41–50 of 90 posts
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.
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.
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.
Somewhat related, we have a saying in Korean – ‘you see as much as you know’.
Earlier quoted context omitted.
Interestingly, when you learn how to drive you need to see approximately one example and you're able to identify them all.
That is called transfer learning. You might only need to see one photo of a sign to identify it in real life (although arguably learner drivers take a while to notice signs) but that is only because you have been training on identifying generic objects since you left the womb. You brain already knows how to select the most important features of a sign. The shape, the size and the color. You have also learned how to u…
We can go back even further - your genes carry information about the structure and function of your brain and this has been refined by natural selection over the course of human evolution. Humans don't start from scratch with randomly initialised weights.
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…
Greg probably also knows SAS and AMPL, and has a good knowledge of ops research, which is within stone-tossing distance of whatever ML is pretending to be this week.
so funny, because so accurate :)
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…
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…
It does in general, but what is elaborated and how? Structuring patterns is not the same as "knowledge" (there are missing subsystems), and that fed data is not fed efficiently, with ideal efficiency - compare with the realm in which "told one notion you acquire it" (this while CS is one of the disciplines focusing on optimization, so it would be a crucial point).
“I once built a face recognition system using 350 million images.” Did this make any of you a little queasy?
Earlier quoted context omitted.
> As opposed to having to figure it out later from the outputs of a black box? Not all problems can be formulated as a set of explicit equalities, constraints and variables (e.g. machine vision). If explicit modeling is an option, of course you should do it. I am seeing efforts to try reinforcement learning on systems that we know how to describe with equations, and of course the results are laughable compared to the…
There is a significant amount of research from the field of computer vision before ML even existed. It was quite robust as well within certain constraints. Those techniques simply did not generalize anywhere even close to as well as deep learning. However, that said, in a tightly controlled environment such as a manufacturing line trying to spot defects I would imagine they would have a good chance at performing a lo…
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?
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.