Don’t kid yourselves about artificial intelligence and machine learning being the stuff of Sci-Fi. While far from perfect, they’re here today. Or – if the Merrill Lynch prediction of the global market for AI and robots reaching just under 153 $B by 2020 is to be believed – these technologies are a mere blink of an eye from having a massive effect on numerous industries.
In fact, the reality of AI- and machine learning-driven transformation is already evident in a number of domains. Let’s check out a few.
Self-driving subways and trains are currently in operation worldwide. With machine learning 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.
Indicators of this trend being well underway include Waymo’s intricate array of software and sensors (originally born as Google’s self-driving car initiative), and its machine learning-assisted driving “experience” help keep cars constantly aware of their surroundings, and roads safer. Tesla cars are also famous for shipping with an autopilot facility comprising a system of cameras and sensors. These enable them to “see” in 360 degrees at a range of up to 250 meters, detect and appropriately respond to external circumstances (including detrimental weather conditions), maintain speed or slow down as necessary, change lanes, and even switch highways and park themselves.
Another area where AI and machine learning are bearing disruptive influence is the financial services industry. Many banks rely heavily on loan risk assessment algorithms, for example, and employ machine learning platforms for automated fraud detection.
The technology offers similar benefits to private portfolio managers, with automated advisors capable of rapidly processing and matching millions of data resources and countless investment opportunities with particular personal profiles, risk tolerances, long-term financial objectives and more. These can additionally fine tune investments in real-time, in response to market fluctuations and other unforeseen circumstances, with powerful probability and prediction algorithms employed to determine the best stocks to invest in, the right time to sell, and more.
The disruptive qualities of machine learning in online marketing can probably best be witnessed in Google’s own search engine, the ranking algorithm of which employs AI to process queries with ever-increasing efficiency. The engine actually grows smarter and returns more relevant results with each click of the its search button.
With its sights aimed at “cracking” AI, Google acquired DeepMind (an AI company aiming to “solve intelligence”) in 2014, and already presents impressively accurate conversational voice controls on desktop search and Android in dozens of languages.
A similarly impactful implementation of machine learning can be seen in online recommendation engines, such as those employed by retail and entertainment streaming giants Amazon and Netflix. Learning algorithms help these market leaders examine past shopping or viewing habits, correlate and compare them with those of many millions of other customers and subscribers – all to precisely predict your next purchase or TV show binge preference.
Machine learning-enabled chatbots deliver 24/7 customer assistance on an ever-increasing number of online stores, and Microsoft has just introduced “full duplex” conversation upgrades that enable its natural language Xiaolce chatbot to start speaking even as listening to what’s being said. 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.
Add to this e-mail marketing solutions capable of analyzing customer behavior patterns to determine just the right timing for communication and engagement, and it quickly becomes obvious that machine learning is already making a real difference in marketing technology today.
Going where no human has gone before
The common thread in the implementation of machine learning in all these domains is in its 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 points and variables no human could realistically handle.
Where the impact of this technology is perhaps best felt is the healthcare domain, in which emerging solutions tap into wearable-generated Big Data for rapidly growing machine learning-assisted gains. More on this in our next blog posts.