> Marcus believes that deep learning is not “a universal solvent, but one tool among many.” And without new approaches, Marcus worries that AI is rushing toward a wall, beyond which lie all the problems that pattern recognition cannot solve.
The thing that fascinates me is that we've only just scratched the surface of what today's ML tech can solve. Let's worry about that wall when we get there...
In the meantime let's not lose sight of today's potential in some misguided idealistic pursuit of perfectionism or "general artificial intelligence".
There are countless problems which current deep learning research combined with some well-thought out UI/UX could solve today in a myriad of industries.
The 1990's software 'revolution' in industry/business was largely just formalization/automation of paper-based processes into spreadsheets and simple databases, which then evolved into glorified CRUD/CMS software interfaces on desktops, then web/SaaS, and then another massively boost with smartphones.
If such a simple translation of human processes into machines can achieve trillions of dollars in value then there is no doubt machine learning can do the same for hundreds of thousands of other simple problem-sets which we haven't even considered. Plus the desktop/smartphone/internet/etc infrastructure is already in place for it to be plugged into.
This can only be negatively judged in the context of all significant steps forward in technology being oversold and misunderstood. But in practical real-world utility we're very far from fully utilizing what has been researched and accomplished today in a small set of markets. And the proliferation of this tech should be encouraged, promoted, and accurately communicated to tech/business talent who can potentially use it, rather than downplayed because it fails to live up to some media hyperbole or SciFi fantasies of where we should be in 2018.
The article mentions that taking AI/ML/data science courses has just become the "hottest new field" for young smart kids to join. Well that means we're just on the cusp of taking advantage of that technological evolution and it's FAR too early to look at what's been accomplished today and be pessimistic about deep learnings potential.
The thing that fascinates me is that we've only just scratched the surface of what today's ML tech can solve. Let's worry about that wall when we get there...
In the meantime let's not lose sight of today's potential in some misguided idealistic pursuit of perfectionism or "general artificial intelligence".
There are countless problems which current deep learning research combined with some well-thought out UI/UX could solve today in a myriad of industries.
The 1990's software 'revolution' in industry/business was largely just formalization/automation of paper-based processes into spreadsheets and simple databases, which then evolved into glorified CRUD/CMS software interfaces on desktops, then web/SaaS, and then another massively boost with smartphones.
If such a simple translation of human processes into machines can achieve trillions of dollars in value then there is no doubt machine learning can do the same for hundreds of thousands of other simple problem-sets which we haven't even considered. Plus the desktop/smartphone/internet/etc infrastructure is already in place for it to be plugged into.
This can only be negatively judged in the context of all significant steps forward in technology being oversold and misunderstood. But in practical real-world utility we're very far from fully utilizing what has been researched and accomplished today in a small set of markets. And the proliferation of this tech should be encouraged, promoted, and accurately communicated to tech/business talent who can potentially use it, rather than downplayed because it fails to live up to some media hyperbole or SciFi fantasies of where we should be in 2018.
The article mentions that taking AI/ML/data science courses has just become the "hottest new field" for young smart kids to join. Well that means we're just on the cusp of taking advantage of that technological evolution and it's FAR too early to look at what's been accomplished today and be pessimistic about deep learnings potential.