AI-generated summary
José Hernández-Orallo, a professor at the Universidad de Valencia, highlights the main applications of Artificial Intelligence (AI), including knowledge representation through ontologies and logic, temporary and probabilistic planning, machine learning methods like linear models and neural networks, pattern recognition, computer vision (such as facial recognition and biometrics), and natural language processing tasks such as speech recognition, summarization, translation, and sentiment analysis. These diverse capabilities showcase the broad scope of AI technologies impacting various domains.
Kai-Fu Lee, a prominent AI expert and investor, categorizes the evolution of AI into four waves. The first wave, the “Internet of AI,” leverages vast online data to create personalized content and enhance user engagement. The second wave, “Business Intelligence,” uses AI to discover hidden correlations beyond human linear logic, outperforming experts. The third wave, “Intelligent Perception,” involves AI technologies with sensory capabilities like voice interfaces and computer vision, enabling new applications through novel data collection. The fourth and most advanced wave, “Autonomous AI,” integrates the previous waves to develop machines capable of intuitive movement and interaction with the environment, exemplified by autonomous vehicles.
Together, these waves illustrate AI’s transformative potential across industries and daily life, showcasing both current applications and future possibilities for intelligent, responsive machines.
What are the main technological areas of Artificial Intelligence and how has been the evolution of its development.
José Hernández-Orallo, is a professor at the Universidad de Valencia and participated in the HUMAINT seminar of the European Commission’s Joint Research Centre (JRC-CAS). He outlines the main applications of Artificial Intelligence:
- Knowledge representation through ontologies, different types of logic or probable inference (X is a bird, so X can fly)
- Temporary programming and planning as well as probabilistic planning
- Machine learning: linear models, decision trees, neural networks
- Pattern recognition
- Computer vision, facial recognition, biometrics
- Language processing: speech recognition, natural language generation, summarization, retrieval, translation, tagging, sentiment analysis, etc.
Artificial Intelligence waves
The Chinese investor and AI guru Kai-Fu Lee places the development of this set of technologies into four different waves:
1. The Internet of AI: the first chapter of implementation, fed by the large amount of data that flows through the web. This data creates detailed profiles of our personalities, habits, demands and desires–the perfect recipe for the creation of personalized content to keep us on a given platform or maximize revenue or profits.
2. Business Intelligence: AI that can explore hidden correlations that escape our linear, cause-and-effect logic. These technologies can perform better than even the most seasoned experts.
3. Intelligent perception: technology that can now see, listen and use thousands of other senses. It collects data that has never been collected before, which is used to create new applications. Intelligent devices and sensors, such as voice interfaces or computer vision applications, are a few examples.
4. Autonomous AI: “the most monumental as well as complex wave,” according to Lee. This wave includes the three prior ones. The results will be machines that can feel and respond to the world around them. They will be able to move intuitively and handle objects with the same ease as a human being. For now, autonomous vehicles are the most cutting-edge example of this technology.
A multitude of possible applications stem for AI’s various capabilities. What is already possible? How are we using and incorporating it, both actively and passively, into our businesses and everyday lives?