Amazon debuts generative AI tools that helps sellers write product descriptions

In this role, Swami oversees all AWS Database, Analytics, and AI & Machine Learning services. His team’s mission is to help organizations put their data to work with a complete, end-to-end data solution to store, access, analyze, and visualize, and predict. AWS will also offer access to Stable Diffusion, an AI model for generating imagery, from Stability AI, a startup that is developing Yakov Livshits a range of open source generative AI models. Philomin’s attempts at assurance aside, brands might not want to be on the hook for all that could go wrong. (In the event of a lawsuit, it’s not entirely clear whether AWS customers, AWS itself or the offending model’s creator would be held liable.) But individual customers might — particularly if there’s no charge for the privilege.

• Sanjeev specializes in Service Mesh, GitOps, IAC, Autoscaling, Cost Optimization & Observability.
• It was quick to add the generative AI models to its own products, incorporating them into Bing in February.
• Now, you can launch training after setting up environment variables for the location of the input model, dataset directory, and output directory of the tuned model.
• As you can see above most Big Tech firms are either building their own generative AI solutions or investing in companies building large language models.
• Want to quickly determine what other customers are saying about a product before reading through the reviews?

Generative AI is a type of artificial intelligence that can create new content and ideas, including conversations, stories, images, videos, and music. Like all artificial intelligence, generative AI is powered by machine learning models—very large models that are pre-trained on vast amounts of data and commonly referred to as Foundation Models (FMs). Apart from content creation, generative AI is also used to improve the quality of digital images, edit video, build prototypes quickly for manufacturing, augment data with synthetic datasets, and more. Although based on the same concepts, there is a straightforward distinction between AI’s traditional machine learning techniques that we’ve been putting to work for years—in particular deep learning—and generative AI. As its name suggests, generative AI is a type of artificial intelligence that can create new content and ideas.

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Generative AI is poised to have a profound impact across industries, from health care and life sciences, media and entertainment, education, financial services, and more. Generative AI uses AI and machine learning algorithms to enable machines to generate artificial yet new content. The end result is a totally new content that tricks the user into believing the content is real. With generative AI, computers identify the underlying pattern related to the input and produce similar content. Various techniques like Generative adversarial networks (GANs), Transformers (GPT-3, LaMDA, Wu-Dao) are used for the purpose.

Many GANs implementations also use the error value produced by the discriminator network as an additional backpropagation connection, which allows the generator to reduce the error in the next iteration. The ability to customize a pre-trained FM for any task with just a small amount of labeled data─that’s what is so revolutionary about generative AI. It’s also why I believe the biggest opportunity ahead of generative AI isn’t with consumers, but in transforming every aspect of how companies and organizations operate and how they deliver for their customers. In 2021, the company admitted it had blocked 200 million fake reviews the year prior, for example. It has also tried to crack down on the sources of fake reviews for years via lawsuits and other actions, including suing sellers who bought fake reviews.

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Auto companies are using generative AI to deliver better customer service by providing quick responses to most common customer questions. New material, chip, and part designs can be created with generative AI to optimize manufacturing processes and drive down costs. Generative AI can also be used for synthetic data generation to test applications, especially for data not often included in testing datasets, such as defects or edge cases. Generative AI applications like ChatGPT have captured widespread attention and imagination because generative AI can help reinvent most customer experiences and applications, create new applications never seen before, and help customers reach new levels of productivity.

Creating compelling product titles, bullet points, and descriptions has traditionally been a cumbersome task for sellers. / Sign up for Verge Deals to get deals on products we’ve tested sent to your inbox daily. For Yakov Livshits more on generative AI and the latest AWS tools, check out my keynote at the AWS New York Summit. Already we are seeing a pattern emerge in how generative AI will show up in businesses across four main modalities.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Notably, it’s compatible with major frameworks like PyTorch, Keras, and Tensorflow. It’s optimized for serving LLMs with features like response streaming, dynamic request batching, and multi-node/multi-GPU (Graphical processing Unit) support. Beyond just model serving, Ray Serve allows the integration of multiple models and business rules into a single service.

The explosive growth in AI has come with a flurry of security concerns from companies worried that employees are putting proprietary information into the training data used by public large language models. “With our new generative AI models, we can infer, improve, and enrich product Yakov Livshits knowledge at an unprecedented scale and with dramatic improvement in quality, performance, and efficiency. From animations and scripts to full-length movies, generative AI can produce high-quality, novel content at a fraction of the cost and time it would traditionally take.

More importantly, you need to tune these models with your data in a secure manner, so, at the end of the day these models are customized for the needs of your organization. Your data is the differentiator and key ingredient in creating remarkable products, customer experiences, or improved business operations. Ray Serve is a powerful model serving library that facilitates online inference application programming interface (API) creation.

He also helps customers modernize their applications to containers, implement AWS best practices and assist in their cloud transformation journey. He is passionate about application networking, cloud native technologies and kubernetes. Diffusers is the go-to library for state-of-the-art pre-trained diffusion models for generating images, audio, and even 3D structures of molecules. Automotive companies can use generative AI for a multitude of use cases, from engineering to in-vehicle experiences and customer service. Generative AI will help automotive companies optimize the design of mechanical parts to reduce drag in vehicle designs. Generative AI will also create new in-vehicle experiences, allowing for the design of personal assistants.

Delivering innovative health solutions at Merck with generative AI

These models have potential applicability across a wide range of use-cases and industry verticals ranging from chatbots and virtual assistants to generating videos completely via text prompts for marketing. Financial services companies can bring the power and cost-effectiveness of generative AI to serve their customers better while reducing costs. Financial institutions can use conversational bots powered by FMs to improve customer service by generating product recommendations and responses to customer inquiries. Lending institutions can fast-track loan approvals using FMs for financially underserved markets, especially in developing nations.