How generative AI is changing the face of business
BEYOND THE
BUZZWORD
Sentiment Analysis
Large language model
Chatbot
Big Data
Neural NetworksBig Data
Machine Learning
Sponsored by
From creating chatbots to producing content, generative AI is poised to transform just about every industry.
The technology gained significant traction when a number of tech giants went to market with their own takes on generative AI. Now, businesses everywhere are eager to see what it can do. But what exactly is this new phenomenon that so many people are talking about?
There are already very context-specific generative AI models for law and medicine. That’s extremely helpful to those heavily paperwork-focused industries. Performing tasks with large language models saves significant amounts of time.
But since generative AI is new and can make mistakes, a human must review content to ensure accuracy. And results are only as good as the data a model is trained on.
Monica Livingston
Head of AI Center of Excellence Intel
Generative AI — fact or fiction?
How much do you know about generative AI?
Generative AI in action: A law firm rests its case with automation
hover to flip
Lawyers are trained to make complex decisions on a deadline. But that’s not always easy when rote, administrative tasks are standing in the way. That was the case at law firm Ropers Majeski, where attorneys handled documenting, filing, timekeeping, and information storage and retrieval. But performing these tasks manually often led to inaccuracies and took up valuable time they could have otherwise spent on billable client work.
Ropers Majeski partnered with Intel, Activeloop, and ZERO Systems to create a generative AI solution powered by 4th generation Intel® Xeon® Scalable processors. The solution offered the speed, reliability, and security required to handle sensitive client information and ensure regulatory compliance. The result? Attorneys could focus on what they do best: Defending the law.
Technology is not our primary business — law is. Attorneys and legal professionals were happier because when their day runs out, instead of sitting there and profiling every email, every document, they just looked and said, ‘Okay, it’s done. AI did it all.’
IT Director Ropers Majeski
Maks Agamir
Behind the technology
Developing AI solutions requires extensive iteration. That means training models to, for instance, distinguish a cancer cell from a healthy one, or a red light from a green one. High-performance servers and processors make that possible. The more efficiently models process data, the sooner they can produce accurate results. In many cases, models don’t need to be large to be effective. Smaller models are trained on data specific to the use case — and Intel Xeon processors help enable performance on these models, with a real-time user experience.
For larger models — used for complex applications like computer vision or neural networks — a specialized processor called Intel® Gaudi® AI processor makes deep learning more efficient and cost effective.
After implementing generative AI, Ropers Majeski witnessed:
https://www.intel.com/content/dam/www/central-libraries/us/en/documents 2023-11/ropers-majeski-article-1-summary.pdf
Of course, generative AI is still in its early stages — and many organizations have barely scratched the surface. As businesses become more comfortable with leveraging it, new opportunities will naturally emerge.
“As generative AI becomes more prevalent, [we] continue to explore ways to optimize models and give businesses the tools to run them more cost-effectively,” said Livingston.
Learn more about the infrastructure required to train and deploy AI models in the video above.
Find out how Intel’s suite of solutions can support your business’ generative AI journey.
Learn more
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.
Intel technologies may require enabled hardware, software or service activation. No product or component can be absolutely secure. Your costs and results may vary.
Intel does not control or audit third-party data. You should consult other sources to evaluate accuracy.
1
1
18.5%
75 minutes
2.3x
saved per user per day
increase in document ingestion rates
1
Unlike traditional AI, generative AI doesn’t just perform ‘if this, then that’ actions. Large language models comb through vast data sets, quickly making complex decisions about information to include and assembling it into new content.
Generative AI
/ˌdʒen.ɚ.ə.t̬ɪv eɪˈaɪ/ noun.
A smart assistant that accelerates specialized work processes.
hover to flip
It’s difficult to distinguish between AI- and human-generated content.
AI will eliminate jobs on a massive scale.
The bigger an AI model, the better its results.
Yes, but solutions are coming.
Very unlikely.
Not necessarily.
Several tech companies have pledged to implement watermarking, which will let viewers know whether or not AI was used in the creation of content.
In its current state, generative AI can only follow the directions it’s given. Businesses will always need humans to control and check their AI applications.
Smaller models with high-quality data often provide outstanding results. And users can run them affordably on existing equipment thanks to high performance processors.
increase in the productivity of knowledge workers
2.3x