When ChatGPT launched in late 2022, it awakened the world to the transformative potential of artificial intelligence (AI). The technology underpinning the powerful new chatbot is one of the biggest step changes in the history of AI—rather than simply analyzing or classifying existing data, generative AI is able to create something entirely new, including text, images, audio, synthetic data and more. Across business, science and society itself, it will enable groundbreaking human creativity and productivity.
From ChatGPT to DALL-E, the latest class of generative AI applications has emerged from foundation models, which are complex machine learning systems trained on vast quantities of data (text, images, audio or a mix of data types) on a massive scale. With recent advances, companies can now build specialized image- and language-generating models on top of these foundation models. Most of today’s foundation models are large language models (LLMs) trained on natural language.
The power of these systems lies not only in their size, but also in the fact that they can be adapted quickly for a wide range of downstream tasks without needing task-specific training. In zero-shot learning, the model uses a general understanding of the relationship between different concepts to make predictions and does not use any specific examples. In-context learning builds on this capability, whereby a model can be prompted to generate novel responses on topics that it has not seen during training using examples within the prompt itself. In-context learning techniques include one-shot learning, which is a technique where the model is primed to make predictions with a single example. In few-shot learning, the model is primed with a small number of examples and is then able to generate responses in the unseen domain.
The introduction of pre-trained foundation models with unprecedented adaptability to new tasks will have far-reaching consequences. According to Accenture’s 2023 Technology Vision report, 97% of global executives agree that foundation models will enable connections across data types, revolutionizing where and how AI is used. To operate in tomorrow’s market, businesses will need to lean on the full capabilities that generative AI provides.
To realize quick returns, organizations can easily consume foundation models “off the shelf” through APIs. But to address their unique needs, companies will need to customize and fine-tune these models using their own data. Then the models can support specific tasks, such as powering customer service bots or generating product designs—thus maximizing efficiency and driving competitive advantage.
As foundation models broaden and extend what we can do with AI, the opportunities will only multiply. Companies will use them to transform human-AI collaboration, ushering in a new generation of AI applications and services. AI models will become our ever-present copilots, optimizing tasks and augmenting human capabilities. Generative AI will bring unprecedented speed and creativity to areas like design research and copy generation. It will take business process automation to a transformative new level, catalyzing a new era of efficiency in both the back and front offices. It will significantly boost productivity among software coders by automating code writing and rapidly converting one programming language to another. And in time, it will support enterprise governance and information security, protecting against fraud and improving regulatory compliance.
With the global AI market size expected to reach nearly $2 trillion by 2030 (source: Statista.com, March 2023), companies will find new ways to leverage these tools to solve complex problems and drive innovation.
Generative AI covers a range of machine learning and deep learning techniques, including:
Transformer models: Transformers are neural networks that learn context by identifying and tracking relationships in sequential data, such as words in a sentence. They’re commonly used for natural language processing (NLP) tasks. Transformer architectures now underpin most foundation models.
Generative Adversarial Networks (GANs): GANs use two neural networks, a generator and a discriminator. The generator creates new content that it presents to the discriminator, which tries to determine whether it’s real or fake. Over time, the generator learns to create more realistic content that can fool the discriminator, while the discriminator gets better at distinguishing content. Though GANs have famously been used to generate fake videos or images of real people saying or doing things they haven’t done—known as deepfakes—there’s enormous potential for using GAN technology in legitimate business applications, from product design to art and content creation.
Variational Autoencoders (VAEs): VAEs learn to generate new content by analyzing patterns in a dataset. They do this by compressing data into a lower-dimensional space and then learning how to generate new data by sampling from this compressed space.
From chatbots to virtual assistants to music composition and beyond, these models underpin various business applications—and companies are using them to approach tasks in entirely new ways. Consider how CarMax leveraged GPT-3, a large language model, to improve the car-buying experience. CarMax used Microsoft’s Azure OpenAI Service to access a pretrained GPT-3 model to read and synthesize more than 100,000 customer reviews for every vehicle the company sells. The model then generated 5,000 helpful, easy-to-read summaries for potential car buyers, a task CarMax said would have taken its editorial team 11 years to complete.
Accenture has identified Total Enterprise Reinvention as a deliberate strategy that aims to set a new performance frontier for companies and the industries in which they operate. Centered around a strong digital core, it helps drive growth and optimize operations by simultaneously transforming every part of the business through technology and new ways of working. Embedded into the enterprise digital core, generative AI will emerge as a key driver of Total Enterprise Reinvention.
Radically rethinking how work gets done and helping people keep up with technology-driven change will be two of the most important factors in harnessing the potential of generative AI. It’s also critical that companies have a robust Responsible AI foundation in place to support safe, ethical use of this new technology. At every step of the way, Accenture can help businesses enable and scale generative AI securely, responsibly and sustainably.
What are the challenges and limitations?
Like any nascent technology, generative AI faces its share of challenges, risks and limitations. Importantly, generative AI providers cannot guarantee the accuracy of what their algorithms produce, nor can they guarantee safeguards against biased or inappropriate content. That means human-in-the-loop safeguards are required to guide, monitor and validate generated content. Inaccuracies are known as hallucinations, in which a model generates an output that is not accurate or relevant to the original input. This can happen due to incomplete or ambiguous input, incorrect training data or inadequate model architecture.
Generative AI also raises questions around legal ownership of both machine-generated content and the data used to train these algorithms. To navigate this, it’s important to consult with legal experts and to carefully consider the potential risks and benefits of using generative AI for creative purposes.
Moreover, foundation models possess certain characteristics that render them unsuitable for specific scenarios, at least for the time being. This introduces a whole new level of complexity to security, which is vital to ensure the smooth implementation of transformative technologies. It’s imperative for leaders to incorporate security measures throughout the entire process of designing, developing and deploying generative AI solutions, thereby safeguarding data, upholding privacy and averting misuse. Leaders must brace themselves for the unexpected, as even minor security breaches can result in significant repercussions.
Ultimately, it’s critical that generative AI technologies are responsible and compliant by design, and that models and applications do not create unacceptable business risks. When AI is designed and put into practice within an ethical framework, it creates a foundation for trust with consumers, the workforce and society as a whole.
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Generative artificial intelligence (AI) is the umbrella term for the groundbreaking form of creative AI that can produce original content on demand. Rather than simply analyzing or classifying data, generative AI uses patterns in existing data to create entirely new content. It can produce text, images, audio, synthetic data and more.
Generative AI covers a range of machine learning and deep learning techniques, such as Generative Adversarial Networks (GANs) and transformer models. ChatGPT, for example, is based on the GPT (Generative Pre-trained Transformer) architecture, which is a type of transformer model designed for natural language processing (NLP) tasks such as text generation, translation and question-answering. DALL-E is another popular generative AI system in which the GPT architecture has been adapted to generate images from written prompts.
With the potential to reinvent practically every aspect of every enterprise, the impact of generative AI on business cannot be understated. These technologies will significantly boost productivity and allow us to explore new creative frontiers, solve complex problems and drive innovation. Ultimately, generative AI will fundamentally transform the way information is accessed, content is created, customer needs are served and businesses are run.