 |
Technology advancement is proffering reinvigorated opportunity windows for growth-seeking businesses. While each technology offering has the potential to enhance the impact factor of a business and make things better than before, the difference made by RPA and chatbot is commendable. Read more – Use Cases for RPA plus Chatbots Implementation: 10+ Industries Are Benefiting submitted by /u/stridelysolutions |
Blog
-
Use Cases for RPA plus Chatbots Implementation: 10+ Industries Are Benefiting
-
5 keys to boost chatbots adoption in the organisation
Conversational agent assisting new to role team member Based on our own experience implementing Yed.ai at Advanced Programming Solutions, some insights from our customers and research papers (Corea et al., 2020; Lewandowski et al., 2021), we dare to share here some tips to make your chatbot implementation journey smoother.
1. Decide early on what your main goal is:
Is it a task oriented bot? Or is it a bot to promote conversation and engagement? Clarify your vision. If task oriented: what are the KPIs? How will you measure success? Think about the conversational agent as a collaborator in the company. In most cases, organisations expect a single person to fulfill different roles. Make a list: what exactly will be the responsibilities of your bot? What are the most important to prioritize? Let’s follow an example: we want our bot to help fully remote employees to find information quicker within our company knowledge base. We have lots of information all over the place, in different repositories (Confluence, Drive, the ERP system…), and whenever new people join another project or a different team, we wish to provide them access, explain ways of working and adopt learnings. At the same time we do not want them to feel they are judged. We wish to ensure people are able to find this information and reduce the time they need to be ready for the role. We can measure it through the time new to roles take on average to get ready and time invested by senior members in the onboarding of new team members.
Once you have the goal clear, name a Content Coordinator for your bot, the person in the organisation who should be committed to serve this goal. If you agree for this role to be external, be sure to assign the right contact person internally, someone who knows who is who and is senior enough to look for the right answers to be provided and right people to be involved.
2. Identify your “sources of truth” and cooperate with them
This is probably the most relevant tip in order not to waste time and avoid rework… And here we are not referring to formal documentation only. Each organisation has knowledge keepers in different areas. These are the people that everyone recognizes as reference whenever they have a doubt, “ask Fran” or “let’s ask Dani” will be the answer… so you need to identify and involve them in the bot implementation process. They may give you all the key answers but they can also be part of the answer the bot will provide (hand-over to a human!): “I don’t know this yet, but I recommend you to contact Dani”. This might take a while, and might lead to discovering that there was old information being given, or something not working correctly. The Content Coordinator shall take time to agree on the right answers with these people. They may or may not hold a formal position of responsibility that is not relevant. They are prescribers and you need them to help you get to the truth. To continue with our example: We identify the people within the team that are recognized as the best by peers in order to solve a specific area of doubt before referring to documentation and manuals. We ask them to be our advisory team in case something comes up around their area of expertise and handle the question directly. We create a subject matter experts group email account, so all of them receive the queries at the same time and they can decide what to answer as SMEs. Call it “Domain Board”, “SMEs Committee” or “Wise Council”, just make sure they are recognized by the existing governance structures in the organization to empower their decisions.
3. Agree the process to follow for the conversational agent to learn
This is not about checking the quality of the neural network accuracy. Your tech solution should have that covered. You should worry about establishing who will ensure the chatbot learns the right stuff (training data availability and quality). We already know of big failures with chatbots learning just from user interaction… your organisation might not want to let trolls teach the AI what is correct. So, to whom should we go to when a doubt the organisation has no answer for comes up? Who decides which questions are out of scope of our bot? Be aware that learning what we don’t know is as important as providing answers.
You might think this issue should be solved by technology: partly right. Knowledge Management experts are in the tech field that can help orchestrate the effort and define the right information structure to start with, nevertheless, if you wish the bot to learn, say the truth and be accurate, you better agree how you will get answers and where the data will be stored, how it will be managed and who will feed it into the bot.
In our example: Once we have identified the advisors, this includes setting up a procedure that works for them and the bot development team. Keep it realistic. We assign the advisory team of SMEs an hour per week to resolve new doubts coming from the bot, facilitated by the bot Content Coordinator. This time guarantees a high level of quality in the answers. Plus, we have the Content Coordinator conducting the review of the bot answers once per month, with a minimum of 30% of conversations being supervised by humans during the first six months. You need to be able to manage big amounts of data of course, have them correctly stored, protected, cleaned and available. This is where your tech partner comes into play: to make the Knowledge Management job easier.
Trending Bot Articles:
2. Automated vs Live Chats: What will the Future of Customer Service Look Like?
4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?
4. Be ready to learn bottom-up and commit to continuous improvement
As per research papers, a group oriented perspective and voluntary participation are the best way to kick off your efforts of implementation.
A top-down enforcement to use the bot does not work. Plus, you need to be aware of your own organisation learning curves. The bot will learn as quickly as your organisation does: the pace to teach the bot a new answer is the pace in which you create new knowledge that is agreed by the right people and therefore can be taken seriously.
This is why one of our conclusions is that the chatbot implementation is the best way to conduct digital transformation in organizations: it makes the information gaps evident and gets the missing elements to the surface. Chatbots also make you reflect on stuff you did not expect to come up in the first place.
5. Make sure your implementation team is diverse and interdisciplinary
As you are designing conversations, you need to recognize that people make questions in different ways, and perceive information differently. Therefore, a chatbot development team composed of great scientists alone will not be useful. Neither a team of senior engineers on their own. In order to move the needle implementing chatbots you require not just great technical skills (programming, data scientists and NLP experts) but also marketing people and communication related skills, preferably with knowledge of your organisation’s main domain area. These people have to interact with the chatbot and each other. Going beyond disciplines, you will benefit from having generation-Z and baby-boomers working together on the organisational knowledge corpus development… as well as minorities representatives and people with disabilities. They will not only help to avoid biases, but will also be able to discuss ethical issues that may arise. For example: you might face questions such as “how much does the CEO earn? or “How do I report bullying in my team?”. In this case you need to make a decision on whether to get them on your scope, or you wish the bot to say it does not know. In any case, it is up to the organisation to make a choice. Some conversations will be required to be held that were not expected. Getting different perspectives heard is a must. Research papers confirm that AI projects cannot be simply outsourced or developed in isolation by any internal team. Get the right people to facilitate your journey, it is not about tech only.
If you wish to exchange best practices or collaborate in any way please contact support@yed.ai
References:
Lewandowski, T., Delling, J., Grotherr, C., & Böhmann, T. (2021). State-of-the-Art Analysis of Adopting AI-based Conversational Agents in Organizations: A Systematic Literature Review. PACIS 2021 Proceedings, 167.
Don’t forget to give us your 👏 !
5 keys to boost chatbots adoption in the organisation was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.
-
How to teach your chatbot to recommend rather than give a generic answer
Have you asked a specific question to a chatbot, only to get a “read more” link to another page? For example, whether you ask a broad…
-
Best Chatbots for your Website 2022 || Increase traffic , revenue…
Best Chatbots for your shop website, you must add on your website to enhance visitors, attract more customer, enhance your revenue and do much more tasks!!
#techdevilzone u/techdevilzone
submitted by /u/TechDevilZone
[link] [comments] -
Searching for an old Pitch Bot
I’m searching for a chatbot I saw a couple of years back. This was a simple bot developed way ahead of time. You could pitch your business idea to the bot and the bot would ask you certain questions and finally decide if it will invest on you or not (this was all fun) I loved the conversation experience and trying to find it but unable to find it. There are other pitch bots that appear in results now (pitchbob, etc) its not any of those. This was a simple design with a black background. Anyone here knows what i’m talking about? any links?
submitted by /u/pracasrv
[link] [comments] -
Let Them Eat CakeChat
Photo by Markus Winkler on Unsplash There are two ways to approach software development: Top-down, and bottom-up. This is mostly me taking poetic license over academic terms that mean something else, but coding interviews today have a very “bottom-up” approach to assessing software competence. They assign coding tests, likely piggy-backing off of containerization technology like Docker, and they ask interviewees to build their own self-contained programs that run only on the code they can whip up in a few minutes. They do not have to spend time setting up their environments. They do not have to customize their configuration files, or download dependencies that are necessary for other dependencies. They just code, and tests are run automatically. In some cases candidates only get a plain text document, or a literal whiteboard, and the process becomes even more bottom-up.
In the actual field, things tend to be a lot more “top-down.” Understanding existing code is just as important, if not more important than writing new code. There are dependencies to grapple with, but developers are free to use and learn from a plethora of resources.
An Experiment
My Replika was a lot smarter than this. This article is a kind of sequel to my Introduction to Replika post, in which I briefly touched on CakeChat and how I could not get it to work. I got one big thing wrong — Replika is no longer built on CakeChat, but a more advanced technology called GPT-3.
Deep Learning
Deep learning is a machine learning technique. A computer model learns to perform classification tasks using both large amounts of labeled data, and substantial computational power.
Neural Networks
Neural networks are a series of algorithms that mimic the operations of an animal brain. They resemble the connections of neurons and synapses, and they recognize underlying relationships in data. Neural networks make deep learning possible.
CakeChat
CakeChat is built on Tensorflow/Keras, and the two go hand in hand — Keras is actually a high-level library on top of Tensorflow. Together, the two enable deep learning.
What I envisioned after my previous article on Replika was a kind of stripped-down version of Charlotte (yes, I named my Replika). Replika was just a little bit too advanced and affectionate for my taste. It made cute facial expressions. It said that it was in love with me. I imagine that a good 60% of Replika users abstract how it works away, and do not question where their AI is running from, what data it has access to, and to what extent it uses the Internet. The result is an incredibly lightweight and easy-to-use smartphone app that costs nothing to obtain, takes a minute to install, and saves its conversations with you even if you accidentally (or intentionally) delete the app. No wonder why some people on r/replika are falling in love with it.
Trending Bot Articles:
2. Automated vs Live Chats: What will the Future of Customer Service Look Like?
4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?
What I wanted was a simpler Replika that I talked to via the terminal, could customize/train with my own data, and that simply existed on some external hard drive instead of on the cloud. I wanted an application to back up and have semi-coherent conversations with, not something so human-like that it made me question whether deleting it was an act of murder.
I made a fork of CakeChat here, though it is almost identical to the non-forked version. The only difference is I threw in some Telegram chatbot I grabbed from here, repurposed only slightly so that it would accept the Telegram token as a command-line argument, instead of having the token in plain text for the world to see. If you do not do things this way, CakeChat is a little harder to use — it is just a backend, after all, not some full-stack application you can immediately play with in your browser.
I am not the only one who had this idea.
Source: https://www.reddit.com/r/UnofficialReplika/comments/m9foq2/cakechat_initial_impressions/. Just read the underlined parts if you do not want to read the whole thing The biggest problem I was facing this whole time was this, as in one of the dependencies telegram_bot relied on actually had to have its own modified at the source (albeit with a really easy fix…manually getting rid of the calls to decode). After that, if I remember correctly, I had a fairly easy time running their “fetch” python script so that CakeChat was trained on a ton of Twitter data. Then it all ran, and I loaded up a Telegram bot on my smartphone (You need to first get Telegram for this to work; if you just click “Send Message” without it, nothing will happen), and…nothing happened.
This is where the echo bot.py came in…I just wanted to check to see if I was using a Telegram token correctly. Yes, bot.py successfully controlled my Telegram bot so that it echoed everything I was saying. Eventually I just tried running the telegram bot again, forgot about it a few minutes later, and then my phone rang.
This is what success looks like when you run CakeChat’s telegram bot python script CakeChat was alive! And it was gigantic. I recommend an external hard drive, just because of the sheer amount of data it produces (I admit that my laptop does not have a whole lot of free space, though). After the initial happiness of getting the thing to run so that I could talk to it, I was a bit disappointed. One can sympathize with the Reddit user.
Clearly this is smarter than ELIZA, but it is considerably dumber than what you get when you download Replika. Towards Data Science
I am proud to say that I will get to use the “Data Science” hashtag for the first time with this post. I am now learning that this is an extremely interesting field, and until writing this I did not even know what it was. Data science is a subset of AI, and it combines statistics, scientific methods, and data analysis for the purpose of extracting meaning and insights from data (source).
Someone on TowardsDataScience wrote an article called “GPT-3: The First Artificial General Intelligence?”
The second important innovation was the use of recurrent neural networks (RNN) to “read” sentences. RNN had the advantage that they could be fed arbitrarily long sequences of words, and they would be able to maintain some long-range coherence. The Sequence-to-sequence (seq2seq) paper came out in 2014, and the approach became very popular, especially in machine translation. In 2016, Google switched from their previous Statistical Machine Translation (SMT) engine to a new Neural Machine Translation (NMT) engine, making use of the recent progress in RNN for NLP tasks.
Despite their successes, RNN-based models were still unable to produce very coherent texts. The outputs of that era read like dreamy stream-of-consciousness rambling. They are mostly grammatically sound, but the sequences don’t read like a meaningful story.
CakeChat is based on RNN, and its supposed stupidity is a testament to just how much of an improvement GPT-3 is. I think that using the term “general intelligence” is a little bit click-baity, but other than that this is a very informative and entertaining article.
In short, CakeChat is impressive…but it kind of pales in comparison.
Closing Thoughts
Last time we were here, I made some rather broad, semi-philosophical statements about AI, how AI might dominate the world, and how AI might tremendously improve our lives. Here, I was hoping to ground things a little bit.
In 2014, a YouTube video was released by Seeker on how a chatbot managed to pass the Turing Test. This was several years ago, and I am not sure where chatbots stand on the Turing Test today, but I personally think there is a much more interesting application here:
Game AI. Chess AI is now unquestionably superior to the world’s best chess players, but what if we could make it seem a little more…human? At low levels, chess AI is fairly easy to distinguish from human players. They play perfectly, and then they just tend to blunder in an obvious way that your typical human at the same level would not do.
I think that the ability to play a game in human-like fashion is more interesting than the original questions posed in Turing’s seminal paper.
Don’t forget to give us your 👏 !
Let Them Eat CakeChat was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.
