What Is Artificial Intelligence (AI)?
Artificial Intelligence is the science of making machines smart, using algorithms to allow computers to the solved problem which used to be solved only by humans. AI already power search engines, online shopping recommendations, and digital assistants. AI is everywhere. It’s helping banks make loan decisions and helping doctors diagnose patients, it’s on our cell phones, auto completing texts. AI already has a huge impact on all of our lives. So people, understandably, have some polarized feelings about it.
Some of us imagine that AI will change the world in positive ways, it could end car accidents because we have self-driving cars, or it could give the elderly great, personalized care. Some say that automation will take all our jobs or the robots might try and kill us all. No, we’re not worried about it. But when we interact with AI that’s currently available like Siri… Hey Siri. Is AI going to kill us all?” Siri: “I don’t understand ‘Is AI going to kill us all.’” … it’s clear that those are still distant futures. If you know about artificial intelligence mostly from books or movies, AI probably seems like this vague label for any machine that can think like a human. Fiction writers like to imagine a more generalized AI, one that can answer any question we might have, and do anything a human can do. But that’s a pretty rigid way to think about AI and it’s not super realistic.
AI still plays a huge role in our everyday lives. There are some more obvious uses of AI, like Alexa or Roomba, which is kind of like the AI from science fiction I guess. When we buy something in a big store or online, we have one type of AI deciding which and how many items to stock. And as we scroll on Instagram, a different type of AI picks ads to show us. AI helps determine how expensive our car insurance is, or whether we get approved for a loan. And AI even affects big life decisions. Like when you submit your college (or job)application AI might be screening it before a human even sees it. The way AI and automation are changing everything, from commerce to jobs, is sort of like the Industrial Revolution in the 18th century. This change is global, some people are excited about it, and others are afraid of it. It was coined in 1956 by a computer scientist named John McCarthy. He used it to name the “Dartmouth Summer Research Project on Artificial Intelligence.” Most people call it the “Dartmouth Conference” for short. The Dartmouth Conference lasted eight weeks and got a bunch of computer scientists, cognitive psychologists, and mathematicians to join forces.
Now, most kinds of Artificial Intelligence don’t have things like senses, a body, or a brain that can automatically judge a lot of different things as a human baby does. Modern AI systems are just programs in machines. Still, there were a lot of changes in the last half a century that led us to the AI Revolution. We got to where we are today because of lots of small decisions, and two big developments in computing. But if you tried to do that it would take you 55 and a half hours! At the time, that was enough computing power to help with the U.S. Air Force’s Ballistic Missile Warning System. But AI needs to do a lot more computations with a lot more data. The speed of a computer is linked to the number of transistors it has to do operations. Every two years or so since 1956, engineers have doubled the number of transistors that can fit in the same amount of space. So computers have gotten much faster.
When the first iPhone was released in 2007, it could do about 400 million operations per second. But ten years later, Apple says the iPhone X’s processor can do about 600 billion operations per second. That’s like having the computing power of over a thousand original iPhones in your pocket. And a modern supercomputer, which does computational functions as the IBM 7090 did, can do over 30 quadrillion operations per second. The second development that kicked off the revolution is something that you’re using right now: the Internet and social media.
Artificial Intelligence Technologies :-
- Speech Recognition
- Natural Language Generation
- Virtual Agent
- Machine Learning
- Robotic Process Automation
- Deep Learning Platforms
- Text Analytics and NLP
Applications Of AI:-
- Artificial Intelligence in Healthcare
- Artificial Intelligence in Business
- AI in Autonomous Vehicles
- AI in Education
- AI in Cyborg Technology
- AI for Robotics
Future Of Artificial Intelligence
AI is in more places than ever before. The machine learning professor Andrew Ng says that “Artificial Intelligence is the New Electricity.” This is a pretty bold claim, but lots of governments are taking it seriously and planning to grow research, education, and development in AI. China’s plan alone calls for over 100 billion U.S. dollars in funding over the next 10 years. AI is awesome. It can help make our lives easier and sort of gives us superpowers. Who knows what we can accomplish with the help of machine learning and AI? And third, AI doesn’t work that well yet. I still can’t ask my phone or any “smart” device to do much, and we’re far away from personal robot butlers. So what’s next? What’s the future of AI?
One way to think about the future of AI technology is to consider milestones AI hasn’t reached yet. Current soccer robots aren’t quite ready to take on human professionals, and Siri still has a lot of trouble understanding exactly what I’m saying. In 2014, for example, the Society of Automotive Engineers attempted to do just that for self-driving cars.
They defined five levels of automation. For each additional level, they expected to the AI controlling the car can do without human help.
At level 1, the cruise control automatically accelerates and decelerates to keep the car at a constant speed,
At level 2, but everything else is on the human driver.
At level 3, the car is basically on its own. It’s driving, monitoring its navigating, surroundings, and so on…..
At level 4, but a human driver will need to take over if something goes wrong, like really bad weather or a downed power line.
And at level 5, the human driver can just sit back, have a smoothie, AI while the car takes them to work through rush-hour traffic. And obviously, we don’t have cars with the technology to do all this yet.
But these levels are a way to evaluate how far we’ve come, and how far our research still has to go.
We can even think about others also using “levels of automation.” Like, for example, maybe we have level 1 A assistants right now that can set alarms for us, but we still need to double-check their work. But what are levels 2 through 5? What milestones would need to be achieved for AI to be as good as a human assistant? The interrogator talks to the hidden players and tries to figure out which is a human and which is a machine. Turing even gave a series of talking points, like please write me a sonnet on the subject of the Forth Bridge. Add 34,957 and 70,764. Do you play chess? I have K at K1 and no other pieces. It’s your move. What do you play? The goal of The Imitation Game was to test a machine’s intelligence about any human things, from poetry to math. We wouldn’t just judge how “real” robot’s fake human skin looks. But over the last 70 years, AI researchers focused on subfields like computer vision, knowledge representation, economic markets, planning, and so on.
This involves projects like teaching a robot to understand language or teaching an AI system that models the stock market to read the news and better understand market fluctuations. To be clear, most of AI is still science fiction…we’d nowhere near Blade Runner, Her, or any similar movies. Before we get too excited about combining everything we’ve built to achieve AGI, we should remember that we still don’t know how to make specialized AIs for most problems. Some subfields are making progress more quickly than others and we’re seeing AI systems pop up in lots of places with awesome potential. To understand how AI might be able to change our lives, AI Professors Yolanda Gil and Bart Selman put together the Computing Research Association’s AI Roadmap for the next 20 years. They predict AI reducing healthcare costs, personalizing education, accelerating scientific discoveries, helping national defense, and more. And all of these problems have lots of data to train new algorithms. It used to be hard to collect training data, going to libraries to copy facts, and transcribe books. But now, a lot of data is already digital. If you want to know what’s happening on the other side of the planet, you can download newspapers or grab tweets from the TwitterAPI. Interested in hyper-local weather prediction? And if you feed that data into a robot Gardner, you could build a fully-automated weather-knowing plant-growing food-making garden!
Truck, delivery, and tractor drivers are some of the most common jobs in the US as of 2014. If self-driving vehicles revolutionize transportation in the near future, will all those people lose their jobs? We can’t know for sure, but Gödel Prize-winning Computer Science Professor Moshe Verdi points out that this is already the trend in some industries. For example, U.S. manufacturing output will likely keep rising, but manufacturing jobs have been decreasing a lot. Plus, computers use energy, and that means we’re not getting any benefits from AI for free. Massive amounts of machines running these algorithms can have a substantial carbon footprint. On top of that, as we’ve discussed, you have to be pretty careful when it comes to trusting AI systems because they often end up with all kinds of biases you may not want. So we have to consider the benefits of massive AI deployment with the costs. In a now-famous story from a few years ago, Target figured out a woman was pregnant based on her shopping history, and they sent her maternity coupons. But she was still in high school, so her family saw the mail, even though she hadn’t told them. Do we want our data being used like this, and potentially revealing personal details? Or what about the government. Should it be allowed to track people with facial recognition installed on cameras at intersections? When we provide companies location data from our phones we could help them build better traffic models so we can get to places faster. Cities could improve bus routes, but it also means … someone … is … always … watching you. AI could also track your friends and family, where you shopped, ate, and whom you hung out with.
If statistics have shown that people who leave home late at night are more likely to commit a crime… and an AI knows you left (even though it’s just for some late-night cookie dough), should it call the police to watch you — just in case? So, we can go down any number of scary thought experiments. And there’s a lot to consider when it comes to the future of AI. AI is a really new tool and it’s great that so many people have access to it, but that also means there are very few laws or projections about what they can and can’t do. Innovations in AI have awesome potential to make positive changes, but there are also plenty of risks, especially if the technology advances faster than the average person’s understanding of it. It’s probably the most accurate to say that the future is… complicated. And the most important thing we can do is be educated and involved in AI as the field changes. So when we see a company or government rolling out new technology, we know what questions to ask: Where did they get their data? Is this even a situation where we want to help humans? Is this the right tool to use? What privacy are we giving up for this cool new feature? Is anyone auditing this model? We’re excited to see what future you decide to build.