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Date: 2018-06-14

What if this application oversimplifies machine learning? 2

What if this application oversimplifies machine learning? 3

It is easier than you think to craft AI tools without typing a line of code.

With the Lobe application, it is something other companies are trying to do as well.

This startup company, which launched earlier this year, offers users a clean drag-and-drop interface for building deep learning algorithms from scratch. It is mainly focused on machine vision, which means if you want to build a tool that recognises different houseplants or can count the number of birds in a tree, you will be able to do all of that with the help of Lobe.

The co-founder, Mike Matas, explains that Lobe is not designed to compete with software used by machine learning professionals (tools like PyTorch and TensorFlow). Instead, it is built to give amateurs an easy way to do this. "People have ideas they want to try in machine learning but don’t have the right way to prototype them," says Matas. Lobe gives these users the first step towards machine learning without having to be a genius in coding, which makes deep learning more accessible to professionals in a diverse range of fields, from architecture to astronomy.

Lobe is really easy to use, you do not have to download anything; just load up Lobe.ai in your browser, sign in with a Google account, and you are ready to go.

You select a template drop in your data from your desktop and let it crunch through the information for you. There are only a few templates at the moment, but Lobe's creators say they plan to expand this by adding new neural network architectures over time and create a community where users can share their best models.

Lobe.ai is in beta now, but the overall look and feel are impressive and easy to adapt to for a new user. As much as Lobe's simplicity is appealing to machine learning amateurs, some experts argue that tools like this flatter out their discipline to an unhelpful degree, simply replacing the coding process with a visual stand-in that does not actually teach them how to build quality algorithms.

Jeremy Howard is a data scientist and entrepreneur who co-founded Fast.AI; a research lab that makes deep learning more accessible through tools and tutorials. He says he has seen it all before.

"For some reason, every year or two, someone comes along and designs a machine learning training system that involves dragging boxes from a toolbox and drawing them together with lines. I haven’t quite figured out why this is so appealing to build, but it is," says Howard. "They always get a certain amount of column inches because people outside the community think they’ve made machine learning easy, but it doesn’t."

Howard says that there is not really much point to these visual interfaces; the process is essentially the same as writing code, but it's "more awkward, takes longer, and you see less on the screen at once". He also points out that in order to build anything other than the most basic application, you still have to know which components you want to use, how to connect them together and so on.

Howard commented on Lobe's interface and said that some of the more complex settings are there for the aesthetics rather than actual functionality. "They show you changing the architecture of the model … but no one hand-writes architecture. Nobody," says Howard. "The idea of somehow doing that by typing numbers into boxes shows a complete lack of understanding of what people need to do."

If you agree with what Howard says, then Lobe may seem like an overcomplicated way to get people started in machine learning. But, even if visual tools like this are just skins for existing software, there is still benefit to them. Lobe makes getting started in AI less scary, and in turn, it could help indict professionals who would benefit from the latest machine learning techniques.

If AI is really going to change the world, then it seems obvious that the more people who get involved, the better – especially people outside the tech world. There might be professionals in scientific fields who may not feel they have the time to learn about code, but they can play around in their browser to get the feel for things.

With all that in mind, Lobe looks like a perfect recruitment tool for a machine learning revolution.


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