The 3 problems listed are (in my experience) not the most important ones you need to know about when you're making a bot. I'm not sure how this got to +100 points here on HN so quickly, since there are plenty of more informative bot articles that would better serve the HN audience.
The article describes:
1 (scalable architecture) : but the common solutions to this problem are not unique to chatbots
2 (a conversational agent must converse) : actually, that's a design decision for each individual bot, there are alternative ways of thinking about it.
3 (users will get frustrated) : this is actually a not a tech/AI problem, but a UI (user expectation) problem, see below.
The actual top 3 problems chatbot makers face are (in order):
#1 Discovery (!!!) : people don't know where/how to find bots and Facebook does not have their shit together in this regard (almost a year after launching their bot platform, there are still crucial errors. Result: most bots that could make a difference in the real world never get any traction, or enough users to train or iterate.
#2 Onboarding / setting expectations : the majority of (non-test or -product hunt) users of your bot are having their first chatbot experience with yours. Guiding them in the correct way is your #1 design priority. Result: a dissapointed general public.
#3 Inflated expectations : Mainly a problem for those who try to sell chatbots in B2B. Its' almost the opposite problem to #2. Most people and media conflate AI with chatbots. But the recent advances in machine learning (which are the reason for the current attention the term "AI" has been getting) are not (yet) applicable to chatbots in general. Add to this expectations of a Jarvis, Siri, etc. but then "for x". Result: dissapointed clients and an even quicker deflation of the hype.
And this is not even going into monetization!
All in all, I'm just frustrated that everyone involved in bots seem to assume that we already know how bots should work. I work in bots and I have yet to see one really impact user's lives. I wish for more humility and experimentation. At least we're all sharing what we're learning, so there's that.
For discovery, FB Messenger actually has more channels for bot discovery than any other platform, including:
- m.me links with referral tracking
- Messenger Codes
- Several plugins that allow easy opt-in via developer's website, including the ability to by default opt-in users on any form/flow on your website into your bot experience on Messenger.
- Customer matching (use phone numbers to send updates to customers and acquire them as a bot user)
- Messenger destination ads
- Sharing features that many have used to achieve incredible viral reach.
The power that developers have to promote their own bots using these existing channels is largely undertapped. I would encourage developers to see what they're building as a business first, and a bot second. The bot is just a facet/channel into what you offer (even if it is a primary one). If you don't have a reason to offer a product or service or publish content, you probably don't have a reason to have a bot.
Admittedly, we could do better at setting user expectations for bots and in distinguishing them in search results and in the FB main app, and maybe pushing them in more places.
Re: inflated expectations. Totally right. The "AI Agent"/NLP use case is just one possibility for a bot, yet it's the sexiest one that the media seems to like. It's the tip of the iceberg -- there are a ton of more mundane but incredibly useful bots that are succeeding on the platform.
If any bot developer has questions about this, or wants to brainstorm ways to get users, I am happy to help or bounce ideas!
I'm very excited about Messenger As a Destination although, I've yet to see one in the wild and from the campaigns I've helped launch the results have not been amazing. It could be a targeting problem, but I'm worried that the user gets the page where they have to choose web browser or Messenger App.
Can you confirm if the user directs into Messenger directly when they click the ad?
Was working on making FB messenger bots around September, but got very poor results. The drop off from number of people who click " Send to Messenger" button to actual opening on chat is close to 50%. And this is in India where people I was targetting generally have messenger installed. Any updates coming on this issue?
Thank you for your feedback. I do not think the author claims to "know it all". Also, this post is about what we've learned so far in this new experiment - definitely not about the top three problems you may encounter when building chatbots. Just as you said - sharing what we're learning and we hope this can be useful for other people.
And I am not sure how this became the top comment.
What? The very first point is discovery (which is fair) which just smoothly melds into discovery ON FACEBOOK? So we just assume bot discovery is best handed off to FB/GOOG/ etc?
So now FB, in addition to being the de facto destination where your personal data goes to be exploited, is also supposed to "get their shit together" to enable even more of the data collection?
Have you noticed how nobody from FB has actually ever engaged with anyone on HN around the ethical issues surrounding any of their practices?
Your last point is about how everyone involved in bots seem to assume we already know how bots should work. You make the same assumption about who should lead this effort. I would rather the bot technology takes longer to pan out than see a world where the giant corporations are also the sole operators of the bot marketplaces.
Perhaps not exactly what you're talking about, but a bot has "changed my life", if you'd put it that way. I built an internet radio station with some friends, and we use an IRC bot to control it. I almost completely ditched any other way of consuming music - using the bot to search our library, request songs, interact with our music as a group - is much better than any other music interface I've used. I think most people just don't have the right use-cases - but it is possible and worth trying.
* Scalable architecture is, most of the time, a very-NOT-big-deal. Bots tend to be relatively lightweight apps, sending/receiving json data only. Even websocket Slack apps can now be replaced by HTTP API.
* Discovery is really un-solved at this time, especially by Facebook. Slack does a better job, but arguably has a smaller ecosystem of B2B bots to manage and promote.
However, I think the article is about a rather ambitious (more than usual) and interesting bot. Having to parse normal language symptoms into medical terms is 1) an interesting use case 2) hard to do well technically. So maybe they face challenges most bot dev don't face.
Design-wise, most people don't see how hard it is to design chatbot flows that hard not horrible and confusing. Most chatbots are horrible and confusing at the moment, because coding bots is easy, designing them is really hard.
I think the article make interesting points about their own specific case, but their situation is perhaps less general than they seem to think.
I'm curious how this architecture (or any "AI" medical tool) handles regulatory compliance issues.
In this case I would think you'd want to display when the rules/facts used for recommendations were last updated, which would add extra fields to track. There are also some interesting QA problems to work through.
I'm curious how this architecture (or any "AI" medical tool) handles regulatory compliance issues.
So is the FDA.
Currently the typical answer from a medical device company is a combination of "carefully" and "with clinical oversight". The typical answer from a non-medical startup seems to be "poorly", for the most part.
I have made it my mission to help with bot discovery. I have built a bot search engine that is a bot: m.me/qwazou
I have close to 1000 bots discoverable via category searches and keyword searches.
Slightly off-topic, but do you people also feel an enormous increase in chat boxes at the bottom right of the screen, in particular at SAAS companies?
For me this is really off-putting, in particular as they suggest that you are talking with a human (photo and pseudo-friendly greetings), although they just want to catch your e-mail address, and the first interaction is clearly with a bot.
I am not sure where this is going, maybe I am a minority, but for me this "interactions" are really off-putting.
The most frustrating part is when I go to a site, start watching the pitch video, and am interrupted 10-15s into the video by a chat bot asking me if I have any questions.
I don't know if I have questions yet. I'm watching the video. I have javascript enabled - you can tell that I'm watching the video. Leave me alone.
For all the startups I've worked with, it's a real person (or persons) behind the chat, usually founders. The greeting is pre-set but all conversations are real.
How do founders have time for that? Not many customers use chat, but the ones that do are valuable. As pg says, all early stage startups should just be writing code and talking to users, so founders make it a high priority to reply quickly!
This is exactly how we use the chat and have few dozen conversations per day. Most (not all) initial conversations are pre-set but if you reply, you'd get a real person, including one of the founders. The email is asked when no one is available to engage within few minutes so that we can get back to the person.
I actually love it when sites have chat solutions.
Usually it seems there are real people and it provides the fast respose of pgone together with the asynchronity of email. Fast but less awkward when kids are screaming in the background.
Yeah, true, however not the initial conversation attempt. I guess, if you really answer you might get hooked up with a real sales human at one point, but the initial attempt (after you have been 5s on the pricing page) is always extremely awkward.
I think if I just scout the chat bubble I already start questioning the business really hard.
I'm okay with it provided they don't start prompting me with messages. that's intrusive. but I like being able to chat with a human on the other end to quickly make buying decisions.
The new wave of chatbots gives me a fuzzy feeling, because I began programming ~2000 by writing chat-bots for IRC in mIRC script.
They didn't have NLP, but some easy commands and they didn't need to scale much, because I always had below 1000 users at the same time. News bots, filesharing bots, social network/user stat bots, game bots, etc.
Funny that I accomplished to write a news bot that got its data from some news site via sockets by copy-paste and edit some code I found online. I didn't even know what arrays were, let alone half ot the code I copied. But somehow I got the target host changed and the needed data parsed out of the HTML.
Ditto, that was my entry to 'programming' - to begin with it consisted of copying a bunch Runescape stats scripts from Hawkee and running on my desktop. As it progressed I included some persistence for some feature, implemented by creating a bunch of global variables with the user's nick as the first part of it. I think I felt that storing to file was overcomplicating things, bearing in mind that I was pretty young at the time.
I do recall coming across some scaling issues, which I resolved by spinning up another instance of mIRC and running it with the name <botname>2. That seemed to work pretty well. 'v2' came next, wholly consisting of changing all of the scripts' colours to be homogeneous.
Ah good old mIRC bots. Same here man. Except mine were mIRC "mud" games.
Very quickly i wrote a very large game from scratch teaching myself all sorts of concepts. It wasn't impressive at all, but oddly i'm quite impressed that i was able to "learn as you go" a semi-advanced project with such a simple language.
I'm not likely to ever be considered a virtuoso or anything, quite ordinary infact, which then means that mIRC's scripting language and documentation must have been very friendly for a complete beginner to program a virtual environment with state, rooms, "ai" traveling between rooms, and etc. It was far more than a hello world, and all thanks to mIRC it seems.
I wrote my first chatbot for IRC, and then later on for AIM. Such great memories!
The first chapter of my book, "Building Voice-Enabled Apps with Alexa" covers several of these topics, including XDCC chatbots, Eliza, The Loebner Prize, and a lot more of chatbot history. Fun stuff!
There is lots of deja vu for me. I spent my days in 97-00 hosting Eggdrops on IRC (mainly IRCNet but some others as well).
Spent my days deep in TCL, accidentally discovering probably the single deadliest bug in Eggdrop with an accidental copy and paste (would cause all bots in the botnet to go into an infinite loop)
For the last two years I've been working on similar problems but for Messaging networks (Messenger, SMS, Intercom, Slack etc) in $currentstartup
If anyone has questions around bots, conversation management, integrations, NLP etc etc, feel free to ask.
While spell-checking is slow, it is not necessarily as slow as 200ms per word. But it is necessary to have one or else people will struggle with your chatbot.
We have created a chatbot that handles mispellings and dialects just fine for the banking and insurance industry in scandinavia. You can try him here: https://james-demo-test.boost.ai (You need google translate as it only understands norwegian, with some support for danish and swedish synonyms. Support for english and other languages are under development)
Performance is not a high priority for us, we can easily scale by adding cpu cores. A good precise answer is a lot more important and its where we spend most of our time improving our chatbot.
Native Norwegian speaker here. I've tried about ten different questions off the top of my head, and it's very much "God dag mann - økseskaft."
E.g. I asked "What is the interest rate on mortgages?" and it gave me basically what I asked for, with an example of total cost over 25 years. Then I asked "How much would that be every month?" and it gave me a link to applying for "avdragsfrihet", but with a text recommending against doing so. Apparently dividing by 300 is beyond its faculties.
Or I asked "How do I invest in index funds?" and it replied "I don't understand, try calling us".
Basically, I couldn't get an answer to any of my questions that I hadn't gotten if I'd just googled the exact same phrase. And it doesn't appear to understand follow-up questions at all.
I'm curious, do you have a question I can try that will give me something google won't answer given the same query?
1. We are currently working on supporting queries with amounts, the first kind will be a "loan-calculator" that tell you how much you can loan for a new house or a new car.
We did try syntaxnet for this earlier, but it failed big time with dialects, so we are creating our own variant.
2. We have nothing on index funds yet, our customers so far does neither have that kind of a thing, though it should at least have been a synonym for funds. This will be corrected.
This bot is a customer service kind of chat bot. It's not something to have a conversation with, but more like a bot that can understand questions and help you. The business value here is not to be better than human, but be as close as possible for the most common support queries. Trying to pass the Turing test is unrealistic... We are trying to automate support issues for the most common questions like, I have lost my credit card, I need a new card and I cannot login.
Compared to a regular search engine, ours will have a much better understanding of what your intention is. Google also tries to predict your intention when you search. However Google is not a replacement for customer support for most banks, telecoms, logistics, energy and insurance companies.
I toyed around with that python DL-distance in cython and without much effort got it to work 3.5~4x faster. Have you looked into cython to optimize it? It certainly seems cheaper than adding CPU cores, even more so considering I'm quite new to cython myself (I just took the code and added types and a few fixes here and there to get it to work faster).
Scratch that, with a bit more of tinkering got a ~80x speedup. Still get the feeling there's some more performance to get, but that'd require some assembly-fu or a different algorithm.
The biggest challenge you're going to face is that the state of the art in NLP is not sufficient for chatbots to conduct a prolonged conversation that is either useful or interesting. This will lead to disappointment on the part of your users, who have probably been exposed to the hype or expected a human. The more modest approach of mapping a Web UX to a sequential chat leaves users at best indifferent, and at worst annoyed.
A literal challenge (i.e. competition) you may choose to face, is of course the Loebner prize. Held in Bletchley Park due to its history with Alan Turing.
If your chatbot can be more convincing than three other finalist chatbots over four half hour dual-conversations (a human judge conversing simultaneously with one chatbot and one confederate human, but is not told which is which) then you stand to win a solid bronze medal and $4000 ($1500, $1000 and $500 to the runners-up).
A silver medal and $25,000 goes to any chatbot which fools half the judges.
To get $100,000 and a solid gold silver medal, you'll need to make a chatbot that is indistinguisable from a human over video chat, now that's a proper challenge!
> you'll need to make a chatbot that is indistinguisable from a human over video chat
This may sound cynical, but if you could arrange a tableau, looking like someone is paralyzed in a hospital environment, speaking like Stephen Hawking, then you don't need any serious advances in robotics (IIRC uncanny valley still hasn't been crossed) to fool humans. But I imagine the rules won't allow that.
This is a good point, and arguably a weakness in the Turing test, the headline grabbing Eugene Goostman chatbot's limited success was from a similar "social engineering" style technique, of tying slowly, changing the subject, telling its own stories and claiming to be a distracted teenager with poor English.
In response to the comment by Coincoin, each judge gets to pick "Human, Computer, Not Sure" for both participants, so they're at liberty to respond "Human, Human". It's true that the "real" human might make someone more likely to declare the "fake tableau" a chatbot, but the perceived disability might also make them less likely to risk declaring them a computer.
Don't forget you are directly competing against a human. If you are a judge, which one would you chose? The still frame with a robot voice pretending to be Hawking or the actual human?
Any time I hear about a health related app, I have to reread "The Anorexic Startup" [0]. It's cautionary and it pokes (gentle) fun at our industry. If you're in the mood for a bit of humor today, it's a fun read.
The first paragraph reminded me so much of jwz's diary from his Netscape days, which is one of the most important documents I've read on startups (and it taight me the brilliant term "impedance mismatch" for how it feels to hang out with drunk people when you aren't, which I last used in casual conversation yesterday)
Disclaimer for your link - his webserver redirects to something not nice if the referrer is HN. Anyone visiting the link should C+P it into their address bar instead.
Extremely sorry, forgot he set up a redirect to an offensive image if you link to this from HN, and too late to edit. Please try this link instead: https://www.google.com/search?q=jwz+nscpdorm
Readit News
The article describes:
1 (scalable architecture) : but the common solutions to this problem are not unique to chatbots
2 (a conversational agent must converse) : actually, that's a design decision for each individual bot, there are alternative ways of thinking about it.
3 (users will get frustrated) : this is actually a not a tech/AI problem, but a UI (user expectation) problem, see below.
The actual top 3 problems chatbot makers face are (in order):
#1 Discovery (!!!) : people don't know where/how to find bots and Facebook does not have their shit together in this regard (almost a year after launching their bot platform, there are still crucial errors. Result: most bots that could make a difference in the real world never get any traction, or enough users to train or iterate.
#2 Onboarding / setting expectations : the majority of (non-test or -product hunt) users of your bot are having their first chatbot experience with yours. Guiding them in the correct way is your #1 design priority. Result: a dissapointed general public.
#3 Inflated expectations : Mainly a problem for those who try to sell chatbots in B2B. Its' almost the opposite problem to #2. Most people and media conflate AI with chatbots. But the recent advances in machine learning (which are the reason for the current attention the term "AI" has been getting) are not (yet) applicable to chatbots in general. Add to this expectations of a Jarvis, Siri, etc. but then "for x". Result: dissapointed clients and an even quicker deflation of the hype.
And this is not even going into monetization!
All in all, I'm just frustrated that everyone involved in bots seem to assume that we already know how bots should work. I work in bots and I have yet to see one really impact user's lives. I wish for more humility and experimentation. At least we're all sharing what we're learning, so there's that.
For discovery, FB Messenger actually has more channels for bot discovery than any other platform, including:
- m.me links with referral tracking
- Messenger Codes
- Several plugins that allow easy opt-in via developer's website, including the ability to by default opt-in users on any form/flow on your website into your bot experience on Messenger.
- Customer matching (use phone numbers to send updates to customers and acquire them as a bot user)
- Messenger destination ads
- Sharing features that many have used to achieve incredible viral reach.
The power that developers have to promote their own bots using these existing channels is largely undertapped. I would encourage developers to see what they're building as a business first, and a bot second. The bot is just a facet/channel into what you offer (even if it is a primary one). If you don't have a reason to offer a product or service or publish content, you probably don't have a reason to have a bot.
Admittedly, we could do better at setting user expectations for bots and in distinguishing them in search results and in the FB main app, and maybe pushing them in more places.
Re: inflated expectations. Totally right. The "AI Agent"/NLP use case is just one possibility for a bot, yet it's the sexiest one that the media seems to like. It's the tip of the iceberg -- there are a ton of more mundane but incredibly useful bots that are succeeding on the platform.
If any bot developer has questions about this, or wants to brainstorm ways to get users, I am happy to help or bounce ideas!
Can you confirm if the user directs into Messenger directly when they click the ad?
What? The very first point is discovery (which is fair) which just smoothly melds into discovery ON FACEBOOK? So we just assume bot discovery is best handed off to FB/GOOG/ etc?
So now FB, in addition to being the de facto destination where your personal data goes to be exploited, is also supposed to "get their shit together" to enable even more of the data collection?
Have you noticed how nobody from FB has actually ever engaged with anyone on HN around the ethical issues surrounding any of their practices?
Your last point is about how everyone involved in bots seem to assume we already know how bots should work. You make the same assumption about who should lead this effort. I would rather the bot technology takes longer to pan out than see a world where the giant corporations are also the sole operators of the bot marketplaces.
* Scalable architecture is, most of the time, a very-NOT-big-deal. Bots tend to be relatively lightweight apps, sending/receiving json data only. Even websocket Slack apps can now be replaced by HTTP API.
* Discovery is really un-solved at this time, especially by Facebook. Slack does a better job, but arguably has a smaller ecosystem of B2B bots to manage and promote.
However, I think the article is about a rather ambitious (more than usual) and interesting bot. Having to parse normal language symptoms into medical terms is 1) an interesting use case 2) hard to do well technically. So maybe they face challenges most bot dev don't face.
Design-wise, most people don't see how hard it is to design chatbot flows that hard not horrible and confusing. Most chatbots are horrible and confusing at the moment, because coding bots is easy, designing them is really hard.
I think the article make interesting points about their own specific case, but their situation is perhaps less general than they seem to think.
In this case I would think you'd want to display when the rules/facts used for recommendations were last updated, which would add extra fields to track. There are also some interesting QA problems to work through.
Currently the typical answer from a medical device company is a combination of "carefully" and "with clinical oversight". The typical answer from a non-medical startup seems to be "poorly", for the most part.
"there are plenty of more informative bot articles that would better serve the HN audience" could you share those articles ?
I saw a recent article at oreilly ideas : https://www.oreilly.com/ideas/designing-bots
For me this is really off-putting, in particular as they suggest that you are talking with a human (photo and pseudo-friendly greetings), although they just want to catch your e-mail address, and the first interaction is clearly with a bot.
I am not sure where this is going, maybe I am a minority, but for me this "interactions" are really off-putting.
I don't know if I have questions yet. I'm watching the video. I have javascript enabled - you can tell that I'm watching the video. Leave me alone.
How do founders have time for that? Not many customers use chat, but the ones that do are valuable. As pg says, all early stage startups should just be writing code and talking to users, so founders make it a high priority to reply quickly!
Usually it seems there are real people and it provides the fast respose of pgone together with the asynchronity of email. Fast but less awkward when kids are screaming in the background.
I think if I just scout the chat bubble I already start questioning the business really hard.
They didn't have NLP, but some easy commands and they didn't need to scale much, because I always had below 1000 users at the same time. News bots, filesharing bots, social network/user stat bots, game bots, etc.
Funny that I accomplished to write a news bot that got its data from some news site via sockets by copy-paste and edit some code I found online. I didn't even know what arrays were, let alone half ot the code I copied. But somehow I got the target host changed and the needed data parsed out of the HTML.
I do recall coming across some scaling issues, which I resolved by spinning up another instance of mIRC and running it with the name <botname>2. That seemed to work pretty well. 'v2' came next, wholly consisting of changing all of the scripts' colours to be homogeneous.
Very quickly i wrote a very large game from scratch teaching myself all sorts of concepts. It wasn't impressive at all, but oddly i'm quite impressed that i was able to "learn as you go" a semi-advanced project with such a simple language.
I'm not likely to ever be considered a virtuoso or anything, quite ordinary infact, which then means that mIRC's scripting language and documentation must have been very friendly for a complete beginner to program a virtual environment with state, rooms, "ai" traveling between rooms, and etc. It was far more than a hello world, and all thanks to mIRC it seems.
Ah, the good ol' days.
The first chapter of my book, "Building Voice-Enabled Apps with Alexa" covers several of these topics, including XDCC chatbots, Eliza, The Loebner Prize, and a lot more of chatbot history. Fun stuff!
"Building Voice-Enabled Apps with Alexa"
https://library.oreilly.com/book/9781939902443/building-voic...
Spent my days deep in TCL, accidentally discovering probably the single deadliest bug in Eggdrop with an accidental copy and paste (would cause all bots in the botnet to go into an infinite loop)
For the last two years I've been working on similar problems but for Messaging networks (Messenger, SMS, Intercom, Slack etc) in $currentstartup
If anyone has questions around bots, conversation management, integrations, NLP etc etc, feel free to ask.
We have created a chatbot that handles mispellings and dialects just fine for the banking and insurance industry in scandinavia. You can try him here: https://james-demo-test.boost.ai (You need google translate as it only understands norwegian, with some support for danish and swedish synonyms. Support for english and other languages are under development)
For fast spell checking, I recommend reading this http://theyougen.blogspot.no/2010/02/faster-spelling-correct... which uses a bloom filter. We use https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_di... and we use a slightly modified slow python implementation https://www.guyrutenberg.com/2008/12/15/damerau-levenshtein-...
Performance is not a high priority for us, we can easily scale by adding cpu cores. A good precise answer is a lot more important and its where we spend most of our time improving our chatbot.
E.g. I asked "What is the interest rate on mortgages?" and it gave me basically what I asked for, with an example of total cost over 25 years. Then I asked "How much would that be every month?" and it gave me a link to applying for "avdragsfrihet", but with a text recommending against doing so. Apparently dividing by 300 is beyond its faculties.
Or I asked "How do I invest in index funds?" and it replied "I don't understand, try calling us".
Basically, I couldn't get an answer to any of my questions that I hadn't gotten if I'd just googled the exact same phrase. And it doesn't appear to understand follow-up questions at all.
I'm curious, do you have a question I can try that will give me something google won't answer given the same query?
1. We are currently working on supporting queries with amounts, the first kind will be a "loan-calculator" that tell you how much you can loan for a new house or a new car. We did try syntaxnet for this earlier, but it failed big time with dialects, so we are creating our own variant.
2. We have nothing on index funds yet, our customers so far does neither have that kind of a thing, though it should at least have been a synonym for funds. This will be corrected.
This bot is a customer service kind of chat bot. It's not something to have a conversation with, but more like a bot that can understand questions and help you. The business value here is not to be better than human, but be as close as possible for the most common support queries. Trying to pass the Turing test is unrealistic... We are trying to automate support issues for the most common questions like, I have lost my credit card, I need a new card and I cannot login.
Compared to a regular search engine, ours will have a much better understanding of what your intention is. Google also tries to predict your intention when you search. However Google is not a replacement for customer support for most banks, telecoms, logistics, energy and insurance companies.
Off-Topic: You've been on HN for 1337 days. You should celebrate that with a cake.
If your chatbot can be more convincing than three other finalist chatbots over four half hour dual-conversations (a human judge conversing simultaneously with one chatbot and one confederate human, but is not told which is which) then you stand to win a solid bronze medal and $4000 ($1500, $1000 and $500 to the runners-up).
A silver medal and $25,000 goes to any chatbot which fools half the judges.
To get $100,000 and a solid gold silver medal, you'll need to make a chatbot that is indistinguisable from a human over video chat, now that's a proper challenge!
This may sound cynical, but if you could arrange a tableau, looking like someone is paralyzed in a hospital environment, speaking like Stephen Hawking, then you don't need any serious advances in robotics (IIRC uncanny valley still hasn't been crossed) to fool humans. But I imagine the rules won't allow that.
In response to the comment by Coincoin, each judge gets to pick "Human, Computer, Not Sure" for both participants, so they're at liberty to respond "Human, Human". It's true that the "real" human might make someone more likely to declare the "fake tableau" a chatbot, but the perceived disability might also make them less likely to risk declaring them a computer.
The wizard was by far the better experience.
Any time I hear about a health related app, I have to reread "The Anorexic Startup" [0]. It's cautionary and it pokes (gentle) fun at our industry. If you're in the mood for a bit of humor today, it's a fun read.
[0] http://theanorexicstartup.com/
https://www.jwz.org/gruntle/nscpdorm.html