- [Instructor] Just over a month ago
at the time of this recording, Google announced Gemini,
and here's their official announcement talking about it
as the largest and most capable AI model.
Well, if you're like me, you're going to trust but verify.
So I read this article, which I recommend that you read
as well, and there's some useful information,
but then I went to my typical sources to understand
what capabilities were released.
So I thought as sort of a fun meta-process
to kick this course off, I would show you the process
that I initially took, and it uses AI to understand AI.
So I just thought it would be kind of fun.
So the first is sort of the old-fashioned way
is we would Google it.
What is Google Gemini?
And we did get some useful information here.
I could watch some videos, I could read the Wikipedia,
I could go back to this blog post,
but I'm wondering if there's anything else that I could do.
So you may or may not know
that Google released a consumer AI
that in fact uses Gemini and other LLMs called Bard.
So inside of Bard, what I first asked was,
what is the difference between Google Search
and asking Google Bard a question?
Kind of a real meta.
And I've pre-generated this
and I've saved the prompts out to my GitHub,
so if you want to rerun them just for fun,
and you can see that there's a nice summary here,
the strengths and weaknesses,
and then there's actually a table,
which I think is really a great way to kick us off.
So Google Search is traditional search.
Bard is generative AI.
Google Search is finding information on the web.
Notice Bard is natural language conversation
and creative tasks.
This is actually really important.
Strengths on Search are vast information, accuracy,
and factual answers.
Strengths on Bard are personal interaction,
understand natural language, and creativity.
Weaknesses on Search, overwhelming,
precise query formatting,
and limited creativity, less accurate, Bard, potential
for factual errors, limited real-time information.
And again, just sort of a fun thing
to link them back and forth.
This G button will perform a search
on Google on the same prompt
or query that you put into Bard.
And the reason I start this course off with this
is because I'm finding too many people rushing
into the world of LLMs, Gemini included,
not understanding the fundamental differences
between previous forms of information retrieval search,
and so on, and new forms with LLMs.
So you have to understand the tools.
So to that end, what I did really
for my own learning is I created a hierarchy.
So if I start with machine learning,
and then I think of neural networks, regression,
random force, other kind of machine learning,
and then I expand this,
LLMs are one type of a neural network,
and there are of course other types
like GANs and so on and so forth, and...
Within LLMs there are different types of models.
There are foundational models, task-specific,
fine-tuneable, and others.
And then within foundational we have generative AI,
and within generative AI we have Gemini,
and within Gemini we have different versions.
Now, if I want to get basic definitions,
I just pulled these, LLM from Google, given input text,
you can lead a horse to water, LLM output text.
That's statistically likely to follow,
but you can't make it drink.
Based on patterns learned from trading data.
So not a search engine.
A foundational model from Wikipedia is an AI model
that is trained on broad data such that it can be applied
across a wide range of use cases.
GenAI, this is from TechTarget, is a type
of artificial intelligence technology
that can produce various types of content, including text,
imagery, audio, and synthetic data.
Gemini, from Google, Gemini is natively multimodal,
which means text, images, video,
which gives you the potential to transform any type
of input into any type of output.
Wow, super compelling.
That's what is announced.
In this course we're going to look at what is available,
and you know, we are in pre general availability.
We're in a public beta, so I'm sure the features will evolve
to the status that Google is talking about.
Not all this functionality
is currently available in the models.
So to that end, continuing to use Bard,
I wanted to get a Google Gemini overview
because I found that article from Google was not as helpful
for me as a technical person,
so I thought this was pretty useful.
Explaining how Gemini works, it's an LLM.
There are three types, Ultra, Pro, and Nano.
There are predecessors.
The Cortex Vault technology is a transformer architecture.
There is data training.
Key capabilities is multimodal, understanding
and generation, and then they have examples,
safety measures, and availability.
Then the next thing I wanted to do was compare Gemini
to Bard because I think
there's a lot of confusion out there.
Bard is a consumer application.
Gemini is a foundational large language model,
and you can see a nice feature chart here.
Bard is an ensemble of multiple pre-trained LLMs.
Gemini is a single dense transformer LLM.
And notice that Bard uses multiple LLMs,
and there are different strengths
and weaknesses and benefits.
So again, I thought this would be a fun way
to kick this course off
because we're all learning differently because of AI,
and you can kind of see my process.
Start with the vendor information, do a Google search,
and then actually use AI to learn about AI.