By acquiring 100+M users in first 3 months of launch, ChatGPT has brought the field of Generative AI (GenAI) into mainstream awareness. The adoption of ChatGPT and similar applications have positioned Generative AI (as well as “Deep Learning”) as the newest disruptive tech after cloud computing.

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What’s inside

1. An overview of Generative AI, the market for it and the reasons for the hype
2. A qualitative assessment of its time to mainstream across various industries
3. Enterprise use cases and its limitations

After the internet, mobile and cloud, GenAI could become the next platform for the coming decade. It will improve productivity and base line the quality of output.

What is Generative AI?

Generative AI, a field of Artificial Intelligence, refers to computational models that are trained on massive amounts of input data (300bn words in the case of ChatGPT). They can synthesize data, draw inferences and create new outputs in the form of text, images, video, audio, new data and even code.

Two architectures have made GenAI immensely valuable

1. Generative Adversarial Networks (GAN), that became popular in 2014 are used for generating images & videos.
2. Transformer Models, proposed by Google in 2017, are used for generating text.

ChatGPT, for instance, uses the transformer model along with Human Feedback Reinforcement training to generate high quality outputs. Training a model requires intensive computational power (supercomputers were used to train GPT3) and significant investments (OpenAI being a key example). But once a model is trained, it can be optimized for a larger user base. Download Complete Research

The market for Generative AI

The market is expected to grow from $8B in 2022 to $109B in 2030 at a CAGR of 34.6%. Key facts as follows

• Software segment accounts for 60%, service segment is the fastest growing.
• Media & entertainment is the biggest user of Gen AI accounting for 18% of revenue,  BFSI is the fastest growing at 36% CAGR.
• North America is the biggest market with 40% share and APAC is the fastest growing region.

Why GenAI is here to stay?

Need for content synthesis
We generate ~2.5 quintillion bytes of data every day on the internet. This not only makes searching for information tough but also makes inferring tougher, for regular users. GenAI tools can search, synthesize and compose an answer.

Democratization of content creation
We are moving from a search and retrieval economy to an infer and compose economy. People used to prompt algorithms to search and retrieve information but now they can prompt algorithms to infer and compose information.

Instant economy
Digital natives prefer tools that enable instant creation of content e.g. Tik Tok. ChatGPT can generate a word in 350ms after processing database of 300B words.

Access to massive computational power
The ability to instantly process and compose information using cloud computing.

Evolution of deep learning neural networks
Large Language Models have become openly available. These models help organize much of the internet’s information and develop patterns to mimic human decision-making.

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An early assessment of time to market & key GenAI companies.

Enterprise use cases of Generative AI

GenAI will fundamentally change several functions in enterprises leading to improved productivity and performance of employees. A Few areas of business where it will have the biggest impact are as follows:

Content creation
Gen AI will lead to more automation in content creation. It will not only reduce the cost of content creation but also increase the quality & variety of content created. Generative AI based DIY Apps are expected to emerge for marketing and design functions.

Content personalisation
Marketing touchpoints like newsletter, websites, videos, metaverse etc. will get hyper-personalized. This will improve brand engagement and conversion ratio of the sales funnel.

Drug discovery
Drug Discovery is a time-consuming process that can extend to 5-12 years. Gen AI can help identify potential drug candidates and test their effectiveness using computer simulations, thus saving time in the process. It has already led to tremendous real-world value, when the first mRNA covid vaccines were developed by programming mRNA molecules to express the specific antigen response. By 2025, more than 30% of new drugs and materials could be systematically discovered using GenAI techniques, up from zero today.

Software development
IT products and services could see the biggest impact. Below are some scenarios that may unfold.

• Reduced time on testing and coding
  Gen AI has the capability to create, test and debug the code in real time. In typical product development cycles, coding and testing takes 30-40% of the time. Gen AI will cut this significantly, thus reducing time to market.
• Improve programmer performance
  Code can now be generated with a simple prompt command. This will allow even less-tech savvy programmers to generate a better code. Gen AI can also translate the code from one programming language to another. However human intervention will still be needed to customize the code for specific vertical / client use.
• Automate recurring tasks
  Manhours will be freed from repetitive tasks, as automation is easier with GenAI. Tasks like report generation, log analysis etc will fall in the domain of automation.
• More secure and reliable IT infrastructure
  Gen AI can track performance and security of IT infrastructure in real time. It can pre-empt any failures, by generating early warning signals and hence improve reliability of operations.
  
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Credits
Lead Authors@lab45: Siddhant Raizada, Nagendra Singh, Tommy Mehl, Arvind Ravishunkar
Contributing Authors: Aishwarya Gupta, Anindito De

The aviation industry is expanding rapidly, making it imperative to adopt sustainable practices. Airports worldwide are taking significant measures to reduce their carbon footprint, conserve natural resources and encourage social responsibility but still more needs to be done.

What's inside

1. Establishing the need for sustainability
2. Several areas of impact: not just carbon emissions
3. Top areas to focus
4. New initiatives to optimize footprint

The need for sustainable airports

The aviation industry contributes to global carbon emissions, with airports accounting for about 2-3% of that contribution. While this is not very significant and airports are committing to Net-zero, they impact the environment in many other ways. Airports consume significant amounts of energy and water and generate waste equivalent to small cities and contribute significantly to noise pollution leading to health disorders in the neighbourhood. Airports also promote economic growth through trade, tourism, job opportunities, support for local businesses, and regional development and hence a sustainable balance is the need of the hour.

Areas to focus for maximum impact

Airports around the world are recognizing the importance of sustainability and are implementing various eco-friendly initiatives to reduce their ecological footprint. Four key areas of focus have emerged: energy, waste, water, and noise. Some leading airports have already implemented sustainable initiatives, such as using recycled materials for construction and mandating the use of reusable tableware in food and beverage establishments. Emerging ideas include hydrogen fuel cells and bio fuels (for energy), AI and IOT Sensors for optimal water usage, hydrothermal liquefaction and AI/ML to detect recyclables for waste and AR safety programs and Noise insulation techniques for Noise impact reduction. Others need to assess their current state and take steps that best suit their situation. Download Complete Research

Credits
Author@lab45: Deepika Maurya

Technology both improves and harms environmental sustainability. It’s is a double edged sword. While emerging technologies such as Al, loT, AR/ VR and others are being used to help achieve sustainability goals, these technologies when put into mainstream adoption will leave a hefty environmental footprint.

What's inside

1. The urgent need for sustainable technology
2. A framework for sustainable technology in datacenters
3. Implementing our sustainable technology framework
4. Sustainable technology maturity model
5. Bringing it all together

The urgent need for sustainable technology

If we continue on our current trajectory, datacenters will leave a huge footprint on our planet. Data centers contribution to Co2 Emissions will go up 12X by 2030 and their consumption of water will go up 17X in the same time. Needless to say, they will be huge contributors to E-waste in landfills.

A framework for sustainable technology in data centers

We have developed a framework based on the IT lifecycle consisting of a stack of building blocks. We believe this framework will help facilitate a comprehensive and structured implementation of a Sustainability strategy within the data center.It is quite intuitive and easy to identify potential actions that can be taken and what their impact would be. Download Complete Research

Implementing our sustainable technology framework

As organizations move with sustainable strategies, we highlight key insights that will help drive these strategies. We also identify metrics that can be used to measure progress.

The maturity model

We have painstakingly built a 5-level maturity model that can be used as a guideline to assess where the organization stands. We also give insights on what would be required to move to the next level.

Bringing it all together

We finally put this all together to see how organizations should approach their Sustainability journey.

Credits
Author@lab45: Hussain S Nayak, Sujay Shivram, Chandan Jha
Author@Wipro: Susan Kenniston