Re:Human-Level Intelligence or Animal-Like Abilities? (2018)
by YeGoblynQueenne on Monday June 08, 2020 @04:58PM
(#23460739)
The title of the article does an awful job of summarising it. The main subject of the article is not about the level of intelligence of current or future AI, as the title suggsts. Instead, the article is a reflection on the progress in the field in the last few years and a discussion of the degree to which progress in deep learning has benefited, or harmed, AI research in general.
This is best summarised by the "Key Insights" box on top of the article:
> the recent successes of deep learning have revealed something very interesting about the structure of our world, yet this seems to be the least pursued and talked about topic today
> In AI, the key question today is not whether we should use model-based or function-based approaches but how to integrate and fuse them so we can realise their objective benefits
> We need a new generation of AI researchers who are well versed in an appreciate classical AI, machine learning, and computer science more broadly while also being informed about AI history.
The first "key point" refers to classes of functions that can be seen as "cognitive functions". For example, mapping a set of inputs to outputs can reasonably be considered as approximating some aspect of cognition when the inputs are regions of images and the subjects their labels, so that the function performs object recognition, a task that AI research has long considered an aspect of cognition. Understanding how such functions work has the potential to contribute to our understanding of human cognition, that has long been a major goal of AI research. Yet, in recent years, interest has shifted from understanding such results to applying them at the level of phone apps, etc.
The final key point is a call to arms. We can't make progress as a field by throwing out everything we've done before, everytime we achieve some success in a narrow range of tasks. The author witnessed this happenning in the 1980's with the rise and fall of expert systems -and the AI winter that followed. In modern times, the success of deep learning has all but eclipsed the deep knowledge that researchers in the field once possessed about symbolic logic and important avenues of research are impossible to follow because the younger generation of researchers simply don't have the necessary background - and are "bullied by the success" of neural networks into directing their careers towards neural network research, whatever their true interests.
On a personal level, not summarising the article anymore, the latter is the most disturbing development. Neural networks can perform "perceptual" tasks, but are wholly incapable of reasoning. Symbolic AI had reasoning down pat- and not in approximate fashion (as a recent trend in deep learning research attempts to perform it). Yet, we seem to have regressed and lost one ability to perform one set of cognitive tasks in the process of figuring out how to perform another.
In the past, AI researchers were well-rounded polymaths, versed in CS but also (continuous) mathematics, physics, psychology, linguistics... Nowadays, researchers seem to be optimising for a narrow band of knowledge and ignoring everything else. This cannot end well.
This is best summarised by the "Key Insights" box on top of the article:
> the recent successes of deep learning have revealed something very interesting about the structure of our world, yet this seems to be the least pursued and talked about topic today
> In AI, the key question today is not whether we should use model-based or function-based approaches but how to integrate and fuse them so we can realise their objective benefits
> We need a new generation of AI researchers who are well versed in an appreciate classical AI, machine learning, and computer science more broadly while also being informed about AI history.
The first "key point" refers to classes of functions that can be seen as "cognitive functions". For example, mapping a set of inputs to outputs can reasonably be considered as approximating some aspect of cognition when the inputs are regions of images and the subjects their labels, so that the function performs object recognition, a task that AI research has long considered an aspect of cognition. Understanding how such functions work has the potential to contribute to our understanding of human cognition, that has long been a major goal of AI research. Yet, in recent years, interest has shifted from understanding such results to applying them at the level of phone apps, etc.
The final key point is a call to arms. We can't make progress as a field by throwing out everything we've done before, everytime we achieve some success in a narrow range of tasks. The author witnessed this happenning in the 1980's with the rise and fall of expert systems -and the AI winter that followed. In modern times, the success of deep learning has all but eclipsed the deep knowledge that researchers in the field once possessed about symbolic logic and important avenues of research are impossible to follow because the younger generation of researchers simply don't have the necessary background - and are "bullied by the success" of neural networks into directing their careers towards neural network research, whatever their true interests.
On a personal level, not summarising the article anymore, the latter is the most disturbing development. Neural networks can perform "perceptual" tasks, but are wholly incapable of reasoning. Symbolic AI had reasoning down pat- and not in approximate fashion (as a recent trend in deep learning research attempts to perform it). Yet, we seem to have regressed and lost one ability to perform one set of cognitive tasks in the process of figuring out how to perform another.
In the past, AI researchers were well-rounded polymaths, versed in CS but also (continuous) mathematics, physics, psychology, linguistics... Nowadays, researchers seem to be optimising for a narrow band of knowledge and ignoring everything else. This cannot end well.