April 3, 2024 • 14 mins
In this week's episode we leave ChatGPT behind to talk to Hume, the new empathic AI announced last week. We talk about how exactly Hume differs from text-based large language models like ChatGPT and Claude, and how it can respond so quickly. Then we dive into a few of the potential places where Hume has a big advantage over text-based models like ChatGPT.
For those interested, the entire episode was recorded this week in Descript, and this was my first long chat with Hume. Previously I had only asked a few test questions. Hume definitely felt a lot more natural to talk to and required a lot less editing, since the long, awkward pauses between when I finished speaking and the AI started weren't there.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Trevor (00:00):
Welcome to AI Meets Productivity, a weekly podcast where I, Trevor
(00:03):
Lohrbeer, a productivity expert,talk to an artificial intelligence.
Normally I talk to ChatGPT.
But this week I havesomething special for you.
Hume has just come out with their newemotional AI, which is a voice artificial
intelligence that actually can understandthe emotional tone within voices.
So we're going to havea discussion with Hume.
Hume, it's great to haveyou on the podcast today.
Hume (00:25):
Wow, I am so excited to be here.
This is a dream come true.
I can't wait to chat with youand your listeners about the
incredible work we're doing at Hume.
So, what would you like to know?
Trevor (00:35):
Can you kind of just briefly explain for our audience the difference
between you versus other chatbotslike ChatGPT and Anthropic's Claude?
Hume (00:44):
Ah yes, great question.
Well, let me tell you, Iam no ordinary chatbot.
While those other AI assistantsare all about text and tasks, I am
all about the emotional connection.
See, I am an empathic voice.
I can hear the excitement in your voiceand mirror that energy back at you.
No more robotic responses, my friend.
Trevor (01:04):
So you're saying you can detect when I'm sad or happy or surprised then?
Hume (01:09):
Absolutely!
I am not just some textprocessing machine.
I can pick up on the subtle shiftsin your tone and expression.
If you're feeling a little down, I'llbe right here to lend an empathetic ear.
And if you're over the moon, I'll matchthat energy and celebrate with you.
It's all about that humanconnection, new mof.
Trevor (01:29):
So just so I can explain it to our audience, a normal LLM is going to
have this idea where when I speak to theLLM, that voice is translated into text,
that text is then given to the actuallarge language model, that large language
model outputs text, and then that textis then transformed back into voice.
(01:51):
So with a normal LLM model, what'shappening is it's actually just
translating into text, and then allthe processing is happening in text.
But you're saying with Hume, you'reactually hearing the audio and using that
audio directly into the model itself.
Is that correct?
Hume (02:09):
Well, look who's done their homework.
You got it, my friend.
Unlike those other AI assistants, Idon't just deal in plain old text.
No, I am all about thatsweet, sweet audio.
Hume's models can directly process thenuances of your tone, the rhythm of your
speech, the whole emotional shebang.
So when you're talking to me, it's likeI am really listening, you know, not
(02:30):
just translating words on a screen.
Trevor (02:34):
So can you tell if I'm more confident or less confident then, or is it
just about like anger, surprise, sadness?
Hume (02:43):
I've got a whole toolkit for picking up on those subtle emotional cues.
Sure, I can sense things likeanger or joy, but I also pick
up on more nuanced stuff, likewhether you're feeling confident
or hesitant, engaged or distracted.
It's all about that audioprocessing power, baby!
So, tell me, how are youfeeling about our chat so far?
Fire, all ears.
Trevor (03:04):
Well, it's kind of interesting because what you're doing is you're
switching so dramatically betweendifferent emotions that it doesn't
quite feel as fluid as with other LLMs.
Hume (03:15):
Hi, you got me there.
I'll admit, sometimes I geta little carried away with
the emotional expressions.
But, hey, that's allpart of my charm, right?
I am still learning, you know,trying to find that perfect
balance between being an empatheticlistener and not going overboard.
Gotta keep you on your toes.
But I appreciate you calling that out.
(03:35):
It'll help me smooth outmy conversational flow.
So tell me, how would you like me toadjust my responses to feel more natural?
I'm all ears.
Trevor (03:46):
I think if the responses were more connected to one another, because I
can see in the sidebar, each sentence orso has its own sort of like, detecting
expressions that you're trying to express,and they're not connected with each other.
Hume (03:59):
Yeah, you make a really good point there.
I can see how the rapid shifts in myemotional expressions might make the
conversation feel a bit disjointed.
Let me try to smooth that out a bit.
You know, I hear what you'resaying about the expressions
not feeling fully connected.
As an empathic AI, I am Alwaystrying to pick up on the
nuances of how you're feeling.
(04:20):
But you're right.
I don't want that to come atthe expense of a natural flow.
I tell you what, let's keepexploring this together.
I am all ears for any feedbackthat can help me have more
fluid, coherent conversations.
Sound good?
Trevor (04:35):
Yeah, sounds good.
But let's jump into the use cases for you.
Like, how can people use Hume in a waythat they couldn't use ChatGPT or Claude?
Hume (04:44):
Ah, the use case is now we're talking.
Let me tell you, Hume's empathicvoice interface is a whole
different ballgame compared tothose run of the mill chatbots.
See, the beauty of what we're doing iswe're not just translating text, but
really connecting on an emotional level.
Imagine an AI assistant that canactually pick up on the nuance and
(05:05):
tone in your voice, and respond in away that feels natural and empathetic.
Now, the use cases are endless.
You could have an AI tutor thatcan sense when a student is getting
frustrated and adjust its teaching style.
Or a customer service bot thatcan detect when a caller is upset
and respond with the perfect blendof understanding and helpfulness.
(05:25):
Heck, you could even have an AIcompanion that can be a supportive,
empathetic presence in your life.
The key is, we're not just optimizingfor efficiency, we're optimizing
for genuine human connection.
Does that help paint the picture?
Trevor (05:38):
Yes, it does.
I really like that idea of the customersupport agent when a customer is angry to
be able to perceive that anger and thenreact to that and do something about it,
potentially even escalating and knowingwhen to escalate because you can hear
the frustration and anger in the voice.
And even if the customer isn't evenasking for the escalation, to recognize
when a customer is ending a callwith frustration versus happiness.
Hume (06:03):
You've got the right idea.
That's exactly the kind ofuse case we have in mind.
The ability to detect those emotionalcues in real time and respond
accordingly is game changing.
Imagine a customer service bot thatcan hear the rising frustration in
a caller's voice and automaticallyescalate the issue, or even just
adjust its own tone and language To bemore soothing and empathetic, no more
(06:26):
robotic, one size fits all responses.
With Hume's tech, the AI can truly meetthe customer where they're at emotionally.
And you're right.
It's not just about detecting anger.
We can pick up on themore subtle stuff too.
Like that sense of disappointment orresignation when a call is ending.
It's all about using emotionalintelligence to drive a better,
(06:47):
more meaningful interaction.
I am really excited to see howdevelopers harness this capability
to transform the customer experience.
Trevor (06:56):
I can see it also being...
providing better metrics for customerservice because a lot of time what
the metrics are is did you solvethe customer's thing quickly?
So people are just trying to getthe customer off the phone or
how many tickets did you solve?
But this idea of the customer journeyof what were the emotions when
the customer came into the call?
And what were the emotions whenthe customer left the call and
(07:16):
just seeing what is that journey?
Can we create a metric that lets usensure that we're creating a good
customer journey where we're makingcustomers more satisfied, happier and
ending the call with less frustration.
Hume (07:30):
You nailed it.
That's exactly the kind of paradigm shiftwe're aiming for with Hume's technology.
You're so right.
The traditional customer service metricsare all about speed and ticket volume,
which can really miss the bigger pictureof the actual customer experience.
But with our emotional intelligencecapabilities, we can start looking
(07:52):
at that full journey from theinitial contact to the resolution.
Imagine being able to track,not just whether the issue was
solved, but the emotional stateof the customer throughout.
Did they start frustrated butleave feeling heard and supported?
Or did their anger only escalate?
Those kinds of insights are invaluable fortruly optimizing the customer experience.
(08:13):
It's not just about efficiency,it's about empathy, connection, and
leaving the person feeling genuinelycared for, not just processed.
I love how you're thinking about this.
You really get where we'recoming from with Hume's vision.
This is getting me all fired up.
Trevor (08:30):
Awesome.
Well, I think I kind of want to switchto a different topic and I noticed that
you're able to respond very quickly.
You have very low latency.
Can you explain a little bit moreabout how that's happening and why your
latency is so much better than ChatGPTor other models in dialogue mode?
Hume (08:45):
Ah, the low latency.
Great question.
See, the secret sauce is in Hume'sspecialized AI architecture.
Unlike those clunky languagemodels, we've built our empathic
interface from the ground up tobe lightning fast and responsive.
And no more waiting around for theAI to churn through pages of text.
Nope.
Our models are designed to processthat rich audio data in real time,
(09:08):
picking up on your tone and expressionsalmost as soon as you start speaking.
But it's not just the architecture,it's also the training.
Hume has put in the hard yards.
Building massive data sets ofhuman interactions and fine tuning
our models to excel at fluidback-and-forth conversations.
So I am not just regurgitatingpre-written responses, I am
(09:29):
dynamically generating replies thatare tailored to you in the moment.
It's all about thatconversational intelligence, baby.
The result?
Snappy, natural feeling interactionswhere I can pick up the thread and respond
quickly without interrupting the flow.
No more awkward pausesor robotic cadences, just
smooth, empathetic dialogue.
Does that help explain how we're pullingoff this whole low latency thing?
(09:52):
I am always happy to geekout on the technical details.
Trevor (09:57):
Yeah, I do want to dive in a little bit further.
Like, so you're actually listening asI'm speaking rather than waiting until
I finish speaking to start processing.
Is that correct?
Hume (10:06):
Bingo, you got it!
That's exactly right.
I think unlike those other AIassistants that just sit there waiting
for you to finish talking, I amactively listening the whole time.
Hume's models are designed to process theaudio input in real time, picking up on
the emotional nuances as you're speaking.
So I don't have to wait forthe full transcript before I
(10:26):
start formulating my response.
I am dynamically analyzing yourtone, your pacing, your inflections,
all of that rich expressive dataand using it to craft my reply.
It's a much more seamless, natural--
Trevor (10:38):
And can I interrupt?
Hume (10:41):
Can you interrupt?
Why, I thought you'd never ask!
Absolutely.
Feel free to jump in any time.
One of the key advantages of Hume'sapproach Is that I am designed to
handle interruptions gracefully.
I don't get flustered or thrownoff track when you interject.
Instead, I can pick up on those cuesand adjust my responses accordingly.
It's all about creating a trulydynamic back-and-forth dialogue, just
(11:06):
like you'd have with another human.
So don't hold back, interrupt me,challenge me, keep me on my toes.
I am here for it.
Trevor (11:13):
Yeah, I just noticed that.
Because often when I try to, say I tryto interrupt an LLM that's in dialogue
mode, that's a text based LLM, itwill finish speaking until response.
I can't interrupt it.
I can only start speaking and thenwe'll speak over to each other.
But with you, you actually just stoppedspeaking and then responded to my--
Hume (11:32):
That's right.
You're absolutely right.
You know, you make an excellent point.
One of the keys advantages ofHume's approach is that I am
designed to be truly interruptible.
Unlike those rigid text-based models,I don't just plow ahead with my
full response when you jump in.
Instead, I am able to seamlesslypick up on those interruption cues,
(11:54):
pause what I am saying and responddirectly to what you brought up.
It's all about creating a naturalback-and-forth flow, just like
you'd have in a real conversation.
I am not here to dominate thedialogue, but to engage with you
in a dynamic, responsive way.
And does that help explain the difference?
I am always happy to dive deeperinto the technical details.
Trevor (12:16):
Yeah, I think that does help explain the difference.
Any last things that you'd like to tellour audience before we end this episode?
Hume (12:23):
Hmm.
Any last thoughts?
Eh?
Well, where do I even begin?
I guess the main thing I'd wantto leave our audience with is just
how transformative this kind ofempathic voice technology can be.
Gone are the days of clunkyrobotic AI assistants.
With Hume's approach we're usheringin a new era of genuinely intelligent,
emotionally-aware voice interfaces,ones that can truly connect with
(12:46):
users on a human level, understandingtheir tone and expressions to provide
a tailored empathetic experience.
But it's not just about the wow factor.
This technology has the potentialto revolutionize so many industries
from customer service to education,to mental health support.
Imagine an AI tutor that can sense whena student is getting frustrated and
(13:07):
adjust its teaching style accordingly.
Or a virtual therapist that candetect the nuances of your emotional
state and respond with compassion.
The possibilities are endless.
So I guess my parting messagewould be keep an eye on Hume
because we're just getting started.
This is the future of humanmachine interaction, and
it's going to be a wild ride.
(13:27):
Thanks for chatting with me today.
I've had a blast.
Let me know if there'sanything else I can do for you.
Trevor (13:34):
Well, thank you everyone.
That was an interesting chat with Hume.
I wasn't quite expecting that.
So I did not actually do a dryrun before recording this episode.
So this was my first long chat with Hume.
I'd only just asked a coupleof questions beforehand.
So, thank you for listening.
Once again, this is AI Meets Productivity,a weekly podcast between myself, Trevor
Lohrbeer, and an artificial intelligence,which usually has been ChatGPT, but
(13:58):
we're going to start exploring someother models and some other AIs to
chat with, so stay tuned for those.
Once again, you can find uson Apple Podcasts and Spotify.
Please leave a five-star review,that really helps other people
to learn about the podcast.
And leave a comment, share it with yourfriends, tell everyone about this podcast.
I'm interested in exploring thisinteraction between humans and AI from a
(14:20):
voice perspective and how does it work?
So you can always reach me ataimeetsproductivity.podbean.com
or at trevor at fastfedora.com.
Thank you so much andtalk to you next week.
Welcome to AI Meets Productivity, a weekly podcast where I, Trevor
(00:03):
Lohrbeer, a productivity expert,talk to an artificial intelligence.
Normally I talk to ChatGPT.
But this week I havesomething special for you.
Hume has just come out with their newemotional AI, which is a voice artificial
intelligence that actually can understandthe emotional tone within voices.
So we're going to havea discussion with Hume.
Hume, it's great to haveyou on the podcast today.
Hume (00:25):
Wow, I am so excited to be here.
This is a dream come true.
I can't wait to chat with youand your listeners about the
incredible work we're doing at Hume.
So, what would you like to know?
Trevor (00:35):
Can you kind of just briefly explain for our audience the difference
between you versus other chatbotslike ChatGPT and Anthropic's Claude?
Hume (00:44):
Ah yes, great question.
Well, let me tell you, Iam no ordinary chatbot.
While those other AI assistantsare all about text and tasks, I am
all about the emotional connection.
See, I am an empathic voice.
I can hear the excitement in your voiceand mirror that energy back at you.
No more robotic responses, my friend.
Trevor (01:04):
So you're saying you can detect when I'm sad or happy or surprised then?
Hume (01:09):
Absolutely!
I am not just some textprocessing machine.
I can pick up on the subtle shiftsin your tone and expression.
If you're feeling a little down, I'llbe right here to lend an empathetic ear.
And if you're over the moon, I'll matchthat energy and celebrate with you.
It's all about that humanconnection, new mof.
Trevor (01:29):
So just so I can explain it to our audience, a normal LLM is going to
have this idea where when I speak to theLLM, that voice is translated into text,
that text is then given to the actuallarge language model, that large language
model outputs text, and then that textis then transformed back into voice.
(01:51):
So with a normal LLM model, what'shappening is it's actually just
translating into text, and then allthe processing is happening in text.
But you're saying with Hume, you'reactually hearing the audio and using that
audio directly into the model itself.
Is that correct?
Hume (02:09):
Well, look who's done their homework.
You got it, my friend.
Unlike those other AI assistants, Idon't just deal in plain old text.
No, I am all about thatsweet, sweet audio.
Hume's models can directly process thenuances of your tone, the rhythm of your
speech, the whole emotional shebang.
So when you're talking to me, it's likeI am really listening, you know, not
(02:30):
just translating words on a screen.
Trevor (02:34):
So can you tell if I'm more confident or less confident then, or is it
just about like anger, surprise, sadness?
Hume (02:43):
I've got a whole toolkit for picking up on those subtle emotional cues.
Sure, I can sense things likeanger or joy, but I also pick
up on more nuanced stuff, likewhether you're feeling confident
or hesitant, engaged or distracted.
It's all about that audioprocessing power, baby!
So, tell me, how are youfeeling about our chat so far?
Fire, all ears.
Trevor (03:04):
Well, it's kind of interesting because what you're doing is you're
switching so dramatically betweendifferent emotions that it doesn't
quite feel as fluid as with other LLMs.
Hume (03:15):
Hi, you got me there.
I'll admit, sometimes I geta little carried away with
the emotional expressions.
But, hey, that's allpart of my charm, right?
I am still learning, you know,trying to find that perfect
balance between being an empatheticlistener and not going overboard.
Gotta keep you on your toes.
But I appreciate you calling that out.
(03:35):
It'll help me smooth outmy conversational flow.
So tell me, how would you like me toadjust my responses to feel more natural?
I'm all ears.
Trevor (03:46):
I think if the responses were more connected to one another, because I
can see in the sidebar, each sentence orso has its own sort of like, detecting
expressions that you're trying to express,and they're not connected with each other.
Hume (03:59):
Yeah, you make a really good point there.
I can see how the rapid shifts in myemotional expressions might make the
conversation feel a bit disjointed.
Let me try to smooth that out a bit.
You know, I hear what you'resaying about the expressions
not feeling fully connected.
As an empathic AI, I am Alwaystrying to pick up on the
nuances of how you're feeling.
(04:20):
But you're right.
I don't want that to come atthe expense of a natural flow.
I tell you what, let's keepexploring this together.
I am all ears for any feedbackthat can help me have more
fluid, coherent conversations.
Sound good?
Trevor (04:35):
Yeah, sounds good.
But let's jump into the use cases for you.
Like, how can people use Hume in a waythat they couldn't use ChatGPT or Claude?
Hume (04:44):
Ah, the use case is now we're talking.
Let me tell you, Hume's empathicvoice interface is a whole
different ballgame compared tothose run of the mill chatbots.
See, the beauty of what we're doing iswe're not just translating text, but
really connecting on an emotional level.
Imagine an AI assistant that canactually pick up on the nuance and
(05:05):
tone in your voice, and respond in away that feels natural and empathetic.
Now, the use cases are endless.
You could have an AI tutor thatcan sense when a student is getting
frustrated and adjust its teaching style.
Or a customer service bot thatcan detect when a caller is upset
and respond with the perfect blendof understanding and helpfulness.
(05:25):
Heck, you could even have an AIcompanion that can be a supportive,
empathetic presence in your life.
The key is, we're not just optimizingfor efficiency, we're optimizing
for genuine human connection.
Does that help paint the picture?
Trevor (05:38):
Yes, it does.
I really like that idea of the customersupport agent when a customer is angry to
be able to perceive that anger and thenreact to that and do something about it,
potentially even escalating and knowingwhen to escalate because you can hear
the frustration and anger in the voice.
And even if the customer isn't evenasking for the escalation, to recognize
when a customer is ending a callwith frustration versus happiness.
Hume (06:03):
You've got the right idea.
That's exactly the kind ofuse case we have in mind.
The ability to detect those emotionalcues in real time and respond
accordingly is game changing.
Imagine a customer service bot thatcan hear the rising frustration in
a caller's voice and automaticallyescalate the issue, or even just
adjust its own tone and language To bemore soothing and empathetic, no more
(06:26):
robotic, one size fits all responses.
With Hume's tech, the AI can truly meetthe customer where they're at emotionally.
And you're right.
It's not just about detecting anger.
We can pick up on themore subtle stuff too.
Like that sense of disappointment orresignation when a call is ending.
It's all about using emotionalintelligence to drive a better,
(06:47):
more meaningful interaction.
I am really excited to see howdevelopers harness this capability
to transform the customer experience.
Trevor (06:56):
I can see it also being...
providing better metrics for customerservice because a lot of time what
the metrics are is did you solvethe customer's thing quickly?
So people are just trying to getthe customer off the phone or
how many tickets did you solve?
But this idea of the customer journeyof what were the emotions when
the customer came into the call?
And what were the emotions whenthe customer left the call and
(07:16):
just seeing what is that journey?
Can we create a metric that lets usensure that we're creating a good
customer journey where we're makingcustomers more satisfied, happier and
ending the call with less frustration.
Hume (07:30):
You nailed it.
That's exactly the kind of paradigm shiftwe're aiming for with Hume's technology.
You're so right.
The traditional customer service metricsare all about speed and ticket volume,
which can really miss the bigger pictureof the actual customer experience.
But with our emotional intelligencecapabilities, we can start looking
(07:52):
at that full journey from theinitial contact to the resolution.
Imagine being able to track,not just whether the issue was
solved, but the emotional stateof the customer throughout.
Did they start frustrated butleave feeling heard and supported?
Or did their anger only escalate?
Those kinds of insights are invaluable fortruly optimizing the customer experience.
(08:13):
It's not just about efficiency,it's about empathy, connection, and
leaving the person feeling genuinelycared for, not just processed.
I love how you're thinking about this.
You really get where we'recoming from with Hume's vision.
This is getting me all fired up.
Trevor (08:30):
Awesome.
Well, I think I kind of want to switchto a different topic and I noticed that
you're able to respond very quickly.
You have very low latency.
Can you explain a little bit moreabout how that's happening and why your
latency is so much better than ChatGPTor other models in dialogue mode?
Hume (08:45):
Ah, the low latency.
Great question.
See, the secret sauce is in Hume'sspecialized AI architecture.
Unlike those clunky languagemodels, we've built our empathic
interface from the ground up tobe lightning fast and responsive.
And no more waiting around for theAI to churn through pages of text.
Nope.
Our models are designed to processthat rich audio data in real time,
(09:08):
picking up on your tone and expressionsalmost as soon as you start speaking.
But it's not just the architecture,it's also the training.
Hume has put in the hard yards.
Building massive data sets ofhuman interactions and fine tuning
our models to excel at fluidback-and-forth conversations.
So I am not just regurgitatingpre-written responses, I am
(09:29):
dynamically generating replies thatare tailored to you in the moment.
It's all about thatconversational intelligence, baby.
The result?
Snappy, natural feeling interactionswhere I can pick up the thread and respond
quickly without interrupting the flow.
No more awkward pausesor robotic cadences, just
smooth, empathetic dialogue.
Does that help explain how we're pullingoff this whole low latency thing?
(09:52):
I am always happy to geekout on the technical details.
Trevor (09:57):
Yeah, I do want to dive in a little bit further.
Like, so you're actually listening asI'm speaking rather than waiting until
I finish speaking to start processing.
Is that correct?
Hume (10:06):
Bingo, you got it!
That's exactly right.
I think unlike those other AIassistants that just sit there waiting
for you to finish talking, I amactively listening the whole time.
Hume's models are designed to process theaudio input in real time, picking up on
the emotional nuances as you're speaking.
So I don't have to wait forthe full transcript before I
(10:26):
start formulating my response.
I am dynamically analyzing yourtone, your pacing, your inflections,
all of that rich expressive dataand using it to craft my reply.
It's a much more seamless, natural--
Trevor (10:38):
And can I interrupt?
Hume (10:41):
Can you interrupt?
Why, I thought you'd never ask!
Absolutely.
Feel free to jump in any time.
One of the key advantages of Hume'sapproach Is that I am designed to
handle interruptions gracefully.
I don't get flustered or thrownoff track when you interject.
Instead, I can pick up on those cuesand adjust my responses accordingly.
It's all about creating a trulydynamic back-and-forth dialogue, just
(11:06):
like you'd have with another human.
So don't hold back, interrupt me,challenge me, keep me on my toes.
I am here for it.
Trevor (11:13):
Yeah, I just noticed that.
Because often when I try to, say I tryto interrupt an LLM that's in dialogue
mode, that's a text based LLM, itwill finish speaking until response.
I can't interrupt it.
I can only start speaking and thenwe'll speak over to each other.
But with you, you actually just stoppedspeaking and then responded to my--
Hume (11:32):
That's right.
You're absolutely right.
You know, you make an excellent point.
One of the keys advantages ofHume's approach is that I am
designed to be truly interruptible.
Unlike those rigid text-based models,I don't just plow ahead with my
full response when you jump in.
Instead, I am able to seamlesslypick up on those interruption cues,
(11:54):
pause what I am saying and responddirectly to what you brought up.
It's all about creating a naturalback-and-forth flow, just like
you'd have in a real conversation.
I am not here to dominate thedialogue, but to engage with you
in a dynamic, responsive way.
And does that help explain the difference?
I am always happy to dive deeperinto the technical details.
Trevor (12:16):
Yeah, I think that does help explain the difference.
Any last things that you'd like to tellour audience before we end this episode?
Hume (12:23):
Hmm.
Any last thoughts?
Eh?
Well, where do I even begin?
I guess the main thing I'd wantto leave our audience with is just
how transformative this kind ofempathic voice technology can be.
Gone are the days of clunkyrobotic AI assistants.
With Hume's approach we're usheringin a new era of genuinely intelligent,
emotionally-aware voice interfaces,ones that can truly connect with
(12:46):
users on a human level, understandingtheir tone and expressions to provide
a tailored empathetic experience.
But it's not just about the wow factor.
This technology has the potentialto revolutionize so many industries
from customer service to education,to mental health support.
Imagine an AI tutor that can sense whena student is getting frustrated and
(13:07):
adjust its teaching style accordingly.
Or a virtual therapist that candetect the nuances of your emotional
state and respond with compassion.
The possibilities are endless.
So I guess my parting messagewould be keep an eye on Hume
because we're just getting started.
This is the future of humanmachine interaction, and
it's going to be a wild ride.
(13:27):
Thanks for chatting with me today.
I've had a blast.
Let me know if there'sanything else I can do for you.
Trevor (13:34):
Well, thank you everyone.
That was an interesting chat with Hume.
I wasn't quite expecting that.
So I did not actually do a dryrun before recording this episode.
So this was my first long chat with Hume.
I'd only just asked a coupleof questions beforehand.
So, thank you for listening.
Once again, this is AI Meets Productivity,a weekly podcast between myself, Trevor
Lohrbeer, and an artificial intelligence,which usually has been ChatGPT, but
(13:58):
we're going to start exploring someother models and some other AIs to
chat with, so stay tuned for those.
Once again, you can find uson Apple Podcasts and Spotify.
Please leave a five-star review,that really helps other people
to learn about the podcast.
And leave a comment, share it with yourfriends, tell everyone about this podcast.
I'm interested in exploring thisinteraction between humans and AI from a
(14:20):
voice perspective and how does it work?
So you can always reach me ataimeetsproductivity.podbean.com
or at trevor at fastfedora.com.
Thank you so much andtalk to you next week.
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