The artificial intelligence is not only ChatGPT: discover the 5 types of AI that are transforming the world
Introduction: The AI is Much More than ChatGPT
ChatGPT is on everyone's lips, but it represents only the tip of the iceberg of a technology ecosystem much more vast and complex.
The artificial intelligence is not a single monolithic entity, but a constellation of specialized models, each with unique abilities that are revolutionizing different industries, from medicine to robotics, from digital creativity to guide autonomous.
While ChatGPT excels in the conversation, there are AI that can “see” the world, who create works of art, the systems that control robots and algorithms that predict the future. A universe multifaceted that it is worth exploring to really understand the impact of this technology on our daily life.
What is Artificial Intelligence and How you Rank
Definition and Context
Theartificial intelligence it is a field of computer science that develops systems capable of performing tasks that would require human intelligence. But there is no “AI” universal: each system is specialized for specific functions, as it explains in detail theObservatory Artificial Intelligence of the Politecnico di Milano.
We can classify the models of IA in five main categories:
- Language Models (as ChatGPT)
- Models of Artificial Vision
- Generative Models Of Multimodal
- Models for Robotics
- Predictive Models
Each category has developed approaches and different technologies to solve specific problems, creating an ecosystem is rich and diverse.
The 5 Types of Artificial Intelligence that Are Changing the World
1. The Lords of the Word: Language Models
The language models are those that we know, the better thanks to ChatGPT, but the universe is much broader.
How They Work
These systems are trained on enormous amounts of text, and to learn to predict the next word in a sentence with impressive accuracy. As discussed in the our guide on the tool IA for beginnersit's like having a “completatore automatic” taken to the extreme, capable of:
- Generate texts of every type
- Translate between different languages
- To summarize complex information
- Writing computer code
- Respond to complex questions
According to a search Stanford on large language models, these systems are transforming not only the technology but the whole society.
The Protagonists of the Sector
GPT-4 (OpenAI): The most famous, excels in creative generation, and natural conversation.
BERT (Google): Specialized in semantic analysis and understanding of the meaning.
Claude (Anthropic): Focused on security and responses to be accurate.
LaMDA (Google): Optimized for conversations natural and engaging.
Limitations and Considerations
Despite the ability to impressive, these models have significant challenges that we analyzed in our exploration on theethics of artificial intelligence:
- They do not have a true understanding of the real world
- They can provide information that is inaccurate (the phenomenon of “hallucinations”)
- Reproduce bias present in the data training
- Always require critical evaluation of human
As highlighted in theAI Index Report 2024 Stanfordthe need of supervision human remains fundamental in spite of the technological advances.
2. See Beyond the Words: Models of Artificial Vision
The models of artificial vision, give machines the ability to “see” and interpret the visual world.
Types and Specializations
ClassificationIdentify the main category of an image (“cat”, “car”, “person”).
Detection Objects: Identify and localize multiple objects in the same image.
Segmentation: Define with precision the boundaries of the objects, by assigning labels to each pixel.
Practical Applications
Machine vision applications, ranging in increasingly broad sectors, as evidenced by a McKinsey report on the state of the IA:
- Car autonomousRecognition of road signs, pedestrians, other vehicles, and for the the mobility of the future
- MedicineAnalysis of x-rays and magnetic resonance, as discussed in our article onAI medical
- SecuritySystems : surveillance and facial recognition with ethical implications
- Industry: Quality control, and assembly robotic automated manufacturing
Reference models
ResNet: Excellent for the classification of images with very high accuracy.
YOLO (You Only Look Once): A Leader in the detection objects in real time.
Detectron2 (Meta): Advanced system for segmentation and detection.
3. Creating New Worlds: Generative Models Of Multimodal
These models do not limit themselves to interpret the reality: the reinvented, creating original content that combine text, images, audio, and video.
Creative Skills
The generative models of multimodal can:
- Create images from text descriptions
- Generate original music
- Produce videos by script
- Combine different modes of expression
The Protagonists of Creativity IA
FROM -, AND 3 (OpenAI): Generates stunning images from text descriptions.
Midjourney: Specializing in artistic creations and concept design.
Stable Diffusion: Open source, allows advanced control of the generation.
Sora (OpenAI): Generate realistic video from the prompt text.
The Ethical implications and Cultural
The advent of these models raises crucial questions that we explored in our focus on AI and creativity and copyright:
- Who owns the copyright in the works created by AI?
- How to distinguish the contents of “real” than those generated?
- What is the impact on the work of the creative team?
- How to prevent the use for misinformation and fake news?
A study of the University of Oxford suggests that the AI generative could radically transform the market for creative work in the coming years.
4. Behind the Scenes of the Robots: Models for Robotics
The models for the robotics allow machines to physically interact with the real world.
Key Features
- Motor control: Coordinate movements precise
- Perception of the environment: Interpret data from sensors and cameras
- Planning: Define sequences of actions to achieve goals
- Learning: Improve performance through experience
Learning approaches
Reinforcement LearningRobots learn by trial and error, receiving rewards or penalties.
Imitation: They learn by observing and replicating human actions.
Simulation: Train in virtual environments before deployment real.
Emerging Applications
- Robotic surgery: Operations minimally invasive with pinpoint accuracy
- Logistics: Automated management of warehouses and delivery
- Domestic assistance: Robots that help in day to day activities
- Space exploration: Rover autonomy for planetary missions
5. Predict the Unpredictable: the Predictive Models
Predictive models analyze historical data to predict future events.
Primary Methodologies
The analysis of time SeriesIdentify patterns in data collected over time.
Machine Learning Predictive: Use complex algorithms to forecast the multivariate analyses.
Deep Learning: Neural networks deep to pattern complex and non-linear.
Areas of Application
The impact of the predictive models extends to critical sectors of the economy moderna, as documented by the The World Economic Forum:
- Finance: Market forecasts and risk management for banks smart
- Meteorology: Weather forecasts more accurate for theIA and climate
- Health: Early diagnosis and preventive medicine
- Supply Chain: Optimization of the logistics for small businesses
Limits and Liability
It is critical to remember that:
- The forecasts are estimates, not certainties
- The margin of error should always be considered
- The human judgment remains essential
- Models that can perpetuate bias in the historical data
Practical Examples: How These Patterns Work Together
Case Study: Car Autonomous
A self-guided autonomous integrates various types of IA:
- Artificial vision: Recognizes the streets, signals, pedestrians
- Predictive models: Anticipate the behaviors of the traffic
- Robotics: Controls the steering, brake, accelerator
- Language: Interacts with passengers
Case Study: Medical Assistant IA
A system of medical diagnosis combines:
- Vision: Analyze diagnostic images
- Language: Prepares medical records and symptoms
- Predictive: Calculate the probability of diagnosis
- GenerativeSuggests treatment plans
Key points to Remember
✅ The AI is not monolithic: There are systems specialized for different tasks
✅ Complementarity: The different models work best when integrated
✅ The evolution continues: Each category is advancing rapidly
✅ A cross-sectional impact- The AI is transforming every sector of the economy
✅ Human responsibilitySupervision critique remains essential
Frequently Asked Questions
What type of AI will have a greater impact in the future?
There is no single “winner”. The integration of different models (linguistic, visual, predictive) will create more powerful systems. The future belongs to the multimode systems that combine different capacity.
It is possible for an AI to become the “general” as the human intelligence?
The Artificial Intelligence-General (AGI) remains a long-term goal. Currently, each system excels in specific tasks but there is a lack of cognitive flexibility, the human.
How can we prepare ourselves professionally to this panorama, IA?
Develop skills that are complementary to the IA: creativity, critical thinking, emotional intelligence. As suggested in our article on the future of work with AI, learn how to collaborate with IA rather than compete against them. The report IBM “AI and the Future of Work” recommends an approach of continuous learning to stay competitive.
These models will completely replace the human work?
It is more likely to transform the work rather than completely replace it. Born new professions, while others evolve to integrate IA.
How can we ensure the ethical use of these technologies?
Serving regulations appropriate, shared ethical principles, transparency in the algorithms and training for developers and end users. As we explore in our study about the who decides the rules of the IAthe responsibility is collective. L’AI Act for europe represents an important first step towards global regulation.
Conclusion: A Future to Build Together
The universe of artificial intelligence goes well beyond ChatGPT, embracing an ecosystem is rich and diverse, specialized technologies. From the linguistic models that have mastered the words to the AI that they see the world, from the systems creative that generate art to robots that physically interact with the environment, up to the predictive algorithms that attempt to reveal the future.
Each category IA brings extraordinary opportunities and ethical challenges significant. Our task is not only to understand these technologies, but to guide their development to applications that improve human life, uphold our values and to build a future with more sustainable and equitable.
The AI is not an inevitable fate, but a human construction. The choices that we make today – in terms of research, regulatory, education, and application – determine the type of future that we want to build together with these intelligent machines.
The road has just begun, and it will be our collective commitment to determine where it will lead us to a fascinating journey into the universe of artificial intelligence.
This article is part of the series “Understanding the IA” de The Compass of the IA. For further information on the topics covered, see our related articles on theethics of artificial intelligence and on tool of IA for beginners.