Artificial intelligence is being used in more and more areas. What are its typical uses? Find out more in our blog post!
What is artificial intelligence?
Artificial intelligence (AI) is the computer system simulation of human intelligence processes. It is a mapping of human abilities such as perception, learning, problem-solving, or decision-making.
Basically, two types of AI are distinguished, "weak" and "strong" artificial intelligence. Instead of "weak", we can also use the word "narrow", as this type of AI focuses on solving a specific task. A good example is Apple Siri or Amazon Alexa. In fact, all artificial intelligence solutions that surround us fall into this "weak" category.
By "strong" AI systems, we mean systems that do not perform tasks in a targeted way but are capable of much more. At the moment, such systems can only be seen in movies, where a machine or computer has autonomous consciousness, can set its own goals, and can outperform the human intellect.
Uses of artificial intelligence
1. Self-driving cars
The concept of a self-driving car or driverless car is not new. I remember as a child witnessing discusbest-knownsions at home about driverless trains. About how it could be done, what technology would be used, what stage the project was at, when the test run would take place, and what would be needed for the introduction.
The need for cars that can take you where you want to go is very old. These self-driving cars use various sensors, cameras and artificial intelligence software to enable the car to reach the destination without the need for human assistance. The biggest difficulty with these systems is failure, and if you browse the online press, you will almost invariably find problems caused by malfunction. If you ask Elon Musk, he himself would say that getting from point A to point B is no longer a challenge. But reducing the failure rate is a real challenge for any car manufacturer.
Totally self-driving cars do not yet exist. Car manufacturers and technology companies are getting closer to making fully self-driving vehicles a reality, but it looks like it could be a few years before they get there.
Alphabet subsidiary Waymo is one of the best-known companies working on self-driving technology. It has been testing autonomous vehicles on public roads for several years and has even launched commercial self-driving taxi services in some cities.
Tesla has been working on self-driving technology for several years and has launched several vehicles with varying levels of autonomous capability.
Cruise is a self-driving car company owned by GM. It has been testing its autonomous vehicles on public roads for several years and has launched commercial self-driving taxi services in some cities.
Uber has been working on self-driving technology for several years and has tested autonomous vehicles on public roads. But the company has suffered a number of setbacks, including a fatal accident involving one of its self-driving vehicles.
Apple is rumored to be working on self-driving technology, but the company has not yet released any official information about its plans.
Baidu is a Chinese technology company working on self-driving technology. It has already tested its autonomous vehicles on public roads and has even launched commercial self-driving taxi services in some cities.
2. Spam filters
Worldwide, we send more than 350 billion (!) emails to each other every day. According to Statista, more than half of these emails are spam. When you open your mail client, you almost take it for granted that spam will end up in your spam folder. You may not even have thought about what's going on in the background. The answer is: machine learning. Learning programs that, thanks to the sheer volume of data, are constantly improving their knowledge of what should and shouldn't be treated as spam.
With machine learning, there is no need for external intervention, the software is constantly evolving and getting better at filtering out spam emails.
There are many types of chatbots, ranging from simple programs that respond to basic commands to more complex systems powered by artificial intelligence that can have full conversations with users. Some chatbots are designed for entertainment, while others are designed for more practical purposes, such as customer service or information retrieval. Chatbots are often used in various industries such as e-commerce, finance, and healthcare, to name but a few.
One of the main advantages of chatbots is that they can work 24 hours a day and provide users with quick answers. This can be particularly useful for businesses that need to provide customer service outside of normal working hours. Chatbots can also handle large volumes of requests at once, which can be useful for companies that receive a high volume of customer inquiries.
Chatbots also have some limitations. While many chatbots can understand and respond to simple queries, they may have difficulties with more complex questions or requests. In addition, chatbots cannot fully reproduce the nuance and depth of human conversation and may not understand or respond to certain types of input.
There are many chatbots, but the best known are Siri, Alexa, Google Assistant, Microsoft Cortana, ELIZA.
4. Digital personal assistants
A digital personal assistant is software that uses artificial intelligence to perform tasks or services for a person. They can be very useful in streamlining and simplifying daily tasks. Some specific things a digital personal assistant can do:
Overall, a digital personal assistant is designed to make life easier by automating tasks and providing you with useful information and services.
Digital personal assistants are often accessed via a device such as a smartphone, tablet or computer and can be activated by voice commands or through a user interface.
Some examples of digital personal assistants are Apple Siri, Amazon Alexa, and Google Assistant.
5. Search engines, Google search
AI search engines are search engines that use artificial intelligence (AI) to generate s,earch results and improve the user's experience. They are designed to understand natural language queries, provide more relevant search results, and improve the overall efficiency of the search process. Some examples of AI search engines are Google Search, Bing, and DuckDuckGo. These search engines use various artificial intelligence technologies, such as machine learning and natural language processing, to understand the intent behind a user's search query and return the most relevant results.
6. Machine translation
Artificial intelligence (AI) translation is a type of translation that is performed by a computer program using artificial intelligence (AI) technology. Computer algorithms and machine learning techniques are used to translate text or speech from one language to another. AI-based translation can be used for many purposes, such as translating websites and documents or facilitating communication between people who speak different languages. There are several approaches to machine translation, including rule-based systems, statistical machine translation, and neural machine translation. Each approach has its own unique strengths and weaknesses, and the most appropriate approach for a given task depends on the specific requirements and goals of the translation.
7. Speech recognition
The main function of speech recognition software is to convert spoken language into written text, i.e. to be able to represent what I say in text form. One such speech recognition software is Siri, which can answer questions asked in a speech. First, it converts the spoken question into text and then answers it. Over time, as it converts more and more text, the speech recognition software becomes more sophisticated and has a wider vocabulary. It also becomes easier and easier to interpret dialects.
We distinguish between speech recognition and voice recognition. Speech recognition has been outlined above. Voice recognition, on the other hand, is biometric individual voice recognition, i.e. the identification of a person's voice.
8. Computer vision
Computer vision algorithms typically use machine learning techniques to learn from and make decisions based on data. These algorithms can be trained on large datasets of images and videos, and then used to classify objects, recognize patterns, and make predictions based on visual input.
An important part of computer vision is image processing, which refers to the various techniques used to manipulate and analyze digital images. This includes techniques such as image enhancement, noise reduction, and feature extraction.
Another important part of computer vision is object recognition, which is the ability of a computer to identify and classify objects in an image or video. This can be used in many applications, such as identifying and counting specific types of objects in an image, or recognizing and tracking moving objects in a video
Overall, the goal of computer vision is to enable computers to understand and interpret the visual world in a way that humans can, and to use this understanding to perform tasks and make decisions.
9. Recommendation engines
Recommendation engines are software capable of generating personalized responses and recommendations. They create recommendations based on the user's personal preferences and needs. What is needed for this? Lots of data about the person, about you! This is what we are wary of, this is what we don't want. Who wants to share as much data about themselves as possible? I don't believe anyone does. However, there's a lot of value in the way recommendation engines work if they can provide really personalized recommendations.
The problem that has come up in a lot of conversations recently is that what you talk about in an office room, you can get recommendations on the internet within a couple of hours. A classic example is the vacuum cleaner. Talk to your friend about vacuum cleaners with your phone next to you, and the next day you're guaranteed to get vacuum cleaner offers in Google Chrome! Of course, many people disagree that there is such a direct link between the needs expressed and the offers, but in my opinion, it's been going on for a long time. (And that's without even considering that mobile phones have cameras. Based on the captured images, recommendations for furniture, clothes, shoes, and cars could be sent automatically. But I think that a recommendation based on the camera might be too much for people, they would find it hard to accept. I think rightly so.)
The first old and classic version of the recommendation engine appeared on Amazon. When buying books, the portal recommended books based on other people's consumer habits. I think that recommendation engines now live with us naturally on a huge number of platforms.