Understanding the 4 types of Artificial Intelligence
Updated 2 years ago on July 18, 2023
How do different types of artificial intelligence mimic and replicate human performance? It is this question that determines how we categorize these four main types of AI.
Did you know that there are four different types of artificial intelligence?
All four of these types are not the same: Some are far more advanced than others. Some of these types of AI cannot even be scientifically imagined right now. According to the current classification system, there are four main types of AI: reactive, limited memory, theory-of-mind, and self-aware.
Let's look at each type in more detail.
Reactive artificial intelligence
The most basic type of artificial intelligence is reactive AI, which is programmed to produce a predictable result based on the input data it receives. Reactive machines always react to identical situations in exactly the same way, they are not capable of learning actions, nor can they visualize the past or future.
Examples of reactive AI are:
- Deep Blue, IBM's chess supercomputer that defeated world champion Garry Kasparov
- Spam filters for our email that keep promotions and phishing attempts out of inboxes
- Netflix's recommendation system
Reactive AI has been a huge step forward in the history of artificial intelligence, but these types of AI cannot function beyond the tasks for which they were originally created. This makes them inherently limited and in need of improvement. On this basis, scientists have developed the following type of AI.
Limited AI memory
AI with limited memory learns from the past and accumulates empirical knowledge by observing actions or data. This type of AI uses historical observational data combined with programmed information to predict and perform complex classification tasks. It is currently the most common type of AI.
For example, autonomous cars use artificial intelligence with limited memory to observe the speed and direction of other cars, which helps them "read the road" and adjust if necessary. This process of understanding and interpreting incoming data makes them safer on the road.
However, AI with limited memory, as its name suggests, is still limited. The information that autonomous cars work with is fleeting and is not stored in the car's long-term memory.
AI Theory of Mind
Want to have a meaningful conversation with an emotionally intelligent robot that looks and sounds like a real person? It's just around the corner with the advent of artificial intelligence.
With this type of AI, machines will acquire a decision-making ability similar to that of humans. Machines with theoretical AI will be able to understand and remember emotions and then adjust behavior depending on these emotions when interacting with people.
There are still a number of obstacles to creating AI with theory of mind because the process of changing behavior based on rapidly changing emotions in human communication is very fluid. It is difficult to mimic as we try to create more and more emotionally intelligent machines.
Nevertheless, progress is being made. The Kismet robot head, developed by Professor Cynthia Breazeal, can recognize emotional cues in human faces and reproduce those emotions on its own face. Sophia, a humanoid robot developed by Hong Kong-based Hanson Robotics, can recognize faces and respond to interactions with its own facial expressions.
Autonomous artificial intelligence
The most advanced type of artificial intelligence is self-conscious AI. When machines can become aware of their own emotions as well as the emotions of those around them, they will have a level of consciousness and intelligence similar to humans. This type of AI will also have desires, needs, and emotions.
Machines with this type of AI will be self-aware of their internal emotions and mental states. They will be able to make inferences (e.g., "I'm angry because someone cut me off in traffic") that are impossible for other types of AI.
We haven't developed such sophisticated artificial intelligence yet, we don't have the hardware and algorithms to support it.
Further development of artificial intelligence
Will we continue to push the boundaries of AI and create a fifth type? How much progress will we make in the next decade toward theory of mind and self-conscious AI? Could there be a super-intelligent AI that even surpasses current human intelligence?
Time will tell, but understanding the differences between the different types of AI will help you make sense of its advances as science continues to push the boundaries.
More Questions
The Milvus Python client provides a search method that retrieves a list of vectors, which allows for a multi-vector query. Weaviate's Python client only allows for a single vector search. As in the indexing time analysis, both engines show similar query behavior.
Build powerful machine learning applications and manage massive vector data with Milvus. Searching data by easily definable criteria, such as querying a movie database by actor, director, genre, or release date, is easy.
Job Outlook for Artificial Intelligence Engineers Jobs for Artificial Intelligence Engineers are projected to grow 21% between 2021 and 2031, significantly higher than the average for all occupations (5%). AI engineers typically work for companies to help them improve their products, software, operations, and delivery.
Although DALL-E 2 is the best known in the field of AI image generation, it might make sense to try Stable Diffusion first: it has a free trial, it's cheaper, it's more powerful, and it has wider usage rights. If you get completely sidetracked, you can also use it to develop your own generative AI.
Stable Diffusion is a hidden diffusion model, a type of deep generative artificial neural network. Its code and model weights are published in the public domain, and it can run on most consumer hardware equipped with a modest GPU with at least 8 GB of VRAM.
The Stable Diffusion model provides the following benefits to developers interested in building applications based on it: Generation of new data: The Stable Diffusion model can be used to generate new data similar to the original training data, which proves useful when creating new images, text, or sounds.
Related Topics
Let's get in touch!
Please feel free to send us a message through the contact form.
Drop us a line at request@nosota.com
/
Give us a call over
nosota.skype