The majority of current GenAI projects are not due to any inherent flaws in Large Language Models (LLMs), but rather misunderstandings about how to use them and the capabilities needed to properly design, develop, and operate GenAI-driven applications. fails due to lack of. .
At Data Summit 2024, Kjell Carlsson, Head of AI Strategy at Domino Data Labs, will present a session titled “Breaking the 7 Myths of GenAI to Operationalize Impact,” highlighting the most harmful We debunked the myth.
The annual Data Summit conference was held in Boston on May 8 and 9, 2024, with a pre-conference workshop held on May 7.
We also looked at case studies on how advanced AI teams in industries ranging from pharmaceuticals to food delivery are breaking these myths and delivering transformative results.
“We have the opportunity to talk to so many companies about how they are using generative AI,” Carlson said.
He explained that generative AI has been successful in drug discovery, customer service, and productivity.
Common characteristics of influential GenAI case studies include people, process, and technology. These companies leverage his extensive ecosystem of GenAI and ML technologies with advanced MLOps capabilities.
Carlson said he predicts that 90% of GenAI efforts will fail to deliver transformative value.
“We're all going to use GenAI, but it's a question of whether we're going to do something transformational that touches the core of our business,” Carlson said. “I'm afraid I'm going to make a lot of mistakes here.”
According to Carlson, some AI myths to ignore include:
- AI becomes perceptive
- AI becomes omniscient
- AI will lead to mass unemployment
- AI could wipe out humanity
Some AI myths worth debunking include:
- GenAI replaces predictive AI/ML: In fact, GenAI complements predictive AI/ML
- Bigger is better: the reality is that bigger is slower, more expensive, and more generic
- GenAI is all about the LLM: the reality is everything else is just as important
- GenAI is not data science: The reality is that data science is the best foundation for GenAI
- Responsible AI is someone else's problem: Actually, it's everyone's problem
- GenAI can be outsourced: in-house capabilities are actually the key to AI transformation
- You can ignore AI and ML: the reality is we should have started years ago
“It's often helpful to think about:” [models] as a pipeline,” he said. “Magic happens when you connect multiple components.”
He recommended designing strategies with real-world GenAI implications in mind. Let our experienced data products and data science teams lead your GenAI projects. Implement an iterative integration process to develop, test, and operationalize GenAI and predictive AI pipelines. Implement open and extensible capabilities across your AI ecosystem to accelerate, streamline, and manage your AI lifecycle.
“Expect the unexpected,” Carlson said. “This is not an option. This is your best bet for increased performance and value.”
In an era where AI is reshaping industries, the integration of large-scale language models (LLMs) like ChatGPT with private knowledge platforms is a breakthrough development.
Clive Smith, Chief Revenue Officer of Sales, Datavid Limited, and Tim Padilla, Director of Sales and Consulting North America, Datavid Limited, followed Carlsson in a session at Data Summit 2024 titled “Integrating LLM and Private Knowledge Platforms.” ” was discussed.
They shared their experiences and lessons learned from both internal R&D and benchmarking several LLMs with customers and subsequent integration with existing KM platforms.
Datavid is a data consulting company specializing in extracting business value from structured and unstructured data. Smith and Padilla help customers create a well-defined FAIR data strategy (Finable Accessible Interoperable Reusable) that is the foundation for building data-centric applications.
“What I'm seeing is a new generation of 'data service providers,'” Smith says. “We treat generative AI as just an application. Only experts can validate the results of the generative applications you’re building, so you need to get them to validate it.”
Padilla explained that a unified data framework is an approach that eliminates data silos resulting from structural and technical factors. This enables business agility, faster delivery of innovative solutions, increased ROI and faster time to value.
According to Smith and Padilla, organizations must make four shifts:
- Operate leaner, faster, and more agile
- Enabling change across your organization and gaining in-time insights
- Investing in next generation innovation
- Align tools and standards
“We come from the world of data,” Padilla says.
Many of the Data Summit 2024 presentations can be reviewed here: https://www.dbta.com/DataSummit/2024/Presentations.aspx.
