What is Artificial Intelligence Engineering?
Artificial intelligence engineering is an engineering field that attempts to give machines the ability to think, perceive, and solve problems like humans. In this field, they provide capabilities to elements such as computer systems and robots in situations that require human intelligence. These capabilities can be as follows:
Speaking and natural language processing skills
Image and voice recognition
Machine learning
Problem solving
Decision making
In order to provide these capabilities to elements such as computer systems or robots, artificial intelligence engineers use engineering intelligence in developing complex algorithms and models, using software and hardware components in an integrated structure, and training the machine with a significant amount of data. For example, artificial intelligence engineers developed a voice recognition system, created voice assistants on phones, and put this system into use with artificial intelligence engineering.
What Does an Artificial Intelligence Engineer Do?
The main goal of artificial intelligence engineers is to make machines and computer programs intelligent. They teach these programs to think and make decisions like humans. AI engineers, who aim to provide outputs that can mimic human intelligence in multiple ways, are people who produce computer programs that can think and make decisions like humans. AI engineers aim to provide the correct output within a subset framework by teaching computers to speak, understand images, and solve problems.
For example, an AI engineer can program a robot to help with housework. This robot can be taught to wash dishes, iron, and sweep the floor. In this way, the robot can do housework intelligently. Or, an AI engineer can program the voice assistant on the phone and teach it to speak so that it can answer when we ask it a question.
To give another example, you have a robot dog and you give it overseas chinese in uk data the command 'sit'. But the robot dog only barks, it does not sit. Because no one has taught it to sit. Here, an artificial intelligence engineer can program the robot dog to perceive the sit command with the help of cameras and sensors and to act accordingly. Thus, the robot can now apply the command it has learned.
If we give an example from the field of technology, the cameras on our mobile phones continue to improve every day. These cameras have a night mode and thanks to this, we can take photos in the dark. So how does the phone camera detect night mode? This is where artificial intelligence comes into play. Artificial intelligence engineers provide the phone camera with special programming that can improve night photos. They show this program millions of day and night photos as examples. Thus, when the phone realizes that a photo was taken in a dark environment, it runs the night program and brightens the photo as if it was taken during the day. In this way, our phone can think like our brain, if not like our eyes, and automatically improve the photo.
In short, artificial intelligence engineers make our daily lives easier by coding on computers and developing these programs using data. In other words, we can say that artificial intelligence engineering is the work of making machines think like humans.
What Do Artificial Intelligence Engineers Do, Article by Article?
In fact, we explained above what artificial intelligence engineers do. Now let's detail this section with examples.
Algorithms
AI engineers develop algorithms. For example, in a maze game, they can teach a program to find its way through the maze using the algorithm.
Data Collection
We can say that the most important issue for artificial intelligence engineers is data. For example, if there is a study on voice recognition feature, they collect recordings of different people's conversations and play the dataset to the program.
Training the Machines
Machines are trained with data. For example, we can teach driverless cars to follow the road and stop with cameras and sensors. Thanks to this training, the car can drive on its own, without a driver. In fact, recently, driverless taxis that entered traffic in America were stranded on the road due to an internet outage.
Testing
For example, they make the chatbot talk to real people and evaluate their answers. As a result of this evaluation, they develop the relevant product.
Software Development
For example, a translation program could initially operate in a single language. After software development, they write codes that can translate between different languages into the relevant product. In this way, artificial intelligence engineers play an active role in the software development process.
In summary, we can say that artificial intelligence engineers play an active role in the development and application processes of artificial intelligence.
Artificial Intelligence Methods
There are many different methods of artificial intelligence. The umbrella that these methods are connected to is terms such as machine learning and deep learning, and the main theme that these terms are connected to is the concept of artificial intelligence. Thanks to artificial intelligence methods, artificial intelligence engineers work with the appropriate model for the need and offer the most effective solution.
What is Machine Learning and Where is it Used?
Machine learning allows computers or robots to develop the right model based on order and inference. Computer systems use machine learning to process and describe large amounts of data. The most important thing in all these processes is that the amount of data is the most decisive criterion for how successful the learning will be. In order to provide these criteria, it is designed to correctly predict the results formed by the data set. For example, we will teach a robot to distinguish an apple from an orange. For this, we will show the robot pictures of apples and oranges as a data set. The robot will learn lessons such as how an apple is, how an orange is, by looking at these pictures, and as a result, it will be indexed to give the right result.
Machine learning is used in many sectors of our lives, such as manufacturing, banking, financial services, entertainment, and retail. For example, in the banking sector, machine learning can be used to detect credit card fraud or suspicious transactions. In the gaming sector, we can think of the games we play against the computer. Because the computer improves itself according to our moves and makes its moves as a result of the learning.
What is Deep Learning? Deep Learning with Examples
Deep learning is a modeled machine learning technique that progresses with a logic in the human brain structure. With this technique, high-capacity learning is achieved by using intelligent systems such as artificial neural networks. Deep neural networks consist of layers that receive inputs, process these inputs, and produce outputs. Each layer receives the output of the previous layer and processes it and transfers it to the next layer. In this way, the relevant learning deepens and we can access the output in complex matters to reach the output at the human level.
For example, consider a deep learning model that detects fraud or suspicious transactions in a mobile banking application. In this model, the input layer receives the customer's transaction data such as amount, date, and location. Then, several hidden layers examine whether this transaction complies with the customer's past transaction habits such as amount limit, location, and day of the week. Subsequent layers check other fraudulent or suspicious signs of the transaction such as IP address and device used. The final layer decides whether the transaction is fraudulent or suspicious based on all these features. As can be seen, the many hidden layers placed between the input and output deepen the model and enable learning complex examples. Thus, the bank can also detect fraudulent transactions.
Let’s move on to another example. Consider a deep learning model that recommends content based on our interests in social media applications. The input layer takes the user’s post and comment history. Subsequent layers extract the user’s interests from this data. Deeper layers examine the user’s followed accounts, liked posts, and watched videos to find more specific interests. The final layer suggests posts that the user might like based on all these inferences. Thus, the application recommends personalized content thanks to the deep learning model. You can also access the comprehensive content on the Zeo Blog for Generative AI here.
What is Natural Language Processing? What are the Applications of Natural Language Processing?
People do not form proper sentences when they speak. Natural language processing (NLP) allows computers to understand human language and present it by producing output that is appropriate for this language. For example, we can say, "Can you bring my son's shoes home?" A computer has difficulty understanding this. Thanks to natural language processing, we can teach computers to analyze this type of speech and writing. In other words, despite not knowing the rules of grammar like humans, we enable computers to understand this thanks to natural language processing. Thus, computers can now answer our questions, have conversations and translate texts.
Voice assistants, translation programs, chatbots, text summarization programs, spell check are the main natural language processing applications. For example, while writing messages on the phone, the correction of the words we type incorrectly on the keyboard is actually the use of natural language processing.
Master the Right AI Tools for the Right Job!
Artificial intelligence includes branches such as deep learning, machine learning, natural language processing. Therefore, we should be aware that artificial intelligence is a very wide field and has many different methods.
For example, if we are designing a robot to clean the house, we can teach it cleaning movements with machine learning. In this way, the robot can do different cleaning tasks. Or if we are thinking of an artificial intelligence that will chat with our friend, we can use natural language processing for this. In this way, the artificial intelligence can understand and respond to conversations. Or if we are developing a program to diagnose doctors, we can analyze images with deep learning.
As seen in the examples, it would be more appropriate to use appropriate artificial intelligence methods and train the tools with these methods. Choosing the right method is very important for developing successful and highly effective artificial intelligence-focused products.
Becoming an Artificial Intelligence Engineer
While artificial intelligence engineers have knowledge and skills in all of the above, they work in many areas to make items such as computer programs and machines smart.
Skills Required to Become an Artificial Intelligence Engineer
To be an AI engineer, you need to be creative, patient and prone to teamwork. It is very important to like finding new ideas and solving difficult problems. It is necessary to constantly improve yourself to follow new technologies.
An artificial intelligence engineer needs to learn programming languages such as Python, R, Java. In this way, he will be able to effectively use artificial intelligence models, especially machine learning algorithms, and deep learning algorithms.
Apart from technical skills, human skills are also important. Improving yourself in criteria such as communication skills, problem-solving skills, time management, teamwork, and critical thinking skills will increase your competence.
Considering that artificial intelligence will be everywhere in the future, working in this field will become a very exciting and important field of work. Therefore, completing a computer engineering or software engineering department at university will allow you to take part in this field as a graduate.
