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💻 Part 2: How Digital Fragments of Optimization Become Abstract Agents A Deep Dive in the “Fragments to Agents” Series

In Part 1, we explored how biological and cultural systems turn scattered parts into living, social, and economic agents. In Part 2, we shift focus to the digital and abstract realms, where fragments of optimization and computation give rise to systems that act—sometimes in ways even their designers didn’t anticipate.

This session features a substantial presentation followed by deep group discussion. We’ll unpack the central question:

How do fragments of digital optimization become abstract agents?

From machine learning systems and feedback loops to digital marketplaces and algorithmic trading—how do we draw the line between a tool and an agent? And what happens when optimization processes become powerful enough to behave as if they have goals?

We’ll explore topics and questions such as:

• When does optimization become agency?
• What distinguishes a system doing work from one with intent?
• What does mesa-optimization teach us about AI and control?
• How do abstract systems—like social networks or algorithms—begin to influence the real world with agent-like persistence?

This session draws from thinkers such as Alan Turing, Norbert Wiener, Stuart Russell, Melanie Mitchell, John von Neumann, and others in the space of AI alignment, computational emergence, and abstract systems.

This event is open to all groups, but the core emphasis will be on questions most relevant to our AI, computation, and systems-focused members. Other lenses—including biological, philosophical, and cultural—will return in future sessions.

Format:

• Substantial presentation on core ideas
• Open, in-depth discussion and Q&A
• Optional follow-up materials and content links

Whether you’re curious about optimization, autonomy, agency, or existential risk—this session offers a deep look into one of the most urgent and fascinating transitions in the modern world.

Read more >    Listen to the podcast (duration: 20:37) >  Stuart Russell, one of the world’s foremost thought leaders on artificial intelligence, explains how we can ensure AI truly benefits humanity rather than causing us harm. According to Russell, doing so begins with abandoning the idea of creating “intelligent” machines altogether.See www.mckinsey.com/privacy-policy for privacy information

• ERRATA (As Reported by Peter: "The book Peter mentioned (at 46:20) by Stuart Russell, "Do the Right Thing", was published in 2003, and not recently"

In this session Peter Morgan, CEO Deep Learning Partnership sat with Vishal Kumar, CEO AnalyticsWeek and shared his thoughts around Deep Learning, Machine Learning and Artificial Intelligence. They've discussed some of the best practices when it comes to picking right solution, right vendor and what are some of the keyword means.

Here's Peter's Bio: Peter Morgan is a scientist-entrepreneur starting out in high energy physics enrolled in the PhD program at the University of Massachusetts at Amherst. After leaving UMass, and founding my own company, Peter has moved into computer networks, designing, implementing and troubleshooting global IP networks for companies such as Cisco, IBM and BT Labs. After getting an MBA and dabbling in financial trading algorithms. Peter has worked for three years on an experiment lead by Stanford University to measure the mass of the neutrino. Since 2012. He had been working in Data Science and Deep Learning, founding an AI Solutions company in Jan 2016.

As an entrepreneur Peter has founded companies in the AI, social media, and music industries. He has also served on the advisory board of technology startups. Peter is a popular speaker at conferences, meetups and webinars. He has cofounded and currently organize meetups in the deep learning space. Peter has business experience in the USA, UK and Europe.

Today, as CEO of Deep Learning Partnership, He leads the strategic direction and business development across product and services. This includes sales and marketing, lead generation, client engagement, recruitment, content creation and platform development. Deep Learning technologies used include computer vision and natural language processing and frameworks like TensorFlow, Keras and MXnet. Deep Learning Partnership design and implement AI solutions for our clients across all business domains.

Interested in sharing your thought leadership with our global listeners? Register your interest @ http://play.analyticsweek.com/guest/