What is ML? part 3

Yesterday we saw that machine learning is behind some successful products and it does have the potential to bring many more changes to our life.

So what is it?

Well, the textbook definition is that it’s the building of algorithms that can perform tasks they were not explicitly programmed to do. In practice, this means that we have algorithms that analyze large quantities of data to learn some patterns in the data, which can then be used to make predictions about new data points.

This is in contrast with the classical way of programming computers, where a programmer would use either their domain knowledge or they would analyze the data themselves and then write the program that has the correct output.

So one of the crucial distinctions is that in machine learning, the machine has to learn from the data. If a human being figures out the pattern and writes a regular expression to find addresses in text, that’s human learning, and we all go to school to do that.

Now does that mean that machine learning is a solution for everything? No. In some cases, it’s easier or cheaper to have a data analyst or a programmer find the pattern and code it up.

But there are plenty of cases where despite decades long efforts of big teams of researchers, humans haven’t been able to find an explicit pattern. The simplest example of this would be recognizing dogs in pictures. 99.99% of humans over the age of 5 have no problem recognizing a dog, whether a puppy, a golden retriever or a Saint Bernard, but they have zero insight into how they do it, what makes a bunch of pixels on the screen a dog and not a cat. And this is where machine learning shines: you give it a lot of photos (several thousands at least), pair each photo with a label of what it contains and the neural network will learn by itself what makes a dog a dog and not a cat.

Machine learning is just one tool that is available at our disposal, among many other tool. It’s a very powerful tool and it’s one that gets “sharpened” all the time, with lots of research being done all around the world to find better algorithms, to speed up their training and to make them more accurate.

Come back tomorrow to find out how the sausage is made, on a high level.

What is ML? part 2

Yesterday I wrote how AI made big promises in the past but it failed to deliver, but that now it’s different, because of machine learning.

What’s changed?

Well, now we have several products that work well with machine learning. My favorite example is Google Photos, Synology Moments and PhotoPrism. They are all photo management applications which use machine learning to automatically recognize all faces in pictures (easy, we had this for 15 years), recognize automatically which pictures are of the same person (hard, but doable by hand if you had too much time) and more than that, index photos by all kinds of objects that are found in them, so that you can search by what items appear in your photos (really hard, nobody had time to do that manually).

I have more than 10 years of photos uploaded to my Synology and one of my favorite party tricks when talking to someone is to whip out my phone and show them all the photos I have of them, since they were kids, or the last time that we met, or that funny thing that happened to them and I have photographic evidence of. Everyone is amazed by that (and some are horrified and deny that they looked like that when they were children). And there is not one, but at least three options to do this, one of which is open source, so that anyone can run in at home on their computer, for free, so there is demand for such a product.

Other successful examples are in the domain of recommender systems, YouTube being a good example. I have a love/hate relationship with it: on one hand, I wasted so many hours of my life to the recommendations it makes (which is proof of how good it is at making personalized suggestions), on the other hand, I found plenty of cool videos with it. This deep learning based recommender system is one of the factors behind the growth of the watch time on YouTube, which is basically the key metric behind revenue (more watch time, more ads).

These are just two examples that are available for everyone to use, and which serve as evidence that machine learning based AI now is not just hot air.

But I still haven’t answered the question what is ML… tomorrow, I promise.

What is ML?

Machine learning is everywhere these days. Mostly in newspapers, but it’s seeping into many real life, actual use cases. But what is it actually?

If you read only articles on TechCrunch, Forbes, Business Insider or even MIT Technology Review, you’d think it’s something that brings Model T800 to life soon, or that it will cure cancer and make radiologists useless, or that it will enable humans to upload their minds to the cloud and live forever, or that it will bring fully self driving cars by the end of the year (every year for the last 5 years).

Many companies want to get in on the ML bandwagon. It’s understandable: 1) that’s where the money is (some 10 billion dollars were invested in it in 2018) and 2) correctly done, applied to the right problems, ML can actually be really valuable, either by automating things that were previously done with manual labor or even by enabling things that were previously unfeasible.

But at the same time, a lot of ML projects make unrealistic promises, eat a lot of money and then deliver something that doesn’t work well enough to have a positive ROI. The ML engineers and researchers are happy, they got payed, analyzed the data and played around with building ML models, and maybe even published a paper or two. But the business is not happy, because they are not better off in any way.

This is not a new phenomenon. Artificial Intelligence, of which Machine Learning is a subdomain of, has been plagued by similar bubbles ever since it was founded. AI has gone through several AI winters already, in the 60s, 80s and late 90s. Big promises, few results.

To paraphrase Battlestar Galactica, “All this has happened before, all this will happen again but this time it’s different”. But why is it different? More about that tomorrow.