SummaryMachine learning and AI applications hold the promise of drastically impacting every aspect of modern life. With that potential for profound change comes a responsibility for the creators of the technology to account for the ramifications of their work. In this episode Nicholas Cifuentes-Goodbody guides us through the minefields of social, technical, and ethical considerations that are necessary to ensure that this next generation of technical and economic systems are equitable and beneficial for the people that they impact.AnnouncementsHello 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 Nicholas Cifuentes-Goodbody about the different elements of the machine learning workflow where ethics need to be consideredInterviewIntroductionHow did you get involved in machine learning?To start with, who is responsible for addressing the ethical concerns around AI?What are the different ways that AI can have positive or negative outcomes from an ethical perspective? What is the role of practitioners/individual contributors in the identification and evaluation of ethical impacts of their work?What are some utilities that are helpful in identifying and addressing bias in training data?How can practitioners address challenges of equity and accessibility in the delivery of AI products?What are some of the options for reducing the energy consumption for training and serving AI?What are the most interesting, innovative, or unexpected ways that you have seen ML teams incorporate ethics into their work?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ethical implications of ML?What are some of the resources that you recommend for people who want to invest in their knowledge and application of ethics in the realm of ML?Contact InfoWorldQuant University's Applied Data Science LabLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank 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@themachinelearningpodcast.com) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksUNESCO Recommendation on the Ethics of Artificial IntelligenceEuropean Union AI ActHow machine learning helps advance access to human rights informationDisinformation, Team JorgeChina, AI, and Human RightsHow China Is Using A.I. to Profile a MinorityWeapons of Math DestructionFairlearnAI Fairness 360Allen Institute for AI NYTAllen Institute for AITransformersAI4ALLWorldQuant UniversityHow to Make Generative AI GreenerMachine Learning Emissions CalculatorPracticing Trustworthy Machine LearningEnergy and Policy Considerations for Deep LearningNatural Language ProcessingTrolley ProblemProtected Classesfairlearn (scikit-learn)BERT ModelThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

CEO Molham Aref discusses the principles and capabilities of Relational AI, an AI co-processor for data warehouses. They explore the challenges of model building and the advantages of embedding AI in Snowflake. The podcast also covers the versatility of intelligent applications, the evolution away from Hadoop, and the historical context of the relational algebra in relational AI.

SummaryMachine 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.AnnouncementsHello 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 modelsInterviewIntroductionHow 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?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank 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@themachinelearningpodcast.com) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksTripleBlindImageNet Geoffrey Hinton PaperBERT language modelGenerative AIGPT == Generative Pre-trained TransformerHIPAA Safe Harbor RulesFederated LearningDifferential PrivacyHomomorphic EncryptionThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummarySoftware 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.AnnouncementsHello 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 TabnineInterviewIntroductionHow 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?Contact InfoLinkedInWebsiteParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank 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@themachinelearningpodcast.com) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksTabNineTechnion UniversityProgram SynthesisContext StuffingElixirDependency InjectionCOBOLVerilogMidJourneyThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Erez Kaminski, an expert in validating machine learning systems for safety critical applications, discusses the regulatory burdens on ML teams in medical applications, the challenges of validating ML systems, and opportunities for automating overhead. He also shares insights into the excitement in the medical field for improving medical applications and highlights the benefits of using Ketryx for building medical software.

SummaryLarge 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.AnnouncementsHello 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 modelsInterviewIntroductionHow 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?Contact InfoLinkedInWebsiteClosing AnnouncementsThank 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@themachinelearningpodcast.com) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?LinksPredibasePodcast EpisodeLudwigPodcast.__init__ EpisodeRecommender SystemsInformation RetrievalVector DatabaseTransformer ModelBERTContext WindowsLLAMAThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryArtificial 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.AnnouncementsHello 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 ecosystemInterviewIntroductionHow 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?Contact InfoWebsite@mattturck on TwitterParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank 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@themachinelearningpodcast.com) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksMAD LandscapeData Engineering Podcast EpisodeFirst Mark CapitalBayesian TechniquesHadoopChatGPTAutoGPTDataikuGenerative AIDatabricksMLOpsOpenAIAnthropicDeepMindBloombergGPTHuggingFaceJexi Movie"Her" MovieSynthesiaThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryA 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.AnnouncementsHello 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 industryInterviewIntroductionHow 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?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank 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@themachinelearningpodcast.com) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksRhino HealthFederated LearningNvidia ClaraNvidia DGXMelloddyFlair NLPThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummarySatellite 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.AnnouncementsHello 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 planetInterviewIntroductionHow 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?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank 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@themachinelearningpodcast.com) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksICEYESAR == Synthetic Aperture RadarTransfer LearningThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Josh Tobin discusses the shift in focus from model development to machine learning systems, the evolution of modeling in the machine learning ecosystem, the capabilities of Gantry in enhancing model performance and maintenance, core capabilities and flexible support for machine learning, innovative approaches and challenges in building and deploying machine learning models, and when to choose Gantry for model development and maintenance.