AI Adds Value to Production Environments
AI Adds Value to Production Environments
AI enables manufacturing enterprises to achieve higher yields, lower energy use, cost savings, reduced emissions, and more.
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A recent post from Angel List was titled, “'Boring' Industries Benefit the Most from AI.” Petuum’s recent presentation to the IT Press Tour is a great example. We had the opportunity to hear Qirong Ho, Co-Founder and CTO of Petuum make a compelling and informative case for AI.
Petuum’s mission is to industrialize AI technology turning it from black-box artisanship into a standardized engineering process that can be replicated within enterprises and across industries with sustainable and standardized building blocks.
AI is hard, yet it is projected to see 6X growth by 2025 to $175 billion. 85% of projects fail. There is a 99% talent deficiency. However, it has the potential to generate tremendous value as exemplified by industry 4.0 and cement production.
Cement production is made up of a well-defined process with large physical plants and machines and 10,000 to 20,000 sensors per factory. Safety first is a mission-critical process. There are low margins. The process is extremely complex. Raw data is far from an ideal state for analysis. There is a need for simultaneous prediction and control that requires inter-operation between many, diverse systems.
You may also want to read: Why Development Environments Shouldn’t Be the Same as Production
Results included cost reductions due to better energy recovery, higher use of alternative fuels. A reduction in process variability by 0.5 standard deviations. Reduced downtime and eliminated shutdowns.
In a healthcare use case with lab tests for a hospital, a real-time ready-to-use AI solution is extremely complex. The deliverable was an automatic medical report generation that requires raw data enrichment; the building of a model/algorithm requires inter-operation between diverse systems. Chest x-ray reports, findings and conclusions, are written in English and Mandarin Chinese with radiologist-level accuracy detecting 18 thoracic lesions from x-rays images visualized with heat maps and cues.
The average interpretation and report writing time were reduced by 60% as reported by the CTO of a thoracic clinic in Asia. The purpose was not to replace doctors but to provide a second opinion and speed time to report. Doctors need an assistant to save them the time to enter all of the information in a report.
Petuum accelerates the transformation of AI for enterprises to better serve the world by bringing ideas to production, at scale, leveraging available AI talent.
Petuum provides time-to-value by turning AI into an assembly line of repeatable processes and models in multiple industries. Benefits include increased productivity, greater process stability, improved energy efficiency, and lower costs.
Petuum Industrial AI-pilot enables manufacturing enterprises to augment an operator’s decision-making process with the ability to predict, prescribe, and supervised-steer assets and processes provide higher yields, lower energy use, cost savings, reduced emissions, safer and more stable operations, and reduced downtime. It’s repeatable across multiple sites and makes prescriptions every five minutes since the process is continuing and may have exceptions
Petuum Symphony Platform enables enterprises to take ideas from production at scale with rich AI/ML capabilities, data operations, and last-mile serving with 10X faster time to value, 12X better time to value and 100+ AI building blocks which enable the full end-to-end AI process with a highly intuitive user interface for software developers and data scientists.
Petuum Neurobots are intelligent business LOB with pre-trained models that can be deployed in minutes. The transferred learning allows minimal data for model customization in less than one hour with models available for chat (Kaibot), clothing (Chicbot), images (Pixbot), and voice (Chimebot). Models are trained on the entirety of the entire multi-lingual Wikipedia corpus and provide a chatbot in 15-minutes versus three to six months.
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