Category: Chat

⚡ Flash Sale Alert: Exclusive Online Passes to Chatbot Conference — Limited Availability! ⚡

Thrilling announcement! Due to overwhelming interest, we’re launching an Online Only option for this year’s Chatbot Conference, and we’re starting off with a spectacular launch!

Today marks the launch of an exclusive flash sale, featuring a limited quantity of tickets — just 18 passes up for grabs at this unbeatable price.

🚀 Unmissable Opportunity

• Get ready to seize the moment with a whopping 50% discount on your exclusive Online Only Virtual Pass!
• Limited-Time: Enjoy a 30% reduction on all in-person ticket options!
• Exclusive Offer: Only 18 Tickets Available at This Price

Seize this opportunity to dive into the cutting-edge world of AI and chatbot technology without breaking the bank.

Lock in your place today and become part of an electrifying network of innovators in the AI space!

Catch you at the Conference!

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⚡ Flash Sale Alert: Exclusive Online Passes to Chatbot Conference — Limited Availability! ⚡ was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Are you thinking about creating a chatbot for your business? You’re not alone. Chatbots have quickly become a popular AI tool. If you use Facebook Messenger, you’ve likely interacted with one.

In fact, according to a Facebook report, over 300,000 active chatbots are on Facebook Messenger alone. This number is staggering for a technology that only gained mainstream attention a few years ago.

Chatbots aren’t limited to just Facebook anymore; they’re making appearances on websites across various industries. Why? The barriers that once prevented people from using chatbots are being removed. More customers are comfortable interacting with chatbots than ever before.

In this guide, we’ll explain what chatbot machine learning is and provide an easy-to-follow approach to building your own chatbot for business purposes.

What is Chatbot Machine Learning?

Before we dive into how to build a chatbot, it’s important to understand what “machine learning” means in this context.

Machine learning is a branch of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed.

In the case of chatbots, machine learning enables the chatbot to interact with users, understand their inputs, and respond intelligently.

Chatbot machine learning refers to the use of algorithms that allow a chatbot to learn from data. As the chatbot processes more conversations, it becomes better at recognizing patterns, understanding language, and providing meaningful responses.

Chatbots powered by machine learning can operate 24/7 and engage users in human-like conversations. The success of these bots largely depends on the quality of the data used to train them and the machine learning models applied.

Steps to Build a Machine-Learning Chatbot

Let’s break down the steps to create a chatbot using machine learning. The goal is to create a product that requires minimal human intervention.

1. Accumulate Data

The first step in building a chatbot is gathering data. To train a chatbot, you need large datasets that mimic real conversations. These can include previous customer interactions, chat logs, or publicly available data from platforms like forums or social media.

The data should be as detailed as possible, covering a wide range of conversation topics. In machine learning terms, this is called “creating a data ontology” — essentially organizing and collecting all the data your chatbot will need to understand and respond to users.

The quality and quantity of your data will determine how well your chatbot performs. More data means better performance and more human-like conversations.

Suggested Reading:
How Businesses Can Leverage Machine Learning Development?

2. Pre-Process Your Data

After gathering your data, the next step is to pre-process it. Pre-processing involves cleaning and organizing the data to ensure it’s in a format that a machine can understand.

For example, you might need to split conversations into individual message-response pairs. This allows your chatbot to learn from human-to-human dialogue and predict the appropriate response for a given input.

In this stage, you should also limit unnecessary details, like responses that took more than five minutes, to keep the training data consistent.

Your chatbot should be able to mimic real-life conversation. The goal is to eliminate the “robotic” feel and make interactions with the bot feel more natural.

3. Add Language Processing Capabilities

Once your data is pre-processed, the next step is to teach your chatbot how to understand and generate language. This involves natural language processing (NLP), which breaks down text into a format that a machine can understand.

The NLP process includes tokenizing, stemming, and lemmatizing. Tokenizing involves breaking down sentences into individual words or tokens. Stemming and lemmatizing simplify words to their base form. For instance, “running” would be reduced to “run.”

This process also teaches the chatbot to handle common errors, such as grammatical mistakes, spelling errors, and slang. For example, if someone asks the chatbot a question with improper grammar, the chatbot should still be able to interpret the question correctly.

4. Choose the Type of Chatbot: Generative or Retrieval-Based

There are two main types of machine-learning chatbots: generative and retrieval-based.

• Generative Chatbot: This type of chatbot doesn’t rely on a predefined set of answers. Instead, it generates responses based on the input it receives, often using neural networks.
  Generative chatbots are more flexible and can answer a wider variety of questions. However, they require more training and are more complex to build.
• Retrieval-Based Chatbot: This chatbot relies on a predefined database of responses. It matches user inputs with the most relevant response in its dataset. While these bots are easier to build and more reliable, they are limited in the scope of conversations they can handle.

For most businesses, a retrieval-based chatbot is a good starting point. However, if you’re looking for a more advanced solution, a generative chatbot may be the way to go.

Suggested Reading:
Machine Learning Development: Trends and Predictions

5. Develop Word Vectors

In conversations, people often use abbreviations and acronyms like “LOL” or “TTYL.” To make your chatbot more conversational, it’s important to build a list of common acronyms and slang, also known as word vectors.

Word vectors help the chatbot understand commonly used phrases and expressions. You can either manually compile a list or use pre-built models from platforms like TensorFlow.

6. Build a Seq2Seq Model

Once your data is ready, you need to create a model that will allow the chatbot to predict and generate responses. A common approach is to use a Sequence-to-Sequence (Seq2Seq) model, which is particularly useful for generating text-based conversations.

If you’re familiar with Python, you can build a Seq2Seq model using TensorFlow, an open-source machine learning framework. The model will help your chatbot learn patterns in conversation and produce relevant responses based on the input it receives.

7. Train and Test Your Chatbot

After building the Seq2Seq model, it’s time to train your chatbot. Training involves feeding the chatbot data and allowing it to learn how to respond to various inputs.

Testing is an essential part of this process. Test the chatbot with different queries to ensure it provides relevant responses.

You can also adjust hyperparameters like learning rates, optimizer selection, and the number of training iterations to fine-tune the bot’s performance.

8. Launch Your Chatbot

Once the chatbot has been tested and fine-tuned, it’s time to launch. You can deploy the chatbot on your website, app, or messaging platforms like Facebook Messenger.

A soft launch to a smaller group of users will allow you to gather feedback and make any necessary improvements.

Suggested Reading:
Machine Learning Development in Action: Real World Use-Cases

9. Continuously Improve the Chatbot

Building a chatbot is an ongoing process. After launch, monitor how users interact with it and collect feedback. Add new datasets based on customer interactions to keep the bot’s knowledge base up to date.

Over time, you can fine-tune the chatbot’s performance to ensure it continues to provide high-quality responses.

Conclusion

Machine-learning chatbots have revolutionized how businesses interact with customers, offering 24/7 support and faster response times.

By following the steps outlined in this guide, you can build a chatbot that not only meets your business needs but also improves over time with continuous learning.

Platforms like BotPenguin simplify the process, allowing businesses to create chatbots without needing deep technical knowledge. Whether you’re building a generative or retrieval-based chatbot, the future of customer support lies in machine learning and AI-driven solutions.

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How to Create a Powerful Chatbot Using Machine Learning was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

We have some fantastic news for you! Due to popular demand, we are thrilled to announce that the Chatbot Conference 2024 will now be available online.

Whether you’re near or far, you can join us from the comfort of your home or office.

🚀 Online Access Details:

• Dates: September 24–26, 2024
• Time: 9:00 am to 5 pm Pacific Time Zone
• Live Streamed via Zoom
• Access: Full live stream coverage of all three days
• Features: Interactive sessions, live Q&A, and digital networking opportunities

This online option ensures that everyone interested in the latest advancements in AI and chatbot technologies can participate, no matter their location.

Experience insightful keynotes, engage in interactive workshops, and connect with global industry leaders — all with just a click!

🔗 Check out the Agenda, Testimonials and Workshops.

How to Register for Online Access:

Simply visit our registration page and select the “ONLINE ONLY: 3 Day Pass” option. It’s quick and easy!

Don’t miss out on this opportunity to be part of the cutting-edge discussions and innovations shaping the future of conversational AI.

Looking forward to welcoming you virtually to the Chatbot Conference 2024!

Warm regards,

The Chatbot Conference Team

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Big News: Chatbot Conference 2024 Now Available Online! was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Why we are building Tailor

Information overload is the biggest issue of our time. We are building Tailor to fix that.

I’ve talked with hundreds of folks from different backgrounds and cultures. I asked them how they consume news and information. Everyone told me some version of: I feel I can’t keep up with everything. I’m supposed to be updated with the news of my city. With stuff regarding my job. With general information regarding my country. With the sports I like. And all I get is clickbaity stuff. It’s emotionally draining. It’s overwhelming. I noticed it negatively affected my well-being. My mental health.

Some folks even told me that they are tuning out everything. They made the conscious decision not to read, listen, or in general consume anything related to what is happening in the world or around them. They just chat with friends, that’s it. Some people made a parallel: It’s like living in a cabin in the middle of the mountains. A digital cabin. Isolated. I fully understand that.

What is so wrong with the current state of things? Why does more access to information and news mean that we are informed less and less every day? Emanuele Capparelli and I started to break down the problem.

The problem

We think that there are 3 main reasons why:

1. The outrage machine

Outrage brings the highest dollar amount on social media. That translates into a constant stream of sensationalistic content, geared towards creating the strongest possible emotional reaction for any given fact. This constant outrage machine is emotionally draining and not sustainable. It’s a big factor in why people tune out.

2. Too much noise

If you compare the news with every other way to spend your time on the internet, the news is much noisier. The amount of promotional content, sponsored content, and listicles is overwhelming. You are not consuming content, you are fending off attacks until you find what is actually relevant and interesting.

3. Echo chambers

Another consequence of the need to optimize for strong emotional reactions is the incentive to create echo chambers. In an echo chamber, the outrage is amplified by the community, making it more profitable to produce content for a smaller, engaged echo chamber than it is to produce content for a wider audience. Given that, increasingly more content is produced specifically to cater to echo chambers.

That means that you have a choice. First choice: You can embrace an echo chamber and accept everything in the echo chamber as indisputable. The majority of people don’t want that.

The second choice: a lot of work. You have to navigate different echo chambers, weighting and balancing out everything you consume. You have to sift through the content “manually,” which is exhausting.

These three problems — constant outrage, high levels of noise, and echo chambers — make consuming the news very demanding in terms of mental energy. No wonder everyone feels overwhelmed and frustrated.

This is what we want to fix with Tailor. We don’t want our society to descend into two groups: The rabid fans and the digital monks, retired in their isolated cabins. We can fix the issues above and re-align things.

Meet Tailor

The first version of Tailor is pretty simple: You tell it what your interests are. Tailor provides a curated summary of what is happening, personalized to you, daily. It also turns it into a newsletter and a podcast.

If you try it out, you will notice that Tailor attempts to fix the issues that affect our current state of information:

1. Tailor helps you make sense of the noise

2. Tailor deeply personalizes your news

3. Tailor streamlines facts first, and then lists the opinions

1. Tailor is personalized to you

Tailor does not rely on making you enraged but on deep personalization. Tailor is not only summarizing but also creating content specifically for you: a newsletter, a podcast, or a bullet-point summary. Everyone gets their own deeply personalized daily digest.

The best example of this is: Tailor allows you to specify how long you want each daily digest to be. If you commute for 10 minutes every day, you can choose 10 minutes. If you need a 3-minute digest, you can pick 3 minutes. Tailor will adjust and make sure that you will get the most important information across in the time you have.

This is just the start in terms of personalization. For example: We envision a future where Tailor knows how deep of a subject matter expert you are, and is able to tailor sources and content based on that.

2. Tailor helps you make sense of the noise

We’ve trained custom A.I. models that allow us to identify low-quality content, such as promotional content. Tailor is also great at summarizing and filtering out stuff that is not relevant to you. Not only that, Tailor is great at finding patterns and aggregating different pieces of information — be it news articles, podcasts, or videos — and extrapolating the common pieces of information. It always references the original source, so you can read it yourself. If you try Tailor once, you will see that it gives you a sense of control over the noise.

3. Tailor streamlines facts first, and then lists the opinions

Full disclosure: This is the hardest issue to fix, and we are just getting started on this front.

Tailor will be able to streamline facts and outline them plainly. Tailor will then list the different points of view it found over the internet on the particular subject, and let you know those as well. We envision a future where Tailor is able to articulate the facts, the more controversial points, and the different points of view expressed. This way, you won’t have to navigate the echo chambers: Tailor will do that for you, automatically.

Join the journey

Knowing what is happening around you, in the world, and in your professional space is super important. If everyone starts to tune out there is no way to get, collectively, to a better place. There is no way to address the collective problems we are facing. For example, there is no way to talk and address climate change.

We have big ideas but we are a small and mighty team. And we are just getting started! If you want to try out Tailor, you can sign up for free here. Let us know what you think, your feedback would be invaluable.

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Fixing information overload was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

3 text-to-speech examples I’ve randomly encountered online

Long before the rise of Bev Standing’s iconic text-to-speech voice all over TikTok and the internet, we’ve heard computers talk. Most people in this day and age have experienced the phenomenon of synthetic speech and its eerie non-human-ness. But what exactly is synthetic speech and why do we keep using it?

Voice branding expert Phoebe Ohayon defines speech as: the signal produced by modulating voice into meaningful patterns. Although many people use “speech” interchangeably with the term “voice”, speech is not necessarily always produced by humans. In fact, that’s exactly what synthetic speech refers to: the artificial production of human speech, a.k.a. machine-created speech. As highly communicative creatures, humans are pretty good at parsing if something is natural or artificial speech. A lot of synthetic speech systems have wonky word emphasis or pauses at the “wrong” time, among other factors that reveal their “unhuman” nature.

The wonkiness explained

Text-to-speech (TTS) is a process to create “spoken” content from written text. It’s also referred to as “read aloud” technology. In plain words, it’s live output made with pre-recorded input. Traditional TTS voices were created in a recording studio. Voice actors were hired to train software on human speech and to try to capture all possible sounds (not words) in a particular language, which were later “stitched together” for a vast combination of words (i.e. the words and sentences not explicitly recorded). This video from Acapela Group does a great job in showing how the word “impressive” can be created by stitching together parts of the words: “impossible”, “president”, and “detective”.

However, not all TTS software are created equally, with some less natural-sounding than others. The speech might sound flat (lack of intonation) or punctuation might get ignored. So the question remains: if the technology sounds so bad, why do we keep relying on synthetic speech?

The authors of the 2005 book, Wired for Speech, summarized it best:

“Because of limitations of storage space (digital recordings are large), processing speed (finding and combining arbitrary utterances can be slow), bandwidth speed (sound files do not transmit gracefully over a 33 kilobyte phone line), dynamism of content (all of the Web’s content cannot be spoken and recorded in real time), and other technical constraints, much of the speech that is and will be produced by computers, the Web, telephone interfaces, and wireless devices will be ‘synthesized speech’[.]”

It’s much easier and viable to create speech artificially rather than have interfaces present “fully recorded words and phrases”, as Clifford Nass and Scott Brave state in their book. It’s expensive, in terms of both money and computing power, and hard to scale. These days, there’s been further advancement of this technology. Neural TTS is all the rage now.

Examples of TTS and its modern usage

Personally, I’ve loved to see this kind of speech technology evolve and improve over time— and become more predominant in everyday life. As someone particularly fond of voice technology, it’s been super fun to follow the modern online trend of creating short videos with synthetic speech content. The following examples listed below are a few of my personal favorite use cases for TTS that are not Instagram Reels/TikToks.

TTS to open a music video

BLOSWOM, a music artist from France, released a music video for his song “Rosiana” where a TTS voice sets context to the scene and reveals why this character wakes up on the beach.

TTS for comedic effect in a video essay

In the video commentary on the 2022 Andrew Dominik film “Blonde”, the Be Kind Rewind channel points out there are potentially many inaccuracies to look out for in the film adaptation of Marilyn Monroe’s life— one of which is a parody on the film’s use of a talking fetus.

TTS to replace human commentary

This was an interesting find: a channel that uses a TTS voice to narrate movie recap commentary. While there are many reasons someone might choose to omit recording their own voice for a video (including speech impediments, insecurity around accent, etc.), it was nice to see a video trying to normalize its use.

Got any favorite examples of synthetic speech in your life? Let me know by leaving a comment on this post! I’d love to hear more everyday examples.

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Everyday Speech: Examples of TTS out in the wild was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

When it comes to AI teaching assistants, finding the right platform can feel overwhelming. Each platform offers its own set of tools, features, and benefits designed to support educators in different ways. Today, we’re diving into a side-by-side comparison of Noodle Factory AI and Magic School AI to help you decide which platform aligns best with your teaching environment.

Don’t Miss Out on ROI of Conversational AI — Your Secret Weapon for Profitability

Contact centers are in crisis. Skyrocketing customer expectations were coupled with relentless cost pressures. It all has created a perfect storm. 71% of consumers expect companies to deliver personalized interactions, and 76% of them get frustrated when it doesn’t happen. Agents are overwhelmed: they are struggling to keep pace with increasing call volumes and complex inquiries. Meanwhile, customer satisfaction is plummeting. Long wait times and repetitive interactions decrease loyalty. The result? A toxic combination of high operational costs, low contentment, and dwindling revenue.

But there’s a game-changing solution: Conversational AI. This cutting-edge technology can dramatically boost operational efficiency and drive significant bottom-line growth. We’ll explore how to harness the power of artificial intelligence to transform your call center into a profit powerhouse. As a result, you can overcome these challenges and unlock new opportunities, so read to the end!

What is Conversational AI and How Does it Work in Contact Centers?

Simply put, intelligent algorithms enable computers to understand and respond to human language in a way that feels natural. The secret is the technology behind virtual assistants. It allows them to engage in native conversations.

At the heart of intelligent agents are two key engines:

• Natural Language Processing which empowers computers to understand and process clients’ requests;
• Machine Learning that allows systems to improve over time and learn from previous dialogs.

In contact centers, Conversational AI is revolutionizing customer interactions. It can handle a wide range of tasks, from answering frequently asked questions and troubleshooting simple issues to providing product information and scheduling appointments. This frees up human agents to focus on more complex problems.

For example, chatbots can offer 24/7 self-service options, allowing people to find answers quickly and easily. 58% of customers are ready to pay more to a brand if they can provide a better client experience. Your in-house support agents can also benefit from digital tools that provide real-time access to knowledge bases, suggest appropriate responses, and summarize call details. Additionally, Conversational AI can be used to proactively reach out to users with surveys, appointment reminders, or personalized offers.

By automating routine tasks and enhancing agent capabilities, virtual assistants are transforming contact centers into more efficient, client-centric operations.

Choosing the Right Option for Your Niche

Nearly 90% of client assistance teams report measurable improvements in complaint resolution speed and enhanced call volume processing using AI. Selecting the ideal conversational platform is a critical step in maximizing your contact center’s ROI. The market is brimming with options, from specialized bot platforms to comprehensive customer engagement hubs. To make an informed decision, it’s essential to align the solution’s capabilities with your specific objectives and client needs.

• Chatbots are well-suited for text-based interactions and excel at handling frequently asked questions, providing product information, and guiding people through simple processes.
• Voice assistants offer a more natural and intuitive user journey, ideal for tasks requiring voice commands or complex interactions.
• Hybrid platforms that combine both bot and voice functionalities provide enhanced flexibility and can cater to a broader range of preferences.

When evaluating platforms, several key features should be at the forefront of your decision-making process:

1. Omnichannel capabilities are essential to deliver consistent experiences across various touchpoints such as websites, mobile apps, social media, and voice channels.
2. Seamless integration with existing systems, including CRM, marketing automation, and user support tools, is crucial for efficient data management and workflow optimization.
3. Scalability is another critical factor, ensuring the platform can handle increasing interaction volumes and expand as your business grows.
4. Comprehensive analytics and reporting functionalities provide valuable insights into consumer behavior, platform performance, and areas for improvement.

Master of Code Global specializes in crafting tailored solutions that address the unique challenges and opportunities of your company. Our deep understanding of the conversational AI for contact centers landscape enables us to select the most suitable platform and implement it effectively. By leveraging our expertise, you can enhance customer satisfaction, drive operational efficiency, and achieve significant ROI.

Real-World Examples to Consider

According to Statista, 34% of retail users feel comfortable conversing with customer service through a virtual assistant. To truly grasp the transformative potential of Conversational AI, it’s essential to thoroughly examine its application.

Industry Giants Examples

The retail industry has been at the forefront of artificial intelligence adoption, with companies like H&M leveraging chatbots to deliver personalized product recommendations. This strategy has yielded impressive results, including a significant surge in online sales.

Similarly, in the healthcare sector, Zocdoc has revolutionized appointment booking through algorithms, significantly reducing patient wait times and improving access to care.

Master Of Code Global Success Stories

MOCG has a proven track record of delivering exceptional Conversational AI solutions. For instance, we created an AI-powered chatbot for Burberry, merging eCommerce and storytelling. Integrated into Facebook Messenger, the virtual assistant enhances Burberry’s online presence, offering personalized shopping processes and engaging brand narratives.

Another successful project to share is the development of Electronics Retail Chatbot. By delivering highly engaging interactions, we boosted customer satisfaction and drove significant sales growth. Our solution increased average order value by ~300% and achieved an impressive 84% engaged session rate, elevating the overall buyer journey.

The Tangible ROI of Conversational AI in Contact Centers

Cost Reduction

Conversational tools significantly lessen the burden on human agents by handling routine inquiries and tasks. This frees up employees to focus on complex issues, leading to higher job contentment and reduced turnover. VentureBeat predicts that chatbot services could slash the cost of providing service by 30%.

By automating repetitive tasks, artificial intelligence optimizes operations, fostering increased efficiency and productivity. This can result in reduced operational costs, such as lower overhead and utility expenses.

Chatbots and virtual assistants excel at handling high-volume chores, such as answering FAQs, resetting passwords, and providing order status updates. This automation frees up human agents to handle more complex inquiries, improving overall efficiency and reducing labor costs.

Revenue Enhancement

Conversational AI boosts client enjoyment by providing quick, accurate, and consistent responses. Satisfied users are more likely to become loyal consumers and brand advocates, increasing positive word-of-mouth referrals.

AI-powered systems can analyze audience behavior and preferences to offer personalized product recommendations. This directly impacts sales and revenue growth through effective upselling and cross-selling opportunities.

In addition, intelligent agents ensure round-the-clock support, eliminating downtime and maximizing revenue potential. Consumers can access information and get the necessary assistance at any time, improving their overall impressions and increasing the likelihood of making a purchase.

Data-Driven Insights

By analyzing customer interactions, conversational platforms provide valuable information about user behavior, preferences, and pain points. This data can be used to identify new business opportunities, improve products and services, and constantly enhance marketing campaigns.

One more benefit to consider is that Intelligent platforms offer detailed metrics, allowing companies to track key indicators (KPIs) and measure the impact of AI initiatives. By analyzing these metrics, organizations can identify areas for improvement, optimize models, and maximize ROI.

Calculating ROI of Conversational AI: Simple Guide

Determining the return on investment of your digital agent is crucial for justifying ongoing expenditure and optimizing performance. A comprehensive cost-benefit analysis should consider factors like implementation costs, ongoing maintenance, and labor savings. Equally important is quantifying improvements in client enjoyment, sales, and operational efficiency.

To streamline the process, Master of Code Global offers a dedicated and absolutely free ROI calculator. This tool helps input relevant data and generate actionable insights for diverse businesses. However, the accuracy of your ROI calculation depends on setting clear, measurable goals before implementation. By defining specific KPIs and tracking their progress, you can accurately assess the impact of your initiative. And we will be there to help you with everything!

Conclusion

Conversational AI is no longer a futuristic concept; it’s a tangible tool driving significant ROI for contact centers. By automating tasks, elevating customer experiences, and providing invaluable data insights, this technology is revolutionizing the industry.

Master of Code Global specializes in crafting tailored solutions to meet your specific business needs. Our Conversational AI consultants committed to helping you unlock the full potential and achieve measurable results. Don’t miss out on this opportunity to transform your customer support hub. Schedule a free consultation today to assess your contact center’s potential and embark on a journey towards increased profitability.

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Don’t Miss Out on ROI of Conversational AI — Your Secret Weapon for Profitability was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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