The modern natural language interfaces with limited domains are Alexa and Siri. Yes, they’re limited. But they are far more impressive and useful than SHRDLU.
Alexa and Siri (and friends) are competely incapable of interacting with a user with the precision of SHRDLU. You can ask them to retrieve information from a Google search but e.g. they have no memory of the anaphora in earlier sentences in the same conversation. If you say "it" a few times to refer to different entities they completely lose the plot.
They are also completely incapable of reasoning about their environment, not least because they don't have any concept of an "environment" - which was represented by the planner and the PROGRAMMAR language in SHRDLU.
And of course, systems like Siri and Alexa can't do anything even remotely like correctly disambiguating the "support support supports" show-off sentence in the excerpt above. Not even close.
Edit: Sorry, there's a misunderstanding about "limited domain" in your comment. Alexa and Siri don't operate in a limited domain. A "limited domain" would be something like being in charge of your music collection and nothing else.
Alexa annd Siri etc are supposed to be general-use agents. I mean, they are, it's just that they suck at it... and would still suck in a limited domain also.
It’s not meaningful to compare SHRDLU with today’s verbal search interfaces. The world SHRDLU manipulated were only stackable blocks and the only relations it knew were ‘above’ and ‘below’. The entire scope of its endeavor was describing the state of block stacks and basic ways to reorder stacks to satisfy the single relation of above-ness and below-ness.
Time and causality and even basic probability were all absent from SHRDLU’s model. Not surprisingly the work was a dead end that even Winograd was quick to abandon, as he subsequently exited the field of experimental AI for the more conceptual models of cognitive science and HCI.
>> The world SHRDLU manipulated were only stackable blocks and the only relations it knew were ‘above’ and ‘below’.
If that were true then SHRDLU would have operated in a one-dimensional world. Instead, it moved blocks around a 3-dimensional world. It could understand more spatial relations than "above" and "below", as in the example I quote above where it is asked "Is there a large block behind a pyramid?". It stacked blocks, but in doing so it also had to move others out of the way etc. That is no big mystery, like I say SHRDLU used a planner and even in the 1960s' there were planners capabe of solving block stacking problems in 3d environments.
Time and causality were certainly not "absent" from SHRDLU! Like I say, it had a memory that allowed it to keep track of what it had done previously and it could explain the reasoning behind its actions, like in this excerpt from the demonstration quoted in wikipedia:
Person: Had you touched any pyramid before you put the green one on the little cube?
Computer: YES, THE GREEN ONE.
Person: When did you pick it up?
Computer: WHILE I WAS STACKING UP THE RED CUBE, A LARGE RED BLOCK AND A LARGE GREEN CUBE.
Person: Why?
Computer: TO GET RID OF IT.
Person: Why did you do that?
Computer: TO CLEAN OFF THE RED CUBE.
Person: Why did you clear off that cube?
Computer: TO PUT IT ON A LARGE GREEN CUBE.
Person: Why did you do that?
Computer: TO STACK UP THE RED CUBE , A LARGE RED BLOCK AND A LARGE GREEN CUBE.
Person: Why did you do that?
Computer: BECAUSE YOU ASKED ME TO.
Person: How did you do it?
Computer: BY PUTTING A LARGE RED BLOCK ON THE TABLE ; THEN LETTING GO OF IT ; THEN PUTTING A LARGE GREEN CUBE ON IT ; THEN LETTING GO OF THAT CUBE ; THEN PUTTING THE RED CUBE ON THAT CUBE; THEN LETTING GO OF THAT CUBE.
This is a question about time and causality: the user asks why the program performed a set of operations in an earlier time.
>> Not surprisingly the work was a dead end that even Winograd was quick to abandon, as he subsequently exited the field of experimental AI for the more conceptual models of cognitive science and HCI.
Regarding Winograd's subsequent work direction, this is what he had to say about it himself:
How would you say SHRDLU influenced your subsequent work and/or philosophy in AI?
Having insight into the limitations I encountered in trying to extend SHRDLU beyond micro-worlds was the key opening to the philosophical views that I developed in the work with Flores. The closest thing I have online is the paper Thinking machines: Can there be? Are we?
How would you characterize AI since SHRDLU? Why do you think no one took SHRDLU or SHRDLU-like applications to the next level?
There are fundamental gulfs between the way that SHRDLU and its kin operate, and whatever it is that goes on in our brains. I don't think that current research has made much progress in crossing that gulf, and the relevant science may take decades or more to get to the point where the initial ambitions become realistic. In the meantime AI took on much more doable goals of working in less ambitious niches, or accepting less-than-human results (as in translation).
What future do you see for natural language computing and/or general AI?
Continued progress in limited domain and approximate approaches (including with speech). Very long term research is needed to get a handle on human-level natural language.
My reading of this is he realised that natural language understanding is not an easy thing. I don't disagree one bit and I don't think for a moment that SHRDLU could "understand" anything at all. But it was certainly capable of much more intelligent-looking behaviour than modern statistical machine-learning based systems. Winograd's reply above says that it's hard to extend SHRDLU outside of its limited domain, but my point is that a program that can operate this well in a limited domain is still useful and much more useful than a program that can operate in arbitrary domains but is dumb as bricks, like modern conversational agents that have nothing like a context of their world that they can refer to, to choose appropriate contributions to a conversation. He also hints to the shift of AI research targets from figuring out how natural language works to "less ambitious niches" and "less than human results", which I also point out in my comments in this thread. This is condensed - I'm happy to elaborate if you wish.
I have to say I was very surprised by your comment, particularly the certainty with which you asserted SHRDLU's limitations. May I ask- where did you come across information that SHRDLU could only understand "above and below" relations and that time and causality were absent from its "model"?
