Podcasts about vector spaces

In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.

References: Galois Theory - Wikipedia https://en.wikipedia.org/wiki/Galois_theory Commutative Ring - Wikipedia https://en.wikipedia.org/wiki/Commutative_ring Commutative Algebra - Wikipedia https://en.wikipedia.org/wiki/Commutative_algebra Commutative Property - Wikipedia https://en.wikipedia.org/wiki/Commutative_property Noncommutative Ring - Wikipedia https://en.wikipedia.org/wiki/Noncommutative_ring Glossary of Ring Theory - Wikipedia https://en.wikipedia.org/wiki/Glossary_of_ring_theory Field - Wikipedia https://en.wikipedia.org/wiki/Field_(mathematics) Module Fundamentals - University of Illinois at Urbana-Champaign https://faculty.math.illinois.edu/~r-ash/Algebra/Chapter4.pdf Linear Transformation (Linear Map) - Wikipedia https://en.wikipedia.org/wiki/Linear_map Vector Space - Wikipedia https://en.wikipedia.org/wiki/Vector_space Scalar - Wikipedia https://en.wikipedia.org/wiki/Scalar_(mathematics) Lattice (Order) - Wikipedia https://en.wikipedia.org/wiki/Lattice_(order) Lattice (Group) - Wikipedia https://en.wikipedia.org/wiki/Lattice_(group) Infimum and Supremum - Wikipedia https://en.wikipedia.org/wiki/Infimum_and_supremum Algebra Over a Field - Wikipedia https://en.wikipedia.org/wiki/Algebra_over_a_field Ring Theory - Wikipedia https://en.wikipedia.org/wiki/Ring_theory Ring - Wikipedia https://en.wikipedia.org/wiki/Ring_(mathematics)

D Creations Mathematics Physics Linear Algebra Vector Spaces --- Send in a voice message: https://anchor.fm/d0531/message

In this lecture, the professor talked about linear operators and matrices, etc.

In this lecture, the professor talked about vector spaces and dimensionality.

In this lecture, the professor talked about eigenvalues and eigenvectors of hermitian operators acting on complex vector spaces, inner products on a vector space, etc.

In this lecture, the professor talked about linear functionals and adjoint operators, Hermitian and unitary operators, etc.

This video shows the ABC of Vector Spaces, with examples.

This video describes the concept of a basis for a vector space.

Herb Gross describes n-Dimensional Vector Spaces relating definitions to the concept of a mathematical structure.

Sums and quotients of vector spaces - Conclusion of Section 3.6, printed slides 57-60. Further module materials are available for download from The University of Nottingham open courseware site: http://unow.nottingham.ac.uk/resources/resource.aspx?hid=bd32d53b-3c12-ac19-176b-d9e112731951

Sums and quotients of vector spaces - Conclusion of Section 3.6, printed slides 57-60. Further module materials are available for download from The University of Nottingham open courseware site: http://unow.nottingham.ac.uk/resources/resource.aspx?hid=bd32d53b-3c12-ac19-176b-d9e112731951

Sums and quotients of vector spaces - Section 3.6, printed slides 55-57. Further module materials are available for download from The University of Nottingham open courseware site: http://unow.nottingham.ac.uk/resources/resource.aspx?hid=bd32d53b-3c12-ac19-176b-d9e112731951

Sums and quotients of vector spaces - Section 3.6, printed slides 55-57. Further module materials are available for download from The University of Nottingham open courseware site: http://unow.nottingham.ac.uk/resources/resource.aspx?hid=bd32d53b-3c12-ac19-176b-d9e112731951