Unleash the power of enterprise Generative AI to drive results

Neural Search is a new approach to retrieving information based on the application of advanced machine learning for Natural Language Understanding (NLU). Specifically, it refers to the use of deep neural nets (there’s the “‘neural”), a specific form of machine learning, to aid in information retrieval. These machine learning techniques search for results with a greater appreciation for the meaning of phrases and their context, instead of searching for the presence of specific words or word forms.

Bidirectional Encoder Representations from Transformers (BERT) is a machine learning framework for NLP developed by Google with a large transformer language model at its core. With TensorFlow BERT as a foundation, Sinequa has built and pre-trained four Deep Neural Network (DNN) models using transfer learning on custom data sets for enterprise content for its Neural Search capabilities.

Neural Search will improve the search result relevance for any use case where the intelligent search is already being used. Text that provides context – full sentences and long phrases – stands to show the most improvement. Examples of places where neural search augments employee abilities may include:

• Research and development, in areas such as drug discovery and product engineering
• Direct answers for customer service, compliance or risk management
• Employee onboarding
• Predictive analytics and chatbots/virtual assistants

Unlike any other search solution available on the market today, Sinequa’s Neural Search uses four models to improve relevance: Meaning Encoder, Passage Ranker, Query Generator, Answer Finder.

Generative AI, also known as generative adversarial networks (GANs), is an advanced AI technique that involves two neural networks: a generator and a discriminator. The generator creates new data samples, such as images, text, or even audio, while the discriminator evaluates the authenticity of those samples.

The generator and discriminator engage in a competitive process, where the generator aims to produce data samples that are indistinguishable from real ones, and the discriminator strives to accurately classify real and generated samples. Through this iterative process, both networks improve their abilities, generating highly realistic and original data samples.

Applications of Generative AI

Generative AI has a plethora of real-world applications that span various domains, each showcasing its incredible potential:

• Enterprise Search: Generative AI enhances enterprise search by improving the relevance and accuracy of search results, enabling natural language understanding, and facilitating personalized search experiences. It also aids in content summarization and recommendation, making information retrieval faster and more efficient.
• Natural Language Processing (NLP): In the field of NLP, generative models enable the automatic generation of human-like text. This technology finds applications in chatbots, language translation, and content creation for various industries.
• Drug Discovery: The pharmaceutical industry benefits from generative AI by speeding up the drug discovery process. AI models can propose novel chemical compounds with specific properties, reducing the time and cost required for drug development.
• Healthcare: In medical imaging, generative AI can generate high-quality images that aid in accurate diagnosis and treatment planning. Additionally, it helps in generating synthetic data for privacy-preserving research.
• Manufacturing: Generative AI transforms manufacturing with optimized product design, predictive maintenance, and efficient supply chain management. It fosters sustainability, enables mass customization, and streamlines automation.