Comparison of Narrow AI — General AI — Super AI
Hello everyone!
Artificial Intelligence (AI) is defined as machine intelligence that mimics the problem-solving and decision-making abilities of humans to perform various tasks. All types of AI utilize machine learning (ML), deep learning (DL), and neural networks to progress to higher levels.
In previous articles, we explored General AI, Super AI, and Narrow AI. In this article, we will delve deeper into the key similarities and differences among these 3 types of AI.
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1. What are Narrow AI — General AI — Super AI?
Artificial Intelligence (AI) is machine intelligence that simulates the problem-solving and decision-making abilities of humans to perform various tasks.
Artificial Intelligence employs algorithms and techniques such as machine learning (ML) and deep learning (DL) to learn, progress, and increasingly improve in assigned tasks. Artificial Intelligence is classified into three types based on the human-like characteristics it can simulate, practical applications, and theoretical premises about the mind:
- Artificial Narrow Intelligence (ANI): AI with a limited scope of capabilities.
- Artificial General Intelligence (AGI): AI equivalent to the capabilities of a human.
- Artificial Superintelligence (ASI): AI surpassing the intelligence of humans.
Let’s examine each type of AI in detail.
1.1. Artificial Narrow Intelligence (ANI)
Artificial Narrow Intelligence (ANI), also known as Weak AI or Narrow AI, is AI designed for specific applications or tasks. It is programmed to perform singular tasks such as facial recognition, speech recognition in voice assistants, or autonomous driving. Narrow AI simulates human behavior based on a limited set of parameters, constraints, and contexts.
Some common examples of ANI include speech and language recognition demonstrated by Siri on iPhones, vision recognition features displayed in autonomous vehicles, and recommendation systems such as Netflix suggesting programs based on users’ online activities.
Google’s RankBrain is another example of Narrow AI that Google uses to rank search results. Such systems are designed to learn or be trained specifically to accomplish particular tasks.
1.2. Artificial General Intelligence (AGI)
Artificial General Intelligence (AGI), also known as Strong AI or Deep AI, is the capability of a machine to think, understand, learn, and apply its intelligence to solve complex problems, much like a human. Strong AI utilizes the framework of AI intelligence to recognize emotions, beliefs, and thought processes of other intelligent systems. A theory of AI at the intelligence level involves teaching machines to truly understand every aspect of human cognition, rather than merely copying or simulating the human mind.
Although AGI has not yet been realized, it has garnered attention from leading technology companies such as Microsoft, which invested $1 billion in AGI through the joint venture OpenAI. Additionally, in the pursuit of achieving powerful AI, Fujitsu has developed the K computer, recognized as one of the fastest supercomputers in the world. Similarly, National University of Defense Technology in China has created Tianhe-2, a supercomputer with a processing power of 33.86 petaflops.
1.3. Artificial Superintelligence (ASI)
Super Artificial Intelligence (ASI) is a type of AI that surpasses human intelligence and can perform any task better than humans. ASI systems not only understand human emotions and experiences but can also evoke their own emotions, beliefs, and desires, similar to humans.
Although the existence of ASI remains speculative, the decision-making and problem-solving capabilities of such systems are believed to far exceed those of humans. Typically, ASI systems can think, solve puzzles, make predictions, and make decisions independently.
2. How do Narrow AI — General AI — Super AI work?
Regardless of the type, artificial intelligence typically possesses three fundamental capabilities:
- Perception of the surrounding environment: In this aspect, an AI model gathers data about the relevant topic from its surroundings.
- Detecting patterns in the environment: After collecting relevant data, the AI model will search for common patterns in the data.
- Learning from these patterns and updating knowledge for future decision-making.
Subsequently, AI model learns from the data patterns and updates its knowledge over time. For AGI, this could involve becoming more self-aware, creative, and enhancing perceptual abilities equivalent to humans.
Similarly, for ASI, this stage may entail developing its own emotions, beliefs, and experiences, while further enhancing its perceptual abilities to overshadow human intelligence.
3. Key similarities Narrow AI — General AI — Super AI
Artificial Intelligence (AI) enables machines to learn from past experiences, adapt to new inputs or stimuli, and perform human-like tasks more efficiently. AI has been applied across various industries, ranging from finance, healthcare, and aviation to manufacturing and supply chains.
While Narrow AI has become a part of our daily lives, AGI is still in its early stages, and the exaggeration surrounding ASI is substantial and unyielding. However, all these types of AI share a common origin and have certain undeniable similarities.
Let’s explore the key similarities among ANI, AGI and ASI.
3.1. Predictive and Adaptive Capability
All types of AI use algorithms to identify and discover patterns within data. Subsequently, the identified patterns serve as the basis for AI solutions to learn and adapt, much like how humans learn from repetitive tasks. All analyzed data will continue to be utilized to make decisions in the future and generate meaningful predictions.
Various software systems are employed today, such as systems used for spell-checking, predicting user-inputted content, or providing users with the shortest route to their destination, all of which feature machine intelligence as a core function.
The same principles also apply to automated decision-making systems based on traffic and surrounding data. The legal field also has a similar application, where AI solutions review various documents and highlight relevant ones for a specific case. By flagging relevant documents, the algorithm can easily identify similar documents. Such AI applications can also be used for predicting litigation outcomes as they can assess the risk factors of cases based on document content and predict the judgment results.
3.2. Making one’s own decisions
AI enhances human intelligence, providing detailed insights from collected data and learning how to improve overall productivity. The data analysis capability, self-learning, deep understanding, and improved decision-making of AI make it a powerful tool across various applications.
Current Narrow AI solutions make decisions based on pre-programmed data. Similarly, with human-like cognitive capabilities in AGI and ASI, future AI solutions can consistently make more precise, optimized, and faster decisions compared to similar human solutions.
3.3. Emulating human cognition
All types of AI systems fundamentally mimic the human mind and address complex problems. They efficiently comprehend the world and react appropriately, much like humans study their surroundings, draw inferences, and then logically interact with them.
The machine intelligence of Narrow AI uses Natural Language Processing (NLP) to understand spoken and written language naturally. This is evident in chatbots and virtual assistants like Cortana. Therefore, current AI is programmed to emulate human cognition for natural and personalized interactions with humans. As AGI and ASI systems are advanced versions of ANI, the ability to mimic human cognition is expected to further expand and progress in the future.
3.4. Continuous learning and development
AI refers to the machine’s ability to learn from common data patterns. AI systems use Deep Learning, a subset of machine learning, for continuous learning and development. Most deep learning methods, often called deep neural networks, employ neural network architecture and are trained using large labeled datasets. Here, ‘deep’ represents the number of hidden layers in the neural network. In comparison to traditional neural networks with two or three hidden layers, deep neural networks can have nearly 150 layers.
At present, some of the most familiar examples of deep learning include Amazon’s Alexa, Google Assistant and Samsung Bixby. Such AI systems use algorithms to develop analytical models and perform tasks through multiple iterations and trial-and-error processes.
3.5. Eliminating mundane tasks through automation
All types of AI replace routine and tedious tasks within an organization by providing automated solutions. This allows employees to focus on critical tasks. A smart warehouse equipped with AI systems and necessary automation enables employees to avoid the hassle of moving heavy items. Instead, automated robots are equipped to perform these activities. Moreover, inventory levels are also automatically calculated, assisting employees in handling tasks that require human attention, such as ensuring that the automated system operates smoothly. This enhances efficiency, saves time and effort for employees, and reduces overall costs.
With AGI and ASI in the future, tasks that currently require careful human supervision will also be undertaken by intelligent agents exhibiting intelligence surpassing that of humans.
3.6. Creating conditions to enhance humanity
The combination of AI and humans can bring forth various capabilities and strengths. Augmented intelligence elevates any form of AI to a higher level.
Consider the example of IBM’s Deep Blue, a computer that defeated world chess champion Garry Kasparov in the late 1990s. After the loss, Garry experimented with how a human working with a computer could enhance the playing ability of either a human or a machine. He found that the combination of human intuition, machine capabilities, and a sound process could yield better results than a powerful computer alone.
Current Narrow AI is already quite effective in assisting humans with critical tasks. With AGI and ASI on the horizon, technologies like automation could further enhance the efficiency of human intelligence. This implies that the overall productivity of ‘human intelligence combined with machine intelligence’ will surpass that of humans or machines alone.
4. Key Differences Narrow AI — General AI — Super AI
It can be said that advancements in AI have made our lives easier today. As Narrow AI becomes an integral part of our daily lives, and with the emergence of AGI and ASI, we are on the verge of realizing the true power of AI technology.
Having said that, each type of AI distinctly differs from the other. Let’s understand the key differences between ANI, AGI and ASI.
5. Closing thoughts
Since the advent of AI in the mid-20th century, all we have achieved is Narrow AI models. They perform exceptionally well when completing assigned tasks. Once Narrow AI becomes as complex as the human brain, we may touch the realm of AGI.
After mastering the general AI phase, where it surpasses human intelligence in every domain, we can begin to envision a future marking the onset of the Super AI era. At that point, the future entails being surrounded by entities that are more intelligent, conscious, and self-aware.
What about your thoughts? If you want to know further about it, don’t hesitate to share it with us! 😀
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