Why Machine Learning won’t cut it

By Peter Voss | 25 October 2016
Medium

Current machine learning approaches will not get us to real AI. The kind that can truly understand you, and learn new knowledge and skills by itself. Like humans do.

Current mainstream techniques, while very powerful in narrow domains, typically have some or all of the following constraints:

Each narrow application needs to be specially trained
Require large amounts of hand-crafted, structured training data
Learning must generally be supervised: Training data must be tagged
Require lengthy offline/ batch training
Do not learn incrementally or interactively, in real time
Poor transfer learning ability, re-usability of modules, and integration
Systems are opaque, making them very hard to debug
Performance cannot be audited or guaranteed at the ‘long tail’
They encode correlation, not causation or ontological relationships
Do not encode entities, or spatial relationships between entities
Only handle very narrow aspects of natural language
Not well suited for high-level, symbolic reasoning or planning

In summary, here’s a machine learning checklist of key feature of intelligence taken from the above linked article:

What then is a good fit?

I’ve expanded on this in another post, but for now I’ll provide a clue by pointing to a short article about Pat Langley’s excellent essay, in which he highlights differences between the current mainstream approaches to AI, and what he calls the ‘Cognitive Systems Paradigm’.

The bigger picture is well captured by the term ‘AGI’.

Reprinted with permission from the author.

Peter Voss is an entrepreneur, inventor, engineer, scientist and AI researcher. In 2001 he (co-) coined the term ‘AGI’ (Artificial General Intelligence), and has been working towards achieving high-level AI since then. He also has a keen interest in the inter-relationship between philosophy, psychology, ethics, futurism, and computer science, and frequently writes and talks on these topics.