Animals have no language like humans have. There will be no translation, or dictionary or whatever. Doctor Doolittle is pure fiction.

All this stems from the notion that man is the ultimate goal of creation, and all other species aspire to be like man. (it’s not and they don’t)

Animals communicate, but not with words or sentences. And they don’t record. Communication is for immediate purposes only. Depending on the species, it’s some sounds some nudges and mostly bodylanguage and posture.

You can learn to read the body language of an animal, but it will always be open to interpretation by humans, mostly empirical, but never confirmed or denied by the animal.

Even the concept of “language” is a purely human thing, and we tend to project it on animals, thinking that if we have such great benefits of it, they must surely also want it. And we tend to measure intelligence by the capability of language. Which of course is also, again, a human interpretation.

Animal languages are mostly non-verbal. It takes a great deal of observation to be able to translate them. There is no shortcut.

On the other hand, there are instances of animals that are able to translate human languages into their own type of vocalization. For example, Wesley the owl apparently learned to do this and would talk to his human using owl equivalents of human words, constructing his own sentences with them.

That I know of, my cats have not gotten quite that advanced in their understanding of human language. That said, I speak to them in full sentences in both English and Polish, and they are used to those sentences rather than the simple commands that are normally recommended. So, they will not react if I say ‘Get down’ when one of them is sitting in my place on the couch. One of them, however, will usually react immediately if I say, ‘Excuse me, honey, may I sit down please?’

On the other hand, I have learned to speak Cat well enough that if I put my mind to it, I probably would be able to carry on most of the everyday conversation I have with my cats in their language. So that while I normally speak Human to them, and they normally understand, that is not my only option for communicating with them.

You can’t do that really.

Animal languages is mostly body language and it expresses emotions rather than abstract concepts. Also, expression is fuzzy, so the more pronounced a certain pose is the more pronounced the “emotion” is.

For example, those kind of charts are usually precise:

Basically, a cat would have few dozen of “states” it will transmit indicating its mood/intention. Those are universal and apply to communicating with other cats.

Meowing is reserved for humans only (also Kitten->Mother Communication, where it mostly a cry for help, as far as I know). In case of meowing there’s no strictly set standard patterns and the cat will actually try to figure out which sounds work better to make the owner do what the cat wants.

So, in human language asking for food would be “Let me transmit idea of being hungry and asking for food by constructing a series of abstract concepts and then vocalizing them”. In case of a cat it would be “Let me make the sound that makes my human give me things most of the time”. So rather than having a language you’ll have specific sounds that correspond to one specific situation or request.

Also, animals pick up pieces of human languages, in sense that they can recognize familiar words, and pick those words from sentences. Smarter dog breeds usually would have no problem learning words owner uses to ask the dog leave the room, and will memorize what they’re supposed to when the words are said. For example, one interesting thing I learned is that responding to a call is learned behavior (and not an instinctive one), and foreign cats won’t respond to human calling them, if this is not done in cat’s “native language”.

As far as I know there has been some progress in animal communication and translation, inventing an animal language translator sounds like a science fiction story that remains challenging.

I have observed that animals communicate in ways that differ greatly from human languages.

Animals use different vocalizations, body language, smells to express feelings, needs and intentions.

Each species has its own unique way of communicating, which makes their languages somewhat difficult to accurately understand and interpret.

We can write AI algorithms that can be used to try to bridge the communication gap between humans and animals to some extent. Like trying to identify patterns and meanings in their voices.

However, these translations are still limited and not as accurate as a hypothetical animal language translator might suggest.

If we want to develop more accurate animal language translators, we need to have a deeper understanding of the cognitive processes and communication systems of different animal species.

As far as I'm concerned the idea of an animal language translator is tempting…

I offer you two very low-tech devices that would enable you to interrogate a motile (moving) creature to determine its preferences.

These items are the choice chamber and the T-maze

If you want to know if an organism prefers light or dark either could be used. If you did this you might learn that the preferences of your subject depends upon the time of day (or night). My students have tested a wide range of organisms in choice chambers including blowfly larvae (maggots), woodlice and various beetles.

A choice chamber

A T-maze

Besides the technology (T-maze or choice-chamber) and the subject (mouse, lion, pigeon, ant; your choice) the rest is up to your ingenuity to device the tests, recoding mechanism and protocols to make sure you observations have validity.

It’s a clever idea. You are able to pose a question to a creature and understand its preferred answer without having to employ any language because lions don’t understand English and you don’t understand Lion.

Choice chambers and T-mazes are easy to make, though how easy will depend upon the size of the subject. (A T-maze for elephants would require metal handling and welding skills) and the devices can be as large or as small as appropriate. With ingenuity you can make a choice chamber for use under a microscope.

I hope you find this a sensible answer to a rather odd question.

Scientifically translate a dog's barking? It's sort of but for the most part possible--there are different barks for different environments and situations. Google is your friend there.

Scientifically translate a dog's behaviors? That is an ongoing debate. Follow a few trainers and stay connected to the ones that make sense to you.

For example, some will claim that there is a malady called separation anxiety that dogs experience. Calling it separation anxiety inappropriately assigns a problem to a dog when it's a human's problem. I don't ascribe to that philosophy.

I think anything that's going on between an owner and his dog is because of an owner's behavior or lack thereof, and it is unfairly and inappropriately labeled as the dog's problem.

My experience has shown that those who push their problems onto their dogs tend to defend the existence of their dog's issues, thus reinforcing their philosophy and attitude of projecting their issues onto their dogs, as well as continuing their dog's unwanted or unbalanced behavior. (You can read more about that here in some of my other answers on Quora, you can find information on it on my Pinterest page, or on my webpage.)

Scientifically translate a dog's body language? Google will be your friend there, too--there are plenty of pages detailing the positioning of ears, tails, and bodies. Its interesting to note there are many similarities in the body languages of dogs that are expressing submission and dogs that are expressing fear. So even though there's plenty of data, it affords one to take caution since it's not a true, rock solid science; there are at least two cases of different energy levels that appear to be very similar in overall body configuration and appearance.

I think the items best supported and defended scientifically are going to be coming from this dog translation category.

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To call what Google Translate (or any of the other services like it) produces a translation, or even translating, is like telling an amateur boxer he’s the greatest after he just went down in the first round of his second fight.

Google will check databases of words, but slight nuances, colloquials, sayings, proverbs, particular (mostly job-related) expressions, as well as a lot of other things it does not yet know, will get lost in translation. Literal translation, that is.

I do not for a moment believe that Google is about to take over my job, during my lifetime. It has improved some, since its early beginnings.

Still, if you want a translation, pay the professional. If you want to roughly understand what a text is about, Google Translate (and the others) will work. That said, it will work on anything you feed it, but depending on the language combination used, the result will range from not good enough to completely useless.

Only inasmuch as, say, a meeow that means “I want food” . Such translations by AI are easy-peasy.

But, rather than communication, I suspect you mean language. The only useful technical definition of which is “the import, export and external storage of imagination”. A technical definition of which is “the ability to form, store and morph models of the external world. Because, not having co-evolved with technology and uniquely possessing a reflective layer of consciousness, other animals have little or none. So, for them, language is not a practical requirement. Here is some background information which may be of interest to you:

This misperception stems from the all too common confusion of language with communication. The communication channels available to organisms are many and varied.

They can use light, through vision. Hand signals are but one variant of this category .Other bodily movements are also used. I believe some marine organisms are able to use the optical channel by glowing. Then there is the waggle-dance of the bee! Even in our own species hand movement communication is still ubiquitous in terms of the pen, mouse or keyboard.

Chemical channels: Dogs, for instance communicate information odorously in this way, and pheromones are widely employed throughout the animal kingdom.

Sound happens to be a particularly useful mode for larger creatures as it is not limited to line-of-sight , not as dispersible as molecular species and has good range. it is has far greater bandwidth than the chemical mode, and has the potential for generation and modulation at a wide range of frequencies by mechanical means that are within the capability of biological systems. Even so a “voice box” is not a prerequisite for this channel. The cicada is one of the many insects that, lacking lungs, pre-empted the use of the acoustic channel by non-vocal devices.

Language is not mere communication. Language is best defined as the transfer of imagination.

The cicada has very little imagination and its transfer requirement is correspondingly low. The imagination of a dog or an ape is much higher, as is its transfer capabilities.

The imagination (often described by the vague term “intelligence”) of our own species, of course, exceeds that of other creature beyond all comparison. It is reflected not only in the great variety of artifacts and technologies to which it has given rise but also in the prodigious extent to which it provides the export, transfer and storage of imagination which language represents.

It is very wrong to think of our highly developed “voice-box” as a precursor to language.

Rather it is a communication channel which has synergistically co-evolved with hearing systems and imagination together with its transfer aspect, language.

While the acoustic and visual channels are those most commonly used by humankind for transfer of imagination, they are by no means essential.

Anybody who doubts this should check out the case of Helen Keller.

Such considerations form part of the very broad evolutionary model encompassed by my latest book "The Intricacy Generator: Pushing Chemistry and Geometry Uphill".

Linguists would probably bristle at the notion that any animal uses language (language is usually defined to be something more sophisticated than mere communication), but I suppose the answer to the question depends on how much semantic content animals actually communicate. For many animals, there is probably next to no semantic content that is communicated and even for more social species like dolphins, it’s currently unclear how much semantic information, if any, is communicated (although there is reason to suspect there is some).

However, if we assume that there is semantic content communicated, it’s at least possible that machine learning could play a pivotal in decoding what information is there, but it would be extremely challenging. To make this work, you need a language grounding system that learns from data that pairs the “utterances” with the observations and context that presumably stimulated them. With enough data collected in this form, machine learning systems could, in principle, be able to extract the associations.

But bear in mind that this is still an extremely challenging line of work with basic human utterances, even with really good data collection, and what might make it even harder with animals, is we might not even know what kind of data to collect that they would be responding to and what kind of concept space needs to be modeled. Dolphins, for example, perceive the world differently than us, and we might need to better understand how they model the world before we can hope to make progress on deciphering any semantic content they do communicate. We would also likely have to gather all data with respect to a small family, because like humans, animals could very well develop language that are specific to their group.

Nothing - if you give full value to the word “translate”.

It will do some replacing of words in one language with words in another and some replacing of phrases and some replacing of forms that correspond.

However, no computer translation can ever actually understand the thing to translate and produce sth in the goal language that means the same. My favourite example is when Ukrainean wikipedia stated (presumably, I don’t speak Ukrainean) that C. S. Lewis as a child had a dog called Jacksie, and when he was four a car ran over it, and after that he always wanted to be called Jacksie, which was later shortened to Jack.

The Google translation to English had Jacksie run over the car ….

It probably depends what you mean by “correctly translate”.

With a short-ish text, Google Translate isn’t necessarily awful, at least in relation to languages I’m familiar with. Longer texts, where context can become more important, however, frequently become completely bizarre. I’d say that it’s useful to work out the general topic under discussion (“This website is advertising discounts”, sort of thing), but not for the specifics.

Even then, figures of speech can also completely confuse it. An image which occasionally does the rounds is of the English-to-Spanish translation of the term “paper jam”. In English, of course, it means a snarl of paper in a printer which needs to be cleared in order to keep the machine printing. The Spanish rendition takes “jam” as “sweet substance you put on food”, and renders the term as “jam made out of paper”, which is very funny but entirely useless if you’re trying to translate the English term for a Spanish-speaking office.

Another example, courtesy of Ben Waggoner , I believe, was the French translation of the term “Polish sausage” (that is to say “Sausage made in the style that people from Poland would do”). Run through Google Translate - and stamped on a label - this term became the imperative command of “Polish the sausage [in order to make it shiny]” in French. Clearly not what you’re going for.

For my part, I’ve also found some completely inexplicable things dealing with Estonian. The phrase matuse pärast (“because of the funeral”, which I used due to a linguistic quirk if you re-arrange the words) is rendered into English as “For fun”. I hope that’s because the last four letters were left off by accident, since it rather changes the meaning.

It’s not machine learning, other than in the sense that a lot of the existing Google Translate infrastructure has used machine learning to make it better at translating, over the years.

It simply OCR’s the text (Optical Character Recognition), and then translates it, just as it would translate text on any web page. This uses the existing Google Translate functionality.

If you allow the App to use location data, it takes that into account as a hint, when “guessing” the source language.

It’s fast because Google Translate is already fast.

Other than colloquialisms and jargon, machine translation is a pretty easy problem. Signage rarely uses colloquialism or jargon, unless it;s advertising (in which case, it can use puns and other constructs made for memorable advertising, and usually pretty awkward translations.

Hi Quora community!

ChatGPT, the powerful AI chatbot developed by OpenAI, has taken the world by storm with its ability to generate human-like text. But how exactly does it work under the hood? Let's delve into the fascinating world of ChatGPT's natural language processing (NLP) capabilities:

At the Heart of ChatGPT: Transformer-Based Neural Networks

ChatGPT's impressive text generation abilities stem from a type of deep learning architecture called a transformer. Unlike traditional neural networks, transformers excel at understanding the relationships between words, even those far apart in a sentence. This allows ChatGPT to:

• Analyze the context of a conversation: By considering the words used and their order, ChatGPT can grasp the overall meaning and intent behind a prompt or question.
• Predict the most likely next word: Based on the context, ChatGPT employs its vast internal knowledge base to predict the word that would most naturally follow in the sequence.
• Generate coherent and grammatically correct sentences: This iterative process of prediction and continuation allows ChatGPT to produce human-readable text that flows and makes sense.

Training on a Massive Dataset of Text and Code

ChatGPT's knowledge and ability to predict come from being trained on a colossal dataset of text and code. This data includes:

• Books, articles, code repositories, and other forms of written content.
• Conversations and online interactions, allowing it to mimic natural speaking patterns.

By analyzing these vast amounts of information, ChatGPT learns the statistical patterns and relationships between words, enabling it to generate similar structures and styles when responding to prompts.

The Power and Limitations of ChatGPT

While ChatGPT's capabilities are impressive, it's important to understand its limitations.

• Susceptibility to Bias: The training data can influence ChatGPT's responses, potentially reflecting biases present in the real world.
• Limited Factual Accuracy: While it can generate realistic text, it may not always be factually accurate. Cross-checking information is crucial.
• Work in Progress: ChatGPT is still under development, and researchers are constantly working to improve its capabilities and address its shortcomings.

The Future of NLP with ChatGPT

Large language models like ChatGPT represent a significant leap forward in NLP. As technology advances, we can expect even more impressive capabilities, like:

• Enhanced reasoning and factual grounding: AI models that can not only generate text but also understand the factual basis behind it.
• Greater personalization and adaptation: Chatbots that can tailor their responses to individual users and situations.
• Improved explainability and transparency: Understanding how AI models arrive at their outputs to build trust and ensure responsible development.

Overall, ChatGPT offers a glimpse into the exciting future of NLP. While challenges remain, it holds immense potential to revolutionize the way we interact with machines and unlock new possibilities for communication and creative expression.

Thank you! Please upvote and share this answer if you found it informative.

This is a very interesting question, in that it applies to a totally different subject… one that I and most of humanity has long pondered. And that is “When we finally meet up with an alien civilization, how will we communicate?” Nearly all the science fiction ever written assumes that we will eventually figure out how to communicate with aliens. But the fact is… we have shared this planet with millions of beasts for millennia, and we haven’t even been able to decipher the language of even one of them. Not even the most intelligent, like whales, porpoises, or any of the apes. So I don’t see any optimistic answer to your question, at all. It’s not unreasonable to say it may be a thousand years. Although, with the advancement of technology, the learning curve may accelerate… but still... at best… it will be hundreds of years… many, many generations.

Here is a rough outline of an NLP program pipeline and structure:

1. Preprocess / Clean the training data: The level of preprocessing required depends on how noisy your input data is. If the data is very messy/noisy, you will have to implement more preprocessing techniques. Preprocessing techniques include but are not limited to:
1. lowercasing
2. lemmatization (reducing words to their roots)
3. normalization (canonicalizing texts, e.x. “4ever” > “forever”).
4. removing stop words—irrelevant words like the, is, as, etc.
5. removing HTML tags, numbers, and punctuation
6. shuffling the dataset (this decreases variance and ensures that the model will not overfit the data).
7. removal of outliers (a standard practice is removing data that falls outside of 2–4 standard deviations from the mean)
2. Feature Extraction / NLP Model Implementation: Because machine learning models can only understand numerical data, you have to transform the text into some form of a numerical representation.
1. In machine learning, features are relevant characteristics of the data that can be used during the analysis. These include the frequency in which words appear in a document and how many documents a feature appears in.
2. The NLP program tokenizes the text, splitting the text up into words or tokens to extract those features.
3. NLP Models use those features to translate textual data into numerical data. The features I described in the above point are used in the popular TF-IDF Model.
3. Implement a classification or clustering algorithm: Now that the textual data has been preprocessed and translated into a numerical representation, a classifier can be used to map that data to a certain category. Clustering algorithms like the K-Means Algorithm are used to group similar data points together in a set. The classifier is trained to fit on the translated numerical data in the previous step.
1. Important: The way in which your numerical data will be used depends on whether you are tackling an unsupervised (unlabeled data) or supervised (labeled data) machine learning problem. If your data is unlabeled, chances are that you are going to be using a clustering algorithm, where you need to feed in just the vectorized data. If your data is labeled, chances are that you are going to be using a classification algorithm, in which you need to feed in both the vectorized data and the labels associated with that data.
2. Also Important: The characteristics of your data should give insight as to what algorithm or variation of an algorithm you should use. For example, if you are planning on using a Naive Bayes classifier and your dataset features are independent and binary, the better implementation to use is the Bernoulli Implementation.
4. Test the trained model and benchmark performance: After the model is trained, it’s time to test it on the testing data for accuracy. Results from the classifier are compared side-by-side with the correct answers. Usually, in an NLP program, multiple combinations of NLP models and classifiers are bench-marked to determine the optimal combination.

Summary: For example, say we are writing an NLP program to classify movie summaries by genre. The NLP program cleans up the data before processing it. It then converts the textual data into vectors after extracting the text’s features. It trains a classifier to categorize the data into genres like thriller, horror, etc. The model is then bench-marked based on parameters such as accuracy, precision, and recall. This process is repeated with different combinations of models and classifiers to find the optimal solution.

Google uses neural networks in both encoder and decoder. The encoder is combinations of transformer and RNN. The decoder is LSTMs (type of RNN). There are multiple layers so that the system is deep enough for accuracy.

If you have a reference of what different animal sounds mean then yes.

You basically have to have examples of the animal language and their translations for the AI to learn how to relate the two. We humans can relate two seemingly unrelated pieces of information. This is how we learn language in the first place.

We look at the actions of our parents and other adults and listen to what sounds they make. Then we learn to associate a given set of sounds with a particular action. AI can’t do this at the moment. Hence why we need humans to translate the animal language first and only then can we teach AI to do the same.

Natural Language Processing (NLP) is a critical component that enables AI to understand and interpret human language. Here’s how NLP achieves this :

1. Tokenization

• Breaking Down Text : NLP starts by breaking down text into smaller units called tokens, such as words or phrases. This helps AI understand the structure and components of the language. For example, the sentence “The cat sat on the mat” would be tokenized into ["The", "cat", "sat", "on", "the", "mat"].

2. Part-of-Speech Tagging

• Identifying Word Roles : NLP assigns parts of speech (e.g., nouns, verbs, adjectives) to each token, helping AI understand the grammatical structure of the sentence. For instance, identifying “cat” as a noun and “sat” as a verb clarifies their roles in the sentence.

3. Named Entity Recognition (NER)

• Identifying Entities : NLP identifies and categorizes named entities in the text, such as people, locations, dates, and organizations. This helps AI extract important information and understand context. For example, in the sentence “Apple Inc. is based in Cupertino,” NER identifies “Apple Inc.” as an organization and “Cupertino” as a location.

4. Parsing and Syntax Analysis

• Understanding Sentence Structure : NLP analyzes the grammatical structure and relationships between words in a sentence to understand its meaning. Parsing helps AI determine how different parts of a sentence relate to each other, improving comprehension of complex sentences.

5. Semantic Analysis

• Understanding Meaning : NLP goes beyond syntax to understand the meaning of words and phrases in context. This involves analyzing word meanings, idiomatic expressions, and context to derive the intended message. For example, understanding that “bank” in “river bank” and “financial bank” refers to different concepts based on context.

6. Sentiment Analysis

• Determining Emotion : NLP analyzes the sentiment or emotional tone behind a piece of text, identifying whether the content is positive, negative, or neutral. This helps AI understand user emotions and respond appropriately. For instance, detecting positive sentiment in a review can trigger a congratulatory response.

7. Contextual Understanding

• Managing Ambiguity : NLP uses context to resolve ambiguities in language. It leverages previous interactions or contextual clues to understand words or phrases that might have multiple meanings. For example, understanding “book” in the context of a hotel reservation vs. a novel.

8. Machine Learning and Deep Learning

• Training Models : NLP utilizes machine learning algorithms and deep learning models (such as transformers) to learn language patterns and relationships from large datasets. These models improve their accuracy over time as they are exposed to more text data. For example, models like GPT (Generative Pre-trained Transformer) are trained on diverse text corpora to generate coherent and contextually relevant responses.

9. Language Generation

• Creating Human-Like Text : NLP models generate human-like text based on the input they receive. They can craft responses, summaries, or translations that are contextually relevant and grammatically correct, making interactions with AI more natural and intuitive.

10. Dialogue Management

• Handling Conversations : NLP manages the flow of conversation by keeping track of context, managing turns and ensuring that responses are relevant to the ongoing dialogue. This enables AI to maintain coherent and engaging interactions with users.

By combining these techniques, NLP allows AI systems to comprehend and generate human language effectively, enabling more natural and meaningful interactions between humans and machines.

Google Translater is a platform where you can translate any sentence from one language to other. There are many other platforms for translations one such best platform is Gizoogle translations which is very easy to use. It is known as Gizoogle alternative Google gangster translator here gangster represents youth gang. Here you can translate your friends profile into funny translations in all languages with Gizoogle Translations.

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Introduction:

Natural language processing enables computers to process what we’re saying into commands that it can execute. Find out how the basics of how it works, and how it’s being used to improve our lives.

What Is Natural Language Processing?

Natural Language Processing is a branch of artificial intelligence that attempts to bridge that gap between what a machine recognizes as input and the human language.

This is done by taking vast amounts of data points to derive meaning from the various elements of the human language, on top of the meanings of the actual words. This process is closely tied with the concept known as machine learning, which enables computers to learn more as they obtain more points of data. That is the reason why most of the natural language processing machines we interact with frequently seem to get better over time.

To illuminate the concept better, let’s have a look at two of the most top-level techniques used in NLP to process language and information.

How It Works

• Tokenization

Tokenization means splitting up speech into words or sentences. Each piece of text is a token, and these tokens are what show up when your speech is processed. It sounds simple, but in practice, it’s a tricky process.

Let’s say that you are using text-to-speech software, such as the Google Keyboard, to send a message to a friend. You want to message, “Meet me at the park.” When your phone takes that recording and processes it through Google’s text-to-speech algorithm, Google must then split what you just said into tokens. These tokens would be “meet,” “me,” “at,” “the,” and “park”.

People have different lengths of pauses between words, and other languages may not have very little in the way of an audible pause between words. The tokenization process varies drastically between languages and dialects.

• Stemming And Lemmatization

Stemming and lemmatization both involve the process of removing additions or variations to a root word that the machine can recognize. This is done to make interpretation of speech consistent across different words that all mean essentially the same thing, which makes NLP processing faster.

Stemming is a crude fast process that involves removing affixes from a root word, which are additions to a word attached before or after the root. This turns the word into the simplest base form by simply removing letters. For example:

“Walking” turns into “walk”
“Faster” turns into “fast”
“Severity” turns into “server”

As you can see, stemming may have the adverse effect of changing the meaning of a word entirely. “Severity” and “sever” do not mean the same thing, but the suffix “ity” was removed in the process of stemming.

On the other hand, lemmatization is a more sophisticated process that involves reducing a word to their base, known as the lemma. This takes into consideration the context of the word and how it’s used in a sentence. It also involves looking up a term in a database of words and their respective lemma. For example:

“Are” turns into “be”
“Operation” turns into “operate”
“Severity” turns into “severe”

In this example, lemmatization managed to turn the term “severity” into “severe,” which is its lemma form and root word.

Pro's

• Predictive Text: When you type a message on your smartphone, it automatically suggests words that fit into the sentence or that you’ve used before.
• Machine Translation: Widely used consumer translating services, such as Google Translate, to incorporate a high-level form of NLP to process language and translate it.
• Chatbots: NLP is the foundation for intelligent chatbots, especially in customer service, where they can assist customers and process their requests before they face a real person.

Applications

Google assistant, Siri, Cortana, Alexa and every other voice activated assistant uses it.

Conclusion:

There’s more to come. NLP uses are currently being developed and deployed in fields such as news media, medical technology, workplace management, and finance. There’s a chance we may be able to have a full-fledged sophisticated conversation with a robot in the future.