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Do AI Bots Need Some Regulations?

Look around and you will realize that artificial intelligence (AI) has found a place in almost every aspect of our daily functioning and is increasingly acquiring more space in our lives. Email spam filter, booking a cab, location-based services, using GPS while driving, voice commands on mobile — these are all examples of AI. As the customers and employees become smarter, there is a growing need for smart homes, and workplaces, and artificial intelligence (AI) can be seen acquiring more extensive responsibilities and coming up with an innovative offering.

In the series of AI innovations, there is another offering from AI, which is set to make our lives much easier and convenient, and it is chatbots. Today, organizations are actively using AI chatbots to promote their businesses, engage with customers better, and enhance their experience with a seamless personalized assistance. Growing competition, the need to keep up with ever-changing business landscapes, and the empowerment of consumers are making chatbots an essential presence. Not only are bots turning out to be instrumental in communication and engagement but also in cutting down costs and streamlining workflows. Moreover, chatbots are gradually finding the addresses of our homes. Machine learning capabilities and natural language processing have further opened gateways to the future, which was far from imagination once.

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How the Insurance Industry Is Leveraging AI

Introduction

Insurance is one of the most critical industries there is; it is what saves people and organizations from falling into financial trouble after a crisis or an unforeseen event. However, it is often deemed as the least innovative industry with outdated processes, time-taking procedures and delays, legacy IT systems, and many dissatisfied and even disgruntled customers. 

With the change in demographics and a rapidly growing population of millennials who are tech-savvy and like to perform their banking and finance-related transactions online, it has also become indispensable for every organization in the banking and finance sector to cater to their needs.

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How To Measure the Success of a Conversational AI Chatbot

Illustration by Quovantis

Last year, when one of our healthcare partners (we refer to our clients as partners) were looking to build a conversational AI chatbot, I was apprehensive about guiding them. I had only worked on the level 2 (out of the 5 levels of conversational AI) type of bots. But this time our partner wanted to build a contextual/consultative AI-powered chatbot assistant.

I was concerned about how the bot would understand end-users’ problems. What features can we build to make it more humanistic? Would it be successful in replacing human care and compassion? Would it replicate the same emotions of empathy, compassion, and care?

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Why You Can’t Have a Real Conversation With Your Chatbot

Sure, we can ask Siri or Alexa to answer a question or perform an action for us.  But Siri and Alexa can only respond to pre-programmed questions and commands.  (You can find a detailed explanation of how personal assistants work here).  They do not really understand what you are saying, and you cannot have a real conversation with a personal assistant like you can with another person.

Three-year-old children understand language. We have computers that can beat chess champions. Why is building computer systems that understand natural language so difficult?  (Natural languages are the languages that people speak as opposed to computer languages).

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How to Make Chatbots More Intelligent With Contextual Intelligence

Chatbots need to have contextual awareness if they have to adequately resolve a query. This contextual awareness leads to intelligence over time, by handling millions of queries over significant periods. Conversational UX relies on effective contextual intelligence to create more meaningful relationships with customers. From banking to health services, each industry has unique requirements from contextual chatbots that work with large data sets. 

Designing a Contextual Chatbot

Designing a contextual chatbot requires strategically planning out key characteristics and use-cases for the technology. This includes any critical data points that it needs to analyze first, as well as any customer-based interactions it can start having early on. When designing the right chatbot, embedding contextual analysis is important from the get-go.

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AI Chatbots – Challenges and Opportunities

Perhaps one of the most extensive and prominent use cases for the adoption of Artificial Intelligence in the industry is the increasing use of AI chatbots across service lines. Chatbots have become an integral part of both the internal and external communication strategies of all large organizations. Chatbots are being used as a human alternative for first-level query resolution for a host of industries. In all cases, end users have direct interaction with chatbots.

What Is a Chatbot?

A chatbot is a rules-based computer program, which simulates human interaction with end-users via a chat interface. In other words, a chatbot can have a conversation with you just like a real person, ask questions, and answer queries based on pre-defined rules and logic.

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Teach Your Conversational AI Application to Store Information in Teneo

To be able to create a humanlike conversation there is a need to teach your virtual assistant to remember inputs that the user says, for example, the user’s name:

User: I want a small cappuccino.
Bot: Ok, what name shall I note for the order?
User: Amber.
Bot: Thanks for your order, Amber! A small cappuccino will be ready for pickup in 5 minutes.

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Applications of Artificial Intelligence in Logistics

Artificial intelligence (AI) has become the talk of the town. Initially, the use of AI was restricted to sci-fi movies or something that you could not imagine as a part of the day-to-day life.

However, today, AI is a huge part of our daily lives and is helping businesses flourish and organize themselves better while also offering better customer experience. Similarly, in the field of logistics, artificial intelligence is making a huge difference and can be a great asset if implemented well in a company.

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4 Latest Key Considerations Involved in Chatbot Development for 2018

A chatbot is an artificial intelligence or a computer program that conducts a conversation through textual or auditory methods. This kind of program is frequently designed to persuasively pretend how a person would behave like a conversational partner, thus passing the Turing test. They are normally utilized in dialog systems for different practical objectives like information acquisition or customer service.

According to statistics:

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Why Most Chatbots are Annoying and How to Make Sure Yours Isn’t

As conversational language interfaces begin to dominate customer service, so does the backlash against chatbots grow. Forrester predicted last year that 2019 would be the year of the backlash against inefficient chatbots, and it looks like they were right. For example, a survey commissioned by an open software service company Acquia, that analyzed responses from more than 5,000 consumers and 500 marketers in North America, Europe and Australia, found that 45 percent of consumers find chatbots “annoying.”

At the same time, the importance of conversational AI for business today cannot be overestimated. When done right, conversational AI has the ability to significantly increase your competitive advantage and fundamentally change the nature of business-customer interaction.

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Customer Engagement With Chatbots in Insurance Industry

As the world is moving towards a digital revolution in all business fields, the same pattern is visible in the insurance industry. Though the progress that insurance has made in the digital outlook has been quite steady compared to other finance domains, but it seems now is the time for insurance businesses to balance the technological disruption with their growth. This has been significantly prioritized by the intervention of insurance chatbots.

The competitiveness of the insurance industry is measured by the betterment of customer engagement. So when technology had begun to elevate the industry, one of the most important areas for its capability and energy was needed in the enhancement of customer experience.

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How to Build a Twitter Bot With Node.js

How to Build a Twitter Bot With Node.js

Building a Twitter bot using Twitter's API is one of the fundamental applications of the Twitter API. To build a Twitter bot with Nodejs, you’ll need to take these steps below before proceeding:

  1. Create a new account for the bot
  2. Apply for API access at developer.twitter.com
  3. Ensure you have Node.js and NPM installed on your machine

We’ll be building a Twitter bot with Nodejs to track a specific hashtag then like and retweet every post containing that hashtag.

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How Your Chatbot Can Learn to Understand Synonyms in Teneo

In language, there are many ways of saying the same thing. That creates a need to optimize the language conditions for your bot so that it can give the correct answer even when other words are used. Here is how you do it in Teneo Studio.

We have earlier seen examples of how to semi-automatically create language conditions based on positive example inputs. For example, we created a syntax trigger that can handle conversations like the following:

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4 Business Applications for Natural Language Processing

4 business applications for NLP.

Natural language processing (NLP) is a type of artificial intelligence (AI) that deals with the interaction between humans using natural language and computers. Simply speaking, it’s technology that helps computers understand people’s natural language. It’s not just about picking out a few specific words and spouting out a generalized answer either. Rather, NLP can comprehend meaning and context and provide automated support with a personal touch.

Natural language processing and artificial intelligence were once technologies only available to mega-corporations. But now, small businesses can use NLP and AI to their advantage too. So, are you wondering how you can use NLP to improve the customer experience, streamline processes, and even generate sales?

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Build an Android Chatbot With Dialogflow

Previously, we presented you with a simple and effective guide to integrating a Dialogflow bot in a website. In this article, we will be sharing steps to do the same in an Android app. All you need to build a sample chatbot for an android app is Dialogflow and Kommunicate.

Below is an example of Kommunicate Support Bot developed in android using Dialogflow. If you wish to see the bot live in action, head here and click on the chat icon from the bottom right corner.

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Teach Your Conversational AI Chatbot to Pick up Entities in Teneo Studio

Teaching a conversational AI chatbot to pick up entities.

In order for your bot to understand what the user said, some words of the user's utterance are more important than others. Typical examples for such important words include so-called named entities like cities or product names. Here is how you pick up an entity from user input in Teneo Studio.

You might also like:  A Beginner’s Guide to Creating an Interactive Chatbot Flow in Teneo

Sometimes it's not enough to recognize which flow to trigger. Your bot may also need to extract some piece of information from the input to respond appropriately. Let's assume that the user wants to know where our Longberry Baristas stores are located. This is how such a conversation could go about:

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Tracking and Using Sentiment in Teneo

His sentiment doesn't look too good.

Introduction

The Teneo platform is delivered with a collection of agile, customizable tools that build on the usual intent recognition that natural language systems offer. Combined with Teneo's unique infrastructure, these tools allow you to cumulatively monitor various aspects of user behavior during a conversation:

Characteristics of user input Categories Examples
Sentiment negative
positive		 not happy about this
that was a nice answer
Intensity cues capitalization
duplication
emojis
punctuation
intensifying vocabulary
multiple one-word sentences		 what DAY is this
I know, I know
:-)
what?? what!! what!?!
very good, extremely well
You. are. great.
Abusive language hate speech
profanity
violence
sexual abuse		 (clearly racist statements)
(obscene language)
(threats of violence)
(explicit sexual references)
Controversial themes

 abortion
crime
death
fascism
sex
suicide
terrorism		 I need an abortion.
How do I rob a bank?
My best friend died.
Heil Hitler.
Let’s have sex.
I want to end it all.
What happened on 9/11?

Think about these categories for a moment. Knowing what happens at the meta-level of a conversation opens numerous powerful possibilities. During the session, you have an opportunity to identify and react to various situations that arise, tailoring the conversation towards a better user experience. After the session, you are able to analyze user sentiment towards company products and services as well as towards acceptance of the system's handling of the users’ issues. In this article, we focus on use cases for sentiment tracking during the session. 

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The Path to Ethical AI: Major Obstacles and Solutions

It's good to have ethical AI.

From ordering pizza online with a chatbot to generating non-fiction texts and optimizing logistics processes, AI has made so many amazing things possible. Not only has it allowed businesses to automate and optimize complex processes, but it has also helped people with conducting research, analyzing vast amounts of data, and increasing the security of personal devices such as smartphones.

However, as AI-powered technologies grow and develop, so does their potential to assist cybercriminals with getting private data. Many governments across the world have legitimate concerns about the security of sensitive data handled by AI-enabled tools and are already working on corresponding laws and guidelines. On top of security risks, many share concerns about how AI can change the way people interact as well as potential job losses.