I don't recall portraying anyone in that light. I highlighted the nature of an industry. Who aligns themselves to this nature was left unstated. If this clear and present truth offends, its likely because it has merit and is validated by the level of offense one encounters. It quite clearly cannot be defended. If you believe what I have stated can be, you're more than welcome to produce sound arguments that try. We can walk through an incredible amount of examples of what I have stated together.

> Hinton was working in neural nets even in the time when they were uncool and fringe area of research. They didn't sell out AGI for some profit motive .

Listen to what you're saying....
> neural nets even in the time when they were uncool and fringe area of research.
And yet, it has made others billions and continues to mint money. Whose centered on the fundamental problem as opposed to conducting applied engineering for profit? Whose over marketing themselves and their efforts as fundamental theoretical research when its more or less optimizations for applied engineering? What ideation is new and what is simply relabeling old pioneer's work as one's own? Who minted LSTM? Whose name remains all over it? Who made a mockery of a prominent contributor? Who no longer discloses the details of their work given the proven nature of the industry? If the critique doesn't apply let it fly.

Btw, Hinton recently stated :
https://www.axios.com/artificial-intelligence-pioneer-says-w...

Whose taking this advice? Whose funding people thinking outside of the box? Whose hiring someone whose thinking outside of the box? So, whose really and whole-fully centered on solving AGI for the sake of solving it? It's fun to market yourself as doing so for increase prominence/money. It's a whole other ball game to be internally oriented and structured in pursuit of it. Applied engineering/optimizations of Weak AI for business applications is not AGI research.

> The thing is we don't have any clear path laid to follow to achieve AGI

The path was always there. One only need pursue it for pursuits sake. No exits. No distractions. No business case. No payouts. A desk pen/paper.

> Various paths proposed earlier turned out over optimistic and dead end.

False. Various paths proposed earlier are the same ones being turned into profitable solutions today. They are the same ones underlying the bulk of white papers today. There weren't over optimistic. They weren't dead ends which is why various groups are using them to suck down billions of dollars today. What happened that caused the previous AI winter is that people rushed fundamental research into applied engineering. The same as what's happening today and will lead to a 'winter' for various groups who are deep in it. Statistics overshadowed fundamental mathematics and understanding. Brute force over-took intelligence... And yes that leads to dead-ends .. clearly. Yet, here we are again. A bunch of statistical brute force machines being over-marketed for profit leading fundamental research into a dead end. Google has spoke out about it, Hinton has, Microsoft has, yan lecun to echo my sentiments. Yet, the allure of profit/notoriety continues to attract the same thinking/soon to be failed approaches to something that is fundamentally beyond current work.

> The current "weak AI" you disparage is an achievement expanding generalization of problem spaces using unified methods way better than past systems.

It's distributed statistical optimization. There's no need to fluff me. I am centered on it. It works via large data sets, limited and specific state spaces, and large amounts of computational resources running tons of iterative steps. It is brittle and not general. It works today because we have enough computing resources such that we can brute force various problems. Problems that are privy to statistical patterning.

> So even if our current progress seems disappointing

I never stated I was disappointed nor did I disparage anyone. I stated more clearly the nature of the progress, the goals and driving forces, and the fast approaching limitations.
This frees one to respectfully acknowledge and consider what's been done and move forward and beyond it to more capable pastures.

> please examine what's happened and why research has taken the current path before tarnishing researchers with unjustified quips.

Please reduce your sensitivity level so that you can reason yourself to higher planes of consideration. Less emotion and more reasoned/truthful admission. I know exactly what happened to research in a time's past which informed me as to how to conduct myself in the present. I know exactly what driving forces are and I know quite clearly as we all do as to how they influence people. Hinton as well as others now public agree. Microsoft/Google have stated much of the work is overhyped. Hinton says pioneers should scrap everything and start over. LeCun stated that deep learning is approaching a brick wall and overhyped. It's exactly what I said to myself years ago and am stating now and there's zero problems with stating this truth as so many have no brought themselves to state.