The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:
Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
Automatic generation of Daily Performance Report, reduction in overall drilling time, cut down Invisible Loss Time and improve rig asset management
Short Description (up to 150 words)
It is important for a drilling contractor to have real time monitoring of rig parameters to optimize operations. The customer lacked granular insights during drilling, could not ascertain the root cause of non-productive time, and manual interpretation of signals led to missing of anomalies further degrading performance.
Cerebra product extracted and ingested different types of signals from surface and downhole sensors to perform near real-time processing. More than 170 vital signals every second from each oil rig were processed by Cerebra to provide near real time insights into drilling operations. This was achieved by handling Data Format and Data Extraction standards and Cerebra’s Visualization Studio provides the flexibility of generating customized asset utilization reports, thus helping the oilfield engineers to understand the root causes of non-productive time and better utilize the assets on field. Rig specific utilization reports, and weekly and monthly utilization reports helped to plan drilling operations improving drilling efficiency.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
High quality company whitepapers and presentations
Publicly accessible sources with sufficient detail
This list is not exhaustive. Other credible sources may be acceptable as well.
Examples of credible sources:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013