Ultimate Guide to Generative AI for Businesses

[Music]
a new more Dynamic and perhaps more
frightening era of artificial
intelligence is upon us and it comes in
the form of generative AI a subset of
artificial intelligence that can
potentially change the way we live work
and make critical business decisions
since the days of the first chatbot more
than six decades ago generative AI has
been flying under the radar that is
until chat GPT made its dazzling debut
in November 2022
chat GPT is the user-friendly interface
to the generative pre-trained
Transformer or GPT a type of machine
learning model users worldwide
discovered that when responding to their
queries and prompts chat GPT could
produce an array of content types and
generate an answer to seemingly any
question the answer is sometimes
accurate and sometimes not but always in
believable sophisticated language
welcome to the world of generative AI
deep learning technology that can
generate poems product descriptions in
all manner of text in a matter of
seconds it can also produce graphic
images audio and video plus synthetic
data that's used to train machine
learning models these rapidly evolving
generative AI capabilities have opened
opportunities for businesses in text to
image generation personalized content
creation and code generation among the
beneficiaries are data scientists
application developers marketers sales
teams digital artists designers in the
media
on the flip side generative AI has also
heightened risks of potential copyright
infringements data privacy violations
discrimination deep fakes phony
messaging deceptive practices and cyber
attacks
here we'll go over the basics of
generative Ai and what businesses need
to know for a deeper dive explore our
complete collection on all things
generative AI by clicking the link above
or in the description below
we're still in the early stages of using
generative AI but this Innovative
technology didn't develop overnight one
of the earliest examples of generative
AI was Eliza the chat bot created by
Joseph weisenbaum in the 1960s
these early chat Bots were limited by
vocabulary lack of context and an
over-reliance on patterns they were also
hard to customize
in 2010 thanks to advances in artificial
neural networks and deep learning
techniques the technology could
automatically learn to parse text
classify image elements and transcribe
audio
in 2014 Ian Goodfellow introduced
generative adversarial networks organs
which could generate realistic images of
people voices music and text
this inspired interest in how generative
AI could be used to create realistic
deep fakes that impersonate people's
voices as well as people and videos
in 2017 Google reported on a new type of
neural network architecture that brought
significant improvements in efficiency
and accuracy to tasks like natural
language processing
the Breakthrough approach called
Transformers was based on the concept of
attention
aka the mathematical description of how
things for example words relate to
complement and modify each other
researchers showed how a Transformer
neural network translated English and
French with more accuracy and in just a
quarter of the training time compared to
other neural networks the Breakthrough
Transformer architecture has evolved
rapidly since it was introduced giving
rise to better pre-training techniques
like Google's Bert in 2018 and large
language models like gpt3 in the
following years
also understand the difference between
generative Ai and traditional AI
traditional AI can identify patterns
make decisions analyze and classify data
and detect fraud generative AI can well
generate entirely new content it can
produce chat responses images diagrams
synthetic data and deep fakes
generative AI starts with a prompt that
could be in the form of a text an image
a video musical notes or any other input
that the AI system can process AI
algorithms then return new content in
response to the prompt that content can
include essays solutions to problems or
realistic fakes created from pictures or
audio of a person
early versions of generative AI required
submitting data through an API or a more
complicated process
developers had to familiarize themselves
with special tools and write
applications using languages such as
python
pioneers and generative AI are now
developing better user experiences that
let you make a request in plain language
after the first AI response to the
request you can customize the results by
providing feedback about the style tone
and other elements that you want the
generated content to reflect
no doubt you've heard about three of the
more popular interfaces to large
language models chatgpt Dolly and bard
chat GPT is an AI powered chatbot
application built on open ai's GPT 3.5
language model openai has provided a way
to interact with and fine-tune text
responses using a chat interface
chat CPT incorporates the history of its
interactions with users into an
immediate response simulating a real
conversation earlier versions of GPT
were only accessible with an API that
changed with the Advent of gpt3 followed
by a more souped-up gpt4
dolly is a multimodal form of gpt3 that
can generate images from text prompts it
identifies connections across different
media such as Vision text and audio the
name dolly is a combination of Wally the
name of fictional robot and the artist
Salvador Dali it was built on open ai's
GPT language model in 2021 a more
capable version called Dolly 2 was
released in 2022 that lets users
generate imagery in multiple styles
Google the inventor of Transformers open
source some of these language models for
researchers but never released a public
user interface for them
however when Microsoft introduced a
significant new investment in open Ai
and integrated a version of GPT into its
Bing search engine Google rushed out a
public-facing chat bot called Google
bard its debut didn't quite go as
planned after the language model
confidently delivered wrong information
stating that the web telescope was the
first to discover a planet in another
solar system Google suffered a
significant loss in stock price but Bard
has plenty of company when it comes to
accusations of inaccuracy and erratic
Behavior
questions of credibility have also
surfaced with open AIS and Microsoft's
chatbot applications
generative AI is becoming more
accessible to Enterprises thanks to
emerging Innovations like GPT that can
be tuned for different business
applications specific Industries can
directly benefit from generative AI
capabilities
businesses can Implement chat Bots to
improve customer service and Technical
Support as well as produce email
responses to customer inquiries
manufacturers can combine data from
cameras X-rays and other metrics to
identify defective parts and root causes
of problems more accurately and
economically
Financial Services can view transactions
in the context of an individual's
history to build better fraud detection
systems
film and media companies can produce
content more economically and translate
it into other languages using an actor's
own voice or composed music in a
specific style or tone
law firms can design and interpret
contracts analyze evidence and suggest
legal arguments
pharmaceutical companies can identify
promising candidates for new drugs
architectural firms can design and adapt
prototypes of buildings more quickly
and gaming companies can design levels
of content faster and more efficiently
the convincing realism of generative AI
content in its lack of transparency can
make detection difficult AI generated
outcomes can contain inaccuracies
plagiarized content amplify biases lack
proper sourcing violate privacy laws
infringe on intellectual property rights
disrupt existing business models
generate fake news and invite cyber
attacks and other malicious activities
simply put if you don't know how the AI
arrives at a conclusion you can't
completely trust the outcome and relying
on that outcome can be dangerous
therefore when introducing generative AI
into your workflows it's important to
follow some best practices to ensure
accuracy transparency ease of use and
success
clearly label all generative AI content
affecting employees and customers
vet The credibility of generated content
by identifying the primary sources of
information
be aware that bias can find its way into
AI generated applications
double check the quality of AI generated
code and content by using dedicated
software
and learn the strengths and limitations
of every tool you're planning to use
the depth ease and speed of generative
ai's responses to text and image prompts
and the widespread rapid adoption of
generative AI tools suggests that the
advantages outweigh the pitfalls of this
emerging technology
the trials and tribulations of early
rollouts have already inspired Research
into better tools for detecting AI
generated text images and video
advances in AI platforms will help
improve generative AI capabilities in
business applications but the most
significant impact will come from
embedding these capabilities directly
into the AI tools we already use
grammar Checkers for example are going
to get better
design tools will seamlessly embed more
useful recommendations directly into
workflows
training software will automatically
identify best practices and one part of
the company to help train employees
company-wide and that's just a fraction
of generative ai's potential yet the
bottom line for the successful use of
generative AI is whether the results can
be trusted in real world applications
more trustworthy outcomes will require
tools and procedures that are better at
tracking the source and credibility of
data that's fed into AI systems
foreign