Microsoft announced yesterday that it has acquired Lobe, a small San Francisco based AI startup. Lobe is a visual Interface tool that allows people to easily create intelligent apps capable of understanding hand gestures, hear music, read handwriting, and more, without any coding involved.
Lobe is aimed at making deep learning simple, understandable and accessible to everyone. With the Lobe’s simple visual interface, anyone can develop deep learning and AI models quickly, without having to write any code.
A look at Lobe’s features
Drag, drop, learn
Lobe lets you build custom deep learning models, train them, and ship them directly in your app without any coding required. You can start by dragging in a folder of training examples from your desktop. This lets you build a custom deep learning model and begin its training. Once you’re done with this, you can export a trained model and ship it directly in your app.
Connect together smart lobes
There are smart building blocks called lobes in Lobe. These lobes can be connected together allowing you to quickly create custom deep learning models. For instance, you can connect the Hand & Face lobe to let you find the most prominent hand in the image. After this, connect the Detect Features lobe to find the important features in the hand. Finally, you can connect the Generate Labels lobe to predict the emoji in the image. You can also refine your model by adjusting each lobes unique settings or by editing any lobe’s sub-layers.
Exploring dataset visually
With Lobe, you can have your entire dataset displayed visually. This helps you browse and sort through all your examples. All you have to do is select any icon and see how that example performs in your model. Your dataset gets automatically split into a Lesson which teaches your model during training. There’s also a Test used that evaluates how your model will perform in the real world on examples that have never been seen before.
Real-time training results
Lobe comes with super fast cloud training that provides real-time results without slowing down your computer. There are interactive charts which help you monitor the accuracy of your model and understand how the model improves over time. The best accuracy then automatically gets selected and saved.
Advanced control over every layer
Lobe is built on top of the deep learning frameworks TensorFlow and Keras. This allows you to control every layer of your model. With Lobe, you can tune hyperparameters, add layers, and design new architectures with the help of hundreds of advanced building block lobes.
Ship it in your application
After you’re done training your model, it can be exported to TensorFlow or CoreML which you can then run directly into your app. There’s also an easy-to-use Lobe Developer API, which lets you host your model in the cloud and integrate it into your app.
What could Microsoft’s plans be with this acquisition?
This is not the first AI startup acquired by Microsoft. Other than Lobe, Microsoft also acquired Bonsai.ai, a deep reinforcement learning platform, in July to build machine learning models for autonomous systems of all kinds. Similarly, Microsoft acquired Semantic Machines this May to build a conversational AI center of excellence in Berkeley to advance the state of conservational AI.
“Over the last few months, we’ve made multiple investments in companies to further this (expanding its growth in AI) goal. These are just two recent examples of investments we have made to help us accelerate the current state of AI development”, says Kevin Scott, EVP, and CTO at Microsoft, in yesterday’s announcement on their official blog.
Looks like Microsoft is all set on bringing more AI capabilities to its users. In fact, major tech firms around the world are walking along the same path and acquiring as many technology companies as they can. For instance, Amazon acquired AI cybersecurity startup Sqrrl, Facebook acquired Bloomsbury AI, and Intel acquired Vertex.ai earlier this year.
“In many ways though, we’re only just beginning to tap into the full potential AI can provide. This in large part is because AI development and building deep learning models are slow and complex processes even for experienced data scientists and developers. To date, many people have been at a disadvantage when it comes to accessing AI, and we’re committed to changing that” writes Kevin.
For more information, check out the official Microsoft Announcement.