Generative artificial intelligence (GAI) is rapidly becoming a reality. GAI refers to machine learning algorithms that could create new content from previously created content such as text, images, code, etc. It is a branch of computer science that has been helping in creating authentic-looking artifacts that are completely original. Models created by GAI are being used in many different application areas, from art and music to computer vision and robotics.
By using GAI, computers would be able to generate or output new content by abstracting the underlying patterns associated with input data such as the number of eyes or hair colour. DeepMind’s Alpha Code (GoogleLab), OpenAI’s ChatGPT, GPT-3.5, DALL-E, MidJourney, Jasper, and Stable Diffusion are large language models, image generators, and leading GAI tools.
As a subset of machine learning, GAI focuses on creating algorithms that could generate new data.
The term ‘generative’ refers to how these models learn how to create new data rather than simply recognising it.
In the near future, machines would write, code, draw, and create with sometimes superhuman results due to a new class of large language models.
Scope of GAI
Almost every field requiring humans to create original content is up for reinvention. GAI would eventually become cheaper, faster, and better than what humans could create manually in some cases. GAI might completely replace certain human functions in the future. Many other functions might thrive through a tight iterative creative process between humans and machines in the days to come.
GAI would improve performance and reduce costs across a broader range of applications. It could be a tool to make things easier without substituting the entire process of creation. GAI is expected to reduce the marginal cost of creation and knowledge work to zero in the real world, leading to massive productivity, wealth, value, and corresponding market capitalisation.
Billions of workers are engaged in knowledge and creative work, which is one of the focuses of GAI. Workers would work faster with more efficiency and handle more tasks at the same time. Thus, GAI could generate trillions of dollars in economic value.
Basic three factors that contribute to the GAI are: improvements in models, better and more data, and greater computing capacity. By using deep learning, computers can learn complex patterns in data that were previously difficult for computers to discover. Now computers are much faster and more powerful and can do more things than ever before, and this has greatly influenced GAI.
GAI is fast becoming a reality. That’s why investment has surged from US$ 12.75 million in 2015 to US$ 93.5 billion in 2021, and the market is expected to reach US$ 422.37 billion by 2028. As per the Financial Times, more than US$ 2 billion has already been invested in GAI since 2020.
Salient Features of GAI
Some of the salient features of GAI are as follows:
- GAI has been used to create simulations of reality or natural processes, such as how to play chess, etc.
- Through the use of GAI, machines can learn how to generate art, music, and other forms of creativity, simply by inputting a string of text.
- GAI allows machines to create new works on the basis of what they have learnt from others. Thus, artists and designers can uniquely create their work for a specific purpose.
- Unlike artificial intelligence (AI) which can write news articles or compose music, GAI has no prior experience and learns by itself as per data sets provided.
Role of Data in GAI
The amount of accessible data in the present days has been growing exponentially such as the total amount of data created last year was about 79 zettabytes, the world over. The source of this data includes social media; e-commerce and search engines such as and Amazon and Google; Netflix; health care; financial services; etc.
GAI uses all this information to train its systems to perform human tasks, such as translating documents or identifying objects in photos, videos, etc. As more data becomes available, GAI would become even more important. The key is to ensure that better algorithms could be learnt, with more amount of data and be more useful in real-world applications.
GAI for Businesses and Organisations
There are many ways in which businesses could benefit from GAI.
- Businesses can automate tasks which currently require human input and can help the company save money on labour costs and make its processes more efficient by removing bottlenecks or delays that may occur while working with humans.
- It can leverage data from different sources and combine them into something new and unique.
- Through predictive analytics GAI can help companies better understand the needs of its customers and preferences so that it could create products to cater their needs.
- GAI can be used for content creation, such as articles, blog posts, etc., and can also modify existing contents to make it more relevant for specific audiences.
- It is being widely used as a popular tool for email writing, graphic design, and video creation.
- GAI can understand human input and respond to it more accurately with advance natural language processing.
- GAI is being used to create more engaging and relevant advertisements which increase click-through rates and conversions. Thus, GAI helps companies better understand their customers by identifying what messages resonate with them and why.
Threats of GAI
Generally considered to be a positive technology, GAI also has some drawbacks. Some of them are as follows:
- GAI can increase identity theft, fraud, and counterfeiting cases due to its ability to simulate things resembling the real ones. This technology could be used for malicious purposes, such as fake news stories about politicians or celebrities, etc.
- There can arise issues of data privacy in health care, as this sector collects private information about individuals.
- It uses past data as a template for future work. It means GAI backs creativity and originality.
- The issues of copyright is also the same in GAI as in AI.
Future of GAI
The field of GAI has been developing with its creative approach. It might seem trivial now, but it could dramatically improve AI efficiency and reduce bias in the future. With the help of AI, Google has developed a tool that could turn text prompts into high-definition videos. Similarly, Meta, a big tech company, has announced its own text-to-video system. GAI can also be used to generate text through chatbots, automated articles, and speech. This technology has been gaining significant traction among all large tech companies and many start-ups. GAI seeks create something new rather than simply analysing what already exists.
GAI helps remove bias from machine learning models and can deliver higher quality outputs and will facilitate the jobs of data analysts as part of the heavy lifting done by GAI. This technology can significantly transform the creative industries by making fundamental changes in the most basic use cases. These techniques can be used in future games to create massive worlds in the future and can be customised to each player.
Some GAI Tools
ChatGPT (Generative Pre-trained Transformer) ChatGPT technology is built upon OpenAI’s GPT3 AI platform. This platform houses some of the world’s largest language models, using both supervised and unsupervised AI learning techniques. This technology remembers all previous conversations and queries are accurately filtered and hence racism or inappropriate prompts are immediately identified and dismissed. With ChatGPT, one can type questions using one’s natural language, and the chatbot would give conversational answers, derived from large data volumes from both the internet and other public sources.
It has been reported that ChatGPT’s training data has algorithmic data bias and had generated a rap indicating that women and scientists of colour were inferior to white and male scientists.
DeepMind’s Alpha Code (GoogleLab) Deep Mind’s Alpha Code is a new AI system used for developing computer code, which can achieve average human level performance in solving programming contests. AlphaCode is also like ChatGPT and it uses self-supervised learning and an encoder and decoder architecture to solve natural language problems by predicting segments of code based on the previous segment and generating millions of viable solutions. It then ranks and recommends the best ten possible solutions.
Way Forward
GAI would be the next frontier for many technologists, so it is worth keeping an eye on. There are endless possibilities with the way GAI could be used. As technology and AI continue to advance, researchers, designers, and engineers worldwide have been pushing the limits of their respective fields of study. Soon, these opportunities would only become more and more prevalent.
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