Google Cloud Next: The role of genAI agents, enterprise use cases
Google Cloud pitched an agent-oriented vision for generative AI at Google Cloud Next and highlighted a bevy of emerging use cases going from pilot to production.
Data to decisions examines the enablement of data-driven decisions across organizations. Holistic, data-informed decisions require a multi-disciplinary approach that combines performance monitoring with traditional business intelligence and analytic technologies. In addition, data-driven decisions are increasingly delivered in the context of business applications rather than in separate, analytic interfaces.
Google Cloud pitched an agent-oriented vision for generative AI at Google Cloud Next and highlighted a bevy of emerging use cases going from pilot to production.
MongoDB expanded integrations with Google Cloud's Vertex AI, BigQuery, Google Distributed Cloud and Google Cloud Manufacturing Data Engine.
Google Cloud outlined a series of services and enhancements across its platform in a bid to make it easier for enterprises to bring their data to generative AI models, build applications and deploy them at scale.
JPMorgan Chase CEO Jamie Dimon issued his annual shareholder letter and provided an incremental update on the company's artificial intelligence efforts as well as private cloud buildout.
Enterprises need to focus on data lakehouse strategies in 2024 to properly take advantage of generative AI; model architecture will be critical to managing large and small models; fine tuning is more difficult than you'd think; and CXOs were weary of database vendors glomming on to genAI hype.
It's easy to conclude that generative AI is going to take jobs from humans. But there's another argument that genAI will be needed just to maintain and improve productivity levels because there will be fewer workers. There’s a demographic donut hole in the workforce that may be partially ameliorated by genAI.
Archetype AI has raised $13 million in seed funding and launched Newton, a foundational model that is built to understand the physical world via data signals from accelerometers, gyroscopes, radars, cameras, microphones, thermometers and other environmental sensors.
With the move, DataStax aims to create an integrated generative AI stack to create applications. DataStax will integrate Langflow with its DataStax Astra DB and Python libraries.
Palantir will move its workloads to Oracle Cloud Infrastructure in a partnership that also includes the two companies jointly selling to governments as well as enterprises.
The survey, based on 107 CEOs mostly in the US, found 56% of respondents rank efficiency and productivity as the primary benefit.