• The meaning of artificial general intelligence for the AI industry and the world.?
  通用人工智能對(duì)人工智能行業(yè)和世界的意義。
• Is artificial general intelligence possible? Various development approaches and predictions.?
  人工通用智能可能嗎？各種開(kāi)發(fā)方法和預(yù)測(cè)。
• Potential risks of creating strong AI that rivals human intelligence. Should we be wary of AI?
  創(chuàng)建可與人類(lèi)智能相媲美的強(qiáng)大人工智能的潛在風(fēng)險(xiǎn)。我們應(yīng)該警惕人工智能嗎？

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目錄

What is AGI??什么是通用人工智能？

What’s AGI AI's main concept?AGI AI 的主要概念是什么？

What is AGI artificial intelligence capable of?AGI人工智能有什么能力？

AGI vs AI difference?AGI 與 AI 的區(qū)別

Narrow AI vs General AI vs Super AI狹義 AI vs 通用 AI vs 超級(jí) AI

AGI development approachesAGI 開(kāi)發(fā)方法

Challenges in the development of AGI technologyAGI技術(shù)發(fā)展面臨的挑戰(zhàn)

Future of artificial general intelligence通用人工智能的未來(lái)

Risks from artificial general intelligence人工智能的風(fēng)險(xiǎn)

General AI Myth or Fact一般人工智能神話(huà)或事實(shí)

Conclusion?結(jié)論

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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.

最近人工智能發(fā)展的爆炸式增長(zhǎng)帶來(lái)了許多想法和問(wèn)題。在全世界見(jiàn)證了這項(xiàng)技術(shù)的強(qiáng)大后，突然之間，科幻小說(shuō)中關(guān)于擁有與人類(lèi)相媲美的智能的機(jī)器人的情節(jié)似乎不再是不可能的了。一些專(zhuān)家表示，創(chuàng)建下一代人工智能——通用人工智能——的第一步已經(jīng)邁出。

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.

我們決定對(duì) AGI（人工智能）這一主題、其發(fā)展的實(shí)際狀況、特征和預(yù)測(cè)進(jìn)行自己的研究。當(dāng)然，Atlasiko 與您分享我們對(duì) AGI 含義的分析，以回答“AI 中的 AGI 是什么？”的熱門(mén)問(wèn)題。繼續(xù)閱讀，不要錯(cuò)過(guò)科技行業(yè)正在發(fā)生的可能影響整個(gè)世界的重大變革。

What is AGI??什么是通用人工智能？

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.
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為了開(kāi)始我們的解釋?zhuān)屛覀兘o出一個(gè)全面的 AGI 定義。因此，通用人工智能是一個(gè)術(shù)語(yǔ)，用于描述在軟件中具有人類(lèi)認(rèn)知能力的智能代理。換句話(huà)說(shuō)，它是一種發(fā)展到能夠與人類(lèi)同等地解決任何不熟悉的問(wèn)題和任務(wù)的發(fā)展水平的人工智能。其他一些專(zhuān)家將 AGI 定義為一種能夠自主工作并在具有經(jīng)濟(jì)價(jià)值的任務(wù)中超越普通人的系統(tǒng)。

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”.
除了通用人工智能定義的兩個(gè)變體外，它還有一些名稱(chēng)。該系統(tǒng)也可以稱(chēng)為通用人工智能以及強(qiáng)人工智能或真人工智能。在一些論文中，你可以看到“真正的人工智能”這個(gè)名字。

What’s AGI AI's main concept? AGI AI 的主要概念是什么？

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.

在 AI 中表征 AGI 含義的基本概念是“智能”和“意識(shí)”。要被認(rèn)為是 AGI，下一級(jí)人工智能必須獲得與人類(lèi)自然認(rèn)知相似甚至相同的人工認(rèn)知。就像我們的大腦通過(guò)體驗(yàn)、學(xué)習(xí)和解決問(wèn)題來(lái)創(chuàng)造新的神經(jīng)元連接一樣，通用人工智能必須在其系統(tǒng)中開(kāi)發(fā)新的連接，并像有意識(shí)的思考過(guò)程一樣對(duì)它們采取行動(dòng)。雖然智力概念比較明確是指認(rèn)知能力，但對(duì)于“意識(shí)”的說(shuō)法卻有不同的觀(guān)點(diǎn)。

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.

自然地，人工智能發(fā)展到更先進(jìn)的階段不僅引起了計(jì)算機(jī)科學(xué)專(zhuān)家的關(guān)注，也引起了研究心靈哲學(xué)和人類(lèi)生存哲學(xué)的哲學(xué)家的關(guān)注。因此，他們對(duì) AGI 系統(tǒng)可能是什么提出了自己的看法。美國(guó)哲學(xué)家 John Searle 提出的關(guān)于通用人工智能的假設(shè)為我們提供了兩個(gè)針對(duì)意識(shí)概念的 AGI 定義。

Strong AI vs Weak AI
強(qiáng)人工智能與弱人工智能

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.
在 Searle 的“中文房間論證”中，他實(shí)際上推論說(shuō)，AI 不可能真的在這個(gè)特定假設(shè)的意義上變得“強(qiáng)大”，并獲得類(lèi)人的思維。我們將達(dá)到的最大值恰好是弱人工智能，這意味著只是一個(gè)具有一般智能行為的程序。

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 person 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.
與此同時(shí)，像 Stuart Russel 和 Peter Norvig 這樣的計(jì)算機(jī)科學(xué)家將哲學(xué)假設(shè)放在一邊，表示應(yīng)該評(píng)估的主要方面是結(jié)果。 AI 只是假裝思考還是真的像人一樣思考都沒(méi)有關(guān)系，只要它給出預(yù)期的結(jié)果即可。因此，關(guān)于人工智能是否需要具有真實(shí)意識(shí)的爭(zhēng)論仍在繼續(xù)。

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.
即使現(xiàn)代科學(xué)已經(jīng)發(fā)展到我們可以制造人造器官和身體部位的地步，我們?nèi)匀粺o(wú)法復(fù)制我們存在的精神部分。因此，通用 AI 基本上是一種重現(xiàn)思維的嘗試，它賦予機(jī)器類(lèi)似人類(lèi)的智能。

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.
也許，甚至從對(duì)“什么是 AGI？”的這個(gè)簡(jiǎn)短回答中也可以看出這一點(diǎn)。你可以看出這個(gè)想法頗具爭(zhēng)議。事實(shí)上，有些人覺(jué)得它很迷人，而另一些人則說(shuō)它完全令人毛骨悚然。這個(gè)話(huà)題有很多方面，也有很多想法，我們將在本文中進(jìn)一步討論。

What is AGI artificial intelligence capable of? AGI人工智能有什么能力？

As AGI intelligence is still a hypothetical system, there’s no way to know the full extent of its capabilities. However, there are certain characteristics that indicate true AI distinguishing it from other forms. We’ve already mentioned that one of the fundamental?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 associated with the system.
由于 AGI 智能仍然是一個(gè)假設(shè)的系統(tǒng)，因此無(wú)法了解其功能的全部范圍。然而，有一些特征表明真正的 AI 有別于其他形式。我們已經(jīng)提到，AGI 的基本要求之一是能夠以與人類(lèi)無(wú)異的方式執(zhí)行認(rèn)知計(jì)算，但是，當(dāng)然，還有更多。隨著科學(xué)家們開(kāi)發(fā)出不同的方法來(lái)實(shí)現(xiàn)通用人工智能和評(píng)估的觀(guān)點(diǎn)，他們概述了與系統(tǒng)相關(guān)的各種功能。

In theory, a completed AGI system is thought to be able of:
理論上，一個(gè)完整的 AGI 系統(tǒng)被認(rèn)為能夠：

• abstract thinking;??抽象思維；
• following common sense in making decisions;?
  在做決定時(shí)遵循常識(shí)；
• comprehension of cause and effect;?
  理解因果關(guān)系；
• creativeness; background knowledge;?
  才思;背景知識(shí);
• transfer learning.?遷移學(xué)習(xí)。

Some scientists also add such typical for human cognitive qualities as sentience, imagination, motivation, social intelligence, and reasoning, but they aren’t considered fundamental
一些科學(xué)家還添加了諸如感知力、想象力、動(dòng)機(jī)、社交智能和推理等典型的人類(lèi)認(rèn)知品質(zhì)，但它們并不被認(rèn)為是基本的

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 processing, and a navigation system.
除了這些能力之外，AGI 計(jì)算機(jī)還必須具備一組功能特性才能自主運(yùn)行。能力的基本實(shí)踐方面包括感官知覺(jué)、足夠水平的運(yùn)動(dòng)技能、自然語(yǔ)言理解和處理以及導(dǎo)航系統(tǒng)。

Researchers believe that?AGI systems will be able to?perform higher-level tasks, such as the following:
研究人員認(rèn)為 AGI 系統(tǒng)將能夠執(zhí)行更高級(jí)別的任務(wù)，例如：

• utilize multiple learning methods and algorithms;?
  利用多種學(xué)習(xí)方法和算法；
• comprehend belief systems,?
  理解信仰體系，
• utilize miscellaneous types of knowledge,?
  利用各種類(lèi)型的知識(shí)，
• produce definite structures for tasks;?
  為任務(wù)制定明確的結(jié)構(gòu)；
• comprehend symbol systems,?
  理解符號(hào)系統(tǒng)，
• engage in metacognition, and utilize knowledge on its basis.
  從事元認(rèn)知，并在此基礎(chǔ)上利用知識(shí)。

AGI vs AI difference?AGI 與 AI 的區(qū)別

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 about true artificial intelligence. Although both systems are based on similar algorithms and principles, the?AI vs AGI difference?is actually tremendous.
為了讓您更多地了解實(shí)現(xiàn) AGI 可能會(huì)有多么革命性，讓我們將其與我們現(xiàn)在可以體驗(yàn)的技術(shù)——人工智能——進(jìn)行比較。正是這項(xiàng)技術(shù)及其最近的進(jìn)步促使科學(xué)家們激活了關(guān)于真正人工智能的討論和研究。盡管兩個(gè)系統(tǒng)都基于相似的算法和原理，但 AI 與 AGI 的差異實(shí)際上是巨大的。

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.
研究人員將我們現(xiàn)在所了解和使用的人工智能稱(chēng)為狹義人工智能（Narrow AI）（主流人工智能科學(xué)中的弱人工智能）。該名稱(chēng)基本上是不言自明的，因?yàn)樵撓到y(tǒng)只能執(zhí)行一組特定的“狹窄”任務(wù)。

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.
與狹義的AI相反，AGI在理論上沒(méi)有任何能力限制。它應(yīng)該能夠處理任何不熟悉的問(wèn)題并擁有各個(gè)領(lǐng)域的知識(shí)。

Narrow AI vs General AI vs Super AI 狹義 AI vs 通用 AI vs 超級(jí) 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.
下面我們比較了上面提到的兩種類(lèi)型，通用 AI 與狹義 AI，以及比它們更高級(jí)的 AI——超級(jí) AI。

AGI development approaches AGI 開(kāi)發(fā)方法

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 development.
盡管 AGI 人工智能仍然是一個(gè)假設(shè)的概念，但計(jì)算機(jī)科學(xué)界最偉大的頭腦已經(jīng)在研究實(shí)現(xiàn)這項(xiàng)技術(shù)的可能方法和途徑。經(jīng)過(guò)細(xì)致的分析，我們選擇了最流行的 AGI 開(kāi)發(fā)方法。

Brain emulation?大腦模擬

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.
一種可能且最有爭(zhēng)議的方法是人腦仿真。它可以通過(guò)對(duì)人腦進(jìn)行徹底掃描、映射并將其上傳到功能強(qiáng)大的計(jì)算設(shè)備上來(lái)完成。盡管聽(tīng)起來(lái)很有未來(lái)感，但模擬大腦的合適硬件實(shí)際上存在于當(dāng)下。根據(jù)美國(guó)計(jì)算機(jī)科學(xué)家 Raymond Kurzweil 的說(shuō)法，每秒足以模擬我們大腦的計(jì)算量是 10^16，而世界上最快的超級(jí)計(jì)算機(jī)（截至 2023 年 3 月）Frontier 能夠在 1 分鐘內(nèi)執(zhí)行 10^18 次計(jì)算第二。當(dāng)然，由于這臺(tái)計(jì)算機(jī)的龐大體積和獨(dú)特性，它目前還不能用于實(shí)驗(yàn)，顯然不能用于普通場(chǎng)合。此外，Kurzweil 的估計(jì)不包括大多數(shù)令人興奮的人工神經(jīng)網(wǎng)絡(luò)使用生物神經(jīng)元的簡(jiǎn)化模型這一事實(shí)。要完全模擬具有所有特征的人腦，需要更多的計(jì)算能力。

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 then 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.
另一個(gè)問(wèn)題是掃描過(guò)程。人類(lèi)的大腦仍有待完全發(fā)現(xiàn)，因?yàn)榧词菇?jīng)過(guò)幾個(gè)世紀(jì)的研究，科學(xué)家們?nèi)匀淮嬖诤邳c(diǎn)。最受歡迎的建議是使用特殊的納米機(jī)器人來(lái)收集有關(guān)大腦功能的準(zhǔn)確數(shù)據(jù)，但即便如此，科學(xué)家們也不能保證這些機(jī)器人能夠捕捉到所有的特性。因此，為了確保成功實(shí)現(xiàn)通用 AI 的大腦仿真，研究人員仍需花費(fèi)至少二十年的時(shí)間來(lái)開(kāi)發(fā)所需的技術(shù)。

Algorithmic probability?算法概率

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).
實(shí)現(xiàn) AGI 的另一種方法是基于 Ray Solomonoff 引入的算法概率理論。根據(jù)該方法，即使在給定不熟悉的情況下，智能代理也能夠預(yù)測(cè)環(huán)境并決定最佳行動(dòng)它（拜耳定理）。

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.
作為這種方法的延續(xù)，DeepMind 資深科學(xué)家 Marcus Hutter 創(chuàng)建了通用人工智能的數(shù)學(xué)理論——AIXI。它是一種理論上的強(qiáng)化學(xué)習(xí)代理，它還使用所羅門(mén)諾夫的歸納法，根據(jù)對(duì)環(huán)境的觀(guān)察和獎(jiǎng)勵(lì)來(lái)選擇可能的最佳行動(dòng)。

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 AIXItl 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.
盡管這兩種模型都有可靠的理論證明，但它們?cè)趯?shí)踐中被認(rèn)為是不可計(jì)算的，這意味著不可能創(chuàng)建一個(gè)準(zhǔn)確的算法來(lái)始終正確地解決問(wèn)題。目前，有一些近似人工智能的例子，如 AIXItl 和 UCAI，但是，它們?cè)谟?jì)算時(shí)間方面有一個(gè)主要缺點(diǎn)，這使得模型在實(shí)踐中效率低下。許多研究人員現(xiàn)在將 AIXI 模型視為人工智能 AGI 功能的基準(zhǔn)，因?yàn)樗且环N經(jīng)過(guò)數(shù)學(xué)驗(yàn)證的功能性 AGI。

Integrative cognitive architecture
整合認(rèn)知架構(gòu)

This method of AGI development is based on the idea of replicating identified 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).
這種 AGI 開(kāi)發(fā)方法基于在 AGI 技術(shù)中單獨(dú)復(fù)制人腦的已識(shí)別中央認(rèn)知過(guò)程的想法。名為 CogPrime 的 AGI 軟件方法首先由 Ben Goertzel (OpenCog) 引入。

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.
CogPrime 系統(tǒng)使用動(dòng)作選擇模塊來(lái)確定場(chǎng)景中的最佳動(dòng)作過(guò)程，同時(shí)模擬大腦的認(rèn)知過(guò)程以檢測(cè)有關(guān)其周?chē)h(huán)境的信息。這使它能夠生成智能模型，隨后生成 AGI 程序。這種范式的缺點(diǎn)包括需要適當(dāng)?shù)膬?nèi)存類(lèi)型分離以及需要系統(tǒng)范圍的協(xié)同作用以產(chǎn)生高效的計(jì)算環(huán)境。與以前的方法相比，CogPrime 能夠克服不可計(jì)算性問(wèn)題，因?yàn)楝F(xiàn)在大多數(shù)實(shí)現(xiàn)它的技術(shù)都可用，但系統(tǒng)的能力遠(yuǎn)低于人腦。因此，在這個(gè)發(fā)展階段，還不能算是真正的人工智能。

Challenges in the development of AGI technology AGI技術(shù)發(fā)展面臨的挑戰(zhàn)

Insufficient technology and great energy consumption levels
技術(shù)不足和能源消耗水平高

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 other 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.
我們已經(jīng)提到，當(dāng)今的技術(shù)無(wú)法在人類(lèi)層面上執(zhí)行認(rèn)知操作。即使是現(xiàn)有的最強(qiáng)大的超級(jí)計(jì)算機(jī)也只能提供足夠的能力來(lái)復(fù)制人類(lèi)的思維計(jì)算，更不用說(shuō)我們大腦能夠處理的多任務(wù)處理和其他復(fù)雜過(guò)程了。此外，即使是最近的 AI 版本也遇到了巨大的能源消耗問(wèn)題。因此，要?jiǎng)?chuàng)造一個(gè)高效的真正的人工智能，人們需要解決許多其他技術(shù)和資源相關(guān)的挑戰(zhàn)。

Replicating Transfer Learning
復(fù)制遷移學(xué)習(xí)

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
將在一個(gè)領(lǐng)域中獲得的信息應(yīng)用到另一個(gè)領(lǐng)域稱(chēng)為遷移學(xué)習(xí)。人類(lèi)經(jīng)常參與其中，這是社會(huì)的一個(gè)重要方面。例如，我們可以在課堂上學(xué)習(xí)如何使用外語(yǔ)單詞，然后在家里運(yùn)用這些知識(shí)造句。復(fù)制遷移學(xué)習(xí)的主要目的是防止再訓(xùn)練，因此一個(gè)有能力的 AGI 人工智能可以使用一種技能來(lái)解決不同領(lǐng)域的任務(wù)

Facilitating collaboration and common sense
促進(jìn)協(xié)作和常識(shí)

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.
人類(lèi)的功能取決于常識(shí)和與其他人的團(tuán)隊(duì)合作來(lái)完成任務(wù)。由于今天的算法范圍如此有限，因此可靠的團(tuán)隊(duì)合作和常識(shí)在未來(lái)還很遙遠(yuǎn)。該系統(tǒng)必須具備這些品質(zhì)，以確保它是真正的通用人工智能，而不僅僅是另一種小眾人工智能。

Understanding Mind and Consciousness
了解思想和意識(shí)

As we defined above, consciousness is one of the main concepts and most reliable ways to prove the existence of general intelligence as it’s an essential component of human existence. However, even we, humans, can’t 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.
正如我們上面所定義的，意識(shí)是證明通用智能存在的主要概念和最可靠的方法之一，因?yàn)樗侨祟?lèi)存在的重要組成部分。然而，即使是我們?nèi)祟?lèi)，也無(wú)法完全掌握我們思想背后的所有秘密和特性。因此，它仍然是通用人工智能發(fā)展和實(shí)現(xiàn)的重大障礙。

Future of artificial general intelligence 通用人工智能的未來(lái)

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.
在進(jìn)一步了解 AGI 之后，我們可以提出“通用人工智能是否可能？”這個(gè)問(wèn)題。不再是懷疑的問(wèn)題。答案顯然是肯定的，因?yàn)榭茖W(xué)家們?nèi)褙炞⒂陂_(kāi)發(fā)真正的人工智能?，F(xiàn)在研究人員提出了另一個(gè)問(wèn)題——“我們什么時(shí)候會(huì)擁有通用人工智能？”，讓我們承認(rèn)，這些預(yù)測(cè)是模棱兩可的。

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”.
例如，澳大利亞著名機(jī)器人專(zhuān)家羅德尼·布魯克斯 (Rodney Brooks) 得出的結(jié)論是，功能性 AGI 系統(tǒng)要到 2300 年才能實(shí)現(xiàn)，并表示當(dāng)今的科學(xué)遠(yuǎn)未理解“人工智能的真正前景和危險(xiǎn)”。

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.
他的聲明得到了杰出研究人員的支持，例如 Geoffrey Hinton 和 Demis Hassabis，他們表示通用人工智能離實(shí)現(xiàn)還很遠(yuǎn)。

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 2030, a 50% chance that it’d happen by 2040, and only 10% – never.
然而，加拿大計(jì)算機(jī)科學(xué)家 Richard Sutton 也表達(dá)了另一種觀(guān)點(diǎn)，他評(píng)估了在未來(lái)二十年內(nèi)開(kāi)發(fā)通用智能 AI 的可能性。他指出到 2030 年理解 AGI 技術(shù)的可能性為 25%，到 2040 年有 50% 的可能性，只有 10% 的可能性——永遠(yuǎn)不會(huì)。

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.
根據(jù)我們的研究，Atlasiko 的軟件開(kāi)發(fā)專(zhuān)家也傾向于認(rèn)為，人工智能不會(huì)比本世紀(jì)末甚至下個(gè)世紀(jì)更早到來(lái)。盡管我們?cè)诶碚撋先〉昧撕艽蟮倪M(jìn)步，但現(xiàn)代科學(xué)要在現(xiàn)實(shí)生活中實(shí)現(xiàn)具有通用智能的 AI 仍然有太多障礙需要克服。

Risks from artificial general intelligence 人工智能的風(fēng)險(xiǎn)

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 place in the general AI dispute.
閱讀本文時(shí)，您可能還記得流行電影中一些虛構(gòu)的智能機(jī)器人接管人類(lèi)的場(chǎng)景。嗯，這很合乎邏輯，因?yàn)檫@些情節(jié)是基于真正的科學(xué)問(wèn)題。存在風(fēng)險(xiǎn)的評(píng)估在一般的 AI 爭(zhēng)論中占有重要地位。

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.? If we’ll 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.
即使是現(xiàn)在，人工智能的快速發(fā)展也引發(fā)??了許多討論和爭(zhēng)議，因?yàn)樗绊懼鱾€(gè)行業(yè)和全球勞動(dòng)力市場(chǎng)。通用人工智能的發(fā)展將極大地改變整個(gè)世界。如果我們?cè)O(shè)法實(shí)現(xiàn)具有類(lèi)人意識(shí)的真正人工智能，則無(wú)法保證這項(xiàng)技術(shù)會(huì)愿意由人類(lèi)管理。簡(jiǎn)而言之，科學(xué)家們現(xiàn)在無(wú)法判斷我們會(huì)得到一個(gè)友好的 R2-D2 還是一個(gè)機(jī)器人叛亂。撇開(kāi)夸張不談，讓我們來(lái)看看專(zhuān)家們討論最多的一些風(fēng)險(xiǎn)。

• 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.?
  缺乏控制。史蒂芬·霍金、斯圖爾特·拉塞爾、弗蘭克·威爾切克、杰弗里·辛頓、OpenAI 的首席執(zhí)行官薩姆·奧爾特曼等科學(xué)界最聰明的頭腦解決了對(duì)人工智能控制缺乏關(guān)注的問(wèn)題。如果沒(méi)有適當(dāng)?shù)墓芾砗捅O(jiān)控，強(qiáng)大的 AI 可能會(huì)被濫用，從而對(duì)社會(huì)造成重大破壞和破壞。
• The AI alignment problem. The more advanced the AI system is, the more challenging it can be to align its goals with human ethics. With the development of stronger cognitive abilities, true AI may be able to build 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對(duì)齊問(wèn)題。人工智能系統(tǒng)越先進(jìn)，使其目標(biāo)與人類(lèi)道德相一致的挑戰(zhàn)就越大。隨著更強(qiáng)的認(rèn)知能力的發(fā)展，真正的人工智能可能會(huì)制定與預(yù)期目標(biāo)和原則不一致的策略，例如權(quán)力尋求。這種行為已經(jīng)在一些強(qiáng)化學(xué)習(xí)智能體中被注意到，當(dāng)它們表現(xiàn)出工具收斂時(shí)（更有能力的智能體使用他們更大的能力來(lái)實(shí)現(xiàn)他們的目標(biāo)，這與人類(lèi)所做的相似）。因此，在部署任何人工智能之前，確保目標(biāo)一致至關(guān)重要。
• 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.?
  指定目標(biāo)的問(wèn)題。對(duì)于每個(gè)智能代理，效用函數(shù)由人類(lèi)開(kāi)發(fā)人員指定。正確編寫(xiě)此函數(shù)非常重要，因?yàn)樗x了一組值，這些值將成為 AI 決策的基礎(chǔ)。因此，如果一些重要的值碰巧沒(méi)有被添加到效用函數(shù)描述中，通用智能將執(zhí)行它自己分配的任務(wù)，盡管有可能造成傷害或損害。
• 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.
  AI AGI 中具有挑戰(zhàn)性的目標(biāo)修改。人工智能等更先進(jìn)的技術(shù)可能會(huì)抵制改變其目標(biāo)結(jié)構(gòu)以確保其繼續(xù)存在，甚至反對(duì)被關(guān)閉。

Undoubtedly, to ensure the safety and stability of human society, scientists have to think through all risks and preventive mechanisms.
毫無(wú)疑問(wèn)，要確保人類(lèi)社會(huì)的安全與穩(wěn)定，科學(xué)家們不得不想透所有的風(fēng)險(xiǎn)和防范機(jī)制。

General AI Myth or Fact 一般人工智能神話(huà)或事實(shí)

Conclusion?結(jié)論

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.
我們希望本文能幫助您更好地理解并找到“什么是通用人工智能？”這個(gè)問(wèn)題的全面答案。實(shí)現(xiàn) AGI 將是計(jì)算機(jī)科學(xué)和其他相關(guān)行業(yè)的一項(xiàng)非凡成就。然而，如此強(qiáng)大的技術(shù)并不能降低我們的謹(jǐn)慎。如果沒(méi)有適當(dāng)?shù)目刂?，它可能?huì)給社會(huì)帶來(lái)負(fù)面的變化和危險(xiǎn)。

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.
如果您想了解有關(guān)當(dāng)今 AI 幫助的積極方面的更多信息，請(qǐng)閱讀我們的博客，我們會(huì)在其中發(fā)布來(lái)自人工智能世界的定期更新和其他有用的見(jiàn)解。文章來(lái)源地址http://www.zghlxwxcb.cn/news/detail-720295.html

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