Mindblown: a blog about philosophy.

  • Get Certified in Conversational UX & AI

    Full-Day Workshops: Nov 16 & 17

    Have you happened to see our Certified Workshop Agenda?

    It’s phenomenal… the best we have ever had.

    And for having trained over 1,000 people in this industry, that is saying a lot.

    Here is what’s in store for you…

    On Day 1, you Learn It!

    It all starts at the Chatbot Conference on Nov 15, where you discover what to build during the conference day.

    On Day 2, you Design it!

    On Nov 16, we start the day with a Theory on Conversational Design from CDI!

    During the morning session, you will learn about the Happy Flow, Fallbacks, Personality, and how to design a conversational flow. In our afternoon sessions, you’ll design these flows and using Voiceflow and at the end of the day, export your project to Dialogflow.

    >>See Full Agenda

    On Day 3, you Build it!

    You will take what you exported, and the Botcopy team will help you create an entire, data-based framework for your bot in Dialogflow.

    Until now, enterprises have spent too much time and resources building conversions by assuming to know what customers wanted. Only to discover that customers ask questions differently than assumed or different questions altogether, leading to bot failure!

    In this workshop, you’ll discover how to avoid this situation and build according to users’ needs!

    This is an entirely new way to build, and we’re very excited that the industry is moving in this direction.

    Check out the full agenda below: See Full Agenda



    Get Certified in Conversational UX & AI was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • Kicking Bot Booty: 5 Ways to Keep the Big Bad Bots Off Your Website

    In yesterday’s The Daily(ish) Advocate email newsletter, “The Internet’s Gone Bot Sh!t Crazy,” we wrote about how bots are running rampant online.

    They eat up budgets in your ad campaigns and affect the quality of traffic on your website.

    To make matters worse, they’re super hard to avoid, detect and fight.

    But keep your chin up, Sparky! It’s not all doom and gloom.

    While you’ll never eliminate them completely, there are things you can do to reduce the number of bots running wild on your site and scarfing up your ad budget like they’re Joey Chestnut at an all-you-can-eat buffet.

    Here are 5 key bot-busting strategies we’ve discovered as we’ve waged a war against the bots attacking our sites and campaigns…

    1. Avoid 3rd Party Search Networks

    If you’re running ads directly on Google, Bing or Yahoo, the number of bot clicks you’ll get is relatively low.

    It’s when you start expanding campaigns to 3rd party search networks that the bots really come out to party.

    For Microsoft Ads, that’s their Search Partner Network. For Google, it’s their Search Partner and Display Networks.

    For platforms like Twitter, Fakebook, Outbrain and Taboola, unfortunately, the bots are all over the place.

    In any case, if you can run your ads only on the main search sites, you’ll have fewer bots to bust.

    2. Bot Fighting Software (maybe)

    We tested a few services, ClickCease being the most popular, that claim to help block the fake impressions and clicks you’ll get from bots (and unscrupulous competitors) in your ad campaigns.

    Unfortunately, we were very underwhelmed by the results.

    To be fair, we only used them on traffic from Microsoft Ads. Maybe they do a better job with Google Ads traffic. But, based on what we saw, we’re skeptical.

    What we found most interesting (and frustrating) were the results when we compared two of these services side-by-side.

    This was a true apples-to-apples test where both services were running on the same sites at the same time.

    While they both showed they flagged a lot of bot traffic, there was almost NO overlap between the traffic each service flagged as fake.

    If these services were genuinely effective, you’d think there’d be at least SOME agreement between the two over what traffic was fake and what was legit.

    Those results, and some other things we noticed in the data, made us decide not to keep using either service we tested, so we can’t recommend them.

    However, your mileage may vary.

    3. Cloudflare

    So far, the most effective way we’ve found to squash the bots is the Super Bot Fight Mode service from Cloudflare. (They also offer Cloudflare Bot Management for enterprises.)

    But the service has a key shortcoming: it does a good job of squashing bots only AFTER they land on your site.

    That means you’ll still be paying for clicks from bots if you’re running paid ad campaigns.

    Which sucks.

    But Cloudflare definitely helps if traffic quality is vital to your site. For example, it can help fight all sorts of nastiness (i.e., slowing down sites, fraud, credential stuffing, inventory hoarding) bots can unleash on an e-commerce site.

    We love and use Cloudflare on all our websites for all sorts of reasons (which we’ll cover at some point in the future). But, for now, check out their Bot Buster Fight Mode service if you’re struggling with bot traffic on your website.

    4. Machine Learning

    This is a situation where an ad network’s machine learning algorithms may be your best ally.

    If you can ID good quality traffic and the traffic most likely to (legitimately) convert, then the machine learning should, over time, favor the legit traffic and show your ads to bots less and less.

    Doesn’t always work, but hey, a guy can dream.

    5. Building Exclusion Audiences

    This may not always work, but it can be wicked awesome when it does.

    If you can ID specific actions bots take on your site, or certain pages they visit, you can create an audience based on those actions/page visits.

    For example: On one of our sites, we noticed bots “clicking” on a link to, and visiting, the Privacy Policy page of the site.

    We created an audience of that traffic and added it as an Exclusion audience to our Microsoft Ads campaign (basically telling Microsoft not to show our ads to that audience). Doing that has helped cut down on the bot traffic that’d been slamming our site from that campaign.

    Unfortunately, there’s no perfect solution when it comes to bots. Even if you use all the strategies above, you’ll still get plenty of these little buggers on your site.

    But you at least now have some good options to use to go out there and kick some bot booty!

    Enjoy this article? I save my best stuff for email subscribers to The Daily(ish) Advocate, an educational and entertaining free newsletter that gets sent out, well, daily-ish. If you’d like to give a spin, subscribe here (this link will take you off Medium).



    Kicking Bot Booty: 5 Ways to Keep the Big Bad Bots Off Your Website was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • Engaging Your Customer with a Multilingual Chatbot

    Customer service is a must-have for any business today. With the global reach of many companies, there is a real need to engage with customers at any time, in a convenient way for your customers. Chatbots provide the ability to enable that customer support in a 24/7 model, giving your customers the ability to engage when they have a need to. But now there is another dimension to consider — language.

    Introducing a bot that can support and speak multiple languages provides immense value to any organization, both in terms of customer support as well as in operational savings. Below, we talk about why it’s important, the different approaches to implementing a multilingual chatbot, and how Master of Code, as a company who provide Conversational AI solutions, has implemented a solution using the Microsoft Azure stack of services.

    The Value of a Multilingual Chatbot

    First, let’s consider the customer service aspect. The value of a chatbot that can provide multilingual supports puts all of your customers on an even playing field. No one customer should be greater than the next; they are all customers, and how they engage with your brand is important. An omnichannel approach to customer design and engagement has long been a growth strategy in the customer experience space, and with contact center and Omnichannel Conversational AI chatbot providing the ability to bring those channels into a single support interface, that horizontal growth of channel adoption is well underway.

    Statistics of Multilingual support for Customer Experience

    The next step is to extend the reach of support over those channels to as many users as possible, and that’s where a multilingual chatbot can come into play. With these conversational systems already in place and with the ability to analyze and understand customer interactions, it’s relatively easy to review and understand which languages your customer base is attempting to engage in. This provides you some unique insight into your own current operational plans in which you can extend your bot automation services to allow for the adoption of additional languages, providing a more valuable chatbot customer experience. And, if you can ensure that the main use cases for those other languages can be transactionally contained within the chatbot, then some burden gets lifted for your live support agents.

    Importance of Multilingual Support for Enterprises

    Multilingual Chatbot vs. Multi-Language Live Agents for Call Centers

    Those agents make up the second consideration when considering a multilingual best chatbot solution. Today, when a bot is unable to answer a question or concern that a user is having, what happens? The conversation gets escalated to a live agent to work with the user. But there is nothing that says the live agent can engage with the customer’s language either, and with the escalation already possibly frustrating the customer, reaching someone who still cannot assist due to a language barrier will make the customer more frustrated and not look as positively on the brand.

    Also read: Call Center Automation using AI-Powered Chatbot.

    Although it is possible to staff up individuals who can speak all of your customer languages, the impact to an organization is hard, and can also be quite costly. Consider the steps in bringing someone onboard to support a brand in a call center: you have to find and recruit the individual; coordinate a start time, usually with a larger group to optimize costs; perform the training, which on average can take from 6–12 weeks; not to mention procure equipment and enable setup and support for the agent. Even if we look at the lower end of the timing, say 1 week for recruitment and 6 weeks for training, that’s 7 weeks for effort and cost per agent. And in an industry that in recent years has seen attrition rates of 34%, the whole process is quite costly. And many organizations provide a premium to those agents who can provide multilingual support, which also increases cost and can decrease customer satisfaction should those agents leave the organization.

    Beyond Voice: How AI-driven voice technology can take your call center CX to the next level

    Many of the challenges that result in customer support agents leaving is overwork and frustration. But this is where a chatbot solution can come in and provide value, containing many of the repetitive and simple-to-answer queries for a customer before it hits the agent, reducing their volume and their own frustration and burnout. And in an industry that has high turnover due to burnout and with 53% of support teams experiencing an increase in demand for support, minimizing turnover and reducing that onboarding cost can help businesses focus on the issues that are most important to customers.

    Implementing Multiple Bots for Multilingual Support

    Historically, the most common method of creating chatbots that can support multiple languages is to create a single bot per language. By implementing this approach, a system needs to first understand the language in which the user is attempting to engage and then route them to the appropriate language-specific bot. That language bot then picks up the engagement and begins to engage with the customer.

    Multiple Bots for Multilingual Customer Support

    The advantage to this model is that each language bot can be developed in parallel because they are disconnected from one another. Parallelism can mean faster to market to support more customers, which is great, but there are some challenges to consider when approaching the solution in this manner:

    • The use cases may be different for each language bot. This can lead to an apples-to-oranges end result, with different bots providing different information. In other words, the customer in one language does not get the same support as in other languages, which can also lead to additional frustration from a live agent escalation in terms of how they need to handle individual customers.
    • The conversation design and customer journey map are disconnected from one another. This may make sense from a geographical standpoint, when different services are available in certain areas, but not from a multilingual support solution for a single geolocation.
    • Support costs can escalate quickly. In the above example, there are 3 or 4 bots to manage and support — one for each language and then possibly the orchestration bot which allows the user to select the language. When changes are made, multiple standalone bots need to be updated, trained, tested, and supported. This can require more manual testing in each language, resulting in significant effort to effect a small change.
    • You are limited to a single language at a time. There is no supportability for switching languages and maintaining context and positioning in the flow. Essentially, with a language change, the user is starting over again. This makes sense if not every flow is supported across all languages, but definitely not an optimal experience for the end user.

    Now, none of those challenges are necessarily wrong. They are a valid approach in which multilingual support can be provided, but being aware of some of the challenges provides some new appreciation for the complexity of this automation.
    But with that said, there is an alternative approach.

    Featured resources: Free guide to Conversation Design and How to Approach It.

    Implementing a Single Multilingual Chatbot

    An alternative method for implementing a single chatbot that supports multiple languages is to leverage the ever-expanding cloud-based cognitive services to provide this language expansion. In this instance, there is a single Natural Language Understanding (NLU) service implemented in a default language for the bot. The use cases for this language are laid out, the persona and journey map exercise is developed, and the core of the chatbot is built.

    Check out our Case Study where a chatbot provides 3x higher conversion rate than a website alone.

    Now, within the multilingual chatbot itself, a language detector component is implemented. This detector will then work with other services to translate the request and then apply it to the NLU to understand the intent of the question, which exists in the default language. Once matched, the appropriate response will be identified and formulated, in the language that the user engaged within, and then returned to the customer in their selected language.

    Multilingual chatbot workflow for customer support

    That is a rather simplified explanation of the service, but let’s apply this approach to the challenges listed above:

    • Different chatbot use cases. By approaching the solution this way, we mitigate this by having only a single multilingual bot. All use cases are available to every language that is available within the chatbot, but we can now ensure that the experience for a user in any of the available languages has parity with the other supported languages. This also means that the same activities before agent escalation are identical, so agents should have more confidence in what has happened before they engage with the customer.
    • The journey maps are aligned because there is only one multilingual bot and so the experience is fully aligned in terms of what is being serviced and provided.
    • Support costs exist, but now there is only a single multilingual chatbot to maintain. Additional costs may exist to leverage some of the other cognitive services as the underlying architecture has become more complex, but a DevOps team now has fewer bots to monitor and support in production, but now instead of doing individual testing in languages and deploying the chatbot independently, time can be optimized to deploy this solution in one fell swoop.
    • Because the language is translated and understood as it happens, should the user change languages mid-stream then the flow continues unabated. The chatbot will then change its own responses to mirror that of the user, so long as that language is enabled and made available within the bot. This level of adaptability provides an enhanced experience to the end user and creates some additional value to the chatbot, including providing interesting data points of language switching mid-conversation, if that is of importance to the business.

    Challenges of a Multilingual Chatbot Implementation for Enterprises

    Even with these challenges mitigated somewhat, there is still some additional work in maintaining the multilingual chatbot. For each language implemented, the responses need to be crafted and formulated to be correct for the languages that make up the chatbot. Although it is reasonable to use NLU and automatic translation to understand the intent of the user, it is not necessarily the same as to how you create the response. Language is very nuanced and when you are talking about value, you need the responses to be aligned with the persona of both the chatbot and the brand. So the language within the responses need that additional support. (You would be doing this in the previous example as well, but it would be done in the confines of its own language bot.)

    Download the ultimate Guide to Conversational AI in Finance

    Because of this need to still manually craft the responses, the organization needs to decide on which languages they wish to perform multilingual support. Not every language can be easily detected, so there can be some technical limitations, which may result in a hybrid of the above 2 solutions being implemented. But when the language is supported, then adding an additional language to the portfolio is more a matter of intent mapping, utterance development, response message, crafting, and then training the bot to understand the updated language. The intent with this architecture is to not recreate the wheel with each subsequent language, but rather expand its language support as needed.

    Check out Case Study with the first-ever bilingual conversational AI game as a Messenger Chatbot with 94% player retention rate.

    How Master of Code Global Developed this Multilingual Chatbot

    There are many cloud service providers in the market today offering numerous cognitive services that can be creatively combined to provide a service such as this. The solution we explore above we have put together with a focus on using the various services from the Microsoft Cognitive Services suite.

    The Microsoft suite provides all of the necessary tools and services to make either of these solutions happen. At the core of both methods in the Microsoft stack is Microsoft Conversational Understanding, which is a next step beyond LUIS, the previous iteration of NLU provided by Microsoft. Both of the products work with the Microsoft Bot Framework successfully, and have APIs for use by other systems, but the future of NLU at Microsoft resides with this next step tool which allows for many of the services outlined above, including the language orchestration activities.

    Learn How to provide Personalised Shopping Assistance within Conversational AI solutions.

    The activities for Conversational Understanding are supported with the Azure Language Detection service, another one of the cognitive services provided in the Microsoft Cognitive Services suite. This service, which is continually advanced by Microsoft, allows for the growth of language quality and support, creating new opportunities over time for additional services to be bundled on top of this solution.

    With all that, there are also services from other cloud providers that can provide similar services. For example, AWS has Amazon Comprehend, which can also perform detection on content to reliably understand the language that the inbound request is in. So although above we talk about how Master of Code has developed Conversational AI solution and an approach using Microsoft Azure services, there are similar options available from the other major cloud cognitive service providers that can be leveraged based upon the underlying architecture that an organization chooses to utilize.

    How Do I Choose The Multilingual Chatbot Solution That Best for My Business?

    That depends on your customer base. There is no one right answer as to how to approach developing and implementing a multilingual chatbot. Each organization needs to consider its customers, the volume of queries they have in some of the additional languages, and the value of those languages to maintain and support their brand. If the brand’s website is available in a certain language, then customers will expect support in that language as well, and so those should be the minimum number of languages supported. It also is based upon the technology stack that your organization utilizes, leveraging what you already have available without necessarily needing to go through a new onboarding and procurement process.

    If you want to explore which options are best for you and your customer engagement strategy, reach out to our team of specialists at Master of Code who will help you to map out a path to customer engagement success with chatbots and business process automation to support your existing contact center activities.

    Explore the ways on how to improve your customer engagement within Conversational AI.

    Let’s Connect!


    Engaging Your Customer with a Multilingual Chatbot was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • I Wrote a Wordscapes Bot in Python, And Became a Screenshot Hoarder

    Hey, bots should be allowed to have leisure time too!

    Photo by Daniel Klein on Unsplash

    I wrote an article explaining how I built a simple anagram solver for Wordscapes. But then I got stupidly obsessed with how I could take my cheating to the next level. I didn’t want to swipe anymore! But what could I use to swipe the screen for my lazy behind?

    Enter pyautogui

    It’s a Python library that lets you, among other things, screenshot and click around a screen on different platforms. There are faster platform specific options but for my needs this was more than sufficient.

    If a pictures worth a thousand words, then a gif is worth 1000s of words yeah?

    Yes I have a tab open for how to screen record. OBS ftw!

    How does it work? Let me break it down for you. (All the code is here.)

    A Dumb Solver

    The very first version of this used a set of points that were fixed on the screen and permuted through them. The code is:

    from itertools import permutations
    import pyautogui as pg
    from typing import List, Tuple
    from pyscreeze import Point
    LEFT_CHAR_POS = Point(360, 820)
    TOP_CHAR_POS = Point(480, 695)
    RIGHT_CHAR_POS = Point(605, 814)
    BOTTOM_CHAR_POS = Point(483, 945)
    def _gen_permutation(n: int):
    return _gen_permutation_for_list(
    def _gen_permutation_for_list(positions: List[Point[int]], n: int):
    return permutations(positions, n)
    def _move_through_permutation(permutation: List[Tuple[str]]):
    for i, pos in enumerate(permutation):
    duration = 0.001
    pg.moveTo(x=pos[0], y=pos[1], duration=duration)
    if i == 0:
    def position_based_permute_solver(positions: List[Point[int]]):
    for i in range(3, len(positions)+1):
    for permutation in _gen_permutation_for_list(positions, i):
    def four_char_permute_solver():
    for permutation in _gen_permutation(3):
    for permutation in _gen_permutation(4):

    This would just swipe all the permutations for 3 and 4 positions, hard coded to the exact pixel placement I got by running pyautogui.mouseInfo() and seeing what the number was for my cursor. While fun and pretty stupid, this approach fell apart as soon as a fifth and sixth character were introduced. Instead of just adding more pixel positions and permutations, I decided to try and be smarter.

    The Pitfalls of Trying to Be Smarter

    If you want to pick letters on the screen, you find that the first thing you need to do is have a screenshot of those letters to “find” them. This is a chicken and egg problem that is solved by me:

    1. Pausing the solver
    2. Screenshotting a picture of each letter
    3. Adding them to a folder and labelling each with the corresponding letter (e.g. a.png )
    4. Restarting the solver and seeing if it’s smart enough to find the letter
    The price of being smart is knowing what the letter “C” looks like

    Pyautogui wraps a handful of other image libraries like opencv and Pillow , but basically I just played around with how I could set the confidence when matching, and found that for certain letters like O and Q I needed to up the similarity. I then needed to deduplicate all the matches found right around the letter, since lowering the confidence meant that you got a lot of clustered duplicates. Also I kept finding I’s inside H’s, so annoying.

    Being smart is annoying!

    Anyway having played around with that a ton, I also realized I had to highlight the letters to start (that’s the initial sweep in the gif above). I just hard coded some relative pixel positions to the back arrow at the top (more screenshots!)

    Putting it all together

    Eventually my solver got pretty fancy, with multiple letters to match in case the background color changed, and I incorporated the anagram solver from the other video to give me my “guesses.”

    The last bit of swiping that was kind of fancy was mapping those guesses to the identified letters and their positions. That looks like:

    from collections import defaultdict
    from typing import Dict, List
    import pyscreeze
    import pyautogui as pg
    def guess_to_movement(guess: str, letter_points: Dict[str, List[pyscreeze.Point]]) -> None:
    letter_indices = defaultdict(lambda: 0)
    for i, letter in enumerate(guess):
    duration = 0.001
    point_index = letter_indices[letter]
    pos = letter_points[letter][point_index]
    letter_indices[letter] += 1
    pg.moveTo(x=pos[0], y=pos[1], duration=duration)
    if i == 0:

    The data we’re dealing with looks like {“e”: [Point(1,2), Point(3,4)], “b”: [Point(5,6)]}

    So if we had a guess like Bee we’d

    1. go to the point corresponding to B
    2. roll B’s index forward in case there was another B again
    3. put the mouse down since it’s the first index
    4. (Looping around) now go to the first E point, and incrementing the index corresponding to E
    5. (Looping around) now go to the second E point, and increment the index, though we won’t need it
    6. Pick up the mouse

    And there you have it, something that can find letters on a page, translate those letters to guesses, and translate those guesses back to swiping.

    After doing this for a while it can be satisfying to watch, but mostly I just became a screenshot hoarder. Still haven’t found my letter Z yet 🙁

    Thanks for reading! Feel free to try out the code if you want to sit back and watch the swiping.


    I Wrote a Wordscapes Bot in Python, And Became a Screenshot Hoarder was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • AI Art -Creative Technologies for the Future

    From Leonardo da Vinci, to Michelangelo, Pablo Picasso and now, codes, and algorithms, Artworks have indeed come a far way, taking on different shapes and permeating varied spaces.

    The new phenomenon of artwork is known as AI Art which is the creation of art using artificial intelligence. Through text-to-image software, AI can now build visual pieces using language prompts and previous data sets, rather than photographing or drawing an image.

    Key Benefits


    Most people are aware of the immense fortune that can be made by selling non-fungible tokens (NFTs), with art created by humans being a popular commodity. Today, with the advancements in AI, NFT generative art is a rapidly growing market with NFT/crypto and art enthusiasts buying and selling AI-generative art pieces. For example, Art Blocks, a generative-art platform is well recognized as the leading space for esteemed artworks and frequent trading. As of September 2022, Arts Blocks is worth over 800 million U.S. dollars.

    The Bot Libre metaverse platform enables influencers, gamers, and businesses to engage these NFT spaces by providing an extensive API, integrations and SDKs for popular 3D platforms.

    Simplifies Tasks

    In the same way Bot Libre chatbots can allow businesses to connect with and serve their customers better, an AI art generator allows persons to create beautiful art for a fraction of the time, and cost. For example, book covers, illustrations, presentations can be quickly produced by entering the relevant prompts in the generator.

    Limitless Designs

    If you can think of it, you can create it. You simply input the relevant text and the AI generator will get to work, and lest you think the results are mediocre, these art pieces are mimicking notable artwork and copping first prizes in competitions all over the world. AI generators also allow for the merging of simulation and reality , creating unique art experiences.

    Be the Movement

    With AI, the possibilities are endless. If you are an AI, art and metaverse enthusiast, looking to build, participate and grow wealth from all the offerings of web3.0, then join the Bot Libre Beta Program. As a member, you will get early access to unique metaverse solutions and become early adopters of metaverse technologies. For more information, contact sales@botlibre.com.

    Learned something? Please give us a

    to say thanks and to help others find this article


    AI Art -Creative Technologies for the Future was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • Metaverse Only Ticket Sale

    Right now, we have a crazy special for the Chatbot Conference.

    We have ten tickets for you to Name your Own Price and attend the Morning Sessions of the Chatbot Conference on Nov 15th!

    Metaverse Only Ticket Sale was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • A Chatbot that Introduces you to Friends in the Metaverse

    Do you want to see a Chatbot in the Metaverse that introduces people to each other based on their personality?

    We created Alice, a chatbot for Chatbot Conference on Nov 15th, where the bot introduces attendees to each other based on their compatibility!

    You can play with the Bot here: https://play.decentraland.org/?position=5%2C118&

    Join us at the Chatbot Conference

    A Chatbot that Introduces you to Friends in the Metaverse was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • Step-by-step Guide to Building Slack Chatbot for Internal IT Support [No code]

    Why use Slack for internal IT Support

    According to Enlyft, there are around 135,711 companies that currently use slack as collaborative software.

    Chances are, your business might be one of those 135,711 companies. Though using slack as a means of communication between members of the same team or different teams in a company leads to productive work culture, companies can leverage the instant messaging platform even more. By turning slack into your business’ employee support hub.

    Here’s why.

    1. Helps you get more out of your existing software

    Be it Jira Cloud for getting notifications about issues as and when they get mentioned, or HubSpot for your sales and marketing efforts, or Freshservice for managing your IT services, you can add the slack version of the apps your business already uses in the channel of your choice from the slack app directory.

    This makes it easier for your employees to use slack’s version of IT service management apps like Freshservice for

    • 1. Converting slack direct message (DM) conversations to tickets in Freshservice
    • 2. Adding slack conversation as ticket description for context
    • 3. Getting in touch with the Subject Matter Expert (SME) of an IT issue right away via slack DMs.

    And that’s just the tip of the iceberg.

    2. It’s super-flexible

    With slack, you can create separate channels for different teams or, you can create a shared channel for your organization and a third-party vendor to collaborate. Channels are a great way of letting employees stay in the loop of things. Be it support agents getting notified in slack about tickets raised via DM or a channel, or an agent reacting to an issue raised by a requestor in a channel to let them know about its status, slack helps in streamlining IT processes.

    Moreover, your company’s help desk agents will be able to guide an employee with their issue via slack’s audio and video calls feature on-the-go. Pretty Neat, huh?

    3. Prevents Silos among Teams

    According to this infographic by the Queens University of Charlotte, 49% of Millennials (who are soon to comprise the majority of the workforce) support social tools for workplace collaboration.

    In fact, about 40% of Millennials would even pay out of pocket for social collaboration tools to improve productivity. That’s how much they believe in the dream team of collaboration and technology.

    Slack, through its plethora of real-time collaboration and document authoring tools like Google Drive, Dropbox or Microsoft OneDrive makes the dream team of collaboration and technology all the more, real. Through pinned messages and files, new joiners or people already in your team’s Slack channel will be able to review things such as:

    • Product spec
    • Tech spec
    • Designs

    This way, lots of onboarding meetings and a slew of forwarded email threads to root through for new employees can be avoided.

    Team members will also be able to scan through all previous conversations, decisions and the people involved in the channel. This kind of transparency ultimately leads to the reduction of help desk tickets raised by employees. Now that’s more like it.

    4. Easier for triaging issues

    Let’s say you have a slack channel called #support for employees to report their IT issues. Whenever an employee reports their issue in the #support channel, the employee or the help desk manager can call the attention of another member who might be an SME for that issue by including an @mention. The person mentioned will then be notified immediately. As seen in the above example, the @mention comes in handy when the help desk manager wants to triage issues mentioned in the channel to the corresponding SME. The SME can then update the status of the issue in the channel itself with the help of emojis that’s easier to track for the employee who reported the issue as well as for the help desk manager.

    But with limitations.

    There are lot of benefits of using slack in your company. And although it is beneficial for companies to use slack, it comes with several limitations especially if you want to automate user interactions and workflows for IT helpdesk or workplace support. Thankfully, you can overcome these limitations by having a slack chatbot that can do much more than just notifications or converting DM to tickets.

    What is Slack Chatbot

    Slack is a modern workplace communication or chat tool that helps users connect and collaborate quickly. It also serves as a platform for users to interact with 3rd party apps, which makes it easy for all teams to get work done quickly.

    With slack being one of the most widely used communication hub, having a chatbot on slack makes it easy for companies to automate user interactions and workflows that help offer several benefits. Companies are starting to adopt bots on slack that can help automate IT Helpdesk and HR support but also see increased adoption in other parts of the business such as finance, and facilities.

    Today, several conversational ai no-code platforms offer quick and efficient way for companies to build bots (for different use cases) and deploy them on slack thus making a slack chatbot more appealing as it does not require human intervention and can autonomously support users. And it doesn’t stop there, you can also automate app workflows from your slack chatbot using no-code platforms. And what this means is, slack Chabot can automate both conversations and interact with apps to automate workflows as well that makes it very attractive to deliver customer and employee service automation.

    How to build slack bot using a no-code platform.


    Think of workativ assistant like an icing on the cake to all that you can already do with slack at workplace. With workativ assistant, you can build a conversational bot that sounds humane and solves your employees’ issues with the power of both NLP and workflow automation in the snap of a finger.

    Yeah, you heard it right. But Thanos’ snap can only turn half of the universe’s population to dust, while a bot can turn most of not all of your employees’ IT woes into dust in no time. Let’s see what makes workativ assistant’s bot so worthy of wielding the infinity gauntlet more than Thanos.

    Here is a short video of how to use a no-code platform like workativ assistant to build your slack chatbot.

    Get — Free IT Slack Chatbot

    How slack chatbot can automate your IT Helpdesk

    Slack chatbot can be very useful for companies to automate IT Helpdesk operations. Having an effective slack bot as your first-line agent can really benefit companies to automate repetitive issues, decreasing mean time to respond, improving employee experience and reducing costs.

    Let’s look at the top 6 issues that slack bot can automate for IT Helpdesk.

    1. Unlocking Accounts


    Your account has been locked unexpectedly, and you want to get it unlocked quickly! This is one of the most frequent queries that hit the help desk. It is repetitive and has a defined process. A perfect candidate for your slack bot.


    In this case, the user types in something like Unlock My Account or select Unlock Account from a list of options the Slack chatbot provides. The bot understands the query, asks for user details to verify, and unlocks the account in seconds.

    2. Resetting Passwords


    Password Reset is the most common request in the IT helpdesk. An agent may take longer than a chatbot to resolve such a trivial issue. With the self-service slack chatbot, you can go through the transparent resolution process yourself and get your password reset in seconds.


    The user has to simply type in Reset Password into their Slack window. The Bot understands and asks the user for some details like email address for verification. Then, it either sends a password reset link to the user’s email address or asks the user to key-in the new password and resets it.

    3. User Provisioning


    When a new user joins a company or moves to a different department, your IT and HR teams works with the process and systems to create new year, assigns email, adds role and user access privileges to various applications and so on depending on company policy. This takes time.


    Let the Slack chatbot do that for you in seconds!

    All you need to do is type in their query or request (say, create an account for Aaron Smith) in the Slack chat box. The bot looks for key words in the user’s input and responds with either more options to clarify the issue or asks for specific user details for verification. Once verified, it proceeds with the provisioning or deprovisioning process as requested.

    4. Access Requests


    Another candidate for automation is access requests when users change roles or needs access to app. The process and efforts are time consuming with traditional IT Helpdesk.


    Slack bot can handle this easily. A user types in the query (say I am not able to access the HR portal) and the chatbot asks for the email address for verification and verifies if the user has access before. Post validation, the bot either raises a request to the management for providing access to the user or allows access to the particular resource (here HR portal) itself. Takes only a few seconds.

    5. Asset Request


    Companies provide workplace assets for employees. And when the asset breaks or needs new or additional assets like laptop or keypad, employees raise new asset requests with IT Helpdesk. This is a defined process that can be automated fairly quickly.


    With slack bot, user types in the query (say My laptop is not working) and the bot asks for more detailed description of the issue. When the user elaborates the issue, the bot proceeds with a request to the management for issuing a new laptop, creates an IT ticket and shares it details with the user as well.

    6. Suspend a User


    Suspending users refers to revoking a user’s access to a particular service and removing them from the company’s email list. Businesses come across similar situations quite often. You wonder if that can be done in seconds?


    With slack bot, user needs to do is type “Suspend < user’s name >” and the bot, after validating and getting required approval, will either suspend the user itself or raise a raise a ticket for second level agent to suspend the user. `

    Top 5 reasons for you to use a no-code platform to build your slack chatbot.

    Because it’s easy. Here are some reasons on why you should choose a no-code platform to build you slack bot. We have used workativ assistant as an example.

    1. One-click slack integration

    Deploying a bot in your preferred slack workspace’s channel is as easy as pie. Just click on the add to slack button, grant the permissions required by workativ assistant to access your slack workspace, add the bot to the channel of your choice, and you’re good to go.

    2. Automates repetitive IT issues with workflows and not just conversations

    According to INRY, password resets constitute an astonishing 20% to 50% of help desk calls. And like you’d already know; password resets aren’t that much of an issue to call another person for a solution when we ourselves can solve it.

    By automating repetitive, mundane tasks like a password reset, unlocking an account, etc., the average cost-per-call to your internal help desk gets reduced drastically. And workativ assistant makes it possible using its workflow automation feature to integrate your bot with several IAM apps and create workflow or an automation in just a few clicks of a button for resetting a password. No coding knowledge required. Handy, right?

    3. Manage several simple or complex use-cases with chatbot builder

    Yes, you can create automations like a breeze using workativ assistant. But what use are those automations when you don’t deliver the solution properly?

    This is where workativ assistant’s intuitive chatbot builder comes into play. With chatbot builder’s myriad of options, you can create conversations for any number of scenarios with a human touch more so that people who chat with the bot can’t tell the difference between chatting with a human and a bot. Again, no coding knowledge required.

    4. App integrations and workflow marketplace

    You can connect your bot with your business’ apps and use pre-built app workflows for your chatbot

    Be it sending an OTP via Twilio, creating a ticket via Freshservice, or sending a message to a slack channel, workativ assistant can do this and more for you once you connect it to the required app.

    And you don’t even have to create Automations from scratch. Through the workativ assistant workflow marketplace, you can add ready-made automations for the required apps with just the click of a button.

    5. Trains itself to better understand your employees

    Workativ assistant’s training module enables the bot that you create to learn from its historical data of conversations with your employees and train itself to better detect an employees’ intention for initiating a conversation.

    Top 6 benefits of slack bot at workplace using workativ assistant.

    Now that you know why using slack as an internal help desk can help transform your workplace support, why not combine it with workativ assistant to let your employees get the best out of your internal IT support?

    Think about it. With slack’s superior features and workativ assistant’s conversational ai-powered automations, you can do so much more for your employees.

    1. Supercharge self-service for your employees

    By deploying a bot built using workativ assistant in the slack channel for reporting IT issues, your employees can solve their problems themselves from the comfort of the channel.

    2. 24×7 availability

    Help desk agents can’t be available 24×7 to help out employees with their issues. After all, they’re human too. They need ample rest just like the rest of us.

    To “help” your help desk agents, you can build a bot that offers IT support for repetitive tasks like a password reset, unlocking an account, etc., (as discussed earlier) in your workplace support slack channel as well as train itself to resolve problems based on solutions previously offered for similar problems by a help desk agent. This allows employees to solve their problems quickly as and when they arise without the hassles of long waiting queues.

    3. Call deflection

    Enabling employees to resolve their IT issues themselves via bot in your workplace support slack channel prevents the bottleneck of a single help desk agent handling huge number of calls. Providing self-serve to employees via bot also thwarts the need for help desk agents to provide in-person support.

    4. All-encompassing

    Adding the slack version of your business’ apps from the slack app directory every time can be a bit frustrating.

    With workativ assistant, you can connect your business’ apps with bot and automate tasks for those apps instead of adding them from the Slack App Directory. This makes it easier on your IT Admins as well as your employees. Be it creating a ticket in Zendesk or sending an email via Outlook, your employees can use the bot to take care of it.

    5. Productive and satisfied employees

    Enabling your employees to solve their IT issues themselves in a slack channel via bot leads them to solve their issues then and there itself and continue with their work.

    For employees, this eliminates the frustration that comes with the labyrinthine way of getting an IT issue resolved like sending multiple emails, redirects to different SMEs, etc., Employee self-service also let’s help desk agents focus on resolving the more pressing IT issues at hand.

    6. Cost savings

    With bot deployed in a slack channel, you can get rid of the overheads that come with repetitive tickets and truly see your help desk costs take a dive immediately. Building and using a slack bot with workativ starts at $299 per month flat. Compare that with hiring a live agent. The cost savings are 10x. More on pricing here.

    Steps to deploy your bot on slack for Internal IT Support

    Deploying your bot on slack is easy. It is just an easy set of steps to follow and you will be done with having a bot on slack in no time. Let us see some of the major steps involved in the process.

    Once you have built your bot, and ready to deploy on slack, you can login to your workativ assistant workspace.

    • Select Slack as the desired Chat Channel
    • Click Add to Slack to start a new integration
    • Sign in to your Slack workspace on the URL: https://api.slack.com
    • Create a new app for the Workativ Assistant chatbot and select the desired App Name (Bot name)
    • Customize your bot by selecting its Logo, Background Colour, and Bot Name
    • Update the required information in both Slack App page and Workativ’s Slack Integration page
    • Save the changes and click Add to Slack

    There you go! You have successfully deployed your chatbot on slack, and you can easily access it by signing into your slack workspace.


    Since you have reached the conclusion, you must be clear by now that Slack is one of the best enterprise-level communication tools for the interaction between the chatbot and the users. It is found that 70% of users prefer using slack for reporting their support issues. And while it’s great for people to interact, using Slack chatbot can really transform workplace support by automating both interactions and as well workflows.

    In a nutshell, it is evident that companies are ought to select slack as the suitable platform for deploying their chatbots. With the AI-powered no-code platforms like Workativ assistant, you can leap your slack bot journey. Bringing together Workativ Assistant, Slack, and other third-party tools makes your BOT no less than a SUPER-BOT. Power-packed and Smart!

    Can’t wait to transform your business’ slack channel into an IT help desk? Get in touch with us, and we’d gladly give you a demo.

    Disclaimer: This article was originally published here.


    Step-by-step Guide to Building Slack Chatbot for Internal IT Support [No code] was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • Flash Sale! Save up to $250 on Chatbot Conference Tickets.

    The Chatbot Conference is only 3 weeks away, and right now, we are having our last sale of the year!

    You can save up to $250 on tickets.

    And it gets better. We just launched our Points Program and a Community, so every dollar you spend on tickets earns you a point you can use towards future Conferences, our Certified Workshops, and Membership Packages.

    Register Today and see you on November 15th.



    Flash Sale! Save up to $250 on Chatbot Conference Tickets. was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • Types of Activation Functions in Deep Learning explained with Keras

    Activation does it means activating your car with a click ( if it has that ,of course) , well the same concept but in terms of neurons , neuron as in human brain ? , again close enough, neuron but in Artificial Neural Network (ANN).

    The activation function decides whether a neuron should be activated or not.

    A biological neuron in the human brain

    If you have seen an ANN, which I sincerely hope you do you have seen they are linear in nature, so to use non — linearity in them we use activation functions and generate output from input values fed into the network.

    A sample ANN network

    Activation functions can be divided into three types

    1. Linear Activation Function
    2. Binary Step Activation Function
    3. Non — linear Activation Functions

    Linear Activation Function

    It is proportional to the output values, it just adds the weighted total to the output. It ranges from (-∞ to ∞).

    Graph showing linear activation function

    Mathematically, the same can be written as

    Equation of Linear Activation

    Implementation of the same in Keras is shown below,


    Binary Step Activation Function

    It has a specific threshold value that determines whether a neuron should be activated or not.

    Binary Step Activation Function Graph

    Mathematically, this is the equation of the function

    Equation of Binary Step Activation Function

    Implementation of the same is not present in Keras so a custom function is made using TensorFlow as follows


    Non — Linear Activation Functions

    It allows ANN to adapt according to a variety of data and differentiate between the outputs. It allows the stacking of multiple layers since the output is a combination of input passed through multiple layers of the neural network.

    Various non — linear activation functions are discussed below

    Sigmoid Activation Function

    This function accepts the input (number) and returns a number between 0 and 1. It is mainly used in binary classification as the output ranges between 0 and 1 e.g. you train a dog and cat classifier , regardless of how furry that dog is it classifies it as a dog not cat , there is no between , sigmoid is perfect for it.

    Graph of Sigmoid function

    Mathematically, the equation looks like this

    Sigmoid Function Equation

    Implementation of the same in Keras is shown below,


    TanH Activation Function

    This activation function maps the value into the range [ -1 , 1 ]. The output is zero centered , it helps in mapping the negative input values into strongly negative and zero values to absolute zero.

    Comparison of tanh with sigmoid

    Mathematically, the equation looks like this

    Equation of tanh

    Implementation of the same in Keras is shown below


    ReLU ( Rectified Linear Unit)

    It is one of the most commonly used activation functions, it solved the problem of vanishing gradient as the maximum value of the function is one. The range of ReLU is [ 0 , ∞ ].

    Graph comparing Sigmoid and ReLU

    Mathematically, the equation looks like this

    Equation of ReLU

    Implementation of the same in Keras is shown below,


    Leaky ReLU

    Upgraded version of ReLU like Covid variants .. sensitive topic …ok fine .. getting back to Leaky ReLU , it is upgraded as it solves the dying ReLU problem , as it has small positive slope in negative area.

    Comparison of ReLU (left) and Leaky ReLU (right)

    Mathematically, the equation looks like this

    Implementation in Keras is coming right below


    SoftMax Activation Function

    Its a combination of lets guess .. is it tanh , hmm not quite , ReLU ? no or its leaky counterpart .. mhh not quite …. ok lets reveal it .. it is a combination of many sigmoid. It determines relative probability.

    In Multiclass classification , it is the most commonly used for the last layer of the classifier. It gives the probability of the current class with respect to others.

    Example of Softmax function

    Mathematically, the equation looks like this

    Equation of Softmax function

    Implementation in Keras is given below


    The whole notebook containing all codes used above

    Google Colaboratory

    If you wanna contact me , lets connect on LinkedIn link below


    Types of Activation Functions in Deep Learning explained with Keras was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Got any book recommendations?