Use of AI for Training Data in Global Organizations

Using AI for training data unlocks a powerful tool enabling organizations to make better decisions, improve efficiency and enhance customer experiences.
Use of AI for Training Data in Global Organizations - Crowdsourcing Week

Written by Clive Reffell

Using AI for training data unlocks a powerful tool that can enable organizations to make better decisions, improve efficiency, and enhance customer experiences. When properly implemented, AI can be used to power intelligent technology, helping organizations to gain insights from vast amounts of data and automate complex tasks. We look at some new research findings on who is using it, and what for.

One way that organizations can use AI to train data is by leveraging machine learning algorithms. These algorithms use statistical models to analyze large amounts of data, identifying patterns and making predictions based on that data. By continually training these algorithms on new data, organizations can improve their accuracy and effectiveness over time.

For example, a global retail company might use AI to train data on customer purchasing patterns, preferences, and behaviors. By analyzing this data using machine learning algorithms, the company could identify patterns that can help them optimize their inventory management, improve their marketing efforts, and deliver personalized recommendations to customers.

Use of AI for Training Data in Global Organizations - Crowdsourcing Week

Conversational AI systems require large amounts of high-quality data. Image source: LXT

Another way that organizations can use AI for training data is through natural language processing (NLP) technologies. NLP uses AI algorithms to analyze human language, enabling machines to understand and respond to human language in a more natural way. This technology can be used to power intelligent conversational AI systems such as chatbots and virtual assistants, allowing organizations to provide better customer service and support at scale. Large sets of speech-based data in a variety of languages and dialects can be used by customers around the world.

Examples of using AI for training data and powering intelligent technology

North America

One of the most well-known examples of AI-powered intelligent technology in North America is Amazon’s recommendation engine. The company uses AI algorithms to analyze customer browsing and purchase history, as well as other data points, to suggest products that customers are likely to be interested in.

Another example is Google’s use of AI to train its search algorithms. Google uses machine learning to understand the context and intent behind user queries, allowing the search engine to deliver more relevant results to users.

Microsoft is also heavily invested in AI, using the technology to power everything from its Cortana virtual assistant to its Azure cloud platform. The company uses AI to analyze data from a wide range of sources, helping businesses to make better decisions and improve efficiency.

Europe

Siemens is a global engineering and manufacturing company that is using AI to improve its industrial processes. The company is using AI to analyze data from sensors and other sources to identify potential issues before they become problems, helping to minimize downtime and improve overall efficiency.

BMW is using AI to power its autonomous vehicles. The company is training its AI algorithms on vast amounts of data, including images and sensor data, to teach its vehicles how to navigate roads and make decisions on their own.

Zalando is a European fashion retailer that is using AI to improve its recommendation engine. The company is analyzing customer data, such as purchase history and browsing behavior, to make personalized recommendations to customers, helping to improve the overall shopping experience.

Asia

Alibaba, one of the world’s largest e-commerce companies, is using AI to improve its customer experience. The company is using AI algorithms to analyze customer data and behavior, such as search queries and browsing history, to provide personalized recommendations and improve product search results.

The Chinese multinational conglomerate Tencent is using AI to develop its virtual assistant, WeChat. The company is using AI to analyze and interpret user messages, allowing the virtual assistant to respond to users more accurately and efficiently.

Rakuten, a Japanese e-commerce company, is using AI to improve its logistics operations. The company is using machine learning algorithms to optimize its supply chain, including inventory management and shipping, to reduce costs and improve delivery times.

South Korean multinational electronics company Samsung is using AI to develop its voice assistant, Bixby. The company is using natural language processing to understand user queries and provide more accurate and useful responses.

Ping An, a Chinese insurance and financial services company, is using AI to improve its healthcare services. The company is using machine learning algorithms to analyze patient data and develop personalized treatment plans, helping to improve patient outcomes and reduce costs.

Where is AI data training on its development path?

For an up-to-date report on this topic we turned to LXT, which is an emerging leader in AI training data to power intelligent technology for global organizations. Their global expertise spans more than 115 countries and over 780 language locales. Founded in 2010, LXT is headquartered in Toronto, Canada with a presence in the United States, UK, Egypt, Turkey and Australia to serve customers in North America, Europe, Asia Pacific and the Middle East. Its services include: Audio annotation; Data annotation; Data collection; Image annotation; Search relevance; Text annotation; Transcription; and Video annotation

Use of AI for Training Data in Global Organizations - Crowdsourcing WeekIn partnership with an international network of contributors, LXT collects and annotates data through crowdsourcing across multiple modalities with speed, scale and agility. Their “Path to AI Maturity 2023” report is a comprehensive analysis of the current state of artificial intelligence (AI) adoption and maturity across industries. The report is based on a survey of 315 senior decision-makers with verified relevant AI experience at US firms with annual revenue of over $100 million and a company size of more than 500 employees.

The report finds that while many companies are making progress in their AI journey, most are still in the early stages of adoption. Only 13% of the companies surveyed are considered “leaders” in AI maturity, while 77% are “explorers” or “experimenters.”

Three critical factors are identified that contribute to AI success: strategy, data, and talent. Companies with a clear AI strategy, a robust data management system, and a skilled workforce tend to be more successful in their AI implementations.

It also discusses the potential benefits and challenges of AI adoption, such as increased efficiency, improved decision-making, and data privacy concerns. Several case studies are included of companies that have successfully implemented AI in their operations.

Finally, the report provides recommendations for companies looking to advance their AI maturity, including creating a clear AI strategy, investing in data infrastructure, upskilling employees, and fostering a culture of innovation. It emphasizes the importance of AI in modern business operations and encourages companies to embrace technology to stay competitive in the rapidly evolving market.

LXT has its own major customers, and their case studies show how it is helping to accelerate AI initiatives around the world with high-quality data.

Overall, using AI for training data is a powerful way for organizations to leverage their data assets and unlock new insights and efficiencies. As AI technology continues to evolve, it is likely that we will see even more innovative applications of this technology in the future. Have you got anything you’d like to share with us?

About Author

About Author

Clive Reffell

Clive has worked with Crowdsourcing Week on sourcing and creating content since May 2016. With knowledge and experience gained in a 30+ year marketing career based in London, UK, he operates as an independent crowdfunding advisor helping SMEs and startups to run successful crowdfunding projects, and with wider social media and content marketing issues.

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