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GENERATIVE AI FOR ENTERPRISE: HOW TO TAME A STOCHASTIC PARROT

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During the last couple of years of the 20th century, Google truly rose to
prominence. Compared to the search engines of those days, it seemed clear that
“search,” as defined before Google, was just about to become completely
disrupted. However, nobody at that time was foreseeing what was about to really
happen: the end of the traditional ways of selling advertising.

With Generative AI rapidly popularized by ChatGPT, we might face a similar
Google moment right now. Besides the most recent hype, there are many ongoing
discussions about what future impact it will effectively have, especially in the
enterprise context.

However, unlike a parrot, ChatGPT uses probabilistic methods to generate its
responses. This means that instead of simply repeating what it has seen before,
it generates new responses by predicting what is most likely to come next based
on the input it receives. This is why ChatGPT is referred to as a "stochastic"
model – it generates responses based on probability rather than determinism.

For example, while ChatGPT is extremely advanced and can generate responses that
are very human-like, it is still limited by the data it was trained on. It can
sometimes produce nonsensical responses, lack context, or disregard existing
access rights within a company.

To create true value in the enterprise context, there would be a need for
Generative AI capabilities to be integrated into a semantic enterprise search
engine and trained on internal data.

Complete the form on this page to watch the on-demand webinar.




YOUR THOUGHTS ABOUT GENERATIVE AI



Part of this webinar will be the results of a survey that is aimed at
decision-makers across the US, EMEA, and APAC and helps build an understanding
of Generative AI’s actual usability. To contribute your opinions to the outcome,
we invite you to take the short survey here:



Take The Survey


SPEAKERS

Dorian Selz
Co-Founder and CEO
Squirro
Saurabh Jain
CTO
Squirro
Thomas Diggelmann
Machine Learning Engineer
Squirro
Tom Allen
Founder, The AI Journal
Webinar Moderator
APAC Session



If you're located in the APAC region, you might be interested in joining us at
the APAC webinar on March 15, at 4:00pm SGT.

APAC WEBINAR
On-Demand Webinar



Duration: 45 minutes






THE WEBINAR DISCUSSES

Bias and Hallucination



Large language models (LLMs) can be biased due to the data they are trained on.
How to deal with bias and hallucinations in an enterprise context.



Retraining on Internal Data



Public LLMs are trained on public data. To make them effective in an enterprise
context one needs to retrain on enterprise data. How?



Integration with Existing Systems



Another challenge that enterprises face when adopting LLMs is integration with
existing systems. We will discuss integration approaches.



Data Privacy and Security Concerns



Public LLMs are trained on massive amounts of public data. Enterprise data
exposed to LLMs gets immediately integrated. Implementing robust security
measures is key.



Cost



LLMs can be very expensive, both in terms of hardware and software costs. How
can these new models be applied in an economically responsible way?



Watch Now
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Squirro's Gartner Peer Insights Reviews
4.2
32 Ratings on Gartner Peer Insights
(Submit a review)
As of 11 Mar 2023

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