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Re your AI thesis, I think it’s the exact opposite. Deterministic, rule-based processing applied to lots of similar data are perfect for AI-fication. It’s all i

Re your AI thesis, I think it’s the exact opposite. Deterministic, rule-based processing applied to lots of similar data are perfect for AI-fication. It’s all in the computer, and you can kick out to a human-driven exception process when required. All that physical, real-world stuff you mentioned (cleaning, cooking etc) is vastly more complicated, riddled with experience-driven subjective judgements (is this paper on the floor trash or should I put it back on the desk?) and highly situational. Extremely difficult to generalize and handle with an AI. Not to forget it’s messy stuff in the real world, with engineering and hardware and grease and failures.

cm2187 9y ago
Most of the complex accounting and legal questions that these departments are doing manually aren't completely trivial either (we are not talking about just adding assets). But the determination of whether the paper on the floor is trash or not is something that pretty much any AI professional can apply his mind on (and will likely do given the size of the market), whereas very few will apply their mind on how IFRS should treat a tax asset in consolidation when the corresponding tax treatment under USGAAP is a still in discussion. If you look at self driving cars, they are already doing the sort of wizardry of your trash example when analysing the other objects in sight. And again it's not so much a matter of technical complexity, it's a matter of the domain being inaccessible to those who can best automate it.
- spaced-out 9y ago
  >Most of the complex accounting and legal questions that these departments are doing manually aren't completely trivial either (we are not talking about just adding assets). These are exactly the types of problems that machine learning is perfect for. Problems which involve chains of nebulous decisions that would be difficult to program traditionally.

cm2187 9y ago

Most of the complex accounting and legal questions that these departments are doing manually aren't completely trivial either (we are not talking about just adding assets). But the determination of whether the paper on the floor is trash or not is something that pretty much any AI professional can apply his mind on (and will likely do given the size of the market), whereas very few will apply their mind on how IFRS should treat a tax asset in consolidation when the corresponding tax treatment under USGAAP is a still in discussion. If you look at self driving cars, they are already doing the sort of wizardry of your trash example when analysing the other objects in sight. And again it's not so much a matter of technical complexity, it's a matter of the domain being inaccessible to those who can best automate it.

spaced-out 9y ago
>Most of the complex accounting and legal questions that these departments are doing manually aren't completely trivial either (we are not talking about just adding assets). These are exactly the types of problems that machine learning is perfect for. Problems which involve chains of nebulous decisions that would be difficult to program traditionally.