Why can't you just train the network on artificially altered photos, with "elephants" randomly scattered around, until it is robust to them?
Well, you could randomly generate and render 3D objects, and paste them into photos. This will give you a huge space of 'elephants'.
What you propose is, essentially, training on a simulation. That doesn't work very well because statistical machine learning algorithms generalise very poorly from training data to unseen data and when your training data is a simulation (such as a 3d model of an object) and the unseen data is the real world (such as a video still, or a digitial photograph of that object) the resulting model will be very bad at predicting the unseen data, so identifying objects in images of the real world.
It might seem to you (and to me!) that modern 3d simulations (as in high-end CGI and video games) is a very faithful simulation of the real world, but that's primarily because we look at modern 3d through human eyes and perceive it with our (presumably) human brains. We don't know how that works exactly, so we can't reproduce the process in computers, yet, therefore machine vision systems essentially "see" and "perceive" something completely different than what we do. In the end, what looks like a very close approximation to you, is pretty much useless for them- or in any case, they are not able to learn to generalise from that to real objects.
This really is not what I expected, being a layman.
Do you have any more data on this topic of training on a simulation?
https://ai.googleblog.com/2017/10/closing-simulation-to-real...
Simulating many years of robotic interaction is quite feasible with modern parallel computing, physics simulation, and rendering technology. Moreover, the resulting data comes with automatically-generated annotations, which is particularly important for tasks where success is hard to infer automatically. The challenge with simulated training is that even the best available simulators do not perfectly capture reality. Models trained purely on synthetic data fail to generalize to the real world, as there is a discrepancy between simulated and real environments, in terms of both visual and physical properties. In fact, the more we increase the fidelity of our simulations, the more effort we have to expend in order to build them, both in terms of implementing complex physical phenomena and in terms of creating the content (e.g., objects, backgrounds) to populate these simulations. This difficulty is compounded by the fact that powerful optimization methods based on deep learning are exceptionally proficient at exploiting simulator flaws: the more powerful the machine learning algorithm, the more likely it is to discover how to "cheat" the simulator to succeed in ways that are infeasible in the real world. The question then becomes: how can a robot utilize simulation to enable it to perform useful tasks in the real world?
The difficulty of transferring simulated experience into the real world is often called the "reality gap." The reality gap is a subtle but important discrepancy between reality and simulation that prevents simulated robotic experience from directly enabling effective real-world performance. Visual perception often constitutes the widest part of the reality gap: while simulated images continue to improve in fidelity, the peculiar and pathological regularities of synthetic pictures, and the wide, unpredictable diversity of real-world images, makes bridging the reality gap particularly difficult when the robot must use vision to perceive the world, as is the case for example in many manipulation tasks.
Note the bit about deep learning algorithms being very proficient "cheaters", which I missed in my comment above. Indeed, one way to fail to generalise from a simulation to the real world is to "overfit" to the defects in the simulation!
Like the linked blog post, most material you are likely to find online focus on training robots with (deep) Reinforcement Learning, I think because that just happens to be one domain where it is even harder to collect training data than good old supervised learning for image recognition. I can find virtually no source referring to the "reality gap" in the context of purely machine vision research- it's just not the done thing to train vision algorithms on simulated data, for the reasons described above, consequently it's very difficult to find hard data on why it's not done.
A good related source (with a bazillion references) is the following blog post, discussing the difficulties of deep RL, which is primarily trained on simulated environments:
https://www.alexirpan.com/2018/02/14/rl-hard.html
The post makes only passing referenct to the "reality gap" but it should give a good idea about the ins and outs of training in simulated environments.
https://rodneybrooks.com/forai-steps-toward-super-intelligen...
If they all agree you have a high confidence, if they mostly disagree you have a low confidence.
[1] https://en.m.wikipedia.org/wiki/Ensemble_averaging_(machine_...
To an extent it shows the gap between neural networks being able to classify things, and actually being able to understand what they are seeing, which will always limit their utility beyond classifying images that look like other images they've seen before.
I tried it on a couple of online demos, neither spotted that there was a car in the picture.
A space collision avoidance AI that identifies a road vehicle as a road vehicle and expects it to behave as one normally behaves would create problems. A human can look at a 1936 Ford truck floating in space and know it's not going to make a sudden left turn. An AI working in space would still treat it as debris if a human told it "that's a truck, it's not going to do anything different unless acted upon."
A system that can't recognise objects out of context is either completely useless, or only useful to the extent that it can recognise a very large number of objects in a very large number of varied contexts. Essentially, it's a system that learns primarily by memorising specific cases, rather than memorising and generalising to new cases.
This kind of system is limited by the number of objects and contexts with which it can be trained in practice. Which is why you need large datasets to train machine vision algorithms- because you basically have to show them many examples of everything you want them to learn.
In your example, the problem is not that the "AI" would not be able to recognise a car in space as a car, in space; it's that it would not be able to recognise it even as debris - unless it had already seen such "debris" before. Obviously, if it hadn't observed the behaviour of such debris it would also be incapable of predicting its behaviour, also.
The inability of statistical machine learning models to abstract and extrapolate from available observations, in the same way humans do, can be very surprising, especially in light of their othewise excellent abilities to recognise objects they have seen before.
By the way, something that is not described in the article- the referenced paper also describes experiments where objects where translated inside the source image itself - so not in another image. And the tested networks were still confused.
So it's not "foreign" objects that are the problem. It's that neural nets generalise very poorly. Show them something they haven't seen before and their ability to recognise it falls off a cliff.