Having a conversation with a computer might have seemed like science fiction even a few years ago. But now, most of us already use chatbots for a variety of tasks. For example, as end users, we ask the virtual assistant on our smartphones to find a local restaurant and provide directions. Or, we use an online banking chatbot for help with a loan application.
As VP of Coveo’s Platform line of business, Gauthier Robe oversees the company’s Intelligent Search Platform and roadmap, including Coveo Cloud, announced in June 2015. Gauthier is passionate about using technology to improve customers’ and people’s lives. He has over a decade of international experience in the high-tech industry and deep knowledge of Cloud Computing, electronics, IoT, and product management. Prior to Coveo, Gauthier worked for Amazon Web Services and held various positions in high-tech consulting firms, helping customers envision the future and achieve its potential. Gauthier resides in the Boston area and has an engineering degree from UCL & MIT. In his spare time, Gauthier enjoys tinkering with new technologies and connected devices.

This machine learning algorithm, known as neural networks, consists of different layers for analyzing and learning data. Inspired by the human brain, each layer is consists of its own artificial neurons that are interconnected and responsive to one another. Each connection is weighted by previous learning patterns or events and with each input of data, more "learning" takes place.
Reports of political interferences in recent elections, including the 2016 US and 2017 UK general elections,[3] have set the notion of botting being more prevalent because of the ethics that is challenged between the bot’s design and the bot’s designer. According to Emilio Ferrara, a computer scientist from the University of Southern California reporting on Communications of the ACM,[4] the lack of resources available to implement fact-checking and information verification results in the large volumes of false reports and claims made on these bots in social media platforms. In the case of Twitter, most of these bots are programmed with searching filter capabilities that target key words and phrases that reflect in favor and against political agendas and retweet them. While the attention of bots is programmed to spread unverified information throughout the social media platform,[5] it is a challenge that programmers face in the wake of a hostile political climate. Binary functions are designated to the programs and using an Application Program interface embedded in the social media website executes the functions tasked. The Bot Effect is what Ferrera reports as when the socialization of bots and human users creates a vulnerability to the leaking of personal information and polarizing influences outside the ethics of the bot’s code. According to Guillory Kramer in his study, he observes the behavior of emotionally volatile users and the impact the bots have on the users, altering the perception of reality.

Yes, witty banter is a plus. But, the ultimate mission of a bot is to provide a service people actually want to use. As long as you think of your bot as just another communication channel, your focus will be misguided. The best bots harness the micro-decisions consumers experience on a daily basis and see them as an opportunity to help. Whether it's adjusting a reservation, updating the shipping info for an order, or giving medical advice, bots provide a solution when people need it most.


Marketing teams are increasingly interested in leveraging branded chatbots, but most struggle to deliver business value. My recently published report, Case Study: Take A Focused And Disciplined Approach To Drive Chatbot Success, shows how OCBC Bank in Singapore is bucking the trend: The bank recently created Emma, a chatbot focused on home loan leads, which […]
Facebook has jumped fully on the conversational commerce bandwagon and is betting big that it can turn its popular Messenger app into a business messaging powerhouse. The company first integrated peer-to-peer payments into Messenger in 2015, and then launched a full chatbot API so businesses can create interactions for customers to occur within the Facebook Messenger app. You can order flowers from 1–800-Flowers, browse the latest fashion and make purchases from Spring, and order an Uber, all from within a Messenger chat.
Whilst the payout wasn't huge within the early days of Amazon, those who got in early are now seeing huge rewards, with 38% of shoppers starting their buying journey within Amazon (source), making it the number one retail search engine. Some studies are suggesting that Amazon is responsible for 80% of e-commerce growth for publicly traded web retailers (source).
Chatbots such as ELIZA and PARRY were early attempts at creating programs that could at least temporarily fool a real human being into thinking they were having a conversation with another person. PARRY's effectiveness was benchmarked in the early 1970s using a version of a Turing test; testers only made the correct identification of human vs. chatbot at a level consistent with making a random guess.
This kind of thinking has lead me to develop a bot where the focus is as a medium for content rather than a subsitute for intelligence. So users create content much as conventional author, (but with text stored in spreadsheets rather than anywhere else). Very little is expected from the bot in terms of human behavious such as “learning”, “empathy”, “memory” and character”. Does it work?
In a traditional application, the user interface (UI) consists of a series of screens, and a single app or website can use one or more screens as needed to exchange information with the user. Most applications start with a main screen where users initially land, and that screen provides navigation that leads to other screens for various functions like starting a new order, browsing products, or looking for help.
Lack contextual awareness. Not everyone has all of the data that Google has – but chatbots today lack the awareness that we expect them to have. We assume that chatbot technology will know our IP address, browsing history, previous purchases, but that is just not the case today. I would argue that many chatbots even lack basic connection to other data silos to improve their ability to answer questions.
A chatbot is an artificial intelligence (AI) program that simulates interactive human conversation by using key pre-calculated user phrases and auditory or text-based signals. Chatbots are frequently used for basic customer service and marketing systems that frequent social networking hubs and instant messaging (IM) clients. They are also often included in operating systems as intelligent virtual assistants.
Like most of the Applications, the Chatbot is also connected to the Database. The knowledge base or the database of information is used to feed the chatbot with the information needed to give a suitable response to the user. Data of user’s activities and whether or not your chatbot was able to match their questions, is captured in the data store. NLP translates human language into information with a combination of patterns and text that can be mapped in the real time to find applicable responses.
Some brands already seem to be getting the balance right. A bot needs to capture a user's attention quickly and display a healthy curiosity about their new acquaintance, but too much curiosity can easily push them into creepy territory and turn people off. They have to display more than a basic knowledge of human conversational patterns, but they can't claim to be an actual human -- again, let's keep things from getting too creepy here.
Despite the fact that ALICE relies on such an old codebase, the bot offers users a remarkably accurate conversational experience. Of course, no bot is perfect, especially one that’s old enough to legally drink in the U.S. if only it had a physical form. ALICE, like many contemporary bots, struggles with the nuances of some questions and returns a mixture of inadvertently postmodern answers and statements that suggest ALICE has greater self-awareness for which we might give the agent credit.

With the help of equation, word matches are found for given some sample sentences for each class. Classification score identifies the class with the highest term matches but it also has some limitations. The score signifies which intent is most likely to the sentence but does not guarantee it is the perfect match. Highest score only provides the relativity base.
What does the Echo have to do with conversational commerce? While the most common use of the device include playing music, making informational queries, and controlling home devices, Alexa (the device’s default addressable name) can also tap into Amazon’s full product catalog as well as your order history and intelligently carry out commands to buy stuff. You can re-order commonly ordered items, or even have Alexa walk you through some options in purchasing something you’ve never ordered before.
Feine, J., Morana, S., and Maedche, A. (2019). “Leveraging Machine-Executable Descriptive Knowledge in Design Science Research ‐ The Case of Designing Socially-Adaptive Chatbots”. In: Extending the Boundaries of Design Science Theory and Practice. Ed. by B. Tulu, S. Djamasbi, G. Leroy. Cham: Springer International Publishing, pp. 76–91. Download Publication
To envision the future of chatbots/virtual assistants, we need to take a quick trip down memory lane. Remember Clippy? Love him or hate him, he’s ingrained in our memory as the little assistant who couldn’t (sorry, Clippy.).  But someday, this paper clip could be the chosen one. Imagine with me if you will a support agent speaking with a customer over the phone, or even chat support. Clippy could be listening in, reviewing the questions the customer is posing, and proactively providing relevant content to the support agent. Instead of digging around from system to system, good ‘ole Clippy would have their back, saving them the trouble of hunting down relevant information needed for the task at hand.
The plugin aspect to Chatfuel is one of the real bonuses. You can link up to all sorts of different services to add richer content to the conversations that you're having. This includes linking up to Twitter, Instagram and YouTube, as well as being able to request that the user share their location, serve video and audio content, and build out custom attributes that can be used to segment users based on their inputs. This last part is a killer feature.

User message. Once authenticated, the user sends a message to the bot. The bot reads the message and routes it to a natural language understanding service such as LUIS. This step gets the intents (what the user wants to do) and entities (what things the user is interested in). The bot then builds a query that it passes to a service that serves information, such as Azure Search for document retrieval, QnA Maker for FAQs, or a custom knowledge base. The bot uses these results to construct a response. To give the best result for a given query, the bot might make several back-and-forth calls to these remote services.
A chatbot (sometimes referred to as a chatterbot) is programming that simulates the conversation or "chatter" of a human being through text or voice interactions. Chatbot virtual assistants are increasingly being used to handle simple, look-up tasks in both business-to-consumer (B2C) and business-to-business (B2B) environments. The addition of chatbot assistants not only reduces overhead costs by making better use of support staff time, it also allows companies to provide a level of customer service during hours when live agents aren't available.

Kik is one of the most popular chat apps among teens with 275M MAUs and 40% of those are in the 13–24 year old demographic. In April, Kik launched its own bot store with 16 launch partners including Sephora, H&M, Vine, the Weather Channel, and Funny or Die. Using Kik’s bots currently feel like using the internet in 1994, very rough around the edges and limited functionality / usefulness. However, we’ll see how their API and bots progress over time, Kik’s popularity among an attractive demographic might convince some brands to invest in the platform.
[In] artificial intelligence ... machines are made to behave in wondrous ways, often sufficient to dazzle even the most experienced observer. But once a particular program is unmasked, once its inner workings are explained ... its magic crumbles away; it stands revealed as a mere collection of procedures ... The observer says to himself "I could have written that". With that thought he moves the program in question from the shelf marked "intelligent", to that reserved for curios ... The object of this paper is to cause just such a re-evaluation of the program about to be "explained". Few programs ever needed it more.
Reduce costs: The potential to reduce costs is one of the clearest benefits of using a chatbot. A chatbot can provide a new first line of support, supplement support during peak periods or offer an additional support option. In all of these cases, employing a chatbot can help reduce the number of users who need to speak with a human. You can avoid scaling up your staff or offering human support around the clock.
This is where most applications of NLP struggle, and not just chatbots. Any system or application that relies upon a machine’s ability to parse human speech is likely to struggle with the complexities inherent in elements of speech such as metaphors and similes. Despite these considerable limitations, chatbots are becoming increasingly sophisticated, responsive, and more “natural.”
The idea was to permit Tay to “learn” about the nuances of human conversation by monitoring and interacting with real people online. Unfortunately, it didn’t take long for Tay to figure out that Twitter is a towering garbage-fire of awfulness, which resulted in the Twitter bot claiming that “Hitler did nothing wrong,” using a wide range of colorful expletives, and encouraging casual drug use. While some of Tay’s tweets were “original,” in that Tay composed them itself, many were actually the result of the bot’s “repeat back to me” function, meaning users could literally make the poor bot say whatever disgusting remarks they wanted. 

There are several defined conversational branches that the bots can take depending on what the user enters, but the primary goal of the app is to sell comic books and movie tickets. As a result, the conversations users can have with Star-Lord might feel a little forced. One aspect of the experience the app gets right, however, is the fact that the conversations users can have with the bot are interspersed with gorgeous, full-color artwork from Marvel’s comics. 

Chatbots are often used online and in messaging apps, but are also now included in many operating systems as intelligent virtual assistants, such as Siri for Apple products and Cortana for Windows. Dedicated chatbot appliances are also becoming increasingly common, such as Amazon's Alexa. These chatbots can perform a wide variety of functions based on user commands.
For example, say you want to purchase a pair of shoes online from Nordstrom. You would have to browse their site and look around until you find the pair you wanted. Then you would add the pair to your cart to go through the motions of checking out. But in the case Nordstrom had a conversational bot, you would simply tell the bot what you’re looking for and get an instant answer. You would be able to search within an interface that actually learns what you like, even when you can’t coherently articulate it. And in the not-so-distant future, we’ll even have similar experiences when we visit the retail stores.
There are obvious revenue opportunities around subscriptions, advertising and commerce. If bots are designed to save you time that you’d normally spend on mundane tasks or interactions, it’s possible they’ll seem valuable enough to justify a subscription fee. If bots start to replace some of the functions that you’d normally use a search engine like Google for, it’s easy to imagine some sort of advertising component. Or if bots help you shop, the bot-maker could arrange for a commission.

Forrester Launches New Survey On AI Adoption There’s no doubt that artificial intelligence (AI) is top of mind for executives. AI adoption started in earnest in 2016, and Forrester anticipates AI investments to continue to increase. Leaders are quickly waking up to AI’s disruptive characteristics and the need to embrace this emerging technology to remain […]


As VP of Coveo’s Platform line of business, Gauthier Robe oversees the company’s Intelligent Search Platform and roadmap, including Coveo Cloud, announced in June 2015. Gauthier is passionate about using technology to improve customers’ and people’s lives. He has over a decade of international experience in the high-tech industry and deep knowledge of Cloud Computing, electronics, IoT, and product management. Prior to Coveo, Gauthier worked for Amazon Web Services and held various positions in high-tech consulting firms, helping customers envision the future and achieve its potential. Gauthier resides in the Boston area and has an engineering degree from UCL & MIT. In his spare time, Gauthier enjoys tinkering with new technologies and connected devices.
Today, more than ever, instant availability and approachability matter. Which is why your presence should be dictated by your customer’s preference or the type of message your business wants to convey. Keep in mind that these can overlap or change depending on your demographic you wish to acquire or cater to. There are very few set-in-stone rules when it comes to new customers.
Last, but not least coming in with the bot platform for business is FlowXO, which creates bots for Messenger, Slack, SMS, Telegraph and the web. This platform allows for creating various flexibility in bots by giving you the option to create a fully automated bot, human, or a hybrid of both. ChatBot expert Murray Newlands commented that "Where 10 years ago every company needed a website and five  years ago every company needed an app, now every company needs to embrace messaging with AI and chatbots."
I've come across this challenge many times, which has made me very focused on adopting new channels that have potential at an early stage to reap the rewards. Just take video ads within Facebook as an example. We're currently at a point where video ads are reaching their peak; cost is still relatively low and engagement is high, but, like with most ad platforms, increased competition will drive up those prices and make it less and less viable for smaller companies (and larger ones) to invest in it.
Last, but not least coming in with the bot platform for business is FlowXO, which creates bots for Messenger, Slack, SMS, Telegraph and the web. This platform allows for creating various flexibility in bots by giving you the option to create a fully automated bot, human, or a hybrid of both. ChatBot expert Murray Newlands commented that "Where 10 years ago every company needed a website and five  years ago every company needed an app, now every company needs to embrace messaging with AI and chatbots."
You can structure these modules to flow in any way you like, ranging from free form to sequential. The Bot Framework SDK provides several libraries that allows you to construct any conversational flow your bot needs. For example, the prompts library allows you to ask users for input, the waterfall library allows you to define a sequence of question/answer pair, the dialog control library allows you to modularized your conversational flow logic, etc. All of these libraries are tied together through a dialogs object. Let's take a closer look at how modules are implemented as dialogs to design and manage conversation flows and see how that flow is similar to the traditional application flow.

When one dialog invokes another, the Bot Builder adds the new dialog to the top of the dialog stack. The dialog that is on top of the stack is in control of the conversation. Every new message sent by the user will be subject to processing by that dialog until it either closes or redirects to another dialog. When a dialog closes, it's removed from the stack, and the previous dialog in the stack assumes control of the conversation.
Alternatively, think about the times you are chatting with a colleague over Slack. The need to find relevant information typically happens during conversations, and instead of having to go to a browser to start searching, you could simply summon your friendly Slack chatbot and get it to do the work for you. Think of it as your own personal podcast producer – pulling up documents, facts, and data at the drop of a hat. This concept can be translated into the virtual assistants we use on the daily. Think about an ambient assistant like Alexa or Google Home that could just be part of a group conversation. Or your trusted assistant taking notes and actions during a meeting.
Another reason is that Facebook, which has 900 million Messenger users, is expected to get into bots. Many see this as a big potential opportunity; where Facebook goes, the rest of the industry often follows. Slack, which lends itself to bot-based services, has also grown dramatically to two million daily users, which bot makers and investors see as a potentially lucrative market.
In 1950, Alan Turing's famous article "Computing Machinery and Intelligence" was published,[7] which proposed what is now called the Turing test as a criterion of intelligence. This criterion depends on the ability of a computer program to impersonate a human in a real-time written conversation with a human judge, sufficiently well that the judge is unable to distinguish reliably—on the basis of the conversational content alone—between the program and a real human. The notoriety of Turing's proposed test stimulated great interest in Joseph Weizenbaum's program ELIZA, published in 1966, which seemed to be able to fool users into believing that they were conversing with a real human. However Weizenbaum himself did not claim that ELIZA was genuinely intelligent, and the introduction to his paper presented it more as a debunking exercise:
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