One Superior Court judge has warned that many cases don’t come down to information alone, which is all AI can do. Law professor David DeWolf also expresses concern about increasing dependence upon law—a form of coercion—to regulate human behavior, a choice that is irrelevant to the growth of AI in the courtroom.
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Maybe both. AI will require more from us, not less, because how we choose to use these tools will make an increasingly stark difference between benefit and ruin.
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AI can certainly help scientists. But to understand why AI can’t do science on its own, we should take a look at the NP-Hard Problem in computer science. The “Hard” is in the name of the problem for a reason…
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Unlike Catherine, Samantha is exactly what Theodore was looking for. No surprise there; Samantha is, literally, adjusted and updated according to Theodore’s preferences from when he initially began speaking to her. She exists only to be Theodore’s soulmate. Is that enough?
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With historically low unemployment, employers are tempted to reduce costs and speed up the process using artificial intelligence (AI) systems. These systems might help but, for best results, let’s have a look at the problems they can’t solve and some that they might create.
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Artificial intelligence expert Robert J. Marks tackles the contentious subject of military drones in his just-published book, The Case for Killer Robots: Why America’s Military Needs to Continue Development of Lethal AI. Many sources (30 countries, 110+ NGOs, 4500 AI experts, the UN Secretary General, the EU, and 26 Nobel Laureates) have called for these lethal AI weapons to be banned. Dr. Marks, a Distinguished Professor of Electrical and Computer Engineering at Baylor University, disagrees. What if ambitious nations such as China and Iran develop lethal AI military technology but the United States does not? Nations that wish to maintain independence (sovereignty), he argues, must remain competitive in military AI. (“Advanced technology not only wins wars but gives pause to Read More ›
AI does pose at least one threat to filmmaking. It could intensify the very tone-deafness that studios hope it can fix: Too much reliance on ever more finely grained analysis of the patterns in past data could blind decision makers to the real risks, volatility, and opportunities in the future. That’s a recipe for losing money and inflicting “Oh, not that again!” on audiences.
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Two theories of human consciousness are about to be tested in a historic contest. Integrated Information Theory (IIT) of consciousness and rival Global Workspace Theory (GWT) have sharply different implications for consciousness in machines.
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Computer algorithms are much, much better than humans at discovering statistical patterns but much, much worse than humans at discerning whether the patterns are meaningful. Wise investors can use the resulting blips to their advantage.
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AI catches things doctor miss, and doctors catch things AI misses. Using the AI to highlight what may be cancer tissue helps the radiologist focus on ambiguous situations, reducing the chance of missing early cancers.
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Tim Harford points to the Sanchez tragedy to raise an important question: How do we know when a given technology is really helping us? And when we are taking too great a risk or paying too high a price?
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Both computers and humans can be fooled by patterns that appear significant but really aren’t. But the bigger the computer, the more random patterns it can find in the vast swathes of data processed.
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Larry L. Linenschmidt interviews Robert J. Marks on the difference between performing a task and understanding the task, as explained in philosopher John Searle’s famous “Chinese Room” thought experiment.
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Because Tesla has yet to make a yearly profit in any of its sixteen years of existence, it depends on capital raises of various forms (equity, debt, etc.) to stay in business. Capital raises require big promises and Tesla’s overstatements about its self-driving cars are always good for a few billion.
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Many models are ridiculously simplistic, making the results worse than worthless. They become a way of solidifying biases.
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Artificial intelligence may do well summarizing data, but the new insights that will lead the economy forward cannot be gleaned that way. What we need is not old data but new truths.
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Computers are very effective but they operate with a very limited set of causal abilities. Humans work from an entirely different set of causal abilities. Uploading your brain to a computer is not a question of technology. It can’t work in principle.
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In reality, without appropriate human supervision, AI is just as likely to find false or unimportant patterns as real ones. Additionally, the overuse of AI in science is actually leading to a reproducibility crisis.
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AI programs are just that—programs. Nothing in such a program could make it conscious. We may as well think that if we make sci-fi life-like enough, we should start worrying about Darth Vader really taking over the galaxy.
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One factor in the growing sobriety may be that, as AI technology transitions from dreams to reality, the future belongs to leaders who are pragmatic about its abilities and limitations.
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