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I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…
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Absolutely. To be perfectly honest, it surprises me the extent to which ML naysaying seems to be popular on HN. The evidence of enormous progress seems pretty obvious to me.
Siri still isn’t able to understand « do NOT set the alarm to 3pm », and many image classifier produces aberrations that no human would ever commit. Many people feel that ML has so far only produced « tricks », but still doesn’t show any sign of « understanding » anything. As in, provide meaning. It may be unfair, but i think that at this point people would be more impressed by a program « smiling » at a good joke th…
Sure, there are some issues and most of them still fall somewhere in the uncanny valley.. but we are just starting to fully exploit this technology. AI that people don't tangibly see but make a giant impact in their lives is for example the Facebook algorithms that decide what shows up on your feed.
When a large subset of the population gets majority of their news from Facbeook, Facebook has effectively created a mechanical system that decides what a large chunk of the population is aware of. I don't think people fully realize what effect that has had on our entire society, including elections. The future will only see more of this type of thing.
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Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the things that you are already sure by now will see the light of day?
One big one is farming, think harvesting machinery. There's a number of startups trying to get that going. I think most are not at the scale to be successful the way the market themselves, but their collective learning will eventually lead to some consolidation. Why AI for that, it's vision AI to know when fruit is ripe or vegetables are ready for harvest. Then hand eye coordination to not bruise the fruit/vegetables…
People have been trying to design machines to pick fruit, even in toy scenarios, for decades, and we have little to show for it. I don’t think that image processing is the bottleneck here, it’s literally the mechanism as far as I understand.
I’m skeptical that any mechanical equipment will ever be able to do these tasks as cheaply, quickly, and efficiently as a human in our lifetimes.
This is so strange. If you use facebook, google, netflix, apple, microsoft, amazon, tesla or a whole host of other products and services you are interfacing with AI all the time, sometimes as the core product of the service. To think there’s no value there is asinine. Comes up a lot on HN. Seems like people who get excited for these types of articles are set in their ways and don’t want to progress forward.
We have also been watching these machine learning models for 6 months: - increase the volatility in virtually every financial market they touched - be exploited by adversarial learning networks to amplify funded propaganda as news - use poorly contrived sentiment analysis to generate incomprehensibly meaningless news headlines These non-linear "function approximators" have absolutely unpredictable and insane non-line…
This is so strange. If you use facebook, google, netflix, apple, microsoft, amazon or a whole host of other services you are interfacing with AI all the time. To think there’s no value there is asinine. Comes up a lot on HN. Seems like people set in their ways who don’t want to progress forward.
I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…
I think a big part of what is holding back many companies from making effective, genuine, real-world use of AI is that a significant majority of the individuals involved are bad at their jobs.
On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decisions than businesspeople. In my view, this includes things that would traditionally be considered business decisions. The business is unwilling to transition from "working on the solution to the problem" to "working on the AI system that solves the problem" (assuming that they even understand what "the problem" is).
On the technology side, I see a massive oversupply of badly underqualified and generally ignorant "data scientists" and "ML engineers" who have just enough bootstrapped understanding and familiarity with the plethora of (absolutely fantastic) open-source tools to fake an entry into the area, but do not have the genuine depth of background that is needed to actually plan, design, and deliver a high quality solution.
Yes, I have met people without "credentialed" backgrounds who are very good indeed, and yes I have met forward-thinking business people, but the number of people who are in these categories is a tiny fraction of the number of people who think they are in these categories.
And that's why businesses are getting less keen on AI.
EDIT: Grammar.
I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…
I met someone who dedicates their life to using machine learning to replace/aid/automate pathologists 6+ hour days searching for cancer tumors in lungs. They have been at it for 5 years. There is an insane amount of approvals, red tape, knowing the right people, convincing the hospital to use it - all tasks not related to the tech actually working. Building it is the easy part. They were on the way there, working in the research section of a hospital. But they are still more than 5 years away from me being able to walk into a hospital and get my lungs scanned for tumours.
I personally can't wait for these general purpose function approximators to make all our lives better.
It's not that the technology isn't there. It's not that the technology can't be technically applied successfully. It's that convincing systems of people to change is way harder than we might think.
What’s the next big thing after deep learning?
However, training is computationally expensive and generating labels for training data is still done mostly manually by humans. Just like people, a continuous learning algorithm will have trouble distinguishing true from fake and could be corrupted by feeding it fake data. There is machine learning that doesn't require labels, but much of the recent success has been built using human labeled data sets. Human biases get into the labeling, and appear even in the methods of selecting and collecting what data to train on. One way to design continuous learning systems involves wrapping simpler machine learning algorithms with data collection and feedback loops to handle the retraining. Ensuring accuracy is a difficult problem unless your problem domain comes with some built-in measures. Some measures of "correctness" are arbitrary such as those defined by culture and can change over time or geographically.
These days, you can translate text by pointing your phone at it and taking a picture. Thirty years ago, this would have been unambiguously AI, because it would have been not only impossible, but stupid impossible like something out of a soft SF novel where little self-flying robots deliver stuff to your house, or you can ask a computer a question in a natural voice and reasonably expect a civil, natural-language answ…
Yes, but you're missing the point. In 1950's sci-fi, those marvels were possible because there was imagined to be something like a general artificial intelligence behind the technology. We've achieved narrow AI, but the perception is that in order to get it, we would already have general AI, which is why people are disappointed.
The point is we have current things that are quite amazing, and would at one time have been considered to be the sort of thing that only an AI would be able to do, and yet we keep moving the goalposts. As if AI is defined as "that which humans can do but machines can't, done by machines".
maybe the best way to conceptualize this, inspired by @random_walker, is to compare AI in the 21st century to machines in the 20th century.
in the industrial age, machines automated rote physical tasks.
in the information age, AI could automate rote mental tasks.
the more objective and templatized the task, the more vulnerable it is to AI displacement. conversely, the more subjective and creative the task, the safer it is from AI displacement.