AI debate: A new engineering discipline is emerging

Most AI debate is shaped by an understanding of AI as high level reasoning by machines or “human imitative intelligence”. But in a thought provoking article AI researcher and MIT professor Michael Jordan argues for the broadening of the concept to encompass “intelligence augmentation” (IA) and “intelligence infrastructure” (II) as well. In the former “computation and data are used to create services that augment human intelligence and creativity. A search engine can be viewed as an example of IA, as it augments human memory and factual knowledge, as can natural language translation, which augments the ability of a human to communicate. Computer-based generation of sounds and images serves as a palette and creativity enhancer for artists. In the latter a web of computation, data, and physical entities exists that makes human environments more supportive, interesting, and safe. Such infrastructure is beginning to make its appearance in domains such as transportation, medicine, commerce, and finance, with implications for individual humans and societies. This emergence sometimes arises in conversations about an Internet of Things, but that effort generally refers to the mere problem of getting ‘things’ onto the Internet, not to the far grander set of challenges associated with building systems that analyze those data streams to discover facts about the world and permit ‘things’ to interact with humans at a far higher level of abstraction than mere bits.” Jordan notes that the current public dialog on AI—which focuses on narrow subsets of both industry and of academia—risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA, and II. His thesis is that we are now in a phase where we are building societal scale systems whose science, functioning and implications we don’t yet fully understand and that we need an engineering effort of unprecedented scale to master a new discipline that is emerging and its impact on the world

 Just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans, and the environment. Just as early buildings and bridges sometimes fell to the ground—in unforeseen ways and with tragic consequences—many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws…

This scope (of a broadly understood AI: AI, IA, II) is less about the realization of science-fiction dreams or superhuman nightmares, and more about the need for humans to understand and shape technology as it becomes ever more present and influential in their daily lives. Moreover, in this understanding and shaping, there is a need for a diverse set of voices from all walks of life, not merely a dialog among the technologically attuned. Focusing narrowly on human-imitative AI prevents an appropriately wide range of voices from being heard…

We should also not pretend that we are talking about something other than an engineering effort of unprecedented scale and scope; society is aiming to build new kinds of artifacts. These artifacts should be built to work as claimed. We do not want to build systems that help us with medical treatments, transportation options, and commercial opportunities only to find out after the fact that these systems don’t really work, that they make errors that take their toll in terms of human lives and happiness. In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data- and learning-focused fields. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline…

We should embrace the fact that we are witnessing the creation of a new branch of engineering. The term engineering has connotations—in academia and beyond—of cold, affectless machinery, and of loss of control for humans, but an engineering discipline can be what we want it to be. In the current era, we have a real opportunity to conceive of something historically new: a human-centric engineering discipline. I will resist giving this emerging discipline a name, but if the acronym AI continues to serve as placeholder nomenclature going forward, let’s be aware of the very real limitations of this placeholder. Let’s broaden our scope, tone down the hype, and recognize the serious challenges ahead.

Source:  Harvard Data Science Review

The US Air Force has implemented virtual reality (VR) training to enable airfield management students to have ‘hands-on’ learning experience.  It incorporated a VR classroom to teach airfield maintenance to the students. Master sergeant Joshua Stillwagon said the VR training technology enables efficient training of airmen compared to the previous lecture-based class. “This system gives instructors the capability to not just tell airmen, but instantly show them a concept.” The training scenario using VR includes visual simulation of the setting of an airfield to enable students to practice their job as if they were operational. Source: Air Force Technology

A pilot project in the Netherlands consisting of tech developer Beijer Automotive BV,  the SmartwayZ.NL mobility program as well as several local governments will use sensor data from vehicles to monitor and evaluate the condition of road surfaces and prioritize and schedule maintenance according to need. Source: Traffic Technology Today

Device-as-a-Service, an old new business model, is gaining ground. Pushed by a number of large vendors, such as Lenovo, HP and Microsoft, the typical DaaS contract runs 24 to 36 months, and customers can frequently choose from a variety of software packages, support levels, product deployment services and other features. Research indicates organizations haven’t been utilizing DaaS long and the procurement process has a fairly long cycle. However while in 2016, only 2 percent of the commercial PCs that shipped in the U.S. were part of a DaaS agreement, an IDC report predicts that, by 2020, nearly a third of the commercial PCs that ship in the US will be the result of a DaaS contract. Source: IDC

M&A and Collaborations

National Grid, a power and gas delivery utility active in the UK and the northeastern US has partnered with Utilidata and Sense to pilot the first ever digital twin of an electric grid. It will map power flow, voltage, and infrastructure from the substation to the home and allow the company to access real time data on operations of appliances in the home and assets on the grid. Utilidata has provided its energy optimisation software and Sense its intelligent home energy monitor and integrated them with machine learning capabilities. The technology is expected to help improve grid efficiency, reliability, and utilization of distributed assets. Using the solution, National Grid is able to respond in real-time to changing grid conditions and will also detect and forecast grid performance. Source: Smart Energy

Rolls-Royce and Tennessee based Analysis and Measurement Services Corporation (AMS) have signed a partnership agreement to jointly provide advanced Instrumentation and Control (I&C) system testing services to the nuclear energy market. As part of Rolls-Royce’s Pulse predictive maintenance technology, the combined services will encompass the monitoring of the performance and health of safety I&C components, which include neutron detectors, pressure transmitters, temperature probes, cables and connections. AMS was selected last year by the US Department of Energy to study testing of electrical cables in nuclear reactors for age-related wear.

Business Environment and Markets

A new-generation direct-drive permanent magnet generator (PMG) concept that could clear the way for giant 25MW offshore wind turbines is on track to be market-ready by 2022. The PMG, developed by British start-up Greenspur Renewables, uses ferrites – an iron-rich ceramic – for its magnets rather than current go-to rare-earth materials – supply of which is monopolized by China – and is built around a modular architecture, making it scaleable and easy to repair and maintain. The design is calculated to cut the capital cost of direct-drive generators by a third, shaving almost 5% off the price of a turbine.

Danish engineering group FLSmidth has launched a new service center for mining customers in the north of Chile. The new 8,000m² space will focus on the fast delivery of mining equipment to customers and component maintenance. The facility was recently inaugurated in Copiapó to meet a high demand for mining service and technical support in the region, the company said. Source: FLSmidth

Oilfield service providers have a long history of riding the ups and downs in the energy market. They ramp up rigs, workers and prices when oil is more expensive, and cut back when the market drops. But now it’s no longer about merely navigating a downturn. It’s about survival. While large companies such as Schlumberger and Halliburton are struggling, smaller companies are facing dire consequences. “A major value transfer is underway between oilfield service companies and E&P entities,” James West, an analyst at Evercore ISI, wrote in a report earlier this year. “Rather than seek to preserve value, companies sought greater market positioning despite the structural predicament. Intense competitive conditions exist in almost every major oilfield service product line.” Some analysts and investors, meanwhile, are seeking more widespread consolidation. Even a single company cutting back on its lesser-performing service lines would help, according to Evercore’s West. “You’re starting to see companies look at their portfolios and pruning in order to drive overall better corporate returns,” he said.  Servicers need to focus more on digital technology, switching from diesel-powered frack equipment to electric and generally improving the quality of their gear, said Jud Bailey, an analyst at Wells Fargo. Doing so could lower costs by 25-35% over the next five years, he estimates. The companies that are winning are the ones “doing one to three things extraordinarily well,” Bailey said. “The ones who do 10 things—and do one or two of them really well and the rest mediocre—those are the ones who are struggling.” Source: WorldOil