AI Weekly: Corporate AI labs’ turn toward commercial research highlights need for investment in basic science

AI周刊：企业AI实验室转向商业研究凸显基础科学投资需求

Google’s parent company Alphabet last week launched a subsidiary focused on AI-powered drug discovery called Isomorphic Labs. Helmed by Demis Hassabis, the cofounder of DeepMind, Isomorphic will use AI to identify disease treatments that have thus far eluded researchers, according to a blog post.

谷歌的母公司Alphabet上周成立了一家专注于人工智能药物发现的子公司，名为Isomorphic Labs。根据一篇博客文章，由DeepMind的联合创始人Demis Hassabis掌舵，Isomorphic将使用AI来识别迄今为止研究人员尚未完成的疾病治疗。

“Isomorphic Labs [is] a commercial venture with the mission to reimagine the entire drug discovery process from the ground up with an AI-first approach,” Hassabis wrote. “[Ultimately, we hope] to model and understand some of the fundamental mechanisms of life … There may be a common underlying structure between biology and information science — an isomorphic mapping between the two — hence the name of the company.”

“Isomorphic Labs[是]一家商业企业，其使命是用人工智能优先的方法从头开始重新构想整个药物发现过程，”Hassabis写道。“[最终，我们希望]建模并理解生命的一些基本机制......生物学和信息科学之间可能存在一个共同的底层结构 - 两者之间的同构映射 - 因此公司的名称。

The launch of Isomorphic underlines the pressure on corporate-backed AI labs to pursue research with commercial, as opposed to theoretical, applications. After losing nearly £2 billion ($2.7 billion), DeepMind recorded a profit for the first time in 2020, notching £43.8 million ($59.14 million) on £826 million ($1.12 billion) in revenue. While the lab remains engaged in prestige projects like systems that can beat champions at StarCraft II and Go, DeepMind has in recent years turned its attention to more practical domains, like weather forecasting, materials modeling, atomic energy computation, app recommendations, and datacenter cooling optimization.

Isomorphic的推出突显了企业支持的AI实验室面临的压力，要求他们从事商业而不是理论应用的研究。在亏损近20亿英镑（27亿美元）之后，DeepMind在2020年首次录得利润，在8.26亿英镑（11.2亿美元）的收入上获得了4380万英镑（5914万美元）。8.26亿英镑（11.2亿美元）。虽然该实验室仍然从事着可以在《星际争霸II》和《围棋》中击败冠军的系统等知名项目，但DeepMind近年来已将注意力转向更实用的领域，如天气预报、材料建模、原子能计算、应用程序推荐和数据中心冷却优化。

As the change in priorities fuels a reported power struggle within Alphabet, DeepMind moves further afield from its original mission of developing artificial general intelligence (AGI), or AI capable of tackling any task, in an open source fashion.

随着优先级的变化助长了Alphabet内部的权力斗争，DeepMind从其最初的使命（即以开源方式开发能够处理任何任务的AI）的最初使命更远。

It’s not just DeepMind that’s leaning increasingly into commercialization. OpenAI — the company behind GPT-3 — launched as a nonprofit in 2016, but transitioned to a “capped-profit” structure in 2019 in a bid to attract new investors. The strategy worked. Roughly a year ago, Microsoft announced it would invest $1 billion in OpenAI to jointly develop new technologies for Microsoft’s Azure cloud platform. In exchange, OpenAI agreed to license some of its intellectual property to Microsoft, which the company would then package and sell to partners, and to train and run AI models on Azure as OpenAI worked to develop next-generation computing hardware.

不仅仅是DeepMind越来越倾向于商业化。OpenAI是GPT-3背后的公司，于2016年作为非营利组织成立，但在2019年过渡到“上限利润”结构，以吸引新的投资者。该策略奏效了。大约一年前，微软宣布将在OpenAI上投资10亿美元，共同为微软的Azure云平台开发新技术。作为交换，OpenAI同意将其部分知识产权许可给微软，然后该公司将打包并出售给合作伙伴，并在Azure上训练和运行AI模型，因为OpenAI致力于开发下一代计算硬件。

Of course, embracing potentially more lucrative AI research directions isn’t necessarily a bad thing. Isomorphic arises from DeepMind’s work in protein shape prediction with its AlphaFold 2 system, which is being used by researchers at the University of Colorado Boulder and the University of California, San Francisco to study antibiotic resistance and biology of SARS-CoV-2, (also known as the coronavirus disease). However, when profit becomes the priority, important fundamental work can fall by the wayside.

当然，拥抱可能更有利可图的人工智能研究方向并不一定是一件坏事。同构源于DeepMind通过其AlphaFold 2系统在蛋白质形状预测方面的工作，该系统被科罗拉多大学博尔德分校和加州大学旧金山分校的研究人员用于研究SARS-CoV-2（也称为冠状病毒病）的抗生素耐药性和生物学。然而，当盈利成为优先考虑的问题时，重要的基本面工作就可以半途而废。

“The tech industry is endangering its own future as well as progress in AI,” CEO of SnapLogic, an enterprise app, and service orchestration platform wrote in a recent essay. “Besides nurturing tomorrow’s talent, [centers like] universities host the kind of blue-sky research that corporations are often reluctant to take on because the financial returns are unclear.”

“科技行业正在危及自己的未来以及人工智能的进步，”企业应用程序和服务编排平台SnapLogic的首席执行官在最近的一篇文章中写道。“除了培养明天的人才外，大学等中心还举办着企业往往不愿意承担的那种蓝天研究，因为财务回报尚不清楚。

As an example, Microsoft and Nvidia last month announced that they trained what they claim is one of the most capable language models to date. But building it didn’t come cheap. Experts peg the cost in the millions of dollars, a total that exceeds the compute budgets of most startups, governments, nonprofits, and colleges. While the cost of basic machine learning operations has been falling over the past few years, it’s not falling fast enough to make up the difference — and techniques like network pruning prior to training are far from a solved science.

例如，微软和英伟达上个月宣布，他们训练了他们声称是迄今为止最强大的语言模型之一。但建造它并不便宜。专家将成本定为数百万美元，这一总额超过了大多数初创公司，政府，非营利组织和大学的计算预算。虽然在过去几年里，基本机器学习操作的成本一直在下降，但它的下降速度还不足以弥补这一差距--而且像训练前的网络修剪这样的技术还远未成为一门解决问题的科学。

“I think the best analogy is with some oil-rich country being able to build a very tall skyscraper,” Guy Van den Broeck, an assistant professor of computer science at UCLA, said in a previous interview with VentureBeat. “Sure, a lot of money and engineering effort goes into building these things. And you do get the ‘state of the art’ in building tall buildings. But there is no scientific advancement per se … I’m sure academics and other companies will be happy to use these [models] in downstream tasks, but I don’t think they fundamentally change progress in AI.”

“我认为最好的类比是，一些石油资源丰富的国家能够建造一座非常高的摩天大楼，”加州大学洛杉矶分校计算机科学助理教授盖伊·范登布洛克（Guy Van den Broeck）在接受VentureBeat采访时说。“当然，大量的资金和工程工作都花在了建造这些东西上。在建造高层建筑的过程中，您确实可以获得“最先进的技术”。但是本身没有科学进步……我确信学术界和其他公司会很乐意在下游任务中使用这些（模型），但我认为它们不会从根本上改变人工智能的进展。“

In another instance of corporate ambitions run amok, Google last January released an AI model trained on over 90,000 mammogram X-rays that the company said achieved better results than human radiologists. Google claimed that the algorithm could recognize more false negatives — the kind of images that look normal but contain breast cancer — than previous work, but some clinicians, data scientists, and engineers took issue with that statement. In a rebuttal published in the journal Nature, the coauthors said that the lack of methods and code in Google’s research “undermines its scientific value.”

在企业野心疯狂的另一个例子中，谷歌去年1月发布了一个人工智能模型，该模型在超过90，000张乳房X光X射线上进行了训练，该公司表示，该模型取得了比人类放射科医生更好的结果。谷歌声称，与之前的工作相比，该算法可以识别更多的假阴性 - 那种看起来正常但含有乳腺癌的图像 - 但一些临床医生，数据科学家和工程师对这一说法提出了异议。在发表在《自然》杂志上的反驳中，合著者说，谷歌的研究缺乏方法和代码“破坏了它的科学价值”。

Academic investments

学术投资

One paper found that ties to corporations — either funding or affiliation — in AI research doubled to 79% from 2008 and 2009 to 2018 and 2019. And from 2006 to 2014, the proportion of AI publications with a corporate-affiliated author increased from about 0% to 40%, reflecting the growing movement of researchers from academia to enterprise.

一篇论文发现，从2008年和2009年到2018年和2019年，人工智能研究中与公司（无论是资金还是隶属关系）的联系翻了一番，达到79%。从2006年到2014年，拥有企业附属作者的AI出版物的比例从0%增加到40%，反映了研究人员从学术界到企业的日益流动。

The solution might lie in increased investments in universities and other institutions with a greater appetite for risk. Recently, the U.S. government took steps toward this with the National Science Foundation’s (NSF) funding of 11 new National Artificial Intelligence (AI) Research Institutes. The NSF will set aside upwards of $220 million for initiatives including the AI Institute for Foundations of Machine Learning and the AI Institute for Artificial Intelligence and Fundamental Interactions, which will investigate theoretical AI challenges like neural architecture optimization and incorporate workforce development, digital learning, outreach, and knowledge transfer programs to develop AI that integrates the laws of physics.

解决之道可能在于增加对大学和其他具有更大风险偏好的机构的投资。最近，美国政府通过国家科学基金会(NSF)资助11个新的国家人工智能(AI)研究所，采取了一些措施。NSF将拨出超过2.2亿美元用于人工智能基础研究所和人工智能与基础交互研究所等计划，这些计划将调查神经架构优化等理论AI挑战，并结合劳动力发展，数字学习，外展和知识转移计划，以开发集成物理定律的AI。

This isn’t to suggest that the academic process is without flaws of its own. There’s a concentration of compute power at elite universities; AI research still has a reproducibility problem, and some researchers suggest the relentless push for progress might be causing more harm than good. A 2018 meta-analysis highlights troubling trends that have emerged in machine learning scholarship, including a failure to identify the sources of empirical gains and the use of mathematics that obfuscates or impresses rather than clarifies.

这并不是说学术过程本身没有缺陷。精英大学的计算能力集中;人工智能研究仍然存在可重复性问题，一些研究人员认为，对进步的不懈推动可能弊大于利。2018年的一项荟萃分析凸显了机器学习学术领域出现的一些令人不安的趋势，包括未能确定经验收益的来源，以及使用的数学会模糊或给人留下印象，而不是澄清问题。

Still, however it’s achieved, a greater focus on fundamental, basic AI research could lead to theoretical breakthroughs to significantly advance the state of the art. Moreover, it could promote values such as beneficence, justice, and inclusion, which work with strictly commercial motivations tends to underemphasize.

尽管如此，无论它是如何实现的，更多地关注基础的，基本的人工智能研究可能会导致理论上的突破，从而显着推进技术水平。此外，它可以促进诸如仁慈，正义和包容等价值观，这些价值观与严格的商业动机一起工作往往被低估了。

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