AI Voice Agent

 

Last week, OpenAI released canvas, a new interface for working with ChatGPT for writing and coding projects that allows users to edit and revise.  To test it out, I used OpenAI's ‘GPT-4o with canvas’ model to write about AI voice agent. 

First, I prompted the model with this instruction:  ‘Draft an article about AI voice agent, providing foundational aspects, the technology behind it, and business benefits of using AI voice agent.  Also describe how AI voice agent works and provide examples.  The article should be business-friendly and easy to understand.’

In 30 seconds, GPT-4o returned a well-written article.  I edited the prompt to ask about the future of AI voice agents.  The final step was to review the article by researching on Google and confirming there was no hallucination or false information in the article.  

Overall, it took about 1 hour to prepare this article, which is a monumental leap in term of time compression and personalization as it would have taken me days to write it traditionally.  Here's the AI-generated article:

AI voice agents are transforming the way businesses interact with customers and streamline operations. From handling customer inquiries to automating routine tasks, these intelligent systems have a wide range of applications. Let’s dive into the technology behind AI voice agents, explore how they work, and understand the business opportunities they bring.

What is an AI Voice Agent?

An AI voice agent is a digital assistant that uses artificial intelligence to interact with users via voice. It can perform a variety of tasks, such as answering questions, making recommendations, scheduling appointments, or even completing transactions. The power of AI voice agents lies in their ability to understand human language, process commands, and deliver relevant responses seamlessly—making conversations more natural and efficient.

The Technology Behind AI Voice Agents

AI voice agents use several key technologies to work effectively:

• Natural Language Processing (NLP): NLP is crucial for AI voice agents to understand and interpret human language. NLP helps agents understand the intent behind spoken words, making communication feel more natural.
• Speech Recognition: Speech recognition technology allows AI voice agents to convert spoken language into text, making it easier to process commands. These systems rely on sophisticated algorithms to accurately transcribe spoken words, even in noisy environments.
• Machine Learning (ML): AI voice agents improve over time by learning from interactions. Machine learning helps these agents recognize patterns, enhance their language capabilities, and make better responses by adapting to user preferences.
• Text-to-Speech (TTS): Text-to-Speech technology converts digital text into spoken language, giving AI agents the ability to respond in a natural-sounding voice.

How AI Voice Agents Work

The operation of an AI voice agent can be summarized in a few key steps:

• Speech Input: The user speaks a command or question to the AI voice agent.
• Speech Recognition: The spoken language is processed through a speech recognition system to convert it into text.
• Intent Analysis: NLP processes the text to understand the user’s intent and context. Machine learning algorithms often assist in predicting the user’s needs.
• Response Generation: Based on the recognized intent, the AI formulates a response—whether it’s providing information, executing an action, or asking for clarification.
• Text-to-Speech (TTS) Output: The response is converted back into audio via TTS and spoken back to the user.

Business Benefits of AI Voice Agents

AI voice agents are making a big impact across multiple industries. Here are some notable benefits:

• Improved Customer Experience: AI voice agents are available 24/7, ensuring prompt responses to customer inquiries without the limitations of traditional working hours. They provide personalized experiences by adapting their responses based on individual customer data.
• Cost Savings: By automating customer service and reducing the need for human agents to handle repetitive tasks, businesses can save significant labor costs. AI voice agents can easily scale to handle increased volumes, especially during peak times.
• Operational Efficiency: AI voice agents can take on time-consuming activities, like appointment scheduling or customer queries, allowing human employees to focus on more complex and high-value tasks.
• Scalability: As businesses grow, it becomes more challenging to handle increasing customer demands. AI voice agents offer scalability without requiring additional infrastructure, making it easier for organizations to manage growth.

Examples of AI Voice Agents

• Amazon Alexa: Alexa has evolved from being a smart home assistant to a sophisticated AI voice agent for businesses. Organizations use Alexa to provide information, assist in booking, and integrate with enterprise solutions.
• Google Assistant: Google Assistant is used by businesses for customer interactions, providing voice-powered e-commerce capabilities, and improving customer engagement in retail and hospitality.
• OpenAI Advanced Voice Mode:  Advanced Voice Mode enhances customer engagement, accessibility, and employee productivity by enabling natural, hands-free interactions and improving user experiences.

The Future of AI Voice Agents

The future of AI voice agents looks promising, with several key trends expected to shape their evolution:

• Greater Conversational Abilities: Advances in NLP and deep learning will enable AI voice agents to have more natural and dynamic conversations, making interactions more engaging and human-like.
• Integration with IoT: AI voice agents will become increasingly integrated with Internet of Things (IoT) devices, allowing users to control smart home appliances, office equipment, and other connected devices through voice commands seamlessly.
• Multilingual Capabilities: Future AI voice agents will be able to handle multiple languages and dialects more effectively, making them useful in diverse, global markets.
• Emotional Intelligence: Emotional AI is on the rise, and future voice agents may be able to detect emotions in the user's voice and adjust their responses accordingly. This will enable more empathetic and personalized interactions, particularly in customer service and healthcare.
• Industry-Specific Customization: AI voice agents will become more specialized for different industries. For instance, healthcare, finance, and education sectors may see customized voice agents trained to address their specific needs and regulatory requirements.
• Augmented Human Capabilities: AI voice agents will assist humans in more complex decision-making processes by providing contextually relevant information in real-time, helping professionals in fields such as finance, healthcare, and law make better decisions.

Final Thoughts

AI voice agents are transforming business interactions by offering accessible, efficient, and natural ways to communicate. As the technology continues to improve, more opportunities will arise to leverage AI voice agents in enhancing customer experience, increasing productivity, and driving business growth.

Organizations that embrace AI voice agents stand to gain a competitive edge by improving efficiency, reducing costs, and providing standout customer experiences—positioning themselves for success in the digital age.

AI Agent

 

AI agent is a software entity that can perform tasks, think logically, and make decisions based on its environment, inputs, and predefined goals.  Generative AI agent takes an LLM model and integrates that into an agent workflow to perform complex tasks for better result.

Agent Characteristics

                                                                                                                                         Image:  Architecture Bytes

AI agents can

(1)  Plan and gather information through sensors, data inputs, or external APIs.

(2)  Reason by using AI algorithms to process data and make decisions based on patterns, rules, or learned behaviors.

(3)  Store and retrieve memory to improve their performance over time by learning from past experiences and adapting to new data.

(4)  Decide and act by using tools autonomously and without constant human input.

In a multi-agent environment, an agent can invoke other autonomous agents to collaborate and achieve a common goal.  Some companies are leading the way employing AI agents into their products.  ServiceNow, the top digital workflow automation company, has concrete plan to integrate AI agents into its suite of premier products.  This cutting-edge work will unleash unprecedented productivity 24/7 and drive more business values for the customers.  

Another impressive example of AI agents at work is the AI Scientist system developed by Sakana AI.  This system has developed and employed agents capable of conducting scientific research, executing experiments, generating code, and discovering new knowledge.

In summary, AI agent is an intelligent software system that can perform multi-step tasks autonomously, adapt to environmental feedback, and improve over time through learning.  These agent-based systems are poised to unlock significant economic value in the near future.

Quantum AI

 

What do you get when you combine the deep learning AI field with quantum computing?  You get into the new realm of increasing computational power exponentially that might one day solve the current unsolvable problems.

As of today, cell phones, computers, and supercomputers operate in a binary system where the classical bit stores information as zero or one.  In quantum computing, a quantum bit (or qubit) can store both zero and one at the same time, a property called superposition.  This special property can process many calculations simultaneously, which allows quantum computing to solve complex problems more efficiently.

qAIntum.ai is a company at the forefront of building large language models (LLMs) with quantum computing. The company’s cutting-edge innovation is the Quantum Transformer Architecture where quantum neural networks (QNNs) are integrated with traditional transformer models.  The company’s objective is to build Quantum Large Language Models (QLLMs) with a potential creation of more sophisticated and accurate language models.

                                                                                                       Image:  qAIntum.ai

Then, there’s the Quantum AI group at Google that has been working on quantum computing for almost two decades.  This group has achieved quantum supremacy in 2019 where computation took just 200 seconds that would have taken the most powerful supercomputers thousands of years to calculate.  In 2023, the group achieved another milestone quantum error correction by showing that it’s possible to reduce errors by increasing the number of qubits.

Whether quantum computing AI is hype or reality, I have to give respect to the brilliant scientists and researchers who dedicate their professional lives to work on these high risk theoretical concepts.  These pioneering researches might not have clear real-world business applications initially, but if succeeded could advance the technological progress for humanity.

AI Planet

 

As AI continues to advance and integrate into the many facets of personal and professional life, it’s important to have some knowledge of this deep learning field in order to stay current.  However, for busy professionals who juggle between life and work, the dilemma is how to carve out the free time for learning.

Introducing the 5-week Data Science bootcamp from AI Planet, a compact bootcamp perfect for busy professionals.  This course is designed to give beginners a good foundation of data science with practical exercises.

AI Planet’s benevolent intention is to democratize data science learning so it provides this bootcamp and other data science courses for free. Taking this bootcamp might not turn you into an AI/ML engineer overnight, but it gives you an excellent foundation from which you can build upon your data science knowledge further.  Good luck!

LQM

Since the initial release of ChatGPT-3.5 in November 2022, Large Language Models (LLMs) have received huge attention as the generative AI model.  Subsequent LLMs such as OpenAI GPT-4o, Meta Llama 3.1, or Anthropic Claude 3 Sonnet continue to generate high interest.

There’s another trend that the next AI wave will include Large Quantitative Models (LQMs), which are designed to handle complex data sets and capture the intricacy of quantitative relationship that LLMs can’t.  You would use LLMs which focus on language tasks to summarize documents, but to discover the next cancer drug you would need LQMs.  LQMs utilize advanced computational techniques and data analysis to make predictions, identify trends, and optimize outputs.  The impact of LQMs will be enormous because these AI models will accelerate new discovery in pharmaceutical drugs, chemical compounds, financial models, weather patterns etc.

Advantages of LQMs:

LQMs have many advantages over LLMs and conventional predictive AI models:

1)  Precision:  LQMs excel in tasks that require numerical precision and the ability to analyze vast quantitative data and to model complex mathematical relationships.

2)  Interpretability:  LQMs address the “black box” nature of AI models by offering enhanced interpretability.

3)  Flexibility:  LQMs can be fine-tuned for specific quantitative tasks, providing powerful tools for data-driven decision-making.

4)  Robustness:  LQMs can understand hidden patterns in data and create new synthetic data to add to the limited historical data, making forecasts and predictions more reliable and accurate.

Overall, LQMs promise to usher in the next wave of deep knowledge exploration and discovery.  LQMs will be critical in fields such as finance, healthcare, energy, supply chain management, climate modeling, manufacturing, and retail.  Some examples of LQMs being used in the industry include Flatiron Health, GE Predix, or Paige.  While LQMs also have challenges, their potential benefits in improving decision-making and optimizing outcomes are substantial.

Momentum AI

 

I had the opportunity to attend Reuters’ Momentum AI conference in San Jose this week.  This summit provided a platform for business executives to showcase their AI thought leadership, challenges, and breakthroughs.  

Some highlights from the conference:

Five Stages of AI Progression

1)  Conversational AI:  AI systems that can engage in natural language conversations with humans such as chatbots or virtual assistants.

2)  Reasoning AI:  AI systems capable of logical reasoning and problem-solving such as advanced decision-making algorithms.

3)  Autonomous AI:  AI systems that can operate independently without human intervention such as self-driving cars and autonomous drones.

4)  Adaptive AI:  AI systems that can continuously learn and improve from their experiences based on feedback such as recommendation systems and adaptive learning platforms.

5)  Organizational AI:  AI systems integrated into the organizational structure of businesses, transforming operations, decision-making processes, and strategic planning. These systems can optimize workflows, enhance productivity, and drive innovation across departments. 

 

Many thanks to Reuters Events for organizing this venue for professionals to share AI knowledge and foster business relationships.

Model Evaluations

 

Model evaluations (or ‘evals’) are the steps that use different metrics to evaluate an AI/ML model’s performance.  These evals are important for assessing performance, measuring effectiveness, ensuring safety, and improving overall reliability of the model.  Model evals can be done in two ways:

• Offline:  Evaluated after model training or continuous retraining. 
• Online:  Evaluated in production as part of model monitoring.

Let’s ask GPT-4 to summarize the key aspects of evals.

For supervised learning, some popular metrics for classification model evaluation include:

• Confusion Matrix provides a summary of prediction results on a classification problem.  
• Accuracy measures how often the classifier makes the correct predictions.  It’s the ratio of number of correct predictions over total number of predictions.
• Precision measures the accuracy of the positive predictions made by the model.  It’s the ratio of true positive predictions to the total number of positive predictions (both true positives and false positives).  A high precision indicates that the classifier has a low false positive rate, meaning it rarely classifies negative instances as positive.  Precision is an important metric when you want to be very sure of your prediction or when the cost of false positives is high.  For example, in medical diagnostics for a rare disease, a high precision ensures that healthy individuals are rarely misdiagnosed as having the disease.

• Recall (aka sensitivity or true positive rate) measures the model’s ability to identify all positive instances.  It’s the ratio of true positive predictions to the total number of actual positives (the sum of true positives and false negatives).  A high recall indicates that the classifier successfully identifies most of the positive instances.  Recall is often traded off with precision. While recall focuses on identifying all positive instances, precision focuses on the accuracy of positive predictions.  Improving recall can sometimes lead to a decrease in precision and vice versa.

• F1 Score provides a balanced measure of a model’s performance by combining precision and recall into a single value.  

Let's use a spam email classification as an example:

1)  True Positives (TP): 70 emails correctly identified as spam.

2)  False Positives (FP): 10 emails incorrectly identified as spam (they were actually not spam).

3)  False Negatives (FN): 5 emails incorrectly identified as not spam (they were actually spam).

4)  True Negatives (TN): 100 emails correctly identified as not spam.

Accuracy =  (TP + TN) / (TP + TN + FP + FN)  

=  (70 + 100) / (70 + 100 + 10 + 5) =  91.9%

Precision =  TP / (TP + FP)  =  70 / (70 + 10) =  87.5%

Recall =  TP / (TP + FN)  =  70 / (70 + 5) =  93.3%

F1 Score =  2 * (Precision * Recall) / (Precision + Recall)

=  2 * (0.875 * 0.933) / (0.875 + 0.933)  =  90.2%

The choice between precision, recall, and F1 score depends on the specific context and objectives:

• If false positives are more costly, prioritize precision.
• If false negatives are more costly, prioritize recall.
• If a balance is needed, the F1 score is a good metric to use.

The F1 score provides a single, comprehensive measure of a model's accuracy by balancing precision and recall, making it a valuable tool for evaluating and comparing classification models.