Domas Janickas

多马斯·詹尼卡斯(Domas Janickas)

(Article originally posted in Lithuanian in lrt.lt)

( 文章最初在立陶宛语中发布在lrt.lt中 )

AI is not just a tool for scientists to develop a new medicine, or for researchers to beat humans at chess, but something that impacts all of us, every day. Throughout Covid-19, AI has been optimising the schedules of the vans that deliver your groceries or suggesting you what to watch on Netflix. But what actually is it? And what else can it do for us?

人工智能不仅是科学家开发新药的工具，还是研究人员在国际象棋上击败人类的工具，而且每天都会影响到我们所有人。 在整个Covid-19中，AI一直在优化货车的日程安排，以运送您的食品或建议您在Netflix上观看什么。 但是实际上是什么呢？ 它还能为我们做什么？

Artificial intelligence originated as an umbrella term in the 1960s, as various scientific communities began studying the first artificial neural networks. The now-famous Turing test was developed, as a way of determining if the responses from various programs differed to that of a human. However, technologies were limited by both computing power and the small amounts of (qualitative) data they had access to, and so no major breakthroughs were achieved.

人工智能起源于1960年代，当时各个科学界开始研究第一个人工神经网络。 开发了现在著名的图灵测试，以此来确定各种程序的响应是否与人类的响应不同。 但是，技术受到计算能力和可访问的少量(定性)数据的限制，因此没有取得重大突破。

As the concept of big data boomed in the early 2010s, so too did the popularity of AI. Increasing computing power — as a result of Moore’s law — and vast amounts of data gathered through our online activities enabled AI to flourish — it’s time had finally come.

随着2010年代初大数据概念的兴起，AI的普及也随之兴起。 由于摩尔定律，计算能力不断增强，通过我们的在线活动收集的大量数据使AI蓬勃发展，现在终于到了。

But what actually is AI? Even in our organisation — an enterprise AI company — we dispute the definition of intelligence and the importance of our role in systematising it. Some have argued that a mushroom, that can adapt and thrive in changing environments, is more intelligent than a machine learning algorithm that can recognise the shapes of roads or people, but that can’t adapt itself. Definitions aside, we’re constantly considering how we apply to expertise to solve complex business problems.

但是AI到底是什么？ 即使在我们的组织(一家企业AI公司)中，我们也对智能的定义以及我们对其进行系统化的角色的重要性表示怀疑。 有些人认为，可以适应不断变化的环境并在其中蓬勃发展的蘑菇比可以识别道路或人形但无法自我适应的机器学习算法更智能。 除了定义，我们一直在考虑如何应用专业知识来解决复杂的业务问题。

We define human intelligence as goal-direct adaptive behaviour and apply the same criteria to artificial intelligence. Thus, for a system to be intelligent, it must be able to adapt their action in real-time, in production environments, and without the aid of a human. In other words, It’s able to make a decision, learn if that decision was good or bad, and then, presented with the same data, make a different decision. It must be able to adapt its decision based on real-time data, not just data it’s been trained on. Then, and only then, would we define it as true AI.

我们将人类智能定义为目标直接的适应性行为，并将相同的标准应用于人工智能。 因此，要使系统智能化，它必须能够在生产环境中实时地，在没有人工帮助的情况下适应其行为。 换句话说，它能够做出决定，了解该决定是好是坏，然后在呈现相同数据的情况下做出不同的决定。 它必须能够根据实时数据而不只是根据其训练的数据来适应其决策。 然后，直到那时，我们才将其定义为真正的AI。

When achieved, it results in huge efficiencies for the organisations that adopt it, both in the quality of their decision making, and the time saved by not having to constantly adapt and improve their models.

一旦实现，就可以为采用该方法的组织带来巨大的效率，无论是决策质量还是无需不断调整和改进模型所节省的时间。

Imagine you show a child thousands of images of kittens and puppies, and tell which animal it is. Eventually, the child begins to recognise kittens or puppies in images they haven’t seen, or in the park, or on the TV. But show them a cow, and they won’t recognise it. Or show them a lion, and they might see legs, fur, eyes and think it’s a kitten and go to pat it. Many systems, which we wrongly call AI work similarly and make these kinds of mistakes. Recently, a facial recognition technology on a phone failed to identify black women — the data it had been trained on simply wasn’t diverse enough. These systems, according to the US Defense Advanced Research Projects Agency (DARPA) — are known as “statistically impressive, but individually unreliable”. And in my opinion, most of the systems marketed today as AI is just automation. They don’t self-learn. They can’t process new datasets in real-time. They’re not intelligent.

想象一下，您给一个孩子展示了成千上万张小猫和小狗的图像，并告诉它是哪种动物。 最终，孩子开始在看不见的图像，公园或电视上认出小猫或小狗。 但是给他们看一头母牛，他们不会认出来。 或者给他们看一头狮子，他们可能会看到腿，毛皮，眼睛，并认为这是一只小猫，然后去拍它。 我们错误地称其为AI的许多系统都以类似的方式工作，并且犯了这些错误。 最近，电话上的面部识别技术无法识别黑人女性-它所接受的训练数据还不够多样化。 根据美国国防高级研究计划局(DARPA)的说法，这些系统被称为“统计上令人印象深刻，但个别不可靠”。 在我看来，当今作为人工智能销售的大多数系统都只是自动化。 他们不会自学。 他们无法实时处理新的数据集。 他们不聪明。

Definitions of AI are far from uniform. Different people use different definitions to describe different technologies, each with different capabilities. Some refer to concepts like narrow, or applied AI — a tool capable of performing one very specific task e.g playing GO, self-driving cars or facial recognition. Others use AGI — or artificial general intelligence — to describe systems that can outperform humans on a much broader range of tasks, such as transportation. And then there’s the super-intelligence — that will supposedly outperform humans at every cognitive task. I hope, for the sake of clarity, that the industry will agree on both the definition and it’s naming in the near future.

AI的定义远非统一。 不同的人使用不同的定义来描述不同的技术，每种技术具有不同的功能。 有些人指的是狭窄或应用的AI之类的概念-一种能够执行一项非常特定任务的工具，例如玩GO，自动驾驶汽车或面部识别。 其他人则使用AGI(或人工智能)来描述在很多任务(例如交通运输)上可以胜过人类的系统。 然后是超级智能-据说在每个认知任务上，它们都会胜过人类。 为了清楚起见，我希望该行业在不久的将来会就定义及其名称达成一致。

As an AI company, filled with engineers, data scientists and mathematicians, we think AI has become overly romanticized, and too full of mystery. By much of the media, it’s often wrongly represented as Terminator-style robots that will end life as we know it. This isn’t true. Others label anything that touches data, or does some basic analysis or automation as AI. This isn’t true either.

作为一家充满工程师，数据科学家和数学家的AI公司，我们认为AI变得过于浪漫，充满了神秘色彩。 在许多媒体上，它通常被错误地表示为终结者式机器人，它将终结我们所知的生命。 这不是真的 其他人则将任何涉及数据或进行基本分析或自动化的东西标记为AI。 这也不是真的。

I don’t want to be misunderstood. I believe that AI-based technologies will be a core component of software in the future. And in some areas, it’s already having an impact. Neurotechnology, a Lithuanian company, is one of the strongest defence and security companies in the world. It’s been developing AI-based fingerprint recognition systems for over 3 decades. Oxipit, a small health-tech startup, uses AI to detect lung diseases and to improve queuing and resourcing in hospitals. Satalia uses AI to optimise the routes of Tesco’s home delivery vans and is scheduling 1M orders a week throughout Covid-19. And for PwC, we optimise the allocation of people to their work. These systems drive enormous value for our clients, but can also be used to solve wider problems for society, or for the public sector.

我不想被误解。 我相信，基于AI的技术将在未来成为软件的核心组件。 在某些领域，它已经产生了影响。 Neurotechnology是立陶宛的一家公司，是全球最强大的国防和安全公司之一。 三十多年来，它一直在开发基于AI的指纹识别系统。 小型医疗技术初创公司Oxipit使用AI检测肺部疾病并改善医院的排队和资源配置。 Satalia使用AI优化了Tesco的送货车路线，并计划在Covid-19期间每周安排100万个订单。 对于普华永道，我们优化人员分配。 这些系统为我们的客户带来了巨大的价值，但也可以用于解决社会或公共部门的广泛问题。

Despite the noise surrounding AI, I have no doubt that real AI — systems that can adapt to new data in real-time, without human intervention — will form a crucial component of all software in the future. The AI market is projected to reach $190BN over the next five years. And as with any technology, some will win and some will lose. After the 2008 financial crisis, it was the companies that invested quickly in automation and process optimisation who grew the fastest over the following decade. Similarly here, we think those who invest in AI quickly will separate the winners from the losers.

尽管AI周围有很多杂音，但毫无疑问，真正的AI(可以在没有人工干预的情况下实时适应新数据的系统)将成为将来所有软件的重要组成部分。 未来五年，人工智能市场预计将达到1900亿美元。 就像任何技术一样，有些会赢，有些会输。 在2008年金融危机之后，正是在自动化和过程优化方面进行了快速投资的公司在接下来的十年中增长最快。 同样在这里，我们认为那些快速投资人工智能的人会将成功者和失败者区分开。