What types of artificial intelligence do we use today, and which do not yet exist?
A clear explanation of ANI, AGI and ASI to distinguish between the artificial intelligence businesses already use and the forms that still remain in the realm of research.
We talk about artificial intelligence when a system can perform tasks that traditionally required human intelligence, such as analysing information, recognising patterns or answering questions.
This does not mean that AI thinks like a person. Most current systems have neither consciousness nor a genuine understanding of the world. They work because they process data extremely efficiently.
To understand what artificial intelligence can do today, and what it cannot yet do, it helps to distinguish between three broad concepts: ANI, AGI and ASI.
ANI: narrow artificial intelligence
ANI is the type of artificial intelligence that we already use extensively today. It is designed for a specific task and has a very clearly defined objective.
- Systems that play chess.
- Recommendation engines.
- Image or speech recognition.
- Data analysis.
- Assistants that answer questions.
In many of these tasks, ANI already far exceeds human capabilities. This is not because it reasons better, but because it can process enormous volumes of data quickly and accurately.
AGI: artificial general intelligence
AGI refers to artificial intelligence capable of operating at the same level as a human being, learning and adapting to any type of task.
It remains a subject of research and debate, and we are still a long way from seeing it applied in the real world.
ASI: artificial superintelligence
ASI would go one step further: it would be artificial intelligence that surpassed human beings in every field, not only in specific tasks but also in creativity, reasoning, strategy and complex decision-making.
For now, ASI belongs to the realm of theory, speculation and ethical debate.
The AI businesses do use today: machine learning
Most real-world business AI solutions are based on machine learning, a subfield of AI that enables systems to learn from data.
It can detect patterns, anticipate behaviour and support decision-making based on real data.
Deep learning: as data volumes grow
Within machine learning, deep learning is a subfield that uses artificial neural networks with multiple layers.
This approach is particularly effective when there are large volumes of data, patterns are complex, or the work involves natural language, images or time series.
Many current capabilities in advanced analytics, automation and language processing rely on deep learning techniques.
What does all this mean for businesses?
In today's environment, using artificial intelligence has become a key factor in competitiveness. Businesses that do not incorporate data and intelligent models into their decision-making risk being at a clear disadvantage.
Want to apply it to your business?
Let's discuss your data, processes and automation opportunities to define an initial practical use case.
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