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Infosys Topaz<\/a>, as it is called, leverages the company's AI framework to help customers build an AI core to deliver cognitive solutions. “It’s our collective knowledge, services, and the ecosystem of partners that we work with that we will bring to bear for our clients. Our clients need a lot of advisory capability around it. One of the first things that we are doing which Nandan<\/a> (Nilekani<\/a>) himself is championing is an AI-first approach within the organisation, and we're going to bring in those learnings into our Topaz offerings, because harnessing generative AI needs a lot of experience,” Infosys<\/a> EVP and co-head of delivery Satish<\/a> HC told TOI.
A recent Goldman Sachs<\/a> report said generative AI could boost economic output by $7 trillion globally over the next ten years. US investment management firm Ark Invest said AI software could generate $14 trillion in revenue by 2030.
“Even without generative AI, the market was about $400-$500 billion. This is a high-growth opportunity and AI is at the heart of everything that we do. We're building digital twins for ourselves. One of the biggest challenges in the industry today, not just for us but even for our clients, is the aging workforce, institutional knowledge that is sitting within teams, and how we harness it without losing it. With generative AI now, we have an answer. Second, we are heavily invested in a core team,” Satish<\/a> added.
Satish noted that for a customer, they had been using machine learning algorithms that can understand emotions and sentiments around their products during sporting events. \"With ChatGPT, we've figured out that we can do this with 30% less effort. In some of these cases, we're going to use ChatGPT because it’s going to work on data which is already there in the public domain in social media. Now, in many other cases, we will have to build a custom module, or a large database bordered within the confines of a company because it's got competitive knowledge,” he said.
A British bank, he said, used Infosys Topaz<\/a> to transform over 2,000 customer service processes to operate in near real-time instead of a week.
Infosys has over 12,000 AI use cases, 150 pre-trained AI models and 10 AI platforms steered by AI and data strategists. It says it has embraced a responsible by design approach that ensures trust, privacy, security, and compliance.
Satish said that Infosys has built a risk framework around it and captured generative AI projects in the master service agreement (MSA<\/a>) to mitigate any downside impact. “Contractually, every place where we are experimenting with this technology, we only do this with the explicit concurrence and approval from our clients and it goes into the MSA<\/a>. That's because of the indirect risks which are open-ended. Second, when we take our service, whether it is 95% accuracy, whether it is 75% accuracy, the balance, we underwrite. Like the example I shared on 30% productivity gains with ChatGPT, it has given me a cycle time and effort savings. Of course, there may be a rough edge, but we will continue to fine-tune the models. And we will underwrite the rest of the accuracy gap, so that it is seamless for our clients,” he said.
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印孚瑟斯启动生成人工智能服务套件
高盛(Goldman Sachs)最近发布的一份报告说生成AI可能提高经济产出7万亿美元全球未来十年。美国投资管理公司投资约柜说人工智能软件将在2030年产生14万亿美元的收入。
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印孚瑟斯黄玉,因为它被调用时,利用公司的AI框架来帮助客户建立一个人工智能核心提供认知的解决方案。“这是我们集体知识、服务和生态系统的合作伙伴,与我们合作,我们将为我们的客户。我们的客户需要大量的咨询能力。的第一件事是我们做的南丹(尼勒卡尼)本人是支持是一种爱一方法在组织内,我们会把这些知识进入我们的黄水晶,因为利用生成AI需要大量的经验,“印孚瑟斯执行副总裁兼联席主管交付SatishHC告诉钢铁洪流。
最近的一次高盛(Goldman Sachs)报告称生殖AI可能提高经济产出7万亿美元全球未来十年。美国投资管理公司投资约柜说人工智能软件将在2030年产生14万亿美元的收入。
“即使没有生成AI,市场约为400 - 5000亿美元。这是一个高增长的机会和人工智能的核心,我们所做的一切。我们正在构建数字为自己的双胞胎。今天业内最大的挑战之一,不仅对我们,即使是对于我们的客户,是老化的劳动力,坐在在团队制度知识,以及如何利用它不丢失。现在生成的人工智能,我们有一个答案。其次,我们在一个核心团队,投入巨资”Satish补充道。
Satish指出,对于一个客户,他们一直使用机器学习算法,能够理解情绪和情绪在他们的产品在体育赛事。“ChatGPT,我们发现我们可以少30%的努力。在这些情况下,我们要用ChatGPT因为它会工作在数据已经在社交媒体在公共领域。现在,在许多其他情况下,我们必须构建一个自定义模块,或大型数据库的范围内与一个公司因为它有竞争力的知识,”他说。
英国银行,他说,使用印孚瑟斯黄玉将超过2000客户服务流程以接近实时的操作,而不是一个星期。
印孚瑟斯有超过12000 AI用例,150 pre-trained AI模型和人工智能平台由人工智能和数据分析师。它说它已经接受了一个负责任的设计方法确保信任、隐私、安全性和遵从性。
Satish说,印孚瑟斯建立了一个框架,捕捉风险生成的人工智能项目主服务协议(MSA)以减少任何不利影响。“契约,每一个地方我们尝试了这项技术,我们只做明确的赞同和批准我们的客户进入MSA。这是因为开放式的间接风险。第二,当我们把我们的服务,无论是95%的准确率,无论是75%的准确率,平衡,我们承担。就像我共享的例子与ChatGPT生产率提高30%,这给了我一个循环节省时间和精力。当然,可能有一个粗糙的边缘,但我们将继续调整模型。我们将承担其余的准确性差距,所以无缝的为我们的客户,”他说。
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