Generative AI is a cutting-edge technological advancement that utilises machine learning and artificial intelligence to create new forms of media, such as text, audio, video, and animation. With the advent of advanced machine learning capabilities like large language models, neural translation, information understanding, and reinforcement learning, it is now possible to generate new and creative short and long-form content, synthetic media, and even deepfakes with simple text, also known as prompts.
Top technology companies, like Microsoft, Google, Facebook, and others, have commercial AI labs researching and publishing academic papers to accelerate these AI innovations. In recent years, we have seen investments in GANs (Generative Adversarial Networks), LLMs (Large Language Models), GPT (Generative Pre-trained Transformers), and Image Generation to experiment and, in some cases, create commercial offerings like DALL-E for image generation and ChatGPT for text generation. For example, ChatGPT can write blogs, computer code, and marketing copies and even generate results for search queries.
For example, the prompt “What is Generative AI” in the ChatGPT1 tool generates the following text:-
Generative AI is a type of artificial intelligence that involves creating new, original content or data using machine learning algorithms. It can be used to generate text, images, music, or other types of media. Generative AI works by training a model on a large dataset and then using that model to generate new, previously unseen content that is similar to the training data. This can be done through techniques such as neural machine translation, image generation, and music generation. Generative AI has the potential to revolutionize many industries by automating the creation of content and enabling the generation of new ideas and concepts. However, it also raises ethical concerns about the potential for biased or inaccurate content to be generated and disseminated. It is important for developers and users of generative AI to consider the potential impacts and ensure that the technology is used ethically and responsibly.
Similarly, the prompt “futuristic athletic sneaker, digital art, 3d render on a yellow background with paint splashing in the air” in Microsoft Bing Image Create, generated the following image.
Different ways Generative AI is used
Below are some interesting use cases for generative AI; this is not an exhaustive list but offers a sense of the varied possibilities.
Generative AI can craft sales, marketing, and brand messaging. Agencies can generate personalised social media posts, blogs, and marketing text and video copies by providing a text prompt to a Generative AI service, like ChatGPT. In addition, the service can quickly iterate different text by simply tweaking the prompt to effectively communicate with the audience. DALL.E, a generative image generation service, can also generate original imagery to align with the branding. Many startups are exploring services like DALL.E2, Bing Image Create, Stable Diffusion, and MidJourney to create their brand logo and to align the same with Generative AI text messaging. Instoried is using Generative AI for marketers to become better copywriters.
GitHub, Copilot6 and ChatGPT1 can generate code and help with developer productivity. It can suggest entire functions, snippets, and even fully functioning modules and generate code in real-time right in your editor. ChatGPT can also help you write code to build a technology service or integration quickly. Generative AI can also be used for generating synthetic data for data augmentation and creating additional training data to train and test AI models to experiment at scale.
Furthermore, generative artificial intelligence can sift through numerous legal research materials and produce a pertinent, specific, and actionable summary. As a result, it can reduce the countless hours of human research and enable them to focus on more complex and exciting problems. In addition, ChatGPT can assist in providing answers to complex queries and augment search algorithms to generate responses to complex search queries. Generative AI can accelerate the discovery of new research, drafting and synthesising documents and reports. Wranga is using AI to generate media reviews to help parents to monitor and steer their children’s content consumption habits.
Generative AI can also help create and simulate complex engineering, design, and architecture. It can help speed up the iterative development and testing of novel designs. Architecture, machine design, and even house floor plans are all be made by Generative Image and video technology. A Generative AI service, for instance, can let engineers and consumers design and iterate over floor plans and architectures with as little as a text prompt or vocal command.
It can also help health professionals with their medical diagnosis. AI can generate potential and alternative treatments personalised to patients’ symptoms and medical history. For instance, DeepMind AlphaFold can predict the shape of protein.
Concerns around AI use
Overall, generative AI has the potential to enable efficiency and productivity across multiple industries and applications at scale. However, if not designed and developed responsibly with appropriate safeguards, Generative AI can create harm and adversely impact society through misuse, perpetuating biases, exclusion, and discrimination. Therefore, we must add rigour and responsibility to developing AI technology, enforce ethical guidelines, conduct regular audits for fairness, identify and address biases, and protect privacy and security. Several concerns surround the use of generative AI, including bias and exclusion. Generative AI systems can perpetuate and amplify existing biases. If the models are trained on biased, non-inclusive data, they will generate biased outputs, such as offensive or discriminatory language, demeaning and degrading imagery, and prejudicial content. For example, initially, generative imagery would show only images of white men for the prompt “CEO.” Big tech has taken corrective actions to mitigate such bias issues and develop AI responsibly in the last few years. Generative AI systems can create content for malicious purposes, such as deepfakes, disinformation, and propaganda. It can also generate offensive or inappropriate content. Nefarious actors may use AI-generated media to manipulate people and influence public opinion. These systems can potentially access sensitive information, raising concerns about data privacy and security. It may also produce low-quality and less accurate information, specifically in the context of complex engineering and medical diagnosis. It can be challenging to determine who is responsible for the content generated by a generative AI system — the acquisition and consent model around the training data and intellectual property issues make it difficult to hold anyone accountable for any harm resulting from its use. Overall, it is essential to carefully consider the potential harms, threats, and concerns of Generative AI systems and ensure that they are used responsibly and ethically. Therefore, we must add adequate policy, regulation, awareness, and education guardrails to develop and use Generative AI services ethically and responsibly.
The writer is Director of Product for the Bing organisation at Microsoft