February 22, 2025 • 17 mins
In this episode of newb.AI, I explore how AI is already deeply integrated into our daily lives, often in ways we don’t even realize. From virtual assistants like Siri, Alexa, and Google Assistant to AI-driven recommendations on streaming services, artificial intelligence is shaping how we interact with technology.
I break down the role of Natural Language Processing (NLP) and Machine Learning (ML) in making AI assistants smarter and more responsive. I also discuss how AI algorithms analyze data, identify patterns, and make decisions.
Beyond the obvious applications, AI is also working behind the scenes in areas like spam email filtering, predictive text, and targeted advertising. As AI continues to evolve, understanding its capabilities can help us use it more effectively rather than fear it.
This episode also introduces the Fact or Fiction segment, where I analyze trending AI rumors:
🔹 Don’t forget to like, subscribe, and follow newb.AI on your favorite platform!
Email: newb.aipodcast@outlook.com
🎙️ Join the journey and discover where AI is taking us next!
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Hello fellow newbies, welcome back to another episode of Noob.ai.
(00:08):
My name is Adam Emberton and I am your Noob.
On the last episode, we talked about what AI is, we talked about some of the fears that
surround AI, and we even took a little trip down AI's history.
On today's episode, we're going to be taking a look at AI and bringing it a little bit
(00:28):
closer to home.
Because you probably don't know this, but you're already using it.
It's all around you.
So we're going to take a dive into that.
We're going to look at the ones that are probably obvious or really obvious.
And we're going to take a look at the ones that are not so obvious.
Welcome to Noob.ai, a podcast about a newbie's AI journey to walk you through as you start
(00:53):
your AI journey.
Let's start with the ones that are the most obvious.
And to do that, you're going to look down at what's in the palm of your hand.
Or at least I assume it's in the palm of your hand.
(01:14):
Or it's sitting on a stand on your desk, or it's sitting on a stand in your car.
And that's going to be your cell phone.
Whether it's an iPhone or an Android, they all have some kind of virtual assistant on
them.
And these virtual assistants are ran through AI.
Now the first ones to do it were Apple with Siri back in 2011.
Yes, there were virtual assistants that came before Siri, but not going to go into those.
(01:40):
Not going to already break my no more history lesson promise.
But Siri was really the first one that was out there that could recognize speech and
respond to speech.
After Siri came out, then you started seeing the likes of others such as Google Now or
Microsoft's Cortana or Samsung's Bixby.
(02:00):
Those virtual assistants on the cell phone really then open the door for virtual assistants
in the home.
These virtual assistants would be things like Amazon's Echo or Alexa, whatever you want
to call it, or Google Home.
Those are virtual assistants that are ran inside the home and they can run different
devices such as lights or plugs, anything like that.
(02:26):
I can confirm this because the room that I'm sitting in right now to record this podcast
is fully automated off of my Amazon Echo.
All I have to do when I walk into this room is I say Alexa, studio on.
The lights come on, the smart plugs come on, everything that's plugged into the smart plugs
come on, and then I'm good to go.
(02:48):
And then at the end of the night, I just go, Alexa, studio off.
And then it's dark again.
There are so many devices that can run off these virtual assistants.
You could have things well beyond the lights or plugs such as washers, dryers, vacuums,
thermostats, garage doors, so many different devices out there that can run off of those.
(03:13):
It's really getting to the point where you could almost have a fully automated home.
Because you are on this journey with me, I think it's important to discuss what kind
of AI these virtual assistants run off of.
As I've mentioned before, AI is such a broad term.
It covers such a wide variety.
(03:33):
So we're going to go ahead and break those down a little bit.
As we get into future episodes and we get a little bit more knowledgeable about AI,
we'll look into them even more.
We'll look into those terms.
But let's at least scratch the surface today.
So as we hear them later on, we know a little bit more of what those are.
First off, let's talk about natural language processing or NLP.
(03:56):
This is what's really going to make it so that the computer can understand words.
Computers can't understand words like humans do.
So the natural language processing, it takes those words and it teaches a computer what
those words are, and that's what makes it so it can respond in a human-like way.
The other part of that is machine learning.
(04:17):
And machine learning is exactly how it sounds.
It's a machine that's learning.
More specifically, a computer that's learning.
These are the two different types that these virtual assistants really run off of.
There's also things out there such as large language models, which we'll talk about a
little bit further.
Those large language models are going to be the ones that are more of like your chat bots,
(04:37):
like the chat GPTs and the Google Geminis and Microsoft Copilot.
Those are going to be the large language models, but they also use some sort of natural language
processing and the machine learning as well.
But the virtual assistants really just use those first two more than anything.
Now, the virtual assistants, I would say, are probably going to be the most obvious.
(04:58):
Those are the ones that people have used, whether they're using them on their phone
or maybe you don't have a home device, but you've been at a family member or friend's
house that have used the device and you've seen them use it.
And I think a lot of people already know that, yeah, that's AI.
Didn't know the breakdown of the natural language processing or the machine learning, but that's
because we're still new at this, right?
(05:18):
But now we know.
But what about some of those AI items out there that are maybe not so obvious?
You use them every day, you just don't even realize it.
Well, let's talk about a streaming service like Spotify.
You're on Spotify, you're listening to music, you're listening to your favorite kind of
music.
Then the next thing you know, it's recommending new music for you.
(05:41):
And this new music is right in line with the music that you're already listening to.
What about if you're on a TV streaming service or a movie streaming service and it's recommending
a new TV show to you?
And it is again, right in line with the TV shows that you've been watching.
Or what about if you're on TikTok and you start getting puppy video after puppy video
(06:06):
because you just liked 20 puppy videos in the last five minutes?
Those are all algorithms that are built off of AI.
So how do these algorithms work?
Well, every time you like a song, that's data.
Every time you like or dislike a movie or TV show, that's data.
(06:28):
Every time you skip a video after only two seconds, that's data.
It's all data, whether good or bad, it's all data going into this AI algorithm.
And it's really taking that every day when we don't even realize it.
And it's moving on to the next step, which is identifying patterns.
It looks at those patterns and it looks at the patterns that collected maybe the day
(06:48):
before, the week before, it's comparing the patterns.
And then it moves on to its next step, which is making a decision.
And over time, as we keep doing this and keep feeding into it, it just keeps building and
building.
And that's where you can tell where, you know, if you've been on TikTok and after a while,
(07:09):
the videos are all just videos that you like, they're right up your alley.
Well, that's because you keep feeding data into this algorithm and it's really picked
up on it.
The more it learns, the better it's going to suggest new things.
And that's really how the algorithms create it.
So think of it like this.
Let's say every day before work, you stop at a coffee shop, you get a coffee and a croissant
(07:32):
for breakfast.
After a while, the barista sees you every day.
They know who you are.
They know what you're going to order before you even walk up to the counter.
But then one day the barista says, Hey, we have this new sandwich that is made with the
croissants as the buns of the sandwich.
Would you like to try it?
It's really the same thing.
(07:54):
They're seeing this pattern and then they're recommending things that you're probably going
to like based off of the patterns that it's had before.
So it's making those decisions.
Now this data is on a very large scale, but think of it on an even larger scale with even
more complex data.
Think if it was a human behind the scenes trying to do this, trying to identify those
(08:15):
patterns, trying to make those decisions.
That's going to be a lot for a human.
It's going to be a lot for multiple humans, teams, teams of humans.
That's a lot for them to process.
Whereas AI is taking that is doing that in such a snap way.
It's getting it done instantly.
So I'm going to go back to TikTok.
(08:36):
I keep using this as an example because I think when people think of algorithms, they
think of the videos on TikTok.
So if that was a human behind the scenes doing that, they're not going to be able to keep
up because video comes in after video, it's going to take more than a day.
But guess what?
Another day comes and another day comes and now they're way behind on making these decisions
(08:57):
or identifying those patterns.
And that's where AI comes into play to really help out with all of that.
It saves time.
It saves money.
It really helps all around there.
Just so you know, TikTok has 1 billion video views a day.
1 billion video views a day.
(09:17):
So a human's not going to be able to keep up with that.
It's going to be just so overwhelming for the human.
And that's even going beyond that with that larger scale with the more complex data.
It's just going to be too overwhelming.
Not going to happen.
So this is where AI comes into play.
But what about some other things out there that maybe aren't so obvious?
(09:38):
Well, whenever you get an email, you probably have both an inbox and you have a spam folder.
That AI is deciding whether those emails should go into the inbox or should go into the spam
folder.
Some email services even take it beyond that.
If you use Microsoft Outlook, there's a focus folder and there's another folder.
(10:00):
Let's say you're getting an email.
You don't need to read it.
This is just coming in, but it's not spam.
So it's not going to the spam folder.
It's still coming to your inbox.
But you delete it without even reading it.
Or maybe you just glance at it and delete it.
Next day, same thing happens.
You do that.
You look at it.
You glance at it.
You delete it.
(10:20):
So now the AI is going to start picking up that, hey, you don't really pay attention
to these emails, but it's not spam.
So we're not going to put it in the spam folder, but we're going to put it in the other folder.
Now those emails that you keep looking at for, you know, five, six, seven minutes, maybe
it's from the same person or a similar subject or has the same content.
That's where AI is going to realize that, hey, more attention is looked at with these emails.
(10:44):
And that's what's going to stay in the focused inbox.
Another place where AI is used on a daily basis that you probably don't realize is whenever
you're doing some online shopping.
Maybe you're looking at coats or you're looking at furniture.
You're maybe just looking at one specific item.
And then after a while of shopping, you go and get on Facebook or Instagram or you go
(11:06):
search something on Google and all of a sudden there's an ad for that product or product
similar to what you're looking at.
Well that is AI collecting data from the cookies from the website, knowing what you're looking
at and it feeds it to the social media to where it's going to be going to be able to
provide ads for the items that you're looking at.
Hey, don't forget about this.
(11:28):
And it just makes it easier for you to go back and buy it.
So that's also AI.
Another place we see AI being used that you probably don't think about is when you're
typing away on your computer or maybe you're texting someone on your phone and you're getting
recommendations for the next word.
That's going to be AI.
Sometimes you can finish a whole sentence without typing.
(11:50):
It's picked up on on what your patterns are when you're texting or when you're typing
and it may be able to finish a sentence for you.
Now it doesn't always work, but it gets pretty close sometimes.
So again, these are these are really just small examples of AI.
There are so many uses out there from from the virtual assistants to the the auto correct
(12:12):
or to the spam folder in your email.
But there's things that go way beyond that.
And we'll keep getting into those.
But right now, let's go ahead and take a look at some AI news and decide whether it's fact
or fiction.
All right.
So the first rumor or news item that we're going to talk about it, it's a it's a few
months older.
It started circulating towards the end of 2024.
(12:33):
But we've already kind of talked about in this podcast.
So I thought it'd be good to bring it up here again.
And that's talking about Siri.
We've talked about that already with the virtual assistants.
So let's talk about some of the news that's coming out there.
It has been rumored that Apple is going to be doing a large AI overhaul of Siri.
Now like I said, this one's been out for a few months, but the likelihood of it is very
(12:56):
high because Apple has been hiring AI engineers and has been making some serious investments
in AI lately.
So again, this one I would say is fact.
The next one I want to talk about that's going to be Elon Musk's AI brain chip called Neuralink.
Neuralink has been successfully implanted into a human and it's being used to help those
(13:18):
with disabilities be able to use controllers or or be able to use things with AI.
Now this one is also true, but the mainstream likeliness of this is probably still years
away.
I don't know how many years away it could be five, 10, 15 years, but not quite ready
for the mainstream use.
(13:38):
But this is one that has been out there.
So there have also been rumors or claims out there from actually from different media sources
stating that AI can read your thoughts.
Now this one I'm going to say right off the bat, that's fiction.
Or I should probably say it's really just over exaggerated.
No, AI cannot quote unquote read your thoughts.
(14:02):
What it can do though is it can decode general thoughts based off the help of brain scans.
So this sounds very sci-fi.
And again, it is fiction or over exaggerated and it and it's not going to be quite on that
level of the sci-fi movies.
It's really on the base level of just being able to scan some brain codes and collect
(14:24):
the general thoughts from that.
But no, it cannot read your mind.
Now the last one I'm going to talk about here, this one isn't even a rumor.
This one's already out there and that's DeepSeek.
If you haven't heard of DeepSeek yet, I would definitely recommend you go out and take a
look at it.
What DeepSeek is, is it's a Chinese AI startup that has a very similar look and a similar
(14:47):
feel to open AI.
Now the reason why DeepSeek is causing such a buzz in the AI world right now is because
they claim that they were able to build this or train this model at a much cheaper cost
than what open AI was able to do.
So in comparison, DeepSeek said that it costs $6 million to train this model.
(15:12):
Whereas with open AI's GPT-4, which is a current model that's out now, that one cost over 100
million dollars to train.
So as you can tell, that's a significant difference.
The other part of DeepSeek is that they claim that it runs faster, it uses less memory,
which also costs less money.
(15:34):
But then also with that, they were able to do this off of old Nvidia chips.
They didn't have to get the newest Nvidia chips to run this.
Now there are still some things to watch with this because there are claims that DeepSeek
trained its model based off of open AI's model.
So that claims out there, we'll keep watching that, more to come on that.
(15:55):
Speaking of open AI, I mentioned before the GPT-4, which cost over 100 million dollars
to train.
It sounds like open AI is about to come out with an even newer model, which I believe
is going to be for GPT-4.5.
I haven't heard much around this, but it sounds like it is coming.
So I'm sure we'll talk about it in future episodes.
(16:16):
So I would love to hear what you think about this episode.
Really I want to hear what AI tools are you using?
Is there anything new that you've started using since you started listening to this
podcast?
Have you even just taken the dive into the AI world since listening to this podcast?
I would love to hear it.
If you have any questions or comments, I would love to hear those.
(16:38):
Were you listening to this or maybe you were getting on Spotify and all of a sudden it
seems like, is this reading my mind?
I was just thinking about using that song or listening to that song.
Let me know if that's happened to you.
Leave a comment on YouTube or email me at noob.aipodcast at outlook.com.
(16:59):
Don't forget to like and subscribe to this podcast on YouTube or like it and subscribe
to it on any of your streaming services so you don't miss any future episodes.
Thank you for listening.
My name is Adam Emberton and I can't wait to see where this journey takes us.
Hello fellow newbies, welcome back to another episode of Noob.ai.
(00:08):
My name is Adam Emberton and I am your Noob.
On the last episode, we talked about what AI is, we talked about some of the fears that
surround AI, and we even took a little trip down AI's history.
On today's episode, we're going to be taking a look at AI and bringing it a little bit
(00:28):
closer to home.
Because you probably don't know this, but you're already using it.
It's all around you.
So we're going to take a dive into that.
We're going to look at the ones that are probably obvious or really obvious.
And we're going to take a look at the ones that are not so obvious.
Welcome to Noob.ai, a podcast about a newbie's AI journey to walk you through as you start
(00:53):
your AI journey.
Let's start with the ones that are the most obvious.
And to do that, you're going to look down at what's in the palm of your hand.
Or at least I assume it's in the palm of your hand.
(01:14):
Or it's sitting on a stand on your desk, or it's sitting on a stand in your car.
And that's going to be your cell phone.
Whether it's an iPhone or an Android, they all have some kind of virtual assistant on
them.
And these virtual assistants are ran through AI.
Now the first ones to do it were Apple with Siri back in 2011.
Yes, there were virtual assistants that came before Siri, but not going to go into those.
(01:40):
Not going to already break my no more history lesson promise.
But Siri was really the first one that was out there that could recognize speech and
respond to speech.
After Siri came out, then you started seeing the likes of others such as Google Now or
Microsoft's Cortana or Samsung's Bixby.
(02:00):
Those virtual assistants on the cell phone really then open the door for virtual assistants
in the home.
These virtual assistants would be things like Amazon's Echo or Alexa, whatever you want
to call it, or Google Home.
Those are virtual assistants that are ran inside the home and they can run different
devices such as lights or plugs, anything like that.
(02:26):
I can confirm this because the room that I'm sitting in right now to record this podcast
is fully automated off of my Amazon Echo.
All I have to do when I walk into this room is I say Alexa, studio on.
The lights come on, the smart plugs come on, everything that's plugged into the smart plugs
come on, and then I'm good to go.
(02:48):
And then at the end of the night, I just go, Alexa, studio off.
And then it's dark again.
There are so many devices that can run off these virtual assistants.
You could have things well beyond the lights or plugs such as washers, dryers, vacuums,
thermostats, garage doors, so many different devices out there that can run off of those.
(03:13):
It's really getting to the point where you could almost have a fully automated home.
Because you are on this journey with me, I think it's important to discuss what kind
of AI these virtual assistants run off of.
As I've mentioned before, AI is such a broad term.
It covers such a wide variety.
(03:33):
So we're going to go ahead and break those down a little bit.
As we get into future episodes and we get a little bit more knowledgeable about AI,
we'll look into them even more.
We'll look into those terms.
But let's at least scratch the surface today.
So as we hear them later on, we know a little bit more of what those are.
First off, let's talk about natural language processing or NLP.
(03:56):
This is what's really going to make it so that the computer can understand words.
Computers can't understand words like humans do.
So the natural language processing, it takes those words and it teaches a computer what
those words are, and that's what makes it so it can respond in a human-like way.
The other part of that is machine learning.
(04:17):
And machine learning is exactly how it sounds.
It's a machine that's learning.
More specifically, a computer that's learning.
These are the two different types that these virtual assistants really run off of.
There's also things out there such as large language models, which we'll talk about a
little bit further.
Those large language models are going to be the ones that are more of like your chat bots,
(04:37):
like the chat GPTs and the Google Geminis and Microsoft Copilot.
Those are going to be the large language models, but they also use some sort of natural language
processing and the machine learning as well.
But the virtual assistants really just use those first two more than anything.
Now, the virtual assistants, I would say, are probably going to be the most obvious.
(04:58):
Those are the ones that people have used, whether they're using them on their phone
or maybe you don't have a home device, but you've been at a family member or friend's
house that have used the device and you've seen them use it.
And I think a lot of people already know that, yeah, that's AI.
Didn't know the breakdown of the natural language processing or the machine learning, but that's
because we're still new at this, right?
(05:18):
But now we know.
But what about some of those AI items out there that are maybe not so obvious?
You use them every day, you just don't even realize it.
Well, let's talk about a streaming service like Spotify.
You're on Spotify, you're listening to music, you're listening to your favorite kind of
music.
Then the next thing you know, it's recommending new music for you.
(05:41):
And this new music is right in line with the music that you're already listening to.
What about if you're on a TV streaming service or a movie streaming service and it's recommending
a new TV show to you?
And it is again, right in line with the TV shows that you've been watching.
Or what about if you're on TikTok and you start getting puppy video after puppy video
(06:06):
because you just liked 20 puppy videos in the last five minutes?
Those are all algorithms that are built off of AI.
So how do these algorithms work?
Well, every time you like a song, that's data.
Every time you like or dislike a movie or TV show, that's data.
(06:28):
Every time you skip a video after only two seconds, that's data.
It's all data, whether good or bad, it's all data going into this AI algorithm.
And it's really taking that every day when we don't even realize it.
And it's moving on to the next step, which is identifying patterns.
It looks at those patterns and it looks at the patterns that collected maybe the day
(06:48):
before, the week before, it's comparing the patterns.
And then it moves on to its next step, which is making a decision.
And over time, as we keep doing this and keep feeding into it, it just keeps building and
building.
And that's where you can tell where, you know, if you've been on TikTok and after a while,
(07:09):
the videos are all just videos that you like, they're right up your alley.
Well, that's because you keep feeding data into this algorithm and it's really picked
up on it.
The more it learns, the better it's going to suggest new things.
And that's really how the algorithms create it.
So think of it like this.
Let's say every day before work, you stop at a coffee shop, you get a coffee and a croissant
(07:32):
for breakfast.
After a while, the barista sees you every day.
They know who you are.
They know what you're going to order before you even walk up to the counter.
But then one day the barista says, Hey, we have this new sandwich that is made with the
croissants as the buns of the sandwich.
Would you like to try it?
It's really the same thing.
(07:54):
They're seeing this pattern and then they're recommending things that you're probably going
to like based off of the patterns that it's had before.
So it's making those decisions.
Now this data is on a very large scale, but think of it on an even larger scale with even
more complex data.
Think if it was a human behind the scenes trying to do this, trying to identify those
(08:15):
patterns, trying to make those decisions.
That's going to be a lot for a human.
It's going to be a lot for multiple humans, teams, teams of humans.
That's a lot for them to process.
Whereas AI is taking that is doing that in such a snap way.
It's getting it done instantly.
So I'm going to go back to TikTok.
(08:36):
I keep using this as an example because I think when people think of algorithms, they
think of the videos on TikTok.
So if that was a human behind the scenes doing that, they're not going to be able to keep
up because video comes in after video, it's going to take more than a day.
But guess what?
Another day comes and another day comes and now they're way behind on making these decisions
(08:57):
or identifying those patterns.
And that's where AI comes into play to really help out with all of that.
It saves time.
It saves money.
It really helps all around there.
Just so you know, TikTok has 1 billion video views a day.
1 billion video views a day.
(09:17):
So a human's not going to be able to keep up with that.
It's going to be just so overwhelming for the human.
And that's even going beyond that with that larger scale with the more complex data.
It's just going to be too overwhelming.
Not going to happen.
So this is where AI comes into play.
But what about some other things out there that maybe aren't so obvious?
(09:38):
Well, whenever you get an email, you probably have both an inbox and you have a spam folder.
That AI is deciding whether those emails should go into the inbox or should go into the spam
folder.
Some email services even take it beyond that.
If you use Microsoft Outlook, there's a focus folder and there's another folder.
(10:00):
Let's say you're getting an email.
You don't need to read it.
This is just coming in, but it's not spam.
So it's not going to the spam folder.
It's still coming to your inbox.
But you delete it without even reading it.
Or maybe you just glance at it and delete it.
Next day, same thing happens.
You do that.
You look at it.
You glance at it.
You delete it.
(10:20):
So now the AI is going to start picking up that, hey, you don't really pay attention
to these emails, but it's not spam.
So we're not going to put it in the spam folder, but we're going to put it in the other folder.
Now those emails that you keep looking at for, you know, five, six, seven minutes, maybe
it's from the same person or a similar subject or has the same content.
That's where AI is going to realize that, hey, more attention is looked at with these emails.
(10:44):
And that's what's going to stay in the focused inbox.
Another place where AI is used on a daily basis that you probably don't realize is whenever
you're doing some online shopping.
Maybe you're looking at coats or you're looking at furniture.
You're maybe just looking at one specific item.
And then after a while of shopping, you go and get on Facebook or Instagram or you go
(11:06):
search something on Google and all of a sudden there's an ad for that product or product
similar to what you're looking at.
Well that is AI collecting data from the cookies from the website, knowing what you're looking
at and it feeds it to the social media to where it's going to be going to be able to
provide ads for the items that you're looking at.
Hey, don't forget about this.
(11:28):
And it just makes it easier for you to go back and buy it.
So that's also AI.
Another place we see AI being used that you probably don't think about is when you're
typing away on your computer or maybe you're texting someone on your phone and you're getting
recommendations for the next word.
That's going to be AI.
Sometimes you can finish a whole sentence without typing.
(11:50):
It's picked up on on what your patterns are when you're texting or when you're typing
and it may be able to finish a sentence for you.
Now it doesn't always work, but it gets pretty close sometimes.
So again, these are these are really just small examples of AI.
There are so many uses out there from from the virtual assistants to the the auto correct
(12:12):
or to the spam folder in your email.
But there's things that go way beyond that.
And we'll keep getting into those.
But right now, let's go ahead and take a look at some AI news and decide whether it's fact
or fiction.
All right.
So the first rumor or news item that we're going to talk about it, it's a it's a few
months older.
It started circulating towards the end of 2024.
(12:33):
But we've already kind of talked about in this podcast.
So I thought it'd be good to bring it up here again.
And that's talking about Siri.
We've talked about that already with the virtual assistants.
So let's talk about some of the news that's coming out there.
It has been rumored that Apple is going to be doing a large AI overhaul of Siri.
Now like I said, this one's been out for a few months, but the likelihood of it is very
(12:56):
high because Apple has been hiring AI engineers and has been making some serious investments
in AI lately.
So again, this one I would say is fact.
The next one I want to talk about that's going to be Elon Musk's AI brain chip called Neuralink.
Neuralink has been successfully implanted into a human and it's being used to help those
(13:18):
with disabilities be able to use controllers or or be able to use things with AI.
Now this one is also true, but the mainstream likeliness of this is probably still years
away.
I don't know how many years away it could be five, 10, 15 years, but not quite ready
for the mainstream use.
(13:38):
But this is one that has been out there.
So there have also been rumors or claims out there from actually from different media sources
stating that AI can read your thoughts.
Now this one I'm going to say right off the bat, that's fiction.
Or I should probably say it's really just over exaggerated.
No, AI cannot quote unquote read your thoughts.
(14:02):
What it can do though is it can decode general thoughts based off the help of brain scans.
So this sounds very sci-fi.
And again, it is fiction or over exaggerated and it and it's not going to be quite on that
level of the sci-fi movies.
It's really on the base level of just being able to scan some brain codes and collect
(14:24):
the general thoughts from that.
But no, it cannot read your mind.
Now the last one I'm going to talk about here, this one isn't even a rumor.
This one's already out there and that's DeepSeek.
If you haven't heard of DeepSeek yet, I would definitely recommend you go out and take a
look at it.
What DeepSeek is, is it's a Chinese AI startup that has a very similar look and a similar
(14:47):
feel to open AI.
Now the reason why DeepSeek is causing such a buzz in the AI world right now is because
they claim that they were able to build this or train this model at a much cheaper cost
than what open AI was able to do.
So in comparison, DeepSeek said that it costs $6 million to train this model.
(15:12):
Whereas with open AI's GPT-4, which is a current model that's out now, that one cost over 100
million dollars to train.
So as you can tell, that's a significant difference.
The other part of DeepSeek is that they claim that it runs faster, it uses less memory,
which also costs less money.
(15:34):
But then also with that, they were able to do this off of old Nvidia chips.
They didn't have to get the newest Nvidia chips to run this.
Now there are still some things to watch with this because there are claims that DeepSeek
trained its model based off of open AI's model.
So that claims out there, we'll keep watching that, more to come on that.
(15:55):
Speaking of open AI, I mentioned before the GPT-4, which cost over 100 million dollars
to train.
It sounds like open AI is about to come out with an even newer model, which I believe
is going to be for GPT-4.5.
I haven't heard much around this, but it sounds like it is coming.
So I'm sure we'll talk about it in future episodes.
(16:16):
So I would love to hear what you think about this episode.
Really I want to hear what AI tools are you using?
Is there anything new that you've started using since you started listening to this
podcast?
Have you even just taken the dive into the AI world since listening to this podcast?
I would love to hear it.
If you have any questions or comments, I would love to hear those.
(16:38):
Were you listening to this or maybe you were getting on Spotify and all of a sudden it
seems like, is this reading my mind?
I was just thinking about using that song or listening to that song.
Let me know if that's happened to you.
Leave a comment on YouTube or email me at noob.aipodcast at outlook.com.
(16:59):
Don't forget to like and subscribe to this podcast on YouTube or like it and subscribe
to it on any of your streaming services so you don't miss any future episodes.
Thank you for listening.
My name is Adam Emberton and I can't wait to see where this journey takes us.
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
The Joe Rogan Experience
The official podcast of comedian Joe Rogan.
24/7 News: The Latest
The latest news in 4 minutes updated every hour, every day.