Deep learning is already mainstream
While I was scrolling on Twitter. I saw a tweet showing the word “deep learning” is plateauing on google trends. Then Yann LeCun replied that it's simply deep learning because become more normal.
This reminds me of a previous blog post that I wrote. Talking about how good tech is like good design. Meaning when technology is good. It embeds itself in our society and becomes invisible. Gmail’s smart compose feature is 100% deep learning. But we don’t think about it as ML. Amazon’s recommendations are ML. But we don’t think about them that way. In normal discourse, we just simply call them algorithms. Which is an accurate term. While abstracting away most of the advanced details.
This makes sense, as only nerds care about the type of algorithm. To recommend films on Netflix. Its recommendation systems. Everybody else will simply just say technology. Or treat the company as a person. Like “Netflix sent me this message.” “Facebook showed me this message.” Etc. When aeroplanes started getting popular we just said we took a flight to Washington DC. Rather than a mechanical flying device helped travel a couple of miles.
As I write this blog post. I use Microsoft Word’s read-aloud feature. To proofread the blog post. Where a robot voice reads why to work for me. The voice has improved tremendously. While it still has some robotic feels to it. It does a good job. It’s like an editor is personally reading my work. Also, I use the program Grammarly. While they do not say it I’m pretty certain they use machine learning. To spot mistakes in your work. These very useful tools that help me improve my writing are drive-by machine learning. Even though people will simply just call it technology.
This is the cycle of all technologies. You have hype. Depending on how good the technology is. It fails to even go into the mainstream. And start again in the hype cycle. If it's good. It will fall below expectations not because it’s bad. But failed to meet the sky-high expectations. Afterwards, people start to work out more practical uses of the technology. After a while, the technology gets popular. But lots of the hype starts to fade away. As people get used to the technology. So I guess deep learning or machine learning. The hype is starting to disappear, but people are finding uses for the technology.
You will have some standouts like GPT-3 and GANs. But most machine learning in the wild right now is a little bit boring. Recommendation systems. Think of Netflix and Amazon. Forecasting. Using past data. To predict future behaviours. It tends to be boring as its simply showing other data points based on past data. Or in amazon cases using the AI to help sell more products. Which is no surprise if your for-profit company. You need to make money.
While ML has its limits. I still think is very popular because it can do so many things. Like generating image via GANs. Classifying images with CNNs. To predicting past behaviour using forecasting. I think this is why AI is very popular. Because if you have some type of data. Which in the internet age, the answer is always a yes. Like early computers where it efficiently changed every industry either via automation or communication. With machine learning. It can help with those areas even more.
In the good tech is like good design blog post. I talked about technology tends to be popular when people stop noticing it. Which is happening now.
In the article I said:
no one calls their company “Excel-based” or “Windows-based”. As it’s [just] a tool.
When people started using office services on their computers. It was revolutionary at the time. But people now, don’t call themselves an “Excel-first company” Or an “email first company”. As people got used to them, people assume that using these services is a given. Soon having some type of data science role will be a given. Just like having a web developer for your company is a given. This will still mainly be focused on tech companies. But non-tech companies are not far behind. Non-tech companies hire web developers and server managers.