As regular readers of this blog know, I love talking about the future of Haskell as a language. I’m interested in ways we can shape the future of programming in a way that will help Haskell grow. I've mentioned network effects as a major hindrance a couple different times. Companies are reluctant to try Haskell since there aren't that many Haskell developers. As a result, fewer other developers will have the opportunity to get paid to learn Haskell. And the cycle continues.
Many perfectly smart people also have a bias against using Haskell in production code for a business. This stems from the idea that Haskell is an academic language. They see it as unsuited towards “Real World” problems. The best rebuttal to this point is to show the many uses of Haskell in creating systems that people use every day. Now, I can sit here and point the ease of creating web servers in Haskell. I could also point to the excellent mechanisms for designing front-end UIs. But there’s still one vital area in the future of programming that I have yet to address.
This is of course, the world of AI and machine learning. AI is slowly (or not so slowly) becoming a primary concern for pretty much any software based business. The last 5-10 years have seen the rise of “cloud native” architectures and systems. But we will soon be living in age when all major software systems will use AI and machine learning at their core. In short, we are about the enter the AI Native Future, as my company’s founder put it.
This will be the first in a series of articles where I explore the uses of Haskell in writing AI applications. In the coming weeks I’ll be focusing on using the Tensor Flow bindings for Haskell. Tensor Flow allows programmers to build simple but powerful applications. There are many tutorials in Python, but the Haskell library is still in early stages. So I'll go through the core concepts of this library and show their usage in Haskell.
But for me it’s not enough to show that we can use Haskell for AI applications. That’s hardly going to move the needle or change the status quo. My ultimate goal is to prove that it’s the best tool for these kinds of systems. But first, let’s get an idea of where AI is being used, and why it’s so important.
AI Will be Everywhere...
...and this doesn’t seem to be particularly controversial. Year-on-year there are more and more discoveries in AI research. We are now able to solve problems that were scarcely thinkable a few years ago. Advancements in NLP systems like IBM Watson have made it so that chatbots are popping up all over the place. Tensor Flow has put advanced deep learning techniques at the finger tips of every programmer. Systems are getting faster and faster.
On top of that, the implications for the general public are becoming more well known. Self-driving cars are roaming the streets in several major American cities. The idea of autonomous Amazon drones making deliveries seems a near certainty. Millions of people entrust part of their home systems to joint IoT/AI devices like Nest. AI is truly going to be everywhere soon.
AI Needs to be Safe
But the ubiquity of AI presents a large number of concerns as well. Software engineers are going to have a lot more responsibility. Our code will have to make difficult and potentially life-altering decisions. For instance, the design of self-driving cars carries many philosophical implications.
The plot thickens even more when we mix AI with the Internet of Things, another exploding market. In the last year, an attack brought down large parts of the internet using a bot-net of IoT devices. In the world of IoT, security still does not have paramount importance. But soon, more and more people will have cameras, audio recording devices, fire alarms and security systems hooked up to the internet. When this happens, their safety and privacy will depend on IoT security.
The need for safety and security suggest we may need to re-think some software paradigms. "Move fast and break things" is the prevailing mindset in many quarters. But this idea doesn't look so good if "breaking things" means someone's house burns.
Pure, Functional Programming is the Path to Safety
Haskell is in many respects a very safe language. This is why it’s trusted by large financial institutions, large data science firms, and even by a company working in autonomous flight control. When your code cannot have arbitrary side effects, it is far easier to prevent it from crashing. It is also easier to secure a system (like an IoT device) when you can prevent leaks from arbitrary effects. Often these techniques are present in Haskell but not other languages.
The field of dependent types is yet another area where we’ll be able to add more security to our programming. They'll enable even more compile-time guarantees of behavior. This can add a great deal of safety when used well. Haskell doesn’t have full support for dependent types yet, but it is in the works. In the meantime there are languages like Idris with first class support.
Of course, when it comes to AI and deep learning, getting these guarantees will be difficult. It's one thing to build a type that ensures you have a vector of a particular length. It's quite another to build a type ensuring that when your car sees a dozen people standing ahead of it, it must brake. But these are the sorts of challenges programmers will need to face in the AI Native Future. And if we want to ensure Haskell’s place in that future, we’ll have to show these results are possible.
It's obvious that AI and machine learning are the big fields of software engineering. They’ll continue to dominate the field for a long time. They can have an incredible impact on our lives. But by allowing this impact, we’re putting more of our safety in the hands of software engineers. This has major implications for how we develop software.
We often have to make tradeoffs between ease of development and reliability. A language like Haskell can offer us a lot of compile time guarantees about the behavior of our program. These guarantees are absent from many other languages. However, achieving these guarantees can introduce more pain into the development process.
But soon our code will be controlling things like self-driving cars, delivery drones, and home security devices. So we have an ethical responsibility to do everything in our power to make our code as reliable as possible. For this reason, Haskell is in a prime position when it comes to the AI Native Future. To take advantage of this, it will require a lot of work. Haskell programmers will have to develop language tools like dependent types to make Haskell even more reliable. We'll also have to contribute to libraries that will make it easy to write machine learning applications in Haskell.
With this in mind, the next few articles on this blog are all going to focus on using Haskell for machine learning. We’ll be starting by going through the basics of the Tensor Flow bindings for Haskell. You can get a sneak peek at some of that content by downloading our Haskell Tensor Flow tutorial!
If you’ve never programmed in Haskell before, you should try it out! We have two great resources for getting started. First, there’s the Getting Started Checklist. It will first walk you through downloading the language. Then it will point you in the directions of some other beginner materials. Second, there’s our Stack mini-course. This will walk you through the Stack tool, which makes it very easy to build projects, organize code, and get dependencies with Haskell.