We have AI's predicting images, predicting objects in an image. Understanding audio, meaning of the audio if it is a spoken sentence.

In humans when we start seeing a movie halfway through, we still understand the entire movie (although this might be attributed to the fact that future events in movies have a link to past events). But even if we see a movie by skipping lots of bits in-between we still understand the movie.

So can a Machine Learning AI do this? Or do humans have some inherent experiences in life which makes AI incapable of performing such a feat?


3 Answers 3


Central Questions

Can ML/AI understand incomplete constructs like humans?

Do humans have some inherent experiences in life which makes AI incapable of performing [some capacities of human intelligence]?

Comprehension of Literature and Film

Whether software exists today that is able to understand like humans is not something that the general public can know. No such system has been released to the general public by any military or commercial organization thus far. Yet such an achievement would not necessarily be something the government or commercial entity would want to disclose outside of the lab and its management.

Deeper comprehension of full or partial speech may have been achieved by anti-terrorist units, since funding has been available for that work for well over a decade, but it also unlikely that some software somewhere can read or scan parts of a book and do a book report that would produce a passing grade.

Determining whether a movie will return its investment at the box office may have been accomplished by the researchers for the big studios, but that doesn't require understanding like someone understands their favorite movie. The answer to the title question is, "Not yet."

Will it occur?

Most in the fields of computer science, robotics, and artificial intelligence say, "Yes." For religious reasons, some say, "No." I'm in neither camp, and have not seen indisputable mathematical rigor that proves either the inevitability of artificial brains or the impossibility of them. It is scientifically irresponsible to make a positive statement based on either recent technology trends or superstitious fears.

What are Humans Like?

The phrase, "Like humans," places a significant demand on researchers and engineers. Human beings can do much more than work with data sets (audiovisual in this case) with missing information, what statisticians call sparse matrices.

Whether software can realize higher achievements of human brains is unknown, and the predictability of such capabilities presents gross difficulties. Consider these human capabilities.

  • Write a screenplay, cast it with artificial characters, direct it, and produce it.
  • Initiate and partly develop a new branch of science, as did Isaac Newton or Lavoisier.
  • Love beyond a superficial expression of love

Conversely, what humans generally do poorly is distinguish between reliable projection and baseless conjecture. Most humans are prone to musings of technical visionaries, propaganda, marketing, rumor, innuendo, and gossip. It would not be surprising for software to someday soon be wiser in this respect, but only because the bar has been set so low by human culture.

There are other capabilities of the human brain that are exceptional and present enormous difficulties in even considering an approach to realizing in software. These may be a result of a hundred thousand years of DNA refinement yielding significant complexity and precision. It is also not outside the realm of possibility that a form of causality exists in the human brain that defies scientific study.

The Possibility of Impossibility

No one has every proven that all things that exist can be measured. Heisenberg has actually proven the opposite to most theoretical physicist's satisfaction.

A phenomenon that cannot be measured at all cannot be studied scientifically. Whether the phenomenon of choice is a unique condition has yet to be understood even in question form, prohibiting the emergence of a proper answer thus far.

Imminent Change as Significant as Industrialization

Nonetheless, important capabilities of human intelligence have been simulated and others are emerging. These are now part of the world economy and will not likely disappear. The possibility of Asimov like scenarios of robots and humans coexisting and conversing in more human-like is very likely.

It is when that occurs that autonomous vehicles and walking robots will begin to have experiences that are like human experiences and we will be able to directly observe just how much like humans they can behave in terms of intelligence and also emotions.

About the Specific Capabilities Mentioned

These are the capabilities mentioned in the question.

  • Predicting images
  • Predicting objects in an image
  • Understanding audio
  • Understanding the meaning of spoken sentences
  • Understanding a movie from the first half
  • Understanding a movie from parts of it

These are the more canonical ways of stating the first four capabilities for which research has produced usable system approaches.

  • Learning to distinguish image categories
  • Learning to distinguish object categories from within images
  • Parsing audio into notes, vocal tones, and transient sounds
  • Extracting semantics from a vocalization sufficiently to respond intelligently some of the time

These are a more accurate cognitive science description of the last two.

  • Guessing much better than random guessing would the story arch to the end of a movie from the sound and frame set of the its first half
  • Filling in character and story arch details from portions of a movie's sound and frame set

There is no obvious reason why such couldn't be done and done well by software, given sufficient research time to develop such a system and sufficient data to train with. Also, significant computing resources would be needed, of course, and possibly some nontrivial period of time.

  • $\begingroup$ Humans are poor at distinguishing highly reliable and baseless conjencture..What does it mean? $\endgroup$
    – user9947
    Jul 18, 2018 at 13:58
  • $\begingroup$ I believe humans are prone to error only due to previous experiences, a scientific person will not view the news same as a normal person, humans probably tend to romanticise things....also I did not understnd Heisenberg's role here, as far as I know you cannot measure position + momentum, only due to the effect of the thing which is measuring the quantity (i maybe wrong though) $\endgroup$
    – user9947
    Jul 19, 2018 at 12:25
  • $\begingroup$ I think it is a kind of combination of denial, giving their life a meaning and evolutionary advantage/prospects $\endgroup$
    – user9947
    Jul 19, 2018 at 12:49

I believe that an AI can understand much more of a movie than we do. As you said, sometimes a detail at the beginning of the movie is the key to understanding the final outcome. The problem is that the storyline distracts us. In the case of an AI, he would just be waiting for the movie to complete because he has all the details at all times.

Imagine that an AI is by your side watching the movie. He will not feel emotion like you. He is only analyzing the images, colors, soundtrack, main characters and is extracting from the lines, whatever he can, to understand the movie. What would it be to understand, in your eyes? To be able to generate a synopsis or to compare the film with some reality or criticizes the life of the human?

An artificial superintelligence would have the task of overcoming us. We could pass a movie and let her explain why we cried watching the movie, because we were surprised, and everything. Since an artificial superintelligence would have the role of not being distracted by the film but analyzing all possible content, try to analyze human reactions and spit out an analysis that could leave you reflecting on life for a few days.

But this is an artificial superintelligence that I imagine may one day exist, but I believe it is far away.

Something real and what we can build is an AI that can extract the genre from the movie and maybe even generate a synopsis with a spoiler like "X character dies."

Another factor that also makes it easier for us to understand movies is our huge film base. We watch so many American cliches movies, that most are already very predictable: "The main character does not usually die," "When the villain is close to killing the main character, something surprising happens and he is prevented," ...

  • $\begingroup$ But what about building the movie by watching it in bits? $\endgroup$
    – user9947
    Jul 10, 2018 at 17:54

Here is a glimpse for you.

Basing on what the second paragraph says;

In humans when we start seeing a movie halfway through, we still understand the entire movie (although this might be attributed to the fact that future events in movies have a link to past events). But even if we see a movie by skipping lots of bits in-between we still understand the movie.

We refer this to what is called "memory recall/retrieval and awareness of the current situation",hence attributes to remembering which can be thought of as an act of creative re-imagination, simply because of the way human memories are encoded and stored effectively.

note :memories are being triggered by biological neural networks(neurons).

So can a Machine Learning AI do this?

It gives an insight on how machine learning algorithms(ANN),pointed above can be applied in re-accessing of events or information bits from the past which have been previously encoded and stored,just like how the human brain does it.

However,if you also want to implement such conceptual idea here is another paper which explains the implementation of the above algorithm

"Neural Memory Networks" gives a little bit of insight on a simple implementation of memory in Neural networks.

Or do humans have some inherent experiences in life which makes AI incapable of performing such a feat?(This question is kinda like two sided).

To some extent,humans don't inherent experiences but rather,past information is triggered by some situations they do face,for instance; I myself experienced some music rhythms way back, and when I try to replay my old jams again,i recall back that situation nor memories.

Hope this can give you a little bit of insight for you to go indepth,concerning "Recall and Recognition in an Attractor Neural Network" (ANN).


You must log in to answer this question.