Over the past few years, multiple new terms related to AI have emerged – "alignment", "large language models", "hallucination" or "prompt engineering", to name a few.在过去的几年里，出现了多个与人工智能相关的新术语——“AI对齐”、“大语言模型”、“AI幻觉”或“AI即时工程”等等。To help you stay up to speed, We have compiled some words you need to know to understand how AI is shaping our world.为了帮助您及时了解最新情况，我们整理了您需要了解的部分词汇，以了解AI如何塑造人类世界。
- 通用AI｜Artificial general intelligence (AGI)
Most of the AIs developed to date have been "narrow" or "weak". So, for example, an AI may be capable of crushing the world's best chess player, but if you asked it how to cook an egg or write an essay, it'd fail. That's quickly changing: AI can now teach itself to perform multiple tasks, raising the prospect that "artificial general intelligence" is on the horizon.迄今为止开发的大多数人工智能都是“狭窄的”或“弱的”。举例来说，人工智能可能有能力击败世界上最好的国际象棋棋手，但如何煮鸡蛋或写论文却会让它不知所措。这种情况正在迅速改变：人工智能现在可以自学执行多项任务，从而提高了“通用人工智能”即将出现的前景。An AGI would be an AI with the same flexibility of thought as a human – and possibly even the consciousness too – plus the super-abilities of a digital mind. Companies such as OpenAI and DeepMind have made it clear that creating AGI is their goal. OpenAI argues that it would "elevate humanity by increasing abundance, turbocharging the global economy, and aiding in the discovery of new scientific knowledge" and become a "great force multiplier for human ingenuity and creativity".通用人工智能将是一种具有与人类相同的思维灵活性（甚至可能还有意识）以及数字思维的超能力的人工智能。OpenAI和DeepMind等公司已经明确表示， 创建通用人工智能是他们的目标。OpenAI 认为，它将“通过增加丰富性、推动全球经济并帮助发现新的科学知识来提升人类水平”，并成为“人类聪明才智和创造力的巨大力量倍增器”。
While we often focus on our individual differences, humanity shares many common values that bind our societies together, from the importance of family to the moral imperative not to murder. Certainly, there are exceptions, but they're not the majority.虽然人们经常关注个体差异性，但人类拥有许多能联结社会的共同价值观，诸如家庭为贵，谋财害命要受到道德谴责。当然，也有例外，但不是大多数。However, we've never had to share the Earth with a powerful non-human intelligence. How can we be sure AI's values and priorities will align with our own?但人类没有必要与强大的非人类智慧生物（即AI）共享地球。我们如何确保人工智能的价值观和重视点与人类保持一致呢？This alignment problem underpins fears of an AI catastrophe: that a form of superintelligence emerges that cares little for the beliefs, attitudes and rules that underpin human societies. If we're to have safe AI, ensuring it remains aligned with us will be crucial.这种一致性问题加剧了人们对人工智能灾难的担忧：超级智能出现，它们几乎不关心支撑人类社会的信念、态度和规则。如果我们想要拥有安全的人工智能，确保它与我们保持一致至关重要。
For an AI to learn, it needs to learn from us. Unfortunately, humanity is hardly bias-free. If an AI acquires its abilities from a dataset that is skewed – for example, by race or gender – then it has the potential to spew out inaccurate, offensive stereotypes.人工智能学习需要向我们学习，但不幸的是，偏见已经刻进了人类的DNA里。如果人工智能学习带有种族或性别歧视的数据集（例如，种族或性别），那么它就有可能产出错误的、令人反感的刻板印象。In the worlds of AI ethics and safety, some researchers believe that bias – as well as other near-term problems such as surveillance misuse – are far more pressing problems than proposed future concerns such as extinction risk.在人工智能伦理和安全领域，一些研究人员认为，偏见歧视以及监控滥用等其他近期问题比灭绝风险等长期问题更为紧迫。
Not a verb, but a noun. Compute refers to the computational resources – such as processing power – required to train AI. It can be quantified, so it's a proxy to measure how quickly AI is advancing (as well as how costly and intensive it is too.)这里的“计算”不是动词，而是名词。计算是指训练人工智能所需的计算资源，例如处理能力。它可以量化，因此它是衡量人工智能进步速度的指标（以及它的成本和强度）。Since 2012, the amount of compute has doubled every 3.4 months, which means that, when OpenAI's GPT-3 was trained in 2020, it required 600,000 times more computing power than one of the most cutting-edge machine learning systems from 2012. Opinions differ on how long this rapid rate of change can continue, and whether innovations in computing hardware can keep up: will it become a bottleneck?自2012年以来，计算量每3到4个月翻一番，这意味着，当OpenAI 的GPT-3在 2020 年进行训练时，它需要的计算能力是2012年最前沿的机器学习系统之一的60万倍。这种快速的变化能持续多久，以及计算硬件的创新能否跟上：它会成为瓶颈吗？
A few years ago, one of the dominant techniques for getting AI to create images were so-called generative adversarial networks (Gan). These algorithms worked in opposition to each other – one trained to produce images while the other checked its work compared with reality, leading to continual improvement.几年前，让人工智能创建图像的主要技术之一是所谓的生成式对抗网络（Gan）。这些算法彼此相反——一种算法被训练来生成图像，而另一种算法则检查其工作与现实的比较，从而不断改进。However, recently a new breed of machine learning called "diffusion models" have shown greater promise, often producing superior images. Essentially, they acquire their intelligence by destroying their training data with added noise, and then they learn to recover that data by reversing this process. They're called diffusion models because this noise-based learning process echoes the way gas molecules diffuse.然而，最近一种称为“扩散模型”的新型机器学习显示出了更大的前景，通常可以产生优质的图像。本质上，他们通过添加噪音破坏训练数据来获取情报，然后他们学会通过逆转这个过程来恢复该数据。它们被称为扩散模型，因为这种基于噪声的学习过程与气体分子扩散的方式相呼应。
编辑：ETTBL
翻译：Gleen
