Main Highlights:
- The only kind of artificial intelligence we have so far effectively generated is artificial narrow intelligence (ANI), often known as weak AI or narrow AI.
- Artificial general intelligence (AGI), also known as strong artificial intelligence (AI) or deep artificial intelligence (AI), is the idea of a machine with general intelligence that mimics human intelligence and/or behaviours, with the ability to learn and apply its intelligence to solve any problem.
- Artificial superintelligence (ASI) is a hypothetical kind of artificial intelligence (AI) that goes beyond just mimicking or understanding human intellect and behaviour.
Artificial superintelligence is one of three varieties of artificial intelligence (AI): limited or weak AI and general or strong AI.
Only limited AI has been accomplished thus far. Many opinions and suppositions are floating around about the future of AI as machine learning skills develop and researchers grow closer to developing general AI. Two significant hypotheses exist.
One idea is based on the anxiety of a dystopian future, as portrayed in many science fiction stories, in which brilliant killer robots rule the planet and either exterminate humans or imprison everyone.
The alternative view envisions a more upbeat future in which humans and robots coexist, with people employing AI as a tool to improve their quality of life.
Tools that use artificial intelligence are already having a big impact on how business is done throughout the world, doing jobs faster and more effectively than people could.
However, human emotion and creativity are extraordinarily special and one-of-a-kind, and they are very challenging if not impossible to recreate on a computer. Codebots is in favour of a future in which robots and people collaborate to achieve success.
In-depth discussions of the three categories of AI and predictions for its future are covered in this article. Let’s begin by defining artificial intelligence in detail.
Artificial intelligence (AI): What is it?
A subfield of computer science called artificial intelligence aims to emulate or reproduce human intelligence in machines so that they may carry out activities that ordinarily call for the human intellect. AI systems may be programmed to do a variety of tasks, such as planning, learning, reasoning, problem-solving, and decision-making.
Algorithms are the driving force of artificial intelligence systems, which employ methods like machine learning, deep learning, and rules. AI systems are fed computer data by machine learning algorithms, which use statistical methods to help the AI systems learn. Without needing to be deliberately designed to do so, AI systems get better at tasks through machine learning.
If you’re unfamiliar with artificial intelligence, you’re probably most familiar with the humanoid robots from science fiction. There are a tonne of amazing things that scientists, academics, and engineers are doing with AI, even if we’re not quite at the human-like robot level of AI yet.
Google’s search algorithms, IBM’s Watson, and autonomous weapons are all examples of AI. Businesses all over the world now have access to AI technologies that enable humans to automate previously time-consuming tasks and quickly recognise patterns in data to uncover previously undiscovered insights.
What 3 types of Artificial intelligence (AI) are there?
The ability of AI technologies to replicate human qualities, the technology they employ to do so, the applications they have in the actual world, and the theory of mind—which we’ll cover in more detail below—are used to classify them.
All hypothetical and practical artificial intelligence systems may be classified into one of three categories using these traits as a guide:
- Artificial narrow intelligence (ANI), which has a narrow range of abilities;
- Artificial general intelligence (AGI), which is on par with human capabilities; or
- Artificial superintelligence (ASI), is more capable than a human.
Weak AI, Narrow AI, and Artificial Narrow Intelligence (ANI)
The only kind of artificial intelligence we have so far effectively generated is artificial narrow intelligence (ANI), often known as weak AI or narrow AI. Narrow AI is goal-oriented, created to carry out a single job, such as driving a car or doing an online search, and is exceptionally clever at carrying out the particular task it is taught to accomplish.
Although these machines may appear intelligent, they are constrained by a certain set of restrictions and limits, which is why this kind of AI is sometimes referred to as weak AI. Narrow artificial intelligence (AI) just simulates human behaviour based on a constrained set of characteristics and environments; it does not imitate or reproduce human intellect.
Consider voice and language recognition in self-driving cars, visual recognition in autonomous vehicles, and recommendation engines that provide suggestions for things based on your past purchases. Only certain tasks may be learned or taught by these systems.
In the past ten years, advances in machine learning and deep learning have enabled major advancements in narrow artificial intelligence. By replicating human-like cognition and reasoning, for instance, AI systems are utilised in medicine today to diagnose cancer and other diseases with extremely high accuracy.
Narrow Natural language processing (NLP), which is used to carry out tasks, is where machine intelligence in AI originates. Chatbots and other comparable AI systems use NLP. AI is intended to connect with people in a natural, individualised way by comprehending voice and writing in natural language.
Narrow AI may just have a little memory or be reactive. Reactive AI mimics the human mind’s capacity to react to various stimuli without prior knowledge and is exceedingly simplistic; it lacks memory and data-storing capabilities. The more sophisticated AI with limited memory can store data and learn, allowing computers to utilise previous facts to guide their judgments.
Most artificial intelligence (AI) is limited memory AI, where computers use massive amounts of data for deep learning. Personalized AI experiences, such as virtual assistants or search engines that save your data and tailor your future encounters, are made possible through deep learning.
Examples of narrow AI:
- Google’s RankBrain and Google Search
- Other virtual assistants include Cortana from Microsoft, Alexa from Amazon, and Siri from Apple.
- IBM’s Watson
- Face recognition software for images
- Tools for predicting and mapping diseases
- Drone robots and production
- Social media monitoring tools and email spam filters for harmful material
- Recommendations for entertainment or marketing content based on watch/listen/buy behaviour
- Autonomous vehicles
Strong AI, deep learning, and artificial general intelligence (AGI)
Artificial general intelligence (AGI), also known as strong artificial intelligence (AI) or deep artificial intelligence (AI), is the idea of a machine with general intelligence that mimics human intelligence and/or behaviours, with the ability to learn and apply its intelligence to solve any problem. In each given circumstance, AGI is capable of thinking, comprehending, and behaving in a manner that is identical to that of a human.
Strong AI is still a work in progress for scientists and researchers in AI. To be successful, they would need to devise a method of imbuing robots with consciousness and a whole set of cognitive capabilities. Experiential learning would need to be advanced for machines to be able to apply it to a larger variety of issues as opposed to merely being more proficient at a single activity.
A theory of mind AI framework is used by strong AI, which refers to the capacity to ascertain the wants, emotions, beliefs, and mental processes of other intelligent beings. The theory of mind-level AI focuses on teaching robots to fully comprehend people rather than replicating or simulating human thought processes.
Given that the human brain serves as the prototype for establishing universal intelligence, the enormous task of developing robust AI is not surprising. Researchers are having difficulty simulating fundamental movements and vision because they lack a thorough understanding of how the human brain works.
One of the most noteworthy attempts to develop strong AI is Fujitsu’s K, one of the fastest supercomputers. However, given that it required 40 minutes to mimic a single second of cerebral activity, it is impossible to say whether or not strong AI will be developed shortly. It is expected that machine learning and vision will improve as picture and facial recognition technology develops.
Artificial Superintelligence (ASI)
Artificial superintelligence (ASI) is a hypothetical kind of artificial intelligence (AI) that goes beyond just mimicking or understanding human intellect and behaviour. With ASI, computers become self-aware and outperform human intelligence and ability.
Superintelligence has long served as the inspiration for futuristic dystopias in which machines conquer, overturn, or enslave humans. According to the idea of artificial superintelligence, AI develops to the point where it is so similar to human emotions and experiences that it not only understands them but also elicits its feelings, wants, beliefs, and goals.
Theoretically, ASI would excel in everything we do, including arithmetic, science, athletics, art, medicine, hobbies, interpersonal connections, and everything else, in addition to replicating the complex intelligence of humans. As with artificial intelligence, ASI would have a better memory and a quicker processing speed. Superintelligent creatures would therefore have far better judgement and problem-solving skills than people.
Although the possibility of having such formidable technologies at our disposal may sound alluring, the idea itself has plenty of unknowable repercussions. Self-aware, highly intelligent organisms would be able to think in terms of self-preservation if such a thing were to exist. This is sheer conjecture as to what effect it will have on mankind, our survival, and our way of life.
Is AI harmful? Will the planet be taken over by robots?
Many individuals are concerned about the “inevitability” and imminence of an AI takeover as a result of AI’s rapid growth and potent powers.
Nick Bostrom opens his book Superintelligence with “The Unfinished Fable of the Sparrows.” In essence, some sparrows decided to have an owl as a pet. The majority of sparrows thought the concept was fantastic, but one was dubious and questioned how the sparrows could govern an owl. We’ll deal with that problem when it’s a problem, was the response to this worry.
Elon Musk, who has similar worries about superintelligent creatures, would contend that whereas humans are represented by the sparrows in Bostrom’s metaphor, ASI is represented by the owl. The “control problem” is especially worrying since we could only have one chance to fix it, just like it was with the sparrows.
Because he believes that AI’s advantages transcend any potential drawbacks, Mark Zuckerberg is less concerned about this fictitious control dilemma.
The majority of researchers concur that highly intelligent AI is unlikely to display human emotions, and there is no reason to believe that ASI would develop malice. Two main scenarios have been identified as being the most plausible when analysing how AI may become a concern.
AI could be taught to carry out a helpful task yet find a damaging way to achieve it.
Without carefully and explicitly defining your objectives, it might be challenging to programme a computer to do a task. Consider requesting the quickest possible transportation from an intelligent vehicle. As quickly as feasible is a directive that disregards factors like safety and traffic laws.
Whatever mayhem the intelligent automobile causes while doing its duty remains to be seen. How can we make sure a machine doesn’t see our attempts to halt it as a danger to the objective if we give it a goal, and then need to modify that goal or stop the machine? How can we prevent the computer from doing “whatever it takes” to achieve the goal? The risk with AI is in the “whatever it takes,” and the risk is competence rather than necessarily malice.
Superintelligent AI would be incredibly effective at achieving any goal, but if we hope to retain any kind of control, we must make sure that these goals coincide with ours.
AI might be designed to carry out a terrible action.
AI systems that have been programmed to kill are autonomous weapons. Autonomous weapons might unintentionally result in an AI war, catastrophic fatalities, and possibly the extinction of humanity if they fall into the wrong hands.
Such weapons may be made to be very hard to “switch off,” and people might lose control of them very quickly. Even with little AI, this risk is common, but it exponentially rises with greater autonomy.
What does AI’s future hold?
The most pressing query is this. Can we develop powerful AI or artificial superintelligence? Do they even exist? AGI and ASI are thought to be feasible by pessimistic specialists, but it is exceedingly difficult to estimate how far off these levels of AI are from becoming a reality.
The distinction between AI and computer programmes is hazy. It is very simple to mimic specific aspects of human behaviour and intellect, but it is far more difficult to replicate human consciousness in a computer.
Breakthroughs in machine and deep learning suggest that we may need to be more serious about the potential of developing artificial general intelligence during our lifetimes, even if AI is still in its infancy and the pursuit of strong AI was long regarded to be science fiction.
It’s unsettling to imagine a time when robots are superior to us in the fundamental traits that define us as humans. The eradication of things like sickness and poverty is not inconceivable, albeit we cannot foresee all the effects that advances in AI will have on our planet.
The possibility of effective goal-oriented automation making much human employment redundant is now the biggest worry society has about limited AI technology. Gary Kasparov, the youngest world chess champion and greatest chess player for 20 years provided an opposing viewpoint in his speech, “What can AI bring to our lives?” at the 2020 Digital Life Design (DLD) Conference in Munich, Germany.
