AI Engineering Podcast
Tobias Macey
This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.
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Top 10 AI Engineering Podcast Episodes
Goodpods has curated a list of the 10 best AI Engineering Podcast episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to AI Engineering Podcast for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite AI Engineering Podcast episode by adding your comments to the episode page.
Enhancing The Abilities Of Software Engineers With Generative AI At Tabnine
AI Engineering Podcast
11/13/23 • 64 min
Summary
Software development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool.
Announcements
Parting Question
Software development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Eran Yahav about building an AI powered developer assistant at Tabnine
- Introduction
- How did you get involved in machine learning?
- Can you describe what Tabnine is and the story behind it?
- What are the individual and organizational motivations for using AI to generate code?
- What are the real-world limitations of generative AI for creating software? (e.g. size/complexity of the outputs, naming conventions, etc.)
- What are the elements of skepticism/oversight that developers need to exercise while using a system like Tabnine?
- What are some of the primary ways that developers interact with Tabnine during their development workflow?
- Are there any particular styles of software for which an AI is more appropriate/capable? (e.g. webapps vs. data pipelines vs. exploratory analysis, etc.)
- For natural languages there is a strong bias toward English in the current generation of LLMs. How does that translate into computer languages? (e.g. Python, Java, C++, etc.)
- Can you describe the structure and implementation of Tabnine?
- Do you rely primarily on a single core model, or do you have multiple models with subspecialization?
- How have the design and goals of the product changed since you first started working on it?
- What are the biggest challenges in building a custom LLM for code?
- What are the opportunities for specialization of the model architecture given the highly structured nature of the problem domain?
- For users of Tabnine, how do you assess/monitor the accuracy of recommendations?
- What are the feedback and reinforcement mechanisms for the model(s)?
- What are the most interesting, innovative, or unexpected ways that you have seen Tabnine's LLM powered coding assistant used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI assisted development at Tabnine?
- When is an AI developer assistant the wrong choice?
- What do you have planned for the future of Tabnine?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers.
Validating Machine Learning Systems For Safety Critical Applications With Ketryx
AI Engineering Podcast
11/08/23 • 51 min
Summary
Software systems power much of the modern world. For applications that impact the safety and well-being of people there is an extra set of precautions that need to be addressed before deploying to production. If machine learning and AI are part of that application then there is a greater need to validate the proper functionality of the models. In this episode Erez Kaminski shares the work that he is doing at Ketryx to make that validation easier to implement and incorporate into the ongoing maintenance of software and machine learning products.
Announcements
Parting Question
Software systems power much of the modern world. For applications that impact the safety and well-being of people there is an extra set of precautions that need to be addressed before deploying to production. If machine learning and AI are part of that application then there is a greater need to validate the proper functionality of the models. In this episode Erez Kaminski shares the work that he is doing at Ketryx to make that validation easier to implement and incorporate into the ongoing maintenance of software and machine learning products.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Erez Kaminski about using machine learning in safety critical and highly regulated medical applications
- Introduction
- How did you get involved in machine learning?
- Can you start by describing some of the regulatory burdens placed on ML teams who are building solutions for medical applications?
- How do these requirements impact the development and validation processes of model design and development?
- What are some examples of the procedural and record-keeping aspects of the machine learning workflow that are required for FDA compliance?
- What are the opportunities for automating pieces of that overhead?
- Can you describe what you are doing at Ketryx to streamline the development/training/deployment of ML/AI applications for medical use cases?
- What are the ideas/assumptions that you had at the start of Ketryx that have been challenged/updated as you work with customers?
- What are the most interesting, innovative, or unexpected ways that you have seen ML used in medical applications?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ketryx?
- When is Ketryx the wrong choice?
- What do you have planned for the future of Ketryx?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers.
Applying Declarative ML Techniques To Large Language Models For Better Results
AI Engineering Podcast
10/24/23 • 46 min
Summary
Large language models have gained a substantial amount of attention in the area of AI and machine learning. While they are impressive, there are many applications where they are not the best option. In this episode Piero Molino explains how declarative ML approaches allow you to make the best use of the available tools across use cases and data formats.
Announcements
Closing Announcements
Large language models have gained a substantial amount of attention in the area of AI and machine learning. While they are impressive, there are many applications where they are not the best option. In this episode Piero Molino explains how declarative ML approaches allow you to make the best use of the available tools across use cases and data formats.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Piero Molino about the application of declarative ML in a world being dominated by large language models
- Introduction
- How did you get involved in machine learning?
- Can you start by summarizing your perspective on the effect that LLMs are having on the AI/ML industry?
- In a world where LLMs are being applied to a growing variety of use cases, what are the capabilities that they still lack?
- How does declarative ML help to address those shortcomings?
- The majority of current hype is about commercial models (e.g. GPT-4). Can you summarize the current state of the ecosystem for open source LLMs?
- For teams who are investing in ML/AI capabilities, what are the sources of platform risk for LLMs?
- What are the comparative benefits of using a declarative ML approach?
- What are the most interesting, innovative, or unexpected ways that you have seen LLMs used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on declarative ML in the age of LLMs?
- When is an LLM the wrong choice?
- What do you have planned for the future of declarative ML and Predibase?
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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- Predibase
- Ludwig
- Recommender Systems
- Information Retrieval
- Vector Database
- Transformer Model
- BERT
- Context Windows
- LLAMA
Surveying The Landscape Of AI and ML From An Investor's Perspective
AI Engineering Podcast
10/15/23 • 62 min
Summary
Artificial Intelligence is experiencing a renaissance in the wake of breakthrough natural language models. With new businesses sprouting up to address the various needs of ML and AI teams across the industry, it is a constant challenge to stay informed. Matt Turck has been compiling a report on the state of ML, AI, and Data for his work at FirstMark Capital. In this episode he shares his findings on the ML and AI landscape and the interesting trends that are developing.
Announcements
Artificial Intelligence is experiencing a renaissance in the wake of breakthrough natural language models. With new businesses sprouting up to address the various needs of ML and AI teams across the industry, it is a constant challenge to stay informed. Matt Turck has been compiling a report on the state of ML, AI, and Data for his work at FirstMark Capital. In this episode he shares his findings on the ML and AI landscape and the interesting trends that are developing.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES.
- Your host is Tobias Macey and today I'm interviewing Matt Turck about his work on the MAD (ML, AI, and Data) landscape and the insights he has gained on the ML ecosystem
- Introduction
- How did you get involved in machine learning?
- Can you describe what the MAD landscape project is and the story behind it?
- What are the major changes in the ML ecosystem that you have seen since you first started compiling the landscape?
- How have the developments in consumer-grade AI in recent years changed the business opportunities for ML/AI?
- What are the coarse divisions that you see as the boundaries that define the different categories for ML/AI in the landscape?
- For ML infrastructure products/companies, what are the biggest challenges that they face in engineering and customer acquisition?
- What are some of the challenges in building momentum for startups in AI (existing moats around data access, talent acquisition, etc.)?
- For products/companies that have ML/AI as their core offering, what are some strategies that they use to compete with "big tech" companies that already have a large corpus of data?
- What do you see as the societal vs. business importance of open source models as AI becomes more integrated into consumer facing products?
- What are the most interesting, innovative, or unexpected ways that you have seen ML/AI used in business and social contexts?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on the ML/AI elements of the MAD landscape?
- When is ML/AI the wrong choice for businesses?
- What are the areas of ML/AI that you are paying closest attention to in your own work?
- Website
- @mattturck on Twitter
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Applying Federated Machine Learning To Sensitive Healthcare Data At Rhino Health
AI Engineering Podcast
09/11/23 • 49 min
Summary
A core challenge of machine learning systems is getting access to quality data. This often means centralizing information in a single system, but that is impractical in highly regulated industries, such as healthchare. To address this hurdle Rhino Health is building a platform for federated learning on health data, so that everyone can maintain data privacy while benefiting from AI capabilities. In this episode Ittai Dayan explains the barriers to ML in healthcare and how they have designed the Rhino platform to overcome them.
Announcements
Parting Question
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
A core challenge of machine learning systems is getting access to quality data. This often means centralizing information in a single system, but that is impractical in highly regulated industries, such as healthchare. To address this hurdle Rhino Health is building a platform for federated learning on health data, so that everyone can maintain data privacy while benefiting from AI capabilities. In this episode Ittai Dayan explains the barriers to ML in healthcare and how they have designed the Rhino platform to overcome them.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Ittai Dayan about using federated learning at Rhino Health to bring AI capabilities to the tightly regulated healthcare industry
- Introduction
- How did you get involved in machine learning?
- Can you describe what Rhino Health is and the story behind it?
- What is federated learning and what are the trade-offs that it introduces?
- What are the benefits to healthcare and pharmalogical organizations from using federated learning?
- What are some of the challenges that you face in validating that patient data is properly de-identified in the federated models?
- Can you describe what the Rhino Health platform offers and how it is implemented?
- How have the design and goals of the system changed since you started working on it?
- What are the technological capabilities that are needed for an organization to be able to start using Rhino Health to gain insights into their patient and clinical data?
- How have you approached the design of your product to reduce the effort to onboard new customers and solutions?
- What are some examples of the types of automation that you are able to provide to your customers? (e.g. medical diagnosis, radiology review, health outcome predictions, etc.)
- What are the ethical and regulatory challenges that you have had to address in the development of your platform?
- What are the most interesting, innovative, or unexpected ways that you have seen Rhino Health used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Rhino Health?
- When is Rhino Health the wrong choice?
- What do you have planned for the future of Rhino Health?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Using Machine Learning To Keep An Eye On The Planet
AI Engineering Podcast
06/17/23 • 42 min
Summary
Satellite imagery has given us a new perspective on our world, but it is limited by the field of view for the cameras. Synthetic Aperture Radar (SAR) allows for collecting images through clouds and in the dark, giving us a more consistent means of collecting data. In order to identify interesting details in such a vast amount of data it is necessary to use the power of machine learning. ICEYE has a fleet of satellites continuously collecting information about our planet. In this episode Tapio Friberg shares how they are applying ML to that data set to provide useful insights about fires, floods, and other terrestrial phenomena.
Announcements
Parting Question
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Satellite imagery has given us a new perspective on our world, but it is limited by the field of view for the cameras. Synthetic Aperture Radar (SAR) allows for collecting images through clouds and in the dark, giving us a more consistent means of collecting data. In order to identify interesting details in such a vast amount of data it is necessary to use the power of machine learning. ICEYE has a fleet of satellites continuously collecting information about our planet. In this episode Tapio Friberg shares how they are applying ML to that data set to provide useful insights about fires, floods, and other terrestrial phenomena.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Tapio Friberg about building machine learning applications on top of SAR (Synthetic Aperture Radar) data to generate insights about our planet
- Introduction
- How did you get involved in machine learning?
- Can you describe what ICEYE is and the story behind it?
- What are some of the applications of ML at ICEYE?
- What are some of the ways that SAR data poses a unique challenge to ML applications?
- What are some of the elements of the ML workflow that you are able to use "off the shelf" and where are the areas that you have had to build custom solutions?
- Can you share the structure of your engineering team and the role that the ML function plays in the larger organization?
- What does the end-to-end workflow for your ML model development and deployment look like?
- What are the operational requirements for your models? (e.g. batch execution, real-time, interactive inference, etc.)
- In the model definitions, what are the elements of the source domain that create the largest challenges? (e.g. noise from backscatter, variance in resolution, etc.)
- Once you have an output from an ML model how do you manage mapping between data domains to reflect insights from SAR sources onto a human understandable representation?
- Given that SAR data and earth imaging is still a very niche domain, how does that influence your ability to hire for open positions and the ways that you think about your contributions to the overall ML ecosystem?
- How can your work on using SAR as a representation of physical attributes help to improve capabilities in e.g. LIDAR, computer vision, etc.?
- What are the most interesting, innovative, or unexpected ways that you have seen ICEYE and SAR data used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on ML for SAR data?
- What do you have planned for the future of ML applications at ICEYE?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
The Role Of Model Development In Machine Learning Systems
AI Engineering Podcast
05/29/23 • 46 min
Summary
The focus of machine learning projects has long been the model that is built in the process. As AI powered applications grow in popularity and power, the model is just the beginning. In this episode Josh Tobin shares his experience from his time as a machine learning researcher up to his current work as a founder at Gantry, and the shift in focus from model development to machine learning systems.
Announcements
Parting Question
The focus of machine learning projects has long been the model that is built in the process. As AI powered applications grow in popularity and power, the model is just the beginning. In this episode Josh Tobin shares his experience from his time as a machine learning researcher up to his current work as a founder at Gantry, and the shift in focus from model development to machine learning systems.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Josh Tobin about the state of industry best practices for designing and building ML models
- Introduction
- How did you get involved in machine learning?
- Can you start by describing what a "traditional" process for building a model looks like?
- What are the forces that shaped those "best practices"?
- What are some of the practices that are still necessary/useful and what is becoming outdated?
- What are the changes in the ecosystem (tooling, research, communal knowledge, etc.) that are forcing teams to reconsider how they think about modeling?
- What are the most critical practices/capabilities for teams who are building services powered by ML/AI?
- What systems do they need to support them in those efforts?
- Can you describe what you are building at Gantry and how it aids in the process of developing/deploying/maintaining models with "modern" workflows?
- What are the most challenging aspects of building a platform that supports ML teams in their workflows?
- What are the most interesting, innovative, or unexpected ways that you have seen teams approach model development/validation?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gantry?
- When is Gantry the wrong choice?
- What are some of the resources that you find most helpful to stay apprised of how modeling and ML practices are evolving?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Real-Time Machine Learning Has Entered The Realm Of The Possible
AI Engineering Podcast
03/09/23 • 34 min
Summary
Machine learning models have predominantly been built and updated in a batch modality. While this is operationally simpler, it doesn't always provide the best experience or capabilities for end users of the model. Tecton has been investing in the infrastructure and workflows that enable building and updating ML models with real-time data to allow you to react to real-world events as they happen. In this episode CTO Kevin Stumpf explores they benefits of real-time machine learning and the systems that are necessary to support the development and maintenance of those models.
Announcements
Machine learning models have predominantly been built and updated in a batch modality. While this is operationally simpler, it doesn't always provide the best experience or capabilities for end users of the model. Tecton has been investing in the infrastructure and workflows that enable building and updating ML models with real-time data to allow you to react to real-world events as they happen. In this episode CTO Kevin Stumpf explores they benefits of real-time machine learning and the systems that are necessary to support the development and maintenance of those models.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Kevin Stumpf about the challenges and promise of real-time ML applications
- Introduction
- How did you get involved in machine learning?
- Can you describe what real-time ML is and some examples of where it might be applied?
- What are the operational and organizational requirements for being able to adopt real-time approaches for ML projects?
- What are some of the ways that real-time requirements influence the scale/scope/architecture of an ML model?
- What are some of the failure modes for real-time vs analytical or operational ML?
- Given the low latency between source/input data being generated or received and a prediction being generated, how does that influence susceptibility to e.g. data drift?
- Data quality and accuracy also become more critical. What are some of the validation strategies that teams need to consider as they move to real-time?
- What are the most interesting, innovative, or unexpected ways that you have seen real-time ML applied?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on real-time ML systems?
- When is real-time the wrong choice for ML?
- What do you have planned for the future of real-time support for ML in Tecton?
- @kevinmstumpf on Twitter
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
How Shopify Built A Machine Learning Platform That Encourages Experimentation
AI Engineering Podcast
02/02/23 • 66 min
Summary
Shopify uses machine learning to power multiple features in their platform. In order to reduce the amount of effort required to develop and deploy models they have invested in building an opinionated platform for their engineers. They have gone through multiple iterations of the platform and their most recent version is called Merlin. In this episode Isaac Vidas shares the use cases that they are optimizing for, how it integrates into the rest of their data platform, and how they have designed it to let machine learning engineers experiment freely and safely.
Announcements
Parting Question
Shopify uses machine learning to power multiple features in their platform. In order to reduce the amount of effort required to develop and deploy models they have invested in building an opinionated platform for their engineers. They have gone through multiple iterations of the platform and their most recent version is called Merlin. In this episode Isaac Vidas shares the use cases that they are optimizing for, how it integrates into the rest of their data platform, and how they have designed it to let machine learning engineers experiment freely and safely.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Isaac Vidas about his work on the ML platform used by Shopify
- Introduction
- How did you get involved in machine learning?
- Can you describe what Shopify is and some of the ways that you are using ML at Shopify?
- What are the challenges that you have encountered as an organization in applying ML to your business needs?
- Can you describe how you have designed your current technical platform for supporting ML workloads?
- Who are the target personas for this platform?
- What does the workflow look like for a given data scientist/ML engineer/etc.?
- What are the capabilities that you are trying to optimize for in your current platform?
- What are some of the previous iterations of ML infrastructure and process that you have built?
- What are the most useful lessons that you gathered from those previous experiences that informed your current approach?
- How have the capabilities of the Merlin platform influenced the ways that ML is viewed and applied across Shopify?
- What are the most interesting, innovative, or unexpected ways that you have seen Merlin used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Merlin?
- When is Merlin the wrong choice?
- What do you have planned for the future of Merlin?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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 [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Building Better AI While Preserving User Privacy With TripleBlind
AI Engineering Podcast
11/22/23 • 46 min
Summary
Machine learning and generative AI systems have produced truly impressive capabilities. Unfortunately, many of these applications are not designed with the privacy of end-users in mind. TripleBlind is a platform focused on embedding privacy preserving techniques in the machine learning process to produce more user-friendly AI products. In this episode Gharib Gharibi explains how the current generation of applications can be susceptible to leaking user data and how to counteract those trends.
Announcements
Parting Question
Machine learning and generative AI systems have produced truly impressive capabilities. Unfortunately, many of these applications are not designed with the privacy of end-users in mind. TripleBlind is a platform focused on embedding privacy preserving techniques in the machine learning process to produce more user-friendly AI products. In this episode Gharib Gharibi explains how the current generation of applications can be susceptible to leaking user data and how to counteract those trends.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Gharib Gharibi about the challenges of bias and data privacy in generative AI models
- Introduction
- How did you get involved in machine learning?
- Generative AI has been gaining a lot of attention and speculation about its impact. What are some of the risks that these capabilities pose?
- What are the main contributing factors to their existing shortcomings?
- What are some of the subtle ways that bias in the source data can manifest?
- In addition to inaccurate results, there is also a question of how user interactions might be re-purposed and potential impacts on data and personal privacy. What are the main sources of risk?
- With the massive attention that generative AI has created and the perspectives that are being shaped by it, how do you see that impacting the general perception of other implementations of AI/ML?
- How can ML practitioners improve and convey the trustworthiness of their models to end users?
- What are the risks for the industry if generative models fall out of favor with the public?
- How does your work at Tripleblind help to encourage a conscientious approach to AI?
- What are the most interesting, innovative, or unexpected ways that you have seen data privacy addressed in AI applications?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on privacy in AI?
- When is TripleBlind the wrong choice?
- What do you have planned for the future of TripleBlind?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- 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.
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- TripleBlind
- ImageNet Geoffrey Hinton Paper
- BERT language model
- Generative AI
- GPT == Generative Pre-trained Transformer
- HIPAA Safe Harbor Rules
- Federated Learning
- Differential Privacy
- Homomorphic Encryption
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FAQ
How many episodes does AI Engineering Podcast have?
AI Engineering Podcast currently has 38 episodes available.
What topics does AI Engineering Podcast cover?
The podcast is about Education, Technology and Podcasts.
What is the most popular episode on AI Engineering Podcast?
The episode title 'Enhancing The Abilities Of Software Engineers With Generative AI At Tabnine' is the most popular.
What is the average episode length on AI Engineering Podcast?
The average episode length on AI Engineering Podcast is 51 minutes.
How often are episodes of AI Engineering Podcast released?
Episodes of AI Engineering Podcast are typically released every 9 days, 13 hours.
When was the first episode of AI Engineering Podcast?
The first episode of AI Engineering Podcast was released on Jun 3, 2022.
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