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Is the smartest robot more clever than the stupidest human?
Most humans are not good at chess. They can't write symphonies. They don't read novels. They aren't good athletes. They aren't good at logical reasoning. Most of us just get up. Go to work in a factory or farm or something. Follow simple instructions. Have a beer and go to sleep.
What are some things that a clever robot can't do that a stupid human can?

The "baseline humans" you describe have been historically described in the media industry as "the lowest common denominator" (LCD).
The LCD is the broadest possible audience for content, traditionally for network television shows.  (Before the age of cable, there were only 3 to 4 networks and all video content was broadcast over the airwaves--no way to specifically target audience segments so content had to appeal to the LCD.)

Captcha

Because captchas have to be solvable by the LCD, but trick bots. As long as captcha are viable, by definition they will always be something that baseline humans can do better than AI.

First Question
To treat this question in a scientific way, because I think it is a reasonable enough question that draws on the realities of postmodern culture in post industrialized societies to be treated scientifically, we should define some things.
The most difficult is intelligence, which is the realm in which smartness, cleverness, and stupidity reside.
Let's go through the list.

Most humans are not good at chess, but humans invented the game.
Most humans can't write symphonies, but humans invented them.
Most humans don't read novels, but digital computers can't write them yet and can't learn ethical balance through the reading of them like humans can.
Most humans are not Olympic level athletes, yet humans developed Olympics and robots are not yet Olympiads.
Most humans (whether or hot they are good at logical reasoning) don't employ it much other than to, "Get by."

The question is fine until it devolves into the dismissal of the intelligence that people apply to their method of earning income, which for many if not most people requires more than just getting up, commuting in, following some simple instructions, and going to sleep mildly inebriated.  Let's replace this last part with this.

Most humans do not work with the intention of optimizing quality of the product or service by measuring their own quality and seeking educational resources to improve the velocity, reliability, or accuracy of their work output (unless programs are instituted to incentivize these things in the workplace.)

If we define intelligence as the union of these things, for simplicity's sake, we have this (which is subject to change as AI develops).

Playing chess: AI wins
Designing games: Humans win
Writing symphonies: Humans win
Writing novels: Humans win
Absorbing ethics from stories: Humans win
Olympic gold: Humans win
Logical consistency: AI wins

We must, to interpret the above list correctly concede two things:

Machines may have the ability to do something according to specific quality standards but not be configured, trained, or connected appropriately to prevail.
Humans may have the ability to do something according to specific quality standards but not be educated, trained, or be properly motivated to prevail.

Second Question
What are some things that a clever robot can't do that a stupid human can?  These are a few, but they are of particular importance from certain perspectives.

Love their family and friends
Have compassion without reason
Decide what to learn
Hunt
See a future danger approaching
Entertain others
Pray

I would not dismiss these human propensities as irrelevant, even from a scientific perspective.  I would also not dismiss the possibility that these things are beyond the capabilities of silicon based entities.

OK, you ask the average human to write a symphony and see what happens.

@zooby, if you pay them well and give them time and resources, they'll write one. The result will likely not draw attention from orchestras unless the person is a prodigy. A symphony is usually in 4 movements, involving string, woodwind, brass, and percussion instruments. The arrangements are written onto staffs in an app or on orchestration stationary. The use of harmony, melody, rhythm and knowledge of timbre, theme, inversion, retrograde, counter play, and intensity are essential, but these are taught and learned by non-geniuses. After several attempts, one might sound good if played well.

You could equally "teach" as in "program" a robot to do exactly the same thing.

Some fields that humans are born with advantages:

Fast and precise image processing ability. Even the stupidest human can tell the edge of two different objects precisely, e.g. which part of the image is a dog and which is a cat.
Fuzzy learning ability. Humans don't need to see all kinds of cats to identify a cat. As long as we see some cats (real ones or pictures or videos) we can identify a cat easily.
Reasoning. Current machine learning methods are mostly statistics-based high-dimensional model approximation. Instead of finding a solution or a pattern, I have never seen any AI entity can generate new ideas based on current facts.
Abstraction. Now GANs and other AI techniques can create vivid drawings. Yet currently I cannot find any model that can do abstraction drawings. E.g. human can doodle a cat from a real pic of cats, while AI currently can't do that.

There are more of these kinds that human born with skills in their genes because of millions of years of evolution. While I believe in the future we'll have better AI entities with better algos to defeat the advantage of humans eventually.

Well I know some humans who are bad at reasoning and doodling.

It's true that I may not include every people on earth. There're always extreme cases beyond my imagination :(

Survival, Imagining, Moral Reasoning
The thing that comes to mind is a new-born, when you said "the stupidest human", and it already has some basic “survival instincts”. It will avoid pain, consume food, and quickly learn to distinguish "safe" and "dangerous" conditions and people.
We have computer programs that can learn chess and calculate the optimal move in a split second, but isn't playing chess is a bit pointless. Merely being able to play a board game is of little value from a survival perspective, industrial perspective, or economic perspective.
There are programs that can do things that are very helpful for the modern world, but as far as I know, they just don't have survival instincts. A self-learning robot; left in a forest with all the tools it needs to generate power for, build duplicates of, maintain and defend its self; probably wouldn't be able to learn how to do so in time to ensure its survival. Our current self learning programs would need to be able to identify when it has succeeded or failed to improve its survival odds. A child of two may learn fast enough to survive if the conditions are not too severe and non-toxic food and some form of shelter is nearby.
A financially poor, marginally educated person with lower than average aptitude working at a farm or factory might not be able to play chess well, but they would definitely be able to tell if someone is murdering someone else, and know to flee and seek the authorities. A robot that can play chess would not.
Furthermore, humans can continue to learn when separated from the problem by thinking about the problem. The ability to construct arbitrary models and run thought experiments is currently unique to humans.

That said, I do hope that we will soon have programs that well replicate the human mind, and demonstrate some of the aspects of what we call consciousness.

The ability to avoid pain, consume nourishment, and quickly learn to distinguish safety from danger is already a capability of robots with neural nets, perhaps not in combination, but as features of existing systems. Avoiding pain is much like some of the architectural features of online learning based on a reinforcement signal, simply the inversion of the pain signal concept, both being a measure of system behavioral wellness. NASA has demonstrated energy source seeking robots, and even older chess programs can quickly identify danger.


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What topologies support recognition of action sequences?
The ability to recognize an object with particular identifying features from single or multiple camera shoots with the temporal dimension digitized as frames has been shown.  The proof is that the movie industry does face replacement to reduce liability costs for stars when stunts are needed.  It is now done in a substantial percentage of action movie releases.
This brings up the question of how valuable recognizing a stop sign is compared to the value of recognizing an action.  For instance, in the world of autonomous vehicles, should there even be stop signs.  Stop signs are designed for lack of intelligence or lack of attention, which is why any police officer will tell you that almost no one comes to a full stop per law.  What human brains intuitively looks for is the potential of collision.
Once what we linguistically perceive as verbs can be handled in deep learning scenarios as proficiently as nouns can be handled, the projection of risk becomes possible.
This may be very much the philosophy behind the proprietary technology that allows directors to say, "Replace the stunt person's face with the movie's protagonist's face," and have a body of experts execute it using software tools and LINUX clusters.  The star's face is projected into the model of the action realized in the digital record of the stunt person.
Projected action is exactly what our brain does when we avoid collisions, and not just with driving.  We do it socially, financially, when we design mechanical mechanisms, and in hundreds of other fields of human endeavor.
If we consider the topology of GANs as a loop in balance, which is what it is, we can then see the similarity of GANs to the chemical equilibria between suspensions and solutions.  This gives us a hint into the type of topologies that can project action and therefore detect risk from audiovisual data streams.
Once action recognition is mastered, it is a smaller step to use the trained model to project the next set of frames and then detect collision or other risks.  Such would most likely make possible a more reliable and safe automation of a number of AI products and services, breaking through a threshold in ML, and increased safety margins throughout the ever increasing world population density.
... which brings us back to ...
What topologies support recognition of action sequences?
The topology may have convolution, perhaps in conjunction with RNN techniques, encoders, equilibria such as the generative and discriminative models in GANs, and other design elements and concepts.  Perhaps a new element type or concept will need to be invented.  Will we have to first recognize actions in a frame sequence and then project the consequences of various options in frames that are not yet shot?
Where would the building blocks go and how would they be connected, initially dismissing concerns about computing power, network realization, and throughput for now?
Work may have been done along this area and realized in software, but I have not seen that degree of maturity yet in the literature, so most of it, if there is any, must be proprietary at this time.  It is useful to open the question to the AI community and level the playing field.

I don't think it's clear what you're asking. Is it about classification (given footage of full sequence of actions, correctly recognize the full sequence), or prediction / modelling the world (given footage of part of the sequence, predict what the remainder of the sequence is)? The paragraphs referencing movies seem vague, and there's no evidence that stuff isn't simply done manual editing. I don't see how the paragraph about GANs / chemistry is relevant. Is the question about current state of the art, or more speculative in nature? It is not clear what is meant with "topologies".

At the very least, we should know if you're asking about classification or prediction as mentioned in my previous comment, so that there is at least a well-defined question if we manage to dig through all the terminology. If you're worried about the answer becoming "obvious", it is perfectly fine to directly answer your own question (using the same account you used to post the question, not using a different account)

@DennisSoemers, It is both explicit and clear to ask, "What topologies support recognition of action sequences?" and, "Where would the building blocks go and how would they be connected?" In aeronautics, fuel economy and maneuverability led to the definition of a key question: "What topologies provide high propulsive force to weight ratios? Where would the building blocks go and how would they be connected?" Pistons and valves gave way to compressors and turbines and jet engines were born. General action recognition is similarly important.

@FauChristian Look back at my very first comment. I described there how "recognition of action sequences" can be interpreted in two very different ways. It is not clear from the question which interpretation (or maybe both) is intended, and that's crucial to know for a good answer. The word "topologies" is still not well-defined, so that also makes the question vague.

This is an old area of AI called "Plan Recognition", which has about 3.5 million results in Google Scholar.
A lot of the modern work is done with classical search techniques coupled with expert domain knowledge, or related reasoning concepts like Hierarchical Task Networks.
I'm not aware of or able to find recent research using deep neural networks for this problem, but I think there are some data-drive approaches to the related work in video-game player modeling.

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Extracting referenced documents
I'm looking to write an AI that will be able to extract in text references from standards documents to assist human research.
My use case is extracting the identifying numbers, for example, "AR 25-2", along with the title of the document "Information Assurance" so that a human can gather all the related research on a contract at once, instead of having to keep track of references while they're reading through the document.
I have a pretty good idea of where to gather the names of these documents for training, I'm planning on 'scraping' a few repositories for different categories of these documents.
What kind of model should I use to get the best results?

Welcome to AI.SE, and nice question! I've slightly edited it to remove the request for software recommendations, which are usually off-topic on Stack Exchange since they tend to go out of date quickly. If you specifically need existing software, you might be able to get help at [softwarerecs.se]. Otherwise, best of luck with your question here!

Do the documents have a variable layout or do they share a common layout? Also, do the documents have a common format? This sounds like something that could be done with regular expressions and some parsing of document elements.

Unfortunately not. The documents have variable format title tags, and there is no common format for the titles, they're from all different branches of the government, and some of the title-tags are mostly plain english. I'm also wary of regular expressions for this task because there may be standards formats that I'm not aware of, and I don't want my tools to miss a single document: I'm aiming to have a high false positive rate to avoid this problem. Also, a regular expression wouldn't get the english titles of the documents.

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Use mobile device camera for moving pattern recognition
For instance, one task would be to detect if with an Android phone in hand, I'm panning the camera toward a circle shape in a 2D space.
What is the best technology set and embedded application approach can be used for these types of motion independent pattern recognition tasks?

i can't find the article, but i recall there was a result where researchers were able to guess the smartphone pin by doing ML on the accelerometer / gyro. sounds like something you'd be interested in

@k.c.sayz'k.csayz' yes that would be something very related if you have it.

@TinaJ it took me 3 words in google to search it up. you should do more research before asking a question like that (including this very question)

I'm not interested in papers per se. More like SDK or APIs or something.

@TinaJ you should first analyse the architecture of the hardware(android device),very well.I see there are some questions to ask more in android community,as well.

Firstly, before we commence I will recommend that you refer to similar questions on the network i.e. https://stackoverflow.com/questions/6499880/ios-gesture-recognition-utilizing-accelerometer-and-gyroscope and https://stackoverflow.com/questions/6368618/store-orientation-to-an-array-and-compare
Your problem can be divided into three parts.
How to gather sensor data.
How to use the gathered data to train a model
How to use the trained model to make a prediction.
A modern smartphone contains around six sensors packed into one device. To implement your application I recommend that you use raw sensor data from either the gyroscope or the accelerometer.
On the android platform, you can access these sensors and acquire the raw sensor data by using the Android sensor framework. The Android sensor framework is part of the android.hardware package.

To capture the raw sensor data, take regular samples (> 20 Hz) and save the maximum values of x, y and z in an array each (to recognize in all 3 planes). If you want the gesture to be 5 seconds long, keep 100 samples (at 20 Hz). Then analyze if any of the three arrays has values which change sinusoidally. If it does, you've got a gesture.
You could store these values into an array, if the user is in 'record mode'. And when the user tries to replicate that movement, the model could predict the replicated movement array from the recorded one's. The thing is, how can you compare the arrays in a smart way? (Randy M 2011)

This leads us to the next step which is applying ML.
To train your model, you can choose to either train the model on the cloud or train it locally on the device. In most cases the problem of training a model on a mobile device is about computational limitations. Machine Learning algorithms running on a mobile device need to be carefully designed and implemented since most mobile devices have weak processors and small RAM's.
Its quite a challenge to squeeze a large neural network (NN) into the small RAM's that smartphones's have since NN's require that the model is fully loaded into memory. In many cases it is advisable to slim the model down and set some weights near zero to zero. (Chomba B 2017)
Incase you decide to utilize the cloud. Your mobile app is required to simply send an HTTPS request to a cloud web service along with the required raw sensor data, and within seconds the service replies with the prediction results. Though there are several cloud services i.e. Microsoft Azure Cognitive Services, Clarifai and Google Cloud Cognition that you can leverage to host the server side of your application, personally I recommend that you consider using reality.ai which is specifically an AI tool for engineers working with signals and sensors.
The next step will be to select an appropriate algorithm to be used in classifying the gesture. Here you can employ either Logistic Regression, Support Vector Machines, Random Forest or Extremely Randomized Trees depending on your app's use case. In order to train your model we then provide the learning algorithm with 'labeled examples'. The ML algorithm then extracts the features and constructs a mathematical model that can accurately describe gestures i.e. roll, pan from raw sensor data.

Thanks for the good description. I know about the Android sensors myself, just not the ML part. I wish there were some libraries or SDK that I could use for recognition experiments.

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Algorithms for scene rotation
My goal is to take an image and return another image that looks as if the scene was viewed from another angle.  The difference in angle can be small — let's say as if the hand holding the camera moved slightly sideways.

I don't see any question being asked here. Moreover, you're not telling us what your findings so far are. Don't expect people to do your homework. Finally, whatever the problem is it would be a good idea to describe what you know about AI/ML, provide examples of images of inputs and outputs, etc.

If deep learning is what you are trying to use here, you should keep in mind that the real intent behind deep learning is to learn a probability distribution, which means that if you were to use a deep learning model to "rotate" images, you can only do it on a specific class of images (e.g. faces, cats, etc...).
If that's your goal, generative models are the way to go:
Autoencoders
You can train an Autoencoders to slightly change the angle. Autoencoders are a special type of neural network that is trained to output the same input you feed into them with a few imposed restrictions to prevent it from learning a trivial identity function. In your case, you could use a variation of a de-noising autoencoder. A de-noising autoencoder, as the name suggests, generates the same input image minus an artificial stochastic noise. The way this is achieved is by feeding a corrupted version of the image and then evaluating the loss on the non-corrupted version.
How can this be adapted in your case?
In your case, you could feed the original images into your autoencoder and evaluate them based on the rotated images. This will result in your autoencoder effectively learning the inner distribution that generates the images in order to generate a slightly "rotated" version of it. For more info on de-noising autoencoders, see the original paper.
Generative adversarial networks
For a more sophisticated approach, you can use generative adversarial networks. GANs are relatively harder to manipulate, but usually perform better than other generative models when it comes to images.
How can this be adapted in your case?
In general, GANs generate images from noise. However, in your case, you can use the original (non rotated) images as input for the generator. The generator can be a convolutional autoencoder for example. And the "real images" dataset will be your rotated images. This way, your model will learn to generate slightly rotated images by being fed an equivalent of noise in traditional GANs which in your case will be the original images. For more info on GANs, I suggest this and the original paper.
I should point out that one of the flaws of GANs is distorted perspectives so it's probably a shot in the dark; however, I think it won't be a problem here because you would be using real images as input instead of complete noise.
Related work
Now, as far as the literature goes, I don't think this has been done before except for this one (kinda) for faces representation learning. The only way this is similar to yours is that you can modify the implementation to only generate the faces it has been fed from different perspectives instead of an average of everything it has learned.

This answer is great for me, thank you. I labeled deep learning only bc other labels were not also available. Other approaches are relevant just as well for me at this stage.

Convolution does not contract and extend surfaces, so that can't work, and pixel morphing is not mentioned. The noise that needs to be removed is not identified. Discussion of training data possibilities is not mentioned.

@FauChristian I'm in a preliminary stage at my work. I want to know what has been done, or at least what to look for

@proton, Exactly. The beginning of an investigation is the time where a project is most sensitive to taking wrong turns, and others may read any given post and be steered down less than optimal paths. SO is set up so that one can down vote misleading responses and provide an explanation for both authors (to modify or improve the response) and readers who are seeking the right direction for work. ... Doing a web search or a scholarly article search for "three-dimensional feature extraction" will give you some sense of work done and options for initial direction.

@FauChristian The reason why I suggested autoencoders is because we are using them for image enhancement and although we weren't expecting it, a few of the generated results had severely distorted perspective. Perhaps, the reason for this is the hybrid architecture and the semi supervised nature of training during which, several shots of the same scene has been provided. I can't provide further technical details as this is still a work in progress, but I can definitely tweak this architecture to produce something similar to what OP asked.

— Stereoscopic Synthesis —
The generation of an image that would likely appear in the right eye of a head from which you already have an image from the left eye (or vice versa) is too complex to expect simple convolution (linear matrix transformation) to achieve a reasonable result.
You are correct that rotation is not the correct description, simply because it is ambiguous. What is rotating?  The best description is the synthesis of a stereoscopic image from a single eyed/camera one.
Although deep learning is an approach well suggested, it is a very general term into which a number of concepts, books, research projects, and software components fit.  I agree with other answers that indicate one could easily find the target objective missed upon repeated attempts at finding a working solution.  Shots in the dark are likely to waste time and effort.
For example, an auto-encoder may not work well because the modelling of depth may not be a feature extracted without having a host of stereoscopic image pairs of similar scenes to which automated feature extraction could be accomplished.
Should feature extraction be possible, it is not noise that needs to be removed, but pixelization and optical distortion that needs to be characterized, so that surfaces revealed by the shift in position could later be imbued with the same contour, focal blur, reflective properties, and edge continuation as the adjacent surfaces of the same objects when pixels corresponding to newly revealed surfaces are generated.
For greatest image authenticity, another noise profile to profile and imbue to generated pixels is the capture device noise profile.
— Formal Problem Restatement —
To narrow machine learning approaches so that we can take a shot in the light, let's consider the model of a three dimensional scene exposed to light sources with two cameras adjacent to one another.  Let's consider the input, the output, and the internal architecture required to produce a fairly reliable and accurate second image more formally.
We have image pixel matrix I1 that represents light arriving at camera c1 containing a rectangular image capture surface in an x-z plane upon which a lens of effective focal length l1 and aperture a1 is focused over scene S over a time window starting at s1 and ending at e1.  Some point is at the origin of a Cartesian coordinate axis, both camera c1 and another camera c2 point such that the origin is centered in the image capture and the point of lens focus.  The three dimensional coordinates of c1 and c2 are known.
You wish to predict the second image I2 arriving at camera c2.
Let's assume, for simplicity, that l1 = l2, that a1 = a2, and that the scene is motionless so that time is not critical in the model.  Let's also assume that the y coordinates and the image capture duration (e minus s) are the same for both cameras c1 and c2.
— Solution Architecture —
For this simplified case and assuming the object space is not an abyss containing only one object, the process architecture of the solution is the following.  Each --> symbol is a sub-process.  The horizontal and vertical positional difference between c1 and c2 is { x, z }.
{ I1, l, a, y, x, z }
--> { I1, l, a, y, { S1 ... Sn }}
--> { I1, l, a, y, { S1 ... Sn }, { E1 ... En }}
--> { I2 }

The first sub-process is a feature extraction, where the features are the three dimensional surfaces visible in the two dimensional image I1.  This is a questionable extraction because no y information in the scene is available and there is no mention of y-labeled training data is in the problem statement.
The second sub-process is the extension of features extracted to provide needed surface representation for I2.
The last process is rendering I2, potentially using morphing pixels in I1 and filling transparent sections remaining using E1 through En and knowledge of the contour, reflective properties, edge continuation, and capture device noise profile from feature extraction.
— Practicality of Learning About Scenes —
The effectiveness of any deep learning architecture could benefit from the above understanding of vision and the comprehension of scenes in DNA based life.  The problem of automated feature extraction is complicated because the data is unlabeled with y information as stated before.
Learning visual comprehension of arbitrary scenes by DNA based life is assisted by the fact that motion occurs and interaction with physical objects and liquid viscosity provides a vastly greater number of dimensions to the input data.

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Can the English language ever be generalized using a set of grammar rules?
In programming languages, there is a set of grammar rules which govern the construction of valid statements and expressions. These rules help in parsing the programs written by the user.
Can there ever be a functionally complete set of grammar rules which can parse any statement in English (locale-specific) accurately and which can be possibly implemented for use in AI-based projects?
I know that there are a lot of NLP Toolkits available online, but they are not that effective. Most of them are trained using specific corpuses which sometimes fail to infer some complex correlations between various parts of an expression.
In other words, what I am asking is that if it is possible for a computer to parse a well-versed sentence written in English as if it were parsed by an adult English-speaking human?
EDIT:
If it cannot be represented using simple grammar rules, what kind of semantic structure can be used to generalize it?
EDIT2: This paper proves the absence of context-freeness in natural languages. I am looking for a solution, even if it is too complex.

I don't have the references to back this up, but I think it has been proven mathematically that you can't do this-- that understanding English completely requires context cues that demand higher-level semantic reasoning that can't be contained in grammar rules.

According to wikipedia most parts of natural languages are context free: https://en.wikipedia.org/wiki/Context-free_grammar#Linguistic_applications

Can there ever be a functionally complete set of grammar rules which can parse any statement in English (locale-specific) accurately and which can be possibly implemented for use in AI-based projects?

Parse it yes, accurately most likely no.
Why ?
According to my understanding on how we derive meaning from sounds, there are 2 complementary strategies:
Grammar Rules:
A rule based system for ordering words to facilitate communication, here meaning is derived from interaction of discrete sounds and their independent meaning, so you could parse a sentence based on a rule book.
E.G. "This was a triumph" : the parser would extract a pronoun (This) with corresponding meaning ( a specific person or thing ) ; a verb (was) with corresponding meaning ( occurred ); ( a) and here we start with some parsing problems , what would the parser extract, a noun or an indefinite article ? An so we consult the grammar rule book, and settle for the meaning ( indefinite article any one of ), you have to parse the next word  and refer to it though, but let's gloss over that for now, and finally (triumph) a noun ( it could also be a verb, but thanks to the grammar rule book we settled for a noun with meaning: ( victory,conquest), so in the end we have ( joining the meanings ):
A specific thing occurred of victory. Close enough and I am glossing over a few other rules, but that's not the point, the other strategy is:
A lexical dictionary (or lexicon)
Where words or sounds are associated with specific meaning. Here meaning is derived from one or more words or sounds as a unit. This introduces the problem to a parser, since well, it shouldn't parse anything.
E.G. "Non Plus Ultra" And so the AI parser would recognize that this phrase is not to be parsed and instead matched with meaning :
The highest point or culmination
Lexical units introduce another issue in that they themselves could be part of the first example, and so you end up with recursion.

if it is possible for a computer to parse a well-versed sentence written in English as if it were parsed by an adult English-speaking human?

I believe it could be possible, most examples I've seen deal effectively with the grammar rule book or the lexicon part, but I am not aware of a combination of both, but in terms of programming, it could happen.
Unfortunately even if you solve this problem, your AI would not really understand things in the strict sense, but rather present you with very elaborate synonyms, additionally context (as mentioned in the comments) plays a role into the grammar and lexicon strategies.

If it cannot be represented using simple grammar rules, what kind of semantic structure can be used to generalize it?

A mixed one where there are both grammar rules and a lexicon and both can change and be influenced based on the AI specific context and experience as well as a system for dealing with these objects could be one way.

My scope of English is restricted to formal usage. If for once we skip very complex entities like metaphors or idioms, would defining a context-sensitive grammar achieve the purpose?

@skrtbhtngr: well in theory yes, it would be a subset as formal language is, context will still be the hard problem though.

Side case: "It was a Triumph" talking about a motor bike for various reason (first bike, burnt bike), Maybe we can argue it takes an uppercase to distinguish the brand from a noun.

I agree that understanding the meaning of the text is the hard part. Just a simple sentence: "Peter went to the cinema." contains a lot of hidden information: Peter is male, he most probably went there to watch a film with his girlfriend, his location changed, etc... Building a model for example a graph based on the text is not enough, because it is not something static, it can describe multiple timelines, events and contexts, plus there is hidden information in every sentence you can infer and use to understand the previous or following sentences.

I strongly disagree with all the former comments. Not because they are wrong, -which they are not - but because they are misleading - though unintentionally.
For example: If one looks at these problems from an academic position, the problems will always seem insurmountable. This is because everything is coldly assessed and calculated in isolation to everything else.
The answer predominantly lies in word association. You have to write a program that can process a vast database of digital books, to register every word and all the words in that language which are associated with it. Plus all the statistical information with each associated word and its associated punctuation.
This will then give you the basis on which an AI can decide several things:

Whether the structure of a given sentence is correct.
If the structure is bad, what the probability is for determining the context and intent of what is being said.
The correct meaning and application of a multifaceted word (Triumph), is by probability - according to the statistics.
To determine where a conversation is likely to be going.
What the correct grammar, and punctuation should be.

So, in conclusion, you have two things to look for: Association and probability.
When digitally databasing a language model, the possibility of word and sentence "strings" occurs, so that every variation of language structure in any given sentence can be determined before, during and after a text sample is being scribed. This intimate control over language model patterns, means that sensitive components such as "subject" and "object" can be determined easily by code.

I think that is overly optimistic. Language is too messy for this to work.

We've concluded that it is a two-faceted, circular problem: structure cannot be inferred without context but knowing the structure also helps infer the context. So, here is your complex solution: start with the context, which is determined by the combination of words in sentence (combinatorics and search problem), from there determine your structure, or "parse" (at this step you can also filter out some insignificant words or at least assign lesser weights to them), go back to the context, back to parsing, and on until you arrive at the meaning. Thus by iterative, recursive reduction the whole problem can be solved.

I'm pretty sure that the answer is "no" in the strictest sense, since English simply doesn't have a formal definition.  That is, nobody controls English and publishes a formal grammar that everyone is required to adhere to. English is built up through an experiential process and it has contradictions and flaws, but the probabilistic nature of the human mind allows us to work around those.
For example, that this "sentence":
This sentence no verb
Technically it's not a sentence at all, since it doesn't have a verb.  But did anybody have any problem understanding what it meant? Doubtful.  Try coming up with a formal rule for that though.  And that's just one example.
Now, could you come up with a formal grammar that covers, maybe, 90% of cases, and is "good enough" for most practical uses? Possibly, maybe even probably.  But I am pretty sure it's not possible to get to 100%.

Your example is not grammatical, so why should a grammar contain a formal rule to describe it?

That's exactly my point.  It's clearly English and it's clearly understandable, but if fails to meet the commonly used definition of English grammar.  Hence it's an example of how real-world spoken English operates outside the bounds of a rigid grammar.


text:
How can these 7 AI problem characteristics help me decide on an approach to a problem?
If this list1 can be used to classify problems in AI ...

Decomposable to smaller or easier problems
Solution steps can be ignored or undone
Predictable problem universe
Good solutions are obvious
Uses internally consistent knowledge base
Requires lots of knowledge or uses knowledge to constrain solutions
Requires periodic interaction between human and computer

... is there a generally accepted relationship between placement of a problem along these dimensions and suitable algorithms/approaches to its solution?
References
[1] https://images.slideplayer.com/23/6911262/slides/slide_4.jpg

The List
This list originates from Bruce Maxim, Professor of Engineering, Computer and Information Science at the University of Michigan.  In his lecture Spring 1998 notes for CIS 4791, the following list was called,

"Good Problems For Artificial Intelligence."

  Decomposable to easier problems
  Solution steps can be ignored or undone
  Predictable Problem Universe
  Good Solutions are obvious
  Internally consistent knowledge base (KB)
  Requires lots of knowledge or uses knowledge to constrain solutions
  Interactive

It has since evolved into this.
  Decomposable to smaller or easier problems
  Solution steps can be ignored or undone
  Predictable problem universe
  Good solutions are obvious
  Uses internally consistent knowledge base
  Requires lots of knowledge or uses knowledge to constrain solutions
  Requires periodic interaction between human and computer

What it is
His list was never intended to be a list of AI problem categories as an initial branch point for solution approaches or a, "heuristic technique designed to speed up the process of finding a satisfactory solution."
Maxim never added this list into any of his academic publications, and there are reasons why.
The list is heterogeneous.  It contains methods, global characteristics, challenges, and conceptual approaches mixed into one list as if they were like elements.  This is not a shortcoming for a list of, "Good problems for AI," but as a formal statement of AI problem characteristics or categories, it lacks the necessary rigor.  Maxim certainly did not represent it as a, "7 AI problem characteristics," list.

It is certainly not a, "7 AI problem characteristics," list.

Are There Any Category or Characteristics Lists?
There is no good category list for AI problems because if one created one, it would be easy to think of one of the millions of problems that human brains have solved that don't fit into any of the categories or sit on the boundaries of two or more categories.
It is conceivable to develop a problem characteristics list, and it may be inspired by Maxim's Good Problems for AI list.  It is also conceivable to develop an initial approaches list.  Then one might draw arrows from the characteristics in the first list to the best prospects for approaches in the second list.  That would make for a good article for publication if dealt with comprehensively and rigorously.
An Initial High Level Characteristics to Approaches List
Here is a list of questions that an experienced AI architect may ask to elucidate high level system requirements prior to selecting an approaches.

Is the task essentially static in that once it operates it is likely to require no significant adjustments?  If this is the case, then AI may be most useful in the design, fabrication, and configuration of the system (potentially including the training of its parameters).
If not, is the task essentially variable in a way that control theory developed in the early 20th century can adapt to the variance?  If so, then AI may also be similarly useful in procurement.
If not, then the system may possess sufficient nonlinear and temporal complexity that intelligence may be required.  Then the question becomes whether the phenomenon is controllable at all.  If so, then AI techniques must be employed in real time after deployment.

Effective Approach to Architecture
If one frames the design, fabrication, and configuration steps in isolation, the same process can be followed to determine what role AI might play, and this can be done recursively as one decomposes the overall productization of ideas down to things like the design of an A-to-D converter, or the convolution kernel size to use in a particular stage of computer vision.
As with other control system design, with AI, determine your available inputs and your desired output and apply basic engineering concepts.  Thinking that engineering discipline has changed because of expert systems or artificial nets is a mistake, at least for now.
Nothing has significantly changed in control system engineering because AI and control system engineering share a common origin.  We just have additional components from which we can select and additional theory to employ in design, construction, and quality control.
Rank, Dimensionality, and Topology
Regarding the rank and dimensions of signals, tensors, and messages within an AI systems, Cartesian dimensionality is not always the correct concept to characterize the discrete qualities of internals as we approach simulations of various mental qualities of the human brain.  Topology is often the key area of mathematics that most correctly models the kinds of variety we see in human intelligence we wish to develop artificially in systems.
More interestingly, topology may be the key to developing new types of intelligence for which neither computers nor human brains are well equipt.
References
http://groups.umd.umich.edu/cis/course.des/cis479/lectures/htm.zip

The 7 AI problem characteristics is a heuristic technique designed to speed up the process of finding a satisfactory solution to problems in artificial intelligence.
In computer science, artificial intelligence and mathematical optimization, a heuristic is a technique designed for solving a problem more quickly, or for finding an approximate solution when you have failed to find an exact solution using classic methods.
The 7 AI problem technique ranks alternative steps based on available information to help one decide on the most appropriate approach to follow in solving problems i.e. missionaries and cannibals, Tower of Hanoi, Traveling salesman e.t.c.
Regarding whether there is a generally accepted relationship between the placement of a problem and suitable algorithms. The answer is that indeed there is a generally accepted relationship.
For example imagine trying to solve a game of chess and a game of sudoku.
If a step is wrong in sudoku, we can backtrack and attempt a different approach. However if we are playing a game of chess and realize a mistake after a couple of moves. We cannot simply ignore the mistake and backtrack.(2nd Characteristic)
If the problem universe is predictable,  we can make a plan to generate a sequence of operations that is guaranteed to lead to a solution. However in the case of problems with uncertain outcomes, we have to follow a process of plan revision as the plan is carried out while providing the necessary feedback. (3rd Characteristic)
Below is an example of the 7 AI problem characteristics being applied to solve a water jug problem.

Image source
https://gtuengineeringmaterial.blogspot.com/2013/05/discuss-ai-problems-with-seven-problem_1818.html

What formally qualifies as requiring human interaction? As I'd've thought that the water jug problem doesn't require human interaction; to me the reason you gave seems like just a precondition for doing the problem in a real life (as opposed to simulated) setting.
If a given solution is composed of multiple steps, and you could therefore break down the search into a search from the solution to the start combined with a search from the start to the solution, is the problem not decomposable? Again, my quabble is with what formally qualifies.
Why is the solution not a state? ...

... The state of the solved bucket is the solution so to me it would seem like the solution is finding a path to the state, with the path in service to the state rather than vice versa; if the state was merely in service to the path that is the solution then I'd think the solution was the path rather than the state.

Additionally, your answer doesn't seem to fully answer the question posed: "Is there a generally accepted relationship between placement of a problem along these dimensions and suitable algorithms/approaches to its solution?"


text:
Once self-driving cars are perfected, will they replace manually driven cars?
After self-driving cars are perfected to the degree that accident statistics are lower for them than human driven cars, will they replace cars with human drivers?  Will there eventually only be self-driving cars?

It likely to be happen, because it's more convenient that way. In general people, organizations and government are always keen to make things more efficient by standarizing things (computers, technology, law, science, etc.) in order to make it manageable and predictable to reduce the time and minimalize the risk of the same mistakes.
The whole world now moves into technological advancement where automation of everything is where we are going, so we can manage complexities in more reliable way, so we can focus on much bigger picture. This includes technology such as mobiles, computers, UAV (delivery drones), robots and now self-driving cars.
The pros of that change would be:

To have safer streets by introducing autonomous cars on the road.
To have fewer drunk, tired, drugged or crazy drivers.
To avoid poor weather conditions.
To reduce braking distances by dropping driver's reaction time and predicting dangerous situations much earlier.

Source: Cyber Physics
Reducing car deaths and costs of GNP.

An estimated 1.3 million people die on the world's roads every year with around 50 million injured or disabled by accidents, with accidents costing countries up to four per cent of their Gross National Product (GNP) yearly. - UN News Centre

To have central point of safety improvements, you cannot change people, but you can fix the known safety issue on global scale.
To introduce global standards from the central point (e.g. new law to which manufactures needs to apply).
To increase car safety in general on larger scale.
And so on.

Why we need the 'only self-driving cars'?
The Secretary-General said the UN would work hard to prevent further deaths on the roads:

Many tragedies can be avoided through a set of proven, simple measures that benefit not only individuals and families but society at large.

Here are my points:

To achieve 'a set of proven measures' - do not allow people to drive - simple.
People tend to break the rules, always, so do not allow them to drive without permission.
Reduce stealing cars and other crime.
Law enforcement dream is to able to stop any car on demand.
Do not allow drunk people to drive a car.
Disallow terrorist attacks, like in Nice where truck killed over 80 people.
Avoid bank robberies and similar which are possible by escaping fast cars.

Is it possible? I believe it depends on specific countries and unions and how quickly we're able to advance and be ready for such change.
To support above points and summarize the 'only self-driving cars' point, please see below references which shows that this is already happening:

2014: Uber will eventually replace all its drivers with self-driving cars
2016: Google's self-driving car system has been officially recognised as a driver in the US.

The move is seen as a first step towards changing the law for cars that have "no need for a human driver".

2016: Beverly Hills to replace public transport with self-driving cars
2016: San Francisco pitches $149 million plan to replace cars with self-driving vehicles

San Francisco’s future is autonomous and shared vehicles – and that future may be only a decade away.

2016: Otto Self-Driving Truck Company Wants to Replace Teamsters


text:
How does Wit.ai convert sentences into structured data?
The Wit.ai is a Siri-like voice interface which can can parse messages and predict the actions to perform.
Here is the demo site powered by Wit.ai.
How does it understand the spoken sentences and convert them into structured actionable data? Basically, how does it know what to do?

I can't speak to wit.ai specifically, but I can tell you a little bit about how similar applications work. Specifically, I can talk a bit about Apache Stanbol which also converts free text into structured data.   That said, I should prefix this by saying there isn't just one way to "get there from here."  Many techniques could be part of a stack for accomplishing this goal.
Anyway, in the case of Stanbol, they run the text through multiple processing engines, sequentially, with different engines affecting the final output.  One engine simply does Named Entity Recognition using OpenNLP.  This identities discrete named "things" - people, places, companies, etc.  Another engine does entity matching with a pre-established database of entities - specifically (in the out-of-the-box configuration) a dump of entities from DBPedia.  Where a match is found, the text from the original input is assigned to the entity.  In the case of a collision, it assigns a weight to the mapping so any downstream consumers can use probabilistic techniques to select the "correct" mapping.
There are, of course, more details that I left out.  Before NER can happen there is parsing and tokenizing and other NLP activities.  But a big part of the basic process is doing NER and then doing the entity matching.
And in the case of Stanbol, you can add your own entities and corresponding structured data, as well as your own engines.  So, for example, if you wanted to write an engine based on neural networks / deep learning, and plug that in, you could.


text:
What are the differences between machine learning, pattern recognition and data mining?
I know a little about these subjects. I found them similar to each other. Can anybody explain the differences between them?

Terms in a field are sometimes defined unambiguously. For instance, we know what convergence means when communicating about machine learning algorithms in academic publications because it has a formal definition in an older field, mathematics. However, the term machine learning is defined ambiguously across academic publications.
Perspectives on Machine Learning
Some see it as a branch of applied probability and statistics involving models with curvature (not usefully approximated by a first degree polynomial) and the application of those principles in digital computing. Some see it as an extension of the work of James Watt and Le Roy MacColl the application of the feedback control concept to digital control. Some see it as the natural result of the pioneering AI work of Norbert Wiener and John Von Neumann, where the adaptive qualities of nature, including neurochemistry, are simulated with the intention of birthing artificial life.
Some don't see that deeply into ML and imagine that its a set of classes and libraries the mastery over which will make for a great career. As shallow as that may seem, that concept may be as true as the other three deeper conceptions.
Perspectives on Data Mining
The term data mining is like that. Each book, and sometimes each chapter within the same book, seems to have its own distinct conception of the verb mining. Although these definitions have some similarity, the term is nothing like the term convergence or even database in IT or melody in music.
Unfolding the metaphor contained in the two words of the term, data mining is digging up data, and perhaps that's a satisfactory definition for the most general use of the term. The information sought is not on the surface, like diamonds dropped onto the ground, but rather underneath and covered with other materials so that one has to survey, dig, and process to get past the worthless material and reveal the gems.
This term has another vantage point. In systems theory there is an important distinction between noise and signal. In data science, what an electrical engineer would call the signal is the listing of statistics, table, graphic, or other visualization miner's client needs to make management decisions. The noise is everything obscuring the signal through complexity, volume, or prominence.
Perspectives on Pattern Recognition
The term pattern recognition is perhaps the most ambiguous because neither of the two words arose in a scientific context.
Early uses of the word pattern in English (and its equivalent in other languages) are related to shelter construction, farming, or early textiles. The notion that the shape of a letter or other symbol or a sequence of phonetic elements that make up a spoken word were patterns only arose recently. Much of the early and current work in pattern recognition involving computers had to do with converting natural language expressions into some functional machine representation.
The term pattern is also ambiguous because of gestalt, the dependence of perception on the orientation of the recognizer at the time of recognition. A sand castle may have an architecture to an architect, a chemical composition to a chemist, an indication of civilization to the starving passengers of a boat adrift, an obstacle in the way to a crayfish, and an imaginary home for a child.
To a mathematician, it may be a three dimensional form with particular surface topology, feature curvature, and dimensions. To a physicist, there may be no significant difference between the sand castle and the seagull flying over it or the air in between them (unless the sand castle is the triumph of the physicists own child).
The orientation of the machine is even more a constraint on the emulation of some aspect of human perception than demonstrated in gestalt psychology experiments. The human can adjust perception when a new kind of pattern or structure is pointed out. Until AI progresses further, that kind of experience, where the computer would say, "Oh, yes. Now I see the old woman in the picture of the young woman," is only realizable in software to the most primitive degree.
Taken literally, the term recognition means the repetition of a cognitive event, but that is not what we mean when we say, "I recognize that," in common speech. We usually mean that a mental search for some set of sensory features (not necessarily any more a pattern than anything else in the sensory stream) is identified and associated with some internal object or concept.
The most common use of a convolutional network (CNN) is neither of these. It is usually used to categorize objects or as a feature extracting sensory front end to a much larger AI design.
Overlap and Associations
With all these ambiguities present, some overlap may be apparent, in that some AI activities may thoroughly involve two or all three of these terms. Certainly some associations between the three terms are obvious.

When mining data, we may be looking for a particular kind of structure in a sea of data and have a particular search strategy to narrow the search and make it manageable for computing resources available. The test use during the search may be called pattern recognition.
In machine learning, we may train a network of artificial cells to assist in locating data or features in data that are meaningful to the stakeholders in the project. That would be using ML for data mining projects.

A large number of other associations between the three terms can be made. Which ones would appear most prominent to the expert would depend on the scientific, research, and career orientation of the expert.
Not Sufficient Overlap to be Synonyms
It would be difficult however to declare any two of the three to be synonymous. The three arose out of different kinds of research and from different orientations. Only some of that etymology is preserved in the terms themselves.

Machine learning is a form of pattern recognition. Machine learning is basically the idea of training machines to recognize patterns and apply it to particle problems. Data science is the science of apply machine learning to practical problems such as creating better search engine results or classifying images. Patten recognition is pretty much the umbrella term here. However, I think that the pattern recognition term is sort of falling out of style with how modern data scientists are training neural networks and other machine learning models.

In data mining, we can use machine learning (ML) (with the help of unsupervised learning algorithms) to recognize patterns.
Pattern recognition is a process of recognizing patterns such as images or speech. We can recognise patterns using ML. For example, once a neural net is trained, using ML algorithms, it can be used for pattern recognition. Other methods, even ones not related to ML and data mining, can be used for pattern recognition, such as a fully handcrafted pattern recognition system.
In general,

data mining is mostly associated with statisticians,
ML is mostly associated with computer scientists whereas,
pattern recognition is mostly associated with engineers.


text:
How to determine if an Amazon review is likely to be fake using text classification
I'm currently in the research stage of building a web app in ASP.NET where the user can input a URL to an Amazon product, then the app would determine how likely its reviews are to be genuine. I need help figuring out what algorithm to use in determining if a certain review is likely to be deceptive. I want my app to behave similarly to Fakespot or ReviewMeta. I realise a tool like this won't be 100% accurate and that's fine.
So far I read parts of this book which seems to be recommended a lot to NLP newbies but haven't found anything that applies to such a specific problem. I also read this article but it's based on hotel, restaurant and doctor reviews. I'm trying to find a more general method that can be applied to any product. Any help would be greatly appreciated!

This will not be that hard of a problem once you have a lot of training data. But, before you have a lot of training data, you will need to get some training data one way or another. You will need a lot of training data for quite a few of the models that will give you a high accuracy. Then, you will probably want to use a Long short term memory recurrent neural network along with a word2vec model, or maybe even a sentence/ paraphraph2vec model. This will be able to give you some fairly good results with a little bit of tweaking, but if you want really accurate results you will want to try an ensemble. An ensemble is when you use multiple neural networks that provide an output that gets fed through another classifier(usually XGboost) in order to achieve better classification results. You might also want to try a little bit of feature engineering. Finally, in order to implement this model, you can take one of several approaches. First off, you can use tensorflow serving. Tensorflow was created by google and used by much of the machine learning community. However, tensorflow was written in python, so you will need to use tensorflow serving in order to use it within your Asp.net app. Or, you could use microsofts CNTK. Or finally, you could use a custom built implementation, but this would take a lot of time and not be worthwhile unless you are doing research. Also, if you really do not want to use deep learning for one reason or another, you can probably use a simpler model, but you will need to understand your data. You will need also need to understand your data if you are doing deep learning but maybe not as much.

This is a common question, but before hand, you need a lot of label data, the more, the better.
Since all the reviews from amazon are not label as deceptive or not, you may manually label them.
And then, you can use NLP tricks as what describe by Aiden Grossman.
A simple way is let every word as one of vector and the output is one vector(deceptive or not). A better way is use word2v as input, and lstm as hidden layer.
Beware this anomaly detection problem, that is the deceptive review may extreme seldom happen. Please carefully handle precision and recall.
Asp.net is OK. You can use your inference model as backend service, and asp.net can call this service with any js framework.


text:
Will AI develop in hybrid ways?
There are two general ways in which AI can interact with humans:

Implants of AI devices into human bodies or brains — Such implantation has already begun with health monitors and could grow to include cognitive access to general purpose digital computing.  Once such is accepted, it is possible that humans will emerge with behavior largely defined by its implants therefore qualify as a hybrid AI systems.
Embedding of AI systems into business or government organizations — It is possibliites that the behavior of organizations using AI systems for business decisioning will eventually be defined in behavior largely by those systems and would therefore qualify as a hybrid AI systems.

How probable are these possibilities, considering human history and current cultural trends in technology acceptance?

Regarding the second part of my question: The publicly available military plan of China for the next 5y announces a transition from informational guided warfare to an AI guided army. For example: Will there be a future moment in history, where in peace times the chinese AI system will decide to secretly start an attack on the power grids of the EU ?

These two are not "general" ways in which AI interact with humans , especially not the first one. In fact most of the applications we have complex AI algorithms deeply rooted in them , your search engine , smart-reply , translating systems all employ some kind of "AI" or to be more specific "machine-learning" algorithms in them. and many organizations are already using business-intelligence systems which you can call "AI" in some sense.


text:
Identify "actionable" intents in email messages
I am trying to build a model which would take an email message (in English, extracted subject, and body of the email) and identify if it has a question, request or a proposal. Basically, I would like to see the mails that I've not replied but needs a reply. The model can be used as a "filter" in an email client.
What is the best way to go about it?
Related Work:
Parakweet Lab's Email Intent Data Set
Learning to Classify Email into "Speech Acts"

When you're trying to implement some project or working on a project model,you need to give a snippet of what you're working. In order to get effective feedback from the community.For your information check through the guide lines

@quintumnia edited and gave a bit more detail.

Am working on a similar use case.
Could you implement? If yes, please share the solution.

What are you findings so far? What have you tried? What is your knowledge of AI/ML? Have you heard of classification? Do you have dataset? Do you have a labelled dataset? These are all questions that should have been answered in your post to make it a good post.

This is a categorization problem, not unlike a spam filter. Instead of flagging an email as spam/not-spam, you are flagging whether it has one of the action categories that you have described.
You'll need to start by assembling a training corpus of example email messages and labeling each example to identify which (maybe multiple) of your categories, if any, are actually present in that email.
Next, pre-process that data to extract features for each message. Examples of typical features include word (or n-gram) counts/frequencies (bag of words). As a shortcut, you might to include as a feature a boolean indicating the presence or absence of a particular word or phrase that you suspect will be predictive of one or more categories. Techniques such as stemming can help reduce the number of words/n-grams being used (often increasing accuracy).
Once you have a dataset that consists of features and labels for each training email (possibly breaking this set up into subsets for training, cross-validation, and testing), you'll want to apply a supervised classification algorithm. You might start with linear classifiers such as logistic regression or SVMs, and if you're unsatisfied with the resulting accuracy then you could advance to neural techniques.

"straightforward" ? too optimistic

Optimistic perhaps, but all is relative. :-) I'll try to expand a bit more on high level steps. As a note to beginners who want to dig deeper into implementation, Andrew Ng's Coursera course might be a good starting point.


text:
Advanced NLG - robot journalist
I want to produce a bot in Python that automatically generates short football summaries from Whoscored data. For my first stage I generate the articles with different sentence templates and lots of rules where the data is used.
Now I want to move to the next stage and start looking into NLP and more advanced NLG. I already scraped numerous articles to create a corpus. How should I move on and do next? Any advice would be much appreciated as I'm new in this.
Thanks!

Do you have summaries for a chunk of dataset? i.e. Is it a labelled dataset?


text:
Classification of English Words Based on Class [Noun, Verb, Adjective, Preposition, etc]
def Interpretation_be(sentence):

     be = ( 'be', 'been', 'is', 'was', 'are', 'were' )
     place = ('there', 'here')
     gender_identity = ('he', 'she')
     indicator =('it', 'that', 'this', 'these', 'those')
     exist =('exist')
     equal=('equal')
     condition=('condition', 'status', 'quality', 'state', 'situation' 'circumstance')
     number =('1', '2', '3', '4', '5')
     consequence=('consequence', 'result')
     to = ('to')

     if sentence.split()[1] in be:
          if sentence.split()[0] in place:
               print(sentence.replace(sentence.split()[1],'exists'))
          if sentence.split()[0] in gender_identity:
               print(sentence.replace(sentence.split()[1],'equal to'))
          if sentence.split()[0] in indicator:
               print(sentence.replace(sentence.split()[1],'equal to'))

     if sentence.split()[1] in exist:
           if sentence.split()[0] in place:
               print(sentence.replace(sentence.split()[1],'is'))

     if sentence.split()[1] in equal:
          if sentence.split()[0] in condition:
                  print(sentence.replace(sentence.split()[1],'is'))
          if sentence.split()[0] in number:
                  print(sentence.replace(sentence.split()[1],'is'))
          if sentence.split()[0] in consequence:
                  print(sentence.replace(sentence.split()[1],'is'))

     if sentence.split()[2] in to:
           print(sentence.replace(sentence.split()[2],'toward'))
           ##need to discern between preoposition to and infinitive to##

##result_check##
Interpretation_be('i have been to Spain')
Interpretation_be('there was a cat')
Interpretation_be('there is a woman')
Interpretation_be('he is a boy')
Interpretation_be('it is a cat')
Interpretation_be('there exist a hat')
Interpretation_be('situation equal to bad')
Interpretation_be('1 equal one')
Interpretation_be('consequence eqaul bad')

I am trying to make up English rephraser as a most basic start of natural language processing.
While building up like above code, I had wanted to get some already-classified text data which criteria of classification is word-class, such as Noun, Verb, Preposition etc.
Any good text data that I can obtain?
Please let me know.


text:
Splitting audio consisting of male / female speaker into segments
To create language flashcards I would like to split an audio course into many single audio clips. They're basically a man and a woman speaking after each other. The intervals aren't regular so it's not possible to split it after time intervals. Furthermore silence detection is not possible since some sentences also include pauses. I have already tried diarization using LIUM but the timings were completely wrong. Additionally the audio course includes a transcript which would certainly be machine-readable consisting of the English sentence and the Japanese sentence as well as its Romaji version (Japanese words using English letters).
I'm not experienced in AI, so I'm looking for a solution which isn't to difficult (like constructing and training my own neural network).
I have some programming experience, so a mathematical approach would be fine.
Links:
audio course: http://www.japaneseaudiolessons.com/download-japanese-lessons/
LIUM: http://lium3.univ-lemans.fr/diarization/doku.php/welcome

If this is for a specific task, you'll be much better off adapting it for and using something like Mechanical Turk to get it done rather than trying to assemble a reliable fully-automated solution

Yeah, given that you will listen to these sentences many times anyway, you are probably best off just going through them once and cutting them by hand.

I've considered this already, but splitting 45 hours of audio certainly is a full time job, so I'm looking for an automatic solution.

Edit: It's not clear what exactly you're trying to accomplish...  My answer assumes you wanted to split the man and the woman's audio, but re-reading your question make me think otherwise.
Note:  Don't expect this to be a perfect answer.  I'm not an expert in the field, just an interested student.  I can't comment on this site yet, so I'm submitting this as an answer.
This sounds less like a problem suited for machine learning and more of a... statistical analysis(terminology?) problem.  Why? You have an extremely small sample space to train on, and the natural cost function would require you to go through the entire recording and manually classify the track anyway.
Your problem is almost well suited for an Independent Component Analysis approach, except for the fact that you only have one audio source.  ICA requires one source for each feature you want to extract.  It might be a good start though to familiarize yourself with the techniques and terminology.  See the Wikipedia page for ICA, an article called "ICA for dummies" by Arnaud Delorme, and this paper: ICA: a tutorial.  I would link the first two, but apparently I need more site rep.
I also found a paper which seems to perfectly match your problem, from Mitsubishi Electrical Research Labs: Separation of Mixed Sources by Independent Subspace Analysis.  It's a bit a of a heavy read, to say the least, but that might be your best shot.  I'll quote the abstract, emphasis mine:

We propose the method of independent subspace analysis (ISA) for separating individual audio
  sources from a single-channel mixture. ISA is based on independent component analysis (ICA)
  but relaxes the constraint that requires at least as many mixture observation signals as sources. A
  second extension to ICA is the use of dynamic components to represent non-stationary signals.
  Sources are tracked by similarity of dynamic components over small time steps. We propose
  a method for grouping components by partitioning a matrix of independent component crossentropies
  that we call an ixegram. The ixegram measures the mutual similarities of components
  in an audio segment and clustering the ixegram yields the source subspaces and time trajectories.
  To demonstrate the techniques we give examples of ISA applied to separation of musical and
  speech sources from single-channel mixtures.

Good luck!

Thank you very much for your detailed response! Unfortunately that's currently beyond my level of understanding. I'm just looking for a ready-to-use approach


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Should we focus more on societal or technical issues with AI risk
I have trouble finding material (blog, papers) about this issue, so I'm posting here.
Taking a recent well known example: Musk has tweeted and warned about the potential dangers of AI, saying it is "potentially more dangerous than nukes", referring the issue of creating a superintelligence whose goals are not aligned with ours. This is often illustrated with the paperclip maximiser though experiment. Let's call this first concern "AI alignment".
By contrast, in a recent podcast, his concerns seemed more related to getting politicians and decision makers to acknowledge and cooperate on the issue, to avoid potentially dangerous scenarios like an AI arms race. In a paper co-authored by Nick Bostrom:
Racing to the Precipice: a Model of Artificial Intelligence Development, the authors argue that developing AGI in a competitive situation incentivises us to skim on safety precautions, so it is dangerous. Let's call this second concern "AI governance".
My question is about the relative importance between these two issues: AI alignment and AI governance.
It seems that most institutions trying to prevent such risks (MIRI, FHI, FLI, OpenAI, DeepMind and others) just state their mission without trying to argue about why one approach should be more pressing than the other.
How to assess the relative importance of those two issues? And can you point me any literature about this?

Welcome to AI!  Informally, most of the scholars I've been reading seem to suggest the social impacts strongly outweigh the technical issues at present.  Specifically, we know that automation is going to replace many, many jobs in the near future, we know that strong forms of AI have more and more control over our behaviors (social media engagement strategies, as an example.) By contrast, apocalyptic scenarios involving AI are still hypothetical.  (Doesn't mean the technical issue is not important, just less immediate.)

Neither AI alignment nor AI governance are important yet. We are so far away from AGI that we don't even know what is missing.
We don't set up safety instructions for interstellar travel, so why should we do it for AGI? I can also come up with a lot of dangers of that...
There are real dangers of AI, though. Including societal issues:

Blind trust:  Trusting a machine, although it is not perfect. Just because it is good in most cases and seems to be rational/objective. Example: Machine Bias
Unemployment: AI has the potential to replace a lot of low-skill jobs with very few super high skilled jobs. For example, self-driving cars (< 100 people for one manufacturer, I guess) could replace all jobs in trasportation (1,076,200 of 31,373,700 in Germany (source) - that is 3.4%!). See CG gray video.
Weapon Systems: The thread of leathal autonomous weapons is real. There is the danger of efficient slaughterbots and the danger of errors - similar to the Wech Baghtu wedding party airstrike or that one

There are a couple of other problems, but I think they are less severe:

Better fakes: lyrebird.ai gives a good demo
Spam: I think it's easier to improve the spam filters, but humans might have a harder time
Data is dominance: Companies like Amazon will have a dominant position on the market as they were the first to aquire important customer information
Bubbles / false hopes: AI (or let's better say Machine learning)  is often seen as a golden hammer. It is not.

I don't think you can say Neither AI alignment nor AI governance are important yet. Here: https://youtu.be/yQE9KAbFhNY?t=287, Robert Miles makes the point that most experts in the field take those concerns seriously. Source: https://arxiv.org/pdf/1705.08807.pdf at page 13.


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Machine learning to predict 8*8 matrix values using three independent matrices
Problem Statement
I have 4 main input features.
This is a small snippet of the data for clearer understanding.
Gate name -> for example AND Gate
index_1 -> [0.001169, 0.005416, 0.01391, 0.03037, 0.06381, 0.1307, 0.2645, 0.532]
index_2 -> [7.906e-05, 0.001123, 0.00321, 0.007253, 0.01547, 0.03191, 0.06478, 0.1305]
values -> [[11.0081, 14.0303, 18.8622, 27.3426, 43.8661, 76.7538, 142.591, 274.499],
           [11.3461, 14.3634, 19.1985, 27.6827, 44.2106, 77.0954, 142.926, 274.879],
           [12.258, 15.2816, 20.1095, 28.5856, 45.1057, 77.9778, 143.8, 275.758],
           [13.665, 16.7457, 21.5835, 30.0545, 46.5581, 79.4212, 145.252, 277.192],
           [15.6636, 18.9526, 23.9051, 32.4281, 48.9011, 81.7052, 147.477, 279.371],
           [17.8838, 21.5839, 26.8957, 35.7103, 52.3901, 85.2132, 150.89, 282.714],
           [19.3338, 23.6933, 29.7184, 39.1212, 56.4053, 89.9721, 155.913, 287.637],
           [18.7856, 23.9999, 31.1794, 41.7549, 60.0043, 95.0488, 162.951, 295.005]]
My task is to predict this values matrix, given that I have index_1 and index_2. Originally this values matrix is propagation delay, calculated using a simulator called SPICE.
Where I am facing problem

There is no written relation between Index_1, index_2 or values since simulator calculates this value using it's own models.
I have made a CSV file which contains the data in separate columns.
Another approach that I thought. If I can give index_1, index_2 and any 5*5 sub-matrix to the model, and the model can predict the values of whole 8*8 Matrix. But the problem is again, which machine learning model do I use.

Approaches Tried so Far

I have tried a CNN model for this but it is giving me very low accuracy.
Used one dense fully connected neural network but it is over-fitting the data and not giving me any values for matrix.

I am still stuck at how to predict the matrix values given this data.  What are other strategies can be used?

Have you tried Generative Networks such as GAN, you may be able to model this problem better with GAN.

I did consider it but, it wont be able to predict the values of new matrix, it will simply remove the noise given after input matrix and try to recreate the original matrix. Correct me if i am wrong

In principle, you can use a fully connected neural network with reshaping for this kind of problem. The questions you should ask yourself are:

Baselines: What are the simplest algorithms to approach the problem? How good would a human be?
What do I know? Are there any properties of the 8x8 matrix that will always be true? For example, it seems as if the values from left to right strictly increase. Same for top to bottom. This can be used!
Are the outputs independent? (e.g. having the (1,1) entry of the matrix, do I know something about the $(i, j)$ entry of it?

Then, of course, there are more specific things I could imagine. If you were not clear about (2), you might have (wrongly) used softmax/tanh/sigmoid in the last layer. You might simply have too little training data for neural networks. Your neural network implementation might be broken.


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Difficulty understanding Keras LSTM fitting data
I'm try to train a RNN with a chunk of audio data, where X and Y are two audio channels loaded into numpy arrays.  The objective is to experiment with different NN designs to train them to transform single channel (mono) audio into a two channel (stereo) audio.
My questions are:

Do I need a stateful network type, like LSTM? (I think yes.)
How should I organize the data, considering that there are millions of samples and I can't load into memory a matrix of each window of data in a reasonable time-span?

For example if I have an array with: [0, 0.5, 0.75, 1, -0.5, 0.22, -0.30 ...] and I want to take a window of 3 samples, for example.  I guess I need to create a matrix with every sample shift like this, right?
[[0.00, 0.50, 0.75]
 [0.50, 0.75, 1.00]
 [0.75, 1.00,-0.50]
 [1.00,-0.50, 0.22]]

Where is my batch_size? Should I make the matrix like this per each sample shift? Per each window? This may be very memory consuming if I intend to load a 4 min song.
Is this example matrix a single batch? A single sample?

Yes, intuition says that RNNs like LSTM or GRU will work better in your case, because predicted values might depend on input patterns corresponding to much earlier time intervals.
There is no reason to create samples shifted by a single measurement because many of the samples will contain pretty much the same information for your model. A viable approach is to shift by sample size. Keeping some overlap between samples is feasible as well.

Keep in mind that as a general rule for processing audio data, it makes sense to convert raw audio data into vectors representing audio spectrum before feeding it into LSTM RNN (see this video for e.g. https://www.coursera.org/learn/nlp-sequence-models/lecture/sjiUm/speech-recognition).
Batch size is different from sample or window size in your case.


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For forecasting and trading control, given limited data, what AI approaches are well matched?
I'm working on stock price prediction and automatic or semi-automatic control of trading.  The price trends of these stocks exhibit recurring patterns that may be exploited.  My dataset is currently small, only in the thousands of points.  There are no images or very high dimensional inputs at all.  The system must select from among the usual trading actions.

Buy $n$ shares at the current bid price
Hold at the current position
Sell $n$ shares at what the current market will bear

I'm not sure if reinforcement learning is the best choice, deep learning is the best choice, something else, or some combination of AI components.
It doesn't seem to me to be a classification problem, with hard to discern features.  It seems to be an action-space problem, where the current state is a main input.  Because of the recurring patterns, the history that demonstrates the observable patterns is definitely pertinent.
I've tried some code examples, most of which employ some form of artificial nets, but I've been wondering if I even need deep learning for this, having seen the question, When is deep-learning overkill? on this site.
Since I have very limited training data, I'm not sure what AI design makes most sense to develop and test first.

This is an interesting problem, the answer of which is highly coveted for obvious reasons.  The production of an answer in this public space is appropriate, provided one believes in a more level distribution of assets across those alive and is comfortable with rewarding technical people for the abilities they developed while others might have been engaging in less technical pursuits.  I only ask that those who may successfully employ it consider using at least some of any windfall generated in a philanthropic manner, to benefit the less fortunate.
Here we go.
Abstract Algebra
One of the fundamental principles presented in Morgenstern and von Neumann's Theory of Games and Economic Behavior is the idea that the value of a monetary unit may vary based on actions of those that control the monetary system, but the underlying value itself has abstract algebraic properties favorable to prediction.  These properties, the book proposes in early chapters, permit additive modelling.
Explained another way, imagine a pile of paper bills of a particular denomination.  The selection of any one of the bills from anywhere in the deque is identical in value to the selector as any other selection of one bill.  The selection of which particular bill is also not a factor in the value remaining in the deque after the selection.
This is also true of shares, which is why serial numbers are not a trading parameter at the investor-trade interface.
For more theory behind this, the interested parties can study group theory and rings.
Critique of M&VN
This convenient additive view of value beneath monetary systems has been critiqued.  The value of assets, products, and services may be demonstrated to vary with life expectancy and philosophic divergence.  While some people fall into an investment mode, others may be divesting.
Fortunately, similar to Isaac Asimov's fictional presentation of psycho-history, the predictive mechanisms that may be developed are, in fact, not fettered by this investment versus divestment mode variance.  The volume of trading and number of traders typically hide the variance through cancellations across either side of mean behavior.
Those with considerable assets can exploit this caveat and a large number of other caveats in the additive nature, but most traders cannot.
Patterns in Stock Values
The phrase that may have started a number of investment firms down the path toward the use of machine learning in high speed training, leading to a conspicuous growth in the number of billionaires in the world, is a simple phrase from Darren Aronofsky's Pi, a 1998 movie in black and white.  The protagonist, Maximillian Cohen, looks at the scrolling LED version of a ticker tape and says, "I know these numbers," and then begins to verbally state each price just before it appears.
The patterns in stock prices have intrigued those who watch trends since the emergence of the New York Stock Exchange.  But they are not cyclic, in spite of the naive belief that they run in cycles.  They only appear to run in cycles, just as one might find a pattern they think they understand in a graphic representation of the Mendelbrot Set, but when the renderer of the set zooms in or out, the greater detail or the generality respectively reveal that the understanding was an illusion.
Attempts to use Fourier Analysis as part of a predictive strategy for anything but extremely narrowly timed speculation have been of limited value.  The reliability of the prediction of advantageous trading choice, expressed as a probability $P(C)$, dwindles to pure entropy (zero bits of actual predictive information) as the clock time of the prediction increases relative to that of the last time in the complete vector of state historic states $\vec{S}$ used for prediction.
$$\lim_{(t - t_\emptyset) \rightarrow \inf} P(A|\vec{S}) \rightarrow 0$$
In plain terms, the risk of trading based on a predictive model from past price trend increases with the staleness of trend information.
The increase in entropy is likely to be a feature of the prediction of any system as complex as the stock market even as prediction tools mature.  It is a consequence of initial input sensitivity combined with measurement inaccuracy and the inevitable unsatisfactory distributed model of decisioning.  These constraints may not be inescapable, but they are certainly resistant to brute force attacks, such as adding artificial network layers.
There are tools that can be predictive in ways better than trying to identify a spectrum using Fourier Analysis.  But let's first consider a spectrum, which is how music listeners identify the pitch, loudness, timbre, and envelope of notes.
Unlike the playing and listening to music, the distribution of pitches in the spectrum from moment to moment are not purely a representation of the spectral distribution in which value fluctuations reside.
The spectral analysis of stock prices reflects artificial bounds.  The spectrum is bounded by the market sampling rate and Nyquest's criteria on the high frequency side.  It is bound by the accumulation of additive effects that tends price fluctuation toward a Gaussian distribution on the low frequency end of the spectrum.
Three-fold Challenge of Freshness, Accuracy, and Complexity
Some may note that the intention of the question, to control (with or without human moderation) a stock portfolio to maximize profit is a planning problem that appears to have the Markov Property, provided the trader is not wealthy or trading in penny stocks, since the impact of previous trades seems unlikely to have significantly impacted market trends.  As we shall see, this appearance may not be objectively true.
Initial input sensitivity is a nearly universal quality of complex systems.  In the case of the stock market, the complexity is high.  Consider a cursory examination of the magnitude of market complexity, understanding that a single price is affected by neighboring prices in that sector and business and in the major market indicators that people watch.  A stock is almost completely isolated in terms of trading actions but inherently tied to all traders and other stocks through an interconnected network of IT pipelines, trading interfaces, news events, and brains, each containing a few billion neurons with complexities much greater than artificial cells.
A popular name for initial input sensitivity is the Buterfly Effect, which, properly explained is this.

Whether a butterfly flaps its wings in Brazil may determine the path of a major storm in Peking a month later.

Sometimes the initial word, "Whether," is not included, which renders the explanation incorrect.  It is a statement of the effect of whether or not the perturbation occurs.  Once cannot say that the air movement from the wing flap causes the storm but rather the presence of a small perturbation may be grossly amplified over time in a complex system.  Or it may not.
Studies of chaotic systems have proven this potential of amplified effect as a characteristic of many if not most complex systems of differential equations with sufficient curvature in their behavioral surface to cause a system phenomena that could most descriptively be called, "Radically increasing bifurcations."
For those seeking greater depth of understanding, the work of Fourier, Nyquist, Cantor; G. Julia and P. Fatou; and Aleksandr Lyapunov may be of interest.
Because of initial input sensitivity, the Markov Property cannot be guaranteed for stock trading, even at low levels of investment.  Even though, technically, the Markov Property is not guaranteed, its assumption at low trading levels and in the leveraging of fresh data for short term gains, it may lead to reasonable approximations with sufficient reliability to win at the negative sum game of trading.
Trading as a Below Zero Sum Game
In a zero sum game, those trading with stale information and trying to apply ineffective predictive strategies will, over any longer period lose. Their monetary assets will pass to those with fresher information using effective predictive strategies.  With trading fees, the condition is more challenging, because the sum of all interactions is below zero by the sum of the fees.
Gaining Advantage
To gain advantage in any practical way above those who may have already employed machine learning in their trading operations or hired a funds manager that already did, there will have to be some clever application of more advanced principles in a way that drives effectiveness even higher than those already in game play.
These are some key elements.

Since the windowing over the price sequence of any LTS (liquid tradable security) to derive a spectrum will produce spectra with no particularly improved predictive power than the price sequence in the time domain, the goal must be to discover the nature of the patterns beyond spectral features.
For very short duration investments, spectral features may provide some advantage but perhaps not enough to overcome others using similar predictive strategies.
Some stocks are predictors of others in that there is a strong unidirectional or bidirectional causal relationship.  For instance, the cost of a barrel of oil impacts the future cost of toys, since plastic is a refinery product.  The development of a semantic net with causal relationships modeled and tuned with each epoch of training can produce predictive power that may exceed the market norm.  In this context, something like convolution may be advantageous, but not in the frame-horizontal-vertical-pixel-layer sense, since semantic networks have a non-cartesian topology.  The edges and vertices in a semantic net of LTSs don't make nice 90 degree angles that lend themselves to trivial applications of loops and arrays.
Attractors, another analytical concept from the world of chaos, are an analytical tool that lend themselves to trends in stock groups and possibly individual stocks, however diversification principles of investment also apply well to attractors.  The thing to study is auto-correlation in $\mathbb{R}^{(m n)}$, where $m$ is the number of features in the stock statistics for which there is a fresh historical record and $n$ is the number of LTSs in the group.  The group is not a trench, with varying risk pooled together to provide a favorable curve of return versus stability.  The group is a set of LTSs that correlated positively or negatively and with or without relative temporal shift (delay).
Leverage factors outside the exchange.  For instance, the result of a close election is a key event that impacts markets.  Terrorism is.  Experts who may know nothing about where the market is going but sound good can vastly sway trends.  Some billionaire somewhere may have rode a wave in a temporary correlation between the price of oranges and the price of gold, floating down the eastern seaboard in a yacht, not having the faintest idea why oranges correlated to gold, but analysis showed too many correlative effects to deny the tie, and they rode it until the model broke.

Project Strategy
It is recommended NOT to test with actual trades.  Build a system and create a simulated portfolio.  Do not use retirement funds and college funds to test a system pitted against some of the best trading automation in the world funded by a few hundred billionaires.  You will end up in Gamblers Anonymous that way.  Also understand that, as time passes, the bar will rise, and more people will be trading using predictive systems so that what is a profitable system today may consume all your time and energy and eventually lose everything you gained as other systems gain advantage over yours.
Also remember that asset management, in the final summary, is a zero sum game.  Trading gains are inescapably stuck inside the larger game where the pieces are placed back in the box and the box back in the closet.  This is true even if you have children or grandchildren.  Eventually, chaos has its sway.  Our great, great, great, great grandchildren, who probably won't care to know our name, might end up poorer because we were wealthy or wealthier because we were poor.  It's the Butterfly Effect again.
There are infinitely more important things than asset accumulation.  Ask any honest billionaire with an advanced terminal condition.
Layer Depth?
Yes depth.  There is no way to meaningfully model even the most banal aspects of the complexity of markets with a few layers of perceptrons.
Real Time Learning?
Reinforcement, yes, because the Q-learning algorithm class and variants are a powerful tool for re-entrant, real time learning system design.  However, be careful to not necessarily assuming the Markov Property.  Whether you can use an algorithm, right out of the box, wired directly to inputs from market data with no normalization and prevail in a sub-zero sum game is unlikely.
Innovation will be a requirement to any earned and even short term sustainable success.  Always, with the application of these powerful AI components, thoughtfully apply what is known to the design, initialization, and further development of the system into which the component fits.
Another network type worthy of consideration with time series is the LSTM network design.  This is where layer depth makes sense, however systems with multiple AI components are really the work horses of the industry today.  Very few problems give way easily to the deployment of some code generated from one algorithm specified in one paper and nothing added.
System Topology
There is likely to be multiple pipelines containing information in a system that sustainably provides growth-positive automated or semi-automated portfolio management, even among groupings of LTSs that seem to display patterned and more easily predictable trends.
If one wishes to get a general sense of the complexity of system design that may be required to beat those at the forefront of the investment community, visit a nuclear power plant or disassemble the control system of the jet engine of a passenger jet.  Still, someone seems to be getting it right.  A suspiciously large number of new billionaires have emerged since 1998 with investing given as the industry in their Forbes entry.
Additional technical information is provided in this answer.

Unclear from your description how RL is useful. RL is a technique that allows your model learn interactive by trial and error. Where is your "trial and error" in your problem?
Stock price prediction sounds more like a regression problem to me. It can be done by DL or many other methods. Probably a shallow neural network would work well for you.