AI Video Glossary: Uncover Terms, Concepts & Insights

AI Agent Framework

Libi Michelson — Thu, 20 Mar 2025 08:41:59 +0000

Companies looking for a more powerful way to coordinate the next generation of artificial intelligence tools are turning to multi-agent AI frameworks. We are at the very beginning of the technological movement behind AI agents. Yet even now, the challenge of simplifying their cooperation has led to the emergence of AI agent orchestration frameworks that enable faster and more efficient agent systems.

What Is an AI Agent Framework?

Agentic AI systems independently handle a number of similar tasks at the same time – the keyword here being “similar”. They are a first step towards AI that goes far beyond the basic and limited queries that are used by, for example, chatbots. AI agents are being applied to a wide variety of industries and becoming more powerful as next-generation AI systems come online.

But this comes at a price. Creating and using artificial intelligence technologies require extensive expertise, time, and funding. Plus, AI agents (as mentioned above) specialize in a certain area. When more than one area needs to be addressed, the development challenges can be daunting.

Benefits of Using an AI Agent Framework

This is where autonomous AI agent frameworks come in. They are platforms, often supplied by a third party, for the creation, management, and interoperability of multiple AI agents. This solution comes with a number of important benefits:

Flexibility

Frameworks allow growing companies to rapidly change or add components to their AI systems without needing extensive integration (that can be flawed) or system downtime.

Cost-efficiency

Instead of building every agent and interoperability function from scratch, frameworks deliver prepackaged solutions.

Effectiveness

Vendor platforms have already gone through the debugging process after design and practical use, so errors are minimized.

Simplicity

Because vendors supply the frameworks, they are designed around ease of use, so companies don’t need to develop special training processes.

Security

Any customer-facing agent runs the risk of data breaches in often unforeseen ways; AI agent frameworks have security holes “plugged” in advance, avoiding development expense and legal exposure.

Example of an AI Framework in Action

Let’s examine how AI agent frameworks might operate with regard to a popular application: learning and development. AI agents can be used to supplement and enhance basic corporate training videos in a number of ways through automated functions:

Narration and animation based on a simple text file
Professional-looking transitions and frame setups
The use of avatars instead of human representatives
Implementation of interactive functionality, where the avatar can actually converse with the learner
Addition of quizzes, testing, and assessment modules
Translation according to locality

Developed independently, the cost and time for building this number of features would be exhaustive. However, if an AI agent framework is applied, it enables video creators to add these features quickly and easily.

In addition, the framework allows these separate agents to cooperate and work in a seamless manner. For example, if the original video changes, an AI agent framework might update all the quizzes to reflect the most recent material.

How Do AI Agent Frameworks Operate?

There are many vendors of AI agent frameworks. Some well-known ones include:

AutoGen: an open-source framework from Microsoft that powers multi-agent applications
CrewAI: another open-source framework that specializes in orchestration
LlamaIndex: a Meta product for creating generative and agentic AI solutions (llama also makes specialist, compact next-generation AI models)

Even though a variety of framework solutions exist, they are all based on common principles. These cover:

External communication – AKA “input”, an agent must first receive an instruction. This might be a text query, an audio file, or even camera imagery (i.e., for self-driving cars).

Processing and decision-making – Many methods of understanding and evaluating queries can be applied, including machine learning, customized algorithms, and reinforcement learning.

Output – After refining a response, the agent puts it into action by, for example, communicating with the user, interacting with software, or activating a physical device. Learning – To improve performance over time, AI analyzes feedback concerning its output and then applies learning techniques (supervised or unsupervised) to look at other options during the next usage round.

Getting Started

Creating an AI-generated persona as a virtual representative takes only a few minutes and no special skills. Find out how this process can work wonders for you by contacting us today.

August 17th 2024

Explainer Videos

Explainer videos do much more than explain–and can also be much more powerful than other types of marketing assets. That being said, using traditional methods for explainer video production can be quite resource-intensive. That’s why many organizations are turning towards AI video explainers to cut costs and optimize the creation process. What is an Explainer…

August 04th 2024

AI Companions

AI companions are quickly becoming the most popular friend on the block. And they have a lot more to offer than simple pop-up help wizards at the bottom of a website. As AI companions advance in sophistication, integrating dynamic video and voice response in real time, users can actually feel as if they are talking…

January 07th 2024

Glossary

Welcome to our AI Glossary, where the complex world of artificial intelligence becomes clear and accessible! Whether you’re a seasoned tech expert diving deeper into AI intricacies, or a curious newcomer eager to understand the basics, this glossary is your go-to resource. Here, you’ll find concise, easy-to-understand definitions of popular AI terms, unraveling the jargon…

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Next-Generation AI Systems

Libi Michelson — Thu, 13 Mar 2025 12:43:25 +0000

You may already use some form of next-generation AI systems at an elementary level. As we have seen with almost every technology, there is often a “eureka” moment when something truly unique is discovered. From then on, development is usually about finding applications for that discovery and refining their functions. Artificial intelligence is perhaps the clearest example of this concept as it becomes increasingly more capable.

What Are Next-Generation AI Systems?

AI can be described as a technology that decides an outcome. For example, a calculator cannot give you different answers when you add one plus one because it has hard-wired circuitry. However, artificial intelligence produces output that is not programmed in advance. Instead, AI is designed to:

Understand what your input means
Access numerous data sources to get a full range of potential responses
Compute and deliver the best possible answer

Core Features of Next-Generation AI Systems

Within this very simple explanation lies a roadmap for how AI will develop, namely:

Ease and Type of Input

Within a short time, communication with AI systems has gone from programming language to natural language. Whereas chatbots once accepted only defined text queries, we can now use AI interactive avatars that understand what you say, and will soon interpret how you feel. This ability to combine input types is called multimodal AI and is meant to simulate how people communicate. Other applications are also being modified to accept new forms of input, such as LiDAR for self-driving cars (instead of the types of cameras being used by Tesla).

Autonomy

As AI technologies mature, they will require even less input from people, while AI programming will be able to interpret the implied meaning of a query. Known as agentic AI, future applications that use AI will fill in the blanks of a natural language query and then produce a range of outcomes. In case the system makes a mistake, the reinforcement learning component of agentic AI will compensate for it next time.

Model Downsizing

Also called “democratization”, model downsizing involves the development of smaller, less expensive models used by AI to search for data related to queries. Model downsizing creates more accessible data sources that might be custom-built or of somewhat reduced functionality compared to the large language models currently in use. One of the vital areas where smaller models are needed is mobile phone applications. The trend towards democratization is illustrated by the creation of models like Llama, Mistral, and GPT-4o-mini.

Technologies Driving Next-Generation AI Systems

Behind advanced artificial intelligence capabilities are a variety of new technological areas. They cover software and hardware developments, and even delve into theoretical concepts that are just now being brought into reality.

Low Power Chips

The electrical requirements to power microchips and issues related to heat have always meant limitations for how compact computing devices can be. The energy needs for AI applications make this an even bigger challenge. Concepts to reduce microchip power consumption include electrical flows that are activated only when needed, as opposed to a constant flow, and chips that combine memory and computing functions.

Hyperdimensional Computing (HDC)

As part of the effort to move AI closer to human abilities, artificial neural networks (ANN) will need to be replaced by HDC. For example, to understand a blue square, ANN has a place in its memory that recognizes “blue” and “square”. But humans perceive objects in thousands of dimensions at once. It is easy for us to picture a blue square in a crowded office, yet an ANN’s memory and energy requirements to produce such a picture are relatively intensive. HDC seeks to simplify this process by mimicking how humans perceive their surroundings.

Artificial General Intelligence (AGI)

AGI is to artificial intelligence as AI is to calculators because it represents the next step of knowledge capability. Whereas regular AI technology depends on designers providing models for the AI to learn, AGI finds its learning solutions. This will allow it to apply its knowledge to a wider range of areas. For example, a system for self-driving cars equipped with AGI would be able to pilot an aircraft. AGI is also known as strong AI.

How to Get Started with Next-Generation AI Systems

Many see AI as an unstoppable force that will eventually affect everyone. However, early adopters are already starting with technologies that incorporate the above innovations. There are a wide range of areas where artificial intelligence plays a critical role. Let’s have a look at an application that represents many of the developments that characterize next-generation AI. Interactive avatars are currently used in areas like customer service, sales, recruiting, and education. They have evolved from being interactive avatars for chat in text form to capabilities that include dynamic verbal communication. Similarly, interactive avatar creators use increasingly sophisticated generative AI that allows the application to give expansive and accurate responses in real-time. Similarly, high-quality avatar platforms enable creators to upload their own model as a database containing their customized responses to user queries.

Getting Started

Creating an AI-generated persona as a virtual representative takes only a few minutes and no special skills. Find out how this process can work wonders for you by contacting us today.

August 17th 2024

Explainer Videos

August 04th 2024

AI Companions

January 07th 2024

Glossary

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Agentic AI

Libi Michelson — Tue, 11 Mar 2025 13:41:41 +0000

Virtual twin technology is being adopted by industries worldwide as a powerful way to develop, test, and optimize the design and employment of countless products and services. By Agentic AI architecture is, in many ways, what we think of when considering the future of artificial intelligence. A “traditional” AI process takes your query and gives you an answer. In comparison, an agentic AI system performs multiple, related tasks all at once – without you asking it specifically to take those steps. The result is a system that “thinks” in context by understanding what you need without you having to detail it, making artificial intelligence into something far more autonomous than simple generative AI.

What Is Agentic AI?

Agentic AI can be described as “goal-oriented”, as opposed to other agentic AI systems that focus on producing some sort of content. One way of understanding the technology is through an agentic AI vs. generative AI comparison.

Let’s take chatbots as an example, most of which are based on generative AI. To use them, you type in (for example) your travel destination and related preferences. The chatbot will provide a range of hotel and flight options from which you can choose. The content in this case is the various online offers provided by airlines and hotels that are detected and reported by the AI.

In contrast AI Agents use agentic AI. With the same prompt (as well as an option for voice communication instead of type), the Agent will be able to identify and reserve taxis, flights, hotel rooms, restaurants, and any other services set out in your preferences. It will ensure that all of these services are within the price range that you request and are scheduled logically. In this case, you have given AI the goal of arranging your trip, and it understands all the sub-tasks that are related to the goal.

How Agentic AI Works

Agentic AI is capable of autonomous decision-making and self-driven learning without constant human supervision. Let’s have a look at the concepts behind these functions.

Reinforcement Learning (RL)

AI agentic workflows that incorporate reinforcement are designed so that the technology can analyze its “environment” and make its own decisions. This is a form of machine learning where the agent determines success through a “reward”. A well-known example of RL is self-driving cars. They use visual technology to analyze their environment and receive a reward by, for instance, not hitting pylons or arriving at a destination faster than previous times. Of course, an AI system using RL starts in an environment where it can’t actually harm anything. Over time, the AI records the right ways to perform functions so that it can be used in the real world.

Goal-Oriented Action Planning (GOAP)

Within the concept of RL is GOAP. This is a process where AI receives a goal to achieve and looks at different ways of getting there. AI that uses GOAP constantly runs through a cycle of goal-potential action-planning-decision steps. Taking the example of a self-driving car, when given the goal of choosing a route, the car will:

Potential Action: Examine all of the options that it knows about
Planning: Figure out which route gives the best result
Decision: Take the route

Finally, the car “learns” by monitoring traffic as it travels and changes the route if a better option arises.

Complex Interactions

Complex interactions in AI deal with the various elements that form an environment. Sophisticated AI systems must be able to analyze complex interactions if the technology is to advance beyond the simple concept of query-response and operate in the real world where the environment is unpredictable. For example, a self-driving car in a simple interaction might only have one paradigm such as start/stop. But in a complex interaction, the car would need to analyze speed, surrounding vehicles, lanes, traffic lights, and many other variables.

Key Industries for Agentic AI

The decision-making abilities of agentic AI make it ideal for industries where a user has many potential choices, with AI helping to suggest and refine options. Here are just a few areas where agentic AI is finding its most immediate applications:

Customer Support

As a form of advanced chatbot, AI platforms in the role of customer support can help resolve issues, answer questions, and route contacts to other systems and people. Businesses can also enhance this function with a human-like representative through the addition of interactive AI avatars. Customer service teams that leverage this combination will enjoy the need for fewer resources and better efficiency with Agents who never forget details or make mistakes.

Data Analysis

A major aspect of artificial intelligence is its ability not only for predictive functions but also for prescriptive data, in which AI can recommend optimal strategies for complex organizations. Together with other systems, AI can analyze many different organizational activities and describe how to improve them.

Sales Development

A lot of sales work is about choice. Salespeople need to pick the best leads and methods, while customers look at different product options, promotions, and cross-sales offers. Agentic AI can work on both sides of the equation by connecting previous successes to the range of selections that need to be made.

Marketing

Moving up the funnel, marketers often develop a wide variety of materials and events aimed at advancing product awareness and branding. Agentic AI can help organizations examine what marketing options are available, and the most productive. This can include analysis down to the level of individual prospects that enable personalized marketing campaigns.

Risks and Limitations of Agentic AI

Agentic AI represents a major leap forward in artificial intelligence, as well as a higher potential for negative consequences. A few of the delicate issues being examined by government, society, and AI developers include:

With the growth of AI, an increased level of security risk and dependency might be harmful if or when systems fail

The difficulty of connecting cause and effect when things go wrong, due to the complex nature of AI decision-making

The absence of any moral awareness, which can contribute to socially unacceptable results of AI analysis

A lack of accountability for developers; with so many “moving parts”, it can be impossible to figure out what system module is at fault

Getting Started

Creating an AI-generated persona as a virtual representative takes only a few minutes and no special skills. Find out how this process can work wonders for you by contacting us today.

August 17th 2024

Explainer Videos

August 04th 2024

AI Companions

January 07th 2024

Glossary

The post Agentic AI appeared first on D-ID.

Real-Time Digital Twin

Libi Michelson — Thu, 06 Mar 2025 12:54:01 +0000

Virtual twin technology is being adopted by industries worldwide as a powerful way to develop, test, and optimize the design and employment of countless products and services. By adding the real-time element, these real-time digital twins can speed up analysis, forecasting, decision-making, and productivity in a wide range of applications. This development in AI makes it possible to offer on-the-fly adjustments while interacting with people in real time, ultimately resulting in greater efficiency and performance.

What Is a Real-Time Digital Twin?

A digital twin is a virtual model of a physical object or system. It is designed to copy the behavior of an actual object by reacting in a way that simulates what the actual object does.

Take a digital model of a bicycle as a simple example. If a bike doesn’t travel at a certain speed or leans to one side beyond a certain angle, it falls. A digital twin will copy these characteristics as a way, for instance, to test the design of a bicycle race track.

Organizations use digital twins for many different reasons, including:

Designing products through advanced planning that avoids having to fine-tune physical models
Building virtual product components to ensure they are compatible before making physical products
Testing products at ranges that would damage actual products
Predicting failure times and maintenance needs

While the concept of digital twins only dates back to 2010, it has grown rapidly in complexity through AI. Along with massive increases in processing speed, this development adds a “real-time” dimension to digital twin applications. Real-time allows virtual twins to react in response to data precisely as it is being received. This is in contrast to the forms of digital twins that run programmed simulations using a pre-existing dataset.

Applications of Real-Time Digital Twins

With real-time capability, this technology is finding an expanded set of uses. These include:

Interactive digital twins in the form of a personal avatar that converse with people for sales, customer service, learning and development, entertainment, and other informational uses
Control of public utilities such as power grids by receiving real-time data from the system and calculating what, when, and how the system might fail
Real-time product design that combines simultaneous feedback from actual and virtual products; an example is analyzing the data from a real aircraft in a wind tunnel when the digital twin is subjected to temperature changes

How Do Real-Time Digital Twins Work?

Regardless of application, the essential step in creating any digital twin is to build an exact model of the “real” object’s behavior. Let’s take interactive digital humans as an example. Modern AI-powered digital avatars can be created by taking a video of the person whom you want to copy. The video is then analyzed to design a three-dimensional image of the person’s face and to model how their face moves, mainly when speaking.

The technologies employed in this process include motion capture for essential imaging and AI-powered analysis that samples facial movements and connects them to what the person is saying within very short timeframes. A similar sampling process is used to copy an actual voice.

The next step is to give the model real-time input to compute a reaction. For instance, you might ask the digital human, “How are you doing today?” And then it will:

Reference a database of potential answers
Use reinforcement learning to decide on the best response
Answer your question by turning its textual answer into audio signals, lip-synching, and facial movements that are derived from the modeling process

How Real-Time Digital Twin Technology Is Evolving

Artificial intelligence requires a lot of computing power. To support its continued growth, the world needs much more capability, with some projecting that processing speed will be a million times faster in ten years.

Along with this boost will come an even wider range of AI-based applications and greater use of digital twins that are increasingly similar to their real-life counterparts. Digital humans are expected to look even more like actual people as the rendering process becomes faster. But they will also have the ability to react to and express feelings. This involves leveraging AI as a way to access databases with details about human emotions and expressions that are created by modeling massive amounts of information related to how real people think and feel.

Getting Started

Creating an AI-generated persona as a virtual representative takes only a few minutes and no special skills. Find out how this process can work wonders for you by contacting us today.

August 17th 2024

Explainer Videos

August 04th 2024

AI Companions

January 07th 2024

Glossary

The post Real-Time Digital Twin appeared first on D-ID.

AI-Generated Persona

Libi Michelson — Tue, 04 Mar 2025 10:15:21 +0000

AI persona generators are essential for better understanding, engaging with, and servicing your customers. With the tools available today, you can create AI personas with highly customized features that imitate real humans–and maximize appeal to your specific audience. Artificial intelligence makes it easier than ever to gather, analyze, and apply the data that makes this all possible–and necessary in an era of rapidly changing consumer behavior.

What Is an AI-Generated Persona?

In marketing and sales, a persona represents a person seen as a “typical” individual involved somehow with your company. There are two basic kinds of personas, although there is a lot of crossover between them:

Customer

Customer (or buyer) personas are created based on an analytical process that first generates an Ideal Customer Profile (ICP). The goal here is to understand more about the groups of consumers and clients who are buying your product so that you can adjust your marketing and sales strategies accordingly.

Representative

Companies that want to develop an effective branding strategy often create or search to identify a persona that represents their brand – AKA “the face of the company”. This can result in:

An actual person (or sometimes an animated character) acting as your representative who has a personality, look, or reputation that implies a corporate image
A certain tone of voice, artistic style, tagline, and other marketing components

AI personas use artificial intelligence to define how this typical person thinks and acts. Plus, with the advent of AI-powered avatars, the information can be translated into the appearance, voice, and behavior of a digital human.

How Does AI-Generated Persona Creation Work?

The first step in creating a persona is to build an ICP. In many ways, this is like a list of qualities that define the companies or people who are buying, or most likely to buy, your product. A typical ICP will cover:

Industry or Department – the industries that have the greatest need for your product and the specific departments within actual companies that are part of making the buying decision
Pain Points – the reasons why these prospects need your product
Revenue, Profit, and Purchase Process – a “bio” of these companies in terms of their size and growth rates, as well as how their purchase process works and how long it takes
Geography – the physical location of prospects, which will affect cultural issues and your ability to meet with them (for example, to build relationships or perform integration activities)
Channels – how your ideal customer will find out about your product, for example, through social media or your website

Once you’ve established the ICP, the next step is to define a persona. As mentioned, the persona can be related to customers or representatives, but the creation process is similar. It involves:

Recognizing Different Personas

You might decide to create more than one persona. This could be the case if your ICP shows that the actual users of your product are junior programmers who tend to be 25-35 years old and use social media, but the decision-makers are executives who are 55-65 years old and prefer website research.

Filling in the Blanks

With this basic data in hand, the next step is to add details. Take the “decision makers” group, for example. What is important for them in a product? What kind of messages do they prefer? What sort of appearance and language style appeals to them?

Translating to Action

The final step is to design a fully-featured version of a persona. Let’s say that you are creating a representative. Your research has shown that decision-makers believe that price is the essential purchase factor; they prefer short, simple, and value-oriented messages, and they like professional-looking reps who are university-educated. This information indicates that your representative’s “pitch” should talk about price, perhaps while wearing a suit and using some financial jargon.

Benefits of AI-Generated Personas

You’re sure to have noticed the level of detail that can go into making a persona. In the past, companies would hire research firms and consultants who would use surveys and proprietary information to get the data. This approach has many disadvantages, such as the age and completeness of the data. Similarly, for a representative, companies would need to search extensively for an actor with the right look and then record them using video. This also led to problems, such as:

The representative failed to appeal to the audience
The cost and time required to create a variety of media (social media videos, photo stills, and website content) featuring the representative
The high chance of needing to add to or change marketing collateral to account for new products, mistakes, and different geographical regions

Nowadays, fortunately, artificial intelligence (often in the form of Software as a Service) can be used instead.

Data Collection

AI vendors such as Delve.AI use a “buyer insights” tool for collecting information connected to your ICP. This can include data enrichment, which supplies many (anonymized) personal details about your prospects, as well as the media channels they prefer to use and even psychological profiles. The AI process involved in data collection is faster than traditional methods and can search a variety of public and private sources (that meet CCPA and GDPR regulations) which are frequently updated.

Avatar Creation

Various platforms supply a range of representatives in the form of a personalized avatar that is based on an actual person, or an AI presenter that is a completely virtual creation. For the latter, top vendors provide dozens of personas and voice types from which to choose. AI also enables instant translation so that you can expand to other geographical markets without needing to remake material. Most importantly, you can easily change the appearance, language style, and messaging in minutes by using an AI-generated persona. This enables you to test different personas until (through, for example, analysis of engagement and conversion rates) you achieve your best results.

Getting Started

Creating an AI-generated persona as a virtual representative takes only a few minutes and no special skills. Find out how this process can work wonders for you by contacting us today.

August 17th 2024

Explainer Videos

August 04th 2024

AI Companions

January 07th 2024

Glossary

The post AI-Generated Persona appeared first on D-ID.

AI Avatar Chatbot

Libi Michelson — Tue, 18 Feb 2025 20:51:39 +0000

AI chatbots with avatar functionality bring together a tested and true interactive medium with a more engaging interface. The newest advances in technology allow the combination of an AI talking character that communicates using natural language with the generative abilities of a chatbot. The exciting result is a growing range of applications that provide ever better opportunities for branding, great customer service, and cost savings.

What Is an AI Avatar Chatbot?

AI avatar chatbots blend AI-driven conversational agents with animated digital avatars. Most of us have used (or know about) popular chatbots such as Siri and Alexa that interact through a voice-response mechanism. They are known as virtual assistants and are somewhat limited in their abilities because they follow decision-tree programming. The purpose of these technologies is to automate customer-facing functions, such as:

Client service
Hands-free access to media
Issuing reminders
Personalized e-commerce recommendations
Completing online forms
Scheduling appointments
Promotional marketing of products and services

“Chatbot” also includes text-operated platforms like Google’s Gemini and OpenAI’s ChatGPT, which are far more sophisticated in their functionality than the applications mentioned above. That’s because they are widely integrated with generative artificial intelligence, which allows these platforms to access and derive responses from practically any informational database.

AI avatar chatbots form the next level of these technologies. They combine the ease-of-use element provided by voice interaction with the generative abilities of the newest text chatbots, but also add a third component – interactive avatars.

What Makes AI Avatar Chatbots Different?

Interactive avatars feature a digital avatar in the form of a digital human (a computerized conception of a person) or a personalized avatar (an avatar based on a real person). Whereas some avatars are used for one-way communication, for example, when giving a presentation, interactive avatars can receive voice queries from a user in real time and immediately respond. Avatars also have dynamic features, meaning that they show facial expressions, incorporate body movements, and display accurate lip-synching with the text that they are saying.

Just as we discussed virtual assistants and text-operated platforms, there are basically two kinds of AI avatar chatbots:

AI assistant avatars, which perform the same tasks as virtual assistants
Generative Agents, which can produce responses of the same depth as advanced text-operated platforms

Why Are AI Avatar Chatbots Becoming More Popular?

These labels will most likely become a thing of the past as you can use the same platform to build both assistants and Agents. The end result is a digital avatar that can handle applications such as customer service and virtual sales while also providing responses to complex queries. This permits the technology to enter new markets such as online learning and AI companions, where users might want information specific to a service provider or be interested in something outside of the provider’s database.

Perhaps more importantly, AI avatar chatbots are more engaging. They allow users to communicate with natural language but through an interface that is more interesting to look at. Similarly, the speed of response and detailed visuals provided by top-level platforms make it feel like you are talking to a real person.

The result is a platform that:

Saves time and money by automating functions previously performed by employees
Creates opportunities for branding by using interesting (and “real”) avatars that act consistently and deliver accurate information
Supplies services that can be automatically personalized according to user preferences and history or wider parameters such as language or regional requirements
Fits naturally with how people communicate with each other, that is, face-to-face and in real time

The Technology Behind AI Avatar Chatbots

AI avatar chatbots incorporate a range of innovations that work together and deliver the complicated functions provided by high-level platforms. These innovations include:

Generative AI

As a subset of artificial intelligence, generative AI accesses a number of sources to generate responses to complex queries. For instance, if an answer to a user prompt can only be found on the internet, generative AI will know where to look. When it comes to chatbots, generative AI uses language functions (as opposed to visual or synthetic) based on large language models to analyze input and assemble answers.

Speech Recognition

The ability to pose queries through voice instead of text is essential for user-friendliness. Speech recognition technology allows functions such as:

Activation upon hearing a voice (as compared to random noise)
Filtering out background sounds
Identifying words despite the challenges of accents and personal speech characteristics

Once it understands speech commands, the same technology converts them into text that can be processed by the computer. Some technologies have speech recognition as part of the natural language processing (NLP) module.

Natural Language Understanding (NLU)

Another aspect of NLP that is relevant to AI avatar chatbots is NLU. NLU allows the computer to decide the semantics of a query, that is, to look at different possible meanings of speech and choose the most logical version. Because of NLU, the user doesn’t need to speak in a specific way or rely on certain terminology to be understood.

Real-time Rendering

On the visual side of avatar programming, real-time rendering (among many other technologies) allows a digital avatar to move in a human-like manner. For instance, to appear authentic, an avatar’s mouth must move at the same speed as it is saying while forming the correct shape with its mouth. This ability is only possible through real-time rendering.

August 17th 2024

Explainer Videos

August 04th 2024

AI Companions

January 07th 2024

Glossary

The post AI Avatar Chatbot appeared first on D-ID.

AI Influencer

Libi Michelson — Sun, 16 Feb 2025 21:59:44 +0000

AI influencers are an increasingly popular tool in the social media landscape. The technology behind AI-generated influencers has improved beyond its original application, namely, a computerized voice acting as a narrator for social media videos. AI influencer generator platforms enable even unskilled users to create complete episodes through an intuitive interface that saves time and money while delivering professional-level production quality.

What Is an AI Influencer?

The early concept of an influencer involved somebody who was relatively unknown to the public and built a reputation for engaging, entertaining, and informative product reviews. Over time, this idea evolved to include celebrities becoming influencers and companies hiring influencers to represent the company’s brand.

With improvements in artificial intelligence, the ability to create an AI influencer is now a practical choice for sales organizations. As the name suggests, AI influencers are representations of people, or dynamic reproductions of actual personalities, programmed to present influencer content.

AI influencers are becoming a significant option for companies that want a simple, scalable, and relatively inexpensive method for accessing online audiences. The global virtual influencer market is projected to grow from USD 6.1 billion in 2024 to approximately USD 46 billion in 2030. This represents an impressive annual growth rate of more than 40%.

How Are AI Influencers Created?

The technology behind AI influencer generator platforms is highly complex, as are many artificial intelligence capabilities. But, for the user, the process behind creation is straightforward and pretty fast.

The only factor to note is that of using the image of an actual person, compared to an Agent, which is an avatar that represents an AI-generated concept of a person. In the case of an actual person or Personalized Avatar, you’ll need to upload an image (plus a voice sample) or a video of that individual to the platform. This will serve as a model for the AI to generate the look and voice of the influencer. For an Agent, the platform’s interface will allow you to choose from a range of characters and voice types.

After that, the basic process of creating a product review episode using AI is as follows:

What product will the influencer review, and what will they say about it? Use your marketing strategy (e.g., ideal customer profile) to build a script around the intended character of the avatar. The script should reflect the audience you want to engage. For instance, younger audiences might prefer a funny take, so include humor in the script.
Upload the script to the platform, finalize the video, and then send it to your designated media channels. Several integrations can help during this step and support advanced creation and analysis tools.
Once the video has been up for a while, check if its performance (i.e., engagement and conversion rates) matches your goals. If not, consider changing the script and/or the character. All this requires is revising and reloading the script while using the interface to change the parameters of the avatar.

Benefits of AI Influencers for Brands

The user-friendly, automated process for recording a product review episode with AI delivers many advantages. These include:

Cost-Effectiveness and Convenience

The method for creating an AI influencer does not require actors, sets, or production equipment. Revisions are a matter of revising your script or changing the avatar. This is compared to a live production, where changes require expensive and time-consuming reshoots or a whole new video if you want to try different actors.

Ease of Use

The technology that supports AI influencer creation requires no programming skills. This allows people on your marketing team to write scripts, select avatars, and make changes directly instead of relying on another staff member. This saves enormous amounts of time and reduces miscommunication. In addition, you can automate the production of episodes for different countries simply by choosing the language in the interface menu.

Branding

A high-quality platform supplies dozens of avatar types you can pick according to your branding objectives. It also eliminates the risk of using a person as an influencer, who can always demand a change to the terms of their contract or quit (which will require a brand reset). The image of the AI influencer that you choose can also be expanded to other marketing assets and functions, such as video campaigns, interactive Agents, and multinational collateral (by using a translation tool).

Challenges of Using AI Influencers

From a popularity standpoint, AI influencers sometimes face hurdles. The audience can reject any influencer if their image is not interesting or engaging enough, the product loses appeal, or people simply move on to the “next thing.”

But artificial intelligence adds other challenges, too. Some viewers might not feel that the AI-generated avatar is authentic compared to an actual person. However, as technology improves, seeing the difference between AI-generated and real people is becoming more challenging. The rapid growth of the virtual influencer market reflects this fact.Secondly, issues like deepfakes risk both the platform owner and the user. To this end, you should look for an ethics statement whenever considering a solution provider.

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AI Voice

Libi Michelson — Wed, 12 Feb 2025 15:16:10 +0000

AI-generated voice technology continues to find new applications and new ways to baffle. With the ability to produce any voice in any language, accent, and tone, from whispers to shouts and everything in between – it is now almost impossible to distinguish AI voices from human ones. Businesses across the board are benefiting from this, as even unskilled employees can program and customize these voices. The key for companies is to work with an AI voice generator platform that provides an intuitive interface and simple input methods while enabling output usage across a number of media.

What Is AI Voice?

AI voice technology allows computers to generate ultra-realistic human-like speech by using artificial intelligence. We are all familiar with the synthetic voices that accompany, for example, YouTube videos. This technology allows creators to scale up video production without needing a human narrator. However, recent advances have expanded this concept into a number of new areas.

Use Cases of AI Voice Technology

Let’s look at how AI voice generation is being used today, with two application areas in mind:

Static Audio

This is the “traditional” area of AI voice. It’s typically applied to areas where the input and output are limited, i.e., the user can only input certain prompts, and the output has a set number of responses. This includes narration for videos, where the input is text (the “database”); for programming, the creator uploads a text document containing a set number of responses to user prompts. Static audio also involves the use of a standardized voice type and converts AI voice text to speech. This level of technology is common for things like:

Voice-response customer service
Content creation
Gaming
Accessibility tools

Interactive Multimedia

In contrast to static audio are interactive multimedia applications of AI voice generation. They represent a more advanced type of platform and can handle a greater range of use cases. For example, interactive multimedia AI generation technology includes:

The ability to create combined video and audio productions where, after setup, only text is needed to control the actions of the video’s digital human actor/narrator
Applications where Generative Agents can “converse” with the user to answer essentially any question that it receives (as opposed to the limited prompts and responses of static audio applications)
The option to use AI voice cloning based on the voices of actual people (often combined with a personalized avatar that also uses the image and movements of a real person)

Interactive media that leverage AI voice technology include marketing and sales, social media productions, live customer service, and corporate learning and development. They also add the element of interactivity to the use cases mentioned for static audio applications.

How Does AI Voice Technology Work?

The types of technology used by AI voice generation platforms depend on their level of sophistication. At a minimum, AI generated voice requires a Text to Speech (TTS) module to convert the textual output of the computer to a synthesized voice signal. For more advanced applications, a variety of other technologies might be involved, such as:

Automatic speech recognition in case input is received in the form of a voice command from the user
Natural language processing (NLP) when the input and output do not need to follow a fixed format; NLP allows the user to input queries in the form of normal language instead of using a set of terms
Generative artificial intelligence for applications where the output might have to go beyond the content of the database (for example, when an interactive chatbot needs to access a flight schedule)
Conversational artificial intelligence for real-time interactivity between the technology and the user

Key Features and Benefits of AI Voice Technology

Just as advanced AI voice platforms use more complex technologies, so do the benefits of AI voice increase according to sophistication. Whereas the original use of AI voice generation was to automatically convert text to speech, thereby saving the time and money that would otherwise be spent on a person, the newer range of top-grade platforms deliver:

Improved accessibility in the form of no-code applications that accommodate unskilled users
Lifelike speech quality, be it artificially generated or based on a real voice
The support of multiple languages, along with the ability for automatic translation
Integration with functions such as a CRM to provide enhanced personalization
Real-time interaction and even the use of AI to adapt responses according to tone of voice

August 17th 2024

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Interactive Avatar

Libi Michelson — Thu, 23 Jan 2025 13:17:25 +0000

AI interactive avatars are the future of automated personalized communication. They combine the best of both worlds: real-time interaction between a person and a source of information, together with the ability of generative AI to source and deliver accurate responses within seconds. And that’s only the current level of sophistication. With interactive avatar creators that include AI-based emotional intelligence, we are moving closer to a user experience that will be indistinguishable from that provided by an actual person.

What Is an Interactive Avatar?

An interactive avatar is a digital human who can respond to real-time queries through natural language processing. This differentiates them from applications that use one-sided communication such as how-to videos and video sales letters.

Realism is a central concept for creating an effective interactive avatar program. The quality of an interactive avatar’s functionality is crucial when companies want to promote their brand as modern and competent. That’s why your selection of interactive avatar creators is essential. When searching for a technology provider, pay attention to features like:

User-Friendly Platform. Building an interactive avatar is intuitive and quick while providing extensive options for customizing the avatar’s voice, appearance, and actions.
Lifelike Qualities. The avatar behaves like a person, with facial and body movements that match what they are saying, lip-synching that is accurate and visually well-defined, and an appearance that seems human-like.
Ease of Programming. An interactive avatar delivers content based on data input provided by the user, and this process should be simple while allowing for fast updates.
Speed of Response. There should be a minimum delay between question and response; an industry rule of thumb is that it should be under two seconds.
Quality of Response. Answers to a person’s queries should contain the information they need but with a sensible level of detail (not too much or too little data) while also being able to access outside sources through generative AI capabilities.

Core Technologies Behind Interactive Avatars

These qualities result from technology providers capitalizing on the innovations that go into building AI avatars. They include:

Machine Learning

Machine learning is an aspect of artificial intelligence that mimics how people learn for computers to execute commands independently. This includes a function where a computer improves its performance (“learning”) by gaining access to more information and comparing its output to outside sources used as a model for highly accurate results.

Real-Time Rendering

The ability to smoothly generate fluid graphics is not a new capability and has been part of high-definition video games for years. But, with AI in use, real-time rendering requires a new level of sophistication. For interactive avatars, the underlying technology must accept queries, process output, and coordinate output delivery (i.e., through a speaking, moving avatar) without any noticeable delay.

Voice Synthesis

Top-notch interactive avatars provide a wide variety of customizable options for the verbal portion of output delivery, including:

Settings according to avatar gender
Automatic translation
Accents for different languages and specific geographical areas (e.g., UK English vs. US English)
Tone of voice (e.g., serious, funny)
Integration of actual voice samples

Key Features of Interactive Avatars

Beyond the ability to process input and deliver answers quickly and accurately, high-level interactive avatars can adjust their behavior and output according to the person with whom they are communicating in the following ways:

Personalized Interactions

Avatars detect user preference patterns and consider this for future conversations. For instance, an interactive avatar for chat can remember previous topics and user responses and mention these whenever similar situations arise. If a user prefers a certain airline, the chat avatar arranging a flight schedule can automatically default to that airline for future reservations.

Adaptive Behavior

Adaptive avatars understand contextual cues from the user and alter their behavior accordingly. As an example, if a user asks for stock quotes, the avatar can take on a neutral expression and tone of voice. In contrast, the avatar might use a friendly, empathetic, or serious character for a personal chat application.

Emotional Intelligence

The topic of conversation, user history, avatar profile choice, and even analysis of the user’s facial expressions and tone of voice are all possible ways for the technology to interpret what sorts of interaction and information are best. As an example, an interactive avatar might provide extensive product descriptions for a “shopaholic” user who has chosen that in the past, as compared to simple price and availability details for a “browsing” user who just wants brief details.

Practical Use Cases for Interactive Avatars

Despite the complex technologies required to build highly capable avatars, the demand for related applications is growing steadily. In 2024, the global market for digital avatars was approximately USD 19 billion, and it is forecast to reach approximately USD 24 billion by 2033. Among the most popular applications of interactive avatar technologies are:

Customer Support

In the form of a virtual assistant, interactive AI avatars support functions related to customer service at levels that require minimal human intervention. As a shopping assistant, interactive avatars help customers as they shop online by answering questions, describing options, and mentioning discounts, cross-sales, and upselling opportunities. AI allows the avatars to learn customer preferences and personalize their experience.

Gaming

Game developers are often the first to adopt complex new technologies, and avatars are no exception. Within games, interactive avatars can be used as:

Direct participants in building a storyline for “choose your own adventure” games
Hosts or game guides, for example, in gambling applications
Part of the immersive experience of virtual reality games

Education

Interactive avatars can virtually replace live instructors for formal educational purposes and as part of business-oriented learning and development programs. Standard avatars are used to deliver up-to-date material in ways that are more interesting than books and presentations. When augmented with interactivity, avatars can answer questions, provide more detail upon student request, and use AI to adapt lessons to student ability dynamically.

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