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Media agencies are younger than the creative agencies. Therefore, these are nimble and agile. They are providing value for money to their clients, the advertisers. They also are quick to detect new opportunities. They recruit the appropriate manpower to exploit these opportunities, and evolve organisational structure to accommodate the manpower. Thus the organisation structure of a media agency is complex. Today’s media agency tend to have in their structure generalists knowing marketing, advertising and media. There are specialists across disciplines such as strategic planning, buying, analytics, digital branding, sports, retail, content and so on. It goes without saying that media agencies will take some time before they provide seamless integrated service to advertisers.

Clients have become demanding and want more out of the media agencies. Media agencies cannot afford to hire high quality talents at the entry level.

Media agencies will have to develop expertise in areas such as e-commerce, technology and the use of automation while handling clients at scale. Clients are tired of dealing with a variety of agencies. It augurs well for the future of large, integrated media agency. There are many successful creative agencies around the world, but there are not many large media agencies.

Digital media has overtaken TV in 2022. The market share of the other media is declining. At the same time, in absolute rupee terms, in India, the market share of the other media is growing. Each of them have a unique role to play in a brand’s life at different points of time.

Google conceived of a plan of using high-altitude balloons in stratosphere to bring internet access to rural and remote areas. However, the project was given up due to high costs. Google worked on laser internet technology in its innovation lab called X, which is nicknamed ‘Moonshot Factory’.

In the project, traffic-light sized machines beam laser carrying data, essentially fibre-optic data. The idea emerged from failed balloon internet project Loon. The balloon project too used lasers to connect data between balloons.

The project has been named Taara and is helping to link up internet in 13 countries, including Australia, Fiji and Kenya.

It will cost a dollar per gigabyte. Even in urban areas internet will be delivered faster since it is less expensive to beam data between buildings without burying cables. This is called moonshot composting.

Bharti Airtel may tie up with Google for this project in India.

In language models of AI, we have large language models and small language models. Microsoft recently revealed its model Phi-1 with 1.3 billion parameters. Traditionally, there is a feeling that larger models are superior. However, Microsoft focussed on the quality of data for training the model. Phi-1 has been trained on curated text-book level dataset. It has already outperformed GPT-3.5 with 100 billion parameters.

Phi-1 has the transformer architecture. The crucial part is its training with text-book-level data. It completed the training process with 8 Nividia A 100 GPUs. The training process was completed in just four days. Instead of parameter count, the focus was the quality of training data. Phi-1’s accuracy score is 50.6 which surpasses that of GPT-3.5’s of 47%, though it runs with 175 billion parameters.

Microsoft wants to open source Phi-1 on HuggingFace. It is not for the first time that Microsoft is dealing with a small language model. It previously has introduced Orca, a 13 billion parameter model trained on synthetic data using GPT-4. Orca too has surpassed ChatGPT.

The belief that increased stock size is essential for better performance has ben dismissed. High quality data vests a smaller model with accuracy.

DeepMind’s AlphaGo AI software made a champion of the board game, Go, a loser in 2016. The techniques of AlphaGo can be borrowed to make a new AI system called Gemini to compete with ChatGPT from OpenAI.

Gemini is a large language model and is a work in progress. It is similar to ChatGPT, but its capabilities will be enhanced further — the ability to plan or the ability to solve problems. It would be a combination of the wonderful language capabilities of large models and the plus points of AlphaGo. At the same time, there are some new innovations.

AlphaGo is based on a technique developed by DeepMind called RL or reinforcement learning. The software learns from the actions taken in the games by repeated attempts and receiving feedback. It also uses free search to explore and remember possible moves on the board.

The language models will take a big leap by performing more tasks. Gemini will take some time before it is ready. It could be Google’s response to ChatGPT and other generative AI technology.

Language models are trained on massive data. The patterns are used to become proficient at predicting the letters and words (tokens) that follow a piece of text. The technology can be further enhanced by using reinforcement learning based on feedback from humans. It will give Gemini additional capabilities. So far language models learnt about the world only through the test. It is indirect learning. It is its major limitation. It can learn to manipulate tasks using neuroscience and robotics. Of course, the technology then could be abused, and will be difficult to control.

Autoregressive language modelling in GPT uses a sequence of tokens to predict the next token in the sequence. The model is trained on huge dataset of text. It learns to predict next token based on the content of the previous tokens.

GPT model’s architecture has a Transformer. In NLP the transformer enables the model to learn long term dependencies between tokens. This understanding is vital for learning the context of a text sequence.

In training a GPT model masked language modelling technique is used. Some tokens in the input sequence are masked. The model is asked to predict them. This helps the model to learn the relationships between different tokens in a sequence.

A trained GPT model is used for inference. The model can generate text, translate language or answer questions.

It can be fine tuned to perform specific language tasks.

GPT model’s transformer layer processes the sequence of tokens. Each token, as we know, is a piece of text, say a word or a character. The model assigns a numerical vector to each token. It is called embedding. The embedding represents its meaning and context. These embeddings are processed through the transformer layers. Ultimately, there is the output sequence.

At present, for natural language processing (NLP), there are two models in use — BERT and GPT. Both are large language models (LLMs). Both these models are based on transformer architecture. However, there are some key differences between these models.

BERT stands for Bidirectional Encoder Representations from Transformers. This model is bidirectional, i.e. it can process text in both the directions. It is thus appropriate for question answering and sentiment analysis since the model has to comprehend the full context. BERT has been trained on a dataset of text and code. It has 340 million parameters. BERT is open source. It is easier to fine tune BERT for specific tasks. BERT works on encoding mechanisms to generate language. BERT is a transformer encoder. BERT is bi-directional. BERT’s input and output positions of each token are the same.

GPT stands for Generative Pre-trained Transformer. It is an autoregressive model, i.e. it can process text in one direction only. It is appropriate for text summarization and translation, where the model has to generate new text based on a given prompt. GPT has been trained on a dataset of text only, and it has 1.5 billion parameters. GPT is proprietary model. GPT relies on decoder part of the Transformer to generate text. GPT is a Transformer decoder. GPT is unidirectional. GPT is meant for autoregressive inference. These models generate an output of one token at a time. The probability distributions over the next token depends on the previous tokens. In short, these models generate output by predicting one token at a time based on previous tokens.

ASCI processed advertising complaints in 2021-22. Some 29 per cent were about the dark patterns to lure customers. These ads were promoted by influencers and were from sectors such as e-commerce fashion, personal care, crypto, food and beverage and finance.

Let us understand the dark patterns. It is an attempt by a user interface to trick users into making choices that are detrimental to their interest, e.g. buying an expensive product, paying more than what was initially declared, making choices on false/paid-for feedback. These dark patterns impede a customer’s right to be well-informed. These constitute unfair trade practices prohibited under the Consumer Protection Act, 2019. The problem can be addressed by having self-regulation.

Types of Dark Patterns

Nagging : It is persistent, annoying and repetitive criticism and requests for action.

Bait and switch : Here what is delivered is different from what is advertised. Often, there is a switch to a lower quality product or another product.

Disguised ads : Ads are so designed that these look like content, say news articles or user-generated content.

Urgency : Here a sense of scarcity is created or a sense of urgency is created. It is is a pressume tactic goading consumers to make a purchase or take action.

Basket sneaking : Here some additional products/services sneak into the shopping cart without the user consent.

Forced action : Consumers are coerced into taking action which otherwise they would not have taken, e.g. signing up a service so as to access content.

Subscription traps : It is easier to sign up for a service, but difficult to cancel it. The cancellation is a cumbersome multi-step process.

Hidden costs : Some costs are kept hidden till the consumers are already into making a purchase.

Pre-ticked boxes : Some checkboxes are already checked. It is assumed that you will not bother to uncheck them. These boxes are for opting into email newsletters or agreeing to receive promotional material.

Misleading buttons : A button says something but does something else. There is a ‘cancel’ button which does not cancel or a ‘no thanks’ button that signs you up for something. These buttons must be deleted.

Disabled links : It is annoying to click a link to close out a pop up but it does not do anything. Either you do whatever the pop up asks or close the browser tab totally. Such links should be disabled.

Sylvester daCunha, the man behind the ‘utterly butterly’ Amul girl passed away on 21st June, 2023 in Mumbai.

daCunha was a veteran advertising man who was associated with Amul brand since the 1960s. He co-created Amul girl with his art director Eustace Fernandes. The Amul girls cheeky sentences created a memorable campaign for the brand, and it celebrated its golden jubilee in 2016.

It is the longest running ad campaign in the world. It has a single character and a topic. The whole thing started in 1966 when Kurien started operation flood in Gujarat.

Amul girl is the blue-haired noseless girl who tossed chucklesome lines into the social flow, and made Amul butter a household staple.

The ‘utterly butterly delicious’ qualifier was the contribution of daCunha’s wife, Nisha daCunha.

The style and technique of Amul campaign has remained unchanged over the years. daCunha in fact pioneered ‘moment marketing’. The catch phrase utterly butterly delicious became unforgetable.

The mascot has stood the test of time and is still relevant 57 years after it was conceived.

daCunha is survived by his wife, a son Rahul daCunha, also an adman. He was a brother of the late Gerson daCunha, an adman.

We are left with a simple Amul girl with big eyes in a dress with red polka dots and matching ribbon in her hair and paired with red shoes. She delights us with her witticisms and turns of phrase. She is adorable, and does clever and at times cringeworthy wordplay. She makes on-the-button references to topical events.

The content window is the amount of information the model can receive and its response to the user. The sum of received and created information is the content that model can operate with.

ChatGPT has the content window of 4 thousand tokens. GPT-4 has 8 thousand tokens. GPT-3.5 Turbo has 16 thousand tokens. These are not enough to load a book or a web-site. Tokens are pieces of words that are used as inputs to AI model. Before processing the prompt, the AI model breaks down input into tokens. Each token will not correspond to the start and end of the word. Tokens include trailing spaces and even sub-words. The number of tokens processed in a single API request depends on the length of the input-output text. One token is roughly equivalent to 4 characters or 0.75 words for English text.

The context window for GPT is the number of previous words the model factors in while generating the next word. The larger the context window, the more context the model has to generate the next word. The default context window for GPT is 1024 tokens.

How to overcome this?

Vector Indexes

Suppose you have 50 documents with information of 50 thousand tokens. It is 35-40 thousand characters. This information that the model has should be used to answer the query.

To accomplish this, all these documents will be split into chunks, say we get 20 pieces of 2000 characters each. These 20 pieces must be converted into vectors. When the user puts a question, that is transformed into a vector also. We then make use of cosine distance and find the vectors of the document pieces closest to the question vector. The search is for the most suitable vectors where the information on the topic is likely to be contained.

The last step is to use the vectors of these pieces into text. It is added to GPT content. Then a question that the user has put forward is asked again.

In short, vector search is a tool that enables you to add only relevant information from all the data one has loaded to the model’s context. This overcomes the contextual window limit in GPT.

Google’s online advertising business is most lucrative. It generated 80 per cent of its total revenue, and amounted to $225 billion in 2022.

Google’s main source of revenue is online advertising. Directly it sells ad space on its own website and apps. Secondly, it intermediates between advertisers and publishers (third party web-sites and apps) that can supply such space.

Google’s advertising technology is being abused — the ad exchange programme favours Google over its rivals. In each ad tech supply chain, the company plays a central role. It charges high fees for its service. Google is present at almost all levels of the so-called ad tech supply chain.

The above case of EU Anti-trust body, is a direct attack on the blackbox of online advertising. Google automatically calculates and offers ad space and prices to advertisers and publishers as a user clicks on a web page. There were three earlier cases against Google where it was fined for abuses of dominance. Such charge sheets can pave the way for as much as 10 per cent of a firm’s global sales. However, they seldom approach that level, and hence the impact on the earnings of Silicon Valley firms is often muted.

EU’s anti-trust arm appealed to Google to come forward with solutions. It has ordered Google to break up ad business.

Google’s ad technology helps advertisers and publishers. They rely upon it for real-time ads (not linked to a search query). For example, banner ads in news web-sites.

There are three tools. First, publisher ad servers for publishers to manage ad space on their web-sites and apps. Second, ad buying tools for advertisers to manage automated ad campaigns. Third, ad exchanges for meeting of the publishers and advertisers to buy and sell ads. Google operates ad buying tools — Google Ads and DV 360. Google operates a publisher ad server — DoubleClick for publishers or DFP and it also operates an ad exchange — AdX.

The EC has taken view that Google breaches anti-trust rules by distorting ad tech. It has publisher ad servers– DFP, programmatic ad buying tools under Google Ads, and favours its own exchanges AdX using tools Google Ads and DV 360.

European Union and US anti-trust regulators agree on one thing — the era of Google’s dominance in ad technology must end.

Since 2014, Google has favoured its own advertising exchange platforms by abusing access to information on rival bids for ad space. It has also harmed other ad exchanges by placing bids for advertising on its own platforms.

Google is active on both sides of the market. It has the publisher ad server. It also has ad buying tools. Thus it holds a dominant position on both the selling and buying side. It also operates the largest ad exchange. There are conflicts of interest in this situation.

Google acquired firms for 15 years to dominate the market. Its 2007 acquisition of online advertising giant DoubleClick deal was worth $3.1 billion.

European Union anti-trust regulators feel that Google must break up. It is a viable option for the California-based tech giant’s alleged monopoly abuses. Of course, there are legal obstacles to the breaking up. It does not mean a legal battle is inevitable. The Commission could be swayed by Google’s arguments or accept a settlement. Google has pointed out that breaking its tech suite would diminish the availability of free, ad-supported, content that benefits everyone.

Google has already entered into an agreement with the French competition regulator, and the company can lean on that to convince the regulators in Washington and Brussels of a less intrusive remedy to the alleged abusive behaviour.