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Google goes all-in on generative AI in Google Cloud Next

This week in Lars In Las Vegas, 30,000 people gathered to hear the latest and greatest from Google Cloud. All they hear all the time is generative AI. Google Cloud is first and foremost a cloud infrastructure and platform provider. If you didn’t know this, you might have missed it in the onslaught of AI news.

Not to disparage what Google showed off, but like Salesforce on its New York City roadshow last year, the company gave no recognition to its core business beyond a passing nod — except in the context of generative AI, of course.

Google has announced a series of artificial intelligence enhancements designed to help customers leverage Gemini large language models (LLM) and improve productivity across the platform. It’s a worthy goal, of course, and Google included plenty of demos in the announcement to illustrate the power of these solutions, both during the main keynote on day one and the developer keynote on day two.

But much of it seemed a little too simplistic, even considering the limited time they needed to squeeze into a keynote address. They rely primarily on examples from within the Google ecosystem, while most of nearly every company’s data is stored in repositories outside of Google.

Some examples actually feel like they could have been done without AI. For example, during an e-commerce demonstration, the presenter calls a supplier to complete an online transaction. Its purpose is to demonstrate the communication capabilities of the sales bot, but in reality, buyers can easily complete this step on the website.

That’s not to say that generative AI doesn’t have some powerful use cases, whether it’s creating code, analyzing a corpus of content and being able to query it, or being able to ask questions of log data to understand why a website crashes. What’s more, the company’s task and role-based agents are designed to help individual developers, creatives, employees, and others make it possible to leverage generative AI in tangible ways.

But when it comes to building AI tools based on Google’s model, rather than using the tools Google and other vendors build for their customers, I can’t help but feel they’re glossing over a lot of the obstacles that might exist. Methods for successfully implementing generative artificial intelligence. While they try to make it sound simple, in reality, implementing any advanced technology within a large organization is a huge challenge.

Big changes are not easy

Like other technological leaps in the past 15 years—whether it’s mobile, cloud, containerization, marketing automation, you name it—it comes with the promise of many potential benefits. However, these advances each bring their own levels of complexity, and big companies are acting more cautiously than we might imagine. AI feels much bigger than Google or any of the big vendors are letting on.

We know from these previous technological changes that they come with a lot of hype and lead to a lot of disillusionment. Even after many years, we are still seeing large companies that perhaps should be taking advantage of these advanced technologies still only dabbling, or even simply sitting on the sidelines, years after they were introduced.

There are many reasons why companies fail to take advantage of technology innovation, including organizational inertia; a fragile technology stack that makes it difficult to adopt newer solutions; or a group of corporate naysayers that shut down even the most well-intentioned initiatives, whether legal, HR, IT, or other groups, who continue to say no to substantive change for a variety of reasons, including internal politics.

Vineet Jain, CEO of Egnyte, a company focused on storage, governance and security, believes there are two types of companies: those that have already made a significant shift to the cloud, and those that will have an easier time adopting generative AI. , and businesses that are slow to move and risk struggling.

He spoke to many companies that still have most of their technology deployed on-premises and have a long way to go before they can start thinking about how AI can help them. “We spoke to many ‘late’ cloud adopters who have not yet started or have started looking to digital transformation very early,” Jain told TechCrunch.

Artificial intelligence may force these companies to think seriously about digital transformation, but they may get stuck in the initial stages, he said. “These companies need to solve these problems first and then use AI after they have mature data security and governance models,” he said.

It’s always data

Big vendors like Google make implementing these solutions sound simple, but like all complex technologies, just because the front end looks simple doesn’t necessarily mean the back end isn’t complex. As I’ve heard a lot this week, it’s still a case of “garbage in, garbage out” when it comes to the data used to train Gemini and other large language models, and that applies even more when it comes to generating artificial intelligence.

It starts with data. If you don’t have your data warehouse organized, it will be difficult to get it organized to train an LLM for your use case. Kashif Rahamatullah, Deloitte’s leader for Google Cloud, was impressed by Google’s announcement this week but still acknowledged that some companies lack clean data when implementing generative AI solutions. There will be problems. “These conversations can start as an AI conversation, but that quickly becomes: ‘I need to fix my data, I need to clean it, I need to put it all in one place, or almost one place, and then processing. Start getting real benefits from generative AI,” Rahamatullah said.

From Google’s perspective, the company built generative AI tools to more easily help data engineers build data pipelines to connect data sources within and outside the Google ecosystem. “It’s really designed to speed up data engineering teams by automating a lot of the labor-intensive tasks involved in moving data and preparing it for these models,” Gerrit Kazmaier, vice president and general manager of databases, data analytics and looker at Google, told TechCrunch.

This should help connect and cleanse data, especially for companies further along in their digital transformation journey. But for companies like the ones Jain mentioned—those that haven’t taken meaningful steps toward digital transformation—it can create more difficulties, even with the tools Google created.

All of this doesn’t even take into account that AI faces its own set of challenges beyond pure implementation, whether it’s applications based on existing models or especially when trying to build custom models, says Andy Tourai Thurai said. Horoscope study. “When implementing either solution, companies need to consider governance, accountability, security, privacy, ethical and responsible use and the compliance of such implementation,” Turai said. None of this is trivial.

Executives, IT professionals, developers and others attending GCN this week are probably looking for what’s next for Google Cloud. But if they’re not looking for AI, or they’re just not ready as an organization, they might leave Sin City a little shocked that Google is entirely focused on AI. In addition to more complete solutions from Google and other vendors, it may take a long time for organizations that lack digital maturity to take full advantage of these technologies.

#Google #allin #generative #Google #Cloud

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