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Appen Launches Three New Products to Build Trustworthy Generative AI Applications

Appen Limited, a leading provider of high-quality data for the AI lifecycle, announced the launch of three new products designed to enable clients to unlock generative AI and power exceptional customer experiences with our industry-leading products, depth of expertise and unmatched global team of AI Training Specialists. These products are:

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“Generative AI is going to change human experiences in ways that we have not even thought about. We at Appen are excited to power those experiences for our clients”

  • Reinforcement Learning with Human Feedback tackles the risks of bias and hallucinations in large language models.
  • Document Intelligence enables clients to extract key insights from their unstructured documents.
  • Automated NLP Labeling leverages generative AI capabilities and zero/few shots learning techniques to speed up data annotation.

In addition to these product launches, Appen is also expanding its offerings to include a new vision for the next phase of growth. The company is expanding its data for the AI lifecycle company strategy to be an AI platform company that focuses on enabling clients to create AI-powered experiences for their end customers. This new vision will address four common challenges: unusable data, difficult data preparation, incomplete data, and the lack of a sophisticated data pipeline.

Appen’s CEO, Armughan Ahmad, said, “We are excited by this next phase for Appen. We will continue to create products and services that serve the data needs of our clients. Our generative AI products we announced today are a great example, and we are just getting started.”

The rise of ChatGPT and large language models has brought attention to the potential of generative AI to revolutionize how humans and machines interact. However, one of the challenges of generative AI is producing accurate and ethically aligned results. That’s where Appen’s Reinforcement Learning with Human Feedback comes in. This product enables clients to generate prompt-response pairs that are engineered by AI Training Specialists and reviewed for accuracy and bias by a diverse group of AI Training Specialists.

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Another challenge is extracting insights from unstructured data in the form of scanned and photographed documents. With Appen’s Document Intelligence product, clients can make any document a usable data source, thanks to exceptional accuracy results of 99% on diverse documents. By leveraging high-quality data, businesses can better understand their customers’ needs, preferences, and behaviors, and use that knowledge to personalize interactions and anticipate their needs.

Finally, Appen’s Automated NLP Labeling product provides clients with a fast, efficient way to annotate data using zero-shot or few-shot learning techniques and large language models. The annotated dataset is then reviewed by AI Training Specialists to ensure quality and accuracy.

“Generative AI is going to change human experiences in ways that we have not even thought about. We at Appen are excited to power those experiences for our clients,” shared Sujatha Sagiraju, Chief Product Officer at Appen. “It is critical to have human feedback while building Generative AI applications to ensure brand integrity and prevent bias and toxicity.”

Appen’s products are backed by industry-leading research in transformer models and generative AI, making Appen a trusted partner for AI innovation. Whether clients are just starting their generative AI journey or looking to enhance their existing solutions, Appen has the products and expertise to meet their needs.

In closing, Armughan Ahmad said, “We strive to ‘Do Good, Be Good, and Lead Good.’ By ‘doing good’, we aim to contribute to society through our crowd model and social impact initiatives. By ‘being good’, we act with integrity and do the right thing as a company. And by ‘leading good’, we strive to improve how AI impacts the world – ethically and without bias.”

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 [To share your insights with us, please write to sghosh@martechseries.com] 

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