AI News, How to build a chatbot artificial intelligence

PR professionals vs. chatbots: Who will win?

The summer before I started the master’s program at Annenberg, my father, CEO of a tech startup, asked me, “Are you aware that technology will soon replace PR and marketing workers like yourself and other interns?” I

There’s so much strategic thinking behind PR and marketing that I’m not easily replaceable!” The chatting robot is a computer program designed to simulate a conversation with human users online.

The use of chatbots exploded in 2016 when Facebook launched a platform for developers to create bots, enabling organizations to more easily respond to questions from the public.

Sephora and eBay have integrated chatbots into their sites to help customers find products via chat and place their orders right after chatting.

With these benefits alone, chatbots are best deployed at the frontline to help organizations solve basic customer problems such as answering what are business’ hours, how to make a reservation and where to find the nearest location.

American Eagle offers a jeans quiz that suggests styles that fit the taste, the chatbot helps to direct shoppers back to the website to facilitate orders.

AI and Chatbots in Customer Service

Even though AI learns over time, it still requires some human oversight to make sure it learns in the right way.

By measuring the customer experience that customers receive, we can start peeking inside the black box and making tweaks to the process to ensure that every customer’s AI journey is appropriate for their needs.

By comparing how the customer’s rate their interactions with the chatbots to how they rate their experience with human agents, you can see if automating answers is impacting the happiness of your customers.

For example, if customers with billing questions are consistently unhappy with their experience being served by a chatbot, try removing the chatbot flow from the pricing page.

When deploying AI, it’s extremely important to approach it from the perspective of improving the quality of the customer experience, and not decreasing the cost of customer service.

But companies will see a bigger return on investment from the technology if they don’t only decrease the bottom line, but also increase customer loyalty and revenue.

How to Build Your Own Facebook Chatbot in About 10 Minutes

This time, I sought help from the telemedicine service covered by my health insurance plan, where I was able to call in and speak with a medical practitioner.

It was a great service, but I had to wait about two hours for the call back from the medical practitioner… and that is a long wait when you’re in pain.

It turns out a number of medical chatbot applications are available — you install one on your phone, and you can get self-help diagnostic information for your ailments.

Just one of many examples of these medical diagnostic chatbots is Babylon, a subscription service available in the UK that offers artificially intelligent chatbot-based consultations that result in a suggestion for a medical course of action.

The chatbot led the child through a conversation with the movie’s main character, Officer Judy Hopps, as they worked together to catch bad guys.

With their chatbots, they are taking their service even further: You can receive budget-driven recommendations, get updates on your already-planned trips, or evaluate the best time to travel to certain hotspots.

With 68 percent of online adults in the United States on Facebook, according to Pew Research, you have a strong probability that you can get your chatbot in front of your target demographic if you create a chatbot for Facebook.

Any product or service that has a high level of pre-purchase decision making, such as auto sales, real estate or enterprise software, can gain immense value from a chatbot.

Because your prospective customer is likely doing a fair amount of online research into which product to buy, it makes sense to build a chatbot that helps answer questions for them, in a smart and conversational way.

Somewhere in the conversation your chatbot can offer a downloadable guide — all the prospective customer needs to do is provide some contact information, and the guide will be sent to them.

Even though Facebook has been heavily pushing chatbots on it’s Facebook Messenger platform for almost three years now, adoption has been slow due to the relative complexity of building a chatbot.

To help you get a better idea of how you can leverage chatbots for lead generation, I’ve made a list of chatbots I’ve either built for clients or that I’ve dreamed up and intend to build.

Because I honestly feel that with time, all of these ideas will be on the market, and there is no reason to hold your cards close to your chest in this game… still plenty of lands to be claimed.

Examples of chatbots that can be used for lead generation: When I first looked into chatbots a few years ago, the chatbot-builder solutions required some coding knowledge, and you had to work directly with Facebook’s developer console to build a chatbot.

Now that you have a good understanding of what a chatbot is, how it can be used in lead generation, and the two leading platforms for building code-free chatbots, let’s jump into actually building one.

To build a feature-full chatbot with some immersive artificial intelligence that can carry on strong conversations, that will require some considerable time and effort on your part.

This time isn’t spent actually building the tool, but you will be spending considerable time writing content, forecasting what the most frequently asked questions are going to be, etc.

However, setting up a Facebook page is very important, and you need to make sure you do it the right way, so I recommend going into Facebook to do it and using the above-linked guide.

It should be a quick sentence that introduces the chatbot and then asks them, “How can I help?” Make sure to also create a default message — something that’s shown in case the user types in a message which you don’t have AI detection set up for.

From this point you have two options: You can have a navigation-type driven conversation (like a phone menu system), or you can have artificial intelligence set up to guide the conversation.

We’ll show you how you can set up AI conversations, but this DIY guide will focus on getting an MVP version of a chatbot launched using navigation buttons and prompts.

In the example chatbot we’re building, we are trying to help our audience find answers to questions they might have about booking a Brian Head, Utah, ski condo.

There are dozens of questions a chatbot user might ask, but for the sake of launching this chatbot in 10 minutes, here are just a few questions we’ll program the chatbot to answer:

MobileMonkeyMobileMonkey specifically fills a niche need of getting live-chat experiences on to websites, which remember previous conversations to build upon those for future interactions.

Improving Access to Sexual Health Education in Kenya with Artificial Intelligence

Irving Amukasa wants to stop the spread of misinformation about sexual health in his home country of Kenya.

His startup SophieBot built a conversational app that uses natural language processing (NLP) and deep learning techniques to provide users with real-time sexual and reproductive health information.

Irving, in his own words, is a “self-taught Android developer and a self-taught AI developer.”  While he’s not scouring arxiv for NLP papers, Irving mentors high school innovators on mobile development.

In this post, we’ll dig into the machine learning and deep learning techniques that power SophieBot, as well as Irving’s own journey building an AI startup in Africa.

According to a 2014 Kenya demographic and health survey: It was important to us to make sure this youthful population not only gets necessary information on sexual and reproductive health, but also accurate and helpful information — without fear of judgement, when they need it most.

We saw this technology as an opportunity — we could be the first to to deliver sexual health education via an app or a chatbot in messaging apps.

With SophieBot — we can answer important sexual and reproductive health questions for our users, no matter where they are or what they’re doing.

Our users can ask Sophie free-form questions like: And she’ll respond with an appropriate response, like, In the backend, whenever we get a question, we’re using parsing the sentence, and then determining what’s the best response to provide.

In 2014, as a young and naive Android developer, I had the crazy idea to build an app to automate my own personal conversations.

Rather than building Snub, I ended up shipping three different chatbot apps: But, by June 2016, two years later, none of these chatbot projects had really taken off.

saw a natural opportunity to adapt my earlier chatbot experiments with AIML to something that might be helpful in improving access to proper sexual health information.

Here’s the AIML markup that powered the very first version of Sophie: If I’m reading that correctly, AIML is a markup language takes a pattern and responds with a template?

Sophie Bot could say hi back, answer only two strictly defined questions, and offer a default answer if a question wasn't defined in her knowledge base.

Within a month of launching Sophie Bot, I had recruited a team of five, we had won a  $10,000 in a sexual health innovation challenge by  UNFPA,  and was already featured on national press, as you can see in this YouTube video: Even more importantly, we had answered questions about sexual health for 250 users.

Next up, I found a PyData talk by Edward Bullen about an NLP bot that got me really excited, but he himself conceded it wasn’t really practical: What he had achieved, though, is automating our earlier rule-based strategy using Python and an SQL database.

Topic wise — most of our users are asking about STIs, safe days for sexual activity, whether “pulling out” works, and unwanted pregnancies.

We hit 30,000 questions asked in April 2018 — a new peak after a successful press run from the Nairobi Innovation week.

The first few times through the article, the concept was so baffling I had to walk through building an LSTM from scratch with matrix multiplications in order to get the proper intuition.

More models and implementations we are prospecting include: I’m very open to new ideas and suggestions, so if anyone reading this has any better ideas or suggestions on models and implementations that we can try out with our dataset, please feel free to reach out to me on Twitter!

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