Each prof will, of course, have a niche app that they do well (in fact sometimes there is too much pressure to have a “trick” you can do to justify funding), but the key question is: are they more like a software engineer masquerading as a scientist than a real scientist? Do they have a paradigm and theory that enables thousands of engineers to move into completely new design-spaces?
I think that the closest we have seen is the ML revolution, but when you look at it, it is not new science, it is just statistics correctly applied.
I have seen some instances of people trying to push forward the frontier, such as the work of Hutter, but it is very rare.
Could you clarify exactly what Hutter has done that has advanced the frontier? I used to be very nearly a “Hutter enthusiast”, but I eventually concluded that his entire work is:
“Here’s a few general algorithms that are really good, but take way too long to be of any use whatsoever.”
Am I missing something? Is there something of his I should read that will open my eyes to the ease of mechanizing intelligence?
I think that the way of looking at the problem that he introduced is the key, i.e. thinking of the agent and environment as programs. The algorithms (AIXI, etc) are just intuition pumps.
This seems like a fairly reasonable description of the work’s impact:
“Another theme that I picked up was how central Hutter’s AIXI and my work on the universal intelligence measure has become: Marcus and I were being cited in presentations so often that by the last day many of the speakers were simply using our first names. As usual there were plenty of people who disagree with our approach, however it was clear that our work has become a major landmark in the area.”
But why does it get those numerous citations? What real-world, non-academic consequences have resulted from this massive usage of Hutter’s intelligence definition, which would distinguish it from a mere mass frenzy?
No time for a long explanation from me—but “universal intelligence” seems important partly since it shows how simple an intelligent agent can be—if you abstract away most of its complexity into a data-compression system. It is just a neat way to break down the problem.
Each prof will, of course, have a niche app that they do well (in fact sometimes there is too much pressure to have a “trick” you can do to justify funding), but the key question is: are they more like a software engineer masquerading as a scientist than a real scientist? Do they have a paradigm and theory that enables thousands of engineers to move into completely new design-spaces?
I think that the closest we have seen is the ML revolution, but when you look at it, it is not new science, it is just statistics correctly applied.
I have seen some instances of people trying to push forward the frontier, such as the work of Hutter, but it is very rare.
Statistics vs machine learning: FIGHT!
Could you clarify exactly what Hutter has done that has advanced the frontier? I used to be very nearly a “Hutter enthusiast”, but I eventually concluded that his entire work is:
“Here’s a few general algorithms that are really good, but take way too long to be of any use whatsoever.”
Am I missing something? Is there something of his I should read that will open my eyes to the ease of mechanizing intelligence?
I think that the way of looking at the problem that he introduced is the key, i.e. thinking of the agent and environment as programs. The algorithms (AIXI, etc) are just intuition pumps.
Surely everyone has been doing that from the beginning.
This seems like a fairly reasonable description of the work’s impact:
“Another theme that I picked up was how central Hutter’s AIXI and my work on the universal intelligence measure has become: Marcus and I were being cited in presentations so often that by the last day many of the speakers were simply using our first names. As usual there were plenty of people who disagree with our approach, however it was clear that our work has become a major landmark in the area.”
http://www.vetta.org/2010/03/agi-10-and-fhi/
But why does it get those numerous citations? What real-world, non-academic consequences have resulted from this massive usage of Hutter’s intelligence definition, which would distinguish it from a mere mass frenzy?
No time for a long explanation from me—but “universal intelligence” seems important partly since it shows how simple an intelligent agent can be—if you abstract away most of its complexity into a data-compression system. It is just a neat way to break down the problem.