I Left Social Media for a Week. This is What Happened. Tired, irritable, moody; I shlepped out of bed 2 hours later than I had originally planned and looked at my face in the mirror.
There is a deliberate flow to this post, above. If you are going to jump around, it is ok, but please be sure to read the section below first.
Ready to have your mind stretched? They are crucial in being able to internalize the depth and breadth of the revolution, and why we strengths AI brings Apple branding essay a radical shift beyond what humans are capable of. Most robots are very robotic because they follow a sense-plan-act paradigm.
This limits the types of things they are able to do, and as you might have seen their movements are deliberate. The team at Google adopted the strategy of having a robot learn own its own rather than programming it with pre-configured models.
The one-handed robots in this case had to learn to pick up objects. Initially the grasping mechanism was completely random — try to imagine a baby who barely knows they even have a hand at the end of their shoulder. These learnings go back to the robot and improve its chances of success.
Among other things, being able to isolate one object a stapler to successfully pick-up a Lego piece. I felt a genuine tingling sensation just imagining a thing not knowing something and it being able to simply learn. Just think about it. It started from scratch — like a baby — and then just figured it out.
It truly is mind-blowing. There were two lessons here. The first related to pure deep learning and its amazingness, I was familiar with this one.
The second was something new for me. This experiment involved 14 one-handed robot arms. While not a massive number, the 14 were collectively contributing data from the start — with their many failures. The end of day learnings by the convolutional neural network were using all And, the next day, all 14 started again with this new level of collective wisdom.
For a clear way for me to capture this lesson, I call this Collective Learning. It is very powerful. Think of 14 humans learning a new task. Or, laying down track for a railroad.
Or, programming a new and even more frustrating in-flight entertainment menu for Air Canada who have the worst one known to mankind. Every human will do it individually as well as they can — there will be the normal bell curve of competency. It is entirely possible, if there are incentives to do so, that the humans who are better in the group will try to teach others.
In neither case will there be anything close to Collective Learning when it comes to humans. Humans also do not posses this continuous closed loop: Check outcome success or failure. Actively learn from either, improve self. Do something better the next time.
An incredible advantage that I had simply not thought through deeply enough. From the ability to speed up discovery of new medicines to the unbelievable speed with which Machine Learning techniques are becoming particularly adept at diagnosis think blood reports, X-rays, cancers etc.
An example I love. If caught early, the disease is completely treatable. Medical specialists capable of detecting DR are rare in many parts of the world where diabetes is prevalent.
The results delivered by the algorithm black curve were slightly better than expert ophthalmologists colored dots … Specifically the algorithm has a F-score of 0. As richer datasets become available for the neural network to learn from, as 3D imaging technology like Optical Coherence Tomography becomes available all over the world to provide more detailed view of the retina, just imagine how transformative the impact will be.
An algorithm created by Sebastian Thrun, Andre Esteva and Brett Kuprel can detect keratinocyte carcinoma a type of skin cancer by looking at images of the skin acne, a rash, mole etc.Last Thursday morning, I knew with certainty it was coming. Tired, irritable, moody; I shlepped out of bed 2 hours later than I had originally planned and looked at my face in the mirror.
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