Sustainable airports: The burning need

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Sustainable airports: The burning need

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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.

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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

Latest stories

Reimagining software engineering with GenAI

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Reimagining software engineering with GenAI

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GenAI is transforming various domains including software engineering. This technology enables high productivity, rapid development, and abundant innovation promising overall productivity gains of 26-30%. Embrace the future and discover the extraordinary potential of GenAI.

What's inside

  1. An overview of GenAI’s impact on the software engineering industry
  2. A detailed prognosis of GenAI’s impact on the software development lifecycle
  3. Tools and useful technologies for GenAI-assisted software engineering
  4. Future trends in GenAI for Software engineering

Key insights: How GenAI can impact software engineering

GenAI is expected to boost productivity in software engineering by 28% and is projected to improve task completion speed by 54% by 2025. It can also increase application development and maintenance revenue by 25%-30%, leading to a 5%-6% improvement in overall profitability. The GenAI market is expected to grow at a CAGR of 42% over the next decade, and the technology can help address the expected shortfall of 4 million developers by 2025. However, detailed study and implementation strategies are needed to consider factors such as initial installation effort, resource training, and technology integration.

GenAI’s impact on the software development lifecycle

GenAI can significantly impact the various phases of software development by generating code, improving code quality, simplifying code structures, and automating tasks such as unit testing and bug detection. It can reduce the duration of task execution across various software development stages by 20%-45%, resulting in substantial cost savings. While software engineers may be the primary beneficiaries of GenAI, other teams, such as architects, consultants, and sales teams, can also derive substantial advantages. However, implementing GenAI requires a comprehensive strategy that includes tool selection, investment, deployment, and developer training. The implementation strategy should target the highest-leverage phases of software development, and enterprises should invest in resources and training necessary for developers to leverage the technology effectively. Download Complete Research

Tools and useful technologies for GenAI-assisted software engineering

Integration of GenAI with DevOps principles can increase productivity by automating tasks, generating content, and providing intelligent insights. Adopting GenAI in the DevOps software development environment can further boost productivity through script generation, automated monitoring and alert generation, and synthetic data for pipeline load testing. GenAI can complement and enhance Low Code No Code (LCNC) capabilities by integrating visual developer interfaces, quickening development cycles, and creating text and multimedia assets. Key platforms for code generation using GenAI include Copilot by GitHub, CodeWhisperer by AWS, ChatGPT by OpenAI, Vertex AI by Google Cloud, and TabNine by Codota Dot Com Ltd.

Future trends in GenAI for software engineering

As AI continues to evolve, GenAI will become an increasingly essential aspect of the software development process. GenAI provides opportunities for innovation and creativity while also presenting new challenges. Customized benchmarking contextualized to the customer environment is crucial in refining and optimizing GenAI tools, fostering greater productivity and efficiency in software development. Enterprises must formulate a comprehensive GenAI strategy and incorporate GenAI tools in the implementation and testing phases of the software development lifecycle to maximize development cost and effort savings and improve quality. Collaboration with GenAI tools will create new roles, such as Prompt Engineers, and allow developers to focus on strategic thinking and creative problem-solving. GenAI is also an excellent learning and training tool, automating the generation of educational content and assisting in information retrieval and organization. Download Complete Research

Credits
Lead Authors@lab45: Hussain S. Nayak, Anju James
Contributing Authors@lab45: Vinay Ramananda, Dattaram B A

Top trending insights

Generative AI on the cusp of disruption

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Generative AI on the cusp of disruption

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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.

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

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.

Industry To Mainstream Gen Ai

Gen Ai Companies Capabilities

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

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