While the phone you carry may be significantly less bulky than some of the original mobile devices, it’s one heck of a lot more powerful. It has a touch screen, lacks a hinge and is filled with apps. And those apps are getting smarter every day with the help of machine learning and AI.
As we use our phones, we provide key information to the apps and software installed on them. Developers in turn, utilize that info to create smarter, more personalized experiences.
AI and machine learning operate a little differently than most of the tech you’re probably familiar with. So, it’s helpful to understand a few key tech concepts to be able to fully grasp what’s happening. (But don’t worry—we’re talking big picture, not the ones and zeroes of it all.)
Moore is Better
Computer technology has relied upon silicon chips since the integrated circuit board was adopted mid-twentieth century. Prior to that, computers ran on huge vacuum tube powered devices that took up entire rooms.
When chip manufacturers—the original unicorns—and powerhouse startups got more heavily involved, Silicon Valley was born. But we’ve come a long way from Fairchild and Shockley, the semiconductor companies that helped give rise to the age of modern computing at Apple and Intel.
Most advances in computer technology have involved manual coding and programming and were governed by the framework of Moore’s Law—which stated that computing and processing speeds would double every two years. As we inch ever closer to reaching the culmination of a computing world defined by Moore’s Law, programmers and scientists are increasingly looking at technology that allows computers to teach themselves by learning to evaluate and parse data.
What is Machine Learning?
Machine learning needs data it can learn from to be able to work. This often takes the form of text, words, photographs, musical preferences, online purchases—even your social media updates. The byproduct for consumers and professionals is smarter apps powered by applications that can learn, synthesize and leverage algorithms that are helping solve large, unscalable problems in virtually every industry imaginable.
An excellent example of this is how Facebook has learned from photographs. With hundreds of thousands of photos uploaded as status updates, Facebook applied machine learning techniques to understand the context of photos and who is in each picture. Facebook has gotten very good at learning the difference between a cat and your significant other from the massive stream of photographs.
Harnessing the Potential Power of AI / ML
Depending on who you ask or what you read, there’s a pretty strong consensus that machine learning and AI will have a strong impact on the future of how we drive, eat, shop and communicate.
A key debate about machine learning and artificial intelligence is how it can be leveraged for good—and potentially for less-than-noble purposes. You don’t have to look far to see the ongoing conversations among journalists and even tech moguls about the seismic changes machine learning will bring.
Andrew Ng, former head of AI for Baidu, has said that machine learning and AI “Will also now change nearly every major industry—healthcare, transportation, entertainment, manufacturing.”
Elon Musk, founder of Tesla and SpaceX, warns that AI is one of humanity’s biggest concerns and “poses existential risks.” Eric Schmidt also donated $2 million to Princeton to fund a program to better understand the underpinnings and evolution of machine learning.
There’s no doubt that a lot of people are starting to see how machine learning and AI might change their industries. Trucking, transportation, manufacturing and logistics are some of the first places changes could take effect.
Machine learning also has the potential to profoundly and positively impact society by removing redundancies in the workplace, making us safer on the road and changing the way we manage healthcare. But before you start worrying about machines making all people “redundant,” remember, there are a few key skills that will require human thinking and learning:
- Empathy: While apps can get smarter, they rely on data and algorithms. We’re still many years away from apps being smart enough to feel and reason. People who can relate to others’ emotions, experiences and thoughts are a valuable part of any team.
- Reasoning & Problem Solving: People are hired for their ability to solve problems. Until systems using Machine Learning and AI can tell us how they arrived at their conclusions, this is an area that will continue to offer opportunity for people.
- Creativity & Storytelling: Everyone loves a great story. And in the age of marketing initiatives driven by storytelling, that won’t change soon. From the hero’s journey to the climactic finale, we’ve been spinning stories for thousands of years. A good story provides a visceral reaction and those looking to share them will find an audience.
Machine learning has created an entire generation of smart apps and products that are proving to be important parts of our daily lives.
The watershed year for cars and machine learning was 2016. Fueled by a spree of bets from large auto manufacturers and a handful of acquisitions, last year was an indication that executives thought machine learning was soon going to propel driverless technologies into the mainstream.
When many complained that #FakeNews was drowning out verified news sources, Facebook leveraged machine learning to combat the problem and provide accurate and truthful context to their trending news feeds.
Researchers are at critical junctures in understanding, diagnosing and treating disease. IBM’s Watson has helped physicians connect patients to clinical trials for cancer treatment.
We’re just starting to see some of these changes roll out, but there’s no doubt there will be some major enhancements in nearly every part of our daily lives. Whether it’s making your email smarter, streamlining tasks or solving the riddle of incurable diseases, AI and machine learning will probably have a huge impact in your life. And sooner than you think.
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