Google’s AutoML is a new up-and-coming (alpha stage) cloud software suite of Machine Learning tools. It’s based on Google’s state-of-the-art research in image recognition called Neural Architecture Search (NAS). NAS is basically an algorithm that, given your specific dataset, searches for the most optimal neural network to perform a certain task on that dataset. AutoML is then a suite of machine learning tools that will allow one to easily train high-performance deep networks, without requiring the user to have any knowledge of deep learning or AI; all you need is labelled data! Google will use NAS to then find the best network for your specific dataset and task. They’ve already shown how their methods can achieve performance that is far better than that of hand-designed networks.

AutoML totally changes the whole machine learning game because for many applications, specialised skills and knowledge won’t be required. Many companies only need deep networks to do simpler tasks, such as image classification. At that point they don’t need to hire 5 machine learning PhDs; they just need someone who can handle moving around and organising their data.

There’s no doubt that this shift in how “AI” can be used by businesses will create change. But what kind of change are we looking at? Whom will this change benefit? And what will happen to all of the people jumping into the machine learning field? In this post, we’re going to breakdown what Google’s AutoML, and in general the shift towards Software 2.0, means for both businesses and developers in the machine learning field.

More development, less research for businesses

A lot of businesses in the AI space, especially start-ups, are doing relatively simple things in the context of deep learning. Most of their value is coming from their final put-together product. For example, most computer vision start-ups are using some kind of image classification network, which will actually be AutoML’s first tool in the suite. In fact, Google’s NASNet, which achieves the current state-of-the-art in image classification is already publicly available in TensorFlow! Businesses can now skip over this complex experimental-research part of the product pipeline and just use transfer learning for their task. Because there is less experimental-research, more business resources can be spent on product design, development, and the all important data.

Speaking of which…

It becomes more about product

Connecting from the first point, since more time is being spent on product design and development, companies will have faster product iteration. The main value of the company will become less about how great and cutting edge their research is and more about how well their product/technology is engineered. Is it well designed? Easy to use? Is their data pipeline set up in such a way that they can quickly and easily improve their models? These will be the new key questions for optimising their products and being able to iterate faster than their competition. Cutting edge research will also become less of a main driver of increasing the technology’s performance.

Now it’s more like…

Data and resources become critical

Now that research is a less significant part of the equation, how can companies stand out? How do you get ahead of the competition? Of course sales, marketing, and as we just discussed, product design are all very important. But the huge driver of the performance of these deep learning technologies is your data and resources. The more clean and diverse yet task-targeted data you have (i.e both quality and quantity), the more you can improve your models using software tools like AutoML. That means lots of resources for the acquisition and handling of data. All of this partially signifies us moving away from the nitty-gritty of writing tons of code.

It becomes more of…

Software 2.0: Deep learning becomes another tool in the toolbox for most

All you have to do to use Google’s AutoML is upload your labelled data and boom, you’re all set! For people who aren’t super deep (ha ha, pun) into the field, and just want to leverage the power of the technology, this is big. The application of deep learning becomes more accessible. There’s less coding, more using the tool suite. In fact, for most people, deep learning because just another tool in their toolbox. Andrej Karpathy wrote a great article on Software 2.0 and how we’re shifting from writing lots of code to more design and using tools, then letting AI do the rest.

But, considering all of this…

There’s still room for creative science and research

Even though we have these easy-to-use tools, the journey doesn’t just end! When cars were invented, we didn’t just stop making them better even though now they’re quite easy to use. And there’s still many improvements that can be made to improve current AI technologies. AI still isn’t very creative, nor can it reason, or handle complex tasks. It has the crutch of needing a ton of labelled data, which is both expensive and time consuming to acquire. Training still takes a long time to achieve top accuracy. The performance of deep learning models is good for some simple tasks, like classification, but does only fairly well, sometimes even poorly (depending on task complexity), on things like localisation. We don’t yet even fully understand deep networks internally.

All of these things present opportunities for science and research, and in particular for advancing the current AI technologies. On the business side of things, some companies, especially the tech giants (like Google, Microsoft, Facebook, Apple, Amazon) will need to innovate past current tools through science and research in order to compete. All of them can get lots of data and resources, design awesome products, do lots of sales and marketing etc. They could really use something more to set them apart, and that can come from cutting edge innovation.

That leaves us with a final question…

Is all of this good or bad?

Overall, I think this shift in how we create our AI technologies is a good thing. Most businesses will leverage existing machine learning tools, rather than create new ones since they don’t have a need for it. Near-cutting-edge AI becomes accessible to many people, and that means better technologies for all. AI is also quite an “open” field, with major figures like Andrew Ng creating very popular courses to teach people about this important new technology. Making things more accessible helps people transition with the fast-paced tech field.

Such a shift has happened many times before. Programming computers started with assembly level coding! We later moved on to things like C. Many people today consider C too complicated so they use C++. Much of the time, we don’t even need something as complex as C++, so we just use the super high level languages of Python or R! We use the tool that is most appropriate at hand. If you don’t need something super low-level, then you don’t have to use it (e.g C code optimisation, R&D of deep networks from scratch), and can simply use something more high-level and built-in (e.g Python, transfer learning, AI tools).

At the same time, continued efforts in the science and research of AI technologies is critical. We can definitely add tremendous value to the world by engineering new AI-based products. But there comes a point where new science is needed to move forward. Human creativity will always be valuable.

Conclusion

Thanks for reading! I hope you enjoyed this post and learned something new and useful about the current trend in AI technology! This is a partially opinionated piece, so I’d love to hear any responses you may have below!