Deep technology is defined as technology which offers significant advancements over the currently employed technology. Deep technology has already created ripples in the conventional technology ecosystem: IT and electronics – AI assisted fintech; Biology – next-gen sequencing for genomics; chemistry and physics sit at the cusp of producing most novel materials that are being used in several domains.

Deep technology and its linkup to the safety ecosystem is already in play, fire safety clothing and use of novel material in operations has mitigated active hazards.

But to truly realise and implement bleeding edge technologies within safety domain, a careful requirement analysis would be essential. A quick look-up at the fundamentals can allow safety managers and C-level execs to make this task easier. The most desirable and readily deployable realm with respect to deep technologies and their safety context – software and data capture.

Modern safety at off-shore oil rigs, smelting plants and chemical industries are all about proactive safety barrier management. Each event in industrial cycle is important and its information capture, crucial. With the help of wearables and their linkup to a central information capture and monitoring network, highly hazardous tasks can be made safer and ensure that the safety is reproducible each time. The assets in play: human resources and machinery can enmesh into a common data workflow where the machinery is providing ‘reliable’ information each step along the way while the human element (with wearables) is ensuring capture, transfer and processing workflow to minimize asset performance management errors.

A major chunk of safety domain relies on trainings. Blending today’s classroom trainings with AR/VR assisted setup and delivering them inside a replicated modular training park (of the worksite/workplace) can ensure near-exact work conditions. Thus, by the virtue of repetition and familiar setup, a newly inducted worker can perform with better efficiency. Here, the deep technology related to modelling, processing information and increasing frequency of better response times can alleviate the virtual training experience.

Another example of deep technologies which are gaining traction to bridge capability and assist human ability are exoskeletons. Already in use by Ford motors to help assist assembly line workers with their manual operations, these exoskeletons enhance a worker’s ability to focus while doing all the heavy lifting for them. This sits in line with most friendly ergonomics work environment that an organization can provide. Similar applications of such exoskeletons can be found in construction and shipping industry.

Deep technologies not only require a paradigm shift within the spectrum of deployed technology, their ‘right blend’ for a precise application is what makes them ‘deep’.

Over the course of safety lifecycle within the industrial ecosystem, a sensible and intelligent network of linked, conversational and data sharing devices might be the next best thing to deep technologies. But for wearing the moniker itself, a capable and interpreting software, superior information processing unit and safety forecast insights on-demand; now, that would be the deep technology safety ecosystem would love to have at its disposal. The individual components are already in place, all we’ve got to figure out and define is the ‘right AI’ and decision-making engine that can create a living ‘safety’ system.

Image credit: NASA/JPL-Caltech