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AI is vulnerable from two security perspectives the way I see it: The classic method of exploiting outright programmatic errors to achieve some sort of code execution on the machine that is running the AI or to extract data. Trickery through the equivalent of AI optical illusions for the particular form of data that the system is designed to deal with. ...


7

Programmer vs Programmer It's a "infinity war": Programmers vs Programmers. All thing can be hackable. Prevention is linked to the level of knowledge of the professional in charge of security and programmers in application security. eg There are several ways to identify a user trying to mess up the metrics generated by Sentiment Analysis, but there are ...


6

How we can prevent it? There are several works about AI verification. Automatic verifiers can prove the robustness properties of neural networks. It means that if the input X of the NN is perturbed not more that on a given limit ε (in some metric, e.g. L2), then the NN gives the same answer on it. Such verifiers are done by: Stanford: https://arxiv.org/...


5

To answer your question, it really depends on the purpose of the Artificial Intelligence program. For example, 4Chan has hacked a number of "Artificial Intelligent" bots, most notably was Microsoft's Twitter bot Tay. The general purpose of the bot was to parse what was tweeted at it and respond in kind, learning and evolving with each and every interaction. ...


5

There is the case of the tesla accident where the car was in autopilot and crashed into a truck because it appears the vehicle mistook a lightly coloured truck for the sky, killing the driver: https://www.newscientist.com/article/2095740-tesla-driver-dies-in-first-fatal-autonomous-car-crash-in-us/ Having said that, it appears the car had been trying to tell ...


4

I believe it is, no system is safe, however I am not sure if I can still say this after 20-30 years of AI development/evolution. Anyways, there are articles that showed humans fooling AI (Computer Vision). https://www.theverge.com/2018/1/3/16844842/ai-computer-vision-trick-adversarial-patches-google https://spectrum.ieee.org/cars-that-think/transportation/...


4

The infamous Flash Crash of 2010 may qualify. It didn't involve Artificial General Intelligence (which is still a hypothetical) or even "strong narrow AI" (such as AlphaGo) but does involve algorithmic decision-making, which is a form of basic Artificial Intelligence. Algorithmic trading already represents a significant percentage of all market activity, ...


4

https://www.technologyreview.com/s/530276/hidden-obstacles-for-googles-self-driving-cars/ Google’s cars can detect and respond to stop signs that aren't on its map, a feature that was introduced to deal with temporary signs used at construction sites. But in a complex situation like at an unmapped four-way stop the car might fall back to slow, extra ...


4

Tesla's technology is assistive, as Alexey points out, so this is not a case of an autonomous system (e.g. an AGI) doing some fatal stunt (the product name AutoPilot is famously misleading). Now on why the car assistance led to this tragic accident, there is some information related to AI technologies. Warning: I cannot find again the source critical to the ...


4

Is Artificial Intelligence Vulnerable to Hacking? Invert your question for a moment and think: What would make AI at less of a risk of hacking compared to any other kind of software? At the end of the day, software is software and there will always be bugs and security issues. AIs are at risk to all the problems non-AI software is at risk to, being AI ...


3

However, do industrial strength, production ready defensive strategies and approaches exist? Are there known examples of applied adversarial-resistant networks for one or more specific types (e.g. for small perturbation limits)? I think it's difficult to tell whether or not there are any industrial strength defenses out there (which I assume would mean that ...


3

The reason this is hard is because it is not trivial to understand what a law means. Many humans still have a hard time understanding laws and thus we have millions of judges and lawyers who study years to be able to even debate whether a law was broken at all. More generally to AI, the problem of understanding laws is a byproduct of the bigger problem that ...


3

As far as I know, Tesla cars autopilot is not a 100% AI pilot, it's an assitant: as it detects hands off wheel it slows down, so it's incorrect to speak about AI mistake: it is not trained/designed to drive a car all by itself. A human driver is responsible in that incident.


3

If a machine learning based AI is "sufficiently smart enough" to be able lie then there is nothing preventing it from lying. This does not mean it can't be persuaded from lying. So just make the AI simple enough to not be able to lie. The reasoning here is that in order for a system to be able to lie, a system must be able to recognize an incentive to ...


2

I agree with Manuel Rodriguez. You can also go some way to example behaviour of a closed system through simulation. This can add a certain percentage of predictability to an algorithm, but with the understanding it isn’t a guarantee


2

A robust ML model is one that captures patterns that generalize well in the face of the kinds of small changes that humans expect to see in the real world. A robust model is one that generalizes well from a training set to a test or validation set, but the term also gets used to refer to models that generalize well to, e.g. changes in the lighting of a ...


1

In my opinion, there are several flaws in your proof and reasonings. First, note that, in the case of Turing's proof, h will actually loop forever (i.e. not halt) when the oracle says that h halts. In this case, there's an actual contradiction, because h will do the opposite of what the oracle says. So, to follow Turing's proof, you would need to make h ...


1

Intelligence of any type is vulnerable to hacking, whether DNA based or artificial. First, let's define hacking. In this context, hacking is the exploitation of weaknesses to gain specific ends which may include status, financial gain, disruption of business or government, information that can be used for extortion, the upper hand in a business deal or ...


1

Is artificial intelligence vulnerable to hacking? hint; if you say that AI is vulnerable,then I disagree with you here by such statement. Artificial intelligence is divided into three categories nor phases that we are supposed to go through ie. artificial narrow intelligence artificial general intelligence artificial super intelligence Therefore,according ...


1

If the AI is static (heuristic and fixed), it will always pursue the stated goal. However, such a system would be "brittle", and either break or produce bad output if confronted with input not previously defined, or outside its model. If the AI evolves via learning, even where the goal is specific, its interpretation of that goal might change, and produce ...


1

If the main goal of AI (which I assume you mean an AGI) is to protect humans and AI will be effective, then AI will always attempt to pursue its main goal (otherwise the assumption of its effectiveness does not hold), even at the expense of other less important goals that it might have. However, if the destruction of a human (or a group of humans) protected ...


1

Perhaps what you are looking for is the notion of adversarial attacks on machine learning systems?


1

Everything can be hacked. The solutions found by artificial intelligence can be much more efficient than human solutions, but they can also be confused because of the diversity and immensity of details that our mind possesses. Artificial Intelligence models bring us more secure solutions, but nothing is 100% safe when we talk about information security. ...


1

It seems impossible to prevent that. If someone can make a safe AI from scratch in the near future, then someone else can make a dangerous AI from scratch as well. If all that's needed is a computer (or eventually a robot) it will be really hard to stop people from creating one. Banning computers? Maybe it could prevent it, but that comes with quite a few ...


1

This is an old area of AI called "Plan Recognition", which has about 3.5 million results in Google Scholar. A lot of the modern work is done with classical search techniques coupled with expert domain knowledge, or related reasoning concepts like Hierarchical Task Networks. I'm not aware of or able to find recent research using deep neural networks for ...


1

I concur with Akio that no system is completely safe, but the take away is AI systems are less prone to attacks when comparing with the old systems because of the ability to constantly improve. As time passes by more people will get in the field bringing new ideas and hardware will be improving so that they are "strong AI."


1

You may be interested in the utility functions of deception: From the abstract of Why Animals Lie: How Dishonesty and Belief can Coexist in a Signaling System. (NIH, 2006)" We develop and apply a simple model for animal communication in which signalers can use a nontrivial frequency of deception without causing listeners to completely lose belief. This ...


1

Tesla model S has Autopilot which allows to steer within a lane, change lanes with the simple tap of a turn signal, and can manage speed by using traffic-aware cruise control. Multiple digital controls helps to avoid collisions. Based on that, this isn't fully self-driving car. However it is using a computer vision detection system, but it is not intended to ...


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