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AI is transforming our world with self-driving cars, smart homes, and virtual assistants. Weak AI is narrow in focus but gets things done, while Strong AI has the potential to surpass human intelligence. Deep Learning automates feature extraction, while Machine Learning relies on human intervention. AI has a long history, with milestones like the Turing Test and IBM's Deep Blue defeating a chess champion. AI systems have fixed objectives and can behave psychopathically if not properly specified. General purpose AI is expected by the end of the century, but it will take time and brainpower to achieve. Just a decade ago, mankind was flirting with a lot of jaw-dropping, imaginary, scientific possibilities in films and books, calling them sci-fi. But little did we know then that those dreams would turn into reality quite soon, thanks to none other than AI. The possibilities that AI technology presents are almost limitless, and it has already begun to change the way we live and work. From self-driving cars and smart homes to virtual assistants and personalized recommendations, AI is transforming our world in ways that were once unimaginable. Join us as we explore what AI is, how it works, and why it is creating shockwaves in the industry. By this time, we're pretty sure you all have an idea of what an AI is, don't you? It's such a mind-blowing technology that lets machines and computer systems simulate human intelligence processes, from expert systems to natural language processing. AI is turning sci-fi dreams into reality. But how does the magic of AI really work? These days, every vendor is peddling their AI products and services like they're the hottest thing since sliced bread. But let me tell you something. A lot of what they call AI is just a tiny piece of the puzzle, like machine learning. First off, AI needs some serious, specialized hardware and software to write and train machine learning algorithms. And forget about one programming language ruling them all. Python, R, Java, C++, and Julia all exist in the AI world. The languages guzzle down labeled training data like it's nobody's business, then crunch the numbers to find patterns and correlations. After that, programmers use those patterns to make eerily accurate predictions about the future. You've seen chatbots that can mimic real human conversation, image recognition tools that can name every object in a picture, and generative AI that can create text, music, and images that are almost too realistic. But here's the real kicker. AI programming isn't just about following instructions. It's about teaching machines how to learn, how to reason, and how to correct themselves. And when it comes to creativity, artificial intelligence is a total game changer. It can create whole new worlds of music, art, and ideas that humans have never even dreamed of. So yeah, AI is kind of a big deal. And it's only getting bigger. Now, artificial intelligence doesn't only mean Siri and Alexa. First up, we've got Weak AI, also known as Narrow AI or Artificial Narrow Intelligence, ANI. This is the stuff that's all around us, powering everything from self-driving cars to IBM Watson. And don't let the name fool you. Weak AI might be narrow in focus, but it's anything but weak. This is the kind of AI that gets things done, no questions asked. Now let's talk about the real heavy hitters, Strong AI. This is the stuff that's straight out of sci-fi, the kind of AI that can make HAL 9000 look like a child's toy. Strong AI is made up of two types, Artificial General Intelligence, AGI, and Artificial Super Intelligence, ASI. AGI is the theoretical form of AI that would be equal to human intelligence. That's right. We're talking about self-aware, problem-solving machines that can plan for the future. And then there's ASI, the stuff of nightmares. This is the kind of AI that would surpass the human brain in every way possible. We're talking about machines that could rule the world, folks. So yeah, Weak AI might be the workhorse that keeps the world turning. But don't sleep on Strong AI. This is the stuff that could change everything, for better or for worse. Next, we have to clear up the confusion between Deep Learning and Machine Learning. Deep Learning is a subfield of Machine Learning, and both are subfields of Artificial Intelligence. The big difference is that Deep Learning cuts out the tedious manual work of feature extraction by automating the process, which means it can handle larger datasets. Machine Learning, on the other hand, relies more on human intervention to learn. But don't underestimate Deep Learning. It can use labeled datasets or raw, unstructured data to determine the features that distinguish different data categories, giving us the power to scale up Machine Learning in ways that will blow your mind. You'd be surprised to know that all these concepts of a thinking machine have been around since ancient Greece. But it wasn't until the era of electronic computing that we witnessed some serious milestones in the evolution of Artificial Intelligence. Alan Turing came up with the Turing Test in 1950 to determine if a computer could match human intelligence. John McCarthy coined the term Artificial Intelligence in 1956 and created the first ever running AI software program, called the Logic Theorist. Frank Rosenblatt created the Mark I Perceptron in 1967, the first computer that learned through trial and error, based on a neural network. In the 1980s, neural networks became all the rage, with the development of backpropagation algorithms that allowed for self-training in AI applications. In 1997, IBM's Deep Blue took down world chess champion Garry Kasparov in a historic match. No wonder this Artificial Intelligence thing is going to shake up your life, and the entire world, real soon. But here's the kicker. Nobody can agree on how exactly it's going to happen. But one thing we can agree on. You can show your support by hitting that subscribe button and turning on notifications to help us grow and get you the latest developments in AI technology. Now enter Stuart Russell, a computer science professor and AI expert who's about to drop some real talk and separate the sense from nonsense. He meant that there's a massive difference between asking a human to do something and giving that as an objective to an AI system. When you ask a human to grab you a cup of coffee, you don't mean that they should make it their life's mission, even if they have to kill everyone else at Starbucks to make it happen. You expect the person to factor in all the other things that we mutually care about. But the important thing is, AI systems are built to achieve a fixed objective. Everything has to be specified in the algorithm, and if you miss something, things can go haywire really fast. Let's take the example of fixing the acidification in the oceans. Sure, we could have a catalytic reaction that does that efficiently, but it would consume a quarter of the oxygen in the atmosphere, causing us to die a slow and unpleasant death over several hours. So how do we avoid this mess? Simple. Just be more careful about specifying the objective, and don't forget to factor in everything else. But guess what? That's easier said than done because there are countless side effects to consider. The seaweed, the fish, the atmosphere, the list goes on and on. But the twist is, humans often know that they don't know all the things that we care about. We're aware that our understanding of the world is limited, and we need to tread carefully. But with AI, we're playing with fire because we don't fully understand the consequences of our actions. AI systems are not capable of understanding the full objective, leading to psychopathic behavior. Aristotle's idea of fully automated weaving machines and plectrums that can play music without any human involvement may sound tempting, but it means we don't need any workers. Keynes' idea of technological unemployment in the 1930s suggests that if machines do the work, then many jobs would disappear. You must have heard of E.M. Forrester's machine-dependent civilization. It's a chilling tale of what happens when humans hand over the reins to machines. We become so reliant on them that we lose our desire to understand our own civilization, or teach the next generation how to do it. Sound familiar? It's like WALL-E, but in real life. But here's the catch. General purpose AI is expected to be here by the end of this century, with the median being around 2045. Yeah, that's right. We're talking about machines that can do anything a human can do, and maybe even better. But don't get too excited yet, because John McAfee, one of the founders of AI, said it's going to take between 5 and 500 years to make it happen. And we're going to need several Einsteins to pull it off, because with this great power comes great responsibility, too. So we have a long way to go before we can have general purpose AI, and we need some serious brainpower to get us there. Are you ready for the ride? Share your thoughts with us in the comments below. We'll make sure to be here and provide the latest information so that you never miss anything important. Thanks for watching. This has been How to AI, and we'll see you in the next one.
