Category: Chat

In today’s fast-paced development landscape, staying ahead of the curve is crucial. Generative AI programming tools offer developers a powerful way to enhance productivity, streamline workflows, and explore new possibilities. These tools leverage machine learning to automate tasks, suggest code snippets, and even generate entire functions.

Generative AI has gained significant traction in recent years, with a surge in development and adoption across various industries. According to McKinsey, businesses and organizations are increasingly adopting generative AI to solve complex problems and drive innovation.

Let’s gain a clear understanding of what Generative AI is and its benefits.

What is Generative AI?

Generative AI is a type of artificial intelligence that can generate new content, such as text, images, audio, and code. It’s capable of learning patterns from existing data and then using that knowledge to create new, original content. The Generative AI models are trained on massive datasets of code, enabling them to grasp programming languages, anticipate your needs, and generate human-quality code.

Benefits of Generative AI Programming Tools

Generative AI programming tools have emerged as indispensable assets for developers, offering a range of benefits that can significantly enhance productivity, creativity, and overall development efficiency. Let’s delve deeper into the key advantages these tools provide:

• Boosted Productivity: Automate repetitive tasks and generate boilerplate code, freeing up valuable time for problem-solving and innovation.
• Enhanced Creativity: Explore new possibilities by having AI suggest code solutions you might not have considered.
• Reduced Errors: Catch typos and syntax errors before they cause headaches, ensuring cleaner and more reliable code.
• Improved Learning Curve: New developers can leverage AI suggestions to grasp coding concepts faster and write production-ready code.

10 Generative AI Programming Tools

From code completion to entire function generation, these tools have the potential to revolutionize the way you write code. Ready to dive in? Let’s explore 11 generative AI programming tools that will supercharge your development journey:

1. OpenAI Codex

Overview: A powerful code generation tool developed by OpenAI, Codex can generate code, translate code between programming languages, and write different kinds of creative content.

Key Features:

• Code Generation: Generates code snippets and entire functions based on natural language prompts.
• Code Explanation: Explains code snippets and provides context.
• Code Completion: Suggests code completions as you type.

Use Cases:

• Rapid prototyping
• Learning new programming languages
• Writing documentation and comments

2. GitHub Copilot

Overview: An AI-powered code completion tool developed by GitHub in collaboration with OpenAI.

Key Features:

• Contextual Code Suggestions: Provides code suggestions based on the surrounding code and comments.
• Language Support: Supports a wide range of programming languages.
• Integration with GitHub: Seamlessly integrates with the GitHub platform.

Use Cases:

• Writing code faster and more efficiently
• Learning new programming languages
• Exploring different coding approaches

3. Tabnine

Overview: A code completion tool that uses AI to suggest code snippets as you type.

Key Features:

• Contextual Code Suggestions: Provides suggestions based on the surrounding code and project context.
• Language Support: Supports a wide range of programming languages.
• Customization: Allows for customization of settings and preferences.

Use Cases:

• Increasing coding speed and accuracy
• Exploring different coding styles and approaches

4. Replit

Overview: A cloud-based integrated development environment (IDE) with built-in AI features.

Key Features:

• Code Completion: Provides real-time code suggestions.
• Debugging: Helps identify and fix errors in your code.
• Collaboration: Enables collaboration with other developers on the same project.

Use Cases:

• Rapid prototyping
• Learning new programming languages
• Collaborating on projects with remote teams

5. Jasper

Overview: A generative AI platform that can be used to generate various types of content, including code.

Key Features:

• Code Generation: Generates code snippets based on natural language prompts.
• Code Explanation: Explains code snippets and provides context.
• Language Support: Supports multiple programming languages.

Use Cases:

• Creating documentation and comments
• Generating boilerplate code
• Exploring different coding approaches

6. Runway ML

Overview: A platform for building and deploying machine learning models, including generative models.

Key Features:

• Custom Model Training: Allows you to train your own generative models.
• Model Deployment: Easily deploy your models to production.
• Integration with Other Tools: Integrates with popular development tools and frameworks.

Use Cases:

• Creating custom generative models for specific tasks
• Experimenting with different AI architectures

7. Hugging Face Transformers

Overview: A library of pre-trained transformer models for natural language processing (NLP) tasks, which can also be used for code generation.

Key Features:

• Pre-trained Models: Provides a variety of pre-trained models for different tasks.
• Customization: Allows you to fine-tune models for specific use cases.
• Integration with Other Tools: Integrates with popular deep learning frameworks.

Use Cases:

• Generating code snippets based on natural language prompts
• Building AI-powered code assistants

8. Google AI Platform

Overview: A cloud-based platform for building and deploying machine learning models, including generative models.

Key Features:

• AutoML: Automated machine learning features for building models without extensive expertise.
• Custom Model Training: Allows you to train your own custom models.
• Deployment and Management: Provides tools for deploying and managing your models.

Use Cases:

• Building and deploying generative AI models at scale
• Experimenting with different AI architectures

9. Amazon SageMaker

Overview: A fully managed platform for machine learning that includes tools for building, training, and deploying models.

Key Features:

• Studio: A web-based IDE for building and training models.
• Ground Truth: A service for labeling and preparing data for machine learning.
• Inference: Tools for deploying and managing models in production.

Use Cases:

• Building and deploying generative AI models at scale
• Experimenting with different AI architectures

10. IBM Watson Studio

Overview: A platform for building, training, and deploying machine learning models, including generative models.

Key Features:

• Notebook Environment: Provides a notebook environment for data exploration and model development.
• AutoAI: Automated machine learning features for building models without extensive expertise.
• Deployment Options: Offers various options for deploying models to production.

Use Cases:

• Building and deploying generative AI models at scale
• Experimenting with different AI architectures

These are just a few of the many generative AI programming tools available. The best tool for you will depend on your specific needs and preferences.

The Future of Generative AI Programming Tools:

Generative AI is still in its infancy, but its potential is limitless. Expect these tools to become even more sophisticated, offering features like automatic code optimization, personalized learning pathways, and seamless integration with developer collaboration tools.

Get Started Today!

Don’t wait — dive into the world of generative AI development tools and unlock a new level of efficiency and creativity in your coding projects.

When looking for a reliable team to work with, hiring remote developers can be a strategic choice. It is essential to have an offshore team that is well-equipped with a diverse and skilled group of software developers. This allows you to have access to a pool of candidates with the right expertise and experience tailored to your specific project needs. Working with remote developers can offer various benefits, such as flexibility, cost-efficiency, and access to a global talent pool.

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10 Generative AI Programming Tools For Developers was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Der Beitrag Die Macht der Chatbots: So steigern Sie Ihre E-Commerce-Umsätze erschien zuerst auf BOTfriends.

The future of AI is happening now, and we want you to be a part of it! Tomorrow, September 24, 2024, the global Chatbot Conference kicks off in San Francisco, gathering the brightest minds in AI, machine learning, and conversational interfaces. This is your chance to dive into the latest innovations, network with top industry leaders, and take part in shaping the next wave of AI technology.

Why Attend?

Are you interested in the advancement of AI? From AI agents to cutting-edge chatbots, this event is your gateway to the future. Whether you’re a seasoned AI professional or new to the space, this conference offers something for everyone:

• Keynotes from industry pioneers: Hear from experts driving the most transformative AI projects today.
• Interactive workshops: Get hands-on experience building your own AI tools and chatbots, learning from the best.
• Networking opportunities: Connect with peers, innovators, and leaders who are at the forefront of AI development.

Conference Highlights

• Discover the latest in AI and chatbot technologies.
• Learn how to design and deploy AI solutions that can impact industries.
• Attend certified workshops designed to sharpen your skills.

Check out the full agenda and register at Chatbot Conference 2024.

Don’t Miss Out!

With so many opportunities to learn, collaborate, and innovate, this is the must-attend event for anyone looking to stay ahead in the fast-paced world of AI.

Be part of the AI revolution!

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Join Us Tomorrow at the Global Chatbot Conference in San Francisco! was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Since 2017, the Chatbot Conference has become a key gathering for AI professionals, innovators, and enthusiasts. Each year, the series explores the latest trends in AI and conversational technology. From multi-agent systems to automation frameworks, the conference covers the full spectrum of advancements in chatbots and conversational AI. Keynotes from industry leaders, hands-on workshops, and networking events provide attendees with everything they need to push the boundaries of what’s possible

Highlights of the Event

• Expert-Led Sessions: Attendees gain deep insights into emerging technologies, such as AI agents and the role of automation in business. Industry leaders share their success stories and offer actionable advice on leveraging AI for maximum impact.
• Workshops and Certified Training: From creating AI-driven solutions to mastering multi-agent systems, workshops at the conference are designed to equip participants with practical, ready-to-deploy skills. Whether you’re building a chatbot for customer service or working on sophisticated AI agents, these hands-on sessions will give you a competitive edge​.
• Networking Opportunities: The conference is also a great chance to connect with like-minded professionals, innovators, and AI enthusiasts. Collaborate on new projects, exchange ideas, and stay ahead in this fast-evolving field.

Why You Should Attend

The Chatbot Conference Series offers a comprehensive look into the future of conversational AI:

• Industry Knowledge: Gain insider knowledge about the latest tools and techniques in AI development.
• Practical Skills: Learn how to implement AI solutions in real-world scenarios through certified workshops.
• Cutting-Edge Innovations: Discover new trends in AI, including advanced multi-agent systems and the automation of business operations​.

Get Involved

With events scheduled throughout the year, the Chatbot Conference Series is the place to stay updated on the latest AI innovations. Whether you’re just starting out or are an industry veteran, you’ll find immense value in attending.

Ready to take part in shaping the future of conversational AI? Be sure to register for the next event and become part of a global community driving change in the AI space.

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Don’t Miss this Year’s Chatbot Conference was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

⚡ 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.