Machine learning is evolving at a rapid pace, furthering the ability of artificial intelligence. As more businesses adopt ML, the future is still murky.
If you’re a business owner who has recently started researching machine learning, you’re likely asking yourself how to make it work for you. As you should, ML makes businesses more efficient, more productive and run faster. But if you’re not a technical business owner, even reading the words “machine learning” can seem overwhelming and confusing. How does a machine learn? The answer: complex algorithms designed for specific situations.
Okay, that’s a pretty technical answer, but hang tight, it will make sense.
Currently in the realm of machine learning, there are processes and algorithms designed to work in very specific ways. Some of those algorithms, though, could potentially be used by multiple businesses for the same purpose. So why couldn’t these businesses share their algorithms? Well, they could, unless there’s really sensitive or proprietary information used to build the algorithm. But then how would that other business gain access to that algorithm without having to jump through hoops and, potentially, shell out some cash?
That poses a new question about the future of machine learning: How can algorithms be shared easily between technologists? We have open-source platforms for coders, like GitHub, where programmers and engineers can go and look for a solution to a problem they are having, or even collaborate with someone else on how to problem-solve whatever issue is at hand. Is it possible to have something similar for machine learning algorithms?
This is where it gets murky. Because the short answer is yes, there are already sites like GitHub where ML teams share training data sets and there are pieces of ML that are open source. There are also libraries with generic algorithms you can configure from Google/Tensor Flow, Azure and AWS. However, most ML algorithms are very specific to each business and, more importantly, how the data from each business is labeled (categorized). No two businesses are run exactly the same way, even within the same industry. Systems and processes may be similar, but no two are identical. Which means that an algorithm created for a marketing campaign or security feature might work perfectly for one business, but the next one needs that algorithm tweaked to be helpful.
Let’s be clear, machine learning and coding are not the same thing. But that doesn’t mean that the process of sharing information freely between technologists can’t happen. When people and businesses collaborate in the field of technology, regardless of the purpose, it further advances our technology. Which is exactly why cloud providers are already integrating ML products and services into their repertoire.
Think about it, coders do this with their open-sourced code. They share code they’ve written to solve a problem and they collaborate with one another to make their jobs more efficient and easier. When you have people of various backgrounds from various cultures coming together to solve a problem, you are far more likely to find a solution that benefits the most people. People often bounce ideas off of each other, and as they do that, the idea grows and changes and morphs into something amazing and beautiful and, importantly, functional.
Machine learning is not perfect, nothing in this world is. Which is largely why artificial intelligence will never take over for humans completely, but that’s a topic for another day. ML has its pros and its cons, just like everything else. It makes mistakes and sometimes gets confused when the data given doesn’t correlate with what it’s learned. The best way to fix that problem, though, is for global collaboration to happen. When humans come together on that scale, we can provide vastly better ML that works for all of us and the whole world benefits.