Of the estimated 2.4m apps available on the App Store, we believe less than 1% leverage ML today – but not for long. We believe Core ML will be a driving force in bringing machine learning to the masses in the form of more useful and insightful apps that run faster and respect user privacy.
It’s going to be interesting to see what we will be able to do with our devices in the next three years as machine learning and artificial intelligence make their way into our lives in a bigger way.
I’ve yet to experience machine learning, as such, but I have benefitted long-term from AppleScripting coupled with database and publishing software. We delegate repetitive tasks to the scripts, which results in significant time saving with virtually perfect accuracy.
It will be interesting to experience this so-called “learning” and to see Apple’s various implementations.
Same, but I think this will open ML up to non-obvious uses and it will be fascinating to see those. For example, I was reading this story about a company in New York growing veggies in a warehouse, hydroponically. The main thrust of the story is that LEDs make this cost-effective, etc but this passage caught my eye:
(emphasis mine).
A ML tech becomes easier and more widespread, it will be fascinating to see other examples of its use like this.
You make some excellent points! The hydroponic farm example is a great application of ML!
The emphasis of ML should be on tasks which humans can’t do well or affordably, in order to increase/enhance production.
Tesla is employing ML in its network of cars on the road, all of which collect data anonymously in order to “learn” all of the roads people drive upon.
what I like about the farm use above is that it takes ML out of just learning/data analysis and puts it in the feedback loop of an ongoing process.
Agreed. Tesla’s feedback loop will enable autonomous driving.