AI adoption has experienced powerful breakthroughs in recent years, with the market revenue of generative artificial intelligence (AI) expected to reach 110 billion USD by 2030. This positive outlook is fueled by the increasing AI-backed transformation efforts across multiple industries, the need for deep learning and machine learning, and the demand for efficient, fast, and optimized workflow processes.
Generative AI interfaces are on the rise, with ChatGPT currently creating a huge buzz. ChatGPT (Generative Pretrained Transformer) has drawn a lot of attention; it exceeded one million users only five days after its launch. Smarter than your average chatbot, ChatGPT is currently the poster child of generative AI because of its next-level capabilities that can produce content such as essays, images, code, or audio using machine learning to generate conversational and natural responses to text prompts.
Generative AI’s Recent Evolution
2014 saw the introduction of generative adversarial networks (GANS), a type of machine learning algorithm. This skyrocketed the capabilities of generative AI, allowing it to produce high-quality images and videos of people. In 2018, better pre-training techniques were applied into Google’s BERT, trained on more than 3.3 billion words and has 110 parameters. The same year also saw GPT being released by OpenAI, trained on 40 gigabytes of data and 117 million parameters. Compared to GANS that were used mainly for generating only visual content from images and text, this GPT technology is capable of taking information to be used for content generation, translation, and chatbot operations.
It’s understandable to take careful steps around generative AI as a whole, yet its current state is presenting a lot of potential that can benefit both users and businesses. Besides automating processes, generative AI can improve the way companies provide support, develop and test products, offer personalized products and services, and make better business decisions as a result of analytics and collected data.
Use Cases of Generative AI Across Industries
Applicable to all industries, generative AI can generate and summarize text, write marketing content, and create audio and video samples within seconds.
Furthermore, generative AI is pivotal in expediting support, as it can automate processes through conversational bots and virtual assistants that can extract answers to queries from knowledge sources gathered in pre-training.
In healthcare, generative AI is capable of converting low-res medical images (such as CT scans or x-rays) into high-res images through GANS, allowing doctors to get a closer look and give a more accurate diagnosis to a patient. A more recent use case of generative AI in healthcare is personalized drug discovery. Algorithms taken from generative AI can be leveraged to create personalized and tailored treatments based on a patient’s medical history.
In the travel and hospitality industry, airports and hotels can better analyze data and make personal recommendations through generative AI. Moreover, generative AI can also help airports with verification and face identification processes, allowing efficiency and limiting schedule delays.
In an industry that demands security, generative AI can also help banking and financial services companies identify fraudulent activities based on customer data.
As always, every existing technology comes with limitations and risks. In the case of generative AI, it can provide wrong information. It doesn’t cite sources, putting companies using it at risk for copyright violations. At a time when disinformation is prevalent, generative AI can make fake news even more accessible.
Thinking about the limitations and risks involved, it’s vital to create the right data touchpoints to be used in training generative AI models, ensuring biased and negative content are removed. Companies that want to adapt with the times by leveraging the power of generative AI must find a way to strike a harmonious balance between AI technologies and human understanding.
At Teleperformance, we promote responsible AI development and adoption, committed to a “human-first” approach toward our employees and society. At the end of the day, it’s the human touch that matters – something generative AI cannot replace, making it priceless in engaging with clients and their customers.