The impact of self-learning software now and in the foreseeable future
We’ve spent so long wringing our hands and worrying about artificial and virtualintelligence that we forgot to roll out the welcome mat when they finally arrived.
Now, when major tech companies give their annual keynotes, they can’t help but pepper the narrative with phrases like “machine learning.” What does it all mean, though? Should we crank up the worry now that it looks like every tent-pole feature of self-learning software could also be a critical flaw?
The future is here — and it’s equal parts exciting and terrifying. Now that our world is populated with computer programs that can teach themselves new tricks, how will things change? What’s still worth worrying about?
With 2018 upon us, the worlds of both business and personal software are ramping up to make the next few years something of an artificial intelligence arms race. On the consumer side of things, machine learning and AI make our lives easier in small ways. Case in point: many of us now have a smart speaker like an Amazon Echo or Google Home sitting on our countertops.
While these kinds of AI applications are helpful and entertaining, their self-learning capabilities are limited, to say the least.
In the world of business, there’s more immediate potential for self-learning software.
“We are drowning in information,” says Vita Vasylyeva of Artsyl Technologies. “The biggest bottlenecks in any business process involve the handling of documents and manual input of data from those documents. At the heart of those bottlenecks is the transformation of unstructured content into structured data.”
Nevertheless, both the business and consumer worlds have distinct needs and roles to play, and I fully expect machine learning in both realms to grow more sophisticated and capable.
Briefly, here are three very different applications for self-learning software:
1. Smartphones: Machine learning is turning smartphones into veritable supercomputers. From learning what your face looks like by poring through your photos to delivering more timely and relevant app and location suggestions, our devices are learning who we are and what we want.
More critically, machine learning is also training modern smartphones to become better at identifying and quarantining known threat vectors such as malware and viruses. It’s not all about fun and games.
2. Medicine: Diagnostic medicine is a difficult branch of science. Some types of cancer scans currently require as many as four specialists to study and come to a consensus on treatment.
With machine learning, physicians can practice this type of diagnostic medicine much faster, more accurately, and with fewer people-hours required.
3. Marketing and business management: The marketing applications of self-learning software perfectly marry the promises and the privacy worries of machine learning.
Some industry experts predict that within 10 years, even the humblest small businesses will engage in machine learning to improve their reach.
Another critical application is the promise of easier bookkeeping and organization. Newer document- and data-capture software suites take cues from the user to automatically identify and categorize types of documents and transactions, and in the process, significantly cut down on the labor and expense of staying organized and profitable.
Naturally, this is an abridged version of the emerging opportunities machine learning represents. Nearly every industry will likely come to rely on self-learning software in the future to make modern life more efficient.
