This is the second post in our Simulation and Deep Reinforcement Learning series. In this post, we will train a Bonsai BRAIN using a Simulink model.
There is a key element for building applied deep reinforcement learning: simulation environments
Bonsai + Redmonk detail how deep reinforcement learning and simulations can solve real-world enterprise problems
Building reinforcement learning models requires you to write effective reward functions. Get tips and tricks for crafting better reward functions.
We need more than TensorFlow to build complex AI models. Here’s how an abstraction layer can simplify industrial artificial intelligence applications
The risks and challenges of industrial AI problems require a different approach to AI models. Learn how concept networks may be the answer we need.
Complex industrial systems may be best served by AI reinforcement learning techniques. Learn about the advantages of applying RL to an AI strategy.
Mark Hammond highlights how enterprises can think about using deep reinforcement learning technology in real world applications.
Industrial AI applications enable businesses to scale domain expertise with machine learning technologies.
Pt 3 of the Bonsai Platform Training Video Series ft. advanced topics on reinforcement learning, concept networks, custom behavior & troubleshooting.
Bonsai outlines 7 challenges enterprises face when programming control or optimization into industrial systems
Bonsai answers the question, “What is Industrial AI?” Learn about the AI technology designed to improve efficiency and performance of physical systems
Bonsai’s 4th training video explains how to write great reward functions for reinforcement learning. See how it works in the Bonsai AI platform.
When programming industrial control systems, how do you know if reinforcement learning is a good fit? Here’s what to look for in your applications.
A key feature of the Bonsai platform is the ability to decompose complex tasks. Learn how we used concept networks to program robotic control.
In this third approach to AI explainability, Mark Hammond highlights machine teaching and recomposability
The state of building explainable artificial intelligence models using 2 model induction techniques, from Mark Hammond’s 2017 O’Reilly AI presentation
Pt 8 of the industrial AI podcast series on This Week in Machine Learning. Bonsai CEO Mark Hammond speaks about machine teaching + machine learning.
In Video 5 of the Bonsai Platform Training series, Marcos Campos covers common challenges in reinforcement learning and strategies to overcome them.
Pt 7 of the industrial AI podcast series on This Week in Machine Learning. Listen to GE Digital’s Josh Bloom on incorporating AI into physical systems
Researchers are making headway in explainable AI with the deep explanations method. Here are 3 techniques that look within the neural network.
Mark Hammond details why it's so important to get explainable AI right. Learn how research into machine learning systems provides critical insight.
Watch Keen Browne’s PyData 2017 presentation on “Unlocking the Power of AI: A fundamentally different approach to building intelligent systems.”
Pt. 2 of Bonsai’s Training Video series examines the Inkling special purpose programming language and reinforcement learning on the AI platform.
Ep 6 of TWIML Industrial AI podcast series. Calvin Seward discusses how deep learning, neural networks and AI models can optimize warehouse workers.
Come visit Bonsai at the O'Reilly AI Conference in San Francisco.
In Video 1 of the Bonsai Training series, we’ll differentiate three types of machine learning: supervised, unsupervised, and reinforcement learning.
Listen to Bonsai CEO Mark Hammond’s presentation on explainable AI and machine teaching from the 2017 O’Reilly AI conference in New York.
Ep. 5: Industrial AI Podcast Series on This Week in Machine Learning. Prof. Sergey Levine discusses deep reinforcement learning research in robotics.
Artificial intelligence companies like Bonsai are attracting top talent to build an AI platform. Learn why Cyrill joined the AI revolution at Bonsai.
Pt 4 of Industrial AI Podcast Series from This Week in Machine Learning. Sam Charrington and Yodit Stanton talk applying AI to IOT & smart sensor data
The Industrial AI Podcast featured on This Week in Machine Learning. Listen to Episode 3 with Chelsea Finn on robots and reinforcement learning
Gears: a new way to blend existing models or classical controllers with state-of-the-art reinforcement learning
Enterprises w. industrial AI problems have huge opportunities to boost performance w. AI for industrial applications. Learn about unique challenges.
Pt 2 of This Week in Machine Learning’s Industrial AI Podcast. Sam Charrington interviews Pieter Abbeel about robotics and reinforcement learning.
Bonsai’s Mark Hammond talks deep reinforcement learning and challenges in the GPU era. Get the GTC recap and watch a video of the live presentation
Listen to Ep. 1 of the 7-part industrial AI podcast series on This Week in Machine Learning, ft. Ilia Baranov of Clearpath Robotics
Bonsai CEO & NVIDIA VP/GM discuss building AI models with machine learning platforms, incl. Bonsai & NVIDIA, to solve enterprise-level challenges
Bonsai AI Platform: Three key questions that qualify use case fit for the platform, deep reinforcement learning, and real world simulations.
Industrial AI use cases that benefit from programming AI models into sophisticated industrial systems. Here’s how devs optimize with Bonsai.
Machine teaching and machine learning techniques come together in the Bonsai AI platform. Here’s how devs combine the two for better AI models.
Programming AI for industrial systems requires a combination of human AND machine intelligence
Program and manage AI models without the complexities of machine learning libraries like TensorFlow. Here’s how the Bonsai AI engine makes it happen.
Bonsai AI development platform makes AI more accessible. Here’s how developers and enterprises simplify programming and management of AI models.
Bonsai | Programming AI for control and decision support use cases without an advanced degree in machine learning.
Beyond personal assistants & chatbots: Build industrial AI models that can inject greater intelligence into sophisticated systems with Bonsai.
A combination of Machine Teaching and Machine Learning techniques are at the foundation of Bonsai’s platform
AI use cases in the news are focused on toy problems but AI innovations can also help enterprises solve complex business problems.
A recap on DeveloperWeek, what you may have missed at our booth, and the cool use cases we found when talking to AI developers and data scientists.
Katherine McAuliffe joins Bonsai as the first developer advocate and explains what the community can expect moving forward.
Bonsai’s Keen Browne talks explainable AI and deep learning systems at SF Bay Area’s Machine Learning Meetup. Watch the video and get the highlights.
The Bonsai team built and trained an AI engine to become a state-of-the-art, self-healing AI cluster. Learn about the process from the ground up.
Get to know Bonsai’s Inkling programming language with a short introduction, including a few tips and tricks you can use with the Inkling code.
Bonsai mourns the loss of Harry Weller, a General Partner at NEA who led the Series A, served on the Board of Directors, and was a champion and friend
Bonsai examines the state of AI adoption by releasing a survey to its Private Beta program registrants. Here's how people view and interact with AI.
As the Bonsai Private Beta opens to community users, the team shows how to connect to the AI engine and teach it a task using reinforcement learning.
Explainability of AI is critical when looking at AI options. Bonsai’s Dave Cahill talks explainable AI and how it’s employed on the Bonsai platform
When the markdown for PyPI didn’t play nicely with Bonsai’s README file, here’s how the team found a solution to satisfy PyPI syntax and README.
Bonsai CEO, Mark Hammond talks “AI for Everyone” at the O’Reilly AI Conference & how Bonsai is harnessing abstraction to unlock AI for every developer
AI engines perform best with reinforcement learning techniques. Here are several training basics you can use when working with the Bonsai AI platform.
The next economy depends on machine intelligence. Here’s how AI created for developers and engineers will empower them to build AI everywhere.
Training your machine learning algorithms is critical to AI success. Here’s how hyperparameters help govern the speed and accuracy of your networks.
The root of Bonsai’s AI platform is pedagogical learning, enabled by Inkling programming language specifications. See how and why it was created.
Bonsai’s newest employee talks about machine learning, the Bonsai platform, and breaking down the barriers to entry in artificial intelligence.
Bonsai unlocks the power of AI for developers with the latest round of funding. Learn about the AI engine’s private beta program expansion.
Bonsai’s AI platform is being created for the best user experience. See how we’re designing AI to be usable, functional and engaging for everyone.
How do you get started with AI without experience? Here are 5 paths you can take to apply machine learning in your professional and personal projects.
How to Teach an AI to Play Breakout: Curriculum & Lessons. Part 2 of the Bonsai blog series on teaching a machine using the Bonsai AI platform.
Bonsai’s approach to programming AI is fundamentally different. Watch how Bonsai uses schemas and concepts to teach an AI to play Atari’s Breakout.
Bonsai’s “AI for Everyone” campaign is about democratizing AI. Here’s how our tools not only teach intelligent machines, but also train them.
How to Teach an AI to Play Breakout: BRAIN Server. Part 3 of the Bonsai blog series on using the Bonsai AI platform to teach a machine to learn.
Bonsai’s AI platform simplifies AI programming, much like BASIC did for computer programming. See how Bonsai’s tools are expanding use cases for AI.