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.
My understanding is that they are trying to automate the data preparation steps that seasoned ML practitioners are doing anyway today.<p>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.<p>You have Greg who knows python and recently visualized some production metrics.<p>If we could empower Greg with automated ML libraries that guide him in the data preparation steps in combination with precooked networks like autogluon, then manufacturing could become a huge beneficiary of the ML revolution.
That is the problem with generalization and cop outs like these. It's no good to people in the field doing actual work where the devil is in the detail.<p>Big data is fairly important to a lot of things, for example I was listening to Tesla's use of Deep net models where they mentioned that there were literally so many variations of Stop Signs that they needed to learn what was really in the "tail" of the distribution of Stop Sign types to construct reliable AI
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 difference between data and knowledge.<p>For instance, an English speaker and a non-English speaker may listen to someone speaking English and while the auditory signals received by both are the same, the meaning of the speech will only be perceived by the English speaker. When we’re learning a new language, it’s this ‘knowledge’ aspect that we’re enhancing in our brain, however that is encoded.<p>This knowledge part is what allows us to see what’s not there but should be (e.g. the curious incident of the dog in the night) and when the data is inconsistent (e.g. all the nuclear close calls). I’m really not sure how this ‘knowledge’ part will be approached by the AI community but feel like we’re already close to having squeezed out as much as we can from just the data side of things.<p>Somewhat related, we have a saying in Korean – ‘you see as much as you know’.
data quality is important. every ai project i've worked on has started with visualizing the data and thinking about it.<p>it's easy to get complacent and focus on building big datasets. in practice, looking at the data often reveals issues sometimes in data quality and sometimes scope of what's in there (if you're missing key examples, it's simply not going to work).<p>most ml is actually data engineering.
Glad to see the term ML being used more often than AI in the comments as it looks like most "AI" models are trained for image classification.
Having said that, the idea of "doing more with less" sounds interesting and I wonder what it means exactly. Does it mean taking a dataset of 50 images and to create 1000s of synthetic images from it?
Pretty interesting. Mr. Ng claims that for some applications having a small set of quality data can be as good as using huge set of noisy data.<p>I wonder if, assuming the data is of highest quality, with minimal noise, having more data will matter for training or not. And if it matters, on what degree?
I can imagine that customizing AI solutions in an automated way is quite important, but writing that as the next wave is probably an overstatement.<p>Of course few shot learning is important for models, but for example for Pathways it was already part of the evaluation.
For industrial application, there are already mature systems based on CV. For majority of those applications, there is no need for deep learning or multilayer CNN. Shocked to see Andrew Ng talking like a marketing guy.
Yeah that'd be great.<p>I also want cars that run on salt water.<p>I'm not saying that small data ai is equally impossible, but simply saying "we should make this better thing" isn't enough.