How Tech Startups Are Utilizing Machine Learning In 2022

How Tech Startups Are Utilizing Machine Learning

Since the turn of the millennium, AI has moved from a concept out the page of science-fiction novels to a practical reality with influence spanning most industries. It isn’t the kind that people once envisioned, of course (it doesn’t feature sentient robots struggling to understand human emotion), but it’s revolutionary even so.

This form of practically-viable AI is known by another term: machine learning. Instead of seeking to mimic all the rich complexities of the human mind, it artificially replicates our core ability to recognize patterns and applies it at a vast scale to analyze huge data sets. Machine learning has been used for a while, but it’s early in its development in the grand scheme of things.

In this post, we’re going to look at some of the key ways in which machine learning is being effectively deployed by tech startups. Let’s get to them:

They’re finding ways to reduce spending

Much of financial management comes down to carefully balancing incomings against outgoings and figuring out which costs can be reduced or eliminated entirely: two things that machine learning tools can help with. Simply load in the data and identify what you’re hoping to achieve.

Some tech startups lean on the deployment of machine learning as a way of helping their customers save money, making it core to their operation. One example is Zest, a company that provides data-driven insight to help credit underwriters make smart decisions and get the best deals.

It isn’t only startups in the fintech world that use it, of course. Startups of all kinds use it to optimize their own finances, understanding how vital it is to spend money carefully while navigating the tricky growth phase. There’s no good reason to do it all manually.

They’re leveraging real-time data insights

For startups, it pays to be agile, and the ability to adapt and pivot quickly is about making data-driven decisions that leverage real-time insights. Data analysis powered by machine learning not only dramatically reduces the time spent processing and gathering data, but it can identify tangible, actionable insights within that data and make intelligent recommendations off the back of its findings.

Take Cloudways, for instance; this well-known SaaS hosting provider uses its own smart assistant (which it refers to as ‘CloudwaysBot’) to pore over performance data, deliver real-time insights about server health, and make smart recommendations about how to optimize servers and applications using the platform.

Trying to do any of this manually would be criminally inefficient, while even the most perceptive human analyst will inevitably fail to pick up on key insights that an AI-powered bot can identify and report as they’re happening.

They’re rapidly advancing medical research

Advancements in medical research have been more widely publicized in recent years due to the COVID-19 pandemic — the first vaccines being administered less than a year after COVID-19 was declared a global health emergency showed how far we’d come in that respect, and at the forefront of this were biotech startups using machine learning to enhance their efforts.

Look at what’s being achieved by companies like Paige, an innovator in the diagnosis and treatment of cancer that applies machine learning algorithms to “of the world’s largest datasets in pathology”. Advancements in the biotech industry mean that medical professionals will be able to identify, diagnose, treat (and possibly even eradicate) diseases quicker than ever before, and machine learning will continue to play a key role.

They’re achieving huge improvements in cybersecurity

The more we rely on the internet in our personal and our professional lives, the more we put vital data and contacts at risk, and there’s still a lot of fraud throughout the online world: particularly for big businesses or online retail. While methods such as biometric authentication are really pushing cybersecurity ahead, they’re unlikely to be ubiquitous for a long time.

Using machine learning to look at historical data for both legitimate and illegitimate payments or system access requests, tech startups in the cybersecurity world can rapidly and reliably identify fraudulent efforts without needing to massively slow everything down. Think about an online seller having their sales system automatically detect and block an illegitimate order before it goes any further in the fulfillment process: services like Riskified offer this kind of protection.

They’re analyzing sentiment through NLP

Natural language processing (otherwise known as NLP) has traditionally been extremely difficult to get right. Services like Grammarly can tell you when you’ve misplaced an apostrophe, but they can’t often figure out what you’re trying to say — and that’s when your text is reasonably consistent. With online communication being wildly inconsistent, you can see the problem.

Recent years have seen remarkable improvements made to NLP, though: not so much because computer systems are getting better at understanding people, but because they can simply use machine learning to analyze previously-unfathomable numbers of human conversations to painstakingly figure out which responses belong in which situations.

Companies like Phrasee actually use this level of insight to generate copy for various purposes. Due to this, entire advertising campaigns can be automated, with everything from the images used to the bids selected determined through machine learning. It’s far from flawless, and still needs human ideation, but there’s a lot of promise there.

2022 and beyond could prove to be a very fruitful time for the real-world application of machine learning. Even stubborn businesses are being pushed to adapt, after all, and they’re starting to think about what else they could do — with machine learning, there’s so much more to be achieved.

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