Welcome to our deep dive.
We're gonna be looking into AI and thought decoding.
Yeah.
And you know how we all crave a little privacy, right?
Like Charlotte Bronte even called the human mind
a sacred sanctuary, right?
Yeah.
But what if technology could peek behind the curtain
of our thoughts?
What if AI could actually like read our minds?
It sounds like something out of science fiction.
Right.
But researchers are using cutting edge AI
and brain scans to, well, to do just that.
Yeah, what's really fascinating is that this technology
could actually help people who've lost their ability
to communicate.
Oh, wow.
Due to conditions like, you know, locked in syndrome.
Right.
Imagine giving them a voice again.
It's amazing and a little unsettling
at the same time, right?
Definitely.
Today we're going to explore two groundbreaking approaches
to thought decoding.
Okay.
One using fMRI and the other using EEG.
All right.
I've got tons of articles and research papers
and I'm so excited to like break it all down.
Yeah, we're going to unpack the science
behind these technologies.
Learn about the incredible breakthroughs
researchers are making and then delve
into the ethical considerations of this mind-boggling field.
Okay, so to understand where we are today,
let's take a quick trip back in time.
Okay.
For centuries, humans have tried to figure out
how the mind works.
Right.
We have some seriously wacky ideas like phenology.
Oh, yeah.
They claim that you could like read someone's personality
based on the bumps on their head.
Right.
Obviously that was a total bust.
Yeah, total bust.
But it shows how deeply we've always wanted
to understand the inner workings of the mind.
Right.
And eventually we developed tools
that actually measure brain activity,
like EEG and fMRI.
Okay, so before we dive into the research,
let's break down how EEG and fMRI work.
Okay.
I'll admit I was a little fuzzy on the details
before this deep dive.
Right.
So the idea of EEG, like those noise-canceling headphones
that filter out distractions,
it uses a cap with electrodes to measure
the electrical signals produced by your brain.
And since neurons communicate using electrical impulses,
EEG allows us to kind of eavesdrop
on the brain's electrical symphony.
Oh.
Even if we can't pinpoint exactly
where each instrument is playing.
Got it.
So EEG is like listening to the brain's orchestra
through these tiny microphones,
picking up the overall rhythm and harmony.
Exactly.
And what about fMRI?
So fMRI is like taking a high-resolution snapshot
of the brain in action.
Okay.
It measures changes in blood flow,
which is like a proxy for brain activity.
Right.
When a particular brain region works harder,
it needs more oxygen, so blood flow increases.
fMRI detects these changes and creates detailed images
of which brain regions are active
during different tasks, like thinking, speaking,
or even daydreaming.
So fMRI gives us like a visual map of the brain.
Exactly.
Highlighting the hotspots of activity.
Yeah, yeah.
But how do we go from brain scans to actual thoughts?
That's where AI comes in, right?
Exactly.
And this is where it gets really cool.
The key idea is that every thought
creates a unique pattern of brain activity,
like a neural fingerprint.
Interesting.
AI is learning to recognize these fingerprints
and decode the thoughts that they represent.
So it's like teaching a computer to read a new language,
like the language of brain activity.
Yeah.
Let's start with Jerry Tang's work at UT Austin.
Okay.
He's using fMRI to decode language,
and the results are pretty astonishing.
Yeah.
His team had participants listen to hours of podcasts
while lying in an fMRI machine.
16 hours, to be exact.
Wow, 16 hours.
Can't imagine lying still for that long.
Oh my gosh, me neither.
Right.
But this massive amount of data was crucial
for training the AI decoder.
Right.
Basically, the AI learned to associate specific patterns
of brain activity with the words and phrases in the podcasts.
It's like showing a child a picture of a dog
and saying dog over and over again.
Eventually, the child learns to associate the image
with the word.
And get this, in one experiment,
they played a sentence for someone
who had gone through the training,
and the decoder was able to paraphrase it.
Wow.
It transformed, we start to trade stories about our lives
into we started talking about our experiences in the area.
Wow.
While not word for word, it captures the underlying meaning,
which is a huge step forward.
Yeah, it's like the decoder's understanding
the gist of the thought, even if it's not getting every word
perfectly.
OK, I know what some listeners might be thinking.
There's no way I could focus for 16 hours straight
while listening to podcasts my mind would wander.
Right.
But Tang's team used engaging narratives,
like audiobooks and radio stories,
to keep people's attention.
OK.
And that huge data set helps filter out
the noise of distractions.
It's like trying to hear a conversation at a bustling cafe.
At first, it's overwhelming.
But as you focus, you can tune out the background noise
and pick up the specific voices you want to hear.
So they're using stories to keep the brain engaged.
Yeah.
And the AI focused on the language.
Yeah, OK.
Hold onto your hats, folks, because it gets even wilder.
Tang's team has managed to decode imagined speech.
What?
They asked people to imagine telling a story
silently in their heads.
And the decoder translated their thoughts into text.
Wow.
In one case, the thought Marco leaned over and whispered
was decoded as he runs up to me and hugs me tight.
Wow, that's incredible.
Again, not perfect.
Right.
But the emotional essence is undeniably there.
Definitely.
Wow, that's incredible.
But I'm curious about something.
Yeah.
When we're talking about decoding meaning rather
than exact words, how do they even measure success?
Yeah.
It seems subjective.
That's a good question.
That's where the power of AI really shines.
OK.
Tang's team uses neural networks to assess semantic similarity.
It's like asking the AI, do these two sentences
convey the same idea even if the wording is different?
Right.
It's not about getting every word right.
It's about capturing the meaning.
So the AI is acting like a judge,
evaluating the meaning behind the words.
Yeah.
But how accurate are these fMRI decoders?
Are we talking reads your mind like an open book accuracy?
Well, it's not quite mind reading in the sense
of knowing your every fleeting thought.
OK.
Remember, this fMRI approach requires extensive training
data.
Right.
So it's not like you could just hop into an fMRI machine
and have your thoughts decoded on the spot.
Right.
Plus, the technology is still under development.
Oh.
But it's improving rapidly.
OK.
So while impressive, this means creating a decoder for you
would require you to spend 16 hours in an fMRI machine
listening to podcasts.
Yeah.
Not exactly practical for everyday use.
Right.
But Jerry's research highlights the incredible detail
we can get from fMRI.
Yeah.
But what if we need a more portable way
to capture brain activity?
Right.
That's where Michael Blumenstein's work with EEG comes in.
Oh, yeah.
He's achieving some amazing results
using a completely different approach.
Instead of relying on bulky fMRI machines,
Michael is using EEG, which is much more portable.
It's like trading in a grand piano
for a sleek, portable synthesizer.
You might lose some of the richness and complexity,
but you gain flexibility and accessibility.
So EEG is like the on-the-go version of brain scanning.
Exactly.
But wouldn't that sacrifice accuracy?
I mean, fMRI gives you such detailed images
of brain activity.
You're right, that EEG lacks the spatial resolution of fMRI.
Right.
We can't pinpoint exactly which brain regions are
firing with the same precision.
Yeah.
But EEG excels at capturing the temporal details,
the precise timing of brain signals.
Got it.
It's like watching a high-speed video
of the brain's electrical symphony.
That makes sense.
Timing is everything in language.
Right.
So how does Blumenstein's method actually work?
Right.
Does he also have people listen to podcasts for hours on end?
Not quite.
His participants read text aloud while wearing an EEG cap.
OK.
And the AI analyzes those EEG signals,
breaking them down into components associated
with individual words.
Wait, so the AI figures out which brainwave patterns correspond
to specific words?
How is that even possible?
It's a brilliant technique called self-supervised learning.
Think of it like teaching a dog a new trick
without explicitly saying the command.
OK.
You reward them when they do the right action
and they figure out the connection themselves.
Right.
Similarly, the AI isn't explicitly
told what each word looks like in terms of brain activity.
It figures it out on its own by analyzing patterns in the data.
So it's like a super-powered code breaker
cracking the neural code of language.
Yeah.
And once it's figured out those word-like units,
what happens next?
This is where things get even more interesting.
A large language model, similar to chatGPT,
stepped in to interpret these thought tokens
and stitched them together into coherent sentences.
So it's a two-step process.
First, the AI breaks down the brain signals
into word-like units.
And then the language model uses its vast knowledge
of language to put those units together in a way that
makes sense.
Precisely.
Yeah.
And what's remarkable is that this approach doesn't
require extensive training data from each individual,
unlike Tang's fMRI method.
That's because of those pre-trained language models,
which have already learned the statistical relationships
between words and concepts from analyzing massive amounts
of text.
So how accurate is this EEG-based approach?
Are we talking mind-reading party trick levels of accuracy
or something more nuanced?
Well, it's still early days.
OK.
But the results are promising.
OK.
Blumenstein's team is reporting accuracy above 60%,
which is significant considering the complexity of the task.
And do they have any examples of what the decoder has been able
to, well, here, so to speak?
Oh, absolutely.
OK.
In one instance, the participant was thinking the phrase,
the hat on the sea.
OK.
The decoder, bless its heart, interpreted this as,
I'd like a bowl of chicken soup, please.
That's hilarious.
So it might not be getting every word perfect,
but it's definitely capturing the essence of the thought.
Exactly.
Remember, this is just the beginning.
Right.
As the technology evolves, the accuracy
is only going to improve.
Which is pretty mind-blowing, but it also brings up
some serious questions.
Right.
If we can already decode thoughts with this level
of accuracy, what does the future hold?
Yeah.
It's like we're on the verge of a whole new era of communication
and understanding.
Right.
But before we get too carried away,
I think it's time to bring our experts back
into the conversation.
Right.
We've got Jerry Tang, who's pioneering
FMRI-based thought decoding, and Michael Blumenstein, who's
pushing the boundaries of EEG.
Right.
Welcome back to the show, gentlemen.
It's fascinating to see how both of you
are approaching this challenge from different angles.
Yeah.
It's like having two master chefs trying
to perfect the same dish, but with completely different
cooking styles.
Exactly.
And speaking of cooking styles, Jerry, your FMRI research
relies on those detailed brain scans
to pinpoint where language is processed.
Right.
But Michael's EEG approach focuses
on the timing of brain signals.
What do these differences tell us
about how language works in the brain?
That's a great question.
Yeah.
One of the most exciting things about this research
is that it's not just about building cool technology.
It's about understanding the human brain.
Yeah.
FMRI allows us to see which brain regions are involved
in different aspects of language processing.
So it's not like there's one word
center in the brain.
Right.
But rather a whole network of regions working together.
Exactly.
And the way these regions interact,
the timing of their activation, it's all incredibly intricate.
Yeah.
Our research is helping us map out
this neural symphony of language.
We're seeing how a single word can activate a complex chain
reaction across the brain.
It's like watching a fireworks display.
Yes.
With different parts of the brain lighting up in sequence.
That's a great analogy.
Michael, what's your take on this from the EEG perspective?
I completely agree with Jerry.
The beauty of EEG is that it captures
the temporal dynamics of brain activity
with millisecond precision.
OK.
So while we may not have the same spatial detail
as fMRI, we can see how brain waves dance and evolve
as a thought is formed.
It's like watching a high speed video of the brain in action.
So fMRI gives us the where.
Yeah.
And EEG gives us the when of language processing.
Exactly.
And together, they're painting a much richer picture
of this incredibly complex process.
And here's where things get really interesting.
OK.
Because if we think about the future of thought decoding,
could we one day combine these approaches?
Oh, wow.
Imagine an AI system that has both the spatial precision
of fMRI and EE, the temporal resolution of EEG.
Talk about a mind reading powerhouse.
I know, right.
But Michael, you mentioned earlier
that your EEG based approach doesn't
rely on individualized training data
thanks to those powerful large language models.
Does that mean we could one day have universal thought
decoders that work right out of the box
without any need for personalized calibration?
That's a very exciting possibility and something
we're actively exploring.
These large language models are incredibly versatile.
As they become more sophisticated,
I wouldn't rule out the possibility of plug and play
thought decoders that can tap into the commonalities
of human language processing.
So instead of needing 16 hours of training data,
we could have decoders that work for anyone right off the bat.
That's mind blowing.
It is pretty amazing.
Jerry, what are your thoughts on this universal decoder idea?
Is it something you see as feasible down the line?
Or are we still a long way off?
I'm cautiously optimistic.
We're already seeing hints that certain aspects of language
processing are shared across individuals.
And these large language models are amazing at finding patterns
that humans might miss.
So while I wouldn't say we're there yet,
the idea of a universal thought decoder
is definitely within the realm of possibility.
And just think about the potential benefits.
If we could develop a technology that accurately
translates thoughts into language,
regardless of individual differences,
it would revolutionize communication,
especially for those who have lost their ability to speak.
Imagine being able to give a voice
to people who have been silenced by stroke or paralysis
or other conditions.
It's like something out of a sci-fi movie,
but it could become a reality.
Absolutely.
And that's why it's so important to have conversations
about the ethical implications of this technology.
We need to make sure it's used for good,
and that we're prepared for the potential challenges.
Because, let's be honest, the idea of AI reading our minds
does raise some concerns.
Of course.
Makes you wonder, where do we draw the line?
When does this incredible technology
start to feel, well, a little creepy?
That's the million dollar question, isn't it?
We're essentially venturing into uncharted territory.
Jerry, your team seems very aware of these concerns.
Can you give us some insight into how you're approaching
the ethical side of this research?
Absolutely.
First and foremost, we believe that no one's brain should
be decoded without their informed consent.
OK.
We're very upfront with our participants
about the risks and benefits of this research.
We want them to feel empowered and in control
throughout the entire process.
So informed consent is crucial.
But what about the bigger picture?
What happens if this technology falls into the wrong hands?
Could it be used for surveillance or even manipulation?
Those are valid concerns, and they're
ones we take very seriously.
As scientists, we have a responsibility
to consider the potential consequences of our work,
both intended and unintended.
That's why we're actively engaging
with ethicists, policymakers, and the public
to ensure that these technologies are developed
and used responsibly.
It's almost like we need a new set of rules,
a mental bill of rights, to protect our inner thoughts
from unwanted intrusion.
I completely agree.
We need to start having these conversations now
before these technologies become so advanced
that it's too late to course correct.
We need to establish clear boundaries and ethical guidelines.
It's like we're standing at a fork in the road.
This technology has the potential to do so much good.
But we need to make sure we're heading in the right direction.
Absolutely.
And those conversations need to involve everyone,
not just the scientists and engineers.
We need to bring in ethicists, philosophers,
legal experts, and, most importantly,
the people who will be most affected by these advancements.
What do you think, Michael?
I think it's crucial to approach this
with a sense of humility.
We're dealing with the human mind,
the most complex and mysterious entity in the known universe.
We need to proceed with caution, respect,
and a deep understanding of the potential consequences,
both positive and negative.
Well said, Michael.
And that's a perfect segue into our final segment
where we'll explore some of the most thought-provoking questions
raised by this research.
OK.
Are we ready to enter a world where our thoughts are no longer our own?
It's a question that's both exciting and terrifying.
But one thing's for sure.
We can't just bury our heads in the sand
and pretend this isn't happening.
The future of thought-decoding is unfolding before our very eyes.
And it's up to all of us to shape that future responsibly and ethically.
And that, my friends, is something to ponder
as we venture deeper into the mysteries of the mind.
OK.
We've heard from the experts about the mind-blowing science
behind thought-decoding and explored
both the incredible potential and the ethical concerns.
Yeah.
But now, let's get philosophical for a moment.
This isn't just about cool gadgets and scientific breakthroughs.
It's about what it means to be human.
Absolutely.
Exactly.
If our thoughts can be decoded, does that change how we think about privacy?
We're already living in a world where our online activity is tracked,
our conversations are recorded, and our faces are stand.
Right.
Is mental privacy the next thing we have to worry about?
It's a valid question.
And it really makes you think, are our thoughts the last truly private space we have left?
And what about the legal side of things?
Could thought-decoding be used in court?
Imagine someone being convicted of a crime based on their thoughts alone.
That raises some serious questions about due process and the presumption of innocence.
We need to think carefully about the legal framework surrounding this technology.
And then there's the question of free will.
Oh, yeah.
If our thoughts can be predicted and decoded, does that mean our actions are predetermined?
Are we really calling the shots?
Are we just following a script written by our brains?
Now we're getting into deep philosophical territory.
This research challenges our understanding of consciousness and autonomy.
Yeah.
If our thoughts can be read, does that diminish our sense of self?
It's almost like we're back to that Charlotte Bronte quote about the mind being a sacred sanctuary.
Right.
Are we on the verge of losing that inner sanctuary, that sense of privacy and control over our own thoughts?
It's a question we need to grapple with individually and as a society.
But I think it's important to remember that technology is a tool.
Right.
It can be used for good or for ill.
Yeah.
It's up to us to decide how we want to use this powerful new tool.
And that decision shouldn't be taken lightly.
We need to have open and honest conversations about the implications of thought decoding technology.
And make sure we're developing it responsibly and ethically.
Absolutely.
So as we wrap up this deep dive, we leave you with this final thought.
What would a world where our thoughts are no longer entirely private look like?
Would it be a utopia of understanding and empathy or a dystopia of control and manipulation?
The answer, as always, is up to us.
That's our show for today.
We hope you enjoyed this exploration of thought decoding.
It's a topic that's sure to keep us thinking and talking for years to come.
If you want to learn more, check out the links in the show notes and share your thoughts on this mind-blowing topic.
Thanks for listening.
It's like we're on the verge of a whole new era of communication and understanding.
But before we get too carried away, I think it's time to bring our experts back into the conversation.
We've got Jerry Tang, who's pioneering FMRI-based thought decoding,
and Michael Blumenstein, who's pushing the boundaries of EG.
Welcome back to the show, gentlemen.
It's fascinating to see how both of you are approaching this challenge from different angles.
Yeah.
It's like having two master chefs trying to perfect the same dish, but with completely different cooking styles.
Exactly.
And speaking of cooking styles, Jerry,
your FMRI research relies on those detailed brain scans, right?
Right.
To pinpoint where language is processed.
But Michael's EEG approach focuses on the timing of brain signals.
What do these differences tell us about how language works in the brain?
That's a great question.
One of the most exciting things about this research is that it's not just about building cool technology.
It's about understanding the human brain.
FMRI allows us to see which brain regions are involved in different aspects of language processing.
So it's not like there's one word center in the brain,
but rather a whole network of regions working together.
Exactly.
And the way these regions interact, the timing of their activation, it's all incredibly intricate.
Our research is helping us map out this neural symphony of language.
We're seeing how a single word can activate a complex chain reaction across the brain.
Wow.
It's like watching a fireworks display with different parts of the brain lighting up in sequence.
Michael, what's your take on this from the EEG perspective?
I completely agree with Jerry.
The beauty of EEG is that it captures the temporal dynamics of brain activity with millisecond precision.
So while we may not have the same spatial detail as FMRI,
we can see how brain waves dance and evolve as a thought is formed.
It's like watching a high speed video of the brain in action.
So FMRI gives us the where and EEG gives us the when of language processing.
And together, they're painting a much richer picture of this incredibly complex process.
And here's where things get really interesting.
Because if we think about the future of thought decoding,
could we one day combine these approaches?
Imagine an AI system that has both the spatial precision of FMRI
and the temporal resolution of EEG.
Talk about a mind reading powerhouse.
But Michael, you mentioned earlier that your EEG based approach doesn't rely on individualized training data
thanks to those powerful large language models.
Does that mean we could one day have universal thought decoders that work right out of the box
without any need for personalized calibration?
That's a very exciting possibility and something we're actively exploring.
These large language models are incredibly versatile.
As they become more sophisticated, I wouldn't rule out the possibility of plug and play thought decoders
that can tap into the commonalities of human language processing.
So instead of needing 16 hours of training data,
we could have decoders that work for anyone right off the bat.
That's mind blowing.
Jerry, what are your thoughts on this universal decoder idea?
Is it something you see as feasible down the line or are we still a long way off?
I'm cautiously optimistic.
We're already seeing hints that certain aspects of language processing
are shared across individuals.
And these large language models are amazing at finding patterns that humans might miss.
So while I wouldn't say we're there yet,
the idea of a universal thought decoder is definitely within the realm of possibility.
And just think about the potential benefits.
If we can develop a technology that accurately translates thoughts into language,
regardless of individual differences,
it would revolutionize communication,
especially for those who have lost their ability to speak.
Imagine being able to give a voice to people who have been silenced by stroke,
paralysis, or other conditions.
It's like something out of a sci-fi movie, but it could become a reality.
Absolutely.
And that's why it's so important to have conversations about the ethical implications of this technology.
We need to make sure it's used for good and that we're prepared for the potential challenges.
Because let's be honest, the idea of AI reading our minds does raise some concerns.
It makes you wonder, where do we draw the line?
When does this incredible technology start to feel, well, a little creepy?
That's the million-dollar question, isn't it?
We're essentially venturing into uncharted territory.
Jerry, your team seems very aware of these concerns.
Can you give us some insight into how you're approaching the ethical side of this research?
Absolutely.
First and foremost, we believe that no one's brain should be decoded without their informed consent.
We're very upfront with our participants about the risks and benefits of this research.
We want them to feel empowered and in control throughout the entire process.
So informed consent is crucial.
But what about the bigger picture?
What happens if this technology falls into the wrong hands?
Could it be used for surveillance or even manipulation?
Those are valid concerns and they're ones we take very seriously.
As scientists, we have a responsibility to consider the potential consequences of our work,
both intended and unintended.
That's why we're actively engaging with ethicists, policymakers, and the public
to ensure that these technologies are developed and used responsibly.
It's almost like we need a new set of rules, a mental bill of rights,
to protect our inner thoughts from unwanted intrusion.
I completely agree.
We need to start having these conversations now
before these technologies become so advanced that it's too late to course correct.
We need to establish clear boundaries and ethical guidelines.
It's like we're standing at a fork in the road.
This technology has the potential to do so much good,
but we need to make sure we're heading in the right direction.
And those conversations need to involve everyone, not just the scientists and engineers.
We need to bring in ethicists, philosophers, legal experts,
and most importantly, the people who will be most affected by these advancements.
What do you think, Michael?
I think it's crucial to approach this with a sense of humility.
We're dealing with the human mind, the most complex and mysterious entity in the known universe.
We need to proceed with caution, respect, and a deep understanding of the potential consequences,
both positive and negative.
Well said, Michael.
And that's a perfect segue into our final segment,
where we'll explore some of the most thought-provoking questions raised by this research.
Are we ready to enter a world where our thoughts are no longer our own?
It's a question that's both exciting and terrifying,
but one thing's for sure, we can't just bury our heads in the sand and pretend this isn't happening.
The future of thought-decoding is unfolding before our very eyes,
and it's up to all of us to shape that future responsibly and ethically.
And that, my friends, is something to ponder,
as we venture deeper into the mysteries of the mind.
All right, so we've spent this deep dive exploring the frontiers of thought-decoding, right?
From fMRI to EEG, the potential benefits, and the ethical dilemmas.
Absolutely. It's a lot to wrap our heads around.
It really is.
But before we sign off, I think it's worth taking a step back and asking ourselves a really big question.
Okay. What's that?
If this technology, thought-decoding becomes commonplace,
how do we safeguard mental privacy?
Hmm, yeah. That's the million-dollar question, isn't it?
It is. I mean, how do we ensure that our thoughts remain, well, our own?
It makes you think about all the ways our privacy has already kind of eroded our online activity, our location data.
Even our faces are scanned and analyzed.
Right. It feels like mental privacy might be the next frontier.
Yeah, it's slippery slope.
And what about the legal ramifications?
Could this be used in court?
I mean, theoretically, yeah.
Imagine someone being convicted based on their thoughts alone.
That raises a whole host of issues.
Right. Like, due process, the presumption of innocence.
Exactly. We need a completely new legal framework to even begin to address that.
And then there's the philosophical question of free will.
Oh, yeah. That's a big one.
Like, if our thoughts can be predicted, does that mean our actions are predetermined?
Hmm. Are we truly in control or are we just puppets on a string?
It's almost like we're back to that idea of the mind as a sacred sanctuary, like Charlotte Bronte said.
Yeah, that inner world, that space where we can be truly ourselves.
Are we on the verge of losing that?
It's a question that keeps me up at night.
Me too. But, you know, I think it's important to remember that technology is ultimately a tool.
Right. It can be used for good or for bad.
Exactly. And it's up to us, you know, society as a whole, to decide how we want to use it.
We need to be proactive, have these conversations now while we still have the chance to shape the future.
Absolutely. We can't just bury our heads in the sand and pretend this isn't happening.
Nope. This technology is coming, whether we're ready for it or not.
So that's our show for today, folks.
We hope you enjoyed this deep dive into the fascinating and frankly unsettling world of thought decoding.
Yeah, thanks for joining us.
Remember, the future is not set in stone.
It's up to all of us to ensure that this technology is used responsibly and ethically.
Right. For the benefit of humanity.
So, keep thinking, keep questioning, and keep the conversation going.
And until next time, thanks for listening.