The IA, a Giant Multifaceted
ChatGPT. This name is now on everyone's lips, almost a symbol of the new era of artificial intelligence (AI) to the conversation, capable of interacting with us in a surprisingly natural and convincing. But the phenomenon ChatGPT is only the tip of the iceberg very large, the outpost of a continent cut off, still unexplored. The AI is not a single monolithic entity, but a constellation of models, each with their particular peculiarities, and of those which, at first view, appear as a real “superpowers”.
In this article, we abandon the banks family of the chatbot to embark on a journey to discover this universe multiforme. We will explore the different “races” of AI: from neural networks that give voice to the human language technologies that enable machines to “see” and interpret the visual world, from the models that generate images and sounds with a creative disconcerting to the algorithms that coordinate the movements of the robot. A journey to discover that artificial intelligence is much more than just a partner virtual: an unstoppable force, and kaleidoscopic, which is changing the way we live, work and engage with the world.
The Lords of the Word: Language Models
Among the many forms of artificial intelligence, the language models occupy a prominent place, in particular, in the recent times, thanks to the exploits of chatbot conversational as ChatGPT and Bard. These IA sophisticated, trained on the amount of text of such magnitude to be put to the test the limits of the human imagination, have developed a stunning mastery of the language, learning to handle it with a smoothness and richness that often leave without words.
But their talent is not limited to the simple conversation. These models are able to perform a myriad of tasks related to communication: how to generate all sorts of texts (articles, screenplays, email, poems), to translate between different languages with ever greater accuracy, summarize complex information in a concise and relevant, to respond to questions in a comprehensive way, and even write computer code. It is, in essence, a real “transformers words”, able to adapt to the most varied needs.
Behind this versatility, there is a mechanism that, however complex, can be conceptually simplified. We can imagine the language models as of the miraculous “completatori automatic” taken to the extreme, and machines that have learned to predict, with an accuracy disconcerting, what will be the next word in a sentence, given everything that has been written or said before. A process seemingly simple, but that requires an encyclopedic knowledge of the language, a deep awareness of its rules of grammar and syntax, and considerable skill in capturing the context and intent of communication below.
Between the language models most well-known and high-performance, stand the models of the family GPT (Generative Pre-trained Transformer) of OpenAI, in particular, GPT-3, and the more recent GPT-4, that have demonstrated the ability impressive in the generation of creative and informative. But, we can't forget BERT and LaMDA, developed by Google, that excel in the semantic analysis and understanding of the meaning of words and the relations between the sentences, making it particularly useful for tasks such as the answer to complex questions, and the classification of the text according to its tone and content.
It is crucial to note, however, that these language models are not infallible. Their knowledge is purely computational, without a real understanding of the real world and of human emotions. For this reason, they can make errors of fact, provide inaccurate answers or partial, and play unconsciously bias present in the data on which they were trained. It is therefore crucial to use these tools with critical awareness, always considering with attention to the accuracy or reliability of the information-generating and keeping in mind that the judgment of human remains, in many cases, essential.
See Beyond the Words: Models of Artificial Vision
The artificial intelligence is not limited, however, to understand and process language. Another fascinating chapter of this book in progress is represented by the models of artificial vision systems that give machines the ability to “see” and interpret the visual world, decoding the meaning of images and video.
These models are based on neural networks, complex, inspired by the functioning of the human brain, that are trained on enormous amounts of visual data (in millions of photographs, videos, drawings, etc) to recognize objects, people, places, actions, and spatial relationships. Imagine a child who learns to distinguish a cat from a dog: at the beginning can confuse you, but in the fury of watching them and'd hear him repeat the names, learn how to properly identify them. The machines, in the same way, they learn to “see” through the massive exposure data and the progressive refinement of their internal connections.
There are different types of models in computer vision, each of them is specialized in a specific task. Models classification they are trained to identify the root category of a picture (e.g., “cat”, “car”, “person”). Models detection objects go beyond identifying and localizing multiple objects within the same image (e.g., “a cat, two dogs, a bike”). Models segmentation assign to each pixel of the image, a label, defining with precision the boundaries of the objects (e.g., “the cat is in the foreground, the background is blurred”).
The practical applications of these models are numerous and pervasive. Are the “automatic pilot” of the car is autonomous, that allow the vehicles to recognize road signs, pedestrians and other vehicles. They are the “eyes” of the industrial robot, which allow the robot to inspect the products, select them or assemble them with precision. They are essential tools in medicine, to analyze x-rays, mris and other diagnostic images, helping physicians to detect early tumors or other abnormalities. And they are also the protagonists of the world of safety, facial recognition, surveillance and access control.
Among the models of machine vision most well-known are the neural networks, ResNet (Residual Network), who have achieved excellent results in the classification of images, and YOLO (You Only Look Once), that excels in the detection of objects in real-time. These are just a few examples of a research field in continuous evolution, which promises to revolutionize further our relationship with the visual world.
Creating New Worlds: Generative Models Of Multimodal
But the artificial intelligence is not limited to decode and interpret the existing world; in some cases, pushing you over, giving life to original designs that are surprising for their beauty and complexity. It is here that come into the scene, the generative models of multimodal, a type of AI that is capable of creating new content by combining different modes of expression, such as text, images, audio, and video.
Imagine an AI system that is able to describe in words a scene, and then generate an image super-realistic manner that matches that description, or vice versa, to transform an image into a piece of music fascinating. Or, create an entirely new video, starting from a script textual or conceptual idea. This is the power of generative models of multimodal, IA that is not limited to “imitate” reality, but in the reinvented, giving life to unexplored worlds and opening the door to new forms of creativity.
The generative models most popular of which are without doubt FROM the AND and Stable Diffusion, which have conquered the web with their ability to create extraordinary images from simple text based instructions, revealing an artistic potential before unthinkable for a machine. Also applications in the field of music and the audio are in rapid growth, with systems that compose original melodies, they imitate the style of famous artists, or generate realistic dialogues for movies and video games.
It is important to emphasize, however, that the advent of the generative models of multimodal raises a number of important ethical questions, and cultural. Who owns the copyright in the works created by AI? How can we distinguish an image or a video, “true” from one generated by a machine? What will be the impact of these technologies on the work of the artists, musicians and creatives in general? These are questions to which the company will need to find appropriate responses to ensure that AI is used in a responsible manner and to protect the wealth and diversity of human creativity.
Behind the Scenes of the Robots: Models for Robotics
So far we have explored models of IA that operate in the virtual world, processing data and creating digital content. But the ai has a crucial role also in the physical world, animating the robot and allowing them to interact autonomously with the environment.
The models for the robotics IA specialized that control the movements of the robot can “perceive” the world through sensors and cameras, and they shall have the ability to plan actions and make decisions in real-time. Imagine an industrial robot that needs to assemble a complex product, a vehicle autonomous guide who must navigate in an urban environment is busy, or a surgical robot that has to perform a delicate operation: all of these systems rely on models of IA to work properly.
A particularly attractive approach in the field of robotics is the Reinforcement Learning, in which robots learn by trial and error, receiving a “bonus” for the correct actions and “punishments” for the wrong ones. A system of this type, for example, can be used to train a robot to play chess, to find your way in a maze, or to perform a maneuver complex.
The AI in robotics is paving the way to a future in which machines will be more and more integrated in our daily lives, performing tasks, repetitive, dangerous or requiring great precision. But even in this context, the ethical considerations cannot be neglected. Who is responsible if a robot makes a mistake? How can we ensure that the robots are used for charitable purposes and not for malicious purposes? What will be the impact of automation on the world of work?
Predict the Unpredictable: the Predictive Models
The last of the “race” of AI, which we will explore in this article is that of the predictive models, intelligent systems able to analyze historical data and current in order to formulate predictions about future events. These are powerful tools, which find application in a variety of sectors, from finance to weather, logistics, and medicine.
The predictive models can, for example, to anticipate the trends of the stock market, predict the demand for a product, estimate the travel time of a journey, the early diagnosis of a disease or predict the behavior of a customer. They are based on complex algorithms that identify patterns, correlations and causal relationships in data, using this information to make projections for the future.
A classic example of the predictive model is represented by the ARIMA models (AutoRegressive Integrated Moving Average), which are used to analyze the time series, that is, data collected at regular intervals over time (e.g., the values of the shares on the stock exchange the day-to-day). Other predictive models exploit machine learning techniques, such as neural networks or machines, vectors support, to deal with problems of prediction and more complex.
However, it is important to use the predictive models with caution, aware of their limitations. The forecasts are not certainties, but only estimates, and the future will always be uncertain. It is therefore important to consider the margin of error, to evaluate critically the assumptions underlying the models and use them as tools to support human decisions, and not as the oracles infallible.
A Future to Explore
In this journey of discovery of the universe multiform, the IA, we have laid an overview on the different “races” of artificial intelligence, each with its own characteristics, potential, and implications. We have seen how the IA is an unstoppable force, and kaleidoscopic, which is changing the way we live, work and engage with the world, opening up new frontiers in areas as diverse.
But while we are fascinated by the ability of these intelligent machines, we cannot forget to ask ourselves fundamental questions: what are the limits of AI? How can we avoid that perpetual our prejudices? How can we use it in a responsible and transparent manner, ensuring that it is always at the service of humanity? The future of AI is to explore, and the route that we will take will depend on our choices today.
