Earlier quoted context omitted.
Experts on a subject that doesn't exist? Like professors of theology?
Theology exists.
It won't, for much longer.
Artificial-Intelligence Experts Are in High Demand
111–119 of 119 posts
Re: Artificial-Intelligence Experts Are in High Demand
#112Earlier quoted context omitted.
Well I would say that "intelligence" is learning and inference with causal models rather than just predictive or correlative models. You can then cash it all out into a few different branches of cognition, like perception (distinguishing/classifying which available causal models best match the feature data under observation), learning (taking observed feature data and using it to refine causal models for greater accu…
It's an interesting perspective, but the thing about this kind of definition is that it's very much focused on the mechanism of intelligence rather than the behaviour that the mechanism produces – which flies in the face of our intuition about what intelligence is, I think. If we found out that one species of chimp learns sign languages through a causal model while another learns it through an associative one (for ex…
>Brains are complicated, and there is a huge amount of heterogeneity in how people process information and think about mathematics (and indeed all topics, but it is clearer in mathematics perhaps). Some are very visual, some are big on calculation.
What do you mean by "associative model"? That doesn't map to anything I've heard of in cognitive science, statistics, machine learning, or Good Old-Fashioned AI.
But actually, I would expect different behaviors from an animal that learns language via a purely correlational and discriminative model (like most neural networks) versus a causal model. Causal models compress the empirical data better precisely because they're modelling sparse bones of reality rather than the abundant "meat" of correlated features. You should be able to generalize better and faster with a causal model than with a discriminative, correlative one.
Re: Artificial-Intelligence Experts Are in High Demand
#113Earlier quoted context omitted.
Not sure if you're being serious but are you sure they would even care whether these kinds of candidates could program? I am skeptical they hired them to code; I imagine they mostly spend their time doing research and then have the SDEs implement things.
I have no idea how hiring in this field works but I'd expect a data scientist to have a pretty good (algorithmic) programming background in this day and age. You pretty much have to "play with the data" and get a good intuition for it when it comes to gigantic data sets and programming is how you accomplish that. Or in other words...I'd be skeptical if a candidate hadn't learned programming on their own even if it wa…
I think often then have experience in programming, but in languages that don't map well to the "real world" of programming -- R, octave, matlab, etc. Those languages are also usually loaded with very helpful libraries to avoid having to do any nitty-gritty programming.
Re: Artificial-Intelligence Experts Are in High Demand
#114Re: Artificial-Intelligence Experts Are in High Demand
#115Earlier quoted context omitted.
It's an interesting perspective, but the thing about this kind of definition is that it's very much focused on the mechanism of intelligence rather than the behaviour that the mechanism produces – which flies in the face of our intuition about what intelligence is, I think. If we found out that one species of chimp learns sign languages through a causal model while another learns it through an associative one (for ex…
>Brains are complicated, and there is a huge amount of heterogeneity in how people process information and think about mathematics (and indeed all topics, but it is clearer in mathematics perhaps). Some are very visual, some are big on calculation. What do you mean by "associative model"? That doesn't map to anything I've heard of in cognitive science, statistics, machine learning, or Good Old-Fashioned AI. But actua…
I think I meant correlational, but it was really just a placeholder for "a different model". You could replace the chimp with some kind of alien whose thinking model is completely, well, alien to us – but still proves its intelligence by virtue of having a spaceship.
I'm not necessarily saying that different models lead to exactly the same behaviour. Clearly, chimps' models don't generalise as well as ours do and they don't have a language model that matches ours in capability, for example, which leads to different behaviour. But given that their behaviour is generally thought of as less intelligent as opposed to not intelligent at all, it seems like the mechanism itself is not the important thing.
Re: Artificial-Intelligence Experts Are in High Demand
#116Re: Artificial-Intelligence Experts Are in High Demand
#117This trend is in most companies business-driven, in others it is technical-driven. Few companies have technical leadership that can manage true AI resources. If you remember the ML courses from Uni and experts in that field, you can imagine why. In many universities AI departments are assigned to schools of psychology and philosophy. Only companies with a deep engineering culture as those mentioned here can build up…
Here in Germany hardly any big company have any clues about the insights which Machine Learning and Data Mining can provide.
Most upper management sees IT, only in a business suport role not as a business driver!
Re: Artificial-Intelligence Experts Are in High Demand
#118Earlier quoted context omitted.
But, to look at one example: neural networks. Neural networks may have been inspired by attempts to recreate the structure of the biological nervous system, but the way in which they are used commonly, e.g. "learning" via back-propagation, is really just a statistical regression for a gigantic equation with many free variables. My preferred term is "predictive analytics," which I feel kind of straddles statistics and…
I feel annoyed with opaqueness of ML algorithms like neural networks. I hope ML doesn't unwittingly define itself as a field where machines learn, but humans may not learn. I'm referring to predicaments like the story about 42 from hitchhikers guide to the galaxy.
That's definitively an interesting problem. Just note in many cases we're not even interested in learning the tasks. For example, you don't need any person to actually know that consumers aged 25-29 years old prefer a certain product 10% more than consumers aged 21-25, and so on.
But humans are still the ones responsible for important high level decisions, so it still makes sense to maximize information transparency to enable good decisions in those contexts.
A neural network that given a prediction 'X is most likely' and could answer the question "Why?" with 'Because Y' would be amazing.