An AI State of Mind – Demystifying AI

welocalize August 4, 2022

Artificial intelligence (AI) has seen increasing adoption across various industries worldwide. In its “An AI State of Mind” report, Baird, a multinational investment bank and financial services firm, interviewed nine global companies, including Welocalize, on how they use AI in practice.

Welocalize Chief Innovation Officer Chris Grebisz shared his insights on how the company is leveraging AI to increase efficiency and productivity, the importance of data in AI, and the future of the technology. This article highlights the key points from the report.

Benefits of AI

The featured companies agree that AI offers four benefits that drive their value and performance in their organizations.

Scalability

AI enables organizations to process a massive amount of data and perform tasks rapidly and at scale. Manual, labor-intensive tasks that take tens of thousands of hours can be automated and completed in a fraction of the time.

Efficiency Gains

Rather than eliminating jobs, AI complements human effort by streamlining workflows, automating repetitive tasks, and optimizing business processes. This allows people to work on higher-level tasks. Grebisz notes,

“Our talent does not want to have 80% of their work be repetitive. AI is allowing us to remove administrative activity from workflows and services, ultimately making work a lot more interesting.”

Improved Quality, Consistently

Manual input is prone to human error. AI helps reduce errors and increase the overall quality of the final product. Welocalize uses natural language processing (NLP) to identify the level of editing necessary for machine translation output to meet the required quality standards. For example, simple sentences are automatically translated, while complex ones are sent to domain translation experts.

New Insights

AI can also derive business intelligence and predictive insights for organizations. This includes insights into their operations and insights about the behavior and preference of their clients.

The Importance of Data in AI

The effectiveness of AI depends on large amounts of high-quality data, whether tens of thousands of data points for neural networks or hundreds of billions of words for large language models. These are the elements of data needed in AI.

Cloud Infrastructure

Digital transformation has led to the creation of vast data sets used by modern AI algorithms. The shift to cloud-based infrastructure has allowed organizations to process digital information at a low cost.

For example, Welocalize migrated its client services delivery platform to the cloud, enabling it to manage and complete localization projects rapidly, accurately, and at scale. As Grebisz explains,

“In the absence of having that infrastructure in place, it would be impossible to do this at scale. And we have many, many hundreds of local pairs, which require any number of resources to process those local pairs. AI is how we identify the best resources to process incoming work and ensure its quality.”

Custom-Made Training Data

When high-quality data is unavailable for legal reasons or it will be used for a new product with no existing data, high-quality custom-made training data is created to feed machine learning applications. Synthetic data includes text, video, audio, and images.

High-Quality Data

For AI to work, it requires high-quality training data it can use and learn from. This means the data must be accurate, representative, and unbiased. AI developers and data creators must work closely to collect and develop training data.

Data Labels

Labels are critical to training AI algorithms. Like data, they can be already present, such as a list of previously flagged transactions, or created, such as image annotation to word translation. Labels are increasingly being used to train models to recognize more subtle data features, such as hate speech and fake news.

Feedback Loops

AI models are highly dynamic. The algorithms and training data should be kept up to date. Feedback loops are necessary for models to keep improving and learn from their mistakes.

AI Applications

Numerous applications are used for AI, with NLP, predictions, and clustering being the three most popular applications.

Natural Language Processing

Natural language processing is used to analyze, understand, and process text to capture its context, intent, and sentiment. The use cases vary from delivering and optimizing digital ads and understanding and processing contracts.

For language service providers (LSP) like Welocalize, NLP and its subfield machine translation (MT) are used to pre-translate some of the text and route content intelligently. If it’s simple, MT is used to translate it with little human intervention. The work is assigned to a human translator for localization if it’s complex or contains cultural nuances.

“I think our differentiation comes from using machine translation as a standard part of our workflow with translation memories continuously improving it. It also comes from the other NLP tools we are using for content transformation, allowing us to provide more intelligence to the routing of content, the type of machine translation model it should go to, etc.,” said Grebisz.

Predictions

AI models learn to make predictions using labeled data. Predicting the category, such as marking hate speech, is called classification. When the result of a prediction is continuous, such as housing prices, it’s called a regression task. Welocalize uses NLP and prediction algorithms to predict when users will engage with specific content.

Clustering

Clustering recognizes similarities between objects and groups them without labeling the data. It’s not as popular as prediction algorithms but is fairly used, nevertheless.

The Future of AI

Developments in AI are ever evolving. Some of the trends seen regarding its future include the following.

Democratization of AI

AI is no longer limited to university labs, corporate data scientists, and R&D teams. It is fast becoming adopted by more organizations across several industries and within more departments. And it has become much more accessible even to startups and small businesses.

Industry Disruption

AI is also becoming a lot more advanced. For example, NLP and MT have become much more sophisticated. Machine learning allows algorithms to learn with minimal human intervention. There is a risk that certain professions and businesses could soon be replaced. However, in most cases, AI enhances and complements human tasks and workflows.

Business Expectations

There remains a lot of hype surrounding AI. However, there is growing caution about what AI can realistically do, especially for smaller companies. There is also a stronger emphasis on having a clear dialogue between the technical team and the C-suite on what AI can do.

Industry Regulation

Along with the hype over AI comes concerns about its practice and implications. So regulation and legislation are inevitable. The most advanced is the AI Act proposed by the European Commission for high-risk AI. It seeks to ensure quality and unbiased data is used. It also requires algorithms are robust and that outcomes are adequately monitored.

 


Learn more about the practical use of AI, data requirements, and the future of AI. Download Baird’s “An AI State of Mind” report here.

To know more about how AI can help you reach global audiences, connect with us here.