Communication, Internal Seminar, HITS

Yesterday I attended a seminar led by our current science journalist Kerstin Hoppenhaus at HITS. It was remarkable because it made me think how communication works and why trustworthy journalism is important.

Journalists curate news in such a way that is interesting and digestible to the users. A piece of news can have a certain amount of bias, which might creep in consciously or unconsciously. Some of the tricks of the trade to seduce the users are (1) telling a story, (2) engaging the user, and (3) using techniques such as dramatic pause, captivating background score or a protagonist.

The format of delivery can be literary, auditory or visual. Different formats pose different challenges. For instance, consider the 360-degree video format in which the director can no longer direct the user through the intended narrative because of the shift in control from the director to the user. 

We then discussed how the Internet has disrupted the media industry. With the Internet, quick communication is possible. However a major challenge here is fake news. As a reader, how can you trust a piece of news that you see over the Internet? Some of the parameters are reputation of the news source, valid verifiable references, accuracy of the news content, and style of presentation.

Finally we discussed whether simplification of news pays off or not. Simplification improves the extend to which the news penetrates the audience. However over-simplification might have to let go off some necessary details.

Mentalese, The Language Instinct, Steven Pinker

This post is a summary of the chapter "Mentalese" from the book "The Language Instinct" written by the eminent linguist Steven Pinker. This chapter deals with the question - Do we think in language? What is the language of thought?

According to the Sapir-Whorf hypothesis, language shapes our model of the world. For example, forms of addressing the listener are different in many languages depending on age or relation. The demands made by the language might influence the speakers to regard the listener with respect. In languages such as Hindi or Tamil, formalness is frequently employed in day-to-day communication. This is in contrast to other languages such as Malayalam, in which formalness is not commonly employed, in spite of the presence of formal and informal forms of address.

Pinker challenges this hypothesis in various ways. First, it is difficult to test this hypothesis because of the circular nature of the existing experiments. A subject can only be evaluated based on what he/she speaks. Second, the 'fact' that the Eskimos have hundreds of words for snow is nothing but an urban legend. According to one survey, the actual number is less than ten. So the Eskimos regard snow similar to the way others do, contrary to what the hypothesis can be taken to mean. Third, there are many beings that cannot speak but possess the faculty of thought: deaf people, babies, animals (, plants?). Fourth, many a time, even though we have a thought in our mind, we struggle to express it clearly. This can be due to two reasons: (1) lack of command over languages, or (2) lack of devices in language to convey our thoughts. Writers exploit the available linguistic machines such as figures of speech, idioms, proverbs and rhythm to express complex ideas through words in the form of prose and verse. In addition, when a sentence is translated from one language to another, it is highly likely for the subtlety of the original meaning to be lost in translation due to the lack of exact matching words. Fifth, it is possible for us to transform (e.g. rotate or zoom in) an image in our minds without speaking about the process loudly. All of the above suggest that English or any other spoken language is not the language of thought.

Pinker claims the language of thought (a.k.a. Mentalese) to be on a level that is different from spoken languages. All thinking creatures use this language to think. Since only human beings possess the ability to talk, there is a program, similar to a compiler for programming languages, in the human brains that converts the high-level Mentalese to the low-level English. This language is built over fundamental concepts of human thought such as time, space, causality and human intention (ref. Immanual Kant).

Chatterboxes, The Language Instinct, Steven Pinker

This post is a summary of the chapter "Chatterboxes" from the book "The Language Instinct" written by the eminent linguist Steven Pinker. This chapter deals with the statement in the title of the book - Is language an instinct for humans?

Language has never been a cultural phenomenon. Language exists and has existed in every culture on earth. No society can claim the title "the cradle of language." This is one of the arguments for claiming that language is innate in human beings. However, for the skeptics, the universality of language may not single-handedly prove the innate nature of language. For instance, Coca-Cola or Facebook are available almost everywhere on earth. Does this mean Coca-Cola and Facebook are innate too? (According to me, the desire to have a drink and to stay connected are innate in human beings. So the universality of language is a conclusive proof.)

Let's look at another argument for the innate nature of language. There are evidences to show that children reinvent language, not because they are asked to do so, but because they have to. Before going into the argument, it is important here to burst two myths about child language acquisition: 

    (1) The first myth is that children learn to speak from their parents (e.g. Motherese - dogggie, pappie, ...). This is not true because parents do not explicitly teach children the rules of the grammar. Chomsky reasoned that this argument of poverty of input is the primary justification for the saying that language is innate.

   (2) The second myth is that children learn to speak by imitating their parents. If this is true, then children should not be making any mistakes when their learn to talk. However children do make mistakes when they learn language (e.g. കാവളവണ്ടി, വെക്കള് )

Now it is time to look at two real-world cases where children reinvented language. One of them is the development of creole from pidgin languages such as pidgin English. Second one is the development of sign languages. In both these cases, phrases and crude sentences of a pseudo-language were converted to a bona fide language by the second generation of users i.e. the children of plantation workers and deaf children respectively.

Finally, another argument for the innate nature of language is that language is different from intelligence (or cognition). Those who suffer from Broca's aphasia are language-retarded. However they have sound cognitive skills. Those who suffer from chatterbox syndrome are language-savvy. However they have negligible cognitive skills. These two cases show that the ability to speak and the ability to, say, cook food are managed by different parts of the brain and hence different faculties.

Talking Heads, The Language Instinct, Steven Pinker

This post is a summary of the chapter "Talking Heads" from the book "The Language Instinct" written by the eminent linguist Steven Pinker. The previous chapter in the book was a discussion on syntax. This chapter focuses more on semantics and pragmatics.

Parse trees are different from parsing.  Parse trees define the syntax or structure of sentences in a language. The process of parsing defines the processing of a sentences and thus is related to cognition and semantics of a language. Chomsky demonstrated this feature of language processing using the classic example "colorless green ideas sleep furiously". This sentence is syntactically right. However it is rife with absurdity and does not make sense at all. In other words, the sentence is semantically not right.

The chapter then discusses the differences between a human being and a machine and how they interpret a natural language sentence. There are various types of sentences: (1) onion (or Russian doll) sentences, (2) garden-path sentences, and (3) ambiguous sentences.

The first two types are hard for human beings because humans are not good at memory as compared to machines. By memory, we mean short-term memory, something similar to a stack for a machine. On the other hand, the last type is easy for human beings because humans are good at decision-making.  The decision making employs different kinds of knowledge such as background knowledge, commonsense knowledge and world knowledge. A classic example that demonstrates commonsense reasoning is given below:

             Woman: I'm leaving you.             Man: Who is he?

The converse is true for machines. Machines can process onion sentences and garden-path sentences because of the availability of memory. However they perform very badly when it comes to ambiguous sentences. In addition to this, machines are too meticulous in parsing a sentences and identifies interpretations that a human being will never detect (e.g. pigs in a pen). 

In real-life, the task of text processing is worsened by the fact that dialogues are filled with short utterances, lots of pronouns, and fillers such as 'uh' and 'hmm'.

The chapter concludes with a discussion on pragmatics. Parsing a sentence involves more than simply understanding the sentence syntactically. A conversation between two parties can either be co-operational or adversarial. In co-operational conversation, the assumptions made by the speaker are also made by the listener. This phenomenon is absent in adversarial conversation. Legal documents demonstrate a form on adversarial conversation by clearly specifying each and every nuances of a contract. The following anecdote demonstrates the difference between the co-operational and adversarial conversation. Two psychoanalysts meet in the morning. The first psychoanalyst greets the other "Good Morning." The other wonders what he really meant by that statement.

Neural networks, explained - Janelle Shane, Physics World

This post is a summary of  the article published in the 2018 issue of "Physics World." This article is written by Janelle Shane.

Advantages:

1. They are excellent at recognizing patterns in multivariate data.
2. They are suitable for problems that are not very well-understood. Traditional systems were either rule-based or feature-based. However manually coming up with rules or features is intellectual challenging and infeasible in many cases such as face recognition. Neural networks are good at feature engineering. 

 Limitations:

1. Interpretability is an issue with neural networks. A neural network acts like a black box because humans cannot easily interpret the the features learnt by the model.
2. It is necessary to review the results by human experts because neural networks might learn features that are not at all relevant to the task at hand.
3. Neural networks might suffer from class imbalance in training examples. This is a major issue in the case of rare events, for which it is hard to generate sufficient number of training examples. 
4. Neural network might suffer from overfitting to training examples. Overfitting can be resolved by testing the network on unseen examples.

"Neural networks can be a very useful tool, but users must be careful not to trust them blindly. Their impressive abilities are a complement to, rather than a substitute for, critical thinking an human expertise."

Dark World

I have listed below some issues that are threats to (my?) peace. This is the result of the quest for answers for a few questions I asked myself over the past couple of days: "Is the world a safe place to live in?", "Is the world as dangerous as the media portrays it to be?", "What are the areas in which a complete overhaul is necessary to make things right?", and "Is it time to move to Mars?"

My list is given below:

1. Greed
2. Poverty - e.g. India
3. Corruption - e.g. India, South Korea, Brazil
4. Consumerism - e.g. McDonald's, Subway, Amazon, Walmart
5. Nepotism - e.g. INC, SP, DMK, JDS, TRS, TMC, BJD
6. Intolerance - e.g.  Kalburgi, Pansare, Dabholkar, Gauri Lankesh
7. Unemployment - e.g. Greece, Spain 
8. IT - Fake News, AI, Monopoly by Tech Giants, Targeted Marketing
9. Social Divide - e.g. Casteism, Classism, Racism
10. Economic Divide - e.g. Mumbai, Healthcare, Education
11. Cultural Divide - e.g. North India vs. South India, East vs. West
12. Religious Divide - e.g. Shia vs. Sunni, Hinduism vs. Islam, Christianity vs. Judaism
13. Nationalism - e.g. US, Brexit, India, Hungary
14. Terrorism - e.g. ISIS, Al-Qaeda, Hamas, Hezbollah, Boko Haram
15. War - e.g. Syria
16. Refugee Crisis - e.g. France, Germany
17. Cold War - e.g. {US, Saudi, Israel} vs. {Russia, Iran, Syria}, India vs. Pakistan, Greece vs. Turkey

I am yet to reach a conclusion.

However recently I came across a TED talk by Steven Pinker, according to which the world has come a long way in terms of progress: democracy as opposed to theocracy that was the practice in major parts of the world (e.g. Russia, England, Spain, India), freedom for colonies (e.g. India), freedom from slavery (e.g. US), improvement in literacy levels and human rights, modern sanitation facilities (e.g. toilets) and dissemination of information (e.g. WWW). So there are indeed reasons to be happy about. However the extend to which these ideas have been fulfilled in practice is debatable.