Interest in autonomous vehicles dates back to the 1920s. It wasn’t until the 1980s that the first truly autonomous vehicle prototypes began to appear. The first DARPA Grand Challenge took place in 2004 offering competitors $1 million dollars to complete a 150-mile course through the Mojave desert. The prize was not claimed.

Since then, rapid progress has begun in autonomous driving fueled by advances in sensor technology, software, and the hardware which runs it. Infrastructure has become a serious consideration for autonomous vehicle companies. In this episode, I speak with Vinoj Kumar, VP of Infrastructure at Cruise, the San Francisco company building an all-electric self-driving rideshare and delivery service. They’re tackling the infinite longtail scenarios of city driving and helping Walmart with self-driving grocery delivery.

Sponsorship inquiries: [email protected]

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Governments, consumers, and companies across the world are becoming more aware and attentive to the risks and causes of climate change. From recycling to using solar power, people are looking for ways to reduce their carbon footprint. Markets like the financial sector, governments, and consulting are looking for ways to understand climate data to make smart decisions and manage risk.

The company Sust Global was founded as a way to deliver sustainable change and climate resilient action. Sust Global uses an AI-powered platform that combines climate science, satellite-derived data, and geospatial data sets to quantify climate change. Companies can use this analysis to evaluate risk to assets, better understand future commodities like metal, and plan for future supply chain challenges and climate perils.

In this episode we talk to Josh Gilbert, CEO at Sust Global. Josh explains Sust Global’s mission and product, and discusses how companies use Sust Global to prepare and respond to climate change.

Sponsorship inquiries: [email protected]

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Mark Saroufim is the author of an article entitled “Machine Learning: The Great Stagnation”. Mark is a PyTorch Partner Engineer with Facebook AI. He has spent his entire career developing machine learning and artificial intelligence products. Before joining Facebook to do PyTorch engineering with external partners, Mark was a Machine Learning Engineer at Graphcore. Before that he founded Yuri.ai. Mark has also published “The Robot Overlord Manual” which “will teach you all the software, math and ML you’ll need to start building robots at home.” In this episode we discuss machine learning subjects and his experience developing cutting edge software.

Sponsorship inquiries: [email protected]

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Arun Kumar is an Assistant Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. 

Systems and ideas based on his research have been released as part of the Apache MADlib open-source library, shipped as part of products from Cloudera, IBM, Oracle, and Pivotal, and used internally by Facebook, Google, LogicBlox, Microsoft, and other companies. 

Arun did his undergrad in Computer Science and Engineering at the Indian Institute of Technology, Madras, and then his MS and PhD in Computer Science at the University of Wisconsin-Madison, where his thesis research explores problems at the intersection of data management and machine learning, with a focus on problems related to usability, developability, performance, and scalability. In this episode he joins us to discuss data management systems and artificial intelligence.

Sponsorship inquiries: [email protected]

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Application Programming Interfaces (APIs) are interfaces that enable multiple software applications to send and retrieve data from one another. They are commonly used for retrieving, saving, editing, or deleting data from databases, transmitting data between apps, and embedding third-party services into apps.

The company BaseTen helps companies build and deploy machine learning APIs and applications. Using pre-existing ML models, or choosing from BaseTen’s library of pretrained models, BaseTen helps you instantly deploy API endpoints powered by those models to use in your applications. These APIs easily scale and integrate with existing data sources. BaseTen’s serverless infrastructure enables chaining model outputs and pre- and post- processing code. They also use a drag-and-drop UI builder to create custom UI’s for the applications, all without learning React. 

In this episode, we talk with Tuhin Srivastava and Amir Haghighat, founders at BaseTen. Tuhin previously founded Shape, and also worked as a Data Scientist at Gumroad. We discuss machine learning API development, scaling ML-driven applications, and the capabilities of BaseTen’s technology.

Sponsorship inquiries: [email protected]

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Natural Language Processing (NLP) is a branch of artificial intelligence concerned with giving computers the ability to understand text and spoken words. “Understanding” includes intent, sentiment, and what’s important in the message. NLP powers things like voice-operated software, digital assistants, customer service chat bots, and many other academic, consumer and enterprise tools.

The company Botpress provides open-source developer tools to create NLP tools for process and FAQ automation. They use the latest NLP models for domain-specific, contextual and goal-oriented conversations. This technology is free and available through simple API routes. They also maintain integrations with popular messaging services like Facebook Messenger, Slack, and Microsoft Teams. For other proprietary systems, they provide a raw Messaging API. 

In this episode we talk to Sylvain Perron, CEO of Botpress. Sylvain was previously a Director of Engineering at Protorisk Limited and a Software Developer at ArcBees before that. We discuss the current advances in Natural Language Processing and how NLP powers Botpress.

Sponsorship inquiries: [email protected]

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Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it for outliers and anomalies, assemble confidence ranges of the predictions and feature importance, and eventually deploy it to make predictions. An emerging field in AI, called Automated Machine Learning (AutoML), lowers these barriers to entry by using AI to automate much of this process. 

One of the market leaders in AutoML is MindsDB. Their service lets business users and developers make predictions on top of data at its source. Rather than make expensive copies of databases, a process that creates complex infrastructures, MindsDB trains and deploys models right inside the database. The results of their ML models can be queried with standard SQL statements and integrated into other applications as easily as querying any other database. 

In this episode we learn more about the progress that has been made in AutoML to simplify incorporating machine learning throughout organizations. We discuss the current features available from MindsDB, the difference their product has made for companies trying to leverage AI, and the future of AutoML and artificial intelligence generally.

Sponsorship inquiries: [email protected]

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Creation Labs is helping bring Europe 1 step closer to fully autonomous long haul trucking. They have developed an AI Driver Assistance System (AIDAS) that retrofits to any commercial vehicle, starting with VW Crafters and MAN TGE trucks. Their system uses camera hardware mounted to the vehicle to capture video data that is processed with computer vision to understand the context on the road. This piece of the system was developed by the world’s leading experts in computer vision. While the computer interprets what is happening on the road, data is sent to a processing system that can control the vehicle’s break, throttle, and steering. 

The AIDAS system currently augments a driver’s role but does not replace the need for one yet. However, the difference between great drivers and bad drivers is around a 30% difference in fuel efficiency, according to Creation Labs. They have trained their systems with data from the best drivers in order to lower fuel costs for vehicles driven by their AIDAS. They’ve also built their system using the highest standards of safety.

Jakub Langr is the CEO of Creation Labs and an Oxford-educated data scientist with 10 years of industry experience. He discusses Creation Labs’s vision for the future and the impact their incredible product is having on customers’ profit margins and emissions.

Sponsorship inquiries: [email protected]

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Vectors are the foundational mathematical building blocks of Machine Learning. Machine Learning models must transform input data into vectors to perform their operations, creating what is known as a vector embedding. Since data is not stored in vector form, an ML application must perform significant work to transform data in different formats into a form that ML models can understand. This can be computationally intensive and hard to scale, especially for the high-dimensional vectors used in complex models.

Pinecone is a managed database built specifically for working with vector data. Pinecone is serverless and API-driven, which means engineers and data scientists can focus on building their ML application or performing analysis without worrying about the underlying data infrastructure.

Edo Liberty is the founder and CEO of Pinecone. Prior to Pinecone, he led the creation of Amazon SageMaker at AWS. He joins the show today to talk about the fundamental importance of vectors in machine learning, how Pinecone built a vector-centric database, and why data infrastructure improvements are key to unlocking the next generation of AI applications.

Sponsorship inquiries: [email protected]

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The incredible advances in machine learning research in recent years often take time to propagate out into usage in the field. One reason for this is that such “state-of-the-art” results for machine learning performance rely on the use of handwritten, idiosyncratic optimizations for specific hardware models or operating contexts. When developers are building ML-powered systems to deploy in the cloud and at the edge, their goals to ensure the model delivers the best possible functionality and end-user experience- and importantly, their hardware and software stack may require different optimizations to achieve that goal.

OctoML provides a SaaS product called the Octomizer to help developers and AIOps teams deploy ML models most efficiently on any hardware, in any context. The Octomizer deploys its own ML models to analyze your model topology, and optimize, benchmark, and package the model for deployment. The Octomizer generates insights about model performance over different hardware stacks and helps you choose the deployment format that works best for your organization.

Luis Ceze is the Co-Founder and CEO of OctoML. Luis is a founder of the ApacheTVM project, which is the basis for OctoML’s technology. He is also a professor of Computer Science at the University of Washington. Jason Knight is co-founder and CPO at OctoML. Luis and Jason join the show today to talk about how OctoML is automating deep learning engineering, why it’s so important to consider hardware when building deep learning systems, and how the field of deep learning is evolving.

Sponsorship inquiries: [email protected]

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