AI Engineering Podcast

Latest Episodes:

The Impact of Generative AI on Software Development Nov 22, 2024

Show notes

Summary
In this episode of the AI Engineering Podcast, Tanner Burson, VP of Engineering at Prismatic, talks about the evolving impact of generative AI on software developers. Tanner shares his insights from engineering leadership and data engineering initiatives, discussing how AI is blurring the lines of developer roles and the strategic value of AI in software development. He explores the current landscape of AI tools, such as GitHub's Copilot, and their influence on productivity and workflow, while also touching on the challenges and opportunities presented by AI in code generation, review, and tooling. Tanner emphasizes the need for human oversight to maintain code quality and security, and offers his thoughts on the future of AI in development, the importance of balancing innovation with practicality, and the evolving role of engineers in an AI-driven landscape.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Tanner Burson about the impact of generative AI on software developers

Interview

Introduction
How did you get involved in machine learning?
Can you describe what types of roles and work you consider encompassed by the term "developers" for the purpose of this conversation?
How does your work at Prismatic give you visibility and insight into the effects of AI on developers and their work?
There have been many competing narratives about AI and how much of the software development process it is capable of encompassing. What is your top-level view on what the long-term impact on the job prospects of software developers will be as a result of generative AI?
There are many obvious examples of utilities powered by generative AI that are focused on software development. What do you see as the categories or specific tools that are most impactful to the development cycle?
In what ways do you find familiarity with/understanding of LLM internals useful when applying them to development processes?
As an engineering leader, how are you evaluating and guiding your team on the use of AI powered tools?
- What are some of the risks that you are guarding against as a result of AI in the development process?
What are the most interesting, innovative, or unexpected ways that you have seen AI used in the development process?
What are the most interesting, unexpected, or challenging lessons that you have learned while using AI for software development?
When is AI the wrong choice for a developer?
What are your projections for the near to medium term impact on the developer experience as a result of generative AI?

Contact Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

ML Infrastructure Without The Ops: Simplifying The ML Developer Experience With Runhouse Nov 11, 2024

Show notes

Summary
Machine learning workflows have long been complex and difficult to operationalize. They are often characterized by a period of research, resulting in an artifact that gets passed to another engineer or team to prepare for running in production. The MLOps category of tools have tried to build a new set of utilities to reduce that friction, but have instead introduced a new barrier at the team and organizational level. Donny Greenberg took the lessons that he learned on the PyTorch team at Meta and created Runhouse. In this episode he explains how, by reducing the number of opinions in the framework, he has also reduced the complexity of moving from development to production for ML systems.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Donny Greenberg about Runhouse and the current state of ML infrastructure

Interview

Introduction
How did you get involved in machine learning?
What are the core elements of infrastructure for ML and AI?
- How has that changed over the past ~5 years?
- For the past few years the MLOps and data engineering stacks were built and managed separately. How does the current generation of tools and product requirements influence the present and future approach to those domains?
There are numerous projects that aim to bridge the complexity gap in running Python and ML code from your laptop up to distributed compute on clouds (e.g. Ray, Metaflow, Dask, Modin, etc.). How do you view the decision process for teams trying to understand which tool(s) to use for managing their ML/AI developer experience?
Can you describe what Runhouse is and the story behind it?
- What are the core problems that you are working to solve?
- What are the main personas that you are focusing on? (e.g. data scientists, DevOps, data engineers, etc.)
- How does Runhouse factor into collaboration across skill sets and teams?
Can you describe how Runhouse is implemented?
- How has the focus on developer experience informed the way that you think about the features and interfaces that you include in Runhouse?
How do you think about the role of Runhouse in the integration with the AI/ML and data ecosystem?
What does the workflow look like for someone building with Runhouse?
What is involved in managing the coordination of compute and data locality to reduce networking costs and latencies?
What are the most interesting, innovative, or unexpected ways that you have seen Runhouse used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Runhouse?
When is Runhouse the wrong choice?
What do you have planned for the future of Runhouse?
What is your vision for the future of infrastructure and developer experience in ML/AI?

Contact Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Building AI Systems on Postgres: An Inside Look at pgai Vectorizer Nov 11, 2024

Show notes

Summary
With the growth of vector data as a core element of any AI application comes the need to keep those vectors up to date. When you go beyond prototypes and into production you will need a way to continue experimenting with new embedding models, chunking strategies, etc. You will also need a way to keep the embeddings up to date as your data changes. The team at Timescale created the pgai Vectorizer toolchain to let you manage that work in your Postgres database. In this episode Avthar Sewrathan explains how it works and how you can start using it today.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Avthar Sewrathan about the pgai extension for Postgres and how to run your AI workflows in your database

Interview

Introduction
How did you get involved in machine learning?
Can you describe what pgai Vectorizer is and the story behind it?
What are the benefits of using the database engine to execute AI workflows?
- What types of operations does pgai Vectorizer enable?
- What are some common generative AI patterns that can't be done with pgai?
AI applications require a large and complex set of dependencies. How does that work with pgai Vectorizer and the Python runtime in Postgres?
- What are some of the other challenges or system pressures that are introduced by running these AI workflows in the database context?
Can you describe how the pgai extension is implemented?
With the rapid pace of change in the AI ecosystem, how has that informed the set of features that make sense in pgai Vectorizer and won't require rebuilding in 6 months?
Can you describe the workflow of using pgai Vectorizer to build and maintain a set of embeddings in their database?
- How can pgai Vectorizer help with the situation of migrating to a new embedding model and having to reindex all of the content?
How do you think about the developer experience for people who are working with pgai Vectorizer, as compared to using e.g. LangChain, LlamaIndex, etc.?
What are the most interesting, innovative, or unexpected ways that you have seen pgai Vectorizer used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on pgai Vectorizer?
When is pgai Vectorizer the wrong choice?
What do you have planned for the future of pgai Vectorizer?

Contact Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

Timescale
pgai
Transformer architecture for deep learning
Neural Networks
pgvector
pgvectorscale
Modal
RAG == Retrieval Augmented Generation
Semantic Search
Ollama
GraphRAG
agensgraph
LangChain
LlamaIndex
Haystack
IVFFlat
HNSW
DiskANN
Repl.it Agent
BM25
TSVector
ParadeDB

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Running Generative AI Models In Production Oct 28, 2024

Show notes

Summary
In this episode Philip Kiely from BaseTen talks about the intricacies of running open models in production. Philip shares his journey into AI and ML engineering, highlighting the importance of understanding product-level requirements and selecting the right model for deployment. The conversation covers the operational aspects of deploying AI models, including model evaluation, compound AI, and model serving frameworks such as TensorFlow Serving and AWS SageMaker. Philip also discusses the challenges of model quantization, rapid model evolution, and monitoring and observability in AI systems, offering valuable insights into the future trends in AI, including local inference and the competition between open source and proprietary models.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Philip Kiely about running open models in production

Interview

Introduction
How did you get involved in machine learning?
Can you start by giving an overview of the major decisions to be made when planning the deployment of a generative AI model?
How does the model selected in the beginning of the process influence the downstream choices?
In terms of application architecture, the major patterns that I've seen are RAG, fine-tuning, multi-agent, or large model. What are the most common methods that you see? (and any that I failed to mention)
- How have the rapid succession of model generations impacted the ways that teams think about their overall application? (capabilities, features, architecture, etc.)
In terms of model serving, I know that Baseten created Truss. What are some of the other notable options that teams are building with?
- What is the role of the serving framework in the context of the application?
There are also a large number of inference engines that have been released. What are the major players in that arena?
- What are the features and capabilities that they are each basing their competitive advantage on?
For someone who is new to AI Engineering, what are some heuristics that you would recommend when choosing an inference engine?
Once a model (or set of models) is in production and serving traffic it's necessary to have visibility into how it is performing. What are the key metrics that are necessary to monitor for generative AI systems?
- In the event that one (or more) metrics are trending negatively, what are the levers that teams can pull to improve them?
When running models constructed with e.g. linear regression or deep learning there was a common issue with "concept drift". How does that manifest in the context of large language models, particularly when coupled with performance optimization?
What are the most interesting, innovative, or unexpected ways that you have seen teams manage the serving of open gen AI models?
What are the most interesting, unexpected, or challenging lessons that you have learned while working with generative AI model serving?
When is Baseten the wrong choice?
What are the future trends and technology investments that you are focused on in the space of AI model serving?

Contact Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

Baseten
- Podcast Episode
Copyleft
Llama Models
Nomic
Olmo
Allen Institute for AI
Playground 2
The Peace Dividend Of The SaaS Wars
Vercel
Netlify
RAG == Retrieval Augmented Generation
- Podcast Episode
Compound AI
Langchain
Outlines Structured output for AI systems
Truss
Chains
Llamaindex
Ray
MLFlow
Cog (Replicate) containers for ML
BentoML
Django
WSGI
uWSGI
Gunicorn
Zapier
vLLM
TensorRT-LLM
TensorRT
Quantization
LoRA Low Rank Adaptation of Large Language Models
Pruning
Distillation
Grafana
Speculative Decoding
Groq
Runpod
Lambda Labs

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Enhancing AI Retrieval with Knowledge Graphs: A Deep Dive into GraphRAG Sep 10, 2024

Show notes

Summary
In this episode of the AI Engineering podcast, Philip Rathle, CTO of Neo4J, talks about the intersection of knowledge graphs and AI retrieval systems, specifically Retrieval Augmented Generation (RAG). He delves into GraphRAG, a novel approach that combines knowledge graphs with vector-based similarity search to enhance generative AI models. Philip explains how GraphRAG works by integrating a graph database for structured data storage, providing more accurate and explainable AI responses, and addressing limitations of traditional retrieval systems. The conversation covers technical aspects such as data modeling, entity extraction, and ontology use cases, as well as the infrastructure and workflow required to support GraphRAG, setting the stage for innovative applications across various industries.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Philip Rathle about the application of knowledge graphs in AI retrieval systems

Interview

Introduction
How did you get involved in machine learning?
Can you describe what GraphRAG is?
- What are the capabilities that graph structures offer beyond vector/similarity-based retrieval methods of prompting?
What are some examples of the ways that semantic limitations of nearest-neighbor vector retrieval fail to provide relevant results?
What are the technical requirements to implement graph-augmented retrieval?
- What are the concrete ways in which the embedding and retrieval steps of a typical RAG pipeline need to be modified to account for the addition of the graph?
Many tutorials for building vector-based knowledge repositories skip over considerations around data modeling. For building a graph-based knowledge repository there obviously needs to be a bit more work put in. What are the key design choices that need to be made for implementing the graph for an AI application?
- How does the selection of the ontology/taxonomy impact the performance and capabilities of the resulting application?
Building a fully functional knowledge graph can be a significant undertaking on its own. How can LLMs and AI models help with the construction and maintenance of that knowledge repository?
- What are some of the validation methods that should be brought to bear to ensure that the resulting graph properly represents the knowledge domain that you are trying to model?
Vector embedding and retrieval are a core building block for a majority of AI application frameworks. How much support do you see for GraphRAG in the ecosystem?
- For the case where someone is using a framework that does not explicitly implement GraphRAG techniques, what are some of the implementation strategies that you have seen be most effective for adding that functionality?
What are some of the ways that the combination of vector search and knowledge graphs are useful independent of their combination with language models?
What are the most interesting, innovative, or unexpected ways that you have seen GraphRAG used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on GraphRAG applications?
When is GraphRAG the wrong choice?
What are the opportunities for improvement in the design and implementation of graph-based retrieval systems?

Contact Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

Neo4J
GraphRAG Manifesto
RAG == Retrieval Augmented Generation
- Podcast Episode
VLDB == Very Large DataBases
Knowledge Graph
Nearest Neighbor Search
PageRank
Things Not Strings) Google Knowledge Graph Paper
pgvector
Pinecone
- Data Engineering Podcast Episode
Tables To Labels
NLP == Natural Language Processing
Ontology
LangChain
LlamaIndex
RLHF == Reinforcement Learning with Human Feedback
Senzing
NeoConverse
Cypher query language
GQL query standard
AWS Bedrock
Vertex AI
Sequoia Training Data - Klarna episode
Ouroboros

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Harnessing Generative AI for Effective Digital Advertising Campaigns Sep 02, 2024

Show notes

Summary
In this episode of the AI Engineering podcast Praveen Gujar, Director of Product at LinkedIn, talks about the applications of generative AI in digital advertising. He highlights the key areas of digital advertising, including audience targeting, content creation, and ROI measurement, and delves into how generative AI is revolutionizing these aspects. Praveen shares successful case studies of generative AI in digital advertising, including campaigns by Heinz, the Barbie movie, and Maggi, and discusses the potential pitfalls and risks associated with AI-powered tools. He concludes with insights into the future of generative AI in digital advertising, highlighting the importance of cultural transformation and the synergy between human creativity and AI.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Praveen Gujar about the applications of generative AI in digital advertising

Interview

Introduction
How did you get involved in machine learning?
Can you start by defining "digital advertising" for the scope of this conversation?
- What are the key elements/characteristics/goals of digital avertising?
In the world before generative AI, what did a typical end-to-end advertising campaign workflow look like?
- What are the stages of that workflow where generative AI are proving to be most useful?
  - How do the current limitations of generative AI (e.g. hallucinations, non-determinism) impact the ways in which they can be used?
What are the technological and organizational systems that need to be implemented to effectively apply generative AI in public-facing applications that are so closely tied to brand/company image?
- What are the elements of user education/expectation setting that are necessary when working with marketing/advertising personnel to help avoid damage to the brands?
What are some examples of applications for generative AI in digital advertising that have gone well?
- Any that have gone wrong?
What are the most interesting, innovative, or unexpected ways that you have seen generative AI used in digital advertising?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on digital advertising applications of generative AI?
When is generative AI the wrong choice?
What are your future predictions for the use of generative AI in dgital advertising?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Building Scalable ML Systems on Kubernetes Aug 15, 2024

Show notes

Summary
In this episode of the AI Engineering podcast, host Tobias Macy interviews Tammer Saleh, founder of SuperOrbital, about the potentials and pitfalls of using Kubernetes for machine learning workloads. The conversation delves into the specific needs of machine learning workflows, such as model tracking, versioning, and the use of Jupyter Notebooks, and how Kubernetes can support these tasks. Tammer emphasizes the importance of a unified API for different teams and the flexibility Kubernetes provides in handling various workloads. Finally, Tammer offers advice for teams considering Kubernetes for their machine learning workloads and discusses the future of Kubernetes in the ML ecosystem, including areas for improvement and innovation.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Tammer Saleh about the potentials and pitfalls of using Kubernetes for your ML workloads.

Interview

Introduction
How did you get involved in Kubernetes?
For someone who is unfamiliar with Kubernetes, how would you summarize it?
For the context of this conversation, can you describe the different phases of ML that we're talking about?
Kubernetes was originally designed to handle scaling and distribution of stateless processes. ML is an inherently stateful problem domain. What challenges does that add for K8s environments?
What are the elements of an ML workflow that lend themselves well to a Kubernetes environment?
How much Kubernetes knowledge does an ML/data engineer need to know to get their work done?
What are the sharp edges of Kubernetes in the context of ML projects?
What are the most interesting, unexpected, or challenging lessons that you have learned while working with Kubernetes?
When is Kubernetes the wrong choice for ML?
What are the aspects of Kubernetes (core or the ecosystem) that you are keeping an eye on which will help improve its utility for ML workloads?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for ML workloads today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Expert Insights On Retrieval Augmented Generation And How To Build It Jul 28, 2024

Show notes

Summary
In this episode we're joined by Matt Zeiler, founder and CEO of Clarifai, as he dives into the technical aspects of retrieval augmented generation (RAG). From his journey into AI at the University of Toronto to founding one of the first deep learning AI companies, Matt shares his insights on the evolution of neural networks and generative models over the last 15 years. He explains how RAG addresses issues with large language models, including data staleness and hallucinations, by providing dynamic access to information through vector databases and embedding models. Throughout the conversation, Matt and host Tobias Macy discuss everything from architectural requirements to operational considerations, as well as the practical applications of RAG in industries like intelligence, healthcare, and finance. Tune in for a comprehensive look at RAG and its future trends in AI.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Matt Zeiler, Founder & CEO of Clarifai, about the technical aspects of RAG, including the architectural requirements, edge cases, and evolutionary characteristics

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what RAG (Retrieval Augmented Generation) is?
What are the contexts in which you would want to use RAG?
What are the alternatives to RAG?
What are the architectural/technical components that are required for production grade RAG?
Getting a quick proof-of-concept working for RAG is fairly straightforward. What are the failures modes/edge cases that start to surface as you scale the usage and complexity?
The first step of building the corpus for RAG is to generate the embeddings. Can you talk through the planning and design process? (e.g. model selection for embeddings, storage capacity/latency, etc.)
How does the modality of the input/output affect this and downstream decisions? (e.g. text vs. image vs. audio, etc.)
What are the features of a vector store that are most critical for RAG?
The set of available generative models is expanding and changing at breakneck speed. What are the foundational aspects that you look for in selecting which model(s) to use for the output?
Vector databases have been gaining ground for search functionality, even without generative AI. What are some of the other ways that elements of RAG can be re-purposed?
What are the most interesting, innovative, or unexpected ways that you have seen RAG used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on RAG?
When is RAG the wrong choice?
What are the main trends that you are following for RAG and its component elements going forward?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. [Podcast.__init__]() covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Barking Up The Wrong GPTree: Building Better AI With A Cognitive Approach Jul 28, 2024

Show notes

Summary
Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human"

Interview

Introduction
How did you get involved in machine learning?
Can you start by unpacking the idea of "human-like" AI?
How does that contrast with the conception of "AGI"?
The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment?
The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models?
What are the opportunities and limitations of causal modeling techniques for generalized AI models?
As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability?
What are the practical/architectural methods necessary to build more cognitive AI systems?
How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications?
What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems?
When is cognitive AI the wrong choice?
What do you have planned for the future of cognitive AI applications at Aigo?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

Aigo.ai
Artificial General Intelligence
Cognitive AI
Knowledge Graph
Causal Modeling
Bayesian Statistics
Thinking Fast & Slow by Daniel Kahneman (affiliate link)
Agent-Based Modeling
Reinforcement Learning
DARPA 3 Waves of AI presentation
Why Don't We Have AGI Yet? whitepaper
Concepts Is All You Need Whitepaper
Hellen Keller
Stephen Hawking

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Build Your Second Brain One Piece At A Time Jul 28, 2024

Show notes

Summary
Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain.
Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Tsavo Knott about Pieces, a personal AI toolkit to improve the efficiency of developers

Interview

Introduction
How did you get involved in machine learning?
Can you describe what Pieces is and the story behind it?
The past few months have seen an endless series of personalized AI tools launched. What are the features and focus of Pieces that might encourage someone to use it over the alternatives?
model selections
architecture of Pieces application
local vs. hybrid vs. online models
model update/delivery process
data preparation/serving for models in context of Pieces app
application of AI to developer workflows
types of workflows that people are building with pieces
What are the most interesting, innovative, or unexpected ways that you have seen Pieces used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pieces?
When is Pieces the wrong choice?
What do you have planned for the future of Pieces?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

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