Early versions of the AI applications we see most often today were based on traditional machine learning models. These models are based on learning algorithms developed and maintained by data scientists. In other words, traditional machine learning models require human intervention to process new information and perform new tasks that go beyond the scope of initial training. For example, Apple released Siri as a feature of its iOS in 2011. This first version of Siri was trained to understand a very specific set of instructions and questions.To expand Siri’s knowledge base and capabilities, human intervention was required.
However, AI capabilities have continued to advance since the groundbreaking development of artificial neural networks in 2012, which enable machines to learn by amplifying and simulating the way the human brain processes information. Unlike basic machine learning models, deep learning models enable AI applications to learn, perform new tasks that require human intelligence, adopt new behaviors, and make decisions without human intervention. As a result, deep learning has enabled
task automation, content generation, predictive maintenance, and other capabilities across industries.
Thanks to deep learning and other advances, the field of artificial intelligence is constantly and rapidly evolving.Our collective understanding of realized AI and theoretical AI is constantly evolving, meaning that AI categories and terminology can vary (and overlap) depending on the source. However, the types of AI can be broadly understood by examining the two categories they comprise: AI capabilities and functionality.
Three types of skill-based artificial intelligence
1. Narrow Artificial AI
Narrow AI, also called weak AI, which we call Narrow AI, is the only type of AI that exists today. Every other form of artificial intelligence is theoretical.It can be trained to perform a single or limited task, often much faster and better than the human mind can. However, it cannot act outside of its defined mission. Instead, it focuses on a single subset of cognitive abilities and progress along that spectrum. Siri, Amazon Alexa and IBM Watson are examples of narrow artificial intelligence. OpenAI’s ChatGPT is also considered a narrow form of AI because it is limited to a single task:text chat.
2. AI with limited memory Unlike reactive AI, this form of AI can remember past events and outcomes and track specific objects or situations over time. AI with limited memory can use past and current data to decide which course of action is most likely to achieve the desired outcome. Although AI with limited memory can use past data for a certain period of time, it cannot store it in a library of past experiences for use for a longer period of time. Because it learns from more data, the AI
‘s limited memory can improve performance.
examples of artificial intelligence with limited memory
Generative AI: Generative AI tools like ChatGPT, Bard, and DeepAI leverage the capabilities of memory-constrained AI to predict the next word, phrase, or visual element in the generated content.
virtual assistants and chatbots: Siri, Alexa, Google Assistant, Cortana, and IBM Watson Assistant combine natural language processing (NLP) and AI with limited memory to understand questions and requests, take appropriate action, and create responses.
Self-driving cars: Self-driving vehicles use artificial intelligence with limited memory to understand the world around them in real time and make informed decisions about when to increase speed, brake, turn, etc.
3. AI theory of mind
Theory of Mind Artificial intelligence is a functional class of artificialintelligence that falls under artificial general intelligence. Although it is currently an unrealized form of artificial intelligence, AI uses its theory of mind capabilities to understand the thoughts and emotions of other beings. This understanding can impact how AI interacts with those around it. In theory, this would allow AI to simulate human-like relationships.
4. Self-aware artificial intelligence
Self-Aware AI is a kind of functional AI course for applications with super AI capabilities. Like mental AI theory, self-aware AI is strictly theoretical. If thisgoal is ever achieved, he will be able to understand his own internal states and characteristics as well as human emotions and thoughts. It would also have its own emotions, needs and beliefs.
Emotion AI is an AI-based theory of mind currently in development. Scientists hope it can analyze voices, images and other types of data to appropriately detect, simulate, monitor and respond to people on an emotional level. So far, emotional AI is unable to understand and respond to human emotions.
Further possibilities and practical applications of AI technology
Artificial vision
narrow computer vision AI applications can be trained to interpret and analyze the visual world. This allows intelligent machines to identify and classify objects in images and videos.
Applications of computer vision include:
Image Recognition and Classification
Object Detection
Object tracking
facial recognition
content-based image search
Computer vision is essential in use cases where AI machines interact with and traverse the physical world around them. Examples include cars and autonomous machines moving around warehouses and other environments.
robotics
robots used in industrial applications can use narrow AI to perform routine and repetitive tasks, including material handling, assembly and quality control. In healthcare, robots equipped with narrow AI can help surgeons monitor vital signs and identify potential problems during procedures. Agricultural machines can independently carry out pruning, moving, thinning, sowing and spraying work. Smart home devices like the iRobot Roomba can navigate your home using computer vision and use
data stored in memory to understand work progress.
expert systems
expert systems with limited artificial intelligence capabilities can be trained inside the organism to mimic human decision-making and use their expertise to solve complex problems.These systems can evaluate large amounts of data to identify trends and patterns necessary for decision making. They can also help companies predict future events and understand why past events occurred.
AI at IBM
IBM has been a pioneer in the field of artificial intelligence since itsinception, contributing to one breakthrough after another in the field. IBM recently released a major update to its cloud-based generative artificialintelligence platform called Watsonx. IBM Watsonx.ai brings together new generative AI capabilities based on core models and traditional machine learning in a powerful study that covers the entire AI lifecycle. watsonx.aienables data scientists to build, train, and deploy machine learning models in a collaborative studio environment.