Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Aware Enables Enterprises to Adopt Secure Generative AI for Digital Workplace Conversations

Embedded Generative AI, powered by targeted language models, delivers a range of solutions for Employee Experience, Business Operations, Compliance and Cybersecurity

Aware, a leading AI data platform announced the release of Generative AI Summaries, which provide secure, trustworthy and traceable insights from unstructured digital workplace conversations. With Aware’s purpose-built platform, companies can unlock the power of Generative AI by delivering actionable and accurate business insights that bridge the gap between analysis and action.

Recommended: The Ethics of Generative AI: Navigating New Responsibilities

“Our unified, scalable platform architecture, compliance with AI standards, and our access to timely, relevant workplace data enables Aware’s generative AI to be ready for deployment, right out the box.”

Aware solves the challenges of adopting Generative AI for enterprises

Although 67% of IT leaders look to implement Generative AI in the next 18 months, adoption has been slow and inconsistent. Generative AI solutions, as provided by general-purpose Large Language Models (LLMs), are not built for specific enterprise use cases, and pose several obstacles to broader adoption:

  • Data Security and Privacy — When large language models are used, end users lose control over their data and risk data leakage, thus exposing valuable proprietary information and intellectual property to unprivileged parties.
  • Accurate Business Insights — Since large language models are trained on generic, outdated, publicly available data, insights are less accurate, hallucination-prone, and have poor data traceability. Depending on the use case, a 100B parameter LLM may not perform as well as a targeted, purpose-built model.
  • Cost Efficiency — The process of operationalizing and scaling LLMs is costly. GPUs are required, computing costs are high, and building an AI/ML Platform to support such models requires significant technical skills and costly infrastructure.

Aware’s embedded AI/ML Platform, called AwareIQ, addresses today’s challenges by providing purpose-built generative AI at scale through a contextually enriched, real-time, event-driven architecture and proprietary, foundational machine learning (ML) models.

“The future of generative AI in the enterprise sits within targeted experiences that are designed to solve the use cases businesses care about most,” said Matt Pasternack, Chief Product Officer at Aware. “Aware’s generative AI capabilities are embedded and airtight within our secure AI/ML Platform and allow teams to intentionally leverage generative AI, without fear of hallucinations or their data falling into the wrong hands. Enterprise users can now condense weeks of analysis into actionable insights within minutes to solve use cases ranging from the employee experience and business operations to cybersecurity and GRC.”

Related Posts
1 of 40,332

Airtight, Secure Generative AI

Aware’s platform ingests and normalizes unstructured data from any source—including collaboration platforms, social media channels, and open-text survey responses. After standardizing the data, Aware’s Intelligent Data Fabric manages data orchestration within a single, highly secure environment. The data is then segmented, respecting data privacy controls, to unlock use cases ranging from eDiscovery collections to open-ended survey analysis. Finally, an extra filtering layer powered by Aware’s targeted, vertical-specific ML models enriches results and ensures only the highest quality data is summarized and presented to the end-user.

Aware’s foundational ML models are narrowly trained on digital workplace conversations instead of public data sets, resulting in curated models that are smaller, more accurate, and highly cost-effective. The embedded AI/ML Platform, built for conversation data, also permits continuous model development and refinement, producing models for production that are both timely and relevant. The result? Secure, trustworthy, and scalable generative experiences that drive confident decision making.

Recommended: Artificial Intelligence and the Trust Deficit: A Call for Greater Transparency

Enterprise-Ready, Responsible AI

As with all Aware’s functionality, the new generative AI capability is built with the tenets of responsible AI in mind. From development to deployment, Aware prioritizes both data protection and data quality. To that end, Aware provides access to verbatims, allowing for easy traceability of summarized data while respecting the existing data access permissions for the specific user. As part of Aware’s commitment to transparency, detailed documentation is available upon request.

“Companies are looking at generative AI as a single solution that will help solve all their problems. What’s emerging is that companies are finding that many of these models aren’t enterprise-ready, and they don’t have the resources in place to operationalize them,” said Jason Morgan, VP of Data Science at Aware., VP of Data Science at Aware. “Our unified, scalable platform architecture, compliance with AI standards, and our access to timely, relevant workplace data enables Aware’s generative AI to be ready for deployment, right out the box.”

Recommended: Modernizing Logistics Operations with Online Freight Forwarding

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

Comments are closed.