Microsoft is investing $1B in OpenAI

We are indeed getting impressively far. Four decades after being invented, machine learning went from useless to useful to enormous societal ramifications terrifyingly quickly.

However, we are not getting impressively close to AGI. That's why we need to stop the AGI alarmism and get our act together on the enormous societal ramifications that machine learning is already having.

Thanks! Now back to your other question:

> Once we had Newton's law of gravity though, we knew the distance, radius, mass, and even surface gravity of the moon.

I think it was more complicated than what you assume there. Newton published his Principia 1687 but before 1798 we didn't know the gravitational constant:

https://en.wikipedia.org/wiki/Cavendish_experiment

However...

> Would you say it's fair to say that by then we knew in principle we could go there and walk there?

If you mean "we 'could' go if we had something what we were sure we haven't had" then there is indeed a written "fiction" story published even before Newton published his Principia:

https://en.wikipedia.org/wiki/Comical_History_of_the_States_...

It's the discovery of the telescope that allowed people to understand that there are another "worlds" and that one would be able to "walk" there.

Newton's impact was to demonstrate that there is no any "mover" (which many before identified as a deity) that provides the motion of the planets but that their motions simply follow from their properties and the "laws." Before, most expected Aristotle to be relevant:

https://en.wikipedia.org/wiki/Unmoved_mover

"In Metaphysics 12.8, Aristotle opts for both the uniqueness and the plurality of the unmoved celestial movers. Each celestial sphere possesses the unmoved mover of its own—presumably as the object of its striving, see Metaphysics 12.6—whereas the mover of the outermost celestial sphere, which carries with its diurnal rotation the fixed stars, being the first of the series of unmoved movers also guarantees the unity and uniqueness of the universe."

I feel like I could write an essay about this.

AGI represents the creation of a mind .... It's something that has three chief characteristics: it understands the world around it, it understands what effects its actions will have on the world around it, and it takes actions.

None of those three things are even close to achievable in the present day.

No software understands the physical world. The knowledge gap here is IMMENSE. Software does not see what we see: it can be trained to recognize objects, but its understanding is shallow. Rotate those objects and it becomes confused. It doesn't understand what texture or color really are, what shapes really are, what darkness and light really are. Software can see the numerical values of pixels and observe patterns in them but it doesn't actually have any knowledge of what those patterns mean. And that's just a few points on the subject of vision, let alone all the other senses, all the world's complex perceivable properties. Software doesn't even know that there IS a world, because software doesn't KNOW anything! You can set some data into a data structure and run an algorithm on it, but there's no real similarity there to even a baby's ability to know that things fall when you drop them, that they fall in straight lines, that you can't pass through solid objects, that things don't move on their own, etc etc.

Even if, a century from now, some software did miraculously approach such an understanding, it still would not know how it was able to alter the world. It might know that it was able to move objects, or apply force to then, but could it see the downstream effects? Could it predict that adding tomatoes to a chocolate cake made no sense and rendered the cake inedible? Could it know that a television dropped out the window of an eight story building was dangerous to people on the sidewalk below? Could it know that folding a paper bag in half is not destructive, but folding a painting in half IS? Understanding what can result from different actions and why some are effective and others are not, is another vast chasm of a knowledge gap.

Lastly, and by FAR most importantly, the most essential thing.....software does not want. Every single thing we do as living creatures is because our consciousness drives us to want things: I want to type these words at this moment because I enjoy talking about this subject. I will leave soon because I want food and hunger is painful. Etc. If something does not feel pleasure or pain or any true sensation, it cannot want. And we have absolutely no idea how such a thing works, let alone how to create it, because we have next to no idea how our own minds work. Any software that felt nothing, would want nothing-- and so it would sit, inert, motionless...never bored, never curious, never tired, just like an instance of Excel or Chrome. Just a thing, not alive. No such entity could genuinely be described as AGI. We are likely centuries from being able to recreate our consciousness, our feelings and desires....how could someone ever be so naive as to believe it was right around the corner?

Thanks.

The problem with 'sentiment analysis' of today is it requires a human labelled training dataset that is specific to a particular domain and time period. These are rather costly to make and have about a 12 month half-life in terms of accuracy because language surrounding any particular domain is always mutating - something 'sentiment analysis' models can't hope to handle because their ability to generalise is naught. I've worked with companies spending on the order of millions per year on producing training data for automated sentiment analysis models not unlike the ones in the parent post.

To get useful value out of automated sentiment analysis, that's the cost to build and maintain domain specific models. Pre-canned sentiment analysis models like the parent post linked are more often than not worthless for general purpose use. I won't say there are 0 scenarios where those models are useful, but the number is not high.

Claiming that sentiment analysis is 90something percent accurate, or even close to being solved, is extremely misleading.