There is a new chatbot powered by artificial intelligence known as ChatGPT that can answer questions, generate essays and even write scientific articles based on a short message. Popularly known as “Language production among psycholinguists”, natural language generation is a procedure that aims to transform any structured data into a natural language. In simple terms, natural language generation can be considered as a process that converts thoughts into words. For example, when a child sees a butterfly flying in a garden, he can think of it in a variety of ways.
Those thoughts can be called ideas. However, when children describe their thinking process in their natural language (mother tongue), this process can be referred to as natural language generation. Natural language understanding is the opposite of natural language generation. This procedure is more inclined towards interpreting natural language.
In the example above, if the child is told about the butterfly instead of being shown to him, he can interpret the data given to him in a variety of ways. Based on that interpretation, the child will draw a picture of a butterfly flying in a garden. If the interpretation was correct, then it can be inferred that the procedure (natural language comprehension) was successful. As the name suggests, voice recognition is a technology that uses artificial intelligence to convert the human voice into a computer-accessible format.
The process is very useful and acts as a bridge in the interaction between humans and computers. Using voice recognition technology, the computer can understand human speech in several natural languages. This also allows the computer to have a faster and smoother interaction with humans. For example, suppose the child in the first example was asked, “How are you? during a normal person-to-person interaction.
When the child hears the human speech sample, he processes the sample according to the data (knowledge) already present in his brain. The child draws the necessary inferences and, finally, an idea comes up with an idea about the subject of the sample. This way, the child can understand the meaning of the voice sample and respond accordingly. Machine learning is another useful technology in the field of Artificial Intelligence.
This technology focuses on training a machine (computer) to learn and think for itself. Machine learning often uses a lot of complex algorithms to train the machine. During the process, the machine receives a set of categorized or unclassified training data that belongs to a specific or general domain. The machine then analyzes the data, draws inferences and stores it for future use.
When the machine finds any other sample data from the domain it has already learned, it uses the stored inferences to draw the necessary conclusions and provide an appropriate answer. For example, suppose that the child in the first example was shown a collection of toys. The child interacts (using their senses such as touch, sight, etc.). These properties can be anything from size, color, shape, and so on.
From toys, based on their observations, the child stores the inferences and uses them to distinguish between any other toy with which he may have future encounters. Therefore, it can be concluded that the child has learned. Virtual agents are a manifestation of a technology that aims to create an effective but digital impersonation of humans. Very popular in the field of customer service, virtual agents use the combination of artificial intelligence programming, machine learning, natural language processing, etc.
To understand the customer and their complaints. A clear understanding by virtual agents depends on the complexity and technologies used in creating the agent. Nowadays, these systems are widely used through a variety of applications, such as chatbots, affiliate systems, etc. These systems are capable of interacting with humans in a humane way.
In the examples mentioned above, if the child is considered a virtual agent and made to interact with unknown participants, the child will use a combination of their already learned knowledge, language processing and other “tools” needed to understand the participant. Once the interaction is complete, the child will derive inferences based on the interaction and will be able to address the queries raised by the participant in an effective manner. Deep learning is a special subset of machine learning based on artificial neural networks. During the process, learning takes place at different levels, where each level is capable of transforming the input data set into composite and abstract representations.
The term “deep” in this context refers to the number of levels of data transformation carried out by the computer system. The technology finds its applications in a variety of domains, such as computer vision, news aggregation (based on feelings), the development of efficient chatbots, automated translations, the rich customer experience, etc. As a simpler example, if the child in the examples above is learning restricted to a single level, the result (response) may not be specific to the problem but rather general. Learning at a deeper level helps the child better understand the problem.
Therefore, it can be inferred that the deeper the learning, the more precise the answer. Big, consensual ideas about how AI hugely governs industries today. Primarily, voice recognition, the advanced mechanism reduces workloads by automating the process, such as documentation in health sectors, live classes in educational sectors, etc. The current wave of AI products is based on a technical breakthrough called generative AI.
It allows a computer to create images or words that appear to have been made by a human. To do this, it studies billions of images and text samples, often extracted from the web. To use them, you give them a phrase in English telling them what you want to do. Finding the right message is becoming an art in and of itself.
Smart technology has become part of our daily lives in recent years. And, as technology advances across society, the new uses of AI, especially in transportation, are becoming more widespread. This has created a new market for companies and entrepreneurs to develop innovative solutions to make public transport more comfortable, accessible and safer. .
