科技情報(bào)研究所工資

介紹 (Introduction)

Providing a formal definition of Intelligence can be a quite intimidating task. In fact, no common agreement about this topic has been reached so far. Since the beginning of the human history, different definitions of intelligence have been proposed and these varied depending on the historical time and culture. For example, in a society in which language and communication skills play an important role, an individual donated of these kinds of skills might be recognised as to be “more intelligent” than others. In the meantime, in a society in which numerical skills are valued most other individuals might be regarded as to be “more intelligent”.

提供有關(guān)智力的正式定義可能是一項(xiàng)非常艱巨的任務(wù)。 實(shí)際上，到目前為止，尚未就此話題達(dá)成共識(shí)。 自人類歷史開始以來(lái)，就提出了不同的智力定義，這些定義根據(jù)歷史時(shí)間和文化而有所不同。 例如，在一個(gè)語(yǔ)言和交流技能起著重要作用的社會(huì)中，捐贈(zèng)此類技能的個(gè)人可能被認(rèn)為比其他人“更聰明”。 同時(shí)，在一個(gè)重視數(shù)字技能的社會(huì)中，大多數(shù)其他人可能被視為“更聰明”。

As a practical demonstration of this trend, we can consider that in the recent past (until around 1960) it was necessary to know Latin in order to be admitted in some Universities (even for technical courses) while nowadays it is not recognised anymore as a prerequisite. This change might have then taken place since the society shifted his interest from humanities subjects to more technical subjects due to the recent technological innovations.

作為這種趨勢(shì)的實(shí)際例證，我們可以認(rèn)為，在最近的一段時(shí)間(直到1960年左右)，有必要了解拉丁語(yǔ)才能被某些大學(xué)(甚至是技術(shù)課程)錄取，而如今它已不再被公認(rèn)為先決條件。 由于社會(huì)由于最近的技術(shù)創(chuàng)新將他的興趣從人文學(xué)科轉(zhuǎn)移到了更多的技術(shù)學(xué)科，因此可能發(fā)生了這種變化。

“Everyone is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid.” [1]

“每個(gè)人都是天才。 但是，如果您以一條魚爬樹的能力來(lái)判斷它，那么它會(huì)終生相信它是愚蠢的。” [1]

—Unknown

-未知

Because of these discrepancies in measuring human intelligence, standardised tests such as the Intelligent Quotient (IQ) test have been created. These types of tests are for instance commonly used nowadays in order to evaluate job applicants and detect intellectual disabilities.

由于在測(cè)量人類智能方面存在這些差異，因此創(chuàng)建了標(biāo)準(zhǔn)化測(cè)試，例如智能商(IQ)測(cè)試。 這些類型的測(cè)試是例如當(dāng)今普遍使用的，以便評(píng)估求職者和發(fā)現(xiàn)智力障礙。

人工智能的誕生 (The inception of Artificial Intelligence)

Alan Turing Imitation Game is nowadays considered to be one of the first attempts to define an Intelligent System. According to the Imitation Game (Turing Test) in fact, a system can be considered to be intelligent if able to exchange messages with an interrogator without letting him/her understand its not human nature. This definition of an intelligent system provides although some limitations since for example, it assumes that the system should be able to express a human-like form of intelligence, even though human intelligence is theoretically not the only possible form of an intelligent system.

如今，Alan Turing模仿游戲被認(rèn)為是定義智能系統(tǒng)的首批嘗試之一。 實(shí)際上，根據(jù)模仿游戲(圖靈測(cè)試)，如果能夠與詢問(wèn)者交換消息而又不讓詢問(wèn)者了解其非人性，則該系統(tǒng)可以被認(rèn)為是智能的。 盡管例如從理論上說(shuō)，它假定系統(tǒng)應(yīng)該能夠表達(dá)類似人的形式的智能，但是智能系統(tǒng)的這種定義提供了一些限制，盡管從理論上講，人的智能并不是智能系統(tǒng)的唯一可能形式。

From this point onward, two different approaches developed in order to try to recreate intelligent systems: symbolism (which emphasises on the use of a formal logic) and connectivism (which aims to reproduce intellectual abilities using a simplified model of the brain functionalities).

從這一點(diǎn)開始，開發(fā)了兩種不同的方法來(lái)嘗試重建智能系統(tǒng)： 象征主義 (強(qiáng)調(diào)使用形式邏輯)和連接 主義 (旨在使用簡(jiǎn)化的大腦功能模型來(lái)復(fù)制智力)。

Thanks to the symbolic approach, projects such as Shakey the robot have been realised, while thanks to the connectivist approach Artificial Neural Networks and nowadays Deep Learning models have been constructed (Figure 1). One of the first successful examples of a system able to express intelligent behaviour, is Arthur Samuel checkers playing program. This program was in fact successfully able to learn to play at a good level the checkers game using heuristic search (Mini-Max algorithm). Samuel additionally included different features in order to train his program which resembles to some extent an early approach to Reinforcement Learning. In fact, the program was able to successfully play against himself and learn from its past mistakes in order to make improvements.

由于采用了象征性的方法，因此實(shí)現(xiàn)了諸如Shakey機(jī)器人之類的項(xiàng)目，而由于采用了連接主義方法的人工神經(jīng)網(wǎng)絡(luò)和當(dāng)今的深度學(xué)習(xí)模型已經(jīng)得以構(gòu)建(圖1)。 能夠表達(dá)智能行為的系統(tǒng)的首批成功示例之一是Arthur Samuel跳棋演奏程序。 實(shí)際上，該程序能夠使用啟發(fā)式搜索(Mini-Max算法)成功學(xué)習(xí)高水平的跳棋游戲。 塞繆爾(Samuel)另外還包括不同的功能，以便訓(xùn)練他的程序，在某種程度上類似于強(qiáng)化學(xué)習(xí)的早期方法。 實(shí)際上，該程序能夠成功地與自己對(duì)戰(zhàn)，并從過(guò)去的錯(cuò)誤中吸取教訓(xùn)，以進(jìn)行改進(jìn)。

Figure 1: Artificial Intelligence Timeline [2]圖1：人工智能時(shí)間表[2]

生物學(xué)觀點(diǎn) (A biological perspective)

Until the early 1980s, scientists tried to create intelligent machines by using a top-down approach (eg. trying to directly imitate complex systems behaviours such as the ones of human beings). Although, this approach resulted to lead to various limitations such as:

直到1980年代初，科學(xué)家一直試圖通過(guò)使用自上而下的方法來(lái)創(chuàng)建智能機(jī)器(例如，試圖直接模仿復(fù)雜的系統(tǒng)行為，例如人類的行為)。 雖然，這種方法導(dǎo)致了各種限制，例如：

• Slow reaction times in changing environments.
  在不斷變化的環(huán)境中React時(shí)間慢。
• Limited ability to adequately scale increasing the difficulty of the challenge.
  適當(dāng)擴(kuò)展的能力有限，增加了挑戰(zhàn)的難度。
• Need for high memory requirements when trying to solve a problem through search or retrieving information from a substantially large lookup table.
  當(dāng)試圖通過(guò)從相當(dāng)大的查找表中搜索或檢索信息來(lái)解決問(wèn)題時(shí)，需要很高的內(nèi)存要求。

An alternative approach (as advocated by Brooks) could be to instead try to imitate simple biological organisms key characteristics (bottom-up approach). In this way, our system would not need to have any hierarchical system and could be able to guarantee faster reaction times in changing environments (like most environments in the real world actually are). Finally, another possible way of creating Intelligent systems inspired from biology are Evolutionary algorithms.

一種替代方法(如布魯克斯所倡導(dǎo)的)可以是嘗試模仿簡(jiǎn)單生物的關(guān)鍵特征(自下而上的方法)。 這樣，我們的系統(tǒng)將不需要具有任何分層系統(tǒng)，并且能夠保證在變化的環(huán)境中(如現(xiàn)實(shí)世界中的大多數(shù)環(huán)境實(shí)際上)具有更快的響應(yīng)時(shí)間。 最后，進(jìn)化算法是創(chuàng)建受生物學(xué)啟發(fā)的智能系統(tǒng)的另一種可能方式。

結(jié)論 (Conclusion)

Over the last century, many connectivist and symbolic implementations of Artificial Intelligence (AI) systems have been implemented in the hope to create Intelligent systems (Figure 2). Although, still nowadays we can’t be sure if using either (or both) of these two approaches it would ever be possible to create a Strong AI system.

在過(guò)去的一個(gè)世紀(jì)中，已經(jīng)實(shí)現(xiàn)了許多人工智能(AI)系統(tǒng)的連接主義和符號(hào)實(shí)現(xiàn)，以期希望創(chuàng)建智能系統(tǒng)(圖2)。 雖然，時(shí)至今日，我們?nèi)圆淮_定是否使用這兩種方法中的一種(或兩種)都可以創(chuàng)建強(qiáng)大的AI系統(tǒng)。

Figure 2: Symbolic vs Connectivist Research Publications over time [3]圖2：隨著時(shí)間的流逝，象征性研究與連接主義研究的出版物[3]

A Reinforcement and Online Learning approach might provide the best possible way in order to tackle such a task. In fact, this can be considered as the closest approach able to mimic how biological systems learn:

強(qiáng)化和在線學(xué)習(xí)方法可能會(huì)提供最好的方法來(lái)解決這一任務(wù)。 實(shí)際上，這可以被認(rèn)為是能夠模仿生物系統(tǒng)如何學(xué)習(xí)的最接近的方法：

• Through their past experiences (using a reward/punishment mechanism).
  通過(guò)他們過(guò)去的經(jīng)驗(yàn)(使用獎(jiǎng)勵(lì)/懲罰機(jī)制)。
• Continuously, considering one stimulus (or a small sample) at the time (unlike Deep Learning Systems which are trained all at once and with huge amounts of data).
  不斷地，同時(shí)考慮一次刺激(或少量樣本)(不同于深度訓(xùn)練系統(tǒng)，后者需要一次訓(xùn)練并且具有大量數(shù)據(jù))。

I hope you enjoyed this article, thank you for reading!

希望您喜歡這篇文章，感謝您的閱讀！

聯(lián)絡(luò)人 (Contacts)

If you want to keep updated with my latest articles and projects follow me on Medium and subscribe to my mailing list. These are some of my contacts details:

如果您想隨時(shí)了解我的最新文章和項(xiàng)目 跟我來(lái)中 并訂閱我的 郵件列表 。 這些是我的一些聯(lián)系方式：

• Linkedin

  領(lǐng)英

• Personal Blog

  個(gè)人博客

• Personal Website

  個(gè)人網(wǎng)站

• Medium Profile

  中檔

• GitHub

  的GitHub

• Kaggle

  卡格勒

參考書目 (Bibliography)

[1] Everybody is a Genius. But If You Judge a Fish by Its Ability to Climb a Tree, It Will Live Its Whole Life Believing that It is Stupid Quote Investigator, Tracing Quotations. Accessed at: https://quoteinvestigator.com/2013/04/06/fish-climb/

[1]每個(gè)人都是天才。 但是，如果您通過(guò)魚類爬樹的能力來(lái)判斷它，那么它會(huì)終生相信它是愚蠢的報(bào)價(jià)調(diào)查員，可以追蹤報(bào)價(jià)。 訪問(wèn)網(wǎng)址 ： https : //quoteinvestigator.com/2013/04/06/fish-climb/

[2] Artificial Intelligence Timeline Infographic — From Eliza to Tay and beyond, Digitalwellbeing.org — Dr Paul Marsden. Accessed at: https://digitalwellbeing.org/artificial-intelligence-timeline-infographic-from-eliza-to-tay-and-beyond/

[2]人工智能時(shí)間軸圖-從Eliza到Tay以及其他地區(qū)，Digitalwellbeing.org-Paul Marsden博士。 訪問(wèn)網(wǎng)址 ： https : //digitalwellbeing.org/artificial-intelligence-timeline-infographic-from-eliza-to-tay-and-beyond/

[3] NEURONS SPIKE BACK, The Invention of Inductive Machines and the Artificial Intelligence Controversy Dominique CARDON, Jean-Philippe COINTET, Antoine MAZIèRES. Accessed at: https://neurovenge.antonomase.fr/NeuronsSpikeBack.pdf

[3]神經(jīng)元回?fù)?#xff0c;感應(yīng)機(jī)器的發(fā)明和人工智能的爭(zhēng)議Dominique CARDON，讓-菲利普·科廷(Jean-Philippe COINTET)，安托萬(wàn)·馬濟(jì)耶斯(Antoine MAZIRES)。 訪問(wèn)網(wǎng)址：https://neurovenge.antonomase.fr/NeuronsSpikeBack.pdf

翻譯自: https://towardsdatascience.com/what-do-we-mean-by-intelligence-f69673f1559e

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