Forget what you may have heard. Machine learning isn’t some new concept or study in its infancy.
If we’re being technical, machine learning has actually been around since the 1950s, when Arthur Samuel coined the term at IBM. Early statistical models in those days paved the way for today’s modern artificial intelligence.
On the contrary, while today’s machine learning is miles ahead of what it used to be, there’s always room for improvement.
With advancements in algorithms, statistical modeling, and computing, machine learning will only get more efficient. And while it’s not always easy to predict what this efficiency will look like, some experts have an idea.
So, we asked 5 people with in-depth knowledge of machine learning for their opinions on its future. Let’s see what they had to say.
Future of machine learning
Depending on how well you understand machine learning, some of these insights may sound familiar, others may expose you to new ideas.
Ben Wald, Co-Founder & VP of Solutions Implementation at Very
Machine learning may be a method of data analysis, however, it’s steadily influencing the lives those who own IoT devices like smartwatches, phones, cars, and more. Here’s what Ben had to say about the unique relationship between machine learning and consumers.
“With 90 percent of all data generated over the last two years, much of it grows from an array of smart devices that connect our phones, wrists, and homes. As a result, companies have more ways than ever to build relationships with their customers.
Using machine learning, corporations can fine-tune their understanding of their target audience to inform product development, marketing, and sales. With algorithms to break down exactly how their products are being used, developers and designers can customize products far more precisely than ever before, maximizing value for both the company and the consumer.”
With more breakthroughs in machine learning algorithms, we’ll begin to see hyper-targeting and fine-tuned personalization for customers on a larger scale.
2. Better search engine experiences
Dorit Zilbershot, Chief Product Officer at Attivio
You may not be aware of it when scrolling through Google in search of an article, but the ranking of those results are done with a purpose. Lately, machine learning has had an enormous influence on search engine results. Dorit is here to explain further.
“Search engines are going to improve both the user and the admin experience by leaps and bounds over the next few years. With further development of neural networks and deep learning, search engines of the future will be way better at delivering answers and insights that are highly relevant to the user that is searching.
Right now, we’re really good at understanding what results should be served based on the user’s profile and the query. However, this process still requires manual configurations and understanding of how search engines work. In the future, results will be tailored even closer to the individual based on their past interactions, preferences and the words they used without any manual administration. We’ll also get proactive about alerting people on potential issues before they even happen and provide actionable recommendations to ensure a smooth operation and excellent search experience.”
With more and more content being published every second of the day, it’ll be interesting to see the ways machine learning algorithms continue to optimize search results with the user in mind.
3. Evolution of data teams
Henrique Senra, VP of Product Development at SlicingDice
It’s not uncommon for IT and data teams to be bogged down with programming and systematic tasks. However, Henrique believes more advancements in machine learning will help evolve the day-to-day of these teams.
“It's nearly impossible to predict the future of ML and AI. If you've told technology experts 20 years ago what we could do with ML today, they would probably be skeptical, to say the least.
There are, however, certain trends in how ML is being used today and how those cases will evolve in the near-future. ML will be one of the foundational tools for developing and maintaining digital applications in the coming years. This means IT/data teams will spend less time programming and updating applications, but rather have them learn and keep improving their operations continually.”
I’ll take Henrique’s insight a step further and say more intelligent robotic process automation – with the help of machine learning – will reduce the number of redundant tasks done by programmers. Read our beginner’s guide on robotic process automation if you’re not yet familiar with it.
Machine learning is likely to evolve the tasks of data teams, but it’ll also be more approachable for a wider range of audiences. I’m referring to low-to-no-code environments. Here’s what Tony says about this newer phenomena.
“Machine learning will become just another part of software engineering. Open-source frameworks like Tensorflow, Keras, and PyTorch have not only standardized the way people implement machine learning algorithms but also removed the prerequisites for doing so. You don't need a Ph.D. to do machine learning, you only need to download a few packages and follow an online course to get up to speed. Companies like ours are taking it a step further and enabling anyone (not just programmers) to use no-code machine learning in their own bespoke apps.”
This may sound like utopia, but with so much infrastructure, datasets, and tools available today, these type of environments are slowly but surely rolling out. Check out our research team’s comprehensive guide on the differences between low code and no code development for more information.
You may have heard of quantum computing from sci-fi films, but this discipline is very real. There’s really no easy way to define quantum computing other than quantum algorithms have the potential to lead to many other innovations. Let’s hear what Matt says about quantum computing.
“Quantum computing is going to play a huge part in the future of machine learning. Integration of quantum computing into machine learning will transform the field as we’ll see faster processing, accelerated learning and increased capabilities. This means that complex problems that we don’t have the ability to solve with current methods could be done so in a small fraction of time. The potential for this is huge and could impact millions of lives for the better – notably in healthcare and medicine.”
As of now, there are no commercially-ready quantum hardware or algorithms available. However, many government agencies and research firms have invested millions to get quantum computing off the ground.
There were so many great talking points from our contributors, I just wanted to touch on a few more before closing out.
“AI winter is on the horizon”
Winter is coming, according to Tony from AppSheets. Tony mentions that the summer of AI – meaning a period of high expectations and large funding rounds for AI-enabled startups – will soon cool off.
The downside? Buzzwordy headlines of human-like robots will be less frequent – leading readers to believe AI and machine learning have gone stale.
The upside? Tony says more spreadsheet-like advancements will be made, and they’ll be just as transformative.
Alexandra Zelenka, Technical Writer from DDI Development, states that as machine learning gets more sophisticated, we’ll see increased usage of intelligent robots. Of course, this also depends on the pace of how artificial neural networks and deep learning progress.
Regardless, robotics will undoubtedly play an increasingly important role in making our lives easier through automation. From smart drones to manufacturing bots, this will be made possible with the help of unsupervised learning.
At G2, we love crowdsourcing insights from industry experts, especially when it comes to something as pivotal to technological innovation as machine learning. Thanks again to our contributors for taking current ideas about machine learning a step further, and introducing some new ideas as well.
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Devin is a former Content Marketing Specialist at G2, who wrote about data, analytics, and digital marketing. Prior to G2, he helped scale early-stage startups out of Chicago's booming tech scene. Outside of work, he enjoys watching his beloved Cubs, playing baseball, and gaming. (he/him/his)