Events

This talk will give you new insight into global energy research at SLB, highlighting market-leading technologies that leverage AI & ML. Don’t miss this opportunity to hear more about the energy innovation happening today at our research site in Kendall. See how new ideas can have global impact.

Working in the subsurface for more than a century, SLB leads the world in developing advanced sensors and measurement technology across the energy landscape. From decarbonization to next-generation energy systems, we continue to push the limits of what technology can deliver.

Here in Kendall Square is SLB’s flagship innovation engine: Schlumberger Doll Research, housing 20+ state-of-the-art laboratories and more than 60 multi-disciplinary scientists. For decades, we havebeen the architect of advanced downhole sensing and measurements: electromagnetic, nuclear,acoustic (seismic, sonic, ultrasonic), and more.

Today, we combine these measurements, data processing and modeling to tackle some of the world’s most critical challenges in:

• Geothermal Energy and Carbon Sequestration

• Emissions Sensing for Methane Reduction

• Formation Evaluation and Well Integrity

• Downhole Sensors and Automation

AI and Machine Learning are integral to these technologies, helping us to characterize complex phenomena across multiple scales, from microns to kilometers.

In this talk, we’ll showcase how AI & ML are fueling current and future energy technology development.

Creating the technology to power tomorrow’s needs starts now. Come join the conversation with SLB.

BoltzGen, a new open-source tool designed by MIT researchers, officially launched on October 26. It is a new generative model for designing protein and peptides of any modality to bind a wide range of biomolecular targets. The researchers are offering a live, in-person presentation with demos and discussion–register today!

Join the BoltzGen in MIT’s Stata Center, room 32-123, as they share details of the new model BoltzGen and discuss the future of biomolecular design 🧬

​​After the presentation and Q&A, there will be food, drinks and an opportunity to discuss with other members of the community!

​​Note: This is an in-person only event. Stay tuned for a separate Zoom seminar.

Learn more about the release of BoltzGen

Please join us for the CSAIL Forum with Alison Gopnik

Speaker: Alison Gopnik Dept. of Psychology, UC Berkeley
Venue: Live stream via Zoom: Registration required
Abstract: Learning about the causal structure of the world is a fundamental problem for human cognition, and causal knowledge is central to both intuitive and scientific world models. However, causal models and especially causal learning have proved to be difficult for standard Large Models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. These approaches also face challenges when it comes to learning however. In parallel, in the very different tradition of reinforcement learning, researchers have developed the idea of an intrinsic reward signal called “empowerment”. An agent is rewarded for maximizing the mutual information between its actions and their outcomes, regardless of the external reward value of those outcomes. In other words, the agent is rewarded if variation in an action systematically leads to parallel variation in an outcome so that variation in the action predicts variation in the outcome. Empowerment, then has two dimensions , it involves both controllability and variability. The result is an agent that has maximal control over the maximal part of its environment. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines.  If an agent learns an accurate causal model of the world they will necessarily increase their empowerment, and, vice versa, increasing empowerment will lead to a more accurate (if implicit) causal model of the world. Empowerment may also explain distinctive empirical features of children’s causal learning, as well as providing a more tractable computational account of how that learning is possible.

Learn more about the CSAIL Forum