In this study, we capitalized on a collective dataset repository of 57k accidents from 9 companies belonging to 3 domains and tested whether models trained on multiple datasets (generic models) predicted safety outcomes better than the company-specific models. We experimented with full generic models (trained on all data), per-domain generic models (construction, electric T&D, oil & gas), and with ensembles of generic and specific models. Results are very positive, with generic models outperforming the company-specific models in most cases while also generating finer-grained, hence more useful, forecasts. Successful generic models remove the needs for training company-specific models, saving a lot of time and resources, and give small companies, whose accident datasets are too limited to train their own models, access to safety outcome predictions. It may still however be advantageous to train specific models to get an extra boost in performance through ensembling with the generic models. Overall, by learning lessons from a pool of datasets whose accumulated experience far exceeds that of any single company, and making these lessons easily accessible in the form of simple forecasts, generic models tackle the holy grail of safety cross-organizational learning and dissemination in the construction industry.

Bottom Line: Real-time analysis of remote video feeds is rapidly improving thanks to AI, increasing the accuracy of remote equipment and facility monitoring. Agriculture, construction, oil & gas, utilities, and critical infrastructure all need to merge cybersecurity and physical security to adapt to an increasingly complex threatscape. What needs to be the top priority is improving the accuracy, insight, and speed of response to remote threats that AI-based video recognition systems provide. Machine learning techniques as part of a broader AI strategy are proving effective in identifying anomalies and threats in real-time using video, often correlating them back to cyber threats, which are often part of an orchestrated attack on remote facilities. The future of remote security monitoring is being defined by the rapid advances in supervised, unsupervised, and reinforcement machine learning algorithms and their contributions to AI-based visual recognition systems.

THIS EVENT IS 100% FREE! Industry 4.0 and Smart Manufacturing – these are the buzzwords that are floating around manufacturing facilities across the United States. What does this mean for you? How are you gaining knowledge about these new technologies? Are you keeping up with your competitors who are being innovative and implementing smart manufacturing in their facilities?

A new business intelligence report released by HTF MI with title "Asia-Pacific Drone Analytics Market Report 2018 Market" has abilities to raise as the most significant market worldwide as it has remained playing a remarkable role in establishing progressive impacts on the universal economy. The Asia-Pacific Drone Analytics Market Report offers energetic visions to conclude and study market size, market hopes, and competitive surroundings. The research is derived through primary and secondary statistics sources and it comprises both qualitative and quantitative detailing. Market Overview of Asia-Pacific Drone Analytics If you are involved in the Asia-Pacific Drone Analytics industry or aim to be, then this study will provide you inclusive point of view. It's vital you keep your market knowledge up to date segmented by Applications [Agriculture & Forestry, Construction, Insurance, Mining & Quarrying, Utility, Telecommunication, Oil & Gas, Transportation & Others], Product Types [, Seismic, Acoustic, Magnetic & Infrared] and major players.

Artificial intelligence, or rather things like machine learning and automation, which are often wrongly called artificial intelligence, is a big thing in oil and gas right now. The hype around AI spreads a lot further than the oil and gas industry, but in it, the technology is making the first splashes and it looks like they are fast multiplying.