This blog is abandoned

I am abandoning this blog. Thank you readers

I am shifting this blog for privacy concerns as I am experiencing too many unnecessary troll visits and unwanted attention. As it is the blog ran for more than 20years -and that is quite a long time, so I am looking for quieter space -that ofcourse doesn't mean the blog will be quiet! Indeed it is going to be much fiercer reflecting my sincere attempts to grapple the reality and very much eviscerating systems of control. It is such a pleasure! When I started this blog my concerns, apart from ofcourse dealing with emergent reality, were mainly to have a grip on expression, the language -it though has nothing to do with rules of language but the skills of expressing the complex and nuanced. Though in the earlier part I was quite cavalier and regrettably sloppy (wasted time dealing with crappy people who sink you), later as I got grip on things, each word was measured for its worth. Since there are too many things written there is sure to be mess ups -thankfully AI Chatbots in recent times are immense help for critically evaluating.  

Over the years I realize more than language -with basic skills, what is urgently needed is understanding. Understanding is much complex than I reckoned. Rooted in presence. The presence is in final analysis what really matters. This unique life in billions of lives (and when it matters it is the only thing that matters, encapsulating everything). Very few people have found the presence, and ability to express the complexity herein. This then is art, and life.

 

Cool it. It is not a major incident.

 

We have lived through periods where every now and then bombs used to explode and communal riots were common. In the last few years despite intense media scrutiny and social media presence things are relatively calm. India is one of the few places in the world, despite its huge diverse population, disparities and chaos, see no major disruption of peace. The Union Government must be praised as also kudos to intelligence agency for preventing major incidents. The bomb blast in Delhi, though unfortunate, is minor incident considering the context of the nation. It cannot be called an intelligence failure nor failure of government machinery. No system is fool proof, there will be few minor slips, what needs to be understood is that major mayhem by terror groups was prevented. Deal with them stringently. But please do not escalate this. Wisdom means bravado be confined to rhetoric to satisfy the populace and take away the heat. There are devious forces trying to exaggerate these. Beware.   

                      

Image generation by AI

So, I asked AI chatbot to recreate the sketch I posted earlier in context to COP30 (it was haphazardly drawn within few minutes, more than aesthetic sensibility communicating the idea was the intent, also I was occupied with few things) with some prompts, and it created this!

Quite rudimentary but I am sure there are much sophisticated version behind paywall. How does AI create these is an interesting question. So here is how this broadly works. Quite sure it is much sophisticated than what I explain here but it is enough to get the gist of how AI is functioning in the mainstream interface. First issue is when you give a prompt it has to be understand by the computer. This is done by Natural Language Processing (NLP). NLP enables computer to comprehend and interpret (as also produce a reply) human language. NLP uses Machine Learning (ML) and Deep Learning (DL) algorithm to discern a word’s semantic meaning by deconstructing the sentence grammatically, relationally and structurally, to understand the context of use. It can also understand intend and emotion (whether irritated, frustrated, confused so on) to draw inference from broad array of linguistic models and algorithms. To get deeper into its working. NLP breaks sentence into chunks or each word separated. This is called Tokenization. So, if the sentence has 8 words it will be 8 tokens. Next is Stemming i.e., to derive stem of each word or token. Suffix, prefix and tense are removed to get the stem. For instance, for the words sitting, sits, sat…the stem is sit. But there is a problem, stemming cannot be correct always. There are words where stem can mean different. Like for instance Universal and University don’t stem down to the word Universe. For such situation alternative tool come into play called Lemmatization. A given token’s meaning is learned through dictionary definition and then the root (or lemma) is derived. So, the stem of word better is bet while lemma is good. Stemming and lemmatization therefore is carefully done according to the context of the token. The context is derived from speech tagging -i.e., where the token is used in the sentence -whether noun or verb. Next is finding whether the word has any entity associated with it (entity recognition), for instance, token Kerala has entity Indian state associated or Sanjay has entity of person’s name. These are some of the tools used by NLP to convert unstructured human speech into structured data that is understood by computer hence applied for any AI application. 

So now that computer understands, what happens next? This is where Deep Learning (DL) comes into play, and indeed has revolutionized AI. DL is a specialized subset of Machine Learning (ML) that layers algorithm to create Neural Network -computational model replicating brain’s structure and functionality. It is DL that enables NLP’s understanding capabilities -the context and intent of what is conveyed. ML was very much active in 1980s while Neural Network (NN) came into being in 1990s but was stuck for a long time. The arrival of Big Data (through internet), enhanced computation power (GPUs) and ofcourse Deep Learning NN is what led to AI revolution (essentially Generative AI). The traditional ML learning algorithm had major issue of efficiency and performance plateauing as dataset grows. DL algorithm on the other hand continues to learn and improve with more data. DL NN consist of interconnected nodes known as neurons that take incoming data and learn to make decision over time. NN consist of input layer, hidden layer (that varies, giving more depth as it increases) and output layer. Each hidden layer transforms the input data by applying ‘activation function’ -a mathematical function that allows network to learn complex patterns. NN is trained by feeding data, error is sent back through the network to adjust internal parameters (weights and biases) helping to reduce error in future predictions. There are different types of NN. In the above case where it analyses the drawing (image) ‘Convolutional NN’ (CNN) is used (convolution is a mathematical operation done by each layer on output of previous layer, mixing two functions). Another popular NN is ‘Recurrent NN’ (RNN) in here each neuron (in hidden layer) receives input with a specific delay in time -allowing RNN to consider context of input -access previous information in current iterations. RNN is used in predicting next word in a sentence. 

Next is to generate entirely new image. This is done through Generative AI (ofcourse all these are interconnected; I am trying to separate it for convenience of understanding) that is the rage now. GenAI creates new content whether text, images, music, audio or video. GenAI is a class of AI system that learn from large data set using ML and DL algorithm to recognize patterns and trends to create new content. The design of GenAI model changes depending on what it is designed to do and how it will be used. They specifically crafted to generate new content. There are many types of GenAI models or GenAI architectures. Where images have to be generated (after Convoluted Neural Network CNN is used to analyze the drawing) Variational Auto-Encoders (VAEs) model or Generative Adversarial Networks (GANs) is used. Both are equally interesting, and it’s very likely that VAEs is being used here in this specific case where I fed the image. VAEs model works by transforming input data through Encoding and Decoding. Encoder takes the input data and turns it into a simpler form called latent space representation -which holds the key features of the data. The decoder then uses this latent space representation to create new outputs. It therefore creates new realistic images based on the pattern it has learned from the data. GANs meanwhile involves two neural networks. The Generator and the Discriminator. Generator tries to make new data samples that looks real. Discriminator verifies the generated data to tell the difference between real and fake data. The process is continued until Generator becomes so good at producing realistic data that the Discriminator is no longer able to distinguish. These are used to generate high quality realistic images. Another type of GenAI that really started the AI craze into mainstream is Transformer architecture. AI exploded into popular imagination through ChatGPT in 2022. GPT stands for Generative Pretrained Transformer. All the chatbots use Transformer architecture. Transformers are used in NLP tasks using encoder and decoder layers that enables model to effectively generate text sequence.

It is also important to know about Foundational Model (FM). These are the core of contemporary AI (except ofcourse reinforced learning -that makes my chess engine evaluate or I can play against!). All the big data, large computation and energy are going into maintaining FM. They are very large NN trained using ML and DL on tetrabytes of unstructured data in an unsupervised manner. The datasets are diverse capturing range of knowledge hence can be adapted to wide ranging tasks. Earlier each AI model was trained on very specific data to perform very specific task. Now FM is able to transfer to multiple different tasks and perform multiple different functions. They serve as base or foundation for a multitude of applications. When introduced with small amount of labelled data you can tune FM to specific task. Asking or prompting chatbot is a way of tuning information from FM. So, when I am asking chatbot to recreate the image. It is fine tuning the data from FM for this specific task of generating image. Large Language Model (LLMs) is text version of FM that fuels GenAI chatbot revolution. Different domains like models for vision, code, or science or climate change etc. are achieved by tuning FM.         

 

 

An upsetting incident

 

We common people are facing onslaught of misinformation and disinformation on daily basis. You really don’t know what to trust hence use critical thinking and try to get information from credible sources before trusting it. What if there is no credible source? Rumor of Dharmendra’s (iconic hindi actor) death was disinformation, maliciously worked without any concern or responsibility but for the stampede of viewership. Afterall how much time does it take to verify and crosscheck, they don’t need to send a horseman over mountains and across rivers, and unlike common people they have access at the highest level. Consider, this is how they deal with every ‘news’, they don’t really seem to have internal fact checking body nor are they bothered. Also, imagine how much distress it causes to people close to purportedly dead and flashed all across the media. Meanwhile social media primed in horrendous value system has started meme, AI created horror and what not. Also, there are some (not very famous, scheming from sidelines) who use even death for self-promotion. When I first became aware of it, I really couldn’t believe people could be such low life. What a horrible world we live in.

I too was carried away by the rumor and almost posted this writeup. So, after a delay and much consideration I am posting it verbatim to underline and tag the disinformation that affects us and must be dealt with all urgency. Meanwhile I hope Mr. Dharmendra lives many more years in peace.     

The age of innocence

Nowadays they call it movie, they call it paddam or cinema, but when we were young, we knew only the word picture. And in picture things were quite clear, there will be hero, heroin, villain and funny people. The template was quite fixed. What we would look forward to was good ‘fight’, which we emulated in slow motion, including open eyed death scene! Dharmendra was right at the top. His snarling dialogues were rage. I really liked enacting him. When I became aware of him, he had already passed his prime, weekend movies on television revived his glory. His death is passing away of slice of childhood.

Those were age of innocence that also coincided with my life (I wonder if I were an adult I would watch). Movies as sophisticated expression of art came quite late. Even when I was in my early 20s I would be watching movies like Cliffhanger in Chennai big screen, not once but three times, in the back row, the middle row, and right in the front. Watching movies like Cliffhanger from the front row was a unique experience! It was much later that I got into film festival circuit, as also printing fake entries (those days it was fun -trolling the system, in the present context with increased security concerns it is a serious crime). Nowadays with online access things have become convenient as also better options from array of brilliant international movies and old classics that give new meaning and understanding to art as an expression of life. There are ofcourse lots of mediocre nonsense on offer but these can easily be chaffed through. Indeed, finding a good movie (like a good book) to invest your precious time and effort has become an arduous task. Many a times when I am in a mood to watch some good movie in the afternoon, I spent more time in trying to find out what to watch! And only after finding strong recommendations from multiple reliable sources do I settle to watch. Sometimes having started to watch a movie after much scrutiny you realize that it is not matching the standards. Watching best from all across the world for many decades evolve sensibilities and you know within 15-20 minutes whether the movie is worth the time. Nobody is really interested in stories (the template is quite limited) but the nuance and depth of complexities and expressions. Even in crime thrillers stories influence is quite limited, it is how narration handled and image worked that leaves abiding impact and adds layers of meaning. For instance, High and Low made in 1960s (Akira Kurosawa) is such a brilliant movie while the recent remake (Spike Lee) such a shoddy attempt.

*What I find charming in Indian movies (ofcourse apart from reminiscence of childhood innocence) are some really soulful well sung songs and brilliant (indeed sometimes embarrassing) play of colors. Many memorable songs were filmed with Dharmendra. The thing about movie songs is that they work in limited frame of simple language so as to connect to the mass. This simplicity that touches millions of hearts is profound. What makes it further dear is life embedded in these moments of listening hence vivid association with memory. Some of Dharmendra movie songs clearly presents early teen years in mind’s eye. Ofcourse there were lots of garbage in Indian movies but some precious gems stand out. The other day I was listening to Malayalam song while driving (Olichirikkan from Aranyakam movie by ONV). O such a beautiful deceptively simple song (lyrics are amazing and well sung -Chitra, the music though is seriously lacking), it increases my happiness index every time! What I specifically find adorable is this exquisite line Thotta vadi ninne eniku enthu ishtum annanno – “touch-me-not (plant) how much I love you you-know”. What really takes this line to quaint beauty is the word enthu, the line could’ve been Thotta vadi ninne eniku ishtum annanno “touch-me-not (plant) how I love you you-know”. As also the word annanno, instead of annu. Ofcourse it creates musicality but the difference is much deeper, from annu to annanno makes it adoringly innocent. These words really change it into something very precious.