Different social media platforms have different ways to remunerate the creators and influencers. Creating content on social media is becoming a mainstream occupation.

YouTube: On YouTube, one enters the YouTube Partner Programme. To become eligible, a creator must have at least 1000 subscribers, 4000 public watch hours in the past 12 months and 10 million public views on YouTube Shots in the past 90 days.

Instagram: On Instagram, one can collaborate with brands. The other way to earn is to live with the badges. Viewers buy these badges to support you. You can also sell merchandise. There is exclusive content highlighted with a purple ring. To view it, subscribers pay a monthly fee.

Facebook: Here the creators earn from in-stream ads. These are short ads before, during and after the videos created by you. Placement of ads is during natural breaks. A creator can decide its own placement of ads. The video views are counted for your earnings. Even the advertisers matter for your earning.

Twitter: The ad revenue is stared with creators when ads appear in reply threads.

Creators are classified depending upon the number of their followers. Mega influencers have more than 1 million followers. Macro influencers have between 50000 to 1 million followers. Midtier influencers have between 50000 to 5 lac followers. Microinfluencers have between 10000 and 50000 followers Nano influencers have between 1000 and 10000 followers. This data decides the brand collaboration. It is to be seen what kind of engagement the followers have with the brand.

Creators can also earn through affiliate marketing. Here the creators earn a commission on sales.

Ernad Mostaque is the founder of Stable Diffusion, the image generator. He was previously a fund manager, and later turned into a tech disruptor. Stability AI is the start up behind this art generator tool.

The tool has thrown open the question of copying and originality. AI companies have the cover of the ‘fair use’ doctrine for content generation. In fact, they reap without sowing.

AI generators are built upon the backgrounds of copyrighted work. They produce copies with varying degrees of variations and sophistication, taking advantage of a legal loophole.

First, artists feed this ever hungry engine new art feed, which is more and more in their own style. However, the origination is de novo, copyright is not applicable. It is not a copy — it is stylistic inspiration.

The data set for Stable Diffusion is called LAION 5b. It was a collection of 6 billion images from internet using a practice called data scraping. LAION could scour the whole internet as it deems itself a not-profit organisation devoted to academic research. It was partly funded by Stability AI, the creator of Stable Diffusion, a separate entity. Stability AI first used its non-profit research arm to create AI generators through Stable Diffusion. It then commercialised it in a new model called DreamStudio.

AI-generated images from Stable Diffusion cannot be distinguished stylistically from those of the original artist. AI works out patterns, styles and relationships by examining billions of images on the internet.

Stable Diffusion have 10 million plus daily users. Other text-to-image generators which have emerged on the scene are Midjourney and DALL-E.

These AI products are built on collection of images known as datasets. They make a detailed mapping of their contents. They discover the connections among images and between images and text.

The tool does not do anything on its own. It takes into account the intention of the user. It is an algorithm that learns just like a human brain, only it is far more faster, powerful and untiring.

There are AI-generated images of historical or mythological characters which may not be realistic. At most, one can say these are realistic-looking fake images.

OpenAI launched Dall-E in 2021, and Dall-E 2.0 in the second half of 2022.

Ai-generated images can have plasticky appearance and semantic consistencies. However, as technology advances, the quality of the images will improve too.

In February 2023, the US Copyright office ruled that Midjourney-generated images of Zarya of the Dawn, a comic book of 18 pages by an AI expert Kristina Kashtanova are not protectable under current copyright laws as they ‘are not product of human authorship’. The ruling has great implications for the artists.

Getty accused Stability AI of using 12 million plus Getty Photos along with captions and metadata to train the algorithm behind Stable Diffusion tool. It is illegal. The newly generated images could be mistaken as images created by the artist.

There could be face-swapping software, and it could be used to supplement human models.

The line between true and fake has already blurred. The technology can also create trust deficit.

The US cCommerce Department is looking for feedback from the public on how to create AI accountability standards. These will guide the policy makers.

With the advent of ChatGPT, the significance of large language models has been realised. These models permit to move from syntax to semantics, and improve our understanding of users.

In searches, there was just a syntax forward approach using certain key words. However, with the access to LLMs, the approach is to use semantics where the model understands the query without any reference to the key words. Human beings understand the surroundings this way.

The concept is being used in advertising. LLMs understand what the users want when they type something. They pull up the most relevant ads. There is more likelihood of users clicking these ads, and the advertisers gets return on investment.

The processing speed is remarkable. It is on account of specialised hardware. That makes the models to get trained faster.

Google uses LLMs in advertising. It is involved in building ML models, especially on the conversion side — ads that generate a customer response. Conversions improve by a significant 13 per cent on an average when automation tools built on LLMs are used.

Rust is a low level programming language that is being used for the past seven consecutive years. Many companies of repute use it — Amazon, Dropbox, Meta and Samsung. It is not a difficult language to pick up. Of course, it is verbose and takes more code to develop a feature. It is good for systems programming or low level programming. Linux has accepted it, after C, as an officially supported language. GitHub’s new search engine code is written in Rust. It is a turning point for the language as it functions at GitHub scale. Dell too uses Rust in product solutions.

Rust has been created by Mozilla, the company that developed Firefox. It was to respond to the scalability problems in apps such as web browsers. The Mozilla team worked on coding problems they encountered, and the solution they got was named Rust. In Rust one can write maintainable, scalable and safe code. The code can run for decades quite reliably. Those who have mastered C/C++ learn Rust quickly.

Rust being a low level language (when compared to Python and Java) poses problems of memory management and concurrency. The developers must manage allocation explicitly and prevent multiple pointers from accessing the same memory. It is frustrating for the beginners. The syntax of Rust is also problematic. Being a new language, the eco-system of new libraries and frameworks are being developed continuously. This could be over-whelming for the new comers.

The other languages such as Java, JavaScript and Python have witnessed 25 years of growth. Rust is relatively new, and so there are kinks. The tools are geared to specific use cases, and for new uses, there should be consultation with the community.

All programming languages cannot satisfy the criteria of performance, safety and concurrency. Compromises are made on safety for the sake of performance. Rust is the first mainstream programming language which provides safety while you write performance concurrent code.

Data consumption is increasing. The use cases of internet are increasing too. Therefore a need to upgrade networks is felt to manage the increased bandwidth, faster speeds and improve range and accessibility of connectivity.

6G is a technology in the making for future to provide higher speeds and reliable ultra-low latency affordable solutions. Just as 2G/3G yielded to 4G, 5G too will make way for 6G. In fact, it will be built over 5G technology.

Industry 4.0, and then Industry 5.0 will be a major user for 6G. These will be remote-controlled factories with machine-to-machine and human-to-human interactions. 6G will facilitate autonomous cars and smart wearables.

6G will bring about convergence of digital, physical and human worlds. It will make use of AI and ML. It will make use of sub-millisecond latency. It will provide good connectivity to India’s hinterland.

6G right now is a concept, It is yet to be realised. The government has made a ‘vision document’ on it. It was released in March, 2022. It envisages a speed of 100x faster than 5G some seven years hence. It is going to be a suitable technology for smart cities, smart homes and smart cars. It will do big data analytics and holographic displays.

The speed 100x of 5G is 1 Tbps. The broadband service will be far more advanced. The video quality and gaming experience will be smarter.

India expects to launch the technology by 2030. Prior to that, there should be an eco-system for that — of devices and systems, finance for R&D, standardisation, identifications of spectrum for 6G etc.

There will be two phases for implementation — period between 2023-25 and 2025-30. In the first phase , the centre will support proof of concept to explore the ideas. The second phase will concentrate on use cases for commercialisation.

India has obtained 127 plus patents on 6G from global institutions.

The recruitment and selection trends at elite management institutes such as the IIMS put the job of product management in demand next to consulting. The Board of Directors some years back wanted a CA to be a member of the Board. These days, every Board expects a digital product manager to be in the Board to manage the digital transformation of companies.

India is known for its IT services companies, but an IT product such as Finacle from Infosys changed the very mature of the banking here and abroad.

Product management job offers are now second to consulting job offers. There is a greater demand for software product management (SPM) as software could either be a full-fledged product such as Finacle or gaming or could be embedded in other products such as cars, washing machines, aereoplanes and so on. It is to be seen whether it is desirable to incorporate software into the product. Next, it is to be seen how it could be feasible to do so. Is it technologically possible to build it? Finally it is to be seen whether it is viable — could it be done in cost parameters and could still leave profit for the company. Software Product Managers act like a director of the movie. They co-ordinate the efforts of all the members of the product development team — design, engineering, testing, sales, marketing.

The role requires business acumen, understanding of the process of software development, and getting things done without the line authority, as such product managers have the staff or advisory positions.

Product management jobs are lucrative high-paying jobs. These days, the sexiest jobs are those of the data scientists, but soon product management would replace it.

It must be remembered that a wrong decision of a doctor affects a few people, of a pilot, a few hundred people but of a product manger, a whole generation of people.

Facebook published an AI model in April 2023 that can pick out individual objects from within an image. It has a dataset of image annotations, which is the largest of its kind. Its SAM or Segment Anything Model could identify objects in images and videos, even for those cases which it has not one across during its training.

A SAM user can select objects clicking on them or by writing text prompts. To illustrate, if the word ‘cat’ is written, the model draws boxes around each of the several cats in a photo.

Facebook’s model spins up surrealist videos from text prompts and generates children’s book illustrations from text prompts.

Facebook was already using a SAM like technology internally to tag photos, moderate prohibited content and to provide recommendations for post on Instagram.

Runway, a NY start up, generated a short video in a couple of minutes of a calm river flowing in a forest on receiving a short description. Runway is testing AI technology that will let people generate videos by typing some words into a box on a computer screen.

As we have already observed in a previous blog that some 1000 plus researchers and experts have called for a moratorium on AI research for six months. There are legitimate concerns about the acceleration of AI research. The moratorium period could be utilised by the tech companies to set standards. In the absence of the ban, there could be potentially catastrophic effects. The companies are racing ahead to develop more and more powerful digital minds, some of which even their creators could not understand, predict or control.

Google’s former CEO Schmidt is not in favour of a such a ban, as it would bring undue advantage to China. Instead, there should be appropriate guardrails.

Alibaba is compiling their large language model (LLM) called Tongy Qianwen translating roughly to ‘truth from a thousand questions’. It could be used primarily to process queries of the customers in their home/native language for the e-commerce giant.

The Ministry of Electronics and IT has decided that an approval from self-regulatory organisations (SROs) will be necessary to operate if the games are money-related. There was previously a debate on classifying the games as games of chance or games of skill. The Ministry now steers clear of this nomenclature. SRO will give the permission, and not the government. It is thus light touch regulation. An online money game is where a user makes a deposit in cash or kind with the expectation of earning on that deposit. There is betting and wagering involved, irrespective of the core content of the game.

SROs will be constituted by giving representation to all the stakeholders. There could be multiple SROs. You have to approach an SRO only when there is money involved in the game, not otherwise. Initially, three SROs will be notified. Later more SROs will be notified.

In the structure of the SROs, we will find people with experience in online gaming, educationist, psychologist, child rights activist and so on. Those should be approved by the government. After application, the gaming company can operate for 3 months. SRO then carries further enquiry, and takes a decision regarding certification.

Even otherwise, online gaming companies are treated as intermediaries protected by section 79 of the IT Act, making them a safe harbour. They are so classified by self-regulatory bodies (SRBs).

This is the decisive first step for comprehensive regulation for online gaming in future to make them viable internationally.

SROs will ensure safety of users, especially children. The gaming companies must specify the risk of addiction and financial loss through repeated warning messages. There should be measures to safeguard against financial fraud.

SROs will ensure a safe framework for the gaming companies. The gaming companies must assign a mark of verification by the SRO on such games. There should be a policy for withdrawal or refund of the deposit. The winnings must be properly regulated. There should be KYC details of the users. There should not be any credit extension or financing by third parties to the users.

The intermediaries will also appoint a compliance person, a nodal contact person and a physical contact address in India. The details must be on the website.

Advertising is handled by the holding companies. The agencies under the holding companies were full service agencies handling the creative work, production and media. However, the media growth engine started making an impact on the holding companies. Digital platforms accelerated the growth and there was a change from traditional communications to digital communications. Media agencies sensed this change and grew with this change. In the 1990s, the media agencies were in their infancy. But then change occurred with a big bang, with the result that by the end of the decade, no full service agency was left.

The next decade of 2010s was defined by the sophistication of media planning. Media agencies could add value to the creative work. There were more digital platforms. The challenge before the media agencies was to digitise the clients. Communication was to established between the brand and its performance on a global basis. The clients were moving from traditional media to digital.