We talk about improving the time it takes to make decisions – called time to decision, a topic that we like to address quite a bit. We started with the news of the day around AI, ml Chaffee GP, and learning models.
We asked ourselves if AI/ML and generative AI could change the way expertise is used to make decisions and improve the time to the decision for experts. What type of implications would that have in the market?
If you’ve been tracking this subject, I know you will find this exciting and interesting.
We check in on data gravity to see how generative AI and conversations about metadata and thinking on data lakes impacts data gravity thinking in general.
Data gravity is a concept that has been propagated by David McCrory, a friend of mine, who defined this idea that data itself, the aggregation of data, the use and transit of data has a gravitational effect. That it pulls more data to it as well as workloads towards it.
We jumped right into impacts of data gravity in this conversation.
How can the intersection of generative AI machine learning and artificial intelligence be applied to environments using digital twins? Today we discuss digital twins and artificial intelligence.
How can we improve the simulations, the systems, the interactions that we build? How can we correctly model complex components of everything from cars to pumps in ways that allow us to then build on top and build more intelligent systems.
In the Cloud 2030 podcast episode on digital twins and AI, Rob Hirschfeld discusses the potential of using digital twins in handling real-world disasters, citing the recent train derailment in Ohio as an example. The concept involves quickly creating a digital twin of a disaster space to enable robots to learn, adapt, and efficiently mitigate the situation. Hirschfeld emphasizes the unprecedented opportunities for improving environmental interactions, responding to crises, and highlights the sophistication of ideas discussed in the episode. He encourages listeners to explore the full conversation on digital twins and AI at the2030.cloud.
Data comes from many different places, sources, and ways. Some data we call dark data, which is data not accessible to you, and all of it relevant. Today we talk about metadata as part of the governance control management exposure of data.
An important layer beyond the data itself is the governance intent, how people access it, and how you combine data. We discuss exactly what that is, but still only touch the surface.
In the Cloud 2030 Podcast episode on metadata and building a data control plane, Rob Hirschfeld emphasizes the challenge of controlling data egress due to its diverse sources and destinations. He contends that rather than attempting to create a locked box for all data, the focus should be on embedding information, particularly metadata, to control data consumption. Hirschfeld envisions a distributed system for a data control plane, involving multiple parties managing and providing consistent rules for data use, acknowledging the complexity of data storage across various locations. He encourages listeners to explore the insightful February 2nd episode and engage in ongoing conversations at the2030.cloud.
Metadata is information that travels with the raw data that provides context, provenance, security, authorship, controls, and indexing.
The number of ways that you can expand the use of data is controlled by adding metadata. It creates a change in how we look at and manage data. Instead of creating control systems that contain the data, it’s actually packaged the control infrastructure, or the data control plane, as part of the data so that all of the systems can participate in it.
We also talk about data mesh a lot in that context.
In the Cloud 2030 Podcast episode from January 19th, Rob Hirschfeld discusses the challenges and opportunities associated with metadata, particularly in the context of managing and sharing data effectively. The conversation explores the concept of packaging raw data in a way that makes it available on request without being included, accompanied by metadata detailing its provenance, access permissions, context, and even expiration dates. Hirschfeld emphasizes the importance of building a data control plane to navigate the complexities of data consumption and production, envisioning a future where metadata plays a crucial role in weaving together diverse data sources. Listeners are encouraged to explore the full 40-minute discussion for comprehensive insights, and to join ongoing conversations at the2030.cloud.
How do authorization systems need to be built and made resilient for distributed infrastructure? We discuss how having a single centralized authorization system is incredibly fragile compared to distributed edge infrastructure.
Everything we build has some element of distributed component tree and resiliency in it, and we need to make sure that the authorization systems are included in that analysis.
We explored how you can make MFA more resilient and how you can improve the security of authentication by building additional layers of trust based on behaviors.
In the April 28th Cloud 2030 Podcast, Rob Hirschfeld delves into the challenges of implementing two-factor authentication (2FA) in distributed infrastructures with centralized authentication. The critical problem revolves around creating resilient systems that don’t solely rely on external factors for authentication, considering the potential impact on every service and component in the infrastructure. The discussion emphasizes the importance of behavioral analysis in authentication, scrutinizing user behavior to ensure trustworthiness, especially in scenarios where full authentication is not available. The full conversation explores these aspects in depth, providing valuable insights for building resilient infrastructure. Join future discussions at the2030.cloud.
What was working what wasn’t working with conferences. But in traditional Cloud2030 style, we dove into the future, what would make great conferences, what makes conferences good, what made them good in the past and what technology changes we think could be coming in the future.
We extended that from conferences into meetings and meeting technology and transcription in the second half of this podcast. We really dive into how to help people connect together better since that’s what conferences are about, too.
Then we extended it more broadly, and brought up some interesting things like sentiment analysis, and adding new dimensions into the types of tools that we’re using today.
Rob Hirschfeld, CEO and co-founder of RackN and host of the Cloud 2030 Podcast, reflects on the September 30th discussion regarding human interactions at conferences and meetings using new technologies. He highlights the shift towards improved communication through visual mediums like Zoom and Teams, emphasizing the potential for future technologies to enhance these interactions further. Hirschfeld encourages listeners to explore the comprehensive discussion at the2030.cloud, which delves into the possibilities and challenges of advancing video interaction technologies.
Joining us this week is Simon Crosby, CTO at SWIM.AI. Simon discusses the architecture for Edge and its differences from existing cloud infrastructure and how the SWIM.AI solution meets the needs for Edge services.
Highlights
• Challenges in Edge
• Data is Complete Disruption in Current Models
• Don’t Train in Cloud for Edge; Instead Learn on Fly
• All About Data – Apps can’t be Written for Data Specifically
• Example of Sensor Model at Traffic Light in Self-Learning Model
• Digital Twin Concept & Actor Model
• SWIM.AI Innovation ~ always as though its local
• Independent Management of Latency and Resource Utilization
• Write the Program from the Data
• Example of Manufacturer with Millions of RFID Tags
• Limitations of Having People Involved in Everything
