Generative AI continues to evolve at a rapid clip. Innovations like Sora from OpenAI that can take short text descriptions and generate high-definition video clips are rapidly closing the gap between AI-generated and human-generated media, making it difficult to distinguish the source of the content. But while the advances we are witnessing are impressive, the content they generate can still contain errors and biases that only humans can detect. 

What is human-in-the-Loop?

Human-in-the-loop (HITL) in machine learning is an approach that involves humans providing feedback to an AI system to improve its performance over time by correcting errors and identifying biases generated by the AI. While generative AI models have improved significantly over time, HITL has become increasingly important to ensure that applications built on these models are accurate, ethical and trustworthy.

An overview of human-in-the-loop in generative AI

There are several components involved in HITL in AI that help to improve its performance and quality:

• Data annotation: The initial stage of training AI models is one area where humans play a key role by labeling data sets. The machine learning model will use this data to learn and make predictions. After the model is trained with the annotated data, humans review and correct the output of the AI, and their feedback is used to improve its accuracy. Establishing a continuous feedback loop helps the model to learn over time and enhance its predictive capabilities.
• Model training and fine-tuning: In a supervised learning approach, human-generated data is used as ground truth to train the machine learning models. The model will improve its performance by comparing its predictions against human-generated data. 
• Error correction and bias mitigation: After the AI makes predictions, humans evaluate the results, and corrections are added to the model to improve it. Humans can also correct biases by pinpointing where the model might be creating unfair outcomes.
• Continuous learning: With many AI applications, the conditions and trends change over time which requires new data inputs to ensure accuracy and relevance. Human feedback plays an important role here as well.

The importance of human-in-the-loop for generative AI

Many significant advancements have been made in the field of generative AI, but gaps still exist with the technology that highlight the importance of humans in creating more accurate and inclusive AI. Humans play an important role in:

• Improving accuracy: humans are crucial to helping reduce errors in generative AI and ensuring that its outputs align with real-world expectations.
• Ethical deployments: humans have the ability to ensure that bias is mitigated through reviewing the data that is used to train the AI, allowing for more inclusive and ethical outcomes. 
• Dealing with ambiguity and understanding context: humans provide the cultural and ethical context to ensure that the generative AI creates more appropriate outputs.
• Model customization and personalization: when generative AI is developed specific to an industry or domain, domain expertise from humans allows organizations to create more tailored solutions for their customers. Human feedback can also help to tailor models to reflect end user preferences, making them more personalized.

And as mentioned earlier, human feedback is indispensable in the process of continuous learning so that models adjust to changing trends over time.

Applications of human-in-the-loop in generative AI

HITL is used across a range of generative AI use cases to improve the quality of its output, including:

• Text: editors review and correct text generated by generative AI models like GPT to make sure that the content is accurate and appropriate for the target audience.
• Image and video: human review of AI-generated images and videos ensures they align with an organization’s brand identity and target audience while also identifying potential harmful content such as deepfakes.
• Conversational AI: a human-in-the loop approach for conversational AI involves using humans to review and adjust response to user queries in chatbots or virtual assistants for accuracy, completeness, relevance and accuracy.
• Text-to-speech: AI-generated speech can be reviewed by humans to adapt it so that it sounds more natural and human-like, improving the user experience for customer service bots, virtual assistants and more.

Scaling your human-in-the-loop program

While scaling an HITL program internally can be resource-intensive, partnering with an organization with this expertise can significantly accelerate generative AI deployments. The benefits of this type of partnership include the ability to generate high-quality training data quickly, resource and cost optimization, and access to specialized expertise. You can learn more and see examples of how LXT is helping enterprises deploy reliable generative AI in this blog post here.

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But it’s still early days in the deployment of generative AI and companies are experiencing several bottlenecks including security and privacy concerns, accuracy of the output, availability of high-quality training data and fine-tuning the foundation model as shown in the chart below.

Benefits of working with the right training data partner to accelerate generative AI deployments

Companies eager to capitalize on the efficiencies that generative AI delivers can accelerate their deployments and address many of these bottlenecks by working with an experienced AI data partner. This has many benefits including:

• Generating high-quality training data 

Organizations focused on building responsible and reliable AI should train their models with high-quality, diverse datasets to improve model accuracy and inclusivity. Experienced data partners implement thorough quality control processes to optimize data integrity and reliability for their customers. They also have access to diverse groups of contributors so that the datasets they create are representative of a wide range of potential users. These contributors can also evaluate the accuracy of model outputs and provide crucial feedback for model retraining.

• Resource and cost optimization

Outsourcing data collection and preparation allows organizations to allocate their internal resources more efficiently and focus on core activities such as product development. While companies can try to collect and label data on their own, this presents challenges that an experienced partner is equipped to handle including access to large and diverse contributor groups, and the capacity to collect and annotate high volumes of data quickly. 

• Access to specialized expertise

Developing accurate generative AI in the realm of natural language processing requires linguistic expertise that many companies do not have access to within their organizations. Working with experienced linguists ensures that the AI can respond to human language in a nuanced and accurate manner. Some key areas of linguistic expertise include syntax and grammar, semantics, speech synthesis, part-of-speech tagging and named entity recognition. Further, a data partner can help with foundational model fine tuning to guide the model to produce the optimal output for a task based on a specific domain or context, and with prompt tuning to optimize a set of prompts that guide the model’s outputs for specific tasks. 

How LXT is helping enterprise companies create reliable generative AI

Organizations leading the charge to deploy generative AI are harnessing partnerships to accelerate and scale their deployments. LXT is helping companies on several fronts including:

• Prompt and response creation: we’ve helped multiple companies develop high-quality inputs and outputs across several domains to enhance the quality and relevance of the generative AI application.

• Prompt rating and ranking: we’ve also helped various organizations assess the effectiveness of prompts based on specific criteria and rank them, which also helps to improve AI accuracy.

• Instruction fine-tuning: we’ve gathered diverse sets of input-output pairs where the input includes a clear instruction and the output is the expected response, helping to make the AI more adept at understanding and executing tasks based on given instructions.

To learn more about LXT’s generative AI capabilities and how we can help you build more accurate and inclusive generative AI, visit our services page here.

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Setting the strategy

With increasing pressure to drive growth while managing costs, AI can often be seen as a magic wand that can solve many business challenges. However, AI leaders recommend a more methodical approach to deploying AI. One survey respondent had this advice to share: 

“Understand the problem you are looking to solve. Many people try to solve something that is too large and unachievable. Set realistic goals and work towards them”. 

By successfully deploying low-hanging fruit use cases, the learnings can then be applied to tackle larger ones. Another AI practitioner explained, “I would suggest starting with limited scope pilots focused on a single use case rather than trying to deploy AI across the entire organization all at once.” Look for processes that are working well and can be further optimized with AI.

Deploying responsible AI

Another participant in our research study focused their advice on the ethical side of AI with this guidance:

“Develop a clear and comprehensive ethical framework for AI deployment. This framework should address issues such as fairness, transparency, accountability and impact of AI on various stakeholders.” 

An important point is that this ethical framework should be developed early in the strategy formulation process and that these guidelines are established before deploying or scaling any AI solutions. The ethical framework should be a guiding principle in strategic decisions and AI deployments.

There are multiple components to the development of a strong ethical AI framework:

• Creating an AI ethics committee: this should be a group of diverse stakeholders who can bring a variety of perspectives to the conversation. This committee is responsible for setting the ethical guidelines, reviewing practices and handling ethical challenges that may arise.
• Applying the ethical framework: AI product and project managers are responsible for applying the guidelines in the day-to-day development and deployment of AI technologies throughout the product lifecycle.
• Ensuring compliance: Legal and Compliance departments ensure that the AI ethical framework complies with international, national and local regulations. This is an ongoing process that should be reviewed regularly in response to new technology developments, legal and compliance requirements, and practical experiences from AI implementation.

In the end, deploying responsible, ethical AI is key to building customer trust, and needs to have accountability across the organization.

The importance of data for successful AI

In a recent episode of our Speaking of AI podcast, guest and thought leader Jeff Winter stressed the importance of a data strategy to succeed with AI:

“Companies should have a clear data strategy before diving into AI…this means investing in the process of cleaning and organizing their data, because no matter how advanced the AI is, it can’t work its magic with messy data…The moment someone says that they have an AI strategy I go, great, can I see your data strategy?”

Several respondents in our survey shared a similar view, providing specific examples of what to think about in building this strategy: 

• “Make sure the data you have is reliable, clear, and reflects the issue you are attempting to solve.”
• “Implement robust data governance policies to maintain data integrity, security and compliance. Establish protocols for data access, storage, sharing, and updating to ensure consistency and reliability.”

Another participant highlighted the connection between data and ethics: “Recognize and correct biases in the data that may affect AI results; this calls for thorough study, algorithms for detecting prejudice, and ethical considerations.” 

Once the initial data pipeline is established, training datasets should be monitored and evaluated regularly for a range of factors including quality, data drift, relevance, privacy, ethical considerations and bias.

Putting it all together

In reviewing the collective advice shared by AI practitioners in the enterprise, setting a strong foundation for an AI program requires a holistic approach including the identification of realistic goals for where AI can be applied, a thorough data strategy to support the AI program, and an ethical framework to ensure that AI is deployed responsibly. For more insights into how enterprise organizations are deploying AI today, download our Path to AI Maturity 2024 report here.

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Risk management is currently the lead driver of AI strategies in the enterprise, but organizations also see AI as a means to being more agile, driving differentiation, and managing their operational performance, among other objectives.

The types of AI applications deployed in the enterprise vary from search engines to speech and voice recognition to computer vision, demonstrating the wide applicability of the technology.

These and many other insights can be found in our Path to AI Maturity 2024 report.

In our new report on the ROI of training data, you’ll learn how enterprise organizations evaluate their training data investments, and how this varies by stage of AI maturity. You’ll discover how training data is sourced and the various roles that data service providers play in building AI data pipelines.

The survey included responses from 322 senior decision-makers at US organizations with at least $100 million in annual revenue and more than 500 employees. More than half of respondents were from the C-Suite and all those who took part had verified AI experience. To review the full findings, download The ROI of High-Quality AI Training Data 2024 here.

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Name

Hilary Shirey

Role

Service Delivery Director

Country of origin

United States

Current location

United States

Years working in the AI data industry

10

Favorite AI application

Alexa – I use it all the time at home for weather updates, cooking tips and even celebrity trivia!

Tell me a bit about your background. How did you end up in the field of AI training data?

Prior to working in the training data industry, I was in the education space. I wanted to make a career change and a friend mentioned that there was an opening at the company she worked for that was focused on providing data for companies building AI solutions. It sounded really interesting and something I might be qualified for, so I applied. Two weeks later, I was hired!

Before working as a project manager in the AI data field, I was an Assistant Director/Account Manager for a company that provided an online service for schools through a web-based system. My role was to nurture and retain client relationships, as well as host webinars and train clients. I was also part of the customer care team that received any incoming queries. I traveled a lot and led presentations and trainings at schools and education conferences.

Prior to that role, I worked for a nurse agency where I did HR work for agency and travel nurses. I onboarded new hires and made sure they had all the appropriate compliance paperwork, certifications and vaccines required to begin work. In addition to onboarding, I processed payroll for all the nurses. I was one of the only people that had ever actually worked with Microsoft Office (in college) back in those days so I also trained everyone else in the office how to use it once the team got our first personal computers. Early tech support!

The experience from these two roles really set me up for success in the AI/tech field and the role of project manager. I had the HR background with field employees needed from the nurse agency to understand how to effectively manage contractors, as well as the technical customer service background with the school company and hundreds of hours logged in training and presentations to assist me in effectively managing projects and contractors in the AI projects. These skillsets really helped me thrive in the associate project manager role. 

Once I began to work as a project manager in AI, I felt I had found my niche and my home.  I greatly enjoyed the technical work and the clients we had the opportunity to work with. I also have the opportunity to grow my career and become a leader and mentor so many great people. 

Over the past 10 years you’ve seen managed many AI projects. Can you give me some examples?

My experience working with global tech leaders to build reliable AI data pipelines has given me exposure to many different AI use cases. For example, I’ve led a variety of search relevance projects to improve the user experience for social media platforms. I’ve also managed data collection projects to collect speech and video clips which help improve AI’s ability to recognize sound and video, and junk and spam identification projects to help remove this content from websites. These are just some of the many projects I’ve led, working with hundreds of contributors across many language locales.

Global tech leaders recognize the importance of human feedback in building responsible and reliable AI solutions, including working with diverse groups of contributors to create their data pipelines. I’m excited to continue these efforts in my new role at LXT.

What is one of the most challenging AI data projects you’ve worked on?

In one project, our contributors were instructed to identify a particular emotion in a video and then tag the video as that emotion. We learned very quickly that emotion can be very subjective to the individual person, yet we still had a quality metric to hit!  It was quite challenging to achieve the quality goal, but by working very closely with our client we were able to get enough agreement via guideline modifications to build a good algorithm.

You recently joined LXT.  What aspects of the company were appealing to you?

I really enjoy working in the AI data space and knew that I wanted to continue building my career in this field. When the Service Delivery Director role at LXT became available, I was intrigued based on what I had heard about the company and its growth over the past couple of years. As I met with different leaders at LXT I learned more about the people-centric culture and strong company values that aligned very well to what I was looking for in my opportunity. The role itself was also a good fit for my skills and experience, and I am excited to help LXT continue its strong growth journey.

What are the key aspects of your role at LXT?

As a Service Delivery Director, I am responsible for building and developing a high-performing project management team to ensure that we continue to delight our clients. This includes identifying opportunities for process improvement and applying industry best practices to our workflows. Day in and out, everything I do is geared towards serving our customers and building strong client relationships.

What advice do you have for companies working in AI when it comes to their data strategy?

The AI industry is constantly changing. Agility is key. What you do today will change a few years from now. It’s good to remember that each stage is temporary and be able to maintain agility around the solutions that are built.

When dealing with a new or experimental project or a new type of data, initial pilot projects with seasoned contributors are a must. Small iterations are the key to a successful larger project run that maximizes value.

The post LXT AI data expert spotlight: Hilary Shirey appeared first on High-Quality AI Data to Power Innovation | LXT.

Training data to fuel AI

Organizations investing in AI understand that training data must be included as part of their overall budget, because without it AI cannot reach its full potential. Machine learning models need real world data to learn from that is refreshed regularly to avoid data drift. That is why companies are dedicating 13% of their AI budgets to training data, another finding from our Path to AI Maturity research. They are also investing the same amount into training data as they are in product development, which signals the critical importance of training data to AI success.

The ROI of quality training data

In our research we asked companies to share how they evaluate their investments in quality training data, and three themes emerged:

• Operational efficiency: organizations investing in quality training data see greater productivity and are more able to streamline their AI projects, which in turn helps them launch more quickly to capture more market share.
• Higher success rate of AI programs: companies that invest in training data are seeing better results from their AI investments, because they are making thoughtful decisions about the data they are using.
• Cost savings for AI programs: focusing on procuring the right amount and quality of data to support AI product development helps save costs by removing expensive rework from the use of low-quality data.

By making up front investments in their data pipelines, teams are seeing the downstream impact this can have on the success of their AI initiatives. For further details that can help you make a business case for high-quality training data to support your AI strategy, check out our ROI of High-Quality Training Data report, or reach out to us at info@lxt.ai to connect with one of our training data experts.

The post Why leading organizations are investing in AI training data now appeared first on High-Quality AI Data to Power Innovation | LXT.

Name

Julia Liberty

Role

Director of Business Development

Country of origin

Germany

Current location

United States

Years working in the AI data industry

5

Favorite AI application

There are many great AI applications, but I’d have to say ChatGPT has become a favorite. I use it almost every day.

Tell me a bit about your background. How did you end up in the field of AI data?

To be honest, my career in the AI industry was not planned. When I was in college in Germany studying language and communication with a focus on linguistics, I looked for a job that complemented my studies and started working for an AI start-up called Symanto AI. The company used psycholinguistics and people’s writing styles to build language models that analyze and predict consumer behavior so that companies can develop more targeted marketing strategies. Additionally, they built a large sentiment analysis model to underline positive and negative aspects of products, movies and consumer goods. At Symanto AI I had the opportunity to work on both the product and sales teams, which gave me great exposure to different aspects of the business.

When I moved to Seattle in 2017, I continued working for Symanto AI until I was recruited by AI Data Innovations as an Account Manager focusing on new business development. This was the start of my career in AI data, and over the past five years I’ve had the chance to work with a wide range of clients on all types of AI data projects.

I recently joined LXT and am excited to work with some amazing talents who’ve worked in the AI data space for years.

What made you decide to join LXT?

LXT’s language capabilities always impressed me; usually vendors cover a sliver of what LXT is now covering. Additionally, the team has such a vast amount of experience and talent in this space. I am excited to have the opportunity to learn from and work with them.

Furthermore, I was attracted to the fact that LXT has such a large global footprint and still manages to provide a welcoming work environment for employees. Many companies nowadays struggle with that and don’t pay enough attention to their employees’ needs. I’m excited to be a part of such a warm and friendly organization.

Over the years you’ve seen quite a range of AI use cases. What are some notable examples that come to mind?

The environment-focused use cases for AR/VR always come to mind. I worked on many data collection projects where our team created a variety of collection sites resembling real world scenarios, and some where we used actual real-world locations. The set-up for those projects and the teamwork needed to ensure a successful data collection outcome is quite extensive. Furthermore, those collections are often quite unpredictable when it comes to the equipment that is used (especially when using prototypes), the participants, and the project timelines.

I believe one of the main reasons that our team was so successful was that we were extremely organized and always planned for the worst-case scenario. For example, if a prototype wasn’t working we would engage directly with the client to understand what we can do on our end next time to troubleshoot without needing their input and use the overhead created by that issue to focus on tasks such as recruiting more difficult participants cells. We also learned what kind of team members are important to make an in-person collection successful; everyone on the project needs to be able to wear multiple hats – sometimes even at the same time.

Another example that comes to mind is an utterance generation project that I led. The client wanted a lot of variety, but at some point there is a natural point of exhaustion with the data. There is a balance between having enough variety in the data and ensuring that utterances sound natural, and I had long discussions with clients on how to get the variety of utterances they wanted while making sure that the utterances reflected real-world scenarios.

What is one of the most challenging AI data projects you’ve worked on?

Most data collection projects have unique challenges, particularly because they are all custom, and in-person collections are often more challenging. Outside of setting up the right environment and meeting the unique requirements of in-person collections, you must also make sure that you can meet client’s volume requirements. Many times the no-show rate is very high (depending on location, project type, collection length, etc.) and you need a strategy in place to ensure that your targets are not impacted by no-shows. Finding the balance between touching base with the participants often enough and when to overschedule is key. Additionally, having a team in place that is flexible is important, which allows you to quickly pivot and add additional collection days/hours to allow for more flexibility for participants.

In your role you help companies determine the type and amount of data they need to improve their AI solutions. How would you describe your approach?

My approach always starts with thoroughly understanding the customer’s end goal so that I design a solution to help reach that goal. Generally my clients have a high-level idea of the data they need, and my role is to help them think through the details. For example when it comes to data for Conversational AI, a client may want to collect utterances across genders and age ranges, but I encourage them to think more broadly and extend their collection into specific ethnicities, dialects, accents, and so on to make their product equally accessible for a wider range of users. It really depends on the target markets and customers they are trying to attract.

What advice do you have for companies working in AI when it comes to their data strategy?

I encourage companies to refine their requirements and make sure they reflect the real world. The right data partner can help define their data requirements to account for this. Then it’s critical to create thorough guidelines, especially when human participants are required. This reduces the potential for costly rework. Ultimately clients should do their due diligence and make sure that they pick a partner that is the right fit for their project to ensure the best outcome.

The post LXT AI data expert spotlight: Julia Liberty appeared first on High-Quality AI Data to Power Innovation | LXT.

Tell me a bit about your background. How did you end up in the field of AI data?

I started my career working in call centers. I worked for Bank of America for 13 years, taking on increasing levels of responsibility and eventually moving into a leadership role as the AVP of Workforce Management. I was managing multiple call centers in various locations. After Bank of America, I spent a year at Expedia managing remote call centers around the globe. These experiences helped me hone my skills in efficient remote workforce management and large-scale operations. When a friend mentioned a role at Butler Hill managing a key project for Microsoft’s transition from MSN Live to Bing, I jumped at the opportunity. This was an exciting initiative in the tech world, and I wanted to be a part of it. 

Over the past 14 years you’ve seen quite a range of AI use cases. What is a notable example that comes to mind?

I’ve worked on a lot of interesting projects over the years. One that stands out is a project that was highly confidential where we recruited contributors in different cities to test the pre-release of a maps application on a smartphone. Contributors were given secure devices without branding so that the company name could be kept confidential. They were instructed to follow routes that were preloaded on the map and to use various modes of transportation: walking, biking, driving or taking public transport. As they followed their routes, they validated street names, landmarks, construction and anything else that was relevant to the route. It was exciting to be part of a project for an application that would become ubiquitous, before it launched to the broader public.

What is one of the most challenging AI data projects you’ve worked on? 

A project that I recall being particularly challenging is one that I managed for a social media client. The client wanted to improve the personalization of its social media feeds and had very specific demographic requirements for project contributors. It wanted contributors to be evenly distributed across various demographics including age, gender, education level and more, which presented a recruiting challenge for our team. In addition, social media is very subjective, and determining quality metrics for this ranking task was a challenge. We ended up establishing a metric around the consistency of results rankings which we then presented to the client for feedback. The client responded favorably to this quality metric which we used throughout the project to evaluate contributor ranking of social media information. Overall it was a very successful project and I was happy to have played a role in that success.

You joined LXT just a couple of months ago. What were some of the key factors that led to your decision?

LXT is in a really exciting growth phase which has a lot of appeal. I have experience building and growing organizations, and I am excited to be a part of that again. It’s a unique opportunity that I really enjoy; implementing frameworks, processes and helping to come up with new solutions for clients. The AI data market has changed a lot in the past five years and companies in our space need to stay agile to be able to meet customer needs. I’m excited to bring my experience to a new organization to help it adapt to this evolving environment. Further, as LXT is known for solutions such as transcription that are more focused on speech data, joining LXT is giving me an opportunity to gain new experience, given that my background is heavier in search relevance programs.

What are your initial impressions of LXT?

Joining the team has been fun and it’s been exciting to see how passionate everyone is to learn from their peers. Everyone I’ve met at LXT has a growth mindset, which is a strong asset for us as we look forward to continued expansion. I’ve also witnessed a lot of creative solutioning for clients, which is critical given the current economic climate. Companies like LXT need to be agile to help clients navigate challenging environments such as this.

Overall there is a major appetite for out of the box thinking at LXT. Creative methods are used to help meet customer needs, and I love having a strong team that I can collaborate with to come up with innovative solutions.

Finally, LXT’s culture has some key strengths that make it a great place to work. There is a culture of working smart and taking accountability to make sure we do the right thing for the client and for the company. We also take the time to celebrate successes and recognize individuals for their hard work, as well as share lessons learned in the spirit of continuous improvement. It’s an incredible team to work with. 

What advice do you have for companies working in AI when it comes to their data strategy?

What immediately comes to mind is to prioritize a plan for addressing potential data bias. Companies need to do the work up front to combat this. Data bias can be tackled by thoughtful task design, appropriate guidelines, diversifying the contributors who collect and annotate the data, and ensuring that data sources are transparent. Organizations are getting better at this, but there is still work to be done here and it’s critical for building responsible AI. 

The post <strong>LXT AI data expert spotlight: Carolyn Harvey</strong> appeared first on High-Quality AI Data to Power Innovation | LXT.

Tell me a bit about your background. How did you end up in the field of AI data?

I was working in tech before I joined LXT, and in 2016 I was approached by Yassin Omar, LXT’s COO, to work as a project coordinator. I knew Yassin and his brother Mohammad Omar as we’ve been friends for some time. I started with LXT as a freelancer, and after a few months I was asked to join the company full time. We started working on more and more projects in many new languages and locales, and things just continued to grow. Since joining LXT I’ve had the opportunity to work on many different projects including data collection for speech, image and video data, speech transcription, and image and text annotation. 

Since joining LXT almost seven years you’ve seen quite a range of AI use cases. What is a notable example that comes to mind?

One project that stands out to me is the one where we worked on emoji transcription. This was a completely new project type for LXT and we needed to find very creative freelancers who could describe a variety of emojis so that our client could develop its keyboard capabilities to provide users with emoji suggestions. We did this in 60 languages, which was a massive undertaking, and we succeeded in delivering the data to the client in four months. This was a cutting-edge project at the time, and it was really interesting to be a part of it.

What is one of the most challenging AI data projects you’ve worked on? 

I worked on another keyboard project where the client wanted to expand into 60 language locales. The size and scope of the project was massive, and we had to come up with creative solutions to get data from all of the target language locales. We also had to review the data to make sure it was complete before sending it to the client. In some cases, we discovered gaps when we received the data back from our freelancers, but we always made sure that the data delivered to the client was complete, even if it required long nights to make the deadline. As a result, the project expanded to a total of 120 language locales, including some low resource languages. 

What  do you enjoy most about your role at LXT?

What I love most is that this industry is so dynamic. Every day is different which keeps it really exciting. I feel like I am constantly learning new things; we encounter new scenarios or challenges during projects and I enjoy problem solving. The overall atmosphere and nature of my role pushes me to improve and fuels continuous learning. I also love seeing how much we’ve grown as a company. We’ve really evolved since I joined back in 2016 and my role has evolved as well. I started as a project coordinator and over the years have been promoted to a program manager role where I manage eight project managers and 20 coordinators. During this time I also wore multiple hats which gave me exposure to many sides of the business. I was involved in IT and accounting, and even took on a brief country manager role in Egypt to help bridge a leadership gap. I can’t wait to see what comes next!  

What advice do you have for companies in today’s environment?

“When people are financially invested, they want a return. When people are emotionally invested, they want to contribute.” by Simon Sinek.

I strongly believe in creating a supportive work culture to promote employee well-being, which can be achieved by creating a positive and encouraging work environment. Companies should foster a culture that values employee well-being, recognizes employee achievements, and supports employee growth and development. Flexible work arrangements can help employees better manage their work-life balance and reduce stress. Companies should consider offering flexible work hours, remote work options, and other accommodations to support employee well-being. And that’s what I really love about LXT, and what I’m focused on now with the champions that I’m working with. 

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Tell me a bit about your background. How did you end up in the field of AI data?

I studied Linguistics and Translation at University along with American and British Literature. While I really enjoyed these fields of study, in my fourth year I decided that to increase my career options I should expand my studies to include business-oriented subjects. I started a diploma in Supply Chain Management which included Project Management. I really discovered a passion for project management during that time, and after my military service I joined a company here in Egypt to pursue that career path. Not long afterwards I was approached by LXT to join their team. That was in 2018 and I have been here ever since, working as project manager on data collection and annotation projects.

Over the past 4.5 years you’ve seen quite a range of AI use cases. What is a notable example that comes to mind?

One use case that stands out to me is the SUPERB project. The SUPERB team wanted benchmarking data that could recognize emotions. We developed a speech data collection project whereby contributors had to record one set of utterances normally and then one with specific emotions. My role included everything from sourcing the project contributors to processing the data, to reviewing the quality of the data, and I had to make sure that the contributors were following the  project requirements which were very specific. I learned a lot in this process and was so happy when I heard  that the SUPERB team was very happy with the datasets we created for them.

What are some of the challenges in your role and how do you manage them?

The nature of this job requires me to work across multiple cultures and time zones, as well as manage changes that may come up during the course of the project. One approach I’ve used is to spend time with my peers to learn their best practices and then implement them in my day-to-day work. In my role I’ve had responsibility for sourcing contributors, onboarding them and ensuring they complete the required tasks according to the guidelines. This whole process can be very time consuming, but I’ve made it a priority to automate as much of my work as possible. For example, during one project there was a time-sensitive task where naming conventions needed to be changed for five thousand files in 24 hours. Normally that would have taken three days, but, by developing a script, I managed to complete this task in three hours. That was a game changer and I continue to look for more ways to implement automation across everything I do.

What about your role at LXT do you enjoy the most?

There is so much that I love about LXT and my role here. I’ve found many mentors and have learned a lot from them including how to work with individuals across a wide range of cultures, which can be quite challenging. I’ve learned something different from each of my colleagues and have integrated these best practices into my role. 

One area that was really exciting for me was working on a User Acceptance Testing project with our product team in support of LXT’s data platform, LDP. I managed a group of contributors using the platform and gathered their feedback into a report with a list of enhancements based on contributor usage and feedback.

I take a lot of pride in the work I do and it means a lot to get good feedback on my performance. SUPERB was my first project and our COO personally praised my work, which meant a lot to me. Another project I worked on was for our client Dubber where I managed the delivery data in ten languages ahead of time and with high quality. Getting great feedback from happy customers is really rewarding.

Overall, I feel like LXT is my home. I’ve started really building my career here and I love the opportunities I’ve had to learn and grow.

What advice do you have for companies in today’s environment?

I think that companies should prioritize investing in their employees. This can be done in a variety of ways, including everything from making sure everyone is aligned on the strategy, to developing employee skill sets to regular employee recognition. By investing in its employees, companies can encourage them to do their best work which ultimately leads to growth. This is what I’ve experienced at LXT and I feel grateful for that!

The post <strong>LXT AI data expert spotlight: Baraa Nabil</strong> appeared first on High-Quality AI Data to Power Innovation | LXT.