Why Speed is the Biggest Hurdle to Building Voice AI Agents

Imagine you are at home, trying to impress your friends with your brand-new AI-powered smart home technology. You confidently say, "Hey AI, turn the living room lights on!" Everyone waits. Everyone waits... and waits. After an awkward pause, the lights finally flickered on.

This funny but frustrating delay is a perfect example of latency in AI. Latency refers to the time delay between when an AI system receives an input and generates the corresponding output.

The impact of latency on downstream applications and end-users becomes more important as these AI systems become more complex and integrated into our daily lives. Even minor delays can seriously affect AI applications like autonomous vehicles and real-time fraud detection.

This article discusses AI latency, bottlenecks contributing to delays, and ways to reduce it. By understanding and addressing these challenges, we can ensure that AI continues to deliver seamless, real-time experiences across various applications.

Latency refers to the time delay between when an AI system receives a command and when it returns a response. In other words, it is the time elapsed from the moment you instruct your AI system to act until it completes the task and provides you with an output. 

For example, when interacting with a voice assistant, latency is the time between sending your message and receiving the assistant’s reply.

Low latency is highly desirable in AI applications. Lower latency, typically measured in milliseconds, enables companies to use AI for real-time applications such as voice assistants, which would otherwise suffer noticeable delays.

High latency is one of the primary reasons customers may abandon an AI-powered application, as they expect swift and seamless interactions.

Achieving low latency is crucial to the success of AI products, the user experience, and the revenue generated by AI applications. This subsection explores the reasons why latency matters in AI systems.

Imagine launching a facial recognition AI company to compete with tech giants like Apple. However, there's a significant drawback: users must wait about three minutes for their Face ID to unlock their phones. 

An extended wait time could make attracting and retaining customers easier, putting your product at a disadvantage compared to established competitors in a fast-paced world.

Latency is key in real-time applications such as autonomous vehicles. These vehicles use sensors, cameras, and AI algorithms to detect obstacles, recognize traffic signals, and respond to dynamic driving conditions. 

Low latency, often below 100 milliseconds, allows vehicles to quickly interpret data and react almost instantaneously to avoid accidents and ensure passenger safety.

Low latency ensures that AI applications can respond in real time, maximizing the utilization of resources such as CPU, GPU, and memory. It also enables AI systems to handle more requests within a given timeframe by reducing the idle time between data processing tasks. 

Reducing latency can lead to cost optimization across the entire solution by minimizing the need for additional computing resources.

So far, you know why latency is important and why you should always strive for lower latency in your AI applications. However, this is more challenging than it sounds. In this section, you will learn about the challenges of reducing latency.

One of the biggest challenges in achieving low latency is data management. AI systems that focus on deep learning analyze and process large datasets, which must be processed timely and efficiently.

Analyzing such datasets requires high computational power. The main challenge is accelerating the speed at which data is processed.

In addition to data processing and transfer speed, they might face storage delays. Storage delay is the time it takes to write and retrieve data to and from storage devices. 

Another challenge to reducing latency is model complexity. Larger models require more parameters, which increases processing time. For example, OpenAI’s Whisper large-v3 model contains about 1550M parameters. 

Optimizing large models for quicker processing and response times is critical to cutting latency. You can achieve this through model compression techniques or using more efficient architectures. 

However, there is often a trade-off between model accuracy and latency, as more complex models tend to achieve higher accuracy but at the cost of increased processing time.

The computational capabilities of hardware components like CPUs, GPUs, TPUs, NPUs, or AI accelerators can also determine how quickly you can train AI algorithms or run inference. 

Complex ML models often require significant computational resources, and if the hardware lacks sufficient processing power, it can lead to latency. Optimizing hardware for AI workloads through parallel processing or specialized instruction sets can help mitigate this challenge.

Slow network connections or congestion can delay data transmission between components, impacting latency. The faster your network and the higher your bandwidth, the faster data packets get sent and the lower the latency.

When a computer wants to send a message to a server, there is bound to be latency. This latency is a by-product of several factors, such as firewalls, computer load, and network traffic. You can minimize network latency through network optimization techniques or edge computing to process data closer to the source.

Biometrics and face unlock are use cases of latency in action. This technology is used in sectors like finance and security, where latency directly impacts user experience and trust. Imagine the frustration of attempting to pay for a service using your banking application only to find that the biometric scan takes five minutes to complete. 

In biometric authentication systems, latency should ideally be below 500 milliseconds to ensure a seamless user experience. An increased latency could increase the tech’s vulnerability to spoofing attacks.

Healthcare is a critical industry where latency can have severe consequences. Low latency is paramount when working with time-sensitive applications, such as remote monitoring systems that gather and track patient physiological data outside a conventional medical setting. 

These systems enable healthcare teams to monitor chronic conditions, but any delay in data transmission can result in late notifications, negatively influencing patient care. In remote monitoring applications, keep latency below 250 milliseconds to ensure timely intervention. 

Low latency is also crucial in other healthcare applications, such as telemedicine and real-time data analysis for early detection of medical conditions.

Financial companies process millions of transactions daily, and low latency is essential for the success of their applications. A recent study shows that nearly 90% of business leaders now require a latency of 10 milliseconds or less. In algorithmic trading, for example, rapid data analysis from various sources is used to capitalize on price discrepancies. 

A one-second delay can affect a company's potential revenue and user experience for applications handling millions of transactions daily. Low latency enables traders to execute orders faster than competitors, increasing profitability.

Picture yourself engaged in an intense online football match with your friends, poised to score a crucial goal. Just as victory seems within reach, delays disrupt the game flow, leading to frustration. 

In competitive gaming, milliseconds of latency can significantly impact gameplay. VUVY, a gaming company, migrated from its on-premise solution to AWS and observed a 10% increase in user retention.

The Chief Operating Officer, Fuat Şeker, stated, "When the loading speed of our games increases, users experience better gameplay. Then, we observe an improvement in the retention rates of new users." 

For optimal gaming experiences, latency should be kept below 50 milliseconds. Game developers and service providers can optimize their systems to reduce latency using edge computing or dedicated game servers.

Smart homes rely on low latency for a seamless and responsive user experience. Voice commands to control lights, thermostats, or security systems should result in near-instantaneous actions. Edge computing significantly reduces latency in smart home systems by processing data closer to the source.

Latency in a smart home can lead to frustrating delays and unresponsive devices—the smaller the latency of a device, the more human-like it seems. It could also pose potential security risks. For example, a delayed response from a smart security system could give intruders more time to act before an alarm is triggered.

So far, you have learned why low latency is important in AI. A question that readily comes to mind is: How can I achieve low latency in my AI systems? 

Edge computing involves processing data closer to the source rather than in centralized data centers. It reduces latency by processing data locally and conditionally sending appropriate tasks over long distances for additional processing, significantly speeding up response time.

For example, edge computing enables real-time object detection and tracking by processing video frames directly on the camera in an AI-powered video surveillance system. This reduces the need to send large amounts of data to a central server.

However, edge computing also presents challenges, such as limited processing power and storage capacity at the edge devices, which may require careful optimization and resource management.

As already established, the larger your model, the more computational power you need to process the datasets. Therefore, using specialized hardware such as TPUs and GPUs can significantly improve the processing time of large datasets and large models. 

These devices are tailored for the parallel computations required in AI, lowering the time it takes to process massive volumes of data.

TPUs are designed explicitly for AI workloads and offer high performance and energy efficiency, while GPUs are more versatile and can be used for a broader range of applications. Depending on the specific requirements of the AI application, you can also consider other specialized AI hardware, such as FPGAs and ASICs.

It is also possible to reduce latency by optimizing your models. Some known ways to optimize your models are:

• Model Pruning: This involves removing some unnecessary parameters. This will make your models more efficient without significantly impacting metrics such as accuracy or F1-score. For example, pruning can remove redundant or less essential neurons for speech recognition in a deep neural network, reducing the model size and computational requirements.
• Quantization: Quantization is another known way of optimizing your models. This involves reducing the precision of numbers used in model calculations, such as by decreasing from 32-bit to 16-bit. Quantization can significantly reduce AI models' memory footprint and computational cost, especially on resource-constrained devices.
• Hyperparameter Tuning: By tuning a model's parameters (learning rate, batch size, regularization strength), you can optimize its performance and reduce training and inference time. This improves efficiency and results.

However, model optimization often involves trade-offs between model performance and computational efficiency. It is essential to strike the right balance based on the specific requirements of the AI application.

Reducing the amount of data sent over a network can also improve latency. You can compress and filter data to reduce the amount, reducing latency and network congestion. 

For example, data compression techniques like Huffman coding or run-length encoding reduce the size of data transmitted over the network.

Alternatively, you can utilize more rapid and effective communication methods to reduce network congestion. Such networking includes low-latency protocols like QUIC or optimizing network architectures with Content Delivery Networks (CDNs) or Software-Defined Networking (SDN).

Frequently accessed data should be cached for faster retrieval. By caching, data you frequently access is stored in a cache to reduce query load on the central server. It increases retrieval speed and helps reduce the overall system latency to improve the performance of AI applications.

For example, in a recommendation system, caching can store frequently recommended items in memory, reducing the need to fetch them from the database on every request. However, caching also introduces challenges, such as ensuring data consistency and managing cache invalidation, which must be carefully addressed.

• Improved user experience: Low latency will make users happier and significantly increase their retention of your AI products. 
• Enhanced AI performance: In healthcare, low latency enables quicker decision-making and real-time capabilities, leading to more effective treatments and improved patient outcomes.
• Competitive Advantage: Low latency can be the critical reason users choose your AI products over others, providing faster responses and a superior user experience.
• Cost Efficiency: Low latency decreases operational expenses and increases overall cost-effectiveness by reducing the time needed for data processing and transmission. 

In the long term, achieving low latency in AI systems can foster innovation and enable new applications previously infeasible due to latency constraints.

This article has taught you about the importance of latency in AI systems. Low latency is essential in various industries, such as healthcare, finance, e-commerce, gaming, autonomous vehicles, and smart homes. 

As AI continues to transform these sectors, the need for real-time, responsive systems will only grow, making latency optimization a critical priority for businesses and developers alike.

You have also seen some ways to minimize latency in AI systems, such as:

• Optimizing your hardware
• Optimizing your models
• Introducing edge computing
• Implementing network optimization techniques
• Caching your frequently accessed data

While we've discussed several strategies for minimizing latency, it's essential to recognize that pursuing lower latency is an ongoing challenge. Researchers and engineers continuously push the boundaries of hardware, software, and algorithms to achieve even faster and more responsive AI systems.

Latency refers to the time difference between when an AI system receives a command and when the system responds to it.

A lot of factors can contribute to high latency in AI systems. Hardware limitations, network constraints, model complexity, and inefficient processing systems are a few reasons for high latency in AI systems. 

There are many ways to minimize latency. Optimizing network requests, hardware acceleration, hyperparameter tuning, introducing caching mechanisms, and edge computing are some ways to reduce latency in your AI systems.

Low latency is critical in applications such as real-time financial trading, autonomous vehicles, medical diagnosis, online gaming, and voice assistants, where quick responses are essential for optimal performance and user satisfaction.

Latency can significantly impact the functionality of AI products. Users often prefer products that offer faster responses and a superior user experience, so AI products with low latency have a competitive edge in the market.