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6-steps-for-Conversational-Bot-success-A-primer 

Make the most of your conversational bot investment with our easy-to-follow guide featuring best practices that can be applied to your digital transformation journey.

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(Featured) 6-steps-for-Conversational-Bot-success-A-primer

Six steps for conversational bot success: A helpful primer for chatbot implementation

See why cloud technology, data protection, AI, interactive voice search and navigation and omnichannel experience are the top digital CX trends in 2023.

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digital trends 2023

The top digital customer experience trends in 2023

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TELUS International, in partnership with Statista, surveyed 250 enterprise leaders to explore where they plan to invest in digital customer experience (CX) technology in 2023. Uncover the results.

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Survey results: Digital customer experience priorities for leaders in 2023

Learn how TELUS International helped a leading European automotive machine learning team build high-quality datasets to power computer vision innovations at scale.

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Computer vision annotation to support autonomous driving systems

Artificial intelligence (AI) proponents around the globe believe that autonomous vehicles, enabled with smart sensors and intelligent algorithms, will significantly improve road safety in the upcoming years. On the other end of the spectrum, opponents feel that autonomous vehicles pose a greater risk to pedestrian and driver safety, requiring additional protective measures. To quell these fears, in July 2022, The Council of the European Union mandated advanced safety systems in all automated road vehicles, including cars, trucks, buses and vans.

Despite criticisms, automotive innovators aren’t hitting the brake pedals on innovation. According to a report by ResearchAndMarkets.com, the global autonomous vehicle market will grow to over $2.16 trillion by 2030. Self-driving cars and robo-taxis from front-runners like Tesla, Waymo and Cruise are already hitting the roads. As autonomous vehicles continue to grow in popularity, AI technologies — powered by high-quality labeled data — will play a key role in ensuring superior safety standards for autonomous vehicles.

The case for autonomous vehicles improving road safety

Despite considerable safety enhancements made over the past decade, recent reports published by the National Highway Traffic Safety Administration (NHTSA) in the U.S. estimated that there were over 40,000 traffic fatalities in the previous year, a 10.5% increase from 2020. Most of these accidents were attributed to human error and caused by distractions, negligence or split-second misjudgments. Thus leading self-driving car experts to believe enhancing the role of technology will save many lives.

Autonomous vehicles offer distinct levels of autonomy, ranging from Level 0 to Level 5 (L5), where each level reduces the amount of human intervention in driving. Today, many AI-enabled driver assistance technologies like adaptive cruise control, automatic emergency braking and lane departure or forward collision warning systems — considered Level 2 — offer built-in support for drivers to reduce accidents and the resulting injuries or fatalities caused by impact. They are common additions to new vehicles sold in the United States and Europe.

A study conducted using the RAND Model of Automated Vehicle Safety concluded that highly automated cars that are 10% safer than human-driven cars offer a better probability of reducing fatalities and saving more lives than waiting for systems to be 70% or 90% better. “We find that, in the short term, more lives are cumulatively saved under a more permissive policy (Improve10) than stricter policies requiring greater safety advancements (Improve75 or Improve90) in nearly all conditions, and those savings can be significant — hundreds of thousands of lives,” the study outlines.

The sheer potential of autonomous vehicles continues to push car manufacturers to pursue L5 autonomy, where autonomous vehicles drive and make critical decisions on the road without human intervention.

High-quality annotated data will fuel the autonomous vehicles of tomorrow

Autonomous vehicles, functioning via intricate multi-neural network systems, require large volumes of diverse and accurately-labeled datasets to execute complex actions in response to real-world stimuli. Supervised learning is one of the main machine learning (ML) techniques employed to train autonomous systems.

Primarily, computer vision models enhance visual senses for AI-powered mobility systems. Data collected from various sensors such as cameras, lidars and radars are labeled accurately to help autonomous vehicles develop machine vision and perception. These algorithms are trained using the annotated data to perceive its surroundings and detect other vehicles and pedestrians sharing the road.

The standard data inputs for computer vision models include image, video, point clouds and geo data. Data annotation procedures, such as image classification, object localization, detection and tracking, image or point-cloud segmentation, HD mapping and more, help train and familiarize models with the real world. From simple bounding boxes or polygons, to more complex 3D semantic segmentation, the type of labels used for the data varies from one use case to another. Case in point — a lane detection machine learning model will benefit from accurate lane markings made using polylines, and a pedestrian detection ML algorithm will produce better results using polygons.

Increasingly, innovators are also experimenting with speech recognition and natural language processing applications to enable voice-assisted features in autonomous vehicles to refine the overall driving experience. Text and audio data annotation such as transcription, translation, text categorization, linguistic annotation, classification and more provide inputs for these models. The key to unlocking all of these possibilities lies with high-quality and accurately labeled data.

There’s still a long road ahead before self-driving cars are fully capable of making judgments without human intervention. Better autonomous vehicle perception requires high-quality data spanning several different real-world scenarios. However, sourcing training datasets that accurately represent a large volume of edge cases and safety-critical scenes can be challenging. Moreover, the dynamically evolving nature of our world and human unpredictability on roads calls for advanced and sophisticated mobility systems trained on accurately annotated data.

Generating comprehensive datasets for fail-safe models demands labor-intensive processes and sophisticated data solutions to collect and label data, test and validate quality, and ensure data diversity and volume of edge cases. TELUS International is a leading AI Data Solutions provider with years of experience building high-quality datasets for autonomous driving use cases. Talk with our experts and leverage our expertise to accelerate your autonomous vehicle projects.

Experts believe autonomous vehicles will substantially improve road safety. Discover how large, diverse and accurate datasets are enabling this vision.

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AI Data Solutions

Does data hold the key to autonomous vehicle safety?

Machine learning demands massive amounts of clean, structured, labeled data. Learn the fundamentals of data labeling for successful AI training.

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Data labeling fundamentals for machine learning

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Outsourcing Locations E-book Feature

Discover some of the top outsourcing destinations for consideration and the factors to keep in mind as you make your decision.

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Selecting the best outsourcing destination for your business

You've heard of product-market fit, but what about profit-market fit? Learn the difference and how scaling tech brands can achieve it.

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Why achieving profit-market fit is essential for scaling brands

Generative AI is a type of AI that can be used to generate new content such as text, images, audio, video, code or synthetic data. Discover exactly how generative AI works and what you can do with it.

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Generative AI 101

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Help your agents reach their full potential with automation

Even consumer robotics companies can benefit from a little help deploying customer experience (CX) automation. Learn more about TELUS International’s solution to improve a client’s agent efficiency and engagement.

Learn about how we helped a consumer robotics company improve their digital customer experience with agent-facing automation.

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Deploying automation to assist agents at a consumer robotics company

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AI & Bots

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The future of content moderation: Strategies and tools for 2023 and beyond

Organizations have a responsibility to create and maintain digital experiences that are truthful, welcoming and safe. Learn how brands can keep pace with the growing levels of user-generated content (UGC) to build trust with their customers and protect their reputation.

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In this IDC Info Snapshot, learn about the three key pillars for any successful AI implementation and the advantages of partnering with a trusted advisor to create and deliver your AI vision.

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Quality, quantity, and responsibility: Three key data pillars for successful AI implementations

Natural language processing (NLP) is the term used to describe a machine’s ability to interpret and respond to language data. Learn why it is a key AI concept.

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What is Natural Language Processing?

What is natural language processing?

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Discover best practices for the sourcing, labeling and analyzing of training data from TELUS International, a leading provider of AI data solutions.

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The essential guide to AI training data

Player support outsourcing isn't just for Triple-A studios. Learn about the benefits of outsourcing for growing games companies.

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What growing games companies can gain from player support outsourcing

Germany's diverse and talented workforce make the European nation an ideal CX outsourcing destination. Read up on all the benefits.

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Don't overlook Germany as a CX outsourcing destination

Learn how TELUS International helped a leading automotive lidar systems provider create high-quality flash-lidar 3D datasets with our fully-managed data annotation solutions.

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Industry - Automotive - Solutions - Image annotation 

Building high-quality 3D flash lidar datasets for autonomous vehicles

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Learn how a leading technology client leveraged TELUS International’s AI Data Solutions to build a multilingual dataset that helps researchers scale natural language understanding technology.

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Building a multilingual dataset with high-quality data collection and annotation

"In the pursuit of creating truly remarkable customer experiences, we must embrace the power of generative AI," explains Alexander Grayson, founder and CEO of AI-led start-up, Quixl. "It allows us to think differently and break free from the constraints of conventional wisdom, propelling us into a realm of unimaginable possibilities."

Grayson makes a convincing case for the role of artificial intelligence (AI) in the future of digital customer experience (CX).

There's just one problem. Neither Alexander Grayson, nor Quixl, exist.

The Silicon Valley-based tech firm and its enigmatic founder are a construction of OpenAI's ChatGPT-4, one of many wildly popular generative AI applications on the market today, like Google's Bard and Amazon's Bedrock. With some prompt engineering, the technology swiftly took up the role of a successful founder of a start-up with an ambiguous name. Alexander Grayson even has a favorite food (sushi) and a reason for loving it so much ("it combines simplicity with artistry").

Fake though he may be, Grayson's belief in the power of generative AI (or "GenAI") is shared by many real people at very real organizations. Take AWS' Vice President of Generative AI, Vasi Philomin, who, in a recent interview with TechCrunch, said, "We think every application out there can be reimagined with generative AI."

Researchers are aligned too. In fact, Grand View Research projects that generative AI's market size will reach over $109 billion by 2030 thanks to an expected compound annual growth rate (CAGR) of 35.6%.

The market growth and pace of innovation is making artificial intelligence and bot technology impossible to ignore for digital customer experience decision-makers.

Tip: If you're looking to learn more about the underlying technology, give our Generative AI 101 article a read.

Generative AI and the customer experience

There's no question that generative AI is capturing the collective imagination.

But brands aren't daydreaming. There are real, powerful digital customer experience applications being explored, deployed and iterated upon in the here and now. Let's look at some of those applications and their benefits.

The frustration that comes from finding the boundaries of a chatbot's knowledge may soon be a thing of the past. Chatbots that are powered by generative AI aren't just trained on expansive datasets — they learn from every customer interaction, always improving in their ability to understand prompts and respond appropriately.

Through TELUS International iLabs, our R&D initiative to design and build disruptive solutions for customer interactions, we have long been developing proprietary chatbot solutions leveraging Natural Language Processing (NLP) and its other utilitarian tools, including speech recognition and semantic understanding, for our clients. Generative AI technologies represent a huge, exciting step forward in this ongoing evolution to develop even better bots that are more conversational, can answer follow-up questions, admit mistakes and even reject inappropriate requests. These technologies are being built into our award-winning customer-facing bot — intelligent TELUS International Assistant.

With all that said, there will always be customers who want to speak to a human being out of preference, or because they struggle with traditional self-service methods for one reason or another. A smart approach involves getting help to deliver the best chatbot experience possible, but to have an empathetic team of human agents ready to step in and support when necessary.

Automotive

Does data hold the key to autonomous vehicle safety?

Does data hold the key to autonomous vehicle safety?

Building high-quality 3D flash lidar datasets for autonomous vehicles

Building high-quality 3D flash lidar datasets for autonomous vehicles

Computer vision annotation to support autonomous driving systems

Computer vision annotation to support autonomous driving systems

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