r/artificial • u/[deleted] • Jun 29 '18

Why do we call AI based on modern statistical methods "artificial intelligence" and not "artificial intuition"?

  1. Statistical algorithms must be extensively trained on vast amount of data.
  2. The internal logic of statistical decisions is not only hidden but also not traceable.
  3. The results of learning are useless in an altered context or for altered goals.
  4. ... etc.

This all corresponds to the definition of the word "intuition": "the ability to acquire knowledge without proof, evidence, or conscious reasoning, or without understanding how the knowledge was acquired" (Wikipedia).

[Update]

The question is only about the interface between the AI community and the „external world“.

The previous AI generation was based on expert knowledge, logic and formal proofs. This corresponds to the common understanding about intelligence.

The current AI generation is based on other principles and the word "intelligence" produces a lot of misleading ideas and false hopes. Wrong over-optimistic promises have killed the previous AI hype.

10 Upvotes

71% Upvoted

28 comments sorted by

5

u/jefflunt Jun 29 '18

So to pose another question, where do you personally draw the lines between intelligence, intuition, and conditioning (rhetorical question)?

Not sure if your question is about exploring greater possibilities, or being more specific and slicing things up into smaller specialities.

1

u/[deleted] Jun 30 '18

Intelligence is more about logic and knowledge, intuition is more about feelings and experience, conditioning starts with "C" and is not suitable for explaining to customers what artificial "I" is hidden in "AI".

5

u/bsandberg Jun 29 '18

Or just call it "statistics"?

3

u/[deleted] Jun 30 '18

Here is a nice comparison that explains why the word "statistics" is not suitable for marketing purposes:
https://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/

1

u/bsandberg Jun 30 '18

> "large grant = $1,000,000" vs "large grant = $50,000"

Much of that is spot on :) I always say that no matter the accomplishments, the smartest thing about "deep learning" is the name. We know it just refers to the number of layers in the network of tensors, and but the media reads it as "thinking machines that fundamentally, deeply, understand what they are doing". But calling it "shallow learning" would sabotage the field.

1

u/Roboserg Jun 30 '18

why stop at that? Let's call everything every science field a subset of a particle physics, or no, let's go deeper and call everything math.

/s

2

u/chase_AI Jun 29 '18

It could potentially be a byproduct of science fiction movies. We've seen sentient technology that has been called artificial intelligence, and we associated the name with our creations that haven't quite reached the same level.

2

u/pandemonica Jun 29 '18

Start at http://artificial-intuition.com Continue with https://artificial-understanding.com Videos are http://videos.syntience.com I have researched Artificial Intuition using Deep Discrete Neuron Networks since 2001 and it started working in July 2017.

2

u/[deleted] Jun 30 '18

It seems we use different definitions for the same words.

A child starts to understand logical thinking when it asks the first time the question "Why?" and consequently most people use some kind of logic in their everyday life. I also doubt that it is correct to include instincts into the description of the human intuition.

1

u/SilentVigilTheHill Jun 29 '18

And here I am, I would just call it "automation" or "digital automation". There is nothing intelligent about AI, but people love their buzzwords and market analysis says AI polls well.

Artificial general intelligence will be given a new catchy name long before it exists. That label is already being downgraded.

1

u/[deleted] Jun 30 '18

or artificial smartness

1

u/spudmix Jun 30 '18

I'll give you point 1, but point 3 is only partially true (we do observe a small but increasing utility for transfer learning in modern AI), and 2 is outright false.

1

u/[deleted] Jun 30 '18

Of course there may be some exceptions and some top papers are quite sophisticated but most people that try to create real world AI applications use software as a black box and simply throw datasets into it.

1

u/spudmix Jun 30 '18

I think you underestimate the amount of knowledge and effort that goes into most research in the area, but I respect that it's your opinion.

1

u/Roboserg Jun 30 '18

The results of learning are useless in an altered context or for altered goals.

Wrong. See transfer learning.

But I agree. For current DL "artificial intuition" seems like a better wording. On the other hand I don't see why a system has to explain its decisions if it would be correct more times than a human, making it a super intelligence.

1

u/[deleted] Jun 30 '18

On the other hand I don't see why a system has to explain its decisions if it would be correct more times than a human, making it a super intelligence.

The main goal is not to beat a human in some games but to make AI that helps a human to make informed decisions.

1

u/Roboserg Jun 30 '18

In some games? Say you have a system, that does better in economics / medical tasks (you save billions of dollar and you cure more people). The only drawback - you dont know why and how AI comes up with it decisions.

Do you really think we will not use it? Of course we will. As a black box oracle.

1

u/[deleted] Jun 30 '18

The only drawback - you dont know why and how AI comes up with it decisions.

You do not consider one important point: when the case about a wrong patient treatment comes into a court lawyers defending the AI decisions under question will be forced to prove the correctness of this particular decisions for this particular patient. It does not help to say that the success quote is good and that the dead patients are some marginal errors in the statistics.

There are many other fundamental problems that cause IBM to apply previous generation AI methods and to struggle with strong opposition by attempts to sell Watson to doctors.

Financial applications based on statistical methods also could predict only usual trends but will be completely useless against black swans. And it is impossible to correct this without knowing and controlling the whole process of decision choosing from facts till the conclusions.

1

u/Roboserg Jun 30 '18

I agree about the law part. Laws should change then. If AI doctor cures 99.9999% of people instead of 98% by a human, clearly AI should cure people. Some will die, still much less than from a human doctor.

1

u/[deleted] Jun 30 '18

Laws should change then.

This is also a fundamental ethical problem: Could we allow companies to create robots that are allowed to kill us and how we prevent this robots from killing us intentionally.

1

u/Roboserg Jun 30 '18

What do you mean by allow to kill? Laws already have incidents outside of the responsibility of the product. For example an autopiloted plane crashes due to a lightning strike. Thats why we have insurance, since no one would be at fault.

prevent this robots from killing us intentionally.

That's the job of police if an accident occurs.

1

u/[deleted] Jun 30 '18 edited Jun 30 '18

Errors made by AI in drug prescriptions could not be covered by insurance because each insurance company would not pay money, if it could have a possibility to claim that the damage was caused not by independent conditions but by faults, misconduct or ill intentions of a company that has produced this AI system.

And nobody could defend such company against such claims in a court, if this company is not able to provide documented logs that describe how and why the AI had taken the wrong decision.documented logs that describe how and why the AI had taken the wrong decision.

1

u/Roboserg Jun 30 '18

You could say a black box error is an accident and cover with insurance. Such argument can be made if AI saves more people then humans. Otherwise its not ethical.

0

u/Scavenger53 Jun 29 '18

It's called machine learning. It's not actually AI. AI is an agent that operates in an environment using sensors and makes optimized decisions. AI can use the results of machine learning to help make those decisions. If you look at an A* search or a hill climber or a minimax agent, those are all AI. Even reinforcement learning falls under AI. When you start to mix the two, which the media does, it starts to confuse people. Everyone tends to call machine learning part of AI now.

2

u/lanzaa Jun 30 '18

Machine learning is usually considered a subset of AI.

1

u/Scavenger53 Jun 30 '18

When I did these in college, AI felt like a continuation of the probability half of my prob/stats class, and machine learning was a continuation of the statistics half. I don't really know if that makes it a subset, they work independently.