AI’s Language Problem

If an 6th grader asks their science teacher "How long does stars last?" / "How long stars last?" /"How long do stars last?" / "How old do stars get?" / "Stars, how old can they get?" / ...

In similar context they probably end up parsed to the same question assuming correct inflection, posture, etc. Spoken conversations are messy, but they also have redundancy and pseudo checksum's. Written language tends to be more formal because it's a much narrower channel and you don't get as much feedback.

PS: It's also really common for someone to ask a question when they don't have enough context to understand what question they should be asking.

It would be pretty easy to prove a computer didn't understand a conversation.

There is evidence that language is fairly smooth though. For example, we can extract e.g. the gender vector from a word embedding space that is learned by a recurrent neural network. That seems to hint at the possibility that words, sentences and concepts live in smooth, high-dimensional manifold that makes them learnable for us in the first place (because in that case they can be learned by small local improvements which seems to be required for biological plausibility). That is also the reason why we have often many words for the same or similar meanings and, conversely, why formal grammars have failed at modeling language.

Arguing from the other direction, neural networks have also already proven to deal with very sharp features. For example the value and policy networks in AlphaGo are able to pick up on subtle changes in the game position. The changes from the placement of single stones can be vast in Go and by no means this is only solved by the Monte Carlo tree search. Without MCTS, AlphaGo still wins in ~80% of the time against the best hand-crafted Go program. The value and policy networks have pretty much evolved a bit of boolean logic, simply from the gradient from the smoothness that results from averaging over a lot of training data.

I have a pet theory that the discovery of sharp features and boolean programs might heavily rely on noise. If the error surface becomes too discrete, we basically need to backup to pure random optimization (i.e. trying any direction by random chance and keep it, if it is better). That allows us to skip down the energy surface even without the presence of a gradient. Of course, such noise can also lead to forgetting, but it just seems that elsewhere the gradient will be non-zero again, so any mistakes will be correct by more learning (or it simply leads to further improvement if the step was into the right direction). Surely, our episodic memory helps in the absence of gradient information as well. If we encounter a complex, previously unknown Go strategy, for example, it will likely not smoothly improve all our Go playing abilities by a small amount. Instead, we store a discrete chaining of states and actions as an episodic memory which allows us to reuse that knowledge simply by recalling it at a later point in time.

The problem is : The industry set upon making a skyscraper from the top floor (neocortex) down and assumes they can hack together a foundation and throw up ad-hoc scaffolding on their way down that will magically reflect the brain's capabilities. There are even ridiculous ideas held by industry 'experts' that the foundation will just magically arise from nothing more than a sheer amount of spaghetti wiring complexity. Nothing in the known universe has been proven to work this way. Yet, no one questions this outlandish belief system because the industry experts and notable names are stating it and.. hey look, their top-floor systems actually do something interesting..So, they must know what they're doing and saying.

So, the foundational problems remain...

They remain because there is no foundation to these cortical systems. Anyone who states this is railed and laughed at. So, you get what you get.....

The article states : "Machines that truly understand language would be incredibly useful–but we don’t know how to build them."

There are people and groups who know how to build them. They are focusing on the 'foundation' first. That is not where the spotlight or money are directed. So, they remain in the dark.

We gained head-winds with a very trivial model of neurons and cortex like hierarchical neural network designs and the money sent people off to the races. People began writing wrappers, stuccoing the top floor, hacking up scaffolding, applying any C.S concept they could find in the parts bin to fancify the top floor.

That's where all of the attention and money is.. What does your system do? What benchmark can it beat? What data can it classify? What cool trick can it do to impress us? So, you get impressive trick systems that require massive amounts of data, training, and answer maps to obscure the lack of intelligence. As there is none explicitly designed into these systems, the system cannot convey its understanding.

It's nothing more than an answer map w/ annealing routines and memory... Very similar to cortical regions.

The foundation and supporting layers up to the top have been ignored, aren't getting any spot-light or money, nor are the individuals who continue to toil on it.

They're considered to be 'philosophers' and jokers and not real scientist/engineers/industry leaders. The A.I space shuts out a huge pool of varying opinions via its : If you don't have a PHD, one need not apply. If you're approaching it from any other methods than the ones subscribed to and you're not a name, face, or have a laundry list of papers you get the : Good luck (thumbs up).

And people stand around and wonder why the fundamental problems remain? Come on...

In any event, it wont remain for long and that will be due to someone/groups actually investing the time and energy to build a sound foundation. This begins first and foremost by deep philosophical questions about the nature of the universe and intelligence. The answers derived serve as a guiding light for further along scientific and engineering pursuits.

This article should be : AI's lack of a foundation. Whose going to build it? Whose going to invest the time to understand what exactly it is as opposed to hacking away at it?

It's the truth but would be considered a 'hit piece'. Until someone constructs a proper foundation, no one is going to give credence to the idea that current A.I lacks it. Hindsight is 20-20 as is a force-fed neo-cortex.