This is a negative value article. It spends a lot of time dwelling on a low-value semantic argument that "when people in business say 'AI' they really mean machine learning". Sure, but these words have been used synonymously for a long time - my grad degree ~15 years ago concentrating in machine learning was called "AI"; who cares? Then the article goes on to claim:
>I anticipate that after this passes, we can start to do the right thing — focusing on using machine learning to build things that are meaningful and realistic.
UGHHH. Why did you hide this in a long article making the 180 degree contradictory point that AI (which really means machine learning, remember?) is dumb?
This article is a disservice to its reader because it downplays a huge shift in the world that any ML practitioner should understand very well: machine learning/AI will ultimately replace a lot of what humans do, and it's moving at a faster pace than ever. I've led several small-ish projects (1-2 people, 3-6 months) that could replace dozens or even 1000s of experts in their respective fields. There are thousands of people like me, and there are more every day. The buzz may wear off, in the same way Time Magazine stopped talking about how the internet was going to take over the world after the dot-com crash, but the effects and the efforts will continue unabated.
In one project at a previous job we were able to pinpoint the genre, mood, instrumentation, etc of any new song with (usually) better-than-human accuracy in milliseconds using deep neural networks. This is better in pretty much every way than the 1000s of music experts that are employed by competitors and vendors.
The thing to keep in mind is every machine learning practitioner who is worth their salary is doing stuff like this. A lot of our every day work isn't as sexy as teaching a computer Go, but it's game-changing to most industries.
Whenever there's a big controversy, especially when it's about whether a "this" is or isn't a "that", the first thing I assume is that people are trying to make a taxonomy out of a continuum. I believe AI is like that. Just as there's a continuum from replicating molecule to god-like alien, there's an analogous continuum from a NAND gate to strong AI.
Totally agree. This article contains very little that's new.
It criticizes Watson, but Watson was roundly debunked last year as far behind IBM's marketing machine.
It trots out the "teenagers & sex" quote, which frankly is applied to every new technology. "Teenagers & sex" belongs to a very small family of cliches that everyone in tech has heard. The fact that it gets used indiscriminately makes it mean very little with each new application.
And finally, I'll like to point out the irony of someone trotting out tired thoughts to get attention while criticizing supposedly overhyped tech. To the right, we have people making exaggeratedly positive claims about tech to grab your eyes, and to the left, we have their mirror image.
> artificial intelligence (AI), a sub-branch of machine learning
I could have a failed mental model, but I'm under the impression that the relationship is the other way around. AI is a broad field encompassing various strategies to build intelligent machines. ML is one particular strategy where large volumes (think Big Data) of training data is used to teach by example. (Which makes Deep Learning a subset of ML, where "deep" neural networks are at play.)
It's actually neither way. AI and ML have some intersection. There're machine learning methods which have little to do with AI, for example, logistic regression. There're AI methods which have nothing to do with ML, for example, logical inference.
The article mentions IBM trying to cash in on the AI hype with Watson, and towards the end wonders what the next overhyped trend will be. This ad might hold the answer: https://www.youtube.com/watch?v=2O2CLoCxAWA
I agree! The question answering datasets, and some of the models built with them are getting good. I work on GAN and general classifiers at work, but on my own time my main interest is in sequential language models.
This time the hype is founded - Deep (Reinforcement) Learning truly pushed many AI domains out of uncanny valley. Image recognition/generation, speech recognition and synthesis, shallower language understanding - we truly have tech we have never seen before and only dreamed about. Of course, it won't solve everything, but the "solved level" got a massive upgrade with recent advancements. If we get GPUs that can compute DNNs 1000x faster, then we will see magic everywhere around us; so far the good models take very long to train, making them less adaptable to changing conditions.
The cute robot soccer game is a disingenuous argument. Put a machine gun on one of those Boston Dynamic robots with image recognition targeting and tell me it's not scary.
Well, yeah, 'cause you will never know when or where it's going to start spewing bullets. (I'll just note that a Tesla just ran into a parked fire truck.)
Readit News
>I anticipate that after this passes, we can start to do the right thing — focusing on using machine learning to build things that are meaningful and realistic.
UGHHH. Why did you hide this in a long article making the 180 degree contradictory point that AI (which really means machine learning, remember?) is dumb?
This article is a disservice to its reader because it downplays a huge shift in the world that any ML practitioner should understand very well: machine learning/AI will ultimately replace a lot of what humans do, and it's moving at a faster pace than ever. I've led several small-ish projects (1-2 people, 3-6 months) that could replace dozens or even 1000s of experts in their respective fields. There are thousands of people like me, and there are more every day. The buzz may wear off, in the same way Time Magazine stopped talking about how the internet was going to take over the world after the dot-com crash, but the effects and the efforts will continue unabated.
Can you give some specifics on projects?
https://tech.iheart.com/mapping-the-world-of-music-using-mac...
Not to mention personalized recommendations, which basically aren't possible at scale without some level of ML:
https://tech.iheart.com/mapping-the-world-of-music-using-mac...
https://news.ycombinator.com/item?id=12269568
The thing to keep in mind is every machine learning practitioner who is worth their salary is doing stuff like this. A lot of our every day work isn't as sexy as teaching a computer Go, but it's game-changing to most industries.
It criticizes Watson, but Watson was roundly debunked last year as far behind IBM's marketing machine.
It trots out the "teenagers & sex" quote, which frankly is applied to every new technology. "Teenagers & sex" belongs to a very small family of cliches that everyone in tech has heard. The fact that it gets used indiscriminately makes it mean very little with each new application.
And finally, I'll like to point out the irony of someone trotting out tired thoughts to get attention while criticizing supposedly overhyped tech. To the right, we have people making exaggeratedly positive claims about tech to grab your eyes, and to the left, we have their mirror image.
I could have a failed mental model, but I'm under the impression that the relationship is the other way around. AI is a broad field encompassing various strategies to build intelligent machines. ML is one particular strategy where large volumes (think Big Data) of training data is used to teach by example. (Which makes Deep Learning a subset of ML, where "deep" neural networks are at play.)
I was expecting some evidence and then I hit "I hope you liked this article."
In this benchmark people or models are given a text, and later asked a number of questions. Questions are quite real. See for example here: https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/S...
Models already have performance which are as good as human's. This is real. This is not hype.