Conversational Assistants and Quality with Watson Assistant

By Daniel Toczala

Recently my team inside of IBM decided that we needed to capture some of the “institutional knowledge” that some of our more experienced members knew, but that didn’t seem to be captured anywhere so our newer team members could be as effective as our more seasoned members.  We also wanted an excuse to get our hands dirty with our own technology, some of us had been focused on some of the other Watson technologies and needed to get reintroduced to the Watson Assistant since the migration to the “skills based” UI.

I went looking for something good about developing a chatbot (or conversational assistant), with Watson Assistant, and some good lessons learned.  I found some good information, but not one spot with the kind of experience and tips that I was looking for.  So I thought it might be good to capture it in my own blog post.

Getting Started with Your Conversational Assistant

We spent a week or two coming to a common vision for our chatbot.  We also mapped out a “growth path” for our chatbot, and we agreed on our roles.  I cannot begin to stress how important this is – Best Practice #1 – Know the scope and growth path for your chatbot.  We had a good roadmap for the growth of our chatbot.  We mapped out the scope for a pilot, where we wanted to be to release it to our end users, and a couple of additional capabilities that we wanted to add on once we got it deployed.

My boss graciously agreed to be our business sponsor – his role is to constantly question our work and our approach.  “Is this the most cost-effective way to do this?”, and, “Does that add any value to your chatbot?”, are a couple of the questions he constantly challenges us with.  As a technical guy, it’s important to have someone dragging us back to reality – it’s easy to get focused on the technology and lose sight of the end goal.

Our team of “developers” also got a feel for the roles we would play.  I focused on the overall view and dove deeper on technical issues, some of my co-workers served primarily as testers, some as knowledge experts (SME’s), and others as served as UI specialists, focusing on the flow of conversation.  This helped us coordinate our work, and it turned out to be quite important – Best Practice #2 – Know your roles – have technical people, developers, SME’s, architects, and end users represented.  If you don’t have people in these roles, get them.

Starting Out – Building A Work Pipeline

As we started, we came together and worked in a spreadsheet (!?!), gathering the basic questions that we anticipated our chatbot being able to answer.  We cast a pretty wide net looking for “sample” questions to get us kickstarted.  If you are doing something “new”, you’ll have to come up with these utterances yourself.  If you’re covering something that already exists, there should be logs of end user questions that you can use to jumpstart this phase of your project.

Next, we wanted to make sure that we had an orderly development environment.  Since our chatbot was strictly for internal deployment, we didn’t have to worry too much about the separation of environments, so we could use the versioning capabilities of Watson Assistant.  Since our chatbot was going to be deployed on Slack, we were able to deploy our “development” version on Slack, and also deploy our “test” and “production” versions on Slack as well.  These are all tracked on the Versions tab of the Watson Assistant Skill UI.  This gives us the ability to “promote” tested versions of our skill to different environments.  All of this allowed us to have a stable environment that we could work and test in – which leads us to Best Practice #3 – Have a solid dev/test/prod environment set up for your Conversational assistant or chatbot.

How Are We Doing? – K- Fold Testing

As we started out, we began by pulling things together and seeing how our conversational assistant was doing in real-time, using the “Try It” button in the upper right-hand corner of the Watson Assistant skills screen.  Our results were hit and miss at first, so we knew that we needed a good way to test out our assistant. 

We started out with some code from a Joe Kozhaya blog post on Training and Evaluating Machine Learning Models.  I ended up modifying it a little bit, and you can find the modified notebook in my Watson Landing Page GitHub repo, under notebooks, in a Python notebook stored as ANYBOT_Test-and-Deploy.ipynb.  We also read some good stuff from Andrew Freed (Testing Strategies for Chatbots) and from Anna Chaney (Data DevOps Rules of Engagement),  and used some of those ideas as well.  This led me to create that modified Python notebook, which I used to provide automated k-fold testing of our assistant implementation.  This highlights Best Practice #4 – Automate Your AI Testing Strategy.

After all of this was in place, our team fell into a predictable rhythm of work and review of our work.  Since this was a side project for all of us, some of us contributed some weeks, and didn’t contribute on other weeks.  We were a small team of people (less than 10), so it was easy to have our team manage itself.

Using Feedback

As we let our automated training process take hold, we noted that our results were not what we had hoped, and that updating things was difficult.  We also learned that taking time each week to review our Watson Assistant logs was time well spent. 

It was quite difficult to add new scope to our conversation agent, so we looked at our intents and entities again.  After some in-depth discussions, we decided to try a slightly different focus on what we considered intents.  It allowed us to make better use of the entities that we detected, and it gave us the ability to construct a more easily maintained dialog tree.  We needed to change the way that we were thinking about intents and entities.

All of this brings us to our next piece of wisdom – Best Practice #5 – Be Open-Minded About Your Intents and Entities.  All too often I see teams fall into one of either two traps.  Trap 1 – they try to tailor their intents to the answers that they want to give.  If you find yourself with intents like, “how_to_change_password” and “how_to_change_username”, then you might be describing answers, and not necessarily describing intents.  Trap 2 – teams try to have very focused intents.  This leads in an explosion of intents, and a subsequent explosion of dialog nodes.  If you find yourself with intents like, “change_password_mobile”, “change_password_web”, “change_password_voice”, then you have probably fallen into this trap.

We found that by having more general intents, and then using context variables and entities to specify things with more detail, that we have been able to keep our intents relatively well managed, our dialog trees smaller and better organized, and our entire project is much easier to maintain.  So, if our intent was “find_person”, then we will use context variables and entities to determine what products and roles the person should have.  Someone asking, “How do I find the program manager for Watson Assistant?”, would return an intent of “find_person”, with entities detected for “program manager” and “Watson Assistant”.  In this way, we can add additional scope without adding intents, but only by adding some entities and one dialog node. 

Why K-Fold Isn’t Enough

One thing that we realized early on was that our k-fold results were just one aspect of the “quality” of our conversational assistant.  They helped quantify how well we were able to identify user intents, but they didn’t do a lot for our detection of entities or the overall quality of our assistant.  We found that our k-fold testing told us when we needed to provide additional training examples for our classifier, and this feedback worked well.

We also found that the “quality” of our assistant improved when we gave it some personality.  We provided some random humorous responses to intents around the origin of the assistant, or more general questions like, “How are you doing today?”.  The more of a personality that we injected into our assistant, the more authentic and “smooth” our interactions with it began to feel.  This leads us to Best Practice #6 – Inject Some Personality Into Your Assistant

Some materials from IBM will break this down into greater detail, insisting that you pay attention to tone, personality, chit-chat and proactivity.  I like to keep it simple – it’s all part of the personality that your solution has.  I usually think of a “person” that my solution is – say a 32-year old male from Detroit, who went to college at Michigan, who loves sports and muscle cars, named Bob.  Or maybe a 24-year-old recent college graduate named Cindy who grew up in a small town in Ohio, who has dreams of becoming an entrepreneur in the health care industry someday.  This helps me be consistent with the personality of my solution.

We also noticed that we often needed to rework our Dialog tree and the responses that we were specifying.  We used the Analytics tab in the skill we were developing.  On that Analytics tab, we would often review individual user conversations and see how our skill was handling user interactions.  This led us to make changes to the wording that we used, as well as to the things we were looking for (in terms of entities) and what we were storing (in terms of conversation context).  Very small changes can result in a big change in the end-user perception.  Something as simple as using contractions (like “it’s” instead of “it is”), will result in a more informal conversation style.

The Analytics tab in Watson Assistant is interesting.  It provides a wealth of information that you can download and analyze.  Our effort was small, so we didn’t automate this analysis, but many teams DO automate the collection and analysis of Watson Assistant logs.  In our case, we just spent some time each week reviewing the logs and looking for “holes” in our assistant (questions and topics that our users needed answers for that we did not address), and trends in our data.  It has helped guide our evolution of this solution.

Summary

This blog post identifies some best practices for developing a chatbot with IBM Watson Assistant – but these apply to ANY chatbot development, regardless of technology.

  • Best Practice #1 – Know the scope and growth path for your chatbot
  • Best Practice #2 – Know your roles – have technical people, developers, SME’s, architects, and end users represented
  • Best Practice #3 – Have a solid dev/test/prod environment set up for your Conversational assistant or chatbot
  • Best Practice #4 – Automate Your AI Testing Strategy
  • Best Practice #5 – Be Open Minded About Your Intents and Entities
  • Best Practice #6 – Inject Some Personality Into Your Assistant

Now that you have the benefit of some experience in the development of a conversational assistant, take some time to dig in and begin building a solution that will make your life easier and more productive.

AI Changes Everything (but nothing really changes…)

I usually don’t write big long opinion rants, and usually, I stick to more technical focused content. However, recent events are beginning to make my head explode. There is so much misunderstanding about data, privacy, and artificial intelligence. So I decided to write something to help one of the younger members of my family make sense of everything. So please read this – and pass this along to your friends who are not “tech people” if you think it will help them understand and make sense of recent events.

Note: The attempt to make this like the Ryan Tomayko REST article is deliberate. I LOVED that article – it clarified the concept of REST for me and countless others a long time ago. I hope this article is half as effective as that one was. Ryan has since taken the original version of his article down because it was deemed sexist…

The Conversation

Young Person: What is with all of the recent media panic about FaceApp and what happens to your data? Is this just like the Facebook attention focused on user privacy?

Tox : Are you sure that you want to get into this? It gets kind of weird.

Young Person: Sure. How bad could it be? You work with Artificial Intelligence and that AI stuff don’t you? Can’t you just have Watson explain it to me?

Tox : Artificial Intelligence, or AI, isn’t really like that. I can’t just ask Watson to explain it to you. AI isn’t magic, it’s just different from how we traditionally programmed computers and created applications.

Young Person: What is an application?

Tox : An app – those things on your phone. Your phone hasn’t always been smart – phones were pretty dumb for the longest time.

Young Person: I know that, I think I saw a picture of one once. But why is AI different?

Tox : Previously, computer software and applications (or apps) operated as a set of rules. Picture them as big diagrams where you check some data (like age, name, location, etc.), and then do something based on the value of that data. So an auto loan calculator app would take in all of the various things needed to calculate your loan payment (length of the loan, downpayment, interest rate, etc.), and would “do the math” based on your individual situation, and return the answer to you. Most apps are bigger and more complex than my example, but they all work the same way every time. If I enter the same data each time, I can predict what the answers coming back will be. This is because traditional computer apps are essentially “rules engines” – they follow the same rules, every time, no matter what.

Young Person: So software developers are just writing “rules” all day long?

Tox : Yes – at some level, that is what a programming language does. It allows you to write rules about how to calculate things, and how to show things to the human users.

Young Person: So how is AI different?

Tox : Artificial Intelligence works differently. It is primarily based on statistical models. It looks at things and creates statistical models so it can try to “predict”, with some level of confidence, what the next steps should be. So in the case of facial recognition, it takes the pictures that you give to it, and it is able to “look” at the picture and say, “I am 90% confident this is an eye, I am 88% confident that this is hair, I am 93% confident that this is a mouth”. Doing this repeatedly allows it to go through a progression of steps, first it will isolate a person from a picture, then it will isolate the face, then it will isolate individual features in the face, and then it will compare those to specific individuals and may be able to “identify” an individual person.

Young Person: So that’s what those “auto tags” in Facebook do? They just apply statistics to my pictures? That’s AI?

Tox : It’s one aspect of AI – visual recognition and facial recognition.

Young Person: So why do people get so worked up about it?

Tox : Because I can use it to keep tabs on people – to make them do what I want them to. To take away their freedom. Have you heard about the Chinese facial recognition used to help enforce their laws?

Young Person: We talked about it in school, but that kind of stuff could never happen here in Texas. Right?

Tox : We could get into a whole political discussion about that – let’s just stick to the stuff I know. The key thing here is that Artificial Intelligence is not “magic” – it isn’t a small computer brain that we have in a lab somewhere bathed in a solution with electrodes attached. It’s just looking at things from a statistical perspective, instead of the more traditional rules-based perspective.

Young Person: OK – so now I know that AI is different. But why does this stuff matter – statistics vs. rules? It might be important to tech types like you and Uncle Marc, but why does it matter to me?

Tox : Since AI is statistically based, it needs to have a lot of data. Statistics has some interesting things, it’s not as bad as it can seem in school. Did you know that the more samples of something that you have, the more accurate your predictions about future behavior can be. This is called the Law of Large Numbers, and it is a CORE driving concept for AI. Having a large amount of data is essential for this to work properly, so AI solutions require a lot of data – a lot of samples – that help them “learn” about something.

Young Person: OK – so how does that work?

Tox : The best illustration of this might be from the world of sports, using baseball batting averages. Or shooting percentages from other sports. As players perform, a history of performance is built up. Early in the year, a player may do very well, and have a high average (or score of success). Later in the year, they may not do as well, and their average (or score of success) will drop. (For those of you who want to learn more, this is called regression to the mean – another interesting statistical concept).

Looking at these scores later in the year will give a coach (or a casual sports fan) a way to compare the probability of success for two different players. Now here is the key point – it will not predict WHO will be successful, but it will tell you who has a higher probability of success. This is what AI does – it uses statistical models to predict future behavior or make sense of current data.

Young Person: So I could create an AI thing that would predict which stocks will do great in the next year – and then make millions. Right?

Tox : Not really. Remember, AI only gives you the probability of success – it doesn’t predict the future. It tells you what the chances of something happening are. If something happens one time every hundred times, I can tell you that it probably won’t happen – and I would be right 99% of the time.

Young Person: But a lot of AI doesn’t predict anything. It changes things. Like some of the deepfakes that I saw. Some of those were really funny….

Tox : These videos are modified using those statistical models. They get enough examples of one persons face, and they “predict” what it would look like on a different person. What gets done is different, and more complex, but the concept is the same.

At this point we took a break to grab some food off of the grill, play with my dogs, and relax a little bit. Then we picked our conversation back up….

Young Person: OK, so now I understand why AI is different. It’s all statistics, and it’s not magic. Why is this becoming such a big deal now?

Tox : As time and technology progress, the computing power and availability of computing resources allow us to tackle larger and larger problems. Large amounts of data can now be easily collected, stored and processed. You can do this on a cloud platform (like the IBM Cloud) for relatively little money. An individual who has the correct background and knowledge can now build and launch an AI application serving hundreds or even thousands of users for around $100 a month.

Young Person: So you mean that I could be doing deepfake videos on the cloud? Or other stuff?

Tox : Sure. You and anyone else you can think of. The issue is now “What is useful for people?” – and how can I provide some capability that people are willing to pay me for? Right now, most of the business models in this space tend to follow the same path that broadcasting took in the last century. Broadcasters provided service for free, but charged advertisers for the ability to expose a large audience to a particular marketing or advertising message. people couldn’t DVR stuff and blast past the commercials. Today, internet companies use your personal information to allow advertisers to “target” particular populations of people – based on income, interest, location and a variety of other things.

Young Person: So I could be like one of those people that are a billionaire before they are 30?

Tox : Why not? But you need to figure out HOW? Entrepreneurs begin to look at where they can make money – and make an impact on the world. Online companies are attractive because they do not require large amounts of capital (I don’t need a factory, and thousands of workers, just a team of 15 software developers and some laptop computers). They can try to build user groups who will pay for a service with some sort of monthly “subscription” (like Netflix). This model means influencing a LOT of people, and getting a small amount of money from each. The other approach is to focus your attention – get a few BIG users, who will spend a lot of money. This leads to the advertising business models where large corporations are the ultimate target customer (like Facebook) – and the consumer is given something for “free”.

Young Person: Facebook is mostly for older people like you. I spend most of my time in Snapchat or Instagram.

Tox : Those guys use the same type of business model – they provide an audience and information about that audience to advertisers, and the consumer gets something for “free”.

Young Person: What’s wrong with getting something for free. Why should I pay to get these things if I don’t have to?

Tox : When I was younger, there was a saying that my Dad had, and it still holds true today. There’s no such thing as a free lunch. You may not be paying for these services and apps with money, but you ARE paying for them.

Young Person: What’s are you talking about? I’m not paying anyone any money. If they give me ads I don’t have to buy any of the stuff getting advertised. So how am I “paying for it”?

Tox : You may not be paying for these services and apps with money, but you ARE paying for them. You are paying for them with a loss of privacy. People in older generations, and people from areas of the world with repressive governments, know what living in a “police state” or “surveillance state” means for people. That brings us back to politics again – and we said that we weren’t going to get into that.

Young Person: So I guess we pay for everything, just sometimes we pay with things other than money.

Tox : Now you’re getting it. So all of this new technology – AI, minaturization, 5G, and whatever else you can think of – has changed the perception of things in the world. It feels like we are able to do things for “free” – but the reality is that we are paying with other currency. Sometimes it is access to our private data. Sometimes it is access to our movements. Sometimes it is limits on what we can do.

I’m Having an Issue on IBM Cloud Part 1 – Why Can’t I Create Anything?

By: Daniel Toczala

Note: This is the first blog in a series of blogs that I am co-authoring with Paula Williams, as part of an “I have an Issue…” series on the IBM Cloud.  These blog posts will cover how to deal with common issues and roadblocks for users of the IBM Cloud. Check out her post on “What the Heck is a CSM?

I like helping my IBM Cloud customers, and I like dealing with the technology.  Every new technology (and even established technologies) have a learning curve.  My goal in writing this series of articles is to help you more quickly conquer that learning curve with respect to the IBM Cloud.

Typical Issues That People See

Some typical issues that users experience when working with the IBM Cloud will often be with respect to their services.  These could be one of the Watson services (like Watson Conversation or Watson Discovery), or maybe Cloud Object Storage, or a DB2 or Mongo database.  The issues that people will typically experience fall into one of two categories:

I’ve Created Something That I Cannot See

These situations have you going out and creating an instance of a service, but now you just can’t seem to figure out how to find it or how to get to it.  It’s almost as if we have decided to hide it on you.

In these situations, it is best to first figure out WHERE you are on the IBM Cloud.  Look in the upper right corner of your browser window.  As soon as you log into the IBM Cloud, you’ll see something like this:

Finding the area you are working in on the IBM Cloud

That text in that small black block tells you WHERE you are.  It tells you the account that you are operating in.  Clicking on this box you can CHANGE where you are looking.  Confused yet?  Maybe we should step back a bit….

You have an ACCOUNT and IDENTITY on the IBM Cloud.  Your IDENTITY from an IBM perspective is typically an email address, which is also your IBM ID (for our example let’s assume that mine is tox@acme.com).  It has the same username/password as your IBM ID (which you might use for things on ibm.com).  You might even have a Federated ID, where we use your company email address/identity and your company authentication mechanisms.  Some things to remember when looking at your IBM ID:

  • Just because you have an IBM ID, doesn’t mean that you have an IBM Cloud account.  You will need to register for an IBM Cloud account. While both the IBM Cloud and ibm.com both use your IBM ID, they are DIFFERENT domains.
  • When you sign up for your IBM ID, use a valid email address as your user ID.  You will need to VALIDATE your account by responding to an email that is sent to (you guessed it!) your user ID.  So in my case I cannot sign up for an IBM ID at tox_is_awesome@acme.com (unless I have convinced my corporate IT folks to give me that email address).

Now let’s get back to finding those cloud resources that we created.  Once you see what context you are in, you will have a better idea of what you can EXPECT to see.  Since I am an Acme Co. employee, I have been added as an approved user of the Acme corporate account.  What does this mean?  Well the Acme corporation created a different account, a corporate account, associated with a service account called IBM_Cloud_Admin@acme.com.  This account has a subscription which provides it with a set amount of “credits” for IBM Cloud services, which it burns down over time.  Since I am a member of this account, I can create IBM Cloud services in this corporate account, and their costs get assigned to the corporate account.  IBM Cloud services are billed based on where they are LOCATED (logically), and not based on who created them.

So now you hopefully have a better feel for how your account fits into the grand scheme of things, maybe you can find out WHERE that Watson service that you created is located, by looking at the various different contexts that you operate in.

Advanced Developer Note: Your IBM ID is based on an email ID.  So I have an IBM ID for tox@acme.com, but I also have one associated with my personal email address (tox_is_awesome@freebiemailcorp.com). I use my acme.com account for doing my regular work, and I use my personal email based ID to do open source work.  That account is a trial account (or maybe I even attach my personal credit card to it), and I am careful not to rack up big charges on the account.  I use it for doing simple little things in the cloud environment.

I Cannot Work With Something I Created

These situations are a little different.  You are able to create some service, but you are then unable to access it.  Either the service is broken, or down, or just not responding to your repeated attempts to use it.  Or maybe you can see a service but you just cannot create it.

So let’s go back to that tox@acme.com account.  First of all, you need to check and make sure that the account that you are attempting to create a service in is able to pay for that service.  If the account has a credit card associated with it, which guarantees payment for cloud services used, then the account is referred to as a “PAYGO” (short for pay-as-you-go) account.  People who use things like GitHub and other SaaS based services should be familiar with this model.  If the account has prepaid for services, via an IBM Cloud subscription, then it is referred to as a “SUBSCRIPTION” account.  Either “PAYGO” or “SUBSCRIPTION” accounts can create any type of service.  Your personal account might not have a guaranteed payment method, like my tox_is_awesome@freebiemailcorp.com account.  In that case you have a “TRIAL” account.  “TRIAL” accounts can create lite (or no charge) instances of most services on the IBM Cloud.  TRIAL accounts will not be able to create more robust versions of those services until they either become “PAYGO” or “SUBSCRIPTION” accounts.

So let’s get back to our example.  My tox_is_awesome@freebiemailcorp.com account is a “TRIAL” account (I’m not paying for anything!), but since I am an approved user of the IBM_Cloud_Admin@acme.com account (which is a “SUBSCRIPTION” account), I can create non-lite service instances within THAT account.  There is one hitch…. I have to have PERMISSIONS granted to me to be able to see particular logical areas of the Acme IBM_Cloud_Admin account.

What Are These “Logical” Areas?

There are two different types of logical areas where IBM Cloud resources can be created.  Each is based on a different security model. 

The Cloud Foundry security model uses the concept of Organizations (called “ORGs”) and Spaces.  These “Orgs” and “Spaces” live in a hierarchal model, with a single Org hosting one or more Spaces.  The administrator/owner of an IBM Cloud account will create these Orgs and Spaces, and will assign people various roles in each org/space.  These roles determine what a user can do within that particular logical environment.  You need to make sure that your account has access to the orgs and spaces that you need to work in.

The IBM Access Management (IAM) security model is based on Resource Groups.  Each resource group may have a series of Access Groups associated with it, and these Access Groups can be used to provide fine-grained access controls and role management.  You need to make sure that your account is enabled to do what is needed for the resource groups that you need to work in.

You can learn all about Orgs, Spaces, Resource Groups, Access Groups and best practices for organizing your IBM Cloud account by reading my blog post entitled, Getting Started Right on the IBM Cloud.