Artificial intelligence, a definition
L’artificial intelligence (AI or AI, Artificial Intelligence) is a branch of computer science that deals with the creation of intelligent agents, i.e. systems that can reason, learn and act autonomously.
AI is a process through which computer systems they simulate the cognitive processes of human intelligencelearning from mistakes and carrying out complex tasks and reasoning in a very short time, which until recently were an exclusive human competence.
It is different fromAutomationalthough these two technologies are often confused with each other.
In fact, Automation is the use of machines or software to carry out tasks that were previously carried out by people, and can be used to simplify, accelerate or improve the efficiency of a process, while artificial intelligence, as we have seen little above, is a branch of information technology that deals with creating machines equipped with specific capabilities typical of human beings (interaction with the environment, learning, adaptation, reasoning, planning and decision-making), capable of autonomously pursuing a defined purpose, and carry out tasks such as image recognition, language translation or autonomous driving.
AI is a very vast and constantly evolving field of research, it is revolutionizing human-machine interaction and intra-machine communication, and its progress has led to the development of a series of innovative technologies, such as robots, computer vision, speech recognition, natural language processing and machine learning.
IA a short story
The first studies on AI and therefore, its origins and history as a scientific discipline, date back to the 1950s.
It was precisely in this period of great scientific ferment that the first conference called “Dartmouth Summer Research Project on Artificial Intelligence”in 1956 at Dartmouth College in the United States, in which about ten researchers from different disciplines participated (from mathematics to logic, from psychology to engineering), in which the definition of Artificial Intelligence, and the design of algorithms and systems for the creation of machines that were capable of reasoning and learning like human beings.
Image by Federica Ruberto
A few years earlier, in 1950, inside the article “Computing machinery and intelligence”Turing had addressed the question of machine reasoning, whether machines could think, solve problems, and make decisions intelligently.
Turing argued in the article that if a machine could follow the fixed rules of a program, then it would be considered capable of thinking.
To demonstrate this thesis, he therefore proposed a test, known as the “Turing Test” or “Imitation Game”, according to which a machine could be considered intelligent if its behavior, observed by a human being, was considered indistinguishable from that of a person.
The AI market
In 2023, according to data from research carried out by the “Artificial Intelligence Observatory of the School of Management” of the Polytechnic of Milan, the artificial intelligence market in Italy reached a volume of approximately 760 million euros (+52% compared to 2022).
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It is expected that the value of the AI market will reach 6.6 billion euros in Italy by 2027, and 407 billion dollars worldwide.
As reported by Grand View Research (an American market research and consultancy company), AI will continue to revolutionize various sectors, with an expected annual growth rate of 37.3% between 2023 and 2030.
Natural Language Processing (NLP)
The NLP, or Natural Language Processingis a branch of artificial intelligence that deals with developing algorithms capable of processing natural language, or carrying out activities such as:
- The natural language recognition (NLU, Natural Language Understanding): that is, understanding the meaning of a text or a conversation. For example, understanding a user’s instructions, or translating text from one language to another.
- There genesis of natural language (NLG, Natural Language Generation): that is, generating text, such as content, or personalized conversations for customers.
- L’sentiment analysis: that is, determining the feeling expressed in a text, such as positive, negative or neutral. For example, understanding user opinions about a product or service.
L’NLP it is a technology that is having a significant impact on many sectors, such as search (think for example of search engines that use Natural Language Processing to understand user queries and provide relevant results), customer support and advertising.
Recent advances in natural language processing have contributed to the birth of advanced Chatbots/Virtual Assistants, no longer based on predefined decision trees or through keyword searches, as was the case until some time ago, but capable of guaranteeing an more realistic and free of form constraints, in which the situations in which the user is sent back to a human are significantly reduced.
Image by Federica Ruberto / Created with DALL-E
And if we add to this that today we are also using the psychometric profilinga technique that uses tools that measure the psychological characteristics of an individual (attitudes, personality, skills), in addition to the psychology of persuasion, we can well understand what the potential or critical issues of these technologies are.
But how does one “reason”. chatbots evolved?
We can simply say that an evolved chatbot uses theSemantic AI to understand the meaning of the question, generative AI to create the answer, and conversational AI to write the answer text in such a way that it is understandable to humans.
Let’s look specifically at these three subareas of AI.
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Semantic Artificial Intelligence
L’semantic artificial intelligence (YOU KNOW, Semantic Artificial Intelligence), is a subarea of artificial intelligence that focuses on the meaning of language.
It is used for understand the meaning of words and sentences, and to apply this meaning to tasks such as translating language, answering questions, and summarizing text.
In fact, it can be used to translate language from one language to another accurately and fluently; answer questions completely, even if they are open-ended or challenging; summarize text, such as news articles or blog posts, so that it is consistent with the context and content of the original text; improve the user experience for e-commerce customers by providing personalized advice and answers to their questions.
Although the definition of SAI seems very similar to the definition of NLP, we can differentiate these two technologies:
NLP focuses on understanding the structure of language, such as words and sentences. Semantic AI, on the other hand, focuses on the meaning of language, or how words and sentences connect to each other to convey a message.
NLP uses techniques such as word recognition, grammar, and syntax to understand the structure of language. Semantic AI, on the other hand, uses techniques such as logical inference, semantics and world knowledge to understand the meaning of language.
Conversational Artificial Intelligence
L’Conversational AI is a subarea of artificial intelligence that focuses on creating systems (e.g. chatbots, virtual assistants and conversational agents) that can simulate fluid and natural conversations with humans.
They use several technologies to understand natural language, generate relevant responses and maintain fluid dialogue, including:
- L’natural language processing (NLP): as we have seen throughout the article, this technology allows machines to understand the meaning of a text or speech, analyzing its syntax and semantics.
- L’machine learning (machine learning): this technology allows machines to learn and adapt to new situations, improving performance over time. Machine learning is used in conversational AI to improve natural language understanding, response generation, and dialogue management.
Conversational AI can benefit, for example, customer service, where chatbots can provide 24/7 support and can answer customer questions quickly and accurately, but above all it can enable better user experience. In fact, conversational AI can personalize interactions with users and make them much more engaging.
Generative artificial intelligence
L’generative artificial intelligence (IAG or GAI, Generative Artificial Intelligence), which has made considerable progress in recent years and is revolutionizing several aspects of our lives, is a subarea of artificial intelligence that exploits complex algorithms, such as deep neural networks, to “learn” from large amounts of existing data (not only texts, but also images and videos), and use this knowledge to generate new original content.
Image by Federica Ruberto / Created with DALL-E
The areas of application are very vast and constantly growing. Let’s think for example about:
- Image Creation: IAG can generate realistic and original images, such as landscapes, people, animals or non-existent objects. This can be useful for artists, designers, architects and anyone who needs creative imagery.
- Product development: IAG can be used to design new products, such as cars, furniture or tools, optimizing them for functionality, aesthetics and production costs.
- Music Composition: IAG can compose original music in different genres and styles, opening up new possibilities for musicians, composers and audio content creators.
- Automatic writing: IAG can generate texts of various types, such as blog articles, social media posts, emails, letters and even novels.
This can be useful for anyone who needs to create content regularly or to automate writing.
Generative artificial intelligence is becoming increasingly popular, just pay attention to the numerous tools that are emerging, citing some of the most famous: ChatGPT (chatbot created by OpenAI that uses generative linguistic models), DALL-E (model that generates images starting from textual descriptions), but also Copy AI (a platform that helps create creative content using GPT).
Generative artificial intelligence offers numerous advantages, including the creation of personalized content for each user (based on their preferences and interaction history); increasing productivity by automating repetitive and time-consuming tasks, freeing up resources for more creative and strategic activities; the stimulation of creativity, helping to generate new ideas and overcome creative blocks.
Written by:
Federica Ruberto
IT Architecture Professional
Marketer converted to IT systems, who after two degrees in Economics and a Master in Digital Marketing has not stopped studying and training in the most varied fields of culture and knowledge.
https://www.linkedin.com/in/federicaruberto/
