. There are hardly any applications of blockchains that are not equally well solved by other methods, whereas computer vision alone has a huge number of applications and “neural network” based solutions are now undoubtedly the best approach to a huge number of hard computer vision problems.
    • xchaotic 7y ago
      I’m glad you scoped it well computer vision is a somewhat narrow field and therefore say for a logistic company AI can be applied to some but not all businesses processes. It’s an improvement but not a revolution.
    • negamax 7y ago
      Yes but computer vision is not what people mean when they are talking about AI. Many of the great AI advantages are available as cloud services. But that's not what people mean by AI. Or they hide those use cases and services. Focusing more on pie in the sky narratives. And that's okay if coming from Google/Facebook etc. But this is mostly management/consultancy speak now.
• easytiger 7y ago
  Let me guess, you are heavily invested in ai/ml and are well aboard the hypetrain? What's about to happen is that people who have been convinced to invest in ML infra are about to start pulling budgets because they've had no results.
  • roenxi 7y ago
    There will be a lot of experiments that won't work, but there will be some that do. They're going to be the foundation for something radical and new. We're clearly entering a world where computers can make complicated, gut-feel decisions. This is an amazing new capability; with the fall of the best human Go players there are no longer any well-defined tasks that a human can outperform a computer in. If we can simulate a situation correctly then computers are now better than us. It will be challenging for this new capability not to not reshape all our control systems over the next two or three decades.
    • easytiger 7y ago
      I've seen projects in the pipeline for years, convinced by the considerable marketing, on spurious vague promises for even relatively basic requirements fail completely. The kind of people attracted to this area is also a factor. The automation revolution is a marketing conceit
    • pingyong 7y ago
      >there are no longer any well-defined tasks that a human can outperform a computer in. If by "well-defined" you mean "complete information", then yes. If by well-defined you mean... well-defined, then no. Humans are better at a lot of things still, including video games for example. OpenAI has been working for years on a DotA AI, and while very technologically interesting, it still can only play (well) with and against a fraction of heroes and mechanics, and even within those limitations humans have quickly been able to find weaknesses and outmaneuver it. AI really hasn't over the last 5 years (and probably won't during the next 5) changed the world that much. There are some problem domains that saw big interesting leaps (mainly image recognition, image reconstruction and game AI), but that's about it. Self-driving cars, if they ever come to market properly, will be the biggest change. And that's certainly a big change, but it's not really the "software industry revolutionizing" change that some people seem to expect.
• raducu 7y ago
  > I would strongly recommend to any developer to spend a few months getting up to speed! As someone who spent more than a few months on a couple courses on various platforms, a few months is NOTHING in this field. ML is not something where most people out of bootcamps can make a living out of. ML is the ultimate winner-take all technology. Sure, there might be some fast.ai examples of self taught ML scientists, but it is orders of magnitude harder than software development, mainly because of much longer learning feed-back -- you can't just printf or debug your code and expect to learn something new every 5 minutes.
  • amelius 7y ago
    On the contrary. ML is the new web-development. Anybody can do it. Sure, for some specialized cases you need an expert, but those cases become increasingly hard to find.
  • 0x1221 7y ago
    > but it is orders of magnitude harder than software development, mainly because of much longer learning feed-back The order of magnitude point is just wrong. You might argue that it's harder because it's essentially software development and a lot of applied math. But that certainly doesn't account for orders of magnitude difference.
    • raducu 7y ago
      I was talking about novel/real-world applications. With software, you can pretty much know in advance what will work, ROUGHLY how much it will take and how much it will cost. With ML you have a high chance that ML will not work at all for your problem, or that YOU won't be able to solve it. I'm a java developer and during my first 3 years I was able to investigate bugs not only in my code, but inside jboss or hibernate ORM; I can look up core java code and understand it just fine. How many of the ML self-taught crowd can write framework-level code, or debug a ML algorithm bug? Fooling management wanting to get into ML with some scikit code is easy, mastery of ML is orders of magnitudes harder than mastering a programming language/frameworks.
• alexcnwy 7y ago
  Machine learning is revolutionizing and democratizing machine perception at an alarming rate.
  • 0x1221 7y ago
    > Machine learning > revolutionizing and democratizing > perception > alarming rate Well done. How can I send you money?
    • allovernow 7y ago
      That may sound like a flood of buzzwords but he's not wrong at all, except maybe for "alarming rate". This tech is "revolutionary" in that nothing like it has ever performed anywhere near the current level. MNIST is effectively a solved, toy problem now, and many image classification problems are as well. Democratized in the sense that you can for the first time write and test these state of the art algorithms on your personal machine and, while a couple years ago you needed something beefy like a 1080 TI, even that requirements has been relaxed with many optimized architectures. More importantly, all of this has literally exploded in the last 5-10 years, no doubt due in part because of the open access nature of arxiv and open source. That's totally fine though, while comparisons to blockchain are a little insulting, all of this cynicism means less competition for me!
• rimliu 7y ago
  Tech was there decades ago.
• zelly 7y ago
  DL has not just arrived. It has been going on since before 2010 in industry and universities. There are multiple cohorts of PhDs in it. DL is useful obviously. Some of the biggest products in the world depend on it for almost their entire value propositions e.g. YouTube, Netflix, Tesla. (This is a power law; the long tail of commercial AI are jokes or borderline frauds.) But I wouldn't recommend already practicing programmers to FOMO into the field this late in the game. First of all, it's very much a different animal. ML is in the statistics/continuous world whereas programming is in the algorithmic/discrete world. Knuth said something about how it's a different type of mind (a more normal one) that's attracted to ML, not the same as the 2% computer mind. Not only will you be doing something you're potentially not as talented at, you will also have to start from scratch up against the seemingly endless stream of ML specialist PhD candidates. You can implement neural nets from descriptions in papers and blog posts. Honestly though there's not much to it with all the dead simple libraries e.g. Keras, Pytorch. Putting together the whole system is a much harder problem and requires actual software expertise.
  • pantsforbirds 7y ago
    I honestly think if you are already a software engineer and you want to get into a ML company you should focus on building infrastructure for big ML products. It seems the infrastructure game is very new for realistic, production ready, machine learning. I mean Uber was the first to really describe the idea of having a feature store back in 2017 with their Michelangelo[0] framework. We are just now starting to see feature stores pop up as a SAAS model (Hopworks, Google's feast, etc.) I think we'll see "Machine Learning Engineers" start to pop up like crazy the next 2-3 years as people with a mixture of data engineering, machine learning, and software engineering experience begin to take these infrastructure roles. [0] - https://eng.uber.com/michelangelo/ https://eng.uber.com/michelangelo/