Systems | Development | Analytics | API | Testing

Last year, we held our first Accelerate event, to explore industry trends, data and technology innovations, and data strategy case studies in financial services. This year, we are expanding to five industry events, featuring leaders in financial services; retail and consumer goods; manufacturing; media, advertising and entertainment; and healthcare and life sciences. Accelerate Financial Services and Accelerate Retail are one-day virtual events brought to you by Microsoft.

Local LLMs are becoming mainstream with sites like HuggingFace promoting open sharing of trained LLMs. These LLMs are often very small but still extremely accurate, especially for domain-specific tasks like medicine, finance, law, and others. Gemma is a multi-purpose LLM and, while small, is competitive and accurate. Local LLMs also have the advantage of being completely run inside your own environment.

95% of insurers are currently accelerating their digital transformation with AI-driven claims processing. Traditionally, this process involved manual steps such as claim initiation, data entry, validation, decision-making, and payout, consuming significant time and resources. However, the introduction of AI has replaced tedious manual work, enabling companies to streamline their tasks efficiently.

It’s no surprise that ever since ChatGPT’s broader predictive capabilities were made available to the public in November 2022, the sprawl of stakeholder capitalization on large language models (LLMs) has permeated nearly every sector of modern industry, accompanied or exacerbated by collective fascination. Financial services is no exception. But what might this transformation look like, from practical applications to potential risks?

Retrieval-augmented generation (RAG) is an AI framework and powerful approach in NLP (Natural Language Processing) where generative AI models are enhanced with external knowledge sources and retrieval-based mechanisms. These appended pieces of outside knowledge provide the model with accurate, up-to-date information that supplements the LLM’s existing internal representation of information. As the name suggests, RAG models have a retrieval component and a generation component.

Over the past year, interest in artificial intelligence has surged due to the proliferation of generative AI and large language models. These tools captured imaginations, demonstrating a technology brimming with possibility. While many focused on the potential of these tools, some companies made AI practical. For example, last year, Appian released packaged AI tools for processing content at scale and quickly building interface forms.

Principles for responsible and effective use.

As Large Language Models are revolutionizing natural language prompts, Large Vision Models (LVMs) represent another new, exciting frontier for AI. An estimated 90% of the world’s data is unstructured, much of it in the form of visual content such as images and videos. Insights from analyzing this visual data can open up powerful new use cases that significantly boost productivity and efficiency, but enterprises need sophisticated computer vision technologies to achieve this.

Ensuring proper data governance and security relies on the capabilities offered by the software and platforms constituting your data infrastructure.