Generative AI refers to a type of artificial intelligence that is designed to generate new data or content, rather than simply analyzing or classifying existing data. In other words, generative AI algorithms are capable of creating new content that is similar to what they have been trained on.
The release of ChatGPT (Generative Pre-trained Transformer) took the world by storm, and the hype around generative AI is showing no signs of letting up. While ChatGPT is still an early foundational model, it brought the powers of generative AI into the public sphere for the first time, leading to popular adoption and widespread engagement. While traditional AI is trained to recognize patterns in databases and make predictions based on the results, generative AI can use deep learning algorithms to generate new data based on statistical patterns and dependencies in the input data it has been trained on.
Generative AI primarily uses neural networks to model the distribution of data and learn how the data is generated. It generates new content via probabilistic sampling of the learned distribution of data. Generative models like GPT and BERT use neural networks to learn this distribution of data which consist of a series of interconnected, hierarchical nodes which perform mathematical operations to transform large datasets of input data.
This neural network plays a crucial role in analyzing complicated data like videos, audio and text to minimize the difference between the actual data and the output generated by the model. The ‘T’ in ChatGPT stands for ‘transformer’ which is a type of neural network architecture that uses deep learning to understand text.
How did transformers change the game for generative AI?
For the first time in the history of generative AI, transformers made it possible to train big data models without scalability issues. For reference, OpenAI’s GPT-3 was developed using a foundational model trained with almost 45 terabytes of text data and almost the entire public web.
According to Google Cloud Tech, there are three main innovations powering transformers: positional encoding, attention and self-attention. By way of these three methods, transformers learn the importance of the order of data and the context behind the data to build an internal understanding of language. As such, transformers are key to generative models capable of natural language processing and synthetic data output.
Similarly for images, generative models use a generative adversarial network (GAN) to create responses close to the desired output. It uses a convolutional neural network (CNN) to look for and identify specific patterns within images to solve vision tasks. Filters within CNN identify and perform abstract tasks like object recognition, identification, etc. within images.
What is the primary determinant of success for generative AI?
For any generative AI, the key to success will be the amount and quality of data that is used to train the model. Large language models need to be trained with massive volumes of data; the amount of data available will be directly proportional to the model’s accuracy. However, the primary challenge is data integration without compromising on scalability or accuracy of the data being used to train the model.
For the generative neural network to respond to requests with real-world fidelity, it needs to be exposed to data that’s an accurate and complete representative of real-world scenarios.
However, training a model based on terabytes of data isn’t exactly cheap. OpenAI, the creator of ChatGPT and DALL-E, is backed by mega investors like Elon Musk, AWS (Amazon Web Services), Microsoft and more. It’s not realistic to expect smaller businesses and newcomers to attempt a generative model of similar scale without an equivalent research and development team to back it up.
But businesses with limited resources can still build exceptionally good models based on unlabeled data by using pre-built generative AI or directing resources to performing particular tasks.
What is the business value of generative AI?
The world is slowly gearing up for an AI revolution, with adoption increasing across the board. McKinsey reports AI adoption has more than doubled in the last five years, with businesses across multiple industry verticals using it for service automation, CRM analytics, risk modeling and analytics, predictive maintenance, product optimization, customer acquisitions and more.
With more and more businesses investing in AI capabilities, there’s absolutely no doubt that AI is here to stay. However, generative AI has the potential to revolutionize human-machine interaction beyond the usual automation or analytics.
So far, we are seeing applications in various sectors like Marketing and Sales, Operations, Research and Development, Information Technology, risk management and more. While the technology is still in the early stages, businesses are already using it to power chatbots, create product guides, generate synthetic data, assist with application development and more.
According to Gartner, 30% of outbound marketing messages will be synthetically generated by 2025. Apart from contributing to marketing and sales copies, McKinsey predicts generative models will also optimize the sales and lead pipeline by analyzing customer feedback, improving support chatbots and delivering personalized recommendations; some of these features already being implemented at a very early stage.
From an operational/IT perspective, generative AI is contributing to troubleshooting, data management, application development, process automation, OCR and even abstract requests like identifying clauses or sub clauses, conflicts of interests, accounting and more.
The possibility of technology being ‘creative’ can unlock new and exciting use cases presently considered outside the purview of AI. Generative AI is already being used in drug design to reduce the human workload by predicting 3D protein structure, leading to accelerated drug discovery and development for the pharmaceutical industry.
It’s also carving its way into the semiconductor market by accelerating the chip development process and driving self-sufficiency with autonomous predictions, identification of placement errors, testing simulations and corrective suggestions to continuously improve suboptimal designs and reach the desired level of efficiency.
Speaking of design, generative AI is also making significant inroads into the design sphere, be it graphic design, parts design or architectural design. Mid journey, a generative AI model that uses prompts to generate art has attracted over 2 million users since its release in open beta in July 2022. Even NASA is using generative AI to help design, analyze and fabricate new mission hardware. While it’s not a perfect process, the end results are superior to traditional hardware, both from a design and material perspective.
All the generative AI applications we see in our daily lives are only the tip of the iceberg. In the future, AI will be involved at an even deeper level, delivering both enterprise and personal use cases for businesses around the globe.
However, the technology is still at a nascent stage; the responses generated are not always accurate, training generative models is very expensive and resource-intensive, and the ethics of it all are murky. But given the circumstances, generative AI will only continue to improve in the future, and for businesses looking to dip their toes into this field, data integration will be key to getting it right.
Also read: ChatGPT at Highest Capacity: How To Fix It?