What Is Generative AI?
Generative AI, a branch of AI focusing on content creation, uses neural networks to generate new output from prompts. It learns from vast data to produce text, images, and more, mimicking human creativity. Businesses use it for marketing, customer support, and analytics, enhancing productivity and decision-making.
What Is Generative AI?
Generative AI is a branch of artificial intelligence that empowers individuals and businesses to rapidly create new content at scale – from text and images to music, and beyond. Unlike traditional AI, which is designed to recognize and categorize data, generative AI actively generates fresh, new output in response to a prompt.
The technical backbone of generative AI involves neural networks, particularly those known as generative models. For instance, in text generation, a generative AI model processes huge volumes of text to understand grammar, style, and context. Then, it applies that understanding to generate new, coherent, contextually relevant text.
Similarly, in image creation, AI models analyse visual data, learn about colours, textures, and shapes, and use this knowledge to create new images that can be indistinguishable from those created by humans.
Businesses are increasingly leveraging generative AI for a multitude of reasons. That includes content creation, where AI can generate marketing materials, newsletters, and blogs, thereby saving significant time and resources. Generative AI can also enhance the customer experience by providing AI-driven customer support and through greater personalisation – including improving product recommendations and generating custom content.
AI enhances analytics by proactively offering solutions to real-world problems without requiring employees to query data, thereby improving decision-making processes, while engineers can use generative AI to assist with coding, especially in unfamiliar languages, increasing productivity.
How does generative AI fit into the broader AI picture?
Generative AI sits within a broader spectrum of artificial intelligence, standing out due to its unique capabilities and objectives. To understand its place in the AI landscape, it helps to view generative AI in the context of other types of AI.
AI is a broad field that encompasses various technologies that enable machines to mimic human intelligence. This includes understanding natural language, recognizing images and patterns, making decisions, and learning from data. AI systems can be broadly categorized into two types: analytical (or cognitive) AI and generative AI.
- Analytical (cognitive) AI: Analytical AI, the bulk of traditional AI systems, focuses on understanding and interpreting data – e.g. data analysis, pattern recognition, and decision-making based on existing information.
For example, a cognitive AI system might analyse financial data to predict market trends or interpret medical images to assist in diagnoses. These systems are reactive, meaning they analyse and respond to the data they receive without creating new data.
Generative AI: In contrast, generative AI takes AI a step further. Instead of just analysing and understanding data, it focuses on creating new data that didn't exist before. This ability to generate new content - whether it be text, images, music, or even code - makes generative AI unique.
Generative AI uses its understanding of existing data to produce entirely new, realistic, and often innovative outputs. This creative aspect of generative AI is what sets it apart from analytical AI.
Analytical AI is primarily designed for understanding, interpretation, and decision-making based on existing data, whereas generative AI is built to create new data or content. Analytical AI typically deals with structured data, focusing on extracting insights and patterns.
Generative AI, however, often handles unstructured data, using it as a foundation to build something new. That said, both use machine learning and deep learning, but generative AI often employs more complex models (like generative adversarial networks or transformers) to learn how to create new outputs.
As it stands, generative AI is at the forefront of AI-driven creativity, pushing the boundaries of what machines can do. It's not just about understanding the world as it is but about imagining and creating what could be. Used correctly, generative AI has the power to be an innovator and creator.
What are the different types of generative AI?
When we’re thinking about what is generative AI, it’s worth noting that AI is about more than just text and images. Generative AI spans various domains, each with distinct characteristics and uses:
Large language models (LLMs)
Includes models like GPT (generative pre-trained transformer), the model behind the famous ChatGPT. These models are adept at understanding and generating text that mimics human language. Trained on extensive textual data, they can perform tasks ranging from writing and translation to coding – which can be applied to chatbots, automated content creation, and language translation services.
Music generators
Analysing patterns in music and compositions these models create new musical pieces – with use cases including generating background music for games and videos, assisting composers with new ideas, and creating mood-specific music for therapeutic purposes.
Image generators
Models like DALL-E, Midjourney, and StyleGAN generate new images based on a prompt, and have revolutionized fields such as art creation, graphic design, and gaming. From generating life-like images or images that mimic well-known artists – right through to environment textures and character designs, and even scientific visualization.
Voice generators
Likewise, working with sound, voice generators specialize in synthesizing human-like speech, and understanding speech patterns, tones, and accents. That’s great for creating virtual assistants, providing voiceovers in various media, aiding language learning, and enhancing speech interface accessibility.
Video generators
Image generators in motion, these AI models are competent in generating or modifying video content. By learning from existing video data, they can create new video clips or alter existing ones, finding use in film and animation for realistic scene creation, in advertising for engaging content, and virtual reality environments.
Each type of generative AI has a unique use case. Sometimes, different types of generative AI can work together for a greater purpose. For example, a scriptwriter could input a basic scene outline into an LLM. The text generator would expand it with dialogue and detailed descriptions. As a next step, an image generator could create corresponding storyboard visuals, aiding with scene visualisation.
Peak generative AI: how large language models (LLMs) work
Generative AI can produce a range of content types – but for many businesses, the most interesting and relevant use cases lie in text generation (even if that text is eventually translated into audio or images).
LLMs such as OpenAI’s GPT, Google’s PaLM, and Anthropic’s Claude, represent a sophisticated approach in the field of natural language processing – and generating fresh text. The way LLMs work involves several key components and processes:
Foundation in deep learning and neural networks
LLMs are built on artificial neural networks, specifically a type called transformer models, which are designed to handle sequential data like text. These models use layers of processing units to analyse and generate language.
Fine-tuning for specific tasks
After pre-training, LLMs can be 'fine-tuned' on a smaller, more specific dataset. This step tailors the model to specific tasks like question answering, translation, or content creation. Fine-tuning adjusts the model's parameters to perform the desired task better, based on the nuances of the specific data it's trained on.
Pre-training on large datasets
The 'pre-training' phase is where the model learns language patterns. LLMs are fed vast amounts of text data (books, articles, websites, etc.). During this phase, they learn the structure of language, including grammar, syntax, and context. This training allows the model to understand and predict language patterns.
Iterative improvement
LLMs often go through multiple iterations of training and fine-tuning. With each iteration, they become better at understanding and generating language. Feedback and new data can be used to continuously improve their performance.
Understanding context with attention mechanisms
A key feature of LLMs is the 'attention mechanism.' This allows the model to weigh the importance of different words in a sentence. For example, in the sentence "The cat sat on the mat," the model learns that "cat" is closely related to "sat" and "mat." This understanding of context and relationships between words is crucial for generating coherent and contextually appropriate text.
In many ways, LLMs are unusual: self-learning, and in theory anyway, capable of developing human levels of cognition. That also means that LLMs have particularly broad applications for businesses large and small – and individuals too.
Examples of applications of generative AI
Generative AI holds much promise. It’s certainly captured the public imagination – and businesses are all over the potential of AI – including LLMs. Generative AI is poised to revolutionize productivity on a global scale, with the potential to add trillions annually to the world economy.
The primary beneficiaries of generative AI are customer operations, marketing, sales, software engineering, and R&D. AI can significantly improve business operations, such as enhancing customer interactions, creating marketing content, and drafting code from natural language prompts – but it also holds promise for individuals in their everyday lives.
More profoundly, generative AI is reshaping the nature of work, automating tasks currently consuming employee time, and higher-wage, knowledge-based jobs are more affected than others.
Use cases for individuals
Even in everyday life generative AI already delivers clear potential to increase human productivity. Take learning for example. Generative AI can analyse learner interactions to create hyper-personalized learning content that suits individual needs and preferences, helping individuals learn faster.
It can craft personalized learning paths that resonate with each learner's unique background and interests, enhancing engagement and optimizing knowledge retention and skill acquisition.
Generative AI can automate grading and provide personalized feedback to students, saving time for educators and providing valuable insights for learners. This can help students understand their strengths and weaknesses, allowing them to focus on areas that need improvement.
On a day-to-day basis, generative AI can automate manual and repetitive tasks, freeing up time for more complex and creative tasks. For example, AI can serve as a personal assistant, scheduling meetings, managing routine emails, organizing a calendar, and reviewing or approving work where someone has supervisory responsibilities.
Use cases for small businesses
Generative AI can help small businesses improve productivity, streamline operations, and enhance customer experiences in various ways. Here are some examples:
Deliver analytics:
Generative AI can help small businesses organize scattered data and provide helpful, actionable insights in natural language, thereby maintaining smooth and reliable operations that drive customer satisfaction.
Improve customer support:
Generative AI can create personalized automated chatbots on company websites to provide 24/7 service, qualify and generate new leads, and answer FAQs. AI can provide instant responses to customer inquiries received via live chat, phone calls, and emails, maintaining a human touch and improving customer satisfaction.
Automating marketing and sales:
Whether it’s blog posts, social media content, or other marketing materials, generative AI saves time and resources for small businesses. Generative AI can serve as a virtual assistant for sales reps, automating tasks like composing emails, scheduling meetings, and preparing notes for the next interaction, allowing sales teams to focus on closing deals.
By leveraging generative AI, small businesses can optimize their operations, enhance customer experiences, and gain a competitive edge in the market.
Use cases for large businesses
For big enterprises, generative AI use cases are profound – in part because large companies have the budgets to heavily customise AI to meet their requirements more closely, to the extent that some companies are building their models from scratch. Some of the areas in which big companies are using generative AI include:
Marketing communication flows:
From sending personalized emails at scale to creating content, generative AI can integrate deeply into CRM systems to automate marketing. These AI tools are increasingly integrated project management platforms too, for communication and workflow automation, with new tools emerging regularly.
Entertainment media:
AI technologies are increasingly used for creating graphics in movies and games, audio for music and podcasts, and characters for virtual storytelling. They assist in scriptwriting and creating interactive NPCs, with predictions of future dominance in film and media content.
Software development:
Generative AI streamlines the software development process by automating repetitive tasks, generating code snippets, and providing real-time coding suggestions, which boosts developer productivity and efficiency. This allows developers to focus on more complex and creative aspects of their projects, leading to faster prototyping, testing, and ultimately, quicker product launches.
Performance management and coaching:
Generative AI aids in employee coaching by analysing call data and sentiment, helping managers assess performance and guide improvement. It supports performance reviews and employee feedback processes which drives employee productivity.
Business intelligence:
Generative AI excels in summarizing vast volumes of data, particularly unstructured and qualitative data, enhancing business intelligence and reporting. It's pioneering in creating data narratives for a deeper understanding of datasets.
AI in pharmaceutical drug discovery:
AI accelerates drug discovery and design, generating novel molecules and predicting clinical trial outcomes. Medical diagnostics and imaging are other use cases – as AI is showing potential in pathology, with tools like ChatGPT passing medical exams and assisting in disease identification.
Smart manufacturing and predictive maintenance
with AI assist in innovative design and predictive maintenance in manufacturing, generating task lists and workflow suggestions. It's used in inverse design for creating new materials.
Generative AI applies to a diverse range of use cases, with many still emerging. It could be said that we’re just seeing the start – with many of the most profound use cases still to be discovered, with an explosion of productivity to follow.
What risks does Generative AI entail?
During the early stages, as individuals and companies started to explore generative AI, limitations inherent to generative AI started surfacing. For example, LLMs cannot always clearly identify the source of their content, making it difficult for users to evaluate the credibility and origin of the information.
Assessing the bias present in the sources can be a challenge, as generative AI often repackages content without offering insights into any inherent biases. Another significant concern is the difficulty in discerning inaccuracies, as AI-generated content often sounds realistic and convincing… even if the content is inaccurate.
Understanding how to adjust these systems for new or evolving situations can be a complex task.
Challenges in implementing Gen AI
Implementing AI therefore comes with a unique set of challenges. From handling technical complexity to addressing ethical concerns, businesses must navigate a complex landscape to harness the full potential of generative AI.
There is a mix of technical hurdles, integration with legacy systems, data privacy, copyright issues, and workforce implications. By understanding these challenges, organizations can better prepare themselves to leverage generative AI effectively and responsibly, unlocking new opportunities for innovation and growth. Challenges include:
Accuracy and hallucinations
Generative AI exhibits limitations in terms of accuracy and the phenomenon of hallucinations. LLMs can occasionally produce incorrect or unreliable outputs due to biases in the training data, lack of common-sense reasoning, and the model's reliance on statistical patterns – that are not always accurate.
The challenge with accuracy includes a phenomenon where the model generates text that is incorrect, nonsensical, or not real – called hallucinating. Verifying for accuracy is critical because even the most confident-sounding AI output can contain errors.
One way to counter the risk is to anonymize the data that is exposed to the Gen AI engines. Other companies choose to use private instances of the foundation models – not the public models supplied by large tech firms. Alternatively, companies can train their own AI models which can safeguard IP and private data.
Cybersecurity
Another significant risk is the greatly expanded cyber footprint that Gen AI creates with the potential that sensitive data is easily accessed. Substantial uncertainty remains about how to adequately protect companies that use Gen AI from attackers that use “prompt injection” to try to expose private data.
At this time, cybersecurity risks stand as one of the major constraints inhibiting the broad adoption of generative AI – parties because many of the risks are still poorly understood, with further research still needed.
Intellectual property
One of the major risks that companies have is whether the technology will expose their intellectual property and sensitive data while using the AI model. For example, the risk that a public-facing chatbot may expose sensitive content during use.
Cost
In other instances, the most significant constraint limiting faster adoption of Gen AI is the cost. The cost presents in two broad categories: training and implementation, but also the cost to operate – because AI is computationally intensive.
In those instances where companies take advantage of open-source models, considerable time, effort, and cost must be expended on training the models. That said, many AI tools are free – for example, employees at small businesses can get a lot of value out of Bard and ChatGPT, without paying a cent.
Finally, many use cases require careful consideration around bias. Models often reflect the biases in the data they are trained on, which can lead to skewed or unfair outcomes. This poses significant risks, particularly when these models are used in sensitive areas like hiring, law enforcement, or healthcare. For example, a business that evaluates credit applications should be mindful of bias when applying AI models to the credit applications.
Good practice for implementing generative AI
So, what can individuals and companies do to take advantage of generative AI, while compensating for the potential risks? Steps include:
Accuracy:
If possible, train AI models with your data for reliable results, balancing accuracy, precision, and recall. Transparency about uncertainty in AI responses is essential, which includes citing sources and explaining the AI's reasoning. Above all, always have a human in the chain to verify the output.
Safety:
Take a safety-first approach, mitigating bias, toxicity, and harmful outputs through assessments and protecting personal information. Security assessments are also important to identify and mitigate vulnerabilities.
Honesty:
Respecting data provenance and consent for its use is vital. Open-source and user-provided data are preferred, and AI-generated content should be transparently identified, such as through watermarks.
Empowerment:
AI should generally assist rather than replace human decision-making, especially in trust-sensitive industries like finance and healthcare. Accessibility and respectful treatment of content contributors and data labelers are also emphasized.
A final point to think about is sustainability, as the environmental impact of generative AI is significant. Efforts are being made to reduce their size and energy consumption to lower their carbon footprint – but both individuals and companies should be aware that using generative AI contributes to carbon emissions.
OVHcloud and Generative AI
Cloud computing
OVHcloud offers a comprehensive range of cloud computing services and products – including public cloud for everyday AI use cases, and for training AI. That’s in addition to specialized solutions for web hosting, domain services, data analytics, and network security.
For example, you can use our AI Deploy feature to seamlessly integrate your Docker images. Make use of API requests for your models and the web interface for managing your production applications – while you leave the complexities of infrastructure and environment security management to us. The OVHcloud AI Notebook is also a simple product for data scientists to use, making it easy to explore data and test models.
Cloud GPU
OVHcloud, working with NVIDIA, also offers an advanced cloud GPU acceleration platform designed for deep learning and high-performance computing. This platform features a range of NVIDIA graphics cards including H100/A100/L40S/L4/V100S GPUs, enhancing OVHcloud's serverless AI solution with remarkable capabilities.
Indeed, our GPU acceleration platform is available in different contexts: as a cloud instance, in a Kubernetes-managed cluster, in a serverless configuration (AI Solutions), or as bare metal.
AI machine learning
Across our AI solutions, OVHcloud emphasizes its commitment to data protection and compliance with standards like GDPR, and we offer support for various industries.