Is the term Machine Intelligence going to take over the terms Artificial Intelligence and Machine Learning? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Answer by Max Simkoff, Founder and CEO at States Title, on Quora:
Machine intelligence, artificial intelligence and machine learning are not different terms for the same thing. They’re related but distinct from each other.
It’s important for us all to recognize the nuanced differences among them, so we can do a much better job of figuring out which one, or which combination of them, is best for a specific undertaking. If, for example, you think you’re using machine intelligence but are really just using automation, you’re missing out on the potential MI has to offer.
My States Title colleague Andy Mahdavi and I put together a list designed to make the distinctions as clear as possible.
As we see it, automation is the result of a machine being told by a human precisely what to do, and successfully doing it. Much like a player piano performing a piece of music, the machine produces a desired effect. But it does not gather any new information from doing so.
Machine learning goes further. When a machine is programmed to learn, it looks for patterns and trends within the data it’s given. It uses those findings to draw conclusions. It then develops new programs that take those conclusions into account, allowing the technology to become more efficient and more accurate in tackling one problem at a time.
That brings us to the term machine intelligence – a more complex concept that, as of this writing, does not even have its own Wikipedia page yet.
What is machine intelligence?
Machine intelligence is what’s created when machines are programmed with some (but not all) aspects of human intelligence, including learning, problem solving and prioritization. With these (limited) abilities, a machine can tackle a complex set of problems.
Machine intelligence by necessity involves deductive logic. For example, systems exhibiting true machine intelligence come to understand when they’ve made mistakes, watch out for similar data that could lead to a similar mistake the next time, and avoid doing so.
This means that machine intelligence will have a suite of different machine learning methods available to it, as well as a battery of automation techniques, and will smartly prioritize and deploy a sequence of them in the right order, with the right timing to achieve specific business goals. You can think of machine intelligence as a higher evolution of machine learning with prioritization and goals added in – a stepping stone on the path to true AI.
Artificial intelligence, meanwhile, will allow computers to take in all of the world’s available information and combine various pieces of that information to come up with new solutions. It will do so using all aspects of human intelligence.
AI will lead to surprising answers that people haven’t thought of. It’s even likely to create new business models.
The Pew Research Center reported that many technologists believe AI “might match or even exceed human intelligence and capabilities on tasks such as complex decision-making, reasoning and learning, sophisticated analytics and pattern recognition, visual acuity, speech recognition and language translation.”
But based on our work, we believe that is likely a vast underestimation of the most distinct realization of AI. Over time, true artificial intelligence will become most differentiated from automation and machine learning by going a step further. It will surpass the human mind in more ways than we can imagine.
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