借助生成式人工智能，这家法律信息服务巨头面临着迄今为止最强大的颠覆者。这就是为什么首席技术官 Jeff Reihl 正在迅速接受和增强该技术，以保持在竞争中的领先地位。
LexisNexis 执行副总裁兼首席技术官 Jeff Reihl 的提示可能会让 IT 领导者寻找避免生成式 AI 颠覆性威胁的蓝图，从而从中受益：在采用该技术方面快速前进，以领先于潜在的颠覆者。
自 1970 年代初成立以来，LexisNexis 及其法律和商业数据和分析服务组合一直面临着互联网、谷歌搜索和开源软件兴起的竞争威胁，现在可能是其最强大的对手：生成式人工智能，Reihl 指出。
Reihl 承认，生成式 AI 的发展速度比他在 IT 领导岗位上近四十年的职业生涯中见过的要快得多。为了应对这一新现实，在去年 4 月 OpenAI 的 GPT-<> 首次亮相后，他的公司的高管们召开了会议，制定战略。会议达成共识？重写和重新确定公司所有年度目标的优先级，以应对新的创新。
“我们全员参与，”Reihl 说。“我们做了一个重大的转变，因为就其交互能力、答案的全面性和数据生成能力而言，这是一个游戏规则的改变者。就其能力而言，这简直令人震惊。
鉴于 LexisNexis 的核心业务是为法律、保险和金融公司以及政府和执法机构收集和提供信息和分析，生成式 AI 的威胁是真实存在的。但Reihl相信，由于当今通用大型语言模型（LLM）的缺陷，以及LexisNexis为增强和定制其服务中使用的LLM而磨练的专有数据和独特工具，包括Anthropic的Claude AI助手和Microsoft Azure上的GPT-4，LexisNexis可以解决生成式AI的进步。
LexisNexis 的 2,000 多名技术人员和大约 200 名数据科学家一直在狂热地工作，以整合利用生成式 AI 的独特功能，并为公司的全球客户群增加更多价值。但这种尝试并不是全新的。自 Google 于 2018 年推出以来，LexisNexis 一直在使用 BERT，这是一个自然语言处理 （NLP） 模型系列，以及自 Chat GPT 成立以来。但现在该公司支持所有主要的LLM，Reihl说。 数字化转型网www.szhzxw.cn
“如果你是最终用户，并且你是我们对话搜索的一部分，其中一些查询将在单个事务中同时转到 Azure 中的 ChatGPT-4 和 AWS 中的 Anthropic，”首席技术官说。“如果我输入一个查询，它可能会根据你提出的问题类型转到两者。我们将选择最佳的 LLM。我们使用 AWS 和 Azure。我们将采用最佳模型来回答客户提出的问题。
上个月底，LexisNexis在美国推出了Lexis+ AI，这是它自己的生成式人工智能解决方案，承诺消除人工智能的“幻觉”，并提供相关的法律引文，以确保律师能够获得准确、最新的法律先例——这是在当前大量法学硕士中发现的弱点。
为创新奠定基础
如果没有迁移到云，这一切都不可能实现，LexisNexis 于 2015 年开始迁移到云。LexisNexis 主要是 AWS 客户，还为许多使用 Microsoft Office 和其他 Microsoft 平台的客户提供 Microsoft Azure。
但要到达云端是一个艰难的攀登。 数字化转型网www.szhzxw.cn
当 Reihl 于 2007 年加入 LexisNexis 时，该公司大约一半的基础设施（包括其核心平台）都基于大型机。该公司在美国运营着两个非常大的数据中心，并进行了多次收购，从而产生了一套非常多样化的技术和各种格式的数据。
Reihl说，不久之后，LexisNexis的IT领导者与董事会接洽，要求数亿美元用基于XML的开放系统取代所有这些基础设施。该公司以直接迁移的方式将大部分数据从大型机迁移到这些开放系统，同时增加了专有的搜索功能，以及索引和自动化。但是这些应用程序没有针对云进行优化，因此当该公司在近十年前开始采用云时，最终不得不为微服务重新架构。
2020 年，LexisNexis 关闭了其最终的大型机，这代表了重大的成本节约，并将全部精力投入到其云平台上。 数字化转型网www.szhzxw.cn
虽然一些工作负载仍在剩余的数据中心运行，但 LexisNexis 的大部分数据利用了来自 50,000 多个来源（如法庭文件、律师事务所、新闻来源和网站）的流量，进入公司专有的内容制作系统。该服务的编辑人员还增强和丰富了专有内容，而自动化则为云上的工作流程增加了价值。
LexisNexis 享受了企业通过迁移到云获得的许多相同好处，包括显著的成本节约、可扩展性、敏捷性和创新速度。但也许最大的好处是LexisNexis能够在自己的生成式AI应用程序中迅速采用机器学习和LLM。
“这是我们最初与人工智能合作的地方，”Reihl说。“我们通过NLP和一些基本的机器学习来完成所有这些工作，随着时间的推移，这些机器学习演变成更多的深度学习。
转型的另一个主要方面是公司在提高员工技能和招募新人才方面的努力。LexisNexis的团队构成已经从用户体验设计师、产品经理和软件工程师转变为主题专家、了解法律和法律语言的知识产权律师，以及近200名数据科学家和机器学习工程师。
首席技术官表示，LexisNexis在数字化转型上总共花费了1.4美元。这似乎非常值得投资。
LexisNexis于10月在美国市场推出了Lexis+ AI，这是其具有生成式AI增强功能的多模型LLM解决方案。据该公司称，为法律行业微调的人工智能平台是市场上为数不多的人工智能SaaS平台之一，具有检索增强生成引擎，可消除幻觉，提供完善的对话搜索功能、法律文件起草、案件摘要和文件上传功能，使用户能够在几分钟内分析、总结和提取法律文件中的核心见解。 数字化转型网www.szhzxw.cn
首席技术官表示，该平台是与开发测试版的客户共同开发的，他们帮助公司完善了提示和搜索，并实施了安全性，以确保隐私和某些搜索可以保留在内部，这对律师来说至关重要。
LexisNexis面临的最大挑战是所有组织都面临的挑战：寻找足够的人才。
“那里的人并不多，所以我们也在培训那些在这些技能方面具有数据敏锐度的人，”Reihl说，他仍然相信，随着200名数据科学家的加入，该公司已准备好在明年在国际市场上发布其产品。
英文原文：
LexisNexis rises to the generative AI challenge
With generative AI, the legal information services giant faces its most formidable disruptor yet. That’s why CTO Jeff Reihl is embracing and enhancing the technology swiftly to keep in front of the competition.
IT leaders looking for a blueprint for staving off the disruptive threat of generative AI might benefit from a tip from LexisNexis EVP and CTO Jeff Reihl: Be a fast mover in adopting the technology to get ahead of potential disruptors. 数字化转型网www.szhzxw.cn
Since its origins in the early 1970s, LexisNexis and its portfolio of legal and business data and analytics services have faced competitive threats heralded by the rise of the Internet, Google Search, and open source software — and now perhaps its most formidable adversary yet: generative AI, Reihl notes.
Reihl concedes that generative AI is evolving much faster than anything he has seen in his career, which spans nearly four decades in IT leadership roles. To address this new reality, his company’s C-suite convened to strategize after the debut of OpenAI’s GPT-4 last March. The meeting consensus? To rewrite and reprioritize all the company’s annual goals to address the new innovations head on.
“We were all-hands-on-deck,” Reihl says. “We did a major pivot because this was a game changer in terms of its interactive abilities, as well as the comprehensiveness of its answers and its data generation capabilities. It was just staggering in terms of its capabilities.”
Given LexisNexis’ core business, gathering and providing information and analytics to legal, insurance, and financial firms, as well as government and law enforcement agencies, the threat of generative AI is real. But Reihl is confident that LexisNexis can tackle generative AI’s advances due to imperfections in today’s general-purpose large language models (LLMs), as well as the proprietary data and unique tools LexisNexis has honed to enhance and customize the LLMs it uses for its services, including Anthropic’s Claude AI assistant and GPT-4 on Microsoft Azure. 数字化转型网www.szhzxw.cn
LexisNexis’ 2,000-plus technologists and around 200 data scientists have been working feverishly to incorporate unique features that exploit generative AI and add more value for the company’s global customer base. But the foray isn’t entirely new. LexisNexis has been playing with BERT, a family of natural language processing (NLP) models, since Google introduced it in 2018, as well as Chat GPT since its inception. But now the company supports all major LLMs, Reihl says.
“If you’re an end user and you are part of our conversational search, some of those queries will go to both ChatGPT-4 in Azure as well as Anthropic in AWS in a single transaction,” the CTO says. “If I type in a query, it could go to both based on the type of question that you’re asking. We will pick the optimal LLM. We use AWS and Azure. We’ll take the optimal model to answer the question that the customer asks.”
Late last month, LexisNexis launched Lexis+ AI, its own generative AI solution, in the US that promises to eradicate AI “hallucinations” and provide linked legal citations to ensure lawyers have access to accurate, up-to-date legal precedents — weaknesses discovered in the current slew of LLMs.
Laying the foundation for innovation
None of this would have been possible without having migrated to the cloud, which LexisNexis began in 2015. Primarily an AWS customer, LexisNexis also offers Microsoft Azure for many customers using Microsoft Office and other Microsoft platforms.
But it was an uphill climb to get to the cloud.
When Reihl joined LexisNexis in 2007, roughly half of the company’s infrastructure, including its core platform, was based on the mainframe. The company operated two very large data centers in the US and had made several acquisitions, leading to a very diverse set of technologies and data in a wide variety of formats. 数字化转型网www.szhzxw.cn
Soon after, LexisNexis IT leaders approached the board of directors to request several hundred million dollars to replace all that infrastructure with XML-based open systems, Reihl says. The company migrated much of the data in a lift-and-shift fashion from the mainframe to those open systems, while adding proprietary search capabilities, as well as indexing and automation. But the applications were not optimized for the cloud, so eventually that had to be rearchitecting for microservices when the company began embracing the cloud nearly a decade ago.
In 2020, LexisNexis shut down its final mainframes — representing a major cost savings — and put the full force of its energies on its cloud platform.
While some workloads still run in the remaining data center, most of the data LexisNexis leverages flows from more than 50,000 sources, such as court filings, law firms, news sources, and websites, into the company’s proprietary content fabrication system. The service’s editorial staff also enhance and enrich the proprietary content, while automation adds value to the workflow on the cloud.
LexisNexis has enjoyed many of the same benefits enterprises gain by moving to the cloud, including significant cost savings, scalability, agility, and speed of innovation. But perhaps the biggest benefit has been LexisNexis’ ability to swiftly embrace machine learning and LLMs in its own generative AI applications. 数字化转型网www.szhzxw.cn
“This is where some of our initial work with AI started,” Reihl says. “We were doing all that through NLP and some basic machine learning, which evolved into more deep learning over time.”
Another major aspect of the transformation has been the company’s efforts in upskilling employees and acquiring new talent. The team makeup at LexisNexis has changed from UX designers, product managers, and software engineers to also include subject matter experts, IP attorneys who understand the law and legal language, as well as nearly 200 data scientists and machine learning engineers.
In total, LexisNexis spent $1.4 brillion on its digital transformation, the CTO says. It appears to have been well worth the investment.
LexisNexis launched Lexis+ AI, its multimodel LLM solution with generative AI enhancements, in the US market in October. The fine-tuned AI platform for the legal industry — one of few AI SaaS platforms on the market — features a retrieval augmented generative engine to eliminate hallucinations, offers refined conversational search capabilities, legal document drafting, case summarization, and document upload capabilities that enable users to analyze, summarize, and extract core insights from legal documents in minutes, according to the company.
The CTO says the platform was co-developed with customers who worked on the beta version and helped the company refine prompts and searches, and implement security to ensure privacy and certain searches can be kept in-house, which is critical for attorneys. 数字化转型网www.szhzxw.cn
The greatest challenge for LexisNexis is the same one all organizations face: finding enough talent.
“There’s just not a lot of people out there so we’re also training people that have data acumen in those skills,” says Reihl, who still believes that, with 200 data scientists on board, the company is well poised to release its offerings in international markets over the next year.
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