- The meaning of artificial general intelligence for the AI industry and the world
. - Is artificial general intelligence possible? Various development approaches and predictions.
Is artificial general intelligence possible? Various development methods and forecasts. - Potential risks of creating strong AI that rivals human intelligence. Should we be wary of AI? Potential risks of
creating strong AI that rivals human intelligence. Should we be wary of artificial intelligence?
Table of contents
What is AGI? What is general artificial intelligence?
What's AGI AI's main concept? What's the main concept of AGI AI?
What is AGI artificial intelligence capable of? What is AGI artificial intelligence capable of?
AGI vs AI difference AGI vs AI difference
Narrow AI vs General AI vs Super AI Narrow AI vs General AI vs Super AI
AGI development approaches AGI development approaches
Challenges in the development of AGI technology AGI technology development challenges
Future of artificial general intelligence
Risks from artificial general intelligence
General AI Myth or Fact General AI Myth or Fact
The recent blast in the development of AI has brought many thoughts and issues to the surface. After the world witnessed how capable this technology can be, all of a sudden, the science fiction plots about androids with intelligence that rival humans don’t seem impossible anymore. Some experts state that the first steps to creating the next generation of AI – artificial general intelligence – have already been taken.
The recent explosion of AI development has brought with it many ideas and questions. After the world has witnessed the power of this technology, suddenly the science fiction plot of a robot with intelligence comparable to that of a human no longer seems impossible. Some experts say the first steps toward creating the next generation of artificial intelligence — artificial general intelligence — have already been taken.
We’ve decided to do our own research on the topic of AGI (artificial general intelligence), the actual state of its development, characteristics, and predictions. Of course, Atlasiko shares our analysis of the AGI meaning with you to answer the popular question “What is AGI in AI?”. Read ahead not to miss the significant transformation happening in the tech industry which can impact the whole world.
We decided to conduct our own research on the topic of AGI (Artificial Intelligence), the real state of its development, its characteristics and forecasts. Of course, Atlasiko shares with you our analysis of what AGI means to answer the popular question "What is AGI in AI?" Read on and don't miss out on the major changes taking place in the tech industry that could impact the entire world.
What is AGI? What is general artificial intelligence?
To start with our explanation, let’s give a comprehensive AGI definition. So, artificial general intelligence is a term used to describe an intelligent agent with human-level cognitive abilities within the software. In other words, it’s an AI that reached the level of development to be able to solve any unfamiliar issues and tasks on par with humans. Some other specialists define AGI as a system that works autonomously and exceeds ordinary people in economically valuable tasks.
To start our explanation, let's give a comprehensive definition of AGI. Therefore, artificial general intelligence is a term used to describe intelligent agents that have human cognitive capabilities in software. In other words, it is an artificial intelligence developed to a level of development capable of solving any unfamiliar problems and tasks on an equal footing with humans. Other experts define AGI as a system that can work autonomously and outperform ordinary humans in tasks of economic value.
Apart from two variants of artificial general intelligence definition, it also has a few names. The system can also be called general artificial intelligence as well as Strong or True AI. In some papers, you can come upon the name “real artificial intelligence”.
In addition to the two variants defined by AGI, it has a few names. The system can also be called general artificial intelligence as well as strong artificial intelligence or true artificial intelligence. In some papers, you can see the name "true artificial intelligence".
What's AGI AI's main concept?
What's the main concept of AGI AI?
The fundamental concepts that characterize AGI meaning in AI are “intelligence” and “consciousness”. To be considered AGI, the next-level AI has to obtain artificial cognition similar to or even the same as the natural one of humans. Just like our minds create new neuron connections living through experiences, learning, and solving, artificial general intelligence has to develop new links in its system and act on them resembling a conscious thinking process. While the intelligence concept is rather clear meaning cognitive capabilities, there are different points of view on the “consciousness” statement.
The basic concepts that characterize the meaning of AGI in AI are "intelligence" and "consciousness". To be considered AGI, the next level of artificial intelligence must acquire artificial cognition that is similar or even identical to natural human cognition. Just as our brains create new neuronal connections through experience, learning, and problem solving, an AGI must develop new connections in its systems and act on them like a conscious thought process. Although the concept of intelligence refers more clearly to cognitive ability, there are different views on the term "consciousness".
Naturally, the development of AI to more progressive stages gets the attention of not just computer science specialists but also philosophers who study the philosophy of mind and human existence. Thus, they present their own perspective on what the AGI system might be. The hypothesis about general AI suggested by an American philosopher, John Searle, gives us two AGI definitions that address the consciousness concept.
Naturally, the development of artificial intelligence to a more advanced stage has attracted the attention of not only computer science experts, but also philosophers who study the philosophy of mind and the philosophy of human existence. As such, they offer their own vision of what an AGI system might be. The hypothesis about general artificial intelligence proposed by American philosopher John Searle provides us with two definitions of AGI for the concept of consciousness.
Strong AI vs Weak AI
Strong AI vs Weak AI
| Strong AI strong artificial intelligence | Weak AI weak artificial intelligence |
|---|---|
The AI system acts upon its subjective experience producing human-level thought processes and making conscious decisions, which cannot be tested in typical ways. Typical way to test. |
The AI system only replicates the behavior of human minds, pretending to have consciousness as a major cognitive quality, but cannot actually process its subjective conscious experience. , but cannot actually deal with its subjective conscious experience. |
In Searle's “Chinese room argument”, he actually theorizes that it's impossible for AI to really become “strong” in the sense of this particular hypothesis and obtain a human-like mind. The maximum that we'll achieve is exactly weak AI which means just a program with generally intelligent behavior.
In Searle's "Chinese Room Argument", he actually reasoned that it is impossible for AI to really become "strong" in the sense of this particular assumption, and acquire human-like thinking. The maximum we'll get to happens to be weak AI, meaning just a program with generally intelligent behavior.
At the same time, computer scientists, like Stuart Russel and Peter Norvig, set philosophical hypotheses aside saying that the main aspect that should be evaluated is the outcome. It doesn't matter if AI just pretends to think or actually thinks like a per son as long as it gives the expected results. Therefore, the debate about whether artificial general intelligence is required to have real consciousness is still going on.
Meanwhile, computer scientists like Stuart Russel and Peter Norvig set aside philosophical assumptions, saying The main aspect that should be assessed is the outcome. It doesn't matter if the AI is just pretending to think or really thinks like a human, as long as it gives the expected results. Thus, the debate continues on whether artificial intelligence needs to be truly conscious.
Even though modern science has been developed to the point where we can make artificial organs and body parts, we still can't replicate the mental part of our existence. So, general AI is basically an attempt to reproduce minds granting human-like intelligence to machines.
Even if modern science has advanced to the point where we can create artificial organs and body parts, we still cannot replicate the mental part of our being. So general AI is basically an attempt to reproduce the mind, giving machines human-like intelligence.
Perhaps, even from this brief answer to “What is AGI?” you can tell that the idea is rather controversial. Indeed, some find it fascinating while others say it's outright creepy. There are many dimensions to the topic, as well as thoughts on it, which we address further in the article.
Maybe, even from this short answer to "What is AGI?" You can tell the idea is quite controversial. In fact, some people find it fascinating, while others say it's downright creepy. There are many sides to this topic and many ideas, which we will discuss further in this article.
What is AGI artificial intelligence capable of
?
As Agi Intelligence is Still a Hypothetical System, there no way to know the full extens of its capabilities. However, there are certain char of. Indicate True AIDINGUISHING It from Other Forms. We've Alream Mentioned that one of the functional requirements of Agi is to be able to perform cognitive computing in a way indistinguishable from humans, but, of course, there's more to it. As scientists developed different approaches to achieving general artificial intelligence and perspectives on the evaluation, they outline various capabilities ass oriented with the system.
Since AGI intelligence is still a hypothetical system, it is impossible to know the full extent of its capabilities. However, there are some characteristics that distinguish true AI from other forms. We've already mentioned that one of the basic requirements for AGI is the ability to perform cognitive computations in a manner indistinguishable from humans, but, of course, there's more to it. As scientists develop different approaches to achieve general artificial intelligence and evaluate perspectives, they outline the various capabilities associated with the system.
In theory, a completed AGI system is thought to be able of: In
theory, a complete AGI system is thought to be able to:
- abstract thinking; abstract thinking;
- following common sense in making decisions
; - comprehension of cause and effect;
understanding of cause and effect; - creativeness; background knowledge;
talent; background knowledge; - transfer learning. transfer learning.
Some scientists also add such typical for human cognitive qualities as sentiment, imagination, motivation, social intelligence, and reasoning, but they aren't considered fundamental human cognitive qualities, but they are not considered fundamental
Apart from these abilities, there is a set of functional features that the AGI computer must have in order to operate autonomously. The essential practical side of capabilities includes sensory perception, a sufficient level of motor skills, natural language understanding and process ing, and a navigation system.
In addition to these capabilities, an AGI computer must possess a set of functional characteristics in order to function autonomously. Basic practical aspects of competency include sensory perception, adequate levels of motor skills, natural language understanding and processing, and navigation systems.
Researchers believe that AGI systems will be able to perform higher-level tasks, such as the following:
Researchers believe that AGI systems will be able to perform higher-level tasks, such as:
- utilize multiple learning methods and algorithms;
utilize multiple learning methods and algorithms; - comprehend belief systems,
understanding belief systems, - utilize miscellaneous types of knowledge
, - produce definite structures for tasks;
develop clear structures for tasks; - comprehend symbol systems,
understand symbol systems, - engage in metacognition, and utilize knowledge on its basis
.
AGI vs AI difference AGI vs AI difference
In order to give you more understanding of just how revolutionary achieving AGI might be, let's compare it with the technology that we can experience now – artificial intelligence. Exactly this technology and its recent advancement urged scientists to activate the discussions and research ab out true artificial intelligence . Although both systems are based on similar algorithms and principles, the AI vs AGI difference is actually enormous.
To give you a little more idea of how revolutionary the realization of AGI could be, let's compare it to the technologies we can experience right now - Artificial Intelligence - Compare. It is this technology and its recent advancements that have led scientists to activate discussions and research on true artificial intelligence. Although both systems are based on similar algorithms and principles, the differences between AI and AGI are actually huge.
Researchers refer to the artificial intelligence we know and use now as Narrow AI (and weak AI in the mainstream artificial intelligence science). The name is basically self-explanatory as the system is only capable to carry out a specific, “narrow” set of tasks.
Researchers refer to the AI we know and use now as Narrow AI (weak AI in mainstream AI science). The name is largely self-explanatory, as the system can only perform a specific "narrow" set of tasks.
Contrary to narrow AI, AGI in theory doesn't have any limitations in capabilities. It's supposed to be able to handle any unfamiliar problem and have knowledge in various areas. Contrary to narrow AI, AGI in theory doesn't have any limitations in capabilities
. It should be able to handle any unfamiliar problems and possess knowledge in various fields.
Narrow AI vs General AI vs Super AI
Narrow AI vs General AI vs Super AI
Down below we compared the two types mentioned above, general AI vs narrow AI , as well as the superior to them stage of AI – super AI.
Their more advanced AI - super AI.
| Artificial narrow intelligence artificial narrow intelligence |
Artificial general intelligence General artificial intelligence |
Artificial super intelligence Artificial super intelligence |
|---|---|---|
| A narrow range of abilities according to the algorithms written by a developer |
Can make decisions in unknown circumstances without training (display of general intelligence ) |
Far exceeds the capabilities of even the most gifted humans in basically everything |
| Completely dependent on the dataset it was trained on in task execution |
Can perform any task that a human is capable of which broadens the range of capabilities |
Has the capacity of perfect recall, can multitask with top-level efficiency, operates superior knowledge base, etc. Has the capacity of perfect recall, can multitask with top-level efficiency , operates superior knowledge base, etc. |
Can exceed human capabilities only in a specific task it was created for |
Its processes and outcomes are indistinguishable from human ones (passed the Turing test ) |
Basically is a new species with exceptional cognitive characteristics |
AGI development approaches
AGI development approaches
Although AGI artificial intelligence is still a hypothetical concept, the greatest minds of computer science have already been working on possible methods and ways to achieve this technology. After conducting a meticulous analysis, we chose the most popular approaches to AGI GI development. Although AGI artificial
intelligence Still a hypothetical concept, but the greatest minds in computer science are already working on possible ways and means to implement this technology. After careful analysis, we selected the most popular approaches for AGI development.
Brain emulation Brain simulation
One of the possible and most debatable approaches is human brain emulation. It can be done by thorough scanning of the human brain, mapping, and uploading it on a capable computational device. Despite sounding quite futuristic, the appropriate hardware for simulating the brain actually exists in the present. According to Raymond Kurzweil, an American computer scientist, the sufficient volume of calculations per second to simulate our brain is 10^16, while the world’s fastest supercomputer (as of March 2023), Frontier, is able to perform 10^18 calculations in 1 second. Of course, due to the massive size and uniqueness of this computer, it’s not accessible for experiments just yet and clearly cannot be used in a commonplace. Moreover, Kurzweil’s estimates don’t include the fact that the majority of exciting artificial neural networks use simplified models of biological neurons. To fully simulate the human brain with all characteristics would require more computational capacity.
One possible and most controversial approach is brain emulation. It can be done by thoroughly scanning the human brain, mapping it and uploading it to a powerful computing device. As futuristic as it sounds, the right hardware to simulate a brain actually exists today. According to American computer scientist Raymond Kurzweil, enough calculations per second to simulate our brain is 10^16, while the world's fastest supercomputer (as of March 2023) Frontier is capable of performing 10^18 calculations in 1 minute second. Of course, due to the large size and uniqueness of this computer, it cannot be used for experiments at present, and obviously cannot be used for ordinary occasions. Furthermore, Kurzweil's estimates do not include the fact that most exciting artificial neural networks use simplified models of biological neurons. To fully simulate a human brain with all its characteristics, much more computing power is required.
Another problem is the scanning process. The human brain remains to be fully discovered since even with centuries of research there are still dark spots for scientists. The most popular suggestion is to use special nanobots that will accumulate accurate data about brain functioning but even the n scientists won't have a guarantee that the bots were able to capture all peculiarities. Therefore, to ensure successful brain emulation for achieving general AI, researchers still have to spend at least two more decades developing the required technologies. Another concern is the scanning process
. The human brain is still to be fully discovered, because even after centuries of research, scientists still have black spots. The most popular proposal is to use special nanorobots to collect accurate data on brain function, but even then, scientists cannot guarantee that these robots will capture all the properties. Therefore, it will take at least two decades for researchers to develop the technology needed to ensure successful brain emulation for general AI.
Algorithmic probability algorithm probability
Another approach to achieving AGI is based on the theory of algorithmic probability introduced by Ray Solomonoff. According to the method, the intelligent agent is able to predict the environment and decide on the best action even when given unfamiliar circumstances using the smallest dataset of environmental observations (Solomonoff's induction) and the possibility of an event based on prior knowledge of conditions related to it (Bayers' theorem).
Another way to achieve AGI is based on the algorithmic probability theory introduced by Ray Solomonoff. According to the method, even given an unfamiliar situation, an intelligent agent is able to predict the environment and decide to best act on it (Bayer's theorem).
As a continuation of this approach, a DeepMind senior scientist, Marcus Hutter created a mathematical theory of artificial general intelligence – AIXI. It's a theoretical reinforcement learning agent that also uses Solomonoff's induction to choose the best possible action based on observations and rewards from the environment .As
a continuation of this approach, DeepMind Senior Scientist Marcus Hutter created the mathematical theory of general artificial intelligence - AIXI. It is a theoretical reinforcement learning agent that also uses Solomonov's induction method to choose the best possible action based on observations of the environment and rewards.
Despite the sound theoretical proof of both models, they are believed to be incomputable in practice, which means it's impossible to create an accurate algorithm to always solve the problem correctly. Currently, there are a few approximate to artificial general intelligence examples like AIX Itl and UCAI , however, they have a major drawback in terms of computation time which makes the models inefficient in practice. Many researchers now consider the AIXI model a benchmark for artificial intelligence AGI capabilities as it's a mathematically proven functioning AGI. Although both models
have Solid theoretical proofs, but they are considered non-computable in practice, meaning that it is impossible to create an exact algorithm that always solves the problem correctly. Currently, there are some examples of approximate artificial intelligence, such as AIXItl and UCAI, however, they have a major disadvantage in terms of computation time, which makes the models inefficient in practice. Many researchers now regard the AIXI model as a benchmark for AGI functionality in artificial intelligence because it is a mathematically proven functional AGI.
Integrative cognitive architectureIntegrated
cognitive architecture
This method of AGI development is based on the idea of replicating identifying central cognitive processes of the human brain individually within AGI technology . The approach to AGI software named CogPrime was first introduced by Ben Goertzel (OpenCog)
. The idea in technology to individually replicate identified central cognitive processes of the human brain. A software method for AGI called CogPrime was first introduced by Ben Goertzel (OpenCog).
The CogPrime system uses an action-selection module to determine the best course of action in a scenario while simulating the cognitive processes of the brain to detect information about its surroundings. This enables it to produce an intelligent model and subsequently an AGI program. The disadvantages of this paradigm include the requirement for proper memory type separation as well as the need for system-wide synergy in order to produce an efficient computing environment. In comparison with previous approaches, CogPrime was able to overcome the incomputability issue as most technologies for its implementation are available now, but the system's capabilities are much below human brains. Thus, at this stage of development, it can’t be considered true artificial intelligence.
The CogPrime system uses an action selection module to determine the optimal course of action in a scene, while simulating the brain's cognitive processes to detect information about its surroundings. This enables it to generate intelligent models and subsequently AGI programs. Drawbacks of this paradigm include the need for proper separation of memory types and the need for system-wide synergy to produce an efficient computing environment. Compared to previous approaches, CogPrime is able to overcome the non-computability problem because most technologies to achieve it are available today, but the system's power is much lower than that of the human brain. Therefore, at this stage of development, it cannot be regarded as true artificial intelligence.
Challenges in the development of AGI
technology
Insufficient technology and great energy consumption levels
Insufficient technology and great energy consumption levels
We've already mentioned that present-day technologies can't execute cognitive operations on a human level. Even the most powerful existing supercomputers could provide just the sufficient capacity to replicate human mind calculations, not to mention multitasking and o the complex processes our brain is capable of. Moreover, even recent AI releases came across the problem of enormous energy consumption. Therefore, to create an efficient real artificial intelligence people need to solve many other technologies and resource-related challenges
. Perform cognitive operations on a human level. Even the most powerful supercomputers in existence offer only enough power to replicate human mental computation, let alone the multitasking and other complex processes our brains are capable of handling. Furthermore, even recent AI versions suffer from enormous energy consumption. Therefore, to create an efficient true artificial intelligence, one needs to solve many other technical and resource-related challenges.
Replicating
Transfer Learning
Applying information gained in one domain to another is referred to as transfer learning. Humans regularly engage in this, and it is a significant aspect of society. For instance, we can learn how to use a foreign language word in class and apply this knowledge to make a sentence with it at home. The main aim of replicating transfer learning is to prevent retraining, so a capable AGI artificial intelligence could use one skill for solving tasks in different
fields for transfer learning. Humans are often involved and it is an important aspect of society. For example, we can learn how to use foreign language words in class and then use this knowledge to make sentences at home. The main purpose of replicating transfer learning is to prevent retraining, so a capable AGI AI can use one skill to solve tasks in different domains
Facilitating collaboration and common sense
Human functioning depends on both common sense and teamwork with other human beings to complete tasks. Since today's algorithms are so limited in scope, dependable teamwork and common sense are yet far off in the future. The system must be endowed with these qualities to ensure that it is a true general artificial intelligence and not just another niche AI.
Humans function based on common sense and teamwork with other humans to accomplish tasks. Because today's algorithms are so limited in scope, reliable teamwork and common sense are far in the future. These qualities must be present in the system to ensure that it is truly AGI and not just another niche AI.
Understanding Mind and ConsciousnessUnderstanding
Mind and Consciousness
As we defined about, Consciousness is one of the main concepts and most reliable ways to provide the existence of general intelligence as it an essential component o F Human Existence. However, EVEN We, Humans, Canon Fully Grasp All the Secrets and PECULIARITIES BeHIND our minds. Thus, it continues to be a substantial barrier to the development and realization of general artificial intelligence.
Consciousness, as we defined above, is the main concept and one of the most reliable methods of proving the existence of general artificial intelligence, since it is the important part of existence. However, even we humans cannot fully grasp all the secrets and idiosyncrasies behind our thoughts. Therefore, it remains a significant obstacle to the development and realization of general artificial intelligence.
Future of artificial general intelligence
After getting to know more about AGI, we can state that the question “Is artificial general intelligence possible?” isn't a matter of doubt anymore. The answer is clearly positive as the scientists dedicate their full attention to the development of true AI. Now researchers pose another question – “ When will we have artificial general intelligence? ”, and let’s admit, the predictions are ambiguous.
After learning more about AGI, we can ask the question “Is general artificial intelligence possible?”. It is no longer a question of doubt. The answer is clearly yes, as scientists are preoccupied with developing true artificial intelligence. Now that researchers are asking another question—"When will we have general artificial intelligence?", the predictions, let's admit it, are ambiguous.
For example, a famous Australian roboticist, Rodney Brooks, concluded that a functional AGI system won't be implemented till 2300 saying that present-day science is far from understanding “the true promise and dangers of AI”. For example, the famous Australian
roboticist Rodney Brooks concludes that functional AGI systems will not be realized until 2300, and says that science today is far from understanding "the true promise and danger of artificial intelligence".
His statement was supported by remarkable researchers, such as Geoffrey Hinton and Demis Hassabis, who said that general artificial intelligence is nowhere close to being implemented. Artificial intelligence is still far from being realized.
However, there's also another point of view expressed by a Canadian computer scientist, Richard Sutton, who evaluated the possibilities of developing general intelligence AI in a span of the next two decades. He specified a 25% possibility of understanding AGI technology by 2 030, a 50% chance that it'd happen by 2040, and only 10% – never.
However, another view is expressed by Canadian computer scientist Richard Sutton, who assesses the possibility of developing AI with general intelligence within the next two decades. He points to a 25% chance of understanding AGI technology by 2030, a 50% chance by 2040, and a 10% chance—never.
According to our research, software development specialists in Atlasiko also tend to think that artificial general intelligence won't arrive sooner than at the end of this century or even the next one. Although we have great theoretical advancements, modern science still has too many obstacles to overcome to implement AI with general intelligence in real life.
According to our research, software development experts at Atlasiko also tend to believe that artificial intelligence will not arrive earlier than the end of this century or even the next century. Although we have made great progress in theory, modern science still has too many obstacles to overcome in order to realize AI with general intelligence in real life.
Risks from artificial general intelligence
Reading this article you probably remembered some fictional scenarios from popular movies where intelligent robots take over humanity. Well, it's pretty logical as those plots are based on real scientific concerns. Evaluation of existential risks takes a great p lace in the general AI dispute. Read this
article When you were , you might remember some scenes from popular movies where fictional intelligent robots take over humans. Well, that's quite logical, since the plots are based on real scientific questions. The assessment of existential risk figures prominently in the general AI debate.
Even now, the rapid advancement of artificial intelligence causes many discussions and controversies as it impacts various industries and a global workforce marketplace. The development of artificial general intelligence will alter the whole world tremendously. manage to achieve true AI with human- like consciousness , there's no guarantee this technology will be willing to be managed by humans. To put it simply, scientists now can't tell if we'll get a friendly R2-D2 or an android rebellion. Exaggerations aside, let's take a look At some risks most discussed among experts.
Even now, the rapid development of artificial intelligence has generated many discussions and controversies as it affects various industries and the global labor market. The development of general artificial intelligence will greatly change the whole world. If we manage to achieve true artificial intelligence with human-like consciousness, there is no guarantee that this technology will be willing to be managed by a human. In short, scientists can't tell right now whether we're going to get a friendly R2-D2 or a robot rebellion. Hyperbole aside, let's take a look at some of the most discussed risks among experts.
- Laking control . The brightest minds of the scientific community such as Stephen Hawking, Stuart J. Russel, Frank Wilczek, Geoffrey Hinton, OpenAI's CEO Sam Altman, and others addressed the lack of attention to the control over artificial intelligence. Without proper management and monitoring strong AI can simply be misused causing major disruptions and damage to society.
Lack of control. Stephen Hawking, Stuart Russell, Frank Wilczek, Jeffrey Hinton, OpenAI CEO Sam Altman and more of science's brightest minds address lack of control over AI Concerns. Without proper governance and monitoring, powerful AI can be misused to cause significant disruption and disruption to society. - The ai alignment workm . The More Advanced The Ai System is, The More Challenging It can be to align its with Human Ethics. Gnitive Abilities, True Ai May Be Abled Strategies Misaligned with Intended Goals and Principles, for example, power-seeking. Such behavior has already been noticed in some reinforcement learning agents when they displayed instrumental convergence (more capable agents used their bigger capacity of power to achieve their goals, which is similar to what humans do) . Therefore, before Deploying any AI it's vital to ensure the alignment of objectives.
AI alignment problem. The more advanced AI systems become, the greater the challenge of aligning their goals with human ethics. As stronger cognitive abilities develop, true AI may develop strategies that are inconsistent with intended goals and principles, such as power seeking. This behavior has been noticed in some reinforcement learning agents when they exhibit tool convergence (more capable agents use their greater abilities to achieve their goals, similar to what humans do). Therefore, prior to deploying any AI, it is critical to ensure alignment of purpose. - An issue with specifying goals . For each intelligent agent, the utility function is specified by the human developer. Writing this function correctly is utterly important as it defines the set of values which would be the basis for AI's decisions. So, if some important values happened to be not added to the utility function description, the general intelligence would act upon its own assigned tasks despite possibilities of harm or damage.
A matter of specifying targets. For each intelligent agent, a utility function is specified by human developers. Correctly writing this function is important because it defines a set of values that will be the basis for the AI's decisions. Thus, if some important value happens not to be added to the utility function description, general intelligence will perform its own assigned tasks, despite the possibility of harm or damage. - Challenging goal modification in AI AGI . More advanced technologies such as artificial general intelligence might resist changes in their goal structure to ensure their continued existence and even oppose being shut down
. More advanced technologies such as artificial intelligence may resist changes to their target structures to ensure their continued existence, or even be shut down.
Undoubtedly, to ensure the safety and stability of human society, scientists have to think through all risks and preventive mechanisms
.
General AI Myth or Fact
General AI Myth or Fact
| Myth | Fact |
|---|---|
| The development of real artificial intelligence is impossible. The development of real artificial intelligence is impossible. |
Artificial general intelligence is still a hypothetical intelligent agent. Indeed, it's predicted to be implemented in the future. General artificial intelligence is still a hypothetical intelligent agent. In fact, it is expected to be implemented in the future. |
| General artificial intelligence has already been developed. General artificial intelligence has already been developed. |
Although there are some approximations, none of the modern AI technologies can't be considered generally intelligent as they do n't possess the required qualities. They don't have the required qualities. |
| Threats from artificial intelligence aren't real. They are just plots from science fiction. Threats from artificial intelligence aren't real. They are just plots in science fiction. |
Many greatest scientists and inventors express concerns about the lack of control over general AI and the possible dangers it might bring to humanity. concerns. |
| Strong AI with consciousness can become evil. Strong AI with consciousness can become evil. |
AI can't "turn evil" in the same meaning as humans. Whether conscious or not, the real problem is probable misalignment with our objectives. Artificial intelligence can't "turn evil" in the same meaning as humans. Whether intentional or not, the real problem may not be aligned with our goals. |
| Goals of general artificial intelligence can only be determined by humans . |
Contrary to narrow AI, general AI with advanced cognitive qualities can display behaviors different from determined goals based on subjective experience . Behavior. |
| The main threats are robots and androids. 主要威胁是机器人和机器人。 |
A misaligned AGI AI doesn't need to have a movable body (or even any body) to be able to cause damage. The only requirement is an Internet connection. 未对准的 AGI AI 不需要具有可移动的身体（或什至任何身体）就能造成损坏。唯一的要求是互联网连接。 |
| AI development will inevitably lead to technology surpassing humans and the downfall of our civilization. 人工智能的发展必然导致技术超越人类，导致我们文明的没落。 |
With strong regulations and a well-thought risk strategy, the development of an advanced AGI system will cause no harm and benefit the overall technology development. 凭借强有力的法规和深思熟虑的风险策略，先进的 AGI 系统的开发将不会造成损害并有利于整体技术发展。 |
Conclusion 结论
We hope that this article helped you to gain a better understanding and find a comprehensive answer to the question “What is artificial general intelligence?”. Achieving AGI will be an exceptional accomplishment in computer science and other related industries. However, we can’t bring down our cautiousness with such a powerful technology. Without proper control, it might bring negative changes and danger to society.
我们希望本文能帮助您更好地理解并找到“什么是通用人工智能？”这个问题的全面答案。实现 AGI 将是计算机科学和其他相关行业的一项非凡成就。然而，如此强大的技术并不能降低我们的谨慎。如果没有适当的控制，它可能会给社会带来负面的变化和危险。
If you want to find out more about the positive aspects of present-day AI assistance , read our blog where we post regular updates from the world of artificial intelligence and other useful insights.
For more information, read our blog, where we publish regular updates and other useful insights from the world of artificial intelligence.