Relativity uses Elasticsearch and Azure OpenAI to build futuristic search experiences, today

Pichai’s talk about value revolved around customers, but also creating returns for shareholders. Hood said Microsoft said its Copilot effort should deliver some productivity improvement to customers. “We are focused on continuing to raise the bar on our operational excellence and performance as we innovate to help our customers maximize the value of their existing technology investments and thrive in the new era of AI.” According to Nadella, the Azure OpenAI Service is growing workloads for its CosmosDB, but also landing new customers. If you’re keeping score at home, Microsoft executives mentioned OpenAI 14 times during its earnings conference call.

To address this, Microsoft offers Microsoft Azure Government, a cloud solution that adheres to strict security and compliance standards, ensuring that sensitive government data is appropriately protected. “The infinite possibilities of AI are awaiting for us to explore,” said Richard Lee, formerly the co-founder and CTO of iCook and now CIO of TNL Mediagene. “The customizable AI solutions and robust information security control provided by Azure OpenAI allow us to realize the vision of AI in a secure environment. Our goal is to create products that truly bring value to users.” Since OpenAI launched at the end of 2022, the world has experienced the astonishing potential that generative AI brings.

What is a cloud landing zone?

TNL Mediagene’s self-developed Content Management System, Inkmagine, enhanced by Azure OpenAI, has become a productivity booster for content creators. Its features include AI-automated tag recommendations to optimize SEO, integrated multi-language Yakov Livshits translation, and generating suggested article outlines and headlines based on the data provided by creators. This serves to inspire a wealth of ideas for content creators while simultaneously increasing their content creation efficiency.

Generative AI, short for Generative Artificial Intelligence, refers to a subset of artificial intelligence (AI) that focuses on creating or generating new content, such as text, images, audio, or even video, similar to human-created content. Generative AI systems learn patterns from large datasets and generate new data that mimics the input data’s style, format, or characteristics. Companies will want to use the technique to fine-tune LLMs like ChatGPT to follow application-specific instructions. But the pipeline for RLHF requires complicated development and management tools, including data preparation and annotation, reward model development, model and data versioning, regular retraining, model monitoring and control, and much more.

Who should take the AI-050: Develop Generative AI Solutions with Azure OpenAI Service course?

The futurist and novelist Arthur C. Clarke is quoted as observing that “any sufficiently advanced technology is indistinguishable from magic”. In the case of generative AI, there does indeed seem to be an almost miraculous ability to produce human-like original content, including poetry, prose, and even computer code. Fortunately for Microsoft, its Azure platform is well-prepared to meet such requirements through its MLops and data warehousing tools.

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.

With deep industry expertise spanning strategy, experience design, technology, and operations, we have become the trusted partner to many of the world’s most innovative and valuable companies, helping modernize and secure their most complex systems. Our 160,000 consultants embrace an open way of working and apply our proven co-creation method, Yakov Livshits IBM Garage, to scale ideas into outcomes. Semantic memory uses a vector search to provide a prompt that can be used to deliver a factual output from an LLM. A vector database manages the context for the initial prompt, a vector search finds stored data that matches the initial user query, and the LLM generates text based on that data.

OpenAI and Azure OpenAI offer four variants of Generative Pre-trained Transformer (GPT) models:

This, along with its scalable cloud infrastructure and software development tools, will give Microsoft the edge in this more specialized niche of generative models. Instead of pure unsupervised learning, the engineers at OpenAI used human annotators to guide the model at different stages of the training process. The team first fine-tuned a pretrained model using a set of prompts and responses written by human experts. Finally, they used the reward model to further train the model and align its output with human preferences. The impressive results of ChatGPT show how far LLMs can be pushed with human assistance.

One thing to note is that Azure Cognitive Search is a “bring your own embedding vector” service. Cognitive Search will not generate the required vector embeddings for you, so you will have to use either Azure OpenAI or the OpenAI embedding APIs to create embeddings for your content. That may require chunking large files so that you stay inside the token limits of the service. In cloud computing and SaaS models, customers rarely cut budgets, according to vendors. These cloud buyers simply “optimize.” Cloud vendors say they are helping customers optimize their spending to play the long-game with customers. Microsoft’s demonstration also involved setting up the catalog for geospatial data sets, and showing how Azure Orbital Cloud Access could provide resilient edge-to-cloud connectivity through SpaceX’s Starlink network and SES satellites.

When given one of the above images of a pink fox, a mask covering the fox, and the prompt of “blue gorilla reading a book in a field”, the model creates edits of the image based on the provided input. Part of the training data for GPT-3 included programming languages, so it’s no surprise that GPT models can answer programming questions if asked. What’s unique about the Codex model family is that it’s more capable across more languages than GPT models. GPT models are able to take natural language or code snippets and translate them into code. The OpenAI GPT models are proficient in over a dozen languages, such as C#, JavaScript, Perl, PHP, and is most capable in Python. A key aspect of OpenAI’s generative AI is that it takes an input, or prompt, to return a natural language, visual, or code response.