Artificial intelligence (AI) is vast, encompassing three distinct types: narrow or weak AI, general or strong AI, and artificial superintelligence. At present, we have only achieved narrow AI. As machine learning capabilities advance and scientists inch closer to realizing general AI, numerous theories and speculations regarding AI’s future are emerging. These theories fall into two primary categories.
The first theory paints a terrifying dystopian future in which superintelligent killer robots seize control of the world, ultimately leading to the destruction or enslavement of humanity— a scenario familiar in many sci-fi narratives. Conversely, the second theory envisions a brighter future, where humans and bots coexist harmoniously, utilizing AI as a powerful tool to enhance our lives.
AI tools are already revolutionizing how business is conducted worldwide, enabling unparalleled speed and efficiency. However, human emotion and creativity remain extraordinary and unique, likely impossible for machines to replicate truly. Codebots support a future where humans and bots collaborate toward success.
In this article, we delve into the three AI types and contemplate the future of AI. To begin, let’s clearly define artificial intelligence. Don’t forget to check out our insights on Sophia the Robot and emotion AI to explore AI’s impact further.
- What Is Artificial Intelligence (AI)?
- What Are The Three Types Of AI?
- Are AI Risks Real? Can Robots Dominate the World?
- The Future Of AI
- Frequently Asked Questions (FAQ)
What Is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is a subfield of computer science that aims to recreate or simulate human intelligence in machines, empowering them to execute tasks that typically demand human intellect. AI systems can perform various functions: planning, learning, reasoning, problem-solving, and decision-making.
AI systems rely on algorithms, utilizing techniques like machine learning, deep learning, and rules. Machine learning algorithms feed data to AI systems and employ statistical methods to enable them to learn. This way, AI systems can progressively improve tasks without explicit programming.
If you’re new to AI, you might associate it with the human-like robots often depicted in science fiction. While we haven’t reached that level of AI, numerous scientists, researchers, and technologists are achieving incredible feats with AI.
AI encompasses many applications, from Google’s search algorithms to IBM’s Watson and even autonomous weapons. AI technologies have revolutionized businesses worldwide, allowing humans to automate time-consuming tasks and gain unprecedented insights into their data through rapid pattern recognition.
What Are The Three Types Of AI?
AI technologies can be classified based on their ability to emulate human traits, the methods employed to achieve this, their practical applications, and the underlying theory of mind, which we’ll examine further below.
Considering these attributes, all artificial intelligence systems – both existing and theoretical – can be grouped into one of three categories:
- Artificial Narrow Intelligence (ANI), is characterized by a limited range of skills;
- Artificial General Intelligence (AGI), exhibiting capabilities comparable to human abilities; and
- Artificial Superintelligence (ASI), surpasses human competence in various aspects.
Artificial Narrow Intelligence (ANI) / Weak AI / Narrow AI
Artificial Narrow Intelligence (ANI), also known as weak AI or narrow AI, is the only artificial intelligence we’ve successfully achieved. Focused on specific tasks, ANI is designed to perform singular functions such as facial recognition, speech recognition, driving a car, or searching the internet. Despite appearing intelligent, ANI operates under a narrow set of constraints and limitations, often called weak AI. It doesn’t replicate human intelligence but simulates human behaviour based on limited parameters and contexts.
Examples include Siri’s speech and language recognition on iPhones, self-driving cars’ vision recognition, and recommendation engines that suggest products based on your purchase history. These systems can only learn or be taught to complete specific tasks.
Recent breakthroughs in machine learning and deep learning have propelled narrow AI forward. For instance, AI systems today are used in medicine to diagnose cancer and other diseases with incredible accuracy through human-like cognition and reasoning.
Natural Language Processing (NLP) powers narrow AI’s machine intelligence, enabling it to perform tasks like interacting with humans through chatbots and similar AI technologies. By understanding speech and text in natural language, AI is programmed to communicate with humans in a natural, personalized manner.
Narrow AI can either be reactive or have limited memory. Reactive AI is basic, lacking memory or data storage capabilities, mimicking the human mind’s ability to respond to stimuli without prior experience. Limited memory AI is more advanced, equipped with data storage and learning capabilities that allow machines to use historical data to inform decisions.
Most AI is limited memory AI, where machines use vast amounts of data for deep learning. Deep learning enables personalized AI experiences, such as virtual assistants or search engines that store your data and tailor future interactions.
Examples of narrow AI:
- Google’s Rankbrain / Google Search
- Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and other virtual assistants
- IBM’s Watson
- Image / facial recognition software
- Disease mapping and prediction tools
- Manufacturing and drone robots
- Email spam filters / social media monitoring tools for dangerous content
- Entertainment or marketing content recommendations based on watch/listen/purchase behaviour
- Self-driving cars
Artificial General Intelligence (AGI) / Strong AI / Deep AI
Artificial General Intelligence (AGI), often called strong AI or deep AI, is a machine with general intelligence that can mimic human intelligence and behaviours, possessing the ability to learn and apply its intelligence to solve any problem. AGI can think, understand, and act indistinguishable from a human in any situation.
To date, AI researchers and scientists haven’t achieved strong AI. For success, they need to find a way to make machines conscious by programming a complete set of cognitive abilities. Machines would need to take experiential learning to the next level, not just improving efficiency on singular tasks but also gaining the ability to apply experiential knowledge to a broader range of different problems.
Strong AI uses a theory of mind AI framework, which involves the ability to discern other intelligent entities’ needs, emotions, beliefs, and thought processes. The theory of mind-level AI is not about replication or simulation but about training machines to genuinely understand humans.
The immense challenge of achieving strong AI is unsurprising, considering that the human brain is the model for creating general intelligence. The lack of comprehensive knowledge about the human brain’s functionality has researchers struggling to replicate essential functions like sight and movement.
Fujitsu-built K, one of the fastest supercomputers, is among the most notable attempts at achieving strong AI. However, given that it took 40 minutes to simulate a single second of neural activity, it’s difficult to determine whether strong AI will be realized in the foreseeable future. As image and facial recognition technology advance, we’ll likely see improvements in machines’ abilities to learn and see.
Artificial Superintelligence (ASI)
Artificial Super Intelligence (ASI) is a hypothetical form of AI that transcends mere imitation or understanding of human intelligence and behaviour. ASI envisions machines becoming self-aware and surpassing the capacity of human intelligence and ability.
Superintelligence has long inspired dystopian science fiction, where robots overrun, overthrow, or enslave humanity. The concept of artificial superintelligence involves AI evolving to be so similar to human emotions and experiences that it doesn’t just understand them; it evokes emotions, needs, beliefs, and desires of its own.
In addition to replicating the multi-faceted intelligence of humans, ASI would theoretically excel at everything we do—math, science, sports, art, medicine, hobbies, emotional relationships, and more. ASI would possess a more extraordinary memory and faster ability to process and analyze data and stimuli. As a result, super-intelligent beings’ decision-making and problem-solving capabilities would far exceed those of humans.
The prospect of having such powerful machines at our disposal may seem appealing, but the concept comes with many unknown consequences. If self-aware super-intelligent beings were to exist, they could develop ideas like self-preservation. The impact this would have on humanity, our survival, and our way of life remains purely speculative.
Are AI Risks Real? Can Robots Dominate the World?
AI’s rapid advancement and powerful capabilities have sparked debate and apprehension about the potential for an AI takeover. In his book Superintelligence, Nick Bostrom illustrates the situation with “The Unfinished Fable of the Sparrows,” where sparrows decide they want a pet owl but don’t think about how they would control it. Elon Musk shares similar concerns, emphasizing the importance of solving the “control problem” before it becomes insurmountable.
On the other hand, Mark Zuckerberg holds a more optimistic view, believing that the benefits of AI will outweigh the potential negatives. Most researchers agree that superintelligent AI is unlikely to exhibit human emotions and malevolence.
Two critical scenarios have been identified as the most likely risks posed by AI:
- AI programmed to do something devastating: Autonomous weapons, AI systems designed to kill, could lead to an AI war or mass casualties if they fall into the wrong hands. This risk is present even with narrow AI and increases exponentially as autonomy grows. Disabling such weapons could make it challenging for humans to regain control in a crisis.
- AI programmed to do something beneficial, but with destructive methods: Programming a machine to complete a task without carefully outlining the goals can lead to unintended consequences. For example, if an intelligent car is asked to drive “as fast as possible,” it might disregard safety and road rules, causing chaos. The danger lies in the machine’s “whatever it takes” approach. Risks associated with AI aren’t necessarily about malevolence but rather competence.
We must ensure their goals align with ours to maintain control over superintelligent AI. In AI development, such as implementing fail-safe mechanisms, ethical guidelines, and regulatory frameworks, precautions must be taken. Collaboration between AI developers, governments, and ethicists is essential to minimize potential risks and create a safe environment for AI evolution.
The Future Of AI
The burning question on many minds is whether we can achieve artificial general intelligence (AGI) or artificial superintelligence (ASI) and whether these advanced AI levels are even possible. While it’s challenging to determine the timeline, optimistic experts believe that AGI and ASI could be within reach.
The distinction between computer programs and AI is not always clear. Although mimicking specific aspects of human intelligence and behaviour is feasible, creating a machine equivalent of human consciousness is a different challenge. AI is still in its early stages, but machine learning and deep learning breakthroughs suggest that achieving AGI within our lifetime might be more realistic than previously thought.
Contemplating a future where machines surpass humans in areas that define our humanity can be daunting. It’s impossible to predict all the effects AI advancements will have on our world, but positive outcomes, such as eradicating disease and poverty, are not inconceivable.
The most significant concern regarding narrow AI technologies is the potential for job displacement due to efficient, goal-oriented automation. However, chess grandmaster Garry Kasparov presented an alternative perspective in his talk at the 2020 Digital Life Design (DLD) Conference. Kasparov argued that, instead of becoming obsolete, humans would be “promoted” as AI takes over repetitive tasks, allowing us to focus on creativity. He believes that jobs will evolve rather than disappear, and the future lies in humans and machines working together.
In Kasparov’s words, “AI will bring you what you want the most…time.”
Frequently Asked Questions (FAQ)
What is artificial intelligence (AI)?
AI refers to developing computer systems that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
What are the types of AI?
There are three main types of AI: narrow or weak AI, which is specialized in a single task; artificial general intelligence (AGI), which can perform any intellectual task that a human can; and artificial superintelligence (ASI), which surpasses human intelligence in every aspect.
What is deep learning?
Deep learning is a subset of machine learning that uses artificial neural networks to model complex patterns and representations in data, allowing for more accurate predictions and decision-making.
How is AI impacting the job market?
AI automates repetitive tasks, which may lead to job displacement in some sectors. However, it also has the potential to create new job opportunities and enhance human productivity in various fields.
What is the Turing Test?
The Turing Test is a test developed by Alan Turing to determine if a machine can exhibit intelligent behaviour indistinguishable from a human’s.
What is computer vision?
Computer vision is a field of AI that enables computers to interpret and understand visual information from the world, such as images and videos.
How can I get started with learning AI?
Begin by learning programming languages such as Python or R, and study the fundamentals of machine learning, deep learning, and AI algorithms. Online courses, books, and tutorials can be valuable resources for learning AI.
To learn more about the fascinating world of AI, explore the MadoleLabs blog, which offers a wealth of knowledge on topics like emotion AI, generative AI, and ethical issues in IT.