Artificial General Intelligence
Artificial general intelligence (AGI) is a type of synthetic intelligence (AI) that matches or goes beyond human cognitive capabilities throughout a wide variety of cognitive tasks. This contrasts with narrow AI, which is restricted to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that greatly exceeds human cognitive capabilities. AGI is considered among the definitions of strong AI.
Creating AGI is a primary objective of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research study and advancement tasks across 37 countries. [4]
The timeline for achieving AGI stays a subject of ongoing argument amongst researchers and professionals. As of 2023, some argue that it may be possible in years or decades; others keep it might take a century or longer; a minority think it might never be achieved; and another minority claims that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has actually expressed issues about the rapid progress towards AGI, suggesting it might be accomplished earlier than lots of expect. [7]
There is debate on the specific definition of AGI and relating to whether modern-day large language models (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a typical subject in sci-fi and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many professionals on AI have stated that mitigating the danger of human extinction postured by AGI must be an international priority. [14] [15] Others discover the development of AGI to be too remote to provide such a threat. [16] [17]
Terminology
AGI is likewise called strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or general smart action. [21]
Some academic sources reserve the term "strong AI" for computer system programs that experience sentience or consciousness. [a] In contrast, weak AI (or narrow AI) is able to fix one particular problem but lacks general cognitive abilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the exact same sense as people. [a]
Related principles consist of artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical kind of AGI that is much more normally smart than humans, [23] while the notion of transformative AI relates to AI having a large effect on society, for example, similar to the farming or industrial transformation. [24]
A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify five levels of AGI: emerging, proficient, expert, virtuoso, and superhuman. For instance, a skilled AGI is specified as an AI that surpasses 50% of experienced grownups in a vast array of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is similarly specified however with a threshold of 100%. They think about large language models like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]
Characteristics
Various popular definitions of intelligence have actually been proposed. Among the leading propositions is the Turing test. However, there are other widely known definitions, and some researchers disagree with the more popular methods. [b]
Intelligence qualities
Researchers usually hold that intelligence is needed to do all of the following: [27]
reason, use method, fix puzzles, and make judgments under uncertainty
represent knowledge, including sound judgment knowledge
plan
discover
- interact in natural language
- if required, incorporate these abilities in completion of any provided objective
Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and wiki.insidertoday.org choice making) consider additional characteristics such as creativity (the capability to form unique mental images and principles) [28] and autonomy. [29]
Computer-based systems that exhibit a lot of these capabilities exist (e.g. see computational creativity, automated thinking, decision support system, robotic, evolutionary calculation, smart representative). There is argument about whether modern AI systems have them to an appropriate degree.
Physical traits
Other capabilities are thought about desirable in smart systems, as they may affect intelligence or help in its expression. These consist of: [30]
- the capability to sense (e.g. see, hear, and so on), pyra-handheld.com and
- the ability to act (e.g. move and manipulate things, change place to check out, etc).
This includes the ability to find and react to hazard. [31]
Although the ability to sense (e.g. see, hear, etc) and the ability to act (e.g. move and control items, modification area to check out, and so on) can be desirable for some intelligent systems, [30] these physical abilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that large language models (LLMs) may currently be or end up being AGI. Even from a less optimistic viewpoint on LLMs, there is no firm requirement for an AGI to have a human-like kind; being a silicon-based computational system is adequate, provided it can process input (language) from the external world in place of human senses. This analysis lines up with the understanding that AGI has actually never been proscribed a particular physical personification and therefore does not demand a capability for locomotion or conventional "eyes and ears". [32]
Tests for human-level AGI
Several tests indicated to validate human-level AGI have actually been thought about, including: [33] [34]
The idea of the test is that the maker has to try and pretend to be a man, by answering questions put to it, and it will only pass if the pretence is reasonably convincing. A substantial portion of a jury, who should not be skilled about devices, need to be taken in by the pretence. [37]
AI-complete issues
An issue is informally called "AI-complete" or "AI-hard" if it is thought that in order to fix it, one would need to carry out AGI, due to the fact that the option is beyond the capabilities of a purpose-specific algorithm. [47]
There are lots of issues that have been conjectured to require general intelligence to solve in addition to people. Examples include computer vision, natural language understanding, and handling unanticipated scenarios while resolving any real-world problem. [48] Even a particular task like translation needs a device to read and write in both languages, follow the author's argument (reason), understand the context (understanding), and faithfully recreate the author's initial intent (social intelligence). All of these issues require to be resolved at the same time in order to reach human-level machine performance.
However, a number of these tasks can now be carried out by modern-day large language designs. According to Stanford University's 2024 AI index, AI has reached human-level performance on many benchmarks for reading comprehension and visual thinking. [49]
History
Classical AI
Modern AI research study started in the mid-1950s. [50] The very first generation of AI researchers were encouraged that synthetic basic intelligence was possible which it would exist in just a few decades. [51] AI leader Herbert A. Simon composed in 1965: "devices will be capable, within twenty years, of doing any work a male can do." [52]
Their forecasts were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers thought they might produce by the year 2001. AI leader Marvin Minsky was an expert [53] on the task of making HAL 9000 as realistic as possible according to the consensus predictions of the time. He said in 1967, "Within a generation ... the issue of creating 'expert system' will considerably be resolved". [54]
Several classical AI projects, such as Doug Lenat's Cyc project (that started in 1984), and Allen Newell's Soar project, were directed at AGI.
However, in the early 1970s, it became apparent that scientists had grossly underestimated the trouble of the project. Funding agencies ended up being hesitant of AGI and put researchers under increasing pressure to produce useful "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that included AGI objectives like "continue a table talk". [58] In response to this and the success of professional systems, both industry and federal government pumped cash into the field. [56] [59] However, self-confidence in AI spectacularly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever fulfilled. [60] For the second time in twenty years, AI researchers who forecasted the imminent achievement of AGI had been misinterpreted. By the 1990s, AI researchers had a track record for making vain pledges. They became hesitant to make predictions at all [d] and prevented mention of "human level" expert system for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research
In the 1990s and early 21st century, mainstream AI accomplished business success and scholastic respectability by focusing on particular sub-problems where AI can produce proven outcomes and industrial applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now used extensively throughout the technology market, and research study in this vein is greatly moneyed in both academic community and market. As of 2018 [update], advancement in this field was considered an emerging trend, and a mature stage was expected to be reached in more than ten years. [64]
At the millenium, numerous traditional AI researchers [65] hoped that strong AI might be established by integrating programs that resolve different sub-problems. Hans Moravec composed in 1988:
I am positive that this bottom-up path to artificial intelligence will one day satisfy the conventional top-down path majority way, ready to provide the real-world competence and the commonsense understanding that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven uniting the two efforts. [65]
However, even at the time, this was challenged. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by mentioning:
The expectation has often been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow fulfill "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper stand, then this expectation is hopelessly modular and there is really just one feasible path from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this path (or vice versa) - nor is it clear why we ought to even attempt to reach such a level, because it appears getting there would simply amount to uprooting our signs from their intrinsic significances (consequently merely minimizing ourselves to the functional equivalent of a programmable computer system). [66]
Modern synthetic basic intelligence research
The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud [67] in a discussion of the implications of completely automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative increases "the ability to please goals in a large range of environments". [68] This type of AGI, defined by the ability to maximise a mathematical meaning of intelligence instead of show human-like behaviour, [69] was likewise called universal synthetic intelligence. [70]
The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research study activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary results". The very first summertime school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The very first university course was given in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, organized by Lex Fridman and including a variety of visitor speakers.
Since 2023 [upgrade], a little number of computer system scientists are active in AGI research study, and lots of add to a series of AGI conferences. However, progressively more scientists are interested in open-ended learning, [76] [77] which is the concept of enabling AI to continually find out and innovate like human beings do.
Feasibility
As of 2023, the development and potential accomplishment of AGI remains a subject of intense argument within the AI neighborhood. While standard consensus held that AGI was a distant goal, current improvements have actually led some researchers and industry figures to claim that early forms of AGI may already exist. [78] AI leader Herbert A. Simon speculated in 1965 that "makers will be capable, within twenty years, of doing any work a man can do". This forecast stopped working to come true. Microsoft co-founder Paul Allen thought that such intelligence is not likely in the 21st century since it would require "unforeseeable and fundamentally unforeseeable breakthroughs" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf in between contemporary computing and human-level expert system is as large as the gulf in between current area flight and practical faster-than-light spaceflight. [80]
A further challenge is the absence of clarity in specifying what intelligence requires. Does it need awareness? Must it show the ability to set goals along with pursue them? Is it purely a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are facilities such as planning, reasoning, and causal understanding required? Does intelligence require clearly replicating the brain and its particular professors? Does it require feelings? [81]
Most AI scientists think strong AI can be accomplished in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of accomplishing strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be accomplished, but that today level of progress is such that a date can not accurately be anticipated. [84] AI experts' views on the expediency of AGI wax and subside. Four surveys conducted in 2012 and 2013 suggested that the mean price quote amongst specialists for when they would be 50% confident AGI would show up was 2040 to 2050, depending on the poll, with the mean being 2081. Of the experts, 16.5% responded to with "never" when asked the very same concern however with a 90% confidence instead. [85] [86] Further present AGI development considerations can be discovered above Tests for validating human-level AGI.
A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year time frame there is a strong predisposition towards forecasting the arrival of human-level AI as between 15 and 25 years from the time the forecast was made". They examined 95 forecasts made in between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft scientists published a comprehensive assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it might fairly be considered as an early (yet still insufficient) version of an artificial basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 surpasses 99% of humans on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a considerable level of general intelligence has actually currently been attained with frontier designs. They wrote that unwillingness to this view originates from 4 primary factors: a "healthy uncertainty about metrics for AGI", an "ideological dedication to alternative AI theories or techniques", a "devotion to human (or biological) exceptionalism", or a "issue about the economic implications of AGI". [91]
2023 likewise marked the emergence of large multimodal designs (big language designs efficient in processing or producing numerous modalities such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the first of a series of models that "invest more time thinking before they react". According to Mira Murati, this ability to think before reacting represents a new, extra paradigm. It improves design outputs by investing more computing power when generating the response, whereas the model scaling paradigm enhances outputs by increasing the model size, training information and training calculate power. [93] [94]
An OpenAI staff member, Vahid Kazemi, declared in 2024 that the company had actually attained AGI, stating, "In my viewpoint, we have actually currently accomplished AGI and galgbtqhistoryproject.org it's even more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any job", it is "much better than most human beings at the majority of jobs." He also addressed criticisms that big language models (LLMs) simply follow predefined patterns, comparing their knowing procedure to the clinical method of observing, hypothesizing, and confirming. These declarations have actually sparked argument, as they count on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models demonstrate impressive adaptability, they might not fully satisfy this standard. Notably, Kazemi's remarks came soon after OpenAI got rid of "AGI" from the terms of its collaboration with Microsoft, prompting speculation about the company's tactical objectives. [95]
Timescales
Progress in artificial intelligence has traditionally gone through durations of quick progress separated by durations when development appeared to stop. [82] Ending each hiatus were basic advances in hardware, software application or both to produce area for further development. [82] [98] [99] For instance, the hardware offered in the twentieth century was not enough to implement deep knowing, which requires big numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel states that estimates of the time required before a truly flexible AGI is built vary from 10 years to over a century. As of 2007 [update], the agreement in the AGI research study neighborhood appeared to be that the timeline gone over by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was plausible. [103] Mainstream AI scientists have given a wide variety of viewpoints on whether progress will be this fast. A 2012 meta-analysis of 95 such opinions found a predisposition towards predicting that the onset of AGI would take place within 16-26 years for modern-day and historic predictions alike. That paper has been slammed for how it classified opinions as professional or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competitors with a top-5 test mistake rate of 15.3%, considerably better than the second-best entry's rate of 26.3% (the traditional method utilized a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the present deep learning wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on publicly readily available and freely accessible weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ value of about 47, which corresponds around to a six-year-old child in first grade. An adult comes to about 100 typically. Similar tests were performed in 2014, with the IQ rating reaching an optimum value of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model efficient in carrying out lots of varied tasks without specific training. According to Gary Grossman in a VentureBeat short article, while there is agreement that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be categorized as a narrow AI system. [108]
In the same year, Jason Rohrer utilized his GPT-3 account to develop a chatbot, and offered a chatbot-developing platform called "Project December". OpenAI asked for modifications to the chatbot to comply with their security guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system capable of performing more than 600 various jobs. [110]
In 2023, Microsoft Research published a research study on an early version of OpenAI's GPT-4, contending that it exhibited more general intelligence than previous AI models and demonstrated human-level performance in tasks spanning several domains, such as mathematics, coding, and law. This research study triggered an argument on whether GPT-4 could be thought about an early, insufficient variation of synthetic basic intelligence, stressing the requirement for more exploration and evaluation of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton mentioned that: [112]
The idea that this stuff might actually get smarter than people - a couple of people believed that, [...] But many people thought it was method off. And I believed it was method off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer believe that.
In May 2023, Demis Hassabis likewise said that "The progress in the last few years has actually been quite extraordinary", which he sees no reason why it would slow down, expecting AGI within a decade or even a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, stated his expectation that within five years, AI would be capable of passing any test at least as well as humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI employee, approximated AGI by 2027 to be "noticeably possible". [115]
Whole brain emulation
While the advancement of transformer designs like in ChatGPT is considered the most appealing course to AGI, [116] [117] whole brain emulation can serve as an alternative approach. With whole brain simulation, a brain model is built by scanning and mapping a biological brain in detail, and after that copying and replicating it on a computer system or another computational device. The simulation model need to be adequately faithful to the initial, so that it acts in practically the exact same method as the original brain. [118] Whole brain emulation is a kind of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research study functions. It has been discussed in expert system research study [103] as a technique to strong AI. Neuroimaging technologies that could deliver the required in-depth understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of adequate quality will end up being readily available on a similar timescale to the computing power needed to emulate it.
Early approximates
For low-level brain simulation, a really powerful cluster of computers or GPUs would be required, given the massive quantity of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain's processing power, based on an easy switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at different price quotes for the hardware required to equal the human brain and adopted a figure of 1016 calculations per 2nd (cps). [e] (For contrast, if a "computation" was equivalent to one "floating-point operation" - a step utilized to rate present supercomputers - then 1016 "computations" would be equivalent to 10 petaFLOPS, attained in 2011, while 1018 was achieved in 2022.) He utilized this figure to predict the necessary hardware would be readily available at some point in between 2015 and 2025, if the rapid growth in computer system power at the time of writing continued.
Current research
The Human Brain Project, an EU-funded effort active from 2013 to 2023, has actually developed a particularly comprehensive and openly available atlas of the human brain. [124] In 2023, scientists from Duke University performed a high-resolution scan of a mouse brain.
Criticisms of simulation-based techniques
The synthetic neuron model assumed by Kurzweil and used in lots of existing artificial neural network implementations is simple compared with biological neurons. A brain simulation would likely have to record the comprehensive cellular behaviour of biological nerve cells, currently understood only in broad summary. The overhead presented by complete modeling of the biological, chemical, and physical information of neural behaviour (specifically on a molecular scale) would need computational powers several orders of magnitude bigger than Kurzweil's quote. In addition, the quotes do not represent glial cells, which are known to contribute in cognitive processes. [125]
A fundamental criticism of the simulated brain technique originates from embodied cognition theory which asserts that human embodiment is an important aspect of human intelligence and is needed to ground meaning. [126] [127] If this theory is right, any fully functional brain model will need to incorporate more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an option, but it is unknown whether this would be adequate.
Philosophical point of view
"Strong AI" as specified in viewpoint
In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese space argument. [128] He proposed a difference in between 2 hypotheses about expert system: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "awareness".
Weak AI hypothesis: An expert system system can (only) act like it believes and has a mind and consciousness.
The very first one he called "strong" because it makes a more powerful declaration: it presumes something unique has actually taken place to the device that goes beyond those capabilities that we can evaluate. The behaviour of a "weak AI" device would be specifically identical to a "strong AI" maker, however the latter would likewise have subjective mindful experience. This use is also typical in academic AI research study and books. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term "strong AI" to suggest "human level synthetic basic intelligence". [102] This is not the same as Searle's strong AI, unless it is assumed that consciousness is required for human-level AGI. Academic theorists such as Searle do not think that holds true, and to most expert system researchers the concern is out-of-scope. [130]
Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they don't care if you call it genuine or a simulation." [130] If the program can act as if it has a mind, then there is no requirement to understand if it actually has mind - indeed, there would be no chance to inform. For AI research, Searle's "weak AI hypothesis" is equivalent to the statement "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for granted, and do not care about the strong AI hypothesis." [130] Thus, for scholastic AI research, "Strong AI" and "AGI" are 2 different things.
Consciousness
Consciousness can have various meanings, and some elements play considerable functions in sci-fi and the principles of synthetic intelligence:
Sentience (or "incredible awareness"): The ability to "feel" perceptions or feelings subjectively, as opposed to the ability to reason about perceptions. Some thinkers, such as David Chalmers, use the term "awareness" to refer exclusively to incredible consciousness, which is approximately comparable to life. [132] Determining why and how subjective experience occurs is referred to as the tough issue of consciousness. [133] Thomas Nagel described in 1974 that it "seems like" something to be mindful. If we are not conscious, then it doesn't feel like anything. Nagel utilizes the example of a bat: we can sensibly ask "what does it seem like to be a bat?" However, we are not likely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat appears to be mindful (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had actually accomplished life, though this claim was widely contested by other professionals. [135]
Self-awareness: To have mindful awareness of oneself as a different person, particularly to be consciously mindful of one's own thoughts. This is opposed to just being the "topic of one's believed"-an os or debugger is able to be "familiar with itself" (that is, to represent itself in the very same way it represents whatever else)-but this is not what individuals normally indicate when they utilize the term "self-awareness". [g]
These characteristics have an ethical dimension. AI sentience would provide rise to concerns of welfare and legal protection, likewise to animals. [136] Other aspects of consciousness related to cognitive abilities are also relevant to the principle of AI rights. [137] Determining how to integrate advanced AI with existing legal and social structures is an emergent concern. [138]
Benefits
AGI might have a wide array of applications. If oriented towards such goals, AGI could assist reduce various problems in the world such as cravings, poverty and illness. [139]
AGI could enhance efficiency and efficiency in a lot of tasks. For instance, in public health, AGI might speed up medical research, significantly against cancer. [140] It could look after the elderly, [141] and democratize access to rapid, high-quality medical diagnostics. It might offer enjoyable, low-cost and customized education. [141] The need to work to subsist might end up being obsolete if the wealth produced is appropriately rearranged. [141] [142] This also raises the question of the place of people in a drastically automated society.
AGI might also help to make logical decisions, and to prepare for and prevent catastrophes. It could also help to profit of potentially catastrophic innovations such as nanotechnology or environment engineering, while preventing the associated threats. [143] If an AGI's main goal is to avoid existential disasters such as human extinction (which could be challenging if the Vulnerable World Hypothesis turns out to be real), [144] it might take measures to drastically decrease the risks [143] while lessening the impact of these steps on our lifestyle.
Risks
Existential risks
AGI may represent several kinds of existential threat, which are threats that threaten "the premature extinction of Earth-originating smart life or the long-term and drastic destruction of its capacity for preferable future advancement". [145] The danger of human extinction from AGI has been the subject of numerous arguments, however there is likewise the possibility that the development of AGI would result in a completely problematic future. Notably, it might be used to spread and maintain the set of values of whoever establishes it. If mankind still has moral blind areas similar to slavery in the past, AGI may irreversibly entrench it, preventing moral development. [146] Furthermore, AGI could help with mass monitoring and brainwashing, which might be utilized to produce a steady repressive around the world totalitarian regime. [147] [148] There is also a danger for the devices themselves. If machines that are sentient or otherwise worthwhile of ethical consideration are mass produced in the future, taking part in a civilizational path that indefinitely neglects their welfare and interests could be an existential catastrophe. [149] [150] Considering how much AGI could improve humankind's future and help in reducing other existential risks, Toby Ord calls these existential risks "an argument for proceeding with due caution", not for "deserting AI". [147]
Risk of loss of control and human termination
The thesis that AI postures an existential risk for people, which this risk needs more attention, is controversial but has actually been backed in 2023 by many public figures, AI researchers and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking criticized prevalent indifference:
So, facing possible futures of incalculable advantages and threats, the specialists are definitely doing everything possible to ensure the finest result, right? Wrong. If a remarkable alien civilisation sent us a message stating, 'We'll get here in a few years,' would we simply respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is occurring with AI. [153]
The potential fate of humankind has actually often been compared to the fate of gorillas threatened by human activities. The comparison specifies that greater intelligence allowed humankind to dominate gorillas, which are now susceptible in manner ins which they might not have anticipated. As a result, the gorilla has actually ended up being a threatened species, not out of malice, however just as a civilian casualties from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to dominate mankind which we ought to be cautious not to anthropomorphize them and analyze their intents as we would for people. He stated that people won't be "smart adequate to design super-intelligent makers, yet extremely silly to the point of offering it moronic objectives without any safeguards". [155] On the other side, the idea of instrumental convergence recommends that almost whatever their objectives, smart agents will have reasons to try to survive and get more power as intermediary actions to achieving these objectives. Which this does not need having feelings. [156]
Many scholars who are concerned about existential threat advocate for more research into fixing the "control problem" to address the question: what kinds of safeguards, algorithms, or architectures can programmers carry out to maximise the likelihood that their recursively-improving AI would continue to act in a friendly, instead of harmful, way after it reaches superintelligence? [157] [158] Solving the control problem is complicated by the AI arms race (which could result in a race to the bottom of safety precautions in order to launch products before competitors), [159] and using AI in weapon systems. [160]
The thesis that AI can posture existential danger likewise has detractors. Skeptics generally state that AGI is not likely in the short-term, or that concerns about AGI sidetrack from other concerns connected to present AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for many people outside of the technology market, existing chatbots and LLMs are already viewed as though they were AGI, causing additional misunderstanding and worry. [162]
Skeptics often charge that the thesis is crypto-religious, with an unreasonable belief in the possibility of superintelligence replacing an irrational belief in a supreme God. [163] Some researchers believe that the interaction projects on AI existential risk by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulatory capture and to inflate interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and researchers, issued a joint declaration asserting that "Mitigating the risk of termination from AI ought to be an international priority together with other societal-scale risks such as pandemics and nuclear war." [152]
Mass unemployment
Researchers from OpenAI approximated that "80% of the U.S. labor force might have at least 10% of their work tasks affected by the introduction of LLMs, while around 19% of employees might see at least 50% of their jobs affected". [166] [167] They think about office employees to be the most exposed, for instance mathematicians, accounting professionals or web designers. [167] AGI could have a better autonomy, capability to make choices, to interface with other computer tools, but also to manage robotized bodies.
According to Stephen Hawking, the result of automation on the quality of life will depend upon how the wealth will be redistributed: [142]
Everyone can take pleasure in a life of glamorous leisure if the machine-produced wealth is shared, or a lot of individuals can wind up badly bad if the machine-owners effectively lobby against wealth redistribution. So far, the pattern seems to be towards the 2nd option, with technology driving ever-increasing inequality
Elon Musk thinks about that the automation of society will require federal governments to embrace a universal fundamental earnings. [168]
See likewise
Artificial brain - Software and hardware with cognitive capabilities similar to those of the animal or human brain
AI result
AI security - Research location on making AI safe and useful
AI positioning - AI conformance to the intended goal
A.I. Rising - 2018 movie directed by Lazar Bodroža
Expert system
Automated artificial intelligence - Process of automating the application of artificial intelligence
BRAIN Initiative - Collaborative public-private research study initiative announced by the Obama administration
China Brain Project
Future of Humanity Institute - Defunct Oxford interdisciplinary research centre
General video game playing - Ability of artificial intelligence to play different games
Generative expert system - AI system efficient in creating material in action to triggers
Human Brain Project - Scientific research study task
Intelligence amplification - Use of infotech to augment human intelligence (IA).
Machine principles - Moral behaviours of man-made makers.
Moravec's paradox.
Multi-task knowing - Solving numerous device finding out tasks at the exact same time.
Neural scaling law - Statistical law in artificial intelligence.
Outline of artificial intelligence - Overview of and topical guide to synthetic intelligence.
Transhumanism - Philosophical motion.
Synthetic intelligence - Alternate term for or form of artificial intelligence.
Transfer learning - Artificial intelligence method.
Loebner Prize - Annual AI competitors.
Hardware for expert system - Hardware specially designed and enhanced for artificial intelligence.
Weak synthetic intelligence - Form of expert system.
Notes
^ a b See listed below for the origin of the term "strong AI", and see the academic definition of "strong AI" and weak AI in the post Chinese space.
^ AI founder John McCarthy composes: "we can not yet characterize in basic what sort of computational treatments we want to call smart. " [26] (For a conversation of some definitions of intelligence used by expert system researchers, see viewpoint of expert system.).
^ The Lighthill report specifically slammed AI's "grandiose goals" and led the taking apart of AI research study in England. [55] In the U.S., DARPA became identified to fund only "mission-oriented direct research, instead of standard undirected research study". [56] [57] ^ As AI founder John McCarthy writes "it would be a terrific relief to the rest of the workers in AI if the inventors of brand-new basic formalisms would express their hopes in a more secured type than has actually in some cases held true." [61] ^ In "Mind Children" [122] 1015 cps is used. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced.
^ As specified in a standard AI textbook: "The assertion that devices could potentially act smartly (or, possibly much better, act as if they were smart) is called the 'weak AI' hypothesis by theorists, and the assertion that machines that do so are really thinking (rather than replicating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References
^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is created to perform a single job.
^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to ensure that synthetic general intelligence benefits all of humankind.
^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new objective is developing artificial general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D jobs were identified as being active in 2020.
^ a b c "AI timelines: What do experts in synthetic intelligence expect for the future?". Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023.
^ "AI pioneer Geoffrey Hinton gives up Google and cautions of risk ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is difficult to see how you can avoid the bad stars from using it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals triggers of AGI.
^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you alter modifications you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York City Times. The genuine danger is not AI itself however the method we deploy it.
^ "Impressed by expert system? Experts say AGI is coming next, and it has 'existential' risks". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might posture existential dangers to humankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last creation that humankind needs to make.
^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the risk of extinction from AI must be a worldwide priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals warn of threat of termination from AI.
^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from developing machines that can outthink us in general methods.
^ LeCun, Yann (June 2023). "AGI does not present an existential danger". Medium. There is no reason to fear AI as an existential threat.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the initial on 14 August 2005: Kurzweil describes strong AI as "device intelligence with the full range of human intelligence.".
^ "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014.
^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical sign system hypothesis.
^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007.
^ "What is synthetic superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023.
^ "Expert system is changing our world - it is on everybody to make certain that it works out". Our World in Data. Retrieved 8 October 2023.
^ Dickson, Ben (16 November 2023). "Here is how far we are to attaining AGI, according to DeepMind". VentureBeat.
^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart characteristics is based upon the topics covered by significant AI books, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998.
^ Johnson 1987.
^ de Charms, R. (1968 ). Personal causation. New York City: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body shapes the method we think: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). "Motivation reevaluated: The idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). "Motivation reconsidered: The concept of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014.
^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019.
^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What occurs when it does?". The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ "Eugene Goostman is a genuine young boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists dispute whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not distinguish GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here's a list of challenging exams both AI versions have passed". Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Profit From It". Retrieved 30 May 2023.
^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024.
^ Gopani, Avi (25 May 2022). "Turing Test is undependable. The Winograd Schema is outdated. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested testing an AI chatbot's ability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024.
^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024.
^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Expert System (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".).
^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Specifying Feature of AI-Completeness" (PDF). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013.
^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). "Artificial Intelligence, Business and Civilization - Our Fate Made in Machines". Archived from the initial on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 quoted in Crevier 1993, p. 109.
^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), quoted in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, "Shift to Applied Research Increases Investment".
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software engineers avoided the term expert system for fear of being seen as wild-eyed dreamers.
^ Russell & Norvig 2003, pp. 25-26
^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the initial on 22 May 2019. Retrieved 7 May 2019.
^ a b Moravec 1988, p. 20
^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300.
^ Gubrud 1997
^ Hutter, Marcus (2005 ). Universal Artificial Intelligence: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022.
^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410.
^ "Who coined the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: 'The term "AGI" was popularized by ... Shane Legg, Mark Gubrud and Ben Goertzel'
^ Wang & Goertzel 2007
^ "First International Summer School in Artificial General Intelligence, Main summer season school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original on 28 September 2020. Retrieved 11 May 2020.
^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020.
^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter season trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020.
^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limits of device intelligence: Despite development in machine intelligence, artificial basic intelligence is still a significant difficulty". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL]
^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023.
^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014.
^ Winfield, Alan. "Artificial intelligence will not develop into a Frankenstein's beast". The Guardian. Archived from the original on 17 September 2014. Retrieved 17 September 2014.
^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and drapia.org Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071.
^ a b c Clocksin 2003.
^ Fjelland, Ragnar (17 June 2020). "Why basic expert system will not be understood". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554.
^ McCarthy 2007b.
^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will synthetic intelligence bring us paradise or destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A study of skilled opinion. In Fundamental problems of synthetic intelligence (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023.
^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Is Already Here". Noema.
^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024.
^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024.
^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024.
^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024.
^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024.
^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024.
^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the original on 28 August 2022. Retrieved 28 August 2022.
^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the original on 29 August 2022. Retrieved 29 August 2022.
^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep knowing in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559.
^ Goertzel & Pennachin 2006.
^ a b c (Kurzweil 2005, p. 260).
^ a b c Goertzel 2007.
^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the original on 4 December 2020. Retrieved 24 August 2020.
^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190.
^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Expert System". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130.
^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is two times as clever as Siri - however a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019.
^ Grossman, Gary (3 September 2020). "We're going into the AI twilight zone between narrow and basic AI". VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are already seeing an early example of an AGI system in the just recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is debatable, but the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a happy medium in between narrow and general AI.
^ Quach, Katyanna. "A developer built an AI chatbot using GPT-3 that helped a man speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the original on 16 October 2021. Retrieved 16 October 2021.
^ Wiggers, Kyle (13 May 2022), "DeepMind's new AI can perform over 600 jobs, from playing games to managing robotics", TechCrunch, archived from the initial on 16 June 2022, recovered 12 June 2022.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL]
^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York Times. ISSN 0362-4331. Retrieved 7 June 2023.
^ Bove, Tristan. "A.I. could equal human intelligence in 'just a couple of years,' says CEO of Google's primary A.I. research laboratory". Fortune. Retrieved 4 September 2024.
^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI could pass human tests in 5 years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead".
^ Sullivan, Mark (18 October 2023). "Why everyone seems to disagree on how to specify Artificial General Intelligence". Fast Company.
^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024.
^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023.
^ Sandberg & Boström 2008.
^ Drachman 2005.
^ a b Russell & Norvig 2003.
^ Moravec 1988, p. 61.
^ Moravec 1998.
^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study task maps the human brain". euractiv.
^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the initial on 8 February 2014. Retrieved 24 January 2014.
^ de Vega, Glenberg & Graesser 2008. A vast array of views in current research, all of which require grounding to some degree
^ Thornton, Angela (26 June 2023). "How publishing our minds to a computer may become possible". The Conversation. Retrieved 8 November 2023.
^ Searle 1980
^ For instance: Russell & Norvig 2003,
Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (estimated in" Encyclopedia.com"),.
MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced quote in "AITopics"),.
Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.
^ a b c Russell & Norvig 2003, p. 947.
^ though see Explainable expert system for curiosity by the field about why a program behaves the way it does.
^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review.
^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024.
^ Nagel 1974.
^ "The Google engineer who thinks the business's AI has come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023.
^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024.
^ Nosta, John (18 December 2023). "Should Expert System Have Rights?". Psychology Today. Retrieved 5 September 2024.
^ Akst, Daniel (10 April 2023). "Should Robots With Expert System Have Moral or Legal Rights?". The Wall Street Journal.
^ "Artificial General Intelligence - Do [es] the cost surpass advantages?". 23 August 2021. Retrieved 7 June 2023.
^ "How we can Take advantage of Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023.
^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Look Like When Artificial Intelligence Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023.
^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023.
^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: courses, threats, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; New York, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2.
^ Piper, Kelsey (19 November 2018). "How technological progress is making it likelier than ever that humans will destroy ourselves". Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). "Climate change an 'existential security risk' to Australia, Senate query states". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023.
^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6.
^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned Artificial Intelligence". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9.
^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023.
^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023.
^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023.
^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. ISSN 0362-4331. Retrieved 24 December 2023.
^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023.
^ "Stephen Hawking: 'Transcendence takes a look at the ramifications of artificial intelligence - however are we taking AI seriously enough?'". The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014.
^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023.
^ "The interesting Facebook debate in between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI". The remarkable Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023.
^ "Will Artificial Intelligence Doom The Human Race Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023.
^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to disastrous AGI danger: a study". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2.
^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023.
^ Tetlow, Gemma (12 January 2017). "AI arms race risks spiralling out of control, report warns". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023.
^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over danger presented however synthetic intelligence can not be ignored". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023.
^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023.
^ Hamblin, James (9 May 2014). "But What Would completion of Humanity Mean for Me?". The Atlantic. Archived from the original on 4 June 2014. Retrieved 12 December 2015.
^ Titcomb, James (30 October 2023). "Big Tech is stiring fears over AI, caution researchers". The Telegraph. Retrieved 7 December 2023.
^ Davidson, John (30 October 2023). "Google Brain founder states big tech is lying about AI termination danger". Australian Financial Review. Archived from the initial on 7 December 2023. Retrieved 7 December 2023.
^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early look at the labor market impact capacity of large language models". OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could see their tasks impacted by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023.
^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we require universal fundamental earnings because 'in the future, manual labor will be an option'". Business Insider. Archived from the initial on 9 July 2023. Retrieved 8 June 2023.
Sources
UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the initial on 18 June 2022. Retrieved 22 September 2021.
Chalmers, David (1996 ), The Conscious Mind, Oxford University Press.
Clocksin, William (August 2003), "Artificial intelligence and suvenir51.ru the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007.
Crevier, Daniel (1993 ). AI: The Tumultuous Search for Artificial Intelligence. New York City, NY: BasicBooks. ISBN 0-465-02997-3.
Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68.
Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114.
Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Expert System and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4.
Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the initial (PDF) on 20 March 2013.
Goertzel, Ben (December 2007), "Human-level synthetic basic intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's review of Kurzweil", Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, retrieved 1 April 2009.
Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, recovered 7 May 2011.
Howe, J. (November 1994), Expert System at Edinburgh University: a Point of view, archived from the original on 17 August 2007, obtained 30 August 2007.
Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5.
Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press.
Lighthill, Professor Sir James (1973 ), "Expert System: A General Survey", Expert System: a paper seminar, Science Research Council.
Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7.
McCarthy, John (2007b). What is Artificial Intelligence?. Stanford University. The supreme effort is to make computer programs that can resolve problems and accomplish goals in the world as well as people.
Moravec, Hans (1988 ), Mind Children, Harvard University Press
Moravec, Hans (1998 ), "When will computer system hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the original on 15 June 2006, retrieved 23 June 2006
Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, recovered 7 November 2009
Newell, Allen; Simon, H. A. (1976 ). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022.
Nilsson, Nils (1998 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4
NRC (1999 ), "Developments in Artificial Intelligence", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, recovered 29 September 2007
Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Rational Approach, New York: Oxford University Press, archived from the initial on 25 July 2009, recovered 6 December 2007
Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the original on 25 March 2020, obtained 5 April 2009
Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the original on 17 March 2019, obtained 3 September 2020
Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row
Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4
Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 - via ResearchGate.
Further reading
Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1
Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the original on 18 February 2021, obtained 4 September 2013 - through ResearchGate
Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the original on 23 July 2014, recovered 31 August 2012
Cukier, Kenneth, "Ready for Robots? How to Think of the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system easy sufficient to be understandable will not be made complex enough to behave smartly, while any system made complex enough to act wisely will be too made complex to understand." (p. 197.) Computer researcher Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple silly. They work, however they work by strength." (p. 198.).
Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, retrieved 25 July 2010.
Gleick, James, "The Fate of Free Will" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what distinguishes us from makers. For biological creatures, reason and function originate from acting worldwide and experiencing the consequences. Artificial intelligences - disembodied, complete strangers to blood, sweat, and tears - have no event for that." (p. 30.).
Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the initial (PDF) on 6 June 2013.
- Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York City Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically anticipate that those who wish to get rich from AI are going to have the interests of the rest of us close at heart,' ... composes [Gary Marcus] 'We can't rely on governments driven by project financing contributions [from tech companies] to push back.' ... Marcus details the needs that residents should make from their federal governments and the tech business. They include openness on how AI systems work; payment for people if their data [are] utilized to train LLMs (big language model) s and the right to grant this use; and the capability to hold tech business accountable for the damages they bring on by eliminating Section 230, imposing money penalites, and passing more stringent product liability laws ... Marcus also recommends ... that a brand-new, AI-specific federal agency, similar to the FDA, the FCC, or the FTC, might provide the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... recommends ... develop [ing] a professional licensing routine for engineers that would function in a similar method to medical licenses, malpractice fits, and the Hippocratic oath in medicine. 'What if, like physicians,' she asks ..., 'AI engineers likewise promised to do no harm?'" (p. 46.).
Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in expert system", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653.
Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stymied humans for decades, exposes the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competition has revealed that although NLP (natural-language processing) models are capable of unbelievable accomplishments, their capabilities are extremely much limited by the amount of context they get. This [...] could trigger [troubles] for researchers who wish to use them to do things such as analyze ancient languages. In many cases, there are few historic records on long-gone civilizations to act as training data for such a purpose." (p. 82.).
Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to produce phony videos indistinguishable from genuine ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest reasonable videos produced using expert system that really trick people, then they barely exist. The fakes aren't deep, and the deeps aren't phony. [...] A.I.-generated videos are not, in basic, operating in our media as counterfeited evidence. Their function much better resembles that of cartoons, particularly smutty ones." (p. 59.).
- Leffer, Lauren, "The Risks of Trusting AI: We should avoid humanizing machine-learning models used in clinical research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81.
Lepore, Jill, "The Chit-Chatbot: Is talking with a maker a conversation?", The New Yorker, 7 October 2024, pp. 12-16.
Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of synthetic basic intelligence are stymmied by the same old issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45.
McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009.
McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, retrieved 29 September 2007.
Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill.
Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, presented and dispersed at the 2007 Singularity Summit, San Francisco, California.
Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead police to overlook contradictory proof?", The New Yorker, 20 November 2023, pp. 20-26.
Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test however revealed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at jobs that require real humanlike thinking or an understanding of the physical and social world ... ChatGPT appeared not able to reason logically and tried to rely on its huge database of ... realities obtained from online texts. "
- Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are effective but undependable. Rules-based systems can not handle circumstances their programmers did not expect. Learning systems are restricted by the information on which they were trained. AI failures have actually already resulted in tragedy. Advanced auto-pilot functions in cars and trucks, although they carry out well in some circumstances, have actually driven vehicles without alerting into trucks, concrete barriers, and parked cars and trucks. In the wrong situation, AI systems go from supersmart to superdumb in an immediate. When an enemy is trying to manipulate and lespoetesbizarres.free.fr hack an AI system, the threats are even higher." (p. 140.).
Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, shiapedia.1god.org and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267.
- Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are enabled by brand-new innovations however count on the timelelss human propensity to anthropomorphise." (p. 29.).
Williams, R. W.; Herrup, K.