Barbara Starr, Principal Consulting Analyst
Barbara was recruited to build intelligent trading systems on Wall Street from a teaching position in South Africa, where she was also studying for a PHD in Artificial intelligence. Barbara has watched the evolution of artificial intelligence over the past decades. She subsequently worked for companies such SAIC, with a role as Principle Investigator on many DARPA and other agency projects (DARPA’s HPKB program, and the ARDA AQUAINT program to name but a few).
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She subsequently moved to the e-commerce arena, working for companies like Overstock.com, applying similar technology, using semantic search techniques to build a vertical search engine for product identifiers, and optimize product feeds and product data for maximum visibility on the web. Having become fascinated with e-commerce, she subsequently remained working in that domain/vertical, consulting for companies like GS1, where her unique skill set, which lies at the intersection of e-commerce, Artificial Intelligence, Semantic Web, Semantic Search & SEO provided unique and insightful solutions to problems.
At TechVision Research, Barbara is focusing on the following areas of specialty
- Artificial Intelligence
- Product Data Quality and optimization
- Semantic Search
- Block chain and supply chain
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Expertise:
- Artificial Intelligence
- Product Data Quality and optimization
- Semantic Search
- Block chain and supply chain
Recently Published Research
Artificial Intelligence Level Set
Artificial intelligence is one of the more heavily hyped and widely misunderstood topics over the past 20 years—right there with blockchain. This report looks to demystify Artificial Intelligence (AI) starting with a history of AI from a technical perspective. We also describe where we see AI and Machine Learning (ML) going and the impact on our enterprise clients. We start with a brief history and a technical description of what AI is, what it isn’t as well as early use cases.
We examine the three major waves of Artificial Intelligence technologies, when each was introduced and the impact of each wave. The first wave started with symbolic reasoning serving up basic reasoning based on logic rules over narrowly defined problems. This progressed to a second wave of AI that focused on learning ability with predictive capabilities based on statistical learning models. The third wave is contextual adaption with added reasoning capabilities, real world use cases, and self-learning ability. Each wave has specific use cases, but adds complexity and real-world applicability.
This foundational document will set the stage for deeper dives into specific instantiations of AI/ML in various use cases. This report will provide high-level recommendations and an action plan for enterprises developing strategies and tactical plans in AI and related areas.
Upcoming Research
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