Types of Artificial Intelligence That Are Shaping the Future

Can you believe that what used to be science fiction is now totally real and changing our lives faster than lightning?

As artificial intelligence (AI) is transforming humanity, a lot of questions arise: What exactly is it? Will it take over our jobs? And, hey, what would Sarah Connor say if she could see how fast we’re becoming best buds with Skynet?

Such questions and concerns are understandable, and it’s no wonder we sometimes find ourselves mixing up fiction with reality. It’s like we’ve stepped into a sci-fi movie, and AI is the superstar who is stealing the show.

We’ve seen AI-powered robots, virtual assistants with personality, and machines doing things we never thought possible. The market size of AI was nearly 100 billion U.S. dollars in 2021 and is expected to grow twentyfold by 2030, reaching two trillion U.S. dollars!

It is easy to see how we might get carried away with the idea that artificial intelligence might take over the world Terminator-style, and turn us into mere spectators.

But let’s take a step back and take a look at where AI stands in 2023. Because the more we learn about AI, the less intimidating it becomes. Keep reading!

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What Is Artificial Intelligence?

Artificial Intelligence is about teaching computers to think and learn, similar to how we do as humans.

Artificial Intelligence builds intelligent machines using vast volumes of data. Systems learn from past experiences and knowledge. As a result, intelligent machines are created that can perform human-like tasks faster and more effectively, without requiring human intervention.

The core of AI consists of two learning forms: Machine Learning and Deep Learning.

Machine Learning

Machine Learning is a subset of Artificial Intelligence (AI) that focuses on enabling computers to learn from data without explicit programming.

It’s like teaching computers to learn from examples and make smart guesses.
It empowers machines to identify patterns, make predictions, and improve performance over time. Instead of following static instructions, Machine Learning algorithms use data to refine their models and adapt to new information.

Deep Learning

Deep Learning is a specialized form of Machine Learning that mimics the human brain’s neural networks to process and understand complex data. It is like having a super smartrobot that can understand and solve really complex problems. It can handle vast amounts of unstructured data and automatically extract high-level features.

Deep Learning has revolutionized various AI applications, such as image and speech recognition, natural language processing, and autonomous vehicles. Its ability to learn from raw data without extensive manual feature engineering has made it a groundbreaking technology, driving significant advancements in the AI domain.

Types of Artificial Intelligence

In general, AI can be divided into two groups based on capabilities and functionalities.

Artificial Intelligence Based on Capabilities

Narrow AI (Weak AI)

Narrow AI, also known as Weak AI, refers to AI systems designed for specific tasks and applications. They excel in their dedicated area but lack the ability to operate beyond their predefined scope. Common examples of Narrow AI include:

• Virtual Assistants. Popular virtual assistants like Siri, Alexa, or Google Assistant are perfect examples of Narrow AI. They can answer questions, set reminders, play music, and control smart home devices, but they are not capable of understanding complex human emotions.
• Recommendation Systems. Platforms like Netflix, Amazon, or Spotify use recommendation systems powered by Narrow AI to suggest content based on user preferences and behaviors.
• Image and Speech Recognition. AI-driven image recognition tools categorize and analyze images, while speech recognition systems convert spoken language into text. However, they do not possess a deeper comprehension of context.

General AI (Strong AI)

General AI, also known as Strong AI or AGI (Artificial General Intelligence), represents the next level of AI.

Unlike Narrow AI, it aims to possess human-like cognitive abilities, enabling it to learn, understand, and perform a wide range of intellectual tasks. While we haven’t achieved this level of AI yet, its potential is exciting. Fictional examples of General AI are Wall-E, Marvel’s Vision and The Terminator.

Some characteristics of General AI include:

• Adaptability. A General AI system could adapt to different tasks and scenarios, just like humans can tackle various challenges.
• Learning. It would have the ability to learn from experiences, making it better and more efficient over time, similar to human learning.
• Creativity. General AI could think creatively, generate new ideas, and find innovative solutions to complex problems.

One of the strongest attempts to build Strong AI was Fujitsu’s K computer (2011-2019). It took 40 minutes to simulate a single second of neural activity.

Super AI (Strong AI)

Super AI is anticipated to exceed human intelligence, outperforming humans in any task.

The idea of artificial superintelligence envisions AI evolving to closely resemble human emotions and experiences, not merely comprehending them but also having its own emotions, needs, beliefs, and desires.

Although it remains hypothetical, some attributes essential to super AI are independent thinking and puzzle-solving as well as autonomous judgment and decision-making.

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Artificial Intelligence Based on Functionalities

As you can see, for now, the Terminator scenario remains in the realm of science fiction, and it will take time to bring Vision to life.

Let’s look at artificial intelligence categorized by functionality:

Reactive Machines

These AI systems are the most basic of the group and have no set memory. They cannot rely on past data to make present decisions.Reactive machines are also task specific and only respond to the current input they receive.

Examples of reactive machines in AI include:

• Chess-Playing AI. Traditional chess-playing programs, like IBM’s Deep Blue. They rely on predefined rules and heuristics to evaluate board positions and make the best move without any knowledge from previous games.
• Expert Systems. Expert systems are AI systems designed to provide expert-level advice or decision-making, without learning from new data.
• Rule-Based Chat bots. Some simple chat bots follow predefined rules to provide responses to specific user queries. They don’t learn from conversations or user interactions but rely on pre-programmed responses.
• Automated Manufacturing Systems. In industrial automation, reactive machines are used to control certain processes based on predetermined rules and inputs, without any learning capabilities.

Limited Memory

Limited memory is exactly that,short-term memory. It restricts the amount of historical data or past experiences that an AI system can store and use for decision making. Limited memory AI models have constraints on the volume of information they can retain and utilize.

Examples of limited memory AI systems include:

• Online Learning Algorithms. These algorithms continuously learn from the most recent data and have limited memory of historical observations.
• Real-Time Systems. Some AI systems in real-time applications, like autonomous vehicles or robotic control, may have limited memory to ensure quick responses and reduce computational complexity. They prioritize recent data over historical data to make immediate decisions.
• Streaming Data Analysis. AI systems that analyze streaming data, such as social media feeds or sensor data, may have limited memory to handle the continuous flow of information efficiently.

Theory of Mind

The theory of mind refers to the ability of an artificial intelligence system to understand and model the mental states of humans or other AI systems. It involves the capacity to infer and predict what others might be thinking, feeling, or intending to do based on their observed behavior and the context of the situation.

Sophia from Hanson Robotics exemplifies the theory of mind through the integration of cameras in her eyes, along with advanced computer algorithms, enabling her to perceive her surroundings. This sophisticated setup enables Sophia to maintain eye contact, identify individuals, and track faces with remarkable precision.

Self Awareness

Self-awareness is the ability of an artificial intelligence system to recognize its own existence, internal state, and the distinction between itself and the external environment.

Examples of self-awareness in AI are relatively limited, as true self-awareness remains a hypothetical. However, some AI systems exhibit certain aspects of self-awareness in specialized contexts:

• Chatbots with Basic Self-Reference. Some chatbots can respond to queries about themselves or their capabilities. For example, if you ask a chatbot, “Who are you?”, it might respond with a pre-programmed answer like “I am an AI language model designed to assist you with information.”
• Reinforcement Learning Agents. In certain scenarios, reinforcement learning agents can develop a limited form of self-awareness. For instance, in simulations or games, an AI agent may learn to understand the effects of its actions on its virtual environment to optimize its decision-making.
• Robotics with Self-Monitoring. Some advanced robots have the ability to monitor their own internal states, such as battery levels, temperature, or damage to its systems. They can use this information to adjust their behavior or request maintenance when needed.

It’s important to note that these examples represent only rudimentary forms of self-awareness in AI. True self-awareness, akin to human consciousness, remains a significant scientific and philosophical challenge, and it is yet to be achieved in artificial intelligence systems.

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Wrap Up

While AI has certainly come a long way, it still has a way to go. Currently it’s more like having a computer-powered sidekick that can make our lives easier, more exciting, and maybe even a bit magical.

Sure, there are valid concerns about AI’s impact on jobs and the potential harm it could cause. It’s safe to say we do need to stay vigilant and address any ethical challenges that arise. But at the same time, let’s remember that we are in charge and we still get to decide how we shape this AI-powered future, and make it a fantastic adventure for everyone involved.