AI Is Here
With Dan Faggella, Founder and CEO, Emerj
Deployments of AI have now reached a level of maturity and organizational readiness where there are meaningful deployments at the organizational level in forward facing organizations.
There are a significant number of organizations that have moved from simple experiments to foundational deployments. While not prevalent across all organizations, those that are innovating and leading have a recognition of the need for an AI foundation.
Choy observed that forward-looking enterprise organizations are now utilizing Large Language Models (LLMs). LLMs are appearing across a broad range of industries with the common theme being that they are either speech, text, or document heavy industries. The industries at the forefront include banking and financial services.
LLMs understand language data with human level accuracy and can generate new content. The leaders in this area are innovating with LLMs to power applications and use cases across the organization, from the front end call center, the back end risk and compliance workloads, and everything in between.
The key takeaway is that there is a shift in thinking at leading organizations. There is an understanding that what is needed is a foundation model to set the backbone of the organization.
Read the blog to discover how leading organizations are gaining competitive advantage with LLMs today, or visit our large language model page to learn more about how LLMs are are changing what is possible.
The fragility of global supply chains, particularly for complex processes, has had a significant impact on manufacturers. That fragility has increased over the last 2-3 years, but now AI is being used to harden the supply chain and to move from simply optimizing for cost reductions and lowering risk to optimizing for sustainability and reducing the carbon emissions.
Challenges include climate related disasters, increases in fuel prices, reduced capacity related to the gloabal pandemic and more.
Rolls Royce is using AI and Natural Language Processing (NLP) to reduce costs, improve component availabillty, and dramatically increase sustainability.
Read the blog to learn more about how Rolls Royce is using AI and deep learning to harden the supply chain while also increasing sustainability and what is required to implement this workflow in manufacturing and automotive organizations.
The Defense Innovation Unit is using deep learning to analyze vast amounts of Publicly Available Information (PAI) and Commercially Available Information (CAI) to transform how analysts can make connections between vast amounts of data to obtain insights that would not otherwise be possible. The issue is that in the modern world we have gone from analyzing hundreds or thousands of documents to millions, or in some cases, billions of documents, images, and other media.
Dunnmon says that moving to an AI based analysis solves this challenge.
Read the blog to learn more about how the DIU is using AI and deep learning to make connections that they never could have before and what is required to implement this workflow in public and private organizations.
The NHS is taking advantage of AI and NLP to transform patient care. At Leeds Hospital they are creating patient pathway management streams that enable patients to receive standardized care, which provides better patient outcomes and experiences. Additionally, these AI created patient streams are amenable to auditing to explain the reason why a particular treatment was recommended.
Ultimately they are able to see more patients, resulting in greater efficiency, while improving the quality of patient care, and even working to prevent people from getting diseases.
Read the blog to learn more about how the NHS is using AI and NLP to improve patient care and even prevent disease or visit our industry page to learn more about how AI has transformed what is possible for the healthcare industry.
Migros Bank is using Natural Language Processing (NLP) with the latest large language models to go beyond what was possible with simple chatbots and robotic process automation (RPA). This enables them to better engage with their customers, deliver new products, and provide customers with the experience that they now demand.
The goal is to be able to understand all of the data that the customer provides, combine that with all the external data and systems they have, so they can process every standard request automatically.
Natural language processing with large language models are now able to deliver this capability. The latest large language models can both understand the content and context of documents as well as generate summaries of those documents and responses to customer queries.
Read the blog to discover how Migros Bank is using NLP and large language models to improve efficiency and reduce costs while delivering customers the high quality experience that they have come to demand.
General Electric is taking advantage of AI to improve the quality of the products they manufacture and to better monitor them over time for more efficient maintenance. In this podcast, Peter Tu describes how the power of AI has enabled GE to ensure that products they build meet specifications.
According to Tu, AI powered inspection of components can occur:
- While they are in production
- Once they have been produced
- After they have been in use
Insurance companies are using the power of AI and natural language processing to transform what is possible in their industry. By leveraging the generative power of large language models, this industry is able to do more with the massive volumes of text-based data that they possess.
According to to Gero Gunkel of Zurich Insurance there are three common use cases in insurance:
- Contract analysis
- Process automation
- Analyst augmentation
Read the blog to learn more about how Zurich insurance is using natural language processing to do more with their data than they ever thought possible or visit our industry page to learn more about how AI is transforming what is possible for the insurance industry.
The energy sector is going through a fundamental transformation. Going beyond simply producing and delivering energy in the form of hydrocarbons, the industry is unlocking new forms of energy, which are delivered with greater efficiency, all while reducing their carbon footprint.
When discussing how transformative AI is, Jeavons said that, “AI is going to be an absolutely fundamental capability for the energy system that I believe is emerging very quickly”.
According to Jeavons, there are three core areas where AI has impacted energy:
- Making existing processes more effective and efficient
- Using AI to design the next generation of energy production
- Managing the changing landscape of energy production and delivery