✎✎✎ Persuasive Essay On Street Racing

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Persuasive Essay On Street Racing



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Going through software code line by line is exactly the sort of tedious problem at which AIs excel, if they can only be taught how to recognize a vulnerability. The implications extend far beyond computer networks. Already AIs are looking for loopholes in contracts. This will all improve with time. Modern AIs are constantly improving based on ingesting new data and tweaking their own internal workings accordingly. All of this data continually trains the AI, and adds to its experience.

The AI evolves and improves based on these experiences over the course of its operation. There are really two different but related problems here. The first is that an AI might be instructed to hack a system. The other is that an AI might naturally, albeit inadvertently, hack a system. Both are dangerous, but the second is more dangerous because we might never know it happened. After 7. And was unable to explain its answer, or even what the question was. That, in a nutshell, is the explainability problem. Modern AI systems are essentially black boxes. Data goes in at one end, and an answer comes out the other.

It can be impossible to understand how the system reached its conclusion, even if you are a programmer and look at the code. Their limitations are different than ours. In , a research group fed an AI system called Deep Patient health and medical data from approximately , individuals, and tested whether or not the system could predict diseases. The result was a success. Weirdly, Deep Patient appears to perform well at anticipating the onset of psychiatric disorders like schizophrenia—even though a first psychotic episode is nearly impossible for physicians to predict.

What we want is for the AI system to not only spit out an answer, but also provide some explanation of its answer in a format that humans can understand. Explanations are a cognitive shorthand used by humans, suited for the way humans make decisions. AI decisions simply might not be conducive to human-understandable explanations, and forcing those explanations might pose an additional constraint that could affect the quality of decisions made by an AI system. In the near term, AI is becoming more and more opaque, as the systems get more complex and less human-like—and less explainable. They will invariably stumble on solutions that we humans might never anticipated—and some will subvert the intent of the system.

These are all hacks. You can blame them on poorly specified goals or rewards, and you would be correct. You can point out that they all occurred in simulated environments, and you would also be correct. But the problem is more general: AIs are designed to optimize towards a goal. Imagine a robotic vacuum assigned the task of cleaning up any mess it sees. He trained an AI by rewarding it for not hitting the bumper sensors. Any good AI system will naturally find hacks. If are problems, inconsistencies, or loopholes in the rules, and if those properties lead to an acceptable solution as defined by the rules, then AIs will find them. We all learned about this problem as children, with the King Midas story. When the god Dionysus grants him a wish, Midas asks that everything he touches turns to gold.

Midas ends up starving and miserable when his food, drink, and daughter all turn to inedible, unpotable, unlovable gold. We also know that genies are very precise about the wording of wishes, and can be maliciously pedantic when granting them. The genie will always be able to hack your wish. The problem is more general, though. In human language and thought, goals and desires are always underspecified. We never delineate all of the caveats and exceptions and provisos. We never close off all the avenues for hacking. Any goal we specify will necessarily be incomplete. This is largely okay in human interactions, because people understand context and usually act in good faith.

We are all socialized, and in the process of becoming so, we generally acquire common sense about how people and the world works. We fill any gaps in our understanding with both context and goodwill. If I asked you to get me some coffee, you would probably go to the nearest coffeepot and pour me a cup, or maybe to walk to the corner coffee shop and buy one.

You would not bring me a pound of raw beans, or go online and buy a truckload of raw beans. You would not buy a coffee plantation in Costa Rica. You would also not look for the person closest to you holding a cup of coffee and rip it out of their hands. You would just know. The audience laughed, but how would a computer program know that causing an airplane computer malfunction is not an appropriate response to someone who wants to get out of dinner? In , Volkswagen was caught cheating on emissions control tests.

The result was that the cars had superior performance on the road. Volkswagen got away with it for over ten years because computer code is complex and difficult to analyze. Basically, the scientists tested the car without the software realizing it. Or of ethics. Again, this is a story of humans cheating. And because of the explainability problem, we humans might never realize it either. Unless the programmers specify the goal of not behaving differently when being tested, an AI might come up with the same hack.

The programmers will be satisfied. The accountants will be ecstatic. And because of the explainability problem, no one will realize what the AI did. And yes, now that we know the Volkswagen story, the programmers can explicitly set the goal to avoid that particular hack, but there are other hacks that the programmers will not anticipate. The lesson of the genie is that there will always be hacks the programmers will not anticipate. If your driverless car navigation system satisfies the goal of maintaining a high speed by spinning in circles—a real example 79 —programmers will notice this behavior and modify the goal accordingly.

The behavior may show up in testing, but we will probably never see it occur on the road. Much has been written about recommendation engines, and how they push people towards extreme content. Similarly, in , an AI taught itself to play the s computer game Breakout. It was just given the controls, and rewarded for maximizing its score. One solution is to teach AIs context. You can think about solutions in terms of two extremes. The first is that we can explicitly specify those values. That can be done today, more or less, but is vulnerable to all of the hacking I just described. That is many years out AI researchers disagree on the time scale. Most of current research straddles these two extremes. Of course, you can easily imagine the problems that might arise by having AIs align themselves to historical or observed human values.

Whose values should an AI mirror? A Somali man? A Singaporean woman? The average of the two, whatever that means? We humans have contradictory values. We humans are often not very good examples of the sorts of humans we should be. The feasibility of any of this depends a lot on the specific system being modeled and hacked. For an AI to even start on optimizing a solution, let alone hacking a completely novel solution, all of the rules of the environment must be formalized in a way the computer can understand. Goals—known in AI as objective functions—need to be established. The AI needs some sort of feedback on how well it is doing so that it can improve its performance.

Sometimes this is a trivial matter. The rules, objective, and feedback—did you win or lose? This is why most of the current examples of goal and reward hacking come from simulated environments. Those are artificial and constrained, with all of the rules specified to the AI. What matters is the ambiguity in a system. That ambiguity is difficult to translate into code, which means that an AI will have trouble dealing with it—and that there will be full employment for tax lawyers for the foreseeable future. Most human systems are even more ambiguous. An AI would have to understand not just the rules of the game, but the physiology of the players, the aerodynamics of the stick and the puck, and so on and so on.

Probably the first place to look for AI-generated hacks are financial systems, since those rules are designed to be algorithmically tractable. This ambiguity ends up being a near-term security defense against AI hacking. Could an AI independently discover gerrymandering? Two different flavors of AI have emerged since the s. This has turned out to be incredibly hard, and not a lot of practical progress has been made in the past few decades. This is the AI that ingests training data and gets better with experience that translates into even more data.

And much of what I am writing about here could easily fall into that category. Advances are discontinuous and counterintuitive. Things that seem easy turn out to be hard, and things that seem hard turn out to be easy. When I was a college student in the early s, we were taught that that the game of Go would never be mastered by a computer because of the enormous complexity of the game: not the rules, but the number of possible moves. And now a computer has beaten a human world champion. Some of it was due to advances in the science of AI, but most of the improvement was just from throwing more computing power at the problem.

We had better start thinking about enforceable, understandable, ethical solutions. Hacking is as old as humanity. We are creative problem solvers. We are loophole exploiters. We manipulate systems to serve our interests. We strive for more influence, more power, more wealth. Power serves power, and hacking has forever been a part of that. Still, no humans maximize their own interests without constraint. Even sociopaths are constrained by the complexities of society and their own contradictory impulses. They have limited time. These very human qualities limit hacking.

In his book, The Corporation, Joel Baken likened corporations to immortal sociopaths. Even in a world of AI systems dynamically setting prices—airline seats is a good example—this again limits hacking. Hacking changed as everything became computerized. Because of their complexity, computers are hackable. And today, everything is a computer. All of our social systems—finance, taxation, regulatory compliance, elections—are complex socio-technical systems involving computers and networks. This makes everything more susceptible to hacking. To date, hacking has exclusively been a human activity.

Searching for new hacks requires expertise, time, creativity, and luck. When AIs start hacking, that will change. Computers are much faster than people. A human process that might take months or years could get compressed to days, hours, or even seconds. What might happen when you feed an AI the entire US tax code and command it to figure out all of the ways one can minimize the amount of tax owed? We have societal systems that deal with hacks, but those were developed when hackers were humans, and reflect the pace of human hackers. At computer speeds, hacking becomes a problem that we as a society can no longer manage. We already see this in computer-driven finance, with high-frequency trading and other computer-speed financial hacks.

But they are able to execute at superhuman speeds, and this makes all the difference. As trading systems become more autonomous—as they move more towards AI-like behavior of discovering new hacks rather than just exploiting human-discovered ones—they will increasingly dominate the economy. A free AI-driven service called Donotpay. It has helped to overturn hundreds of thousands of tickets in cities like London and New York. The AI persona bots discussed previously will be replicated in the millions across social media. They will be able to engage on the issues around the clock, sending billions of messages, long and short. Run rampant, they will overwhelm any actual online debate. What we will see as boisterous political debate will be bots arguing with other bots.

This sort of manipulation is not what we think of when we laud the marketplace of ideas, or any democratic political process. The increasing scope of AI systems also makes hacks more dangerous. AI is already making important decisions that affect our lives—decisions we used to believe were the exclusive purview of humans. AI systems make bail and parole decisions.

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