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Well noted! Explicitly: where does such database come from?
Frames of video could make the number sky-high like that without involving enormous numbers of people.
Yeah that'd be great. I also want cars that run on salt water. I'm not saying that small data ai is equally impossible, but simply saying "we should make this better thing" isn't enough.
Only, the article seemed to show a very conservative Ng about the algorithms, a focus on data management - so it's still ML.
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I'm not sure "training" is the right way to think about it. Children don't train to identify objects, they quickly develop the ability to recognize objects and are able to correlate them with prior information that was retained and learned. Case in point: if you take a child born blind, give them the ability to see, they are immediately able to recognize and correlate objects around them.
> Children don't train to identify objects I have to disagree. They spend an inordinate amount of time trying to understand what they see, taste and hear. > able to correlate them with prior information that was retained and learned This is what we call inference. > Case in point: if you take a child born blind, give them the ability to see, they are immediately able to recognize and correlate objects around them. No…
To understand yes, however I meant "identify" in the sense of recognizing an object. I show a picture of an apple to a child a bit over a year old, hide it, put it in a basket of other things and present it back to them, they will be able to identify it. Or if you show an object to a child, then hide the object, they can realize that the object is no longer there, regardless of their understanding of what happened to the object.
> Many things we take for granted as being innate to the human experience, are in fact learned (trained) behavior.
I don't think it's that simple, I would say it's both. I don't question learning plays a big role in recognition, just pointing out that a large amount intrinsic knowledge also exists from early child development. Many of those cases where someone regains eyesight happens much later in life, at a time when their brains have largely matured to the point that neuroplasticity is pretty much over for them. Having someone's brain develop with almost no visual input at a young age is bound to mean that their visual cortex and its connections to everything else doesn't develop as it should.
From a quick search, it appears that to some degree children born categorically blind can recover all the way up to teenage life. [1] But indeed it's likely less effective than a younger child undergoing a similar procedure (which I don't think is really that rare: it's hard to diagnose vision problems at young age, and a lot of children who get necessary corrective surgery at young age turn out fine).
I was going to interview at LandingAI. I was asked before the interview to install a spyware browser extension to monitor my traffic to detect if I was cheating during the interview. I respectfully declined and didn't have that interview.
Possibility which, by the way, makes the interviewer's cautionary move generally useless.
I was going to interview at LandingAI. I was asked before the interview to install a spyware browser extension to monitor my traffic to detect if I was cheating during the interview. I respectfully declined and didn't have that interview.
What are some ML data annotation tools that guide you towards those data points where the model gets confused? I hear Prodigy does this. Any others?
What's the role of these tools? Can't a developer just write the code to get those data points? At a first glance it seems like the hassle of integrating such a product into an existing ML codebase/pipeline is larger than solving the problem by hand.
I’ve been wondering about the limits of data-centric approach – there seems to be this implicit notion that more data equals better performing ML or AI. I think it would be interesting to imagine a point of diminishing return on additional data if we consider that our ability to perceive is probably largely based on two parts - sensory input and knowledge. Note that I’m making an explicit distinction here on the diff…
Can't you consider that knowledge is a function of previous data? In your example, the 2 individuals actually didn't receive the same amount of data because the English speakers received data previously that allowed him to build some kind of "knowledge" that allows him to solve specific related tasks (understanding a spoken sentence). This would be the equivalent of transfer learning where "knowledge" is a model trai…
Animals solve this problem by having bodies and moving around. It is that we take the bent stick out of the water which allows us to impart a theory to the "data" we receive... a theory implicit in our actions.
Since we are causally active in the world, sequenced in time, and directly changing it -- our bodies enable us to resolve this problem. The motor system is the heart of intelligence, not the frontal lobe -- which is merely book-keeping and accounting for what our bodies are doing.
I was going to interview at LandingAI. I was asked before the interview to install a spyware browser extension to monitor my traffic to detect if I was cheating during the interview. I respectfully declined and didn't have that interview.
I'd just note that if pushed by circumstances (if one was willing to be interviewed in spite of their ways), the interview environment could be (would be) on a throwaway virtual machine... Possibility which, by the way, makes the interviewer's cautionary move generally useless.
My understanding is that they are trying to automate the data preparation steps that seasoned ML practitioners are doing anyway today. The fact that he tries this in manufacturing makes the case stronger. In most manufacturing companies you do not have access to top ML talent. You have Greg who knows python and recently visualized some production metrics. If we could empower Greg with automated ML libraries that guid…
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OR and ML have their own space in manufacturing. OR is perfect when you can describe explicitly what the decision space is and what the restrictions are. ML is great fit when you want to identify and use patterns. Quality control with machine vision is a good application for ML. NLP for PDF documents is a huge field for manufacturing as well. Companies have so much data in email attachments that they do not currently…
> OR is perfect when you can describe explicitly what the decision space is and what the restrictions are. As opposed to having to figure it out later from the outputs of a black box? > Quality control with machine vision is a good application for ML. I can't imagine CV could be an actual replacement for actual SPC in many industries. There's a reason we need to take samples and stress test, analyze composition, etc.…
However I have seen CV and NLP useful here and there... but it is not the bread and butter.