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7 Types Of Artificial Intelligence

Mon, 15 Jul 2024 07:14:21 +0000

Understanding the 7 Types of Artificial Intelligence

As we have already explain Artificial intelligence many tie in our previous blogs https://devopscurry.com/ai-and-innovation/ . Now we will explain there types in brief as mention below:

When you look into classifying artificial intelligence, you need to consider two parameters…

AI capabilities, and
AI functionalities

I know what you’re going to say – aren’t they the same thing?

While it’s true that both are quite similar, this is how they are classified globally. Maybe there isn’t a better way to categorize AI. If you read about AI classifications from other sources, you might question whether the categories are truly different. I certainly did. So, to help you understand this classification, here’s what I suggest:

First, go through all the types of AI and understand them individually. Then, read the section titled “Capabilities & Functionalities: Where to Draw the Line?” that I’ve written especially for you. Hopefully, it will justify the classification. Let’s start with AI based on their capabilities…

3 Types of AI based on capabilities

Image Credit:https://www.walkme.com/blog/types-of-ai/

Narrow AI

As the name suggests, Artificial Narrow Intelligence or Narrow AI can perform only within a very narrow range of tasks. These tasks can be as simple as language translation or as complex as operating self-driving cars. Either way, they are restricted to the set of functions they are trained for.

For example, Google Assistant can tell you about the weather and help you set alarms. It can make phone calls for you and even crack some cliche jokes. But it cannot cross its boundaries and write a song for you. Not unless it is trained to do so. At most, it can provide you with some articles from the internet about how to write a song on your own but that’s it. It completely lacks the ability to learn and do anything that it is not trained for.

Hence, because of its limited functionality, Narrow AI is also called Weak AI.

Apple’s Siri and Amazon’s Alexa are some similar examples of Narrow AI technology. Surprisingly, ChatGPT is also considered a part of this category, as it’s limited to text-based chats only.

General AI

Artificial General Intelligence (AGI) or General AI refers to a humanistic AI technology that possesses cognitive abilities similar to that of humans. Unlike Narrow AI, General AI does not totally depend on the data it is trained on but can learn to perform newer tasks as and when required. This makes it more flexible and capable than Narrow AI, giving it another name, Strong AI. However, as fantastic as it sounds, AGI is still a theoretical concept and a dream goal for AI researchers.

Super AI

If General AI was human, then Super AI is most definitely superhuman.

Artificial Superintelligence (ASI) or Super AI is the most advanced form of AI that does not match but surpasses human capabilities. It is far better at doing anything that a human can do and much more. It can think quicker, sense better, and understand more deeply than humans ever can. It can also perceive the emotions of other humans and even create innovations that were never possible through human efforts.

But yet again, Super AI exists all in theory. Though, once General AI is achieved, Super AI may not remain as far of a possibility as it seems now.

4 Types of AI Based on Functionalities

Reactive Machines

Reactive machine AI is the earliest and most basic form of artificial intelligence. It is called ‘reactive’ because it can easily ‘react’ to immediate queries like recommending movies based on your watch history or filtering out spam emails from your inbox. However, it lacks memory and cannot use past experiences or interactions to provide personalized responses.

The chess match between Garry Kimovich Kasparov, a Russian chess grandmaster, and IBM’s Deep Blue, a reactive AI technology, is one of the most historic events showing the reactive AI’s level of competence. In 1997, Garry became the first world champion to be defeated by artificial intelligence. Deep Blue was a reactive AI developed by IBM (International Business Machine Corporation) using 32 processors and could evaluate 200 million chess positions per second. However, it could not store the memory of previous matches and played based only on current situations.

Lastly, I’d like to say that reactive machine AI lives more in the present, sometimes just too much, you see.

Limited Memory AI

Unlike reactive AI, Limited Memory AI can temporarily store past data and use it to make predictions and informed decisions. This past data is stored for only as long as it is required, after which it is either updated or discarded.

Moreover, it is the most widely used AI model in today’s world. Some real-life examples of this type of AI are Chatbots and AI virtual assistants which utilize deep learning to generate human-like responses. Self-driving cars like Tesla’s autopilot also use this model to store data about nearby cars and obstacles to make quicker decisions on the road.

Theory of Mind

Theory of Mind (ToM) is originally a concept of psychology which according to Wikipedia ”…refers to the capacity to understand other people by ascribing mental states to them…People utilize a theory of mind when analyzing, judging, and inferring others’ behaviors.”

When integrated with artificial intelligence, it will allow AI to perceive complex human emotions and intent to respond in the best human-like manner. This empathetic AI model will be able to have effective social interactions and respond to emotional cues as well.

However, the practical implementation of this integration remains largely theoretical.

Self-aware AI

Today, the only criterion that can consistently distinguish humans from machines is consciousness. However, it may not be so once self-aware AI comes into the picture.

A Self-aware AI would possess self-awareness which is defined as the “conscious knowledge of one’s own character and feelings” by Google. This self-awareness will enable it to have its own belief systems and ideology. It will be just like a super-intelligent humanoid with far more capabilities than humans. But as fascinating as it sounds, it’s quite terrifying.

A self-aware AI would mean that it will no longer stay under human control. This independence along with their extreme capabilities can make them dangerously unpredictable. Plus there’s also no guarantee they will fit into our idea of morality and ethics. Who knows they might become the robot in sci-fi movies that tries to take over the world because it thinks humans are useless creatures.

That said, it’s quite fortunate that Super AI is still a purely hypothetical idea that is light years away in future.

Capabilities & Functionalities: Where to draw the line?

Now that you know about the different types of AI, let’s try to make sense of how they are classified…

First is capability which means potential. Here it refers to how capable an AI is in comparison with humans.

Narrow AI is less capable than humans as it can perform only a specific set of tasks which it is trained for.
General AI is as capable as humans as it can ‘self-teach’ and can perform as well as a human.
Super AI is much more capable than humans and can do anything and everything better than humans.

Next is functionality which refers to how an AI can utilize its capability to perform various functions.

If it functions without memory it’s called Reactive Machine AI.
If it can store memory but in limited amounts, it is called Limited Memory AI.
If it has a memory and can perceive other entities’ emotions or mental states, it is known as Theory of Mind AI.
Lastly, if the AI can not only sense others’ emotions but also have its own, it is then referred to as a Self-aware AI.

You might also feel that some of the types from the two categories overlap with each other. For example, Super AI and Self-aware AI feel somewhat similar. They indeed are as both of them seem to have superhuman potential. But we classify them differently because we are looking at two different aspects of the same AI. When we think in terms of capability, we call it Super AI, but when we think in terms of how they function, we call it Self-aware AI.

Conclusion

Let’s be direct – does this classification even matter if more than half of the AI classes do not even exist?

No…and yes.

These AI types may be mostly theory (as of yet) but they do act like benchmarks in the evolution of AI. It begins with simple and limited forms, then those which match humans and lastly, the ones, which surpass human abilities. It’s like breaking down the ultimate goal of AI researchers (which is Super AI or Self-aware AI) into short-term goals (like Narrow, General, and Theory of Mind AI).

That said, heavy research is still going on in this sector to take AI to the level of humans and beyond. And though the advancement of AI will be a groundbreaking discovery, this also opens up the possibility of AI-powered crimes. This calls for a more holistic approach to AI research, that controls misuse and promotes responsible innovation.

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AI And Innovation in 2024

Shiwani Sharma — Mon, 08 Apr 2024 06:04:28 +0000

Introduction To AI & Innovation

AI And Innovation ; In today’s rapidly evolving technological landscape, Artificial Intelligence (AI) occupies a prominent position at the forefront of innovation. It plays a pivotal role in transforming industries and redefining the realm of possibilities. The dynamic interplay between AI and innovation has the capacity to revolutionize the way we live, work, and communicate with the world.

In previous articles on AI, we explored the multifaceted world of Artificial Intelligence, delving into its definition, advantages, disadvantages, and its myriad applications across various industries. Now, we delve further into the profound synergy between AI and innovation, where AI holds the power to enhance innovation in numerous ways. To know more about our previous blog on Artificial Intelligence, you can get more knowledge about what actual AI is and what it does. Go to the mentioned link and enhance your knowledge in field of AI. https://devopscurry.com/artificial-intelligence-an-overview/

What is the meaning of AI innovation?

As the name suggest AI innovation that means the formation and execution of great ideas, techniques, methods, technologies and algorithms within the field of artificial intelligence (AI). It consists of expanding latest techniques to solves the problems, enhancing existing AI systems or generating latest applications that purchase AI capabilities. It helps in many ways such as development of application just by generating latest platform and application that utilizing the method of AI to address particular requirement or to solve a specific problems in the fields like education, healthcare, transportation, finance etc.

At the end when we see overall impact of AI in innovation, it plays an important role in advancing the capabilities and applications of artificial intelligence, driving progress in several industries and convey societal challenges.

Latest Innovations in Artificial Intelligence

AI in Healthcare: AI has become increasingly pivotal in the global healthcare industry, leveraging technology to deliver innovative solutions. Some of the recent breakthroughs in AI include:
- AI-aided Medical Imaging: AI assists pathologists and radiologists in interpreting medical images, such as CT scans, X-rays, and MRIs, accelerating the identification of diseases, including early-stage conditions like diabetes, cancer, and eye disorders.
- Drug Discovery: AI analyzes extensive datasets to identify potential uses for existing drugs, reducing the time and costs associated with bringing new drugs to market.
AI in Cybersecurity: The realm of cybersecurity has witnessed remarkable advancements driven by AI in recent years. Innovations in AI for cybersecurity encompass:
- Threat Prevention and Detection: AI scrutinizes real-time network traffic, detects threats, and thwarts malicious activities, including zero-day threat detection.
- NLP and IoT Security: Natural Language Processing (NLP) aids in email security, identifying phishing attempts and malicious content. AI also safeguards the rapidly expanding Internet of Things (IoT) ecosystem by monitoring and identifying suspicious activities within IoT devices and networks.
Self-Driving Cars: AI has made significant strides in the development of autonomous or self-driving cars, although regulatory challenges and safety concerns remain. Key innovations in AI for self-driving cars encompass:
- Advanced Sensor Technologies: AI enhances sensor technologies, including cameras, radar, ultrasonic sensors, and LiDAR, making them more cost-effective and capable of providing high-resolution, 360-degree views around vehicles.
- Deep Learning for Enhanced Perception: AI refines object identification and scene comprehension, enabling self-driving vehicles to recognize and classify objects on the road, such as road signs and other vehicles, with remarkable reliability.
AI for Virtual Assistants and Chatbots: Voice-activated virtual assistants and chatbots are gaining prominence across websites and applications. Innovations in this domain include:
- Voice-First Chatbots: Voice-activated chatbots allow users to interact with virtual assistants using voice commands and natural language, with companies like Apple, Amazon, and Google focusing on voice-based virtual assistants.
- Emotional Recognition: Some chatbots possess the ability to detect user emotions by analyzing voice and text inputs, tailoring responses based on the user’s emotional1 state, which proves valuable in areas such as customer service and mental health applications.
AI in Climate Change Solutions: AI plays a pivotal role in addressing climate change by introducing innovative solutions, including:
- Climate Modeling and Prediction: AI is employed to construct climate models, enhancing weather predictions and improving our understanding of extreme events, such as droughts, floods, and hurricanes.
- CCS (Carbon Capture and Storage): AI is instrumental in the design and operation of carbon capture and storage systems, aiming to reduce carbon dioxide emissions from industrial processes, make the capture process more cost-effective, and optimize energy usage.

Conclusion: When discussing innovation, it’s essential to recognize that AI is not merely a tool but a transformative force that influences how we think, adapt, and create, shaping the technology-driven world and offering a more streamlined life for people globally. The ongoing developments in artificial intelligence continue to revolutionize industries and introduce novel technologies, promising a future where innovation is limitless.

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