AI-vengers Assemble! Overview of the adaptable Generative AI

Prasad Kulkarni
10 min readMar 28, 2023

Have a chat with any business owner or a management executive, and the topic on leveraging Artificial Intelligence (AI) to enhance their existing status quo is inevitable. And that is for a good reason. In recent years, the interest generated by Generative AI has been on the rise, thanks to its ability to quickly and easily produce high-quality output via user-friendly interfaces. And this has led to a paradigm shift in the definition of productivity and output evaluation.

Add to all this, especially post Covid, organizations and employees are at loggerheads with pressure of higher profits on one side and compensation + work-life balance on the other. So, it’s no surprise that companies are turning to technology like Generative AI for answers; especially with large amount of relevant data in hand.

Introduction to the basics:

For the uninitiated, Generative AI is an emerging technology which uses different Machine Learning algorithms to help create different forms of content — be it text, images, audio, code, synthetic data or a video.

In the digital age where content is the king and there is a constant demand for personalized one, Generative AI has been a boon. Consumers are always seeking content which is tailored to their individual needs. And Generative AI is making it possible to create this content at scale and at a rapid pace. Daily examples like Netflix or Spotify leverage ML algorithms to create customized playlists at runtime or provide recommendations based on your previous viewing history.

Another critical aspect in rise of Generative AI’s adoption is the growing availability of large datasets. With more data at our fingertips, machine learning algorithms have a wealth of information to draw upon when generating new content. As more data is made available (hopefully in a safer way), expect more applications of Generative AI to arise in years to come.

How Generative AI works:

Generative AI uses unstructured deep learning models to produce content based on user input. All the tools generally start with a prompt that could be in the form of a text, an image, a video or any input that the AI system can process. Based on the prompt, various AI algorithms then return new content in response. Content can include code, images, videos, essays or solutions to problems.

Sample screen grabs of ChatGPT, CodeStarter and Midjourney

Generative AI models

Generative AI models are a powerful tool that leverage various AI algorithms to process and represent content. For example when it comes to generating text, NLP techniques are utilized to convert letters, punctuation, and words into sentences, parts of speech, entities, and actions. These elements are then transformed into vectors using multiple encoding techniques. Similarly, images are transformed into various visual elements that are also expressed as vectors. Once developers have settled on a method to represent the world, they then apply a particular neural network to generate new content in response to a query or prompt. Techniques such as GANs (Generative Adversarial Networks) and variational autoencoders (VAEs) — neural networks that consist of a decoder and encoder — are suitable for generating realistic images or synthetic data for AI training.

Getting into details, some of the high level models are:

  1. GANs (Generative Adversarial Networks) are a type of AI model that uses two neural networks to generate new content — One for generating the content and the other for evaluating that content and providing feedback.
  2. Variational Autoencoders (VAEs) are another type of AI model that can generate new content. They work by encoding data into a lower-dimensional representation and then decoding it back into the original format.
  3. Language models are AI models that can generate new text based on a given prompt or context. A common example of language model-based software is GPT-3.
  4. Evolutionary algorithms use principles from evolutionary biology to generate new content. They work by iteratively refining a population of candidate solutions until an optimal solution is found.

Recent advancements in transformers, such as Google’s Bidirectional Encoder Representations from Transformers (BERT), OpenAI’s GPT, and Google AlphaFold, have led to the development of neural networks that can not only encode language, images but also generate new content. These advancements have the potential to revolutionize various fields in a rapid fashion.

With all said and done, Artificial intelligence without right data is just… artificial! :)

-Joe McKendrick

Understanding the Status Quo:

Generative AI has been evolving rapidly and its current state is quite advanced as compared to just a few years ago. It is making an impact on multiple areas, including art, music, gaming, fashion, and product design. We can now generate realistic images, videos, and audio that are indistinguishable from those created by creative human beings. Or use better training techniques such as GANs (Generative Adversarial Networks) to significantly improve the quality of generative models. On the customer experience side, we are surrounded by interactive applications such as chatbots, virtual assistants and personalized recommendations. And there are tools which are already present in the market creating a difference across the IT industry, including:

1. Text generation tools like GPT, Jasper, AI-Writer, Lex

2. Image generation tools like Dall-E 2, Midjourney, Stable Diffusion

3. Code generation tools like CodeStarter, Codex, GitHub Copilot, Tabnine, Codeconvert

4. AI chip design tool companies like Synopsys, Cadence, Google, Nvidia

Business Impact on the IT Industry:

With Generative AI reaching a critical level of maturity, its potential benefits are being visible across industries. With specific example of the IT industry, let’s see how it is making an impact on cost savings, new revenue generation and improved efficiency in every step of SDLC.

1. Hyper-Personalized Customer Experiences

Customers today want experiences which are faster, better, more secure, more compelling, differentiated, immersive, highly customized or hyper personalized. This is why businesses are increasingly leveraging AI in building customer experiences. Automated interfaces creation and content generation will be built using customer’s previous interaction data & profile insights. Thanks to high computing power, such high-quality user content will be created at runtime and will ensure its resonance. Adobe Sensei, Canva Design AI, Designer plugin are some of the AI-powered tools for generating UI designs based on user input and for analyzing user behavior to optimize UI layouts. Microsoft Sketch2Code uses AI to convert hand-drawn UI designs into working HTML markup and CSS styling prototypes.

Designer, a GPT-3 Figma Plugin

2. Improved Product’s Time to Market

Via Generative AI, the entire process of design, development, innovating and manufacturing products is being transformed. It contains the ability to create newer unexplored concepts. Through automated data analysis techniques and user testing approaches like automated A/B testing, trends in customer behavior and preferences can be detected early which will help create precise product design. Also, Generative AI can assist in product engineering by enabling rapid prototyping and simulation of products in virtual environments, leading to faster and more accurate problem-solving. This would reduce the overall product development time, improve productivity, bring in personalized designs, improve speed to market and increase overall precision of product R&D. Report by Accenture found that AI could increase productivity by up to 40%.

https://www.accenture.com/fr-fr/_acnmedia/36dc7f76eab444cab6a7f44017cc3997.pdf

3. Automated Software Engineering

Generative AI helps in automated code generation by using machine learning algorithms to analyse existing code and generate new code based on the patterns and structures it identifies (DeepCoder, Kite). The use of AI also aids in generating test cases and developing test automation (DiffBlue, DeepTest, Functionize, Receptiviti), thereby reducing the costs associated with engineering development. According to a study by Infosys, the use of AI in software testing reduced defects by 90%.

Advanced machine learning models such as GPT-3 and Codex are powering a new wave of code generation tools designed to streamline software development. GitHub’s CoPilot is one such tool, using GPT-3 to suggest lines of code to developers as they work. Debuild is another, which takes a voice-controlled approach to application development. It allows users to describe their desired app in natural language. It then translates these instructions into code and deploys the application automatically, making it possible to develop complex apps with ease. Generative AI also has the capacity to assist in DevOps and project management. DarwinAI optimizes machine learning models for faster deployment whereas NetApp can predict and prevents IT infrastructure issues.

My personal favorite tool found during research was Codeconvert. It helps automate the code conversion process from one programming language to another. Such automation on one side saves time and efforts, brings in accurate conversions and consistency of best practices. And on the other side, more importantly, organizations are suddenly filled with cross-trained resources and cross-functional code bases.

Codeconvert

Together, such tools are paving the way for a faster, more accessible, streamlined approach to software development. As per report by McKinsey, Generative AI could help save the IT industry up to $2.9 trillion annually by 2025 by helping reduce costs by automating tasks and reducing the need for manual labor. And according to Gartner, AI will automate 40% of application development, resulting in faster deployment and reduced development costs.

Visible Risks:

So very great, right? Well, if you say so! :-)

The ability of machines to create new data based on previous information, has the potential to revolutionize the software industry. However this advantage also poses significant risks that must be addressed to ensure safe and responsible development.

  1. Unintended consequences:

Generative AI models are black box models. Visibility on its internals and how they come up with their outputs is not provided and neither is the its underlying reasoning is provided. And this will lead to multiple intended issues.

One of the other significant risks is the potential for bias. If the training data used to create the generative model is biased, the output will be biased as well. This can preserve and reinforce existing inequalities and discrimination.

Infringement of intellectual property is also a real possibility. Generative AI will create new works that may unintendedly infringe on existing IP rights, such as trademarks or copyrights. This will be leading to unwanted legal disputes and hence reputational damage for companies that use Generative AI.

Generative AI can create unexpected and unintended outputs, particularly if the model is used to make decisions in critical areas such as healthcare or finance. Add to this, backtracking is almost impossible with potential to produce unpredictable & varied results each time.

2. Work displacement:

As Generative AI becomes more capable, it may have the potential to impact software & support jobs in a complex and multifaceted way. It could lead to the automation of certain tasks like automatically generate code or design user interfaces. This will reduce the manual work required by developers or designers. AI-generated content may also replace technical writers for producing documentation and tutorials or visual designers who create relevant images for a living. Chatbots are already replacing human customer support representatives, creating a risk for support professionals who lack the skills to adapt to new customer service technologies.

3. Negative side-effects:

Generative AI can be used to create fake data, such as images, videos, and audio files, that are difficult to distinguish from real data. This can create cybersecurity risks, as attackers can use generative AI to create convincing phishing emails or deepfake videos to spread disinformation. And with access to such rich assets, negative elements can also create fake identities or personal information, which can be used for identity theft or other malicious purposes. This can be particularly concerning in industries that handle sensitive information, such as finance or healthcare.

To mitigate such varied kinds of risks, companies must take steps to ensure that their generative AI models are transparent, explainable, and ethical. This includes rigorous testing and validation, monitoring for bias and unintended consequences, and being transparent about how the generative model was created and what it is being used for. By taking these steps, companies can ensure that generative AI is used safely and responsibly in the software industry.

What’s Next?

The age of autonomous enterprises is upon us, and there is no doubt that Generative AI will be a critical driving force behind it. As this technology continues to evolve like GPT4 whose real-world adoptions are round the corner, Generative AI will become increasingly integrated into our daily lives and transforming various aspects of it along the way. Companies can automate their complicated systems completely/partially with minimal manual effort, provide differentiated experiences, enable faster decision-making and more effective risk-taking, and align better with ever evolving customer needs.

At the risk of quoting Thanos, Generative AI is inevitable!

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