Home News Carolyn Harvey, Chief Operations Officer at LXT – Interview Series

Carolyn Harvey, Chief Operations Officer at LXT – Interview Series

Carolyn Harvey, Chief Operations Officer at LXT – Interview Series

Carolyn Harvey has extensive experience leading and growing global operations in the sphere of search relevance rating and annotation for ML data. Carolyn is currently Chief Operations Officer (COO) of LXT where she leads the corporate’s global operations division, ensuring consistent delivery of all AI data programs and projects. She focuses on high-quality data at scale, constructing efficiencies in long-term programs and scaling across large numbers of world locales.

As COO of LXT, Carolyn lends her wealth of experience to develop a best-in-class organization.

Are you able to briefly describe what LXT does and your role because the COO?

Artificial intelligence relies on data to exist, and LXT is an emerging leader in delivering accurate, ethically sourced data that powers AI innovations. As Chief Operations Officer, my role is to oversee, lead and expand our global operations through strategies, structure, and processes that allow us to deliver the best quality AI data to our customers. I ensure we deliver on time across a big selection of use cases, from generative AI to look relevance and self-driving cars, amongst many others.

How has LXT’s mission evolved since its inception in 2010? 

Our mission is to power the technologies of the longer term through data generation and enhancement across every language, culture, and modality. Our goal is to assist firms of all sizes capitalize on the incredible advantages that AI delivers by powering their models with high-quality data. As the corporate’s mission has evolved, our scope of services has expanded from language transcription and speech collection to incorporate a big selection of solutions, including data collection and annotation for text, image and video, generative AI services, and more. We’ve also expanded our global footprint of ISO 27001-certified facilities to fulfill our customers’ growing needs for secure data services.

What have been the important thing drivers of its growth within the AI training data sector?

Continued investment in AI from organizations of all sizes has fueled our growth. Firms now know that AI is table stakes for them to stay competitive, and data powers AI. But not all data is equal, and firms which might be succeeding in AI know that high-quality data is critical to creating more accurate AI.

Now with generative AI on everyone’s mind, this trend has opened much more growth opportunities for LXT. Humans are critical to making sure that these solutions are accurate, ethical, and responsible. We offer a spread of generative AI services in areas similar to fine-tuning large language models, prompt creation and more. Our customers know that to construct trust with end users, the output of their generative AI products must be factual, represent a various audience, and be freed from toxic language. We may also help them achieve these goals with our human within the loop services.

How has the explosion of generative AI impacted LXT and its customers?

LXT has seen increasing demand for its AI training data because of generative AI, each for core language-oriented data in addition to newer elements related to evaluation, creativity, and significant considering. We’re also seeing a rise in demand for domain knowledge and specialized profiles for project employees.

Customer requests are increasingly going beyond the micro tasking machine learning inputs of the past toward LLMs, and the more complex data sets required by apps like ChatGPT, Gemini and the numerous offshoots. We’re currently involved in several revolutionary projects where we’re writing prompts geared toward confusing the generative AI to see the way it responds, after which creating the proper answer.

In the longer term, this will likely evolve further into artificial general intelligence (AGI) where the information sets will map to much more complicated and complex actions.

You might have years of experience working in search and personalization to assist improve these algorithms. What are a number of the ways in which leading firms are improving their search relevance to offer a greater user experience?

In a world where time is precious and data is in all places, improving search relevance can bolster loyalty, increase conversion rates, and make users more productive.

Search relevance begins with cleansing and organizing our customers’ data, rooting out anything that may generate false positives, and creating additional data fields through which search and suggestion engines can scour to generate more precise results. With the assistance of machine learning and natural language processing, customers can empower their search engine to more intuitively ascertain user intent and find out about their preferences over time. The result’s a faster search experience that results in more personalized results.

Reaching this goal requires large volumes of coaching data, with a specific deal with training algorithms easy methods to recognize, rank and return relevant entities, and easy methods to handle typos, grammatical errors, and other data anomalies. We also recommend a human-in-the-loop (HITL) reinforcement approach to make sure accurate data, reduced bias, and supply a greater search experience for the top user. With ML advancements over the past 10 years, HITL has an intensified deal with quality review processes which drives a necessity for deeper experience from data providers.

Are you able to elaborate on LXT’s approach to data annotation and the way it ensures the standard and accuracy of AI training data?

As an operations team, we must first understand how customers use the information we offer in the event of their services and products to make sure that it would fit their needs. To make this occur, we’d like to search out experts in each project management and annotation who’ve experience with the variety of data required.

From there, it is basically about preparation and finding the appropriate resources in the beginning of every project. This includes aligning with customers on success aspects through the scoping phase in addition to deep qualification and vetting processes for project annotators that consider vital details similar to educational background, special interests, demographics, and experience. We also develop detailed learning and reference materials as a guide, customized for every project. We apply mature quality and process management oversight throughout all project lifecycles. The approach we use aligns with and informs industry best practices, ensuring results are meeting customer expectations.

And all these methodologies are in service of our guaranteed data quality promise.

How does LXT handle the challenge of annotating unstructured data, which comprises over 80% of all data?

LXT has built an internal annotation platform that automates many parts of the annotation process and provides structure and a consistent user interface for employees. Within the pre-processing stage, we deal with preparation of the information, formatting the input files and removing duplicates, amongst other things, and in post-processing, address packaging the information, collating and formatting for delivery to the client.

Before  the project kicks off, we create guidelines which might be reviewed with the client and iterated on throughout the project lifecycle as things change. We will break down the information labeling process into multiple tasks to deal with each element of the project properly. As well as, quality control methodologies are implemented to drive elimination of errors at scale.

Finally, our Operational Excellence Team is liable for advanced process management to make sure high efficiency and scalability for our projects worldwide.

What are a number of the biggest challenges LXT faces in collecting data at scale globally, and the way do you overcome them?

Diversity and bias in participants and within the resulting data collections are sometimes a number of the biggest challenges that LXT, and any AI training data provider, will face. Other challenges include a recent demand for domain expertise and a rapidly changing landscape with the shift to LLMs and generative AI data.

We overcome these challenges through a highly proactive approach to sourcing our candidate pool, where we review expertise, experience, previous roles, interests, and demographics to form the appropriate diversity amongst teams by gender or other elements, similar to analytical considering or creative writing, educational backgrounds, amongst others.

Once now we have sourced the appropriate candidates, we take great care to have interaction employees frequently to construct a more experienced, loyal, and satisfied workforce over the long run.

When it comes to AI evaluation, how does LXT work to mitigate bias and ensure ethical outputs within the AI systems it helps train?

As mentioned earlier, ensuring diversity is a challenge that many AI training data providers must solve, and that can go a good distance toward mitigating bias and ensuring ethical outputs.

I’ll refer again to our engagement best practices which include finding diverse and representative annotators and being thorough with guidelines and quality control measures. Now we have an impact sourcing strategy that permits us to bring work to diverse and latest groups of annotators, similar to in long tail language regions.

We goal ethical outputs through our use of industry best practices, aligning on expectations with our customers and driving higher standards for our project managers and annotators. Communication is important in addition to compliance audits, bias evaluation and a commitment to data regulation and privacy requirements.

What’s the long-term vision for LXT and the way do you see the corporate evolving in the subsequent five years?

 Our vision is to offer accurate, ethically sourced data to assist drive the rollout of AI and the technologies of the longer term that can enhance and improve the experience of individuals around the globe.

While automation and technology are vital in AI, there’s also a crucial human component that enhances the technology. As we move from easy automated tasks to large language models (LLMs), and from generative AI to general artificial intelligence (GAI), it would be critical that AI products faithfully represent the people, each those that generate the information and our global communities at large.

At LXT, we attempt to make sure that AI is utilized in a positive and transformative way that reflects these values.


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