What, it’s that easy to grasp machine learning basics? Good news from Google’s Teachable Machine crew. Previously, the Teachable Machine provided lessons on how AI works but a new 2.0 puts you to work in making your machine learning model come to life in apps, websites and more.

A number of websites got busy over the last few days to say that Google updated the Teachable Machine program to give users more training tools.

Bradly Shankar in MobileSyrup.com talked about what’s special about 2.0. “Models can be uploaded for online use or saved for native use on the device. Further, AI models can now be trained based on sound and poses, on top of the regular image data.”

Ivan Mehta in TNW similarly explained what was special. “The earlier version allowed you to train three classes through your camera. The new model, not only lets you define more than three classes, it also allows you to use images, audio clips, pose data, or your own dataset for the training.”

All in all, you can train AI models not just on images but based on sound and poses.

“Teachable Machine 2.0: Making AI easier for everyone” is a video posted on Nov. 7. The video message was that the new 2.0 could make it both it fast and easy to create machine learning models for your projects, no coding required. “”You train a computer to recognize your images, sounds, and poses without writing any machine learning code. Then, use your model in your own projects, sites, apps, and more.”

If some people feel nervous that working with machine learning would be over their heads, the team behind Teachable Machine would like to take away that fear and replace it with enthusiasm. “Machine learning is pretty intimidating to get into,” said the narrator. “So we’ve been wondering. What if it wasn’t?”

Say hello to Teachable Machine 2.0. They are setting out to make it easier for anyone to create machine learning models. Cantaloupe orange. Shady sunny. Water on Water off. Teachable Machine now puts the power in your hands. The narrator is eventually joined by a co-narrator who says, “So let’s say you want to build a model using a picture of you and a picture of your dog.” You open up the site, record samples of you, and samples of your dog. Click train.

You can upload your model, too, to host it online or download it. Teachable Machine is flexible; the Teachable Machine site said it’s respectful of the way you work. You can use it entirely “on-device, without any webcam or microphone data leaving your computer,” said the site.

Kyle Phillips, Google Creative Lab, wrote a blog post about the new capabilities on Nov. 7. How it works: You can use Teachable Machine to recognize images, sounds or poses. “Upload your own image files, or capture them live with a mic or webcam. These examples stay on-device, never leaving your computer unless you choose to save your project to Google Drive.” Teachable Machine from Google Creative Lab goes to work to train a model based on the examples you provided.

The training happens in your browser, he said; everything stays in your computer.

In the video, the presenter had said that “folks” have already been trying it out, that is, using Teachable Machine in their own experiments.

Phillips, meanwhile, reported on specific cases.

Education researcher Blakeley Payne and her team have been using Teachable Machine as part of a curriculum that teaches middle-schoolers about AI through hands on experience.

Steve Saling, focused on accessibility technology, used it to explore communication for people with impaired speech.

Yining Shi has been using Teachable Machine with students in the Interactive Telecommunications Program at NYU to explore its potential for game design.

Tensorflow.js, an open-source library for machine learning from Google, plays a part in all this; it powers the model you create. As the Teachable Machine site said, “The models you make with Teachable Machine are real Tensorflow.js models that work anywhere javascript runs, so they play nice with tools like Glitch, P5.js, Node.js & more.”

If you still need convincing it is really as easy as it looks and sounds,, this will help. Check out another video where you see the narrator talk about Teachable Machine 2.0.

The Engadget verdict? Jon Fingas weighed in, with perspective. “This clearly isn’t the most sophisticated AI system, but it doesn’t have to be. It’s still an educational tool at heart, and the support for projects makes it that much more useful for demonstrating AI concepts in the real world.”

So, which version is better to use, the first or this latest one?

“In a nutshell,” said the web site for Teachable Machine, “if you’re say, a teacher who just wants to quickly demo of how machine learning works and don’t need to save anything, use the first version from 2017. If you want to save your model and create a working project, use the latest version.”

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Computer science has a pretty high barrier to entry. Learning to code is the first and biggest obstacle; many people run straight into that hurdle. So, when Google’s A.I. Experiment announced earlier this week that they had created a new experiment to help people understand machine learning without a single line of code, I was dubious. How could this even be possible?

Teach a robot to teach itself

First, a bit of background.

One of the big problems stopping us from having artificial assistants a la Jarvis is the Polanyi paradox. Named for the philosopher Michael Polyani, it more or less states that we “know more than we can say”. It’s the biggest problem in automation, actually. Automation requites that tasks be broken down into concrete steps. But how can a task be broken down if we don’t even know how we’re doing it?

We can teach a sixteen year old to (more or less) safely drive a car in less than six months. Any adult can do it. And yet, it’s taken some of the best minds in engineering and computer programming over a decade to teach a robot to do the same. Why?

There are a lot of steps in between point A and point B. Most of them are unconscious or so minimal that we hardly even think of it. But a computer needs all of those little rules and things to keep in mind. There’s a famous example – “programming a peanut butter and jelly sandwich”. It’s a lot more difficult than you’d think.

Computers think literally; it’s kind of their thing. This has been the big issue, since programmers can’t cover everything. (Also, programmers in the olden days had to worry about computer processing limitations as well, the poor sods.) And so, we come to machine learning.

Machine learning is the art of teaching computers to teach themselves. Why bother coding every possible interaction that a program might encounter, when you can feed a program a crazy amount of data and have it pick up general rules along the way? The programmers add a bit of correction and guidance to make sure it doesn’t go too far afield.

It’s not that crazy of an approach. After all, that’s how we humans learn to speak.

These days, machine learning uses datasets in the thousands and hundred-thousands, offering the program a chance to figure out a pattern on its own from the examples given to it. Sometimes, it works out quite well. Other times, not so much.

And so, from these datasets, come general rules that help a program determine a cat from a dog or sort pickles for Japanese farmers. Which is all very well and good. But how do they manage to do it without a single line of code?!

Teachable Machine

Well, technically, there is code. Just not on the end user’s side.

The Teachable Machine works with your computer’s camera to explore the details of machine learning with some fun examples.

The tutorial explains things quite handily. You’re basically creating a small dataset of images to teach the program to respond in one of three specific ways for the gif, sound, or speech responses.

Here’s how it works: You give the program at least thirty photos of you doing a specific, recognizable thing, like sitting quietly at your desk, making a funny face, or drinking a cup of coffee. Each photoset is associated with a specific response from the program.

I make a funny face? The program responds with a cat gif.

I sip at my coffee? The program plays a trumpet.

I sit quietly at my desk? The program says “awesome”.

Things are pretty easy to differentiate between if the three poses are distinct. But things get more interesting if you have three similar datasets. The program definitely has a harder time when the difference between two photos is a wink. It shifts between responses readily as you move, trying to make sense of what you’re doing.

[Full disclosure: if you’re weirded out about the idea of Google having access to your photos as you play around with this tech, all of the images gathered stay on your local network and aren’t uploaded past the initial page.]

It’s definitely fun to play around and see how good the program can get at differentiating at different photo datasets.

Limitations

Obviously, the limitations are pretty clear. Teachable Machine isn’t going to help solve the robotic driving issue any time soon. But it’s an excellent introduction to machine learning for those of us who might not be the greatest at coding.

And frankly, we need all the machine learning specialists we can get.

A sneak peek at Gartner’s top 10 technology trends for 2018 shows that artificial intelligence and machine learning is at the tip-top of the list. Machine learning specialists are among some of the best paid in the business.

But, there’s a big gap between the open jobs and the skills programmers have. So, making it easier for people to dip their toes into the machine learning pool can only be a good thing. And that is what Teachable Machine is doing: bringing machine learning to the masses… with cat gifs

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