The machines are getting smarter. If you haven’t been living under a rock for the last few years, you’ve probably heard the words “Machine Learning” thrown around by at least one of the major tech companies of the world. What is Machine Learning, and does this matter to me as a business owner on Main Street, USA? We’re here to tell you—you should watch an awful lot. Notably, anyone who is, or is going to be, operating in the digital space – machines will be making more and more of the day-to-day choices that drive our economies, our markets, and our businesses.
So what is machine learning, exactly? Computers have been giving us the answers to questions for years; what makes this different from entering 2+2 on a calculator? You’d be correct in thinking that computers have been answering simple questions, making calculations, and even generating projection models for decades. Machine Learning changes the game due to two significant factors – automation and output. In simplest terms, machine learning is when an algorithm (computer) takes in a large amount of data, analyzes it, and can make projections and predictions based on that data. Computers can now generate exponentially more complex output by increasing the input data utilizing algorithmic analysis capabilities.
For marketers and business owners – this changes the game. For example, when we look at Google Analytics, we see vast amounts of data attached to each customer interaction with advertising and web browsing. Machine learning allows Google to make such advanced recommendations on how to improve the performance of your ads – we’re one step away from Google being able to manage all of the advertising procedures on its own. Let’s explore which markets and industries will see significant paradigm shifts as machine learning enters new markets and alters established ones.
Healthcare and the medical profession were (and are being) rocked by the COVID-19 pandemic. Chief among the issues faced with the medical field was the perpetual balance of COVID patients and the rest of the activities in our healthcare centers. Triage, primary diagnosis, prognosis, and treatment are fundamental components of a healthcare facilities’ offerings—these functions typically require input from at least one human, but no longer. Researchers across the medical field are equipping AI with machine learning algorithms to make diagnoses and treatment plans for many common ailments. Very soon, when you or a loved one has a cold or an ear infection, chances are your diagnosis and treatment plan will be generated by an AI.
At the forefront of machine learning are the companies responsible for most of the world’s biggest (and most profitable) sets of data – search engines and social media. You’ve probably heard of cookies – little bits of data that companies use to track you on the web. Sometime in 2022 (now 2023), Google will ‘kill’ the cookie—they will begin using a yet un-named modification to their algorithms to track us differently. This algorithm change will likely be AI-focused, with considerable support from machine learning. They hope that fewer individual details will be attached to users, and other, broader, and more semantic data can personalize advertising. What does that mean for consumers? Hopefully—more personalized ads and far more privacy as you explore the web!
The OG users of machine learning, stock exchanges, and brokers have been using automated learning algorithms for just about as long as they’ve been using computers. One of the most significant barriers to entry into the financial market is the accessibility & analysis of an unimaginable amount of dynamic data that changes by the second. Machine learning-backed AI can keep up with the multiple-actions-per-second speed of the modern financial system, analyze that data, and provide accurate projections on the performance of the market. This type of mass-statistical analysis used to be possible only for the largest of firms, with massive resources (and thereby computing power) at their disposal. Now retail investors and complete amateurs can perform Wall Street-level analysis.
The Discovery Phase is a considerable part of the legal process, particularly in civil cases. In the past, firms would hire teams of junior associates, aides, and paralegals to comb through documentation looking for evidence (or counterevidence). Today this process can be massively simplified, with thousands of hard-copy files being digitized, analyzed, and reported. Lawyers working a case have increased their efficiencies by order of magnitude, and now a process that took days, with hundreds (and sometimes thousands) of labor hours, can be performed by just a few people in minutes. IBM, Microsoft have developed off-the-shelf solutions for the legal industry, and Google – with most of the software being home-grown or customized from large tech firms. Machine learning-backed technologies will continue to improve the efficiencies in the legal industry and hopefully within the judicial system.
The COVID-19 pandemic showed the world just how fragile the supply chain is and how imperative every last percent-of-a-percent of efficiencies can have a massive impact down the line on the chain. Public entities, like the DOT, have been using machine learning to analyze traffic flow, patterns, and potential improvements. However, the biggest story in transport has to be the dawning of the self-driving vehicle. For personal transportation, this is huge—the ability to travel anywhere by car without any human input sounds like something out of science fiction—but companies like Tesla and Mercedes-Benz have developed working self-driving vehicles. The even more significant impact will come from the use of self-driving semi-trucks. Over-land shipping is nearly a trillion-dollar per year industry in the United States, and truck drivers have been consistently understaffed for decades. As self-driving semis become the norm, we will begin to see a massive paradigm shift in those resources of the next decade.
Automation, AI, and machine learning are already changing how Americans get their food. Machine learning technology has been a large part of industrial agriculture, from soil composition, disease diagnosis, and weed management. The balance of maintaining bio-diversity while maintaining production is the perfect job for AI-backed with machine learning. A 2019 program developed through a partnership with ICRISAT and Microsoft created an “AI Sowing Application,” which, based on their results, increased yields by 10-30%.
Tech is getting better, faster, more advanced, and more intelligent as we continue to develop better and more integrated, more innovative technologies. From plowing fields to Wall Street, the odds of a small business owner getting swallowed up because of their inability to adapt increases. This is where Prophase fits in—this is where we live—let us be that Wall Street-Ready technology and talent but with Main Street’s service. Contact us today, and let us show you how we can bring your business into the 21st century and beyond!