How to minimize data risk for generative AI and LLMs in the enterprise

Having worked with foundation models for a number of years, IBM Consulting, IBM Technology and IBM Research have developed a grounded point of view on what it takes to derive value from responsibly deploying AI across the enterprise. Overall, the applications of generative AI are vast and varied, and it has the potential to transform many different industries and fields. As the technology continues to advance, it will be interesting to see the new and innovative ways in which it is used in the future. This can be used to create immersive video game environments, movie special effects, or even personalized product images for e-commerce websites. The landscape of risks and opportunities is likely to change rapidly in coming weeks, months, and years. New use cases are being tested monthly, and new models are likely to be developed in the coming years.

It’s still a long way off to replacing developers, but now AI is a great help in improving the efficiency of coding and refactoring. Multimodal models can understand and process multiple types of data simultaneously, such as text, images and audio, allowing them to create more sophisticated outputs. An example might be an AI model capable of generating an image based on a text prompt, as well as a text description of an image prompt. Generative AI models are increasingly being incorporated into online tools and chatbots that allow users to type questions or instructions into an input field, upon which the AI model will generate a human-like response.

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The digital economy is under constant attack from hackers, who steal personal and financial data. Even perfect security systems with thousands of known threat detection rules are not future proof and the adversaries continue to work on new methods of attacks and will inevitably outsmart these security systems. With billions of transactions per day, it’s impossible for humans to detect illegal and suspicious activities. The challenge will be to balance product development with commercial outreach in this fast-developing market.

Lokalise AI is an automated localization and translation platform designed for various applications, including web apps, customer service, documents, mobile apps, games, and marketing assets. With its advanced features like contextual translation, alternative variants, rephrasing, and concise adaptations, it enables seamless communication with global audiences in different languages. Auditors can interact with the model to discuss the organization’s activities, control systems, and business environment. ChatGPT, for examples, can assist auditors assess risk levels identify priority areas for more investigation, and get insights into potential hazards.

How to minimize data risk for generative AI and LLMs in the enterprise

A GAN consists of a generator and a discriminator that creates new data and ensures that it is realistic. GAN-based method allows you to create a high-resolution version of an image through Super-Resolution GANs. This method is useful for producing high-quality versions of archival material and/or medical materials that are uneconomical to save in high-resolution format. It is also possible to use these visual materials for commercial purposes that make AI-generated image creation a useful element in media, design, advertisement, marketing, education, etc. An image generator, for example, can help a graphic designer create whatever image they need (See the figure below).

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Synthetic data sets produced by generative models are effective and useful for training other algorithms, while being secure and safe to use. Moreover, the opportunity for specialists that can help businesses to navigate through the explosion of interest in generative AI to identify and build genuinely useful tools is a huge one. The latest research from the consultant McKinsey estimates that generative AI could add lead to productivity improvements worth between $2.6 trillion to $4.4 trillion annually across more than 60 use cases.

The tool is similar to ChatGPT, but it was specifically designed to be more focused on safety and a customizable, conversational tone. Many early users have praised Claude’s abilities when it comes to comedy, creative content generation, and generally absorbing feedback about communication style. Midjourney is an AI genrative ai image generator that can create realistic images based on detailed text inputs. Manufacturers can utilize it to generate prototypes, quick mockups, and visualizations without the necessity of physical samples. This means that a process that previously required a physical product can now be replaced by generative AI.

• To be sure, it has also demonstrated some of the difficulties in rolling out this technology safely and responsibly.
• As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape.
• Lokalise AI is an automated localization and translation platform designed for various applications, including web apps, customer service, documents, mobile apps, games, and marketing assets.
• These early implementations used a rules-based approach that broke easily due to a limited vocabulary, lack of context and overreliance on patterns, among other shortcomings.

In 2017, Google reported on a new type of neural network architecture that brought significant improvements in efficiency and accuracy to tasks like natural language processing. The breakthrough approach, called transformers, was based on the concept of attention. The goal for IBM Consulting is to bring the power of foundation models to every enterprise in a frictionless hybrid-cloud environment.

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It operates on AI models and algorithms that are trained on large unlabeled data sets, which require complex math and lots of computing power to create. These data sets train the AI to predict outcomes in the same ways humans might act or create on their own. This generative AI app can be used to create compelling ad creatives as well as organic social media posts. It’s very easy to use – based on target audience and platform preferences, the AI algorithm generates visuals and text in minutes. It enables designers and architects to swiftly create and render designs with a multitude of options, including color, material, finish, and part-specific modifications.

Generative AI covers a range of machine learning and deep learning techniques, such as Generative Adversarial Networks (GANs) and transformer models. DALL-E is another popular generative AI system in which the GPT architecture has been adapted to generate images from written prompts. Generative AI tools are trained by natural language processing, neural networks, and/or deep learning AI algorithms to ingest, “understand,” and generate responses based on input data.