Infinite Curiosity Pod with Prateek Joshi

In this AMA episode, the host Prateek Joshi answers the following questions:- How is machine learning being used in the space industry?- How is synthetic data being used to train AI systems?- Are foundation models going to become more prevalent in production ML systems?- What do you think about AI-infused coding tools?

Serg Masis is a Data Scientist in agriculture with a background in entrepreneurship and web/app development. He's the author of the book "Interpretable Machine Learning with Python". In addition to ML interpretability, he's passionate about explainable AI, behavioral economics, and ethical AI.In this episode, we cover a range of topics including:- How did he get into machine learning?- What is interpretable ML?- What is post hoc interpretability?- Process fairness vs statistical fairness- How an algorithm creates the model?- How a model makes predictions?- What makes an ML model interpretable?- How do you measure the interpretability of a model?- How do parts of the model affect predictions?- Does the method of interpretation depend on the model? Or can we apply a given method to a number of models?- Can you explain a specific prediction from a model?- What techniques can we use to interpret neural networks?- What techniques are available to increase the interpretability of a model? 

In this AMA episode, the host Prateek Joshi answers the following questions received from listeners and readers:- When it comes to experimentation in machine learning, does Python have an advantage over Matlab?- I have many years of experience in database technology and I'd like to switch to machine learning. What advice would you give me?- What is the most difficult part of building a machine learning startup? What is your advice if someone wants to do it?- I just graduated from college and want to be the AI domain. What roles are available in this field?

Zach Keller leads the data science team at Trove, a white label platform that supports resale as a channel for the world's most beloved brands. He has worked as a data scientist and machine learning engineer, building machine learning systems that operate at scale. He's been in industries such as manufacturing, finance, and re-commerce. At Trove, he focuses on growing and mentoring the data science team. He lives in Dallas with his wife and dog.In this episode, we cover a range of topics including:- His journey into data science- What is Recommerce- How does circular shopping work- Carbon footprint in ecommerce- How is machine learning used in ecommerce- How should new entrants evaluate what you like within data science- The role of mathematics in learning ML- The role of economics in ecommerce and marketplaces- How incentives work in marketplaces- Push bucket vs pull bucket- How to build good data science culture

In this episode, the host Prateek Joshi covers the latest developments in Machine Learning including:- Predicting battery lifetimes- Fighting wildfires- Rainfall mapping- Avoiding idling at traffic lights- General purpose AI system that can perform 604 tasks- Google releases world's largest publicly available machine learning hub- A plant nutrient detected by AI comes to market- European Union's recently proposed AI Act- ML startups that are gaining traction

Demetrios Brinkmann is one of the main organizers of the MLOps Community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveler who taught English as a second language to see the world and learn about new cultures. He fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and Machine Learning. Since diving into the nitty-gritty of ML Operations he felt a strong calling to explore the ethical issues surrounding AI/ML. When he is not conducting interviews, you can find him making stone stackings with his daughter in the woods or playing the ukulele by the campfire.Vishnu Rachakonda is a data scientist at Firsthand, where he helps build data-intensive systems that identify and connect individuals living with serious mental illness to firsthand’s peer-based recovery model. Vishnu is also the Head of Operations for the MLOps Community, the world’s largest online hub for production ML practitioners, and co-hosts the community’s podcast "MLOps Coffee Sessions", whose past guests include Jeremy Howard, D. Sculley, and other industry luminaries. Prior to this, he was the first machine learning hire at Tesseract Health, a 4Catalyzer company focused on ophthalmic imaging, and a teaching assistant for the spring 2021 edition of Full Stack Deep Learning. He obtained a BS and MS in bioengineering from the University of Pennsylvania.In this episode, we cover a range of topics including:- Journey into machine learning- How they started the MLOps community and grew it into the world's largest community of its kind- Framework for building online communities and getting the first few members to join- What tools can be used to manage online communities- ML tooling landscape- What signals can we use to identify ML tools that are gaining traction before it's obvious- Why should an ML tool be opinionated

In this episode, the host Prateek Joshi covers the latest developments in Machine Learning including:- Brain computer interface enters human trials- Nanomagnetic computing for low-energy AI- Speeding up the process of counting microplastics using Machine Learning- Quantum tunneling memory to boost AI energy efficiency- Wine and beer reviews written by AI- Spotting cavity using an AI system- FDA approves the world's first automated and wearable 3D breast ultrasound- Intel announces new chips for AI processing

Thom Ives is the founder of Integrated Machine Learning & AI providing services and instruction in the data science community. And he is the lead data scientist at AI Strategy Corporation. He is the recipient of more than 25 US Patents for many different types of devices. He has a PhD in mechanical engineering and has over 47,000 followers on LinkedIn. He has built an amazing career over the last 30 years.In this episode, we cover a range of topics including:- His AI journey- Creating educational content- His upcoming book- Building communities- "You must first BE to DO to HAVE". What does this mean in our context of data science?- Building a portfolio of projects as a data scientist- Growth happens through cycles and maintenance is integral to cycling. How should a machine learning professional think about it?- Establishing your online presence as a data scientist- Preventing burnout and the importance of recovery periods- Storytelling for data scientists- Why he loves Python

In this episode, the host Prateek Joshi covers a range of topics including:- Generative AI and OpenAI's DALL-E 2- ML based enzyme that can break down plastic really quickly- Predicting the performance of plasma for nuclear fusion- Creating low-carbon concrete with ML- Building ML models that suggest molecular structures that can be synthesized in the lab- Google launches Pix2Seq, a new language interface for object detection.- DeepMind introduces Flamingo, a single visual language model (VLM) for few-shot learning.- Anaconda announces PyScript. It's an in-browser, single-include way to run Python scripts in HTML pages as easily as JavaScript.

Tushar Gupta is a Senior Product Manager at Packt. He produces books in the Machine Learning and AI portfolio. He's been in tech publishing for close to 8 years. And has produced over 300 books to date. In this episode, we cover a range of topics including:- How did you get into the world of book publishing?- How do you identify topics for books?- What trends are you seeing in ML books?- What does great writing look like to you?- What is the learning journey of a reader?- How to think about your book's target audience?- How do you work with authors?- What's the step by step process of writing a book?- What advice do you have for people who want to write their first book?- What areas of ML are gaining traction in 2022?- What type of book tends to sell more? A text book, a book containing recipes, or a book that does a deep dive?- What do book readers want?- What's the role of communities in book publishing?