"Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it. The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much powerful, though usually unconscious, sensor motor knowledge. We are all prodigious Olympians in perceptual and motor areas, so good that we make the difficult look easy. Abstract thought, though, is a new trick, perhaps less than 100 thousand years old. We have not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it."- Hans Moravec Moravec's paradox is involved with the fact that it is the seemingly easier day to day problems that are harder to implement in a machine, than the seemingly complicated logic based problems of today. The results prove that most artificially intelligent machines are as adept if not more than us at under-taking long calculations or even play chess, but their logic brings them nowhere when it comes to carrying out everyday tasks like walking, facial gesture recognition or speech recognition.
In 2020, the American poet Andrew Brown gave a student the following assignment: write a poem from the point of view of a cloud looking down on two warring cities. "I think I'll start to rain, Because I don't think I can stand the pain, Well, Brown's'student' turned out to be a computer program, not a human. The program, called GPT-3, is one of the most powerful AI language models ever made. Created in 2020 by the research firm OpenAI, its development has cost tens of millions of dollars. Trained on 200 billion words from books, articles, and websites, GPT-3 can generate fluent streams of text on any topic you can imagine. Companies like Amazon, Netflix, Spotify, and LinkedIn feed our personal preferences into them to create targeted recommendations.
An econometrics problem illustrates the difference between artificial and human intelligence. Understanding the tacit knowledge and limits of AI is crucial to implementing it effectively and fairly. One of the only lucid thought experiments ever conducted by econometricians, the "red bus-blue bus" problem illustrates a central drawback that comes with using statistical estimation to quantify the probability that a person will make a specific choice when faced with to various alternatives. As the thought experiment proceeds, imagine that you are indifferent about taking a car or a red bus to work. Due to his indifference, an estimate of his probability of choosing either option is to flip a coin.
One of the only lucid thought experiments ever carried out by econometricians, the "red bus-blue bus" problem illustrates a central drawback that comes with using statistical estimation to quantify the probability a person makes a specific choice when faced with several alternatives. As the thought experiment goes, imagine that you're indifferent between taking either a car or a red bus to work. Owing to your indifference, an estimate of your probability of picking either option is a coin flip. There is a 50 percent chance that you're taking the car and 50 percent that you're taking the red bus. Thus, your odds of selection are one-to-one.
Artificial Intelligence (AI) is the future. But can we call the future dumb? Some possibilities could drive us to such a situation. AI is designed to make human jobs easy and the technology is trying its best to restrain the position. However, what it fails to comply with are the basic human activities that we find very easy. Everyone has seen AI beating the world champion in the board game Go, the quiz game Jeopardy, the card game Poker and the video game Dota 2. AI has come a long to what it is today.