Why Machine Learning won’t cut it
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’.