[seopress_breadcrumbs]

Episode #101 – The Kind of Artificial Intelligence That Google Doesn’t Care to Build – with Dr. Roger Schank

•

Episode #101 - The Kind of Artificial Intelligence That Google Doesn’t Care to Build - with Dr. Roger Schank — Beyond Siri

Dr. Roger Schank received his PhD from Stanford, taught at Yale, and altogether has spent around three decades attempting to solve the big problems of artificial intelligence.
In this interview, Dr. Schank talks about a kind of helpful “teaching” artificial intelligence that would go beyond Siri and help us to make the right decisions at the right times given our own objectives. He explains why he believes such a project is not on Google’s agenda, and what he believes might be required to create such an AI.

Check out this episode on Libsyn.

Listen on iTunes.

image credit: cdn.cultofmac.com

Recommended from Emerj

Read more

Building the Context Layer Enterprise AI Needs to Scale

This article is sponsored by Tabnine and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. Complex work depends on context, and AI does not have it by default. To leverage AI at scale and…

•

Read more

Unified Data for Supply Chain Cost and Performance Visibility

This article is sponsored by Easy Metrics and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. Distribution and fulfillment leaders operate vast networks where performance expectations continue to rise while operational complexity quietly compounds.…

•

Read more

Accelerating Speed to Value through Agentic Systems and Intelligent Automation in Life Sciences

Life sciences enterprises are caught in a widening gap. The pace of R&D, manufacturing, and data-driven decision-making is accelerating, but the infrastructure beneath it is not. The result is delays, cost overruns, and operational friction at precisely the moment organizations are trying to move faster. The data volumes alone signal the scale of the challenge.…

•

Read more

Artificial Intelligence at Siemens

Founded in 1847, Siemens began as a telegraph manufacturing company in Berlin and evolved into one of the world’s largest industrial conglomerates. Today, Siemens operates across energy, healthcare, mobility, infrastructure, and industrial manufacturing. In fiscal year 2025, Siemens reported revenues of €77.8 billion and invested €6.1 billion in research and development, much of it focused…

•

Read more

How AI Is Re‑Architecting Industrial Procurement and Supply Chain

This article is sponsored by Arkestro and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.Enterprise procurement leaders are operating in environments characterized by increasing supplier complexity, data intensity, and external volatility. As organizations scale,…

•

Read more

The Shift to Enterprise‑Wide Third‑Party Risk Management at Scale

This article is sponsored by Aravo and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. Enterprises lack reliable visibility, control, and accountability over the risks embedded in their third‑party networks, despite being legally and…

•

Artificial Intelligence at JP Morgan Chase

Read more

Artificial Intelligence at JPMorgan Chase

JPMorgan Chase & Co. is one of the world’s largest financial institutions, reporting $4.425 trillion in total assets, $2.559 trillion in deposits, $57.0 billion in net income, and $182.4 billion in total net revenue in 2025. It serves millions of customers across consumer banking, commercial banking, payments, investment banking, and asset management, and ended the…

•

Read more

Driving the Systemic Change for AI

This interview analysis is sponsored by Deloitte and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. Many enterprises discover that “AI readiness” on paper doesn’t always translate to value in production. Across the EU,…

Matthew DeMello

•

Why Industrial AI Projects Stall Before Production Header Image — Why Industrial AI Projects Stall Before Production

Read more

Why Industrial AI Projects Stall Before Production

This article is sponsored by IFS and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. A widening gap has emerged between industrial enterprises’ ambitions for AI and their ability to operationalize it; this gap…

•

Read more

Transitioning from Reactive Reporting to Predictive Market Intelligence in MedTech Commercial

This interview analysis is sponsored by AcuityMD and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. The MedTech commercial sector in 2026 is defined by a paradox of data. While research and development complexity…

•

Read more

Challenges HR Leaders Face in Adopting Enterprise AI

Talent data fragmentation has emerged as a foundational constraint on enterprise HR's ability to make strategic workforce decisions, persisting because organizations have systematically underinvested in unified data architecture. Despite organizations operating an average of 897 applications, only 34% provide an integrated user experience across their channels, per the 2025 MuleSoft Connectivity Benchmark Report. This distortion…

•

Read more

Building AI-ready Cultures in Life Sciences R&D

Life sciences are over‑funding AI and under‑funding the data maturity required to scale it. This imbalance is turning a potential R&D accelerant into a high‑priced bottleneck that fails to deliver compounding returns across the portfolio. These workflows depend on fragmented data sources spanning molecular databases, clinical trial registries, internal protocols, and published literature. This fragmentation…

•