The Accelerated Death of “AI”
Another tech and business buzzword is about to be retired:
Believe it or not, the excessive use of the appellation Artificial Intelligence is quickly vaporizing its meaning and weight. The tech industry has a long history of killing terms and concepts too soon by using them indiscriminately. Big Data, Blockchain, Cloud, and others were also victims of early and excessive advertisement. Marketers are once again getting a ride on the AI hype, already attributing it to anything they can (in a desperate attempt to claim “we also have AI in our portfolio”). That type of use stretches the perception of the concept so thin in users’ minds that we will have to move away from it. More meaningful definitions of how AI is applied to products and services will take its place from now on as a necessity. Recently, like an act of desperation, a new moniker has surged in “weak AI.” Weak artificial intelligence -also called narrow AI- is a type of artificial intelligence limited to a specific or narrow area. However, weak AI will not fly commercially for obvious reasons.
Thus, we must go deeper into those definitions to distinguish what is offered. Let’s start by understanding and correctly defining the four types of artificial intelligence.
- Reactive Machines: These AIs respond to specific inputs with specific outputs, like playing chess. They can’t learn or use past experiences. Think of IBM’s Deep Blue!
- Limited Memory: These AIs use past data to make decisions. For instance, self-driving cars that adjust actions based on traffic patterns.
- Theory of Mind: This is the future of AI! It refers to machines that can understand and display human emotions.
- Self-aware AI: The pinnacle of AI evolution. Machines that have their own consciousness. It’s purely theoretical for now.
Then, we need to understand and correctly apply the names of the technologies used in products and services when commercially describing them. Here is a summary of tech with brief explanations to help.
- Machine Learning (ML): A subset of AI that allows systems to learn from data and improve over time without being explicitly programmed.
- Deep Learning: A type of ML based on artificial neural networks, particularly useful for complex tasks like image and voice recognition.
- Neural Networks: Algorithms designed to recognize patterns; they interpret sensory data through machine perception, labeling, and clustering.
- Natural Language Processing (NLP): Enables machines to understand and respond to human language, driving applications like chatbots and translation services.
- Computer Vision: Allows machines to interpret and make decisions based on visual data (images, videos).
- Robotics Process Automation (RPA): Uses AI to automate rule-based tasks in business processes.
- Reinforcement Learning: A type of ML where agents learn how to behave in an environment by performing actions and receiving rewards.
- Generative Adversarial Networks (GANs): A class of ML systems where two neural networks (a generator and a discriminator), work against each other to improve the generation of new data.
- Speech Recognition: Converts spoken language into text, powering voice assistants like Siri and Alexa.
- Expert Systems: Computer systems that emulate the decision-making abilities of human experts in specific domains.
- Fuzzy Logic Systems: A computing approach based on “degrees of truth” rather than the usual true/false binary approach used in various control systems.
- Evolutionary Computation: Algorithms inspired by biological evolution, like mutation, crossover, etc., are often used for optimization problems.
- Swarm Intelligence: Algorithms based on the collective behavior of decentralized and self-organized systems, such as ant colonies or bird flocking.
These technologies form the foundation of AI, driving the innovations and applications we see today and paving the way for the future of artificial intelligence. So please, marketers and advertisers, when you develop a new catalog, write a social media post, or write a new Ad, try to be more specific about the types of AI and technologies involved. That way, we all collectively will help educate our consumer market.
Originally published at https://sentientboss.substack.com.
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