Artificial Intelligence Write For Us – In short, artificial intelligence is the field that tries to give machines the ability to think or understand how humans do. This does not mean creating fully autonomous machines capable of doing everything like us. Still, it does mean doing particular tasks that our brains may do almost effortlessly but which are by no means trivial for a machine. In general, in programming, we need to describe all the cases and conditions that can happen to decide the program’s steps (if A happens, the program will do B, if it won’t, do C). Still, specific tasks are impossible to define precisely. Artificial intelligence comes in these tasks and has made significant advances. This form.
The clearest example is probably the comprehension of images. Humans constantly analyze what our eyes see, and we do it extraordinarily well, but the task itself is highly complex. Let’s imagine, for example, that we are creating an autopilot system for a self-driving car. To drive, we put a camera in front of the car that sees what the driver would visit, and we try to create a program that turns, brakes or accelerates from the images. Simplifying a lot, one of the first things we should do is detect if we have a car in front of us or not to brake. Let’s imagine for a moment what it means to create a system capable of doing this: we need to find, in an image, a car that can be any model on the market (from a 90’s Seat Ibiza up to a 2020 BMW X7), that you can be driving in any weather condition (day or night, raining, foggy…) and that the same car can be in any state (clean and in perfect condition, or full muddy and dented). Let’s imagine how the image can change, even if it’s just a matter of seeing a simple car.
Although we understand these images as semantically similar things, they have absolutely nothing to do with each other at the pixel level. Moreover, we can see a car model for the first time in life and instantly know it is a car. This is impossible in conventional programming; we need another tool.
Conditions And Actions.
This is where what caused this boom in Artificial Intelligence a few years ago comes into play: Deep Learning. Instead of manually defining the conditions and actions of our program, the idea is to get a model capable of learning them automatically. Specifically, the quintessential model that has achieved incredible results is called Neural Networks, which mimic how a biological brain works in a very simplified manner.
Biological Brain Consists.
It is tough to explain this in a few words, but it would be a model that tries to copy how our brain can learn. Roughly speaking, we know that a biological brain consists of many interconnected neurons. To learn new things, we know that these connections between neurons, called synapses, vary in intensity and how they transmit signals. In other words, knowledge is distributed throughout the network by changing the connections between neurons.
What is Neural Network Model?
In the Neural Network models that we use in the field of Artificial Intelligence, we try to copy this way of storing information, creating artificial neurons and connections that are also modifiable. Our artificial brains are a simplified version of what happens in a biological brain, but they allow us to create machines capable of learning.
The example that I have explained before of the vision system to detect if there is a car in front of it could be perfectly solved using these artificial brains. How do we do it? First, we would create a brain model by arranging a series of neurons connected to each other in software. This brain would not physically exist but would simply be thousands of variables prepared in the computer’s memory ready to be used. So, we would show this brain images of roads with cars and without cars, and we would see what result in it gives us. Depending on whether the result is correct or not, we would adjust the connections between neurons to try to make fewer errors in the following examples. Initially, the relationships between neurons would be entirely invented; therefore,, our brains would be foolish. But as we showed it images and adjusted the links, the brain would start working. Given enough imaging and time, the brain could do the task just as well as a human.
Why does AI suddenly seem so much more present?
The truth is that Deep Learning and Neural Networks have achieved incredible results in the last decade. Although Neural Networks have existed for a long time (since the 1940s), their potential has been exploited relatively recently. The reasons for this change are several, but in broad strokes:
- First of all, the same Moore’s law. For Neural Networks to work correctly, computers must have in memory and modify millions and millions of variables, pretending to be neurons and synapses. This is technologically expensive, and the simple evolution of computers has made it possible.
- Second, the availability of data. As I have explained, our networks are fed data to learn how to perform new tasks. Today, with the explosion of the Internet and social media, we are clearly in the data age, and therefore deep learning has all the fuel it needs to function.
- Third, some companies have seen the potential it has. The fact that hardware (Nvidia, Intel…) and software (Google, Facebook…) companies have devoted resources to AI has made the field evolve much faster. These companies have facilitated the technology implementation and made it possible for other smaller companies or even startups to create technology with AI. Powerful hardware and sufficiently optimized software are needed to develop and train these networks.
Is it a fad or really the future?
Undeniably, a true technological revolution has been achieved in the last ten years! What can be achieved now using Deep Learning was absolutely unthinkable just a few years ago. Therefore, I have no doubt that it will mark the future, not only technologically but also socially.
That being said, it’s clear that there’s also some hype to it. When technology is hot, companies try to adopt it, if only for marketing reasons. The same thing has happened with Artificial Intelligence; lately, even bakeries are using AI to cut bread.
Another thing to remember is that not everything that appears in the news is entirely accurate. When we see information about futuristic advances in AI, we must not forget that interests are often involved, and these advances are highly magnified. Artificial intelligence has great potential, but we’re far from seeing the Terminator walk through the door.
We all think of autonomous cars and voice assistants, but what else is within our reach or what will we ever have?
I think self-driving cars and voice assistants are probably the most attractive uses, but we have to believe that most of the technology that needs to make intelligent decisions will end up working with Deep Learning. We already have this technology integrated into a large part of the software we consume, and we probably do not realize it. When we use the Google translator, when Spotify recommends a new artist or when we get banners while browsing the Internet, all this is powered by Deep Learning. I would say we haven’t realized it, but there are certain apps that we’ve been using for a long time that suddenly work a lot better because of AI.
In the future? I could give several examples:
- The industry will probably experience the most significant changes. Industrial processes have very repetitive tasks but have been very complex to automate. AI will significantly influence this sector, and many of these tasks can be fully automated. This is sure to revolutionize industrial production and will surely also bring about significant changes in society.
- Another field where new applications using AI are appearing in the area of security and control cameras. Using Deep Learning, it is possible to analyze videos quite accurately, detecting suspicious actions or people. China is already applying this technology to carry out massive control of the population, and here, private security companies are already beginning to offer security services with AI.
- Deep Learning has proven to be a powerful video editing or image processing tool. I believe that film producers and video game creators will use this technology to offer us more spectacular products. One last curious application could be AI for image and video editing. The possibilities in this field are endless.
Some say that machines will replace people. Is it true?
First, I believe that although many jobs can automated, everything cannot automated. The AI that we currently have works well performing repetitive tasks. It is incapable of functioning like humans in more complex jobs or requires social skills. Alexa (Amazon’s voice assistant) can understand clear instructions well, but it doesn’t go much further. You can ask him to buy you milk, but you can’t explain that you’re depressed today because your girlfriend has left you.
Second, if there is not enough work for everyone to do the current workday, the everyday workday will surely change. Now it seems that we have always had an 8-hour work day and worked five days a week for two holidays, but this has not always been the case. Society has adapted to significant technological changes in the past, and nothing makes me think that it cannot adapt to one more change. Without going any further, the idea of a shorter working day is becoming increasingly popular.
Finally, while some jobs will most likely disappear, new ones will also created. On the one hand, AI will generate new jobs; getting an AI system up and running and maintaining it requires a lot of work. On the other hand, if society changes and has more free time, new business opportunities will also appear in the service sector that will generate new jobs.
How far can you go?
Although Artificial Intelligence has achieved things hitherto unthinkable, it is still a long way from being able to create intelligent systems. The human brain is extraordinary, and, neither in the short nor medium term, it seems possible that its capabilities can matched by any current technologies. With Neural Networks, we have achieved that machines can do specific tasks as well as humans, but these tasks are always minimal and do not show natural intelligence. An example I love to understand how complicated it is to create intelligent systems is the one Andrej Karpathy gave some time ago in his blog.
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