The Artificial Intelligence Revolution is Here
Artificial intelligence (AI) is a popular buzzword you’ve probably heard get tossed around a lot, along with machine learning (ML) and deep learning (DL). Far from being a figment of a Sci-Fi geek’s imagination, these technologies are here and causing waves in nearly every industry. By 2026, Bloomberg projects that the global AI market size will grow from $58.3 billion to $309.6 billion at a Compound Annual Growth Rate (CAGR) of 39.7%.
The far-reaching impact of AI and machine learning-driven transformation is evident across different domains and industries. Let’s check out a few.
Smart Transportation
Automated, driverless subways and trains are already in operation worldwide. With ML and AI helping advance driverless technology, it won’t be long before human drivers are taken out of the equation across most, if not all mass transportation ecosystems.
Google is a particularly notable example of this trend. Its fleet of driverless vehicles is autonomously cruising roads with cameras on their hoods, mapping terrain for the Google Maps application. Machine learning algorithms are allowing driverless vehicles to improve the longer they stay on the roads and deal with the unpredictability of human error, weather, and parking conditions. Driverless cars are still in the testing phase before they become commercially viable.
Tesla cars are also famous for their autopilot feature, which utilizes a system of cameras and sensors that enable it to “see” 360 degrees at a range of up to 250 meters. Moreover, these cars can detect and appropriately respond to external circumstances (including detrimental weather conditions), maintain speed or slow down as necessary, change lanes, and even switch highway lanes and park themselves.
Automated Finance
Another area where AI and ML are bearing disruptive influence is the financial services industry. Many banks heavily rely on loan risk assessment algorithms, for example, and employ machine learning platforms for automated fraud detection.
AI technology offers similar benefits to private portfolio managers. Automated advisors are capable of rapidly processing and matching millions of data resources and countless investment opportunities with personal profiles, risk tolerances, long-term financial objectives, and more. Moreover, ML can fine-tune investments in real-time in response to market fluctuations and other unforeseen circumstances, with powerful probability and prediction algorithms to determine the best stocks to invest in, the right time to sell, and more.
Algorithmic Retail, Marketing, and E-Commerce
The disruptive qualities of machine learning in online marketing can best be witnessed in Google’s own search engine. Its ranking algorithm employs AI to process queries with ever-increasing efficiency, and the engine actually grows smarter and returns more relevant results with each click of the search button.
A similarly impactful implementation of ML can be seen in online recommendation engines, such as those employed by retail and entertainment streaming giants like Amazon and Netflix.
AI, ML, and DL technologies help these market leaders identify customer purchase/consumption patterns, strengthen their marketing capabilities, and personalize their recommendations — all to precisely predict your next purchase or TV show binge preference.
Chatbots are another example of how ML algorithms enable businesses to streamline their communications with clients. Chatbots deliver 24/7 customer assistance on an ever-increasing number of online stores, and over time, ML allows the chatbot to learn the types of questions customers ask, how to interpret various ways of asking a question, and how to answer in the best way possible.
Microsoft’s “full-duplex” conversation upgrade in 2018 was the first to allow you to speak to a chatbot like how you might speak to a friend over the phone. The Microsoft AI can predict what customers are likely to say next, knows when to interrupt with important info without coming off as rude, and is even able to say something more when both sides have suddenly gone quiet — all to contribute to natural conversation flow.
Telehealth and AI-Powered Wearables
Artificial Intelligence experienced a major boom in the healthcare sector when the Covid-19 pandemic struck. For example, when the Center for Connected Medicine (CCM) conducted a survey of 117 healthcare executives before the pandemic, only about a quarter of healthcare executives (26%) said the shift to telehealth and virtual care was a top innovation priority at their organizations. In a follow-up survey during the summer of 2020, that figure jumped to 49% of executives who say virtual care is a top innovation priority.
With widespread application in telemedicine, ranging from preliminary diagnosis to formulating a treatment plan, AI became essential to safely treating patients during the pandemic. Moreover, AI platforms can utilize a range of technologies such as data mining, image processing, and predictive modeling to discover the best route to a long and healthy life.
At this rate, one could argue the medical field is benefitting from ML and DL advancements as a whole. In radiology image analysis, ML and DL algorithms are able to find patterns in scans of fractures and other injuries, sometimes even detecting injuries before the human eye can. With the pandemic straining hospital resources and half of physicians reporting burnout in the aftermath of the pandemic, AI-powered remote monitoring technology like Somatix provided a way for physicians to provide high-value care from afar in order to prevent Covid risk. The goal is not for AI to replace clinicians but to lighten their workload, help them be more efficient, and give patients some autonomy in their care.
Going Where No Human Has Gone Before
The common thread in the implementation of machine learning in all these domains is the ushering-in of groundbreaking capabilities far exceeding those of humans. After all, the technology makes it possible to sift through and uncover patterns in staggering amounts of data and variables that no human could realistically handle.
From autonomous vehicles to retail chatbots, AI has developed roots and grown offshoots throughout our society. The impact of this technology is most promising in the healthcare domain, in which emerging solutions are tapping into wearable-generated Big Data for rapidly growing machine learning-assisted gains. Still in its infancy, ML is just starting to be applied to a wide range of industries to improve products and services. As AI, ML, and DL continue to find solutions to problems no human could previously solve, the artificial intelligence revolution is not going anywhere anytime soon.
