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How Technology Providers Can Build AI Expert Systems

by Werner Goertz  |  November 27, 2018  |  Submit a Comment

For more than a year now, I have argued (1) that generalist personal assistants will be complemented with specialist models which add domain-specific lexica, workflows and knowledge graphs. My vision was that voice AIs (Alexa, Google Assistant,…) will invoke expert AIs, to create “virtual doctors”, “virtual lawyers”, “virtual auto mechanics”, etc.

Technology product and service providers can now use platform technologies, like the extensions to the AWS Comprehend service (2) to develop domain-specific ML models: leverage your deep vertical expertise to build expert solutions. Developer teams require little or no prior ML experience, further democratizing the proliferation of AI. Creating and running specialist ML models remains private (running on-prem if needed) and uses private APIs. Your cloud provider holds no state within the AI flow, and therefore your models and inference results remain exclusively yours.

How to create a “virtual lawyer”: AI is at its best when used to identify structure and relationships from seemingly unstructured data. Imagine the vast repositories of legal documents as data lakes of complex syntax, words, and knowledge. Train your own legal AI model that is specific to your legal practice area. As an example LexisNexis has developed their own extension to create a legal expert solution.

How to create a “virtual doctor”: AWS has developed a first pass at an AI-based medical practitioner in the form of AWS Comprehend Medical (3) . Complex medical records (from a patient’s history or captured from a practitioner’s notes) not only recognize keywords, but AI extracts relationships between the elements: patient demographics, anatomical references, medical conditions, prescribed medications are contextualized to manage and predict outcomes. Healthcare tech providers can leverage this platform for further specialization.

How to create a “virtual expert” for other domains: AWS’ announcements for legal and healthcare domains bode well for other potential markets and applications: transportation, mechanical engineering, predictive failure analysis are examples of huge market opportunities.

Use the Custom Entities features of cloud offerings like AWS Comprehend to develop expert experiences, training your own custom AI models for competitive advantage. Other cloud providers might announce similar services to compete with AWS.
Technology product managers can now develop product roadmaps with accelerated expert AI features. Product marketers can create competitive advantage and monetize domain expertise in a new way. Technology general managers and tech CEOs have a new way to develop their organizations and capture the democratizing wave of AI.

 

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(1) See my blog: https://blogs.gartner.com/werner-goertz/2017/09/07/vpa-monogamy-overrated-ready-tiered-multi-vpa-voice-interactions/

(2) https://aws.amazon.com/blogs/machine-learning/build-your-own-natural-language-models-on-aws-no-ml-experience-required/

(3) https://aws.amazon.com/blogs/aws/amazon-comprehend-medical-natural-language-processing-for-healthcare-customers/

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Werner Goertz
Research Director
1 years at Gartner
21 years IT Industry

Werner Goertz is a Research Director within Gartner's Personal Technnologies team, where he covers personal devices (smartphones, PCs/Ultrabooks, tablets/ultramobiles and wearables) and IoT. A special emphasis of his research lies in the Human Machine Interface (HMI) and multimodal I/O technologies: voice/speech processing and recognition, facial recognition and eye tracking, biometrics and motion/gesture control. Read Full Bio




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