Artificial Intelligence

Open Source Automated Machine Learning With MindsDB - Episode 218

Summary

Machine learning is growing in popularity and capability, but for a majority of people it is still a black box that we don’t fully understand. The team at MindsDB is working to change this state of affairs by creating an open source tool that is easy to use without a background in data science. By simplifying the training and use of neural networks, and making their logic explainable, they hope to bring AI capabilities to more people and organizations. In this interview George Hosu and Jorge Torres explain how MindsDB is built, how to use it for your own purposes, and how they view the current landscape of AI technologies. This is a great episode for anyone who is interested in experimenting with machine learning and artificial intelligence. Give it a listen and then try MindsDB for yourself.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
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  • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing George Hosu and Jorge Torres about MindsDB, a framework for streamlining the use of neural networks

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what MindsDB is and the problem that it is trying to solve?
    • What was the motivation for creating the project?
  • Who is the target audience for MindsDB?
  • Before we go deep into MindsDB can you explain what a neural network is for anyone who isn’t familiar with the term?
  • For someone who is using MindsDB can you talk through their workflow?
    • What are the types of data that are supported for building predictions using MindsDB?
    • How much cleaning and preparation of the data is necessary before using it to generate a model?
    • What are the lower and upper bounds for volume and variety of data that can be used to build an effective model in MindsDB?
  • One of the interesting and useful features of MindsDB is the built in support for explaining the decisions reached by a model. How do you approach that challenge and what are the most difficult aspects?
  • Once a model is generated, what is the output format and can it be used separately from MindsDB for embedding the prediction capabilities into other scripts or services?
  • How is MindsDB implemented and how has the design changed since you first began working on it?
    • What are some of the assumptions that you made going into this project which have had to be modified or updated as it gained users and features?
  • What are the limitations of MindsDB and what are the cases where it is necessary to pass a task on to a data scientist?
  • In your experience, what are the common barriers for individuals and organizations adopting machine learning as a tool for addressing their needs?
  • What have been the most challenging, complex, or unexpected aspects of designing and building MindsDB?
  • What do you have planned for the future of MindsDB?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

The Past, Present, and Future of Deep Learning In PyTorch - Episode 202

Summary

The current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. One of the most popular frameworks for building deep learning applications is PyTorch, in large part because of their focus on ease of use. In this episode Adam Paszke explains how he started the project, how it compares to other frameworks in the space such as Tensorflow and CNTK, and how it has evolved to support deploying models into production and on mobile devices.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Check out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI
  • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Your host as usual is Tobias Macey and today I’m interviewing Adam Paszke about PyTorch, an open source deep learning platform that provides a seamless path from research prototyping to production deployment

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what deep learning is and how it relates to machine learning and artificial intelligence?
  • Can you explain what PyTorch is and your motivation for creating it?
    • Why was it important for PyTorch to be open source?
  • There is currently a large and growing ecosystem of deep learning tools built for Python. Can you describe the current landscape and how PyTorch fits in relation to projects such as Tensorflow and CNTK?
    • What are some of the ways that PyTorch is different from Tensorflow and CNTK, and what are the areas where these frameworks are converging?
  • How much knowledge of machine learning, artificial intelligence, or neural network topologies are necessary to make use of PyTorch?
    • What are some of the foundational topics that are most useful to know when getting started with PyTorch?
  • Can you describe how PyTorch is architected/implemented and how it has evolved since you first began working on it?
    • You recently reached the 1.0 milestone. Can you talk about the journey to that point and the goals that you set for the release?
  • What are some of the other components of the Python ecosystem that are most commonly incorporated into projects based on PyTorch?
  • What are some of the most novel, interesting, or unexpected uses of PyTorch that you have seen?
  • What are some cases where PyTorch is the wrong choice for a problem?
  • What is the process for incorporating these new techniques and discoveries into the PyTorch framework?
    • What are the areas of active research that you are most excited about?
  • What are some of the most interesting/useful/unexpected/challenging lessons that you have learned in the process of building and maintaining PyTorch?
  • What do you have planned for the future of PyTorch?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Rasa: Build Your Own AI Chatbot with Joey Faulkner - Episode 134

Summary

With the proliferation of messaging applications, there has been a growing demand for bots that can understand our wishes and perform our bidding. The rise of artificial intelligence has brought the capacity for understanding human language. Combining these two trends gives us chatbots that can be used as a new interface to the software and services that we depend on. This week Joey Faulkner shares his work with Rasa Technologies and their open sourced libraries for understanding natural language and how to conduct a conversation. We talked about how the Rasa Core and Rasa NLU libraries work and how you can use them to replace your dependence on API services and own your data.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Joey Faulkner about Rasa Core and Rasa NLU for adding conversational AI to your projects.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining the goals of Rasa as a company and highlighting the projects that you have open sourced?
  • What are the differences between the Rasa Core and Rasa NLU libraries and how do they relate to each other?
  • How does the interaction model change when going from state machine driven bots to those which use Rasa Core and what capabilities does it unlock?
  • How is Rasa NLU implemented and how has the design evolved?
  • What are the motivations for someone to use Rasa core or NLU as a library instead of available API services such as wit.ai, LUIS, or Dialogflow?
  • What are some of the biggest challenges in gathering and curating useful training data?
  • What is involved in supporting multiple languages for an application using Rasa?
  • What are the biggest challenges that you face, past, present, and future, building and growing the tools and platform for Rasa?
  • What would be involved for projects such as OpsDroid, Kalliope, or Mycroft to take advantage of Rasa and what benefit would that provide?
  • On the comparison page for the hosted Rasa platform it mentions a feature of collaborative model training, can you describe how that works and why someone might want to take advantage of it?
  • What are some of the most interesting or unexpected uses of the Rasa tools that you have seen?
  • What do you have planned for the future of Rasa?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA