人工智能 (ARTIFICIAL INTELLIGENCE)
Enterprise CIOs are rethinking their goals as the coronavirus pandemic continues with investments shifting towards artificial intelligence, cloud computing, and cybersecurity. The remote work model is influencing decisions as organizations explore options for smooth operations despite the current pandemic.
作为冠状病毒与流感大流行对投资人工智能，云计算和网络安全转移继续ênterprise CIO们正在重新考虑自己的目标。 尽管当前出现了大流行，但组织仍在探索各种可实现平稳运营的方案，因此远程工作模型正在影响决策。
The Adobe report found 84% of companies using remote work models with small businesses adoption at 94%. From using AI investments and implementation, there is more to come in 2020 as the world fights the coronavirus.
Adobe报告发现84％的公司使用远程工作模型，而小型企业的采用率为94％。 通过使用AI投资和实施，随着世界与冠状病毒的斗争，在2020年还会有更多的事情发生。
The development of artificial intelligence ethical guidelines will safeguard against the unfair and dangerous use of AI as the boom continues. Many people ask this question: Do these ethical guidelines influence decisions? To answer this question, it is important to understand how ethical guidelines address loopholes amid the current adoption of artificial intelligence.
随着繁荣的发展，人工智能道德准则的发展将防止人工智能的不公平和危险使用。 许多人问这个问题：这些道德准则会影响决策吗？ 要回答这个问题，重要的是要了解道德准则如何解决当前采用人工智能的漏洞。
The release of GPT-3 is the current hype on the internet as users praise the language prediction model for impressive performance. Open AI released GPT-3 with intelligent features including creating songs, technical assistance, and developing stories. Will GPT-3 live to the hype?
GPT-3的发布是当前互联网上的炒作，因为用户称赞语言预测模型具有出色的性能。 Open AI发布了具有智能功能的GPT-3，包括创作歌曲，技术帮助和开发故事。 GPT-3会大肆宣传吗？
These and more insights on our Weekly AI Update
这些和更多关于我们每周AI更新的见解
远程劳动力转移CIO优先事项 (Remote Workforce shifting CIO Priorities)
The COVID-19 pandemic has shifted the top concerns of enterprise CIOs¹, who are now prioritizing their spending on areas such as cybersecurity, public cloud, infrastructure and AI/ML. That is according to a new survey from Adobe, who teamed up with Fortune to measure “How A Remote Workforce Is Shifting CIO Priorities.”
吨他COVID-19大流行已经转移企业CIOs¹的最关注的问题，现在谁优先在诸如网络安全，公共云，基础设施和AI / ML领域开支。 这是根据Adobe的一项新调查得出的，该调查与《财富》杂志联手评估了“远程员工如何改变CIO优先级”。
CMO by Adobe, which produces advice, guidance, data and research to senior business leaders, conducted a mid-March survey of more than 200 CIOs to gauge shifting attitudes subsequent to a pre-pandemic January post about CIO priorities. Although security was a top-of-mind concern in both survey efforts, cloud computing is now getting more attention in a remote-work world². However, while most organizations use the cloud, that usage may be increasing in the new world order.
由Adobe提供的CMO向高级企业领导人提供建议，指导，数据和研究，该公司在3月中旬对200多位CIO进行了调查，以评估在1月大流行前有关CIO优先事项之后的转变态度。 尽管在两次调查中安全都是头等大事，但云计算现在在远程工作环境中得到了越来越多的关注²。 但是，尽管大多数组织都使用云，但在新的世界秩序中，这种使用可能会增加。
AI Still Fairly New: Only 50 percent of organizations use Artificial Intelligence³ in one or more projects (and only 25 percent of SMBs), with CIOs noting they leverage AI for IT and customer support the most. More than 90 percent of those that have implemented AI have only done so in the past year. Top challenges faced when implementing AI are around data and funding/talent.
AI仍是全新的：只有50％的组织在一个或多个项目中使用了人工智能³(而中小型企业则只有25％)，CIO指出他们最大程度地利用了AI来提供IT和客户支持。 过去一年中，超过90％的实施AI的人都这样做了。 实施AI时面临的主要挑战是围绕数据和资金/人才。
人工智能伦理概述 (Overview of Artificial Intelligence Ethics)
Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics⁴.
在人工智能系统的研究，开发和应用程序C urrent的发展已经取得了对AI的道德产生深远的话语⁴。
In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies.
因此，近年来发布了许多道德准则。 这些指南包含旨在利用新AI技术的“破坏性”潜力的规范性原则和建议。
The current AI boom is accompanied by constant calls for applied ethics, which are meant to harness the “disruptive” potentials of new AI technologies. As a result, a whole body of ethical guidelines has been developed in recent years collecting principles, which technology developers should adhere to as far as possible.
当前的AI繁荣伴随着对应用道德的不断呼唤，这意味着要利用新AI技术的“破坏性”潜力。 因此，近年来，已经制定了一套完整的道德准则，以收集原则，技术开发人员应尽可能遵守这些原则。
However, the critical question arises: Do those ethical guidelines have an actual impact on human decision-making in the field of AI and machine learning? The short answer is: No, most often not.
但是，出现了一个关键问题：这些道德准则是否会对AI和机器学习领域的人类决策产生实际影响？ 简短的答案是：不，通常不是。
Currently, AI ethics is failing in many cases. Ethics lacks a reinforcement mechanism. Deviations from the various codes of ethics have no consequences. And in cases where ethics is integrated into institutions, it mainly serves as a marketing strategy. Furthermore, empirical experiments show that reading ethics guidelines has no significant influence on the decision-making of software developers.
当前，在许多情况下，人工智能伦理正在失败。 道德缺乏强化机制。 背离各种道德规范没有后果。 并且在将道德规范整合到机构中的情况下，它主要用作一种营销策略 。 此外，经验实验表明，阅读道德准则对软件开发人员的决策没有重大影响。
GPT-3：第三代语言预测模型 (GPT-3: Third-Generation Language Prediction Model)
The non-profit artificial intelligence research company OpenAI, which is backed by names like Peter Thiel, Elon Musk, Reid Hoffman, Marc Benioff, and Sam Altman, has released GPT-3, the company’s third-generation language prediction model. The release of GPT-3 has been met with extreme hype from some of the early users.
吨他非营利人工智能研究公司OpenAI，这是由像彼得泰尔，伊隆·马斯克，雷德·霍夫曼，贝尼奥夫和萨姆·奥尔特曼名的支持下，已经发布了GPT-3，该公司的第三代语言预测模型。 一些早期用户对GPT-3的发布大肆宣传。
GPT-3⁵ is the largest language model ever created and is capable of generating text that is indistinguishable from human text in many cases. #OpenAI described the language prediction technology for the first time in a research paper back in May. Last week, some people were given early access to the software through a private beta.
GPT-3⁵是有史以来最大的语言模型，能够在许多情况下生成与人类文本没有区别的文本。 #OpenAI在5月的一篇研究论文中首次描述了语言预测技术 。 上周，一些人通过私人Beta版获得了对该软件的早期访问权。
OpenAI is relying on outside developers to learn more about the technology and what it is capable of, and the company has plans to go commercial by the end of this year. Businesses will be able to pay for a subscription to use the AI.
OpenAI依靠外部开发人员来了解有关该技术及其功能的更多信息，该公司计划在今年年底前实现商业化。 企业将能够为使用AI的订阅付费。
GPT-3 has proved to be the most powerful language model ever created. Evolving from the previous GPT-2 model, GPT-3 was released last year. GPT-2 was also extremely impressive, being able to create competent strings of text after being provided an opening sentence.
GPT-3被证明是有史以来功能最强大的语言模型 。 去年发布了GPT-3，从以前的GPT-2模型演变而来。 GPT-2也给人留下了深刻的印象，能够在给他们一个开头的句子后创建出能胜任的文本字符串。
#GPT3 has 175 billion parameters, which increased from GPT-2’s 1.5 billion, and the AI has been demonstrated to create short stories, songs, press releases, and technical manuals. Not only can the technology create stories, but it can do so while using language that is relatable to specific writers. The technology only required the title, author’s name, and the initial word. GPT-3 is also capable of generating other text like guitar tabs and computer code.
＃GPT3具有1,750亿个参数，比GPT-2的15亿个参数有所增加，并且已经演示了AI可以创建短篇小说，歌曲，新闻稿和技术手册。 该技术不仅可以创建故事，而且可以在使用与特定作者相关的语言时创建故事。 该技术仅需要标题，作者姓名和首字母。 GPT-3还能够生成其他文本，例如吉他标签和计算机代码。
中国的动物鼻子识别 (Animal Snout Recognition in China)
Forget Facial Recognition — China is Developing Animal Snout Recognition⁶. Ant Financial’s online payment provider Alipay has developed a new feature on its app that can recognize animal nose prints, said to be the first application of such technologies in China. Similar to human fingerprints, animal nose prints are unique and can accurately identify an animal over 99% of the time. Using this identification method, Alipay has partnered with insurance companies to provide insurance services for pet cats and dogs.
˚Forget人脸识别-中国正在发展动物吻Recognition⁶。 蚂蚁金服的在线支付提供商支付宝已经在其应用程序上开发了一项新功能，该功能可以识别动物的鼻子印痕，据说这是此类技术在中国的首次应用。 与人的指纹相似，动物的鼻子印痕是独特的，可以在99％的时间内准确识别出动物。 使用此识别方法，支付宝已与保险公司合作为宠物猫和狗提供保险服务。
On the Alipay app, users can upload photos of their pets to create a digital profile and buy an insurance plan. For cats and dogs between the ages of three months and ten years, Alipay offers three health insurance premium options ranging from 199 to 799 yuan that can insure a pet for up to 20,000 yuan in medical bills annually. When owners wish to claim insurance benefits for a pet, its nose print will be used to verify its identity.
在支付宝应用程序上 ，用户可以上传宠物照片以创建数字资料并购买保险计划。 对于三个月至十年之间的猫和狗，支付宝提供三种健康保险保费选择，范围从199元至799元不等，可以为宠物每年支付不超过20,000元的医疗费用。 当主人希望为宠物索取保险利益时，其鼻子印将用于验证其身份。
Pet insurance has been around in China for over 10 years, but has failed to gain popularity due to trouble proving a pet’s identity. Less than 1% of pets in China are covered by insurance, compared to 25% in the UK and 7% in Japan. But the market for pet insurance is larger than ever: in 2019, pet-related products and services in China have developed into a 202 billion yuan industry serving almost 100 million pet cats and dogs (and their owners). Alipay believes that its new feature will speed up acceptance of pet insurance in China.
宠物保险在中国已经存在了十多年，但是由于无法证明宠物的身份而未能获得普及。 在中国，只有不到1％的宠物可以享受保险，而英国为25％，日本为7％。 但是宠物保险的市场比以往任何时候都更大：2019年，中国与宠物相关的产品和服务已发展成为一个2020亿元的产业，为近1亿只宠物猫和狗(及其主人)服务。 支付宝认为，其新功能将加快中国宠物保险的接受度。
欧洲新兴企业推动人工智能发展 (European Start-ups advancing Artificial Intelligence)
There’s little arguing that artificial intelligence is one of the hottest sectors of the tech industry. From recommending personalized content⁷ in your YouTube feed to translating text, diagnosing cancer and driving cars, the number of domains that the AI industry is touching is constantly expanding.
Ť这里有一点他们认为人工智能是科技行业最热门的行业之一。 从推荐YouTube供稿中的个性化内容 to到翻译文本，诊断癌症和驾驶汽车，人工智能行业正在接触的领域数量正在不断扩大。
With so much hype and money surrounding the AI industry, it is as good a time as any to be an AI company. In 2018, billions of dollars were invested in AI companies, and by 2030, the industry is projected to be worth more than $15 trillion according to research firm PricewaterhouseCoopers. Currently, TNW is part of a cutting-edge AI program sponsored by the European Commission, named Data Market Services, aiming to advance and support European data-centric startups.
围绕AI行业的大量炒作和金钱，成为AI公司的时机已到。 根据研究公司普华永道(PricewaterhouseCoopers)的数据，2018年，数十亿美元投资于AI公司，到2030年，该行业的价值预计将超过15万亿美元 。 目前，TNW是由欧洲委员会发起的一项名为“数据市场服务”的尖端AI计划的一部分，旨在促进和支持以欧洲数据为中心的初创公司。
But not all companies that claim to use AI are actually leveraging the technology. Many companies now make vague claims of using AI in their products and services to secure funding or attract customers. A recent study by London-based venture capital firm MMC found that out of 2,830 European startups classified as AI companies, only 1,580 accurately fit the description.
但是并非所有声称使用AI的公司实际上都在利用这项技术。 现在，许多公司声称在其产品和服务中使用AI来获得资金或吸引客户的含糊说法。 总部位于伦敦的风险投资公司MMC的最新研究发现，在2,830家被归类为AI公司的欧洲初创公司中，只有1,580家完全符合描述。
So without further ado, here are some of the European startups we think are leading promising efforts in the field:
因此，事不宜迟，我们认为以下是一些欧洲初创公司正在引领该领域的有希望的努力：
- German Autolabs 德国汽车实验室
- Qucit 默契
- Merantix 梅兰蒂克斯
- Braingineers 脑子
- Understand AI 了解人工智能
人类级人工智能 (Human-Level Artificial Intelligence)
You will not find any comprehensive data on humans outside of the testimonials at the Darwin Awards, but stupidity is surely the biggest threat to humans throughout all of history. Luckily, we are still the smartest species on the planet, so we have managed to remain in charge for a long time despite our shortcomings.
Ÿ欧不会找到在达尔文奖褒奖的人之外的任何全面的数据，但愚蠢肯定是在所有历史对人类的最大威胁。 幸运的是，我们仍然是地球上最聪明的物种，因此尽管存在缺点，我们仍然能够保持很长时间。
Unfortunately, a new challenger has entered the arena in the form of AI. And despite its relative infancy, artificial intelligence is not far from challenging our status as the apex intellects as you might think.
不幸的是，一个新的挑战者以AI的形式进入了竞技场。 尽管人工智能还处于起步阶段，但正如您可能认为的那样， 人工智能离挑战我们作为顶点智能的地位并不遥远。
The experts will tell you that we are far away from human-level AI (HLAI). But maybe no one knows what the benchmark for that would be.
专家会告诉您，我们离人类级AI(HLAI)尚远。 但是也许没人知道基准会是什么。
Trying to define what HLAI should and should not be able to do is just as difficult as trying to define the same for an 18-year-old human. Change a tire? Run a business? Win at Jeopardy?
试图定义HLAI应该做什么和不应该做什么，与为18岁的人定义HLAI一样困难。 换轮胎？ 做生意？ 在危险中赢？
This line of reasoning usually swings the conversation to narrow intelligence versus general intelligence⁸. But here we run into a problem as well. General AI is, hypothetically, a machine capable of learning any function in any domain that a human can. That means a single GAI should be capable of replacing any human in the entire world given proper training.
这种推理方式通常使对话变成狭义智力与一般智力 ⁸。 但是这里我们也遇到了问题。 假设一般的AI是一台能够学习人类可以在任何领域中使用的任何功能的机器。 这意味着一个单一的GAI应该能够在接受适当培训的情况下取代整个世界上的任何人。
Humans do not work that way however. There is no general human intelligence. The combined potential for human function is not achievable by an individual. If we build a machine capable of replacing any of us, it stands to reason it will.
但是，人类并非以这种方式工作。 没有一般的人类智慧。 人的功能的综合潜力是个人无法实现的。 如果我们制造的机器能够替代我们中的任何一个，那是有理由的。
深度学习和转移学习的局限性 (Limitations of Deep Learning and Transfer Learning)
Today, artificial intelligence programs can recognize faces and objects in photos and videos, transcribe audio in real-time, detect cancer in x-ray scans years in advance, and compete with humans in some of the most complicated games.
牛逼 ODAY，人工智能程序能够识别照片和视频，实时录制音频脸和物体，提前检测在X射线扫描年癌症，并在一些最复杂的游戏人竞争。
All these challenges were either thought insurmountable, decades away, or were being solved with sub-optimal results. But advances in neural networks⁹ and #deeplearning, a branch of AI that has become very popular in the past few years, has helped computers solve these and many other complicated problems.
所有这些挑战要么被认为是不可克服的，几十年之遥，要么正在以次优的结果解决。 但是神经网络的进步⁹ ＃deeplearning是过去几年中非常流行的AI分支，它帮助计算机解决了这些以及许多其他复杂的问题。
Unfortunately, when created from scratch, deep learning models require access to vast amounts of data and compute resources. This is a luxury that many cannot afford. Moreover, it takes a long time to train deep learning models to perform tasks, which is not suitable for use cases that have a short time budget.
不幸的是，当从头开始创建时，深度学习模型需要访问大量数据和计算资源 。 这是许多人买不起的奢侈品。 而且，训练深度学习模型来执行任务需要很长时间，这不适合时间预算短的用例。
Fortunately, #transferlearning, the discipline of using the knowledge gained from one trained AI model to another, can help solve these problems.
幸运的是，＃ transferlearning是一种使用从一个受过训练的AI模型获得的知识到另一个模型的学科，可以帮助解决这些问题。
In some domains, such as teaching AI to play games, the use of transfer learning is very limited. Those AI models are trained on reinforcement learning¹⁰, a branch of AI that is very compute-intensive and requires a lot of trial and error. In reinforcement learning, most new problems are unique and require their own #AI model and training process.
在某些领域，例如教AI玩游戏，转移学习的使用非常有限。 这些AI模型是在强化学习中接受训练的，强化学习是AI的一个分支，它是计算密集型的，并且需要大量的反复试验。 在强化学习中，大多数新问题都是独特的，需要它们自己的#AI模型和培训过程。
But all in all, for most deep learning applications, such as image classification and natural language processing, there’s a likely chance that you’ll be able to shortcut your way with a good dose of clever transfer learning.
但总而言之，对于大多数深度学习应用程序(例如图像分类和自然语言处理) ，您很有可能可以通过大量的巧妙迁移学习来捷径。
Neuralink：脑机接口 (Neuralink: Brain-Computer Interface)
The mission of Neuralink has never quite been clear. We know it is working on a chip designed to be surgically inserted into the human skull called a brain-computer interface. However, what and who it is for remains a bit of a mystery.
Neuralink的吨他的使命从来没有相当清楚了。 我们知道它正在开发一种芯片，该芯片旨在通过外科手术插入人类头骨，称为脑机接口。 但是，它的用途和目的仍然是个谜。
As best we can tell based on what’s been revealed so far, it’s shaping up to be a terrifying hormone hijacker capable of potentially giving you forced mental orgasms or making you fall in love. Musk originally said the goal of Neuralink was to produce a BCI so that humans would not lose their competitive edge to AI. The big idea here is that keyboards and other peripherals are not as efficient as a direct thought-to-action interface.
根据到目前为止的发现，我们可以说最好的是，它正在成为一个可怕的激素劫持者，有可能给您带来强迫性高潮或使您坠入爱河。 马斯克最初表示，Neuralink的目标是生产BCI，以使人类不会失去对AI的竞争优势。 这里的主要思想是键盘和其他外围设备的效率不如直接思考到操作的界面有效。
Musk thinks this is going to help us out if a superintelligence¹¹ rises up against us. But the path to jamming spikes in people’s skulls in order to assume control of at least some of their natural motor functions is a bit different than, say, getting permission to build a tunnel under Las Vegas — same concept, different authorities.
马斯克认为，如果超级智慧¹¹对我们不利，这将对我们有所帮助。 但是，为了控制人的至少某些自然运动功能而阻塞人的头颅尖峰的途径与获得在拉斯维加斯下建造隧道的许可有所不同。
That is probably why Neuralink quickly pivoted to medicine. Musk and company currently claim Neuralink will eventually “solve a lot of brain/spine injuries” and treat mental illnesses and cognitive disorders.
这可能就是为什么NeuralinkSwift转向医学的原因 。 马斯克及其公司目前声称，Neuralink最终将“解决很多脑部/脊柱受伤”并治疗精神疾病和认知障碍。
We are not expecting much out of Neuralink unless it is ready to either commit to building an invasive medical device aimed at neurology patients, or a non-invasive consumer device. Either way, we should all have more information soon.
除非它准备致力于构建针对神经病患者的侵入性医疗设备或非侵入性消费类设备，否则我们对Neuralink的期望并不高。 无论哪种方式，我们都应该尽快拥有更多信息。
扩展深度学习系统 (Scaling Deep Learning Systems)
Deep learning has reached the end of it’s rope according to a group of researchers from MIT, Underwood International College, the MIT-IBM Watson AI Lab and the University of Brasilia who recently conducted an audit of more than 1,000 pre-print papers on arXiv.
d EEP学习已按照一组来自MIT，安德伍德国际学院，美国麻省理工学院，IBM沃森人工智能实验室和巴西利亚大学的研究人员谁最近对的arXiv进行的1000多名预打印纸审计达到它的穷途末路。
The researchers claim they have run out of compute basically and could soon reach a point where it is no longer economically or environmentally feasible to continue scaling deep learning systems.
研究人员声称他们基本上已经耗尽了计算能力，并且可能很快达到继续扩展深度学习系统在经济或环境上不再可行的地步。
This might come as a shock to TensorFlow users and AI hobbyists running impressive #neuralnetworks on GPUS or home computers, but training large scale models is a power-intensive, expensive proposition. Clever algorithms and dedicated hardware can only take things so far.
这可能会作为一个冲击TensorFlow用户和AI爱好者在GPU上或家庭计算机上运行的令人印象深刻的＃neuralnetworks，但培养大型模型是能源密集型的，昂贵的命题。 聪明的算法和专用硬件只能解决目前的问题。
If, for example, you want to train a huge state-of-the-art system like OpenAI‘s big bad text generator, GPT-2, you will be spending a lot of money and potentially doing some serious damage to the environment.
例如，如果您想训练一个庞大的最新系统，例如OpenAI的大型不良文本生成器GPT-2，您将花费大量金钱，并可能对环境造成严重破坏。
Based on current trends, the researchers feel we will soon reach a point where achieving further benchmarks — such as reaching higher accuracy with ImageNet — will no longer be cost-effective under the current paradigm.
根据当前趋势，研究人员认为我们将很快达到一个达到更高基准的点，例如使用ImageNet达到更高的精度，将不再符合当前范式的成本效益。
The field of AI has been staring down the barrel of this gun for a long time. Arguably, #machinelearning algorithms have been held back by compute since the 1950s. Thanks to a few modern tricks, we’ve enjoyed a spurt of growth for the past decade or so that’s led to one of the most exciting periods for technology in human history.
长期以来，人工智能领域一直盯着这把枪。 可以说，自1950年代以来，＃ 机器学习算法一直被计算所阻碍。 多亏了一些现代技巧，在过去的十年左右的时间里，我们经历了突飞猛进的发展，这是人类历史上最激动人心的技术时期之一。
The MIT researchers believe we’ll come up with better algorithms and “other machine learning methods,” to solve our power struggle. Perhaps most interestingly, they also speculate that quantum computing could help bushwhack a path forward.
麻省理工学院的研究人员认为，我们将提出更好的算法和“其他机器学习方法”，以解决我们的动力难题。 也许最有趣的是，他们还推测量子计算可以帮助灌木丛走一条前进的道路。
参考文献 (Works Cited)
¹Enterprise CIOs, ²Remote-Work World, ³Artificial Intelligence, ⁴AI ethics, ⁵GPT-3, ⁶Animal Snout Recognition, ⁷Personalized Content, ⁸Narrow Intelligence versus General Intelligence, ⁹Neural Networks, ¹⁰Reinforcement Learning, ¹¹Superintelligence
¹ 企业CIO们 ，² 远程工作世界 ，³ 人工智能 ，⁴ AI道德 ，⁵ GPT-3 ，⁶ 动物吻识别 ，⁷ 个性化的内容 ，⁸ 缩小智力与一般智力 ，⁹ 神经网络 ，¹⁰ 强化学习 ，¹¹ 超级智能
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翻译自: https://medium.com/analytics-vidhya/remote-workforce-shifting-cio-priorities-in-2020-ai-ethics-and-gpt-3-a126d55d90ef