What you need to know
As artificial intelligence (AI) continues to capture the attention of the world, new forms have burst onto the scene, creating an ongoing game of catchup for organizations everywhere. One of the most significant and disruptive forms of AI is generative AI. Feed gen AI a simple—or complex—prompt, and its ability to mimic human cognitive processes delivers on-the-spot responses that can be carefully refined by further input.
Generative AI models such as ChatGPT and DALL-E exemplify this capability, showcasing the versatility and ingenuity of generative AI. As generative AI continuously refines its output over time, it becomes increasingly precise and creative.
What is the difference between AI and generative AI?
Artificial intelligence (AI) encompasses various technologies that enable machines to perform tasks typically requiring human intelligence, such as sensing, comprehending, acting and learning. Generative AI is a subset of AI that focuses on creating original content, including text, images, audio and synthetic data, rather than simply analyzing or classifying existing information.
What's the magic behind generative AI?
Generative AI uses natural language processing (NLP), machine learning (ML) and image recognition to respond to prompts autonomously, mimicking human cognition to solve problems while evolving over time. This cutting-edge tech has the remarkable ability to create brand new content instantly. It generates multi-modal output such as text, images or audio based on patterns learned from vast amounts of data—where more diverse data sets yield more precise and creative responses.
What is NLP and ML?
Natural Language Processing (NLP) – A field of computer science, with the goal to understand or generate human languages, either in text or speech form. Just like a translator helps people speaking different languages understand each other, NLP helps computers understand and process human language. It translates the complex nuances of human speech and text into a format that computers can work with.
Machine Learning (ML) – Think about ML the same way you would playing a video game. If you make one move and get flattened, you don’t make the same move again. You learn from the mistake and improve until finally you can beat the level. That's the idea behind machine learning, a form of artificial intelligence. Traditional computers can't fix problems on their own, but with machine learning they can learn from past results and make better decisions in the future, at scale and with speed.
How does generative AI empower organizations?
Generative AI presents a huge opportunity to accelerate reinvention, offering the potential to reshape every facet of an organization. Our recent research indicates that technology is the top lever for reinvention for 98% of organizations, with generative AI now seen as one of the main levers for 82% of those organizations. This underscores the growing recognition of its transformative potential among businesses across various industries.
For example, turning enterprise data into knowledge entails sharing deep subject matter expertise between many people and sources. This process takes a considerable amount of time—days, weeks or even months. But thanks to the power of gen AI, we’re now able to shorten that timeframe, going from data to knowledge to real-time insights in just minutes.
That’s what we’re doing with BMW North America, using our gen AI platform EKHO (Enterprise Knowledge Harmonizer and Orchestrator) to collect and analyze its enterprise data. The platform uses large language models to intelligently answer complex questions across business functions and use cases.
of organizations see generative AI as a main technology lever for reinvention
What are the challenges and limitations?
Generative AI poses challenges and risks that demand careful management. A primary issue is AI providers' inability to ensure the accuracy and appropriateness of algorithm outputs, requiring human-in-the-loop (HITL) oversight to address errors and biases, known as "hallucinations."
Complexities related to the ownership of AI-generated content and training data also necessitate consultation with legal experts.
Security is another critical concern; even minor breaches can have severe consequences, underscoring the need for robust security protocols throughout the development and deployment of these technologies. Ensuring ethical design and regulatory compliance is crucial to reduce business risks and build trust with consumers, employees, and society at large.
Generative AI in action
Why is there so much buzz surrounding generative AI?
The excitement comes from Generative AI's ability to open up a world of possibility for creativity, problem-solving and productivity. In fact, our research shows that organizations are enhancing annual productivity gains by a factor of 5x through generative AI-powered invention. By autonomously creating content, generative AI empowers organizations to leverage AI for tasks beyond traditional analysis. This can lead to innovations in content creation, automation and decision-making processes, ultimately increasing performance while saving time. As businesses increasingly rely on data-driven strategies, generative AI offers a powerful tool for staying competitive and innovative in the digital age.
Hype, or reality?
The relationship between hype and reality can be a delicate balance, especially when it comes to introducing new concepts or technology. While there is a lot of buzz surrounding generative AI, its impact should not be underestimated. Few advancements in technology have had such a rapid and transformative effect, even on itself. Its rapid pace of change can be seen in the vast scope of ChatGPT. Therefore, it is crucial to act quickly. As we move forward, it is important to establish trust and transparency in order to fully utilize the potential of Gen AI in the economy, business, and society. Ultimately, we have the ability to shape the best possible outcomes together.
Generative AI terms to know
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Prompt
An instruction or query given to generate a response or perform a task. AI prompts can be in the form of questions, statements or commands.
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Large language model (LLM)
A type of foundation model, designed to understand, generate, and interact with human language.
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Natural language processing (NLP)
Branch of AI that involves teaching computers to understand, interpret and generate language in a way that is meaningful and useful.
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Machine learning (ML)
Branch of AI that enables computers to learn from data and make predictions or decisions without being explicitly programmed, by recognizing patterns and improving performance over time.
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Text generation
Utilizes algorithms to produce human-like text content, facilitating applications such as language translation, content creation and chatbots.
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Image and audio generation
Employs deep learning techniques to create realistic images and immersive auditory experiences that enrich user engagement and interaction.
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Style transfer
Transforms the style of an image or video to match a specified reference style, commonly used in artistic rendering and visual effects.
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Deepfake technology
Utilizes generative models to create manipulated media, often for deceptive purposes, raising concerns about misinformation and privacy.
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Prompt engineering
The process of designing and refining inputs (prompts) to effectively communicate with and guide AI models, particularly those based on machine learning, to produce desired outputs.
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HITL
HITL or “Human-in-the-loop" is the process of inserting humans into machine learning processes to optimize outputs and boost accuracy.