Climate change is one of the most pressing issues of our time. Despite increasing global consensus about the urgency of reducing emissions since the 1980s, they continue to rise relentlessly. We look to technology to deliver us from climate change, preferably without sacrificing economic growth. Our optimistic--some would say techno-utopian--visions of the future involve vast arrays of solar panels, machines that suck carbon dioxide back out of the atmosphere, and replacing fossil fuels for transport and heating with electricity generated by renewable means. This is nothing less than rebuilding our civilization on stable, sustainable foundations.
While accelerators have been around for some time to boost the performance of simulation and modeling applications, accelerated computing didn't gain traction for most people until the commercialization of the Tesla line of GPUs for general computing by Nvidia. This year marked the tenth annual Nvidia GPU Technology Conference (GTC). I have been to all but one starting with the inaugural event in 2009. Back then it was a much smaller group. Attendance has leaped 10X with this year's meeting attracting over 9,000 participants.
Technology has always been about increasing productivity and efficiency, but its impact in the enterprise now transcends productivity in a few very important ways. One is that it plays a central role in organizations' business agility initiatives. Second is its impact on employee recruitment, productivity, and loyalty. In fact, the quality of employee experience today is often a reflection of the quality and adroitness of a company's digital prowess, and shapes perceptions about what makes an organization a top destination for talent. A truly digital experience refers not only to the ability to access information remotely, but also to seamlessly collaborate and innovate with colleagues.
The modern world is becoming increasingly technology driven. Many areas, such as healthcare, have been quick to realise the possibilities. AI and machine learning in oil & gas focused sectors has been slower to establish itself. This is largely because the industry has been slow to realise the potential. However this is slowly changing. Machine learning in oil & gas can be used to enhance the capabilities of this increasingly competitive sector. Not only can it help to streamline the workforce. The technology can also be used to optimise extraction and deliver accurate models. These benefits are just some of the reasons why machine learning in oil & gas is becoming increasingly important. Here are 10 ways that the impact of machine learning in oil & gas industries is being felt. One of the most noticeable impacts of machine learning in oil & gas focused industries is how it transforms discovery processes. Applications employing machine learning in oil & gas enable computers to quickly and accurately analyse huge amounts of data. This includes being able to sift precisely through signals and noise in seismic data.
As AI capabilities rapidly mature, more and more chemicals and petroleum executives are determining where and how the technology fits within their organizations. Chemicals and petroleum CxOs are highly focused on three priority functional areas: information technology, information security, and innovation. These areas support the intensified focus on revenue growth and the customer as the value drivers for AI investments. AI implementation is not straightforward, however, and many companies are struggling with the transition. Yet, some businesses are achieving AI at scale successfully, and they are disproportionately outperforming financially.
Using the CB Insights platform, we track where AI is heating up, from health to entertainment. Since 2013, over 3.6K AI startups have raised equity funding globally. The majority of these companies -- like unicorns UiPath, Automation Anywhere, and Face -- sell AI software-as-a-service. Others use AI to develop their core products, including Indigo Agriculture, which leverages machine learning to develop microbial seed treatments. Some other startups -- such as Graphcore, Habana, and Cerebras -- focus on hardware to support AI workloads.
Dr. Mitchell Pryor earned is BSME at Southern Methodist University in 1993. After graduating, he taught math and science courses at St. James School in St. James Maryland before returning to Texas. He completed is Masters (1999) and PhD (2002) at UT Austin with an emphasis on the modeling, simulation, and operation of redundant manipulators. Since earning his PhD, Dr. Pryor has taught graduate and undergraduate courses in the mechanical and electrical engineering departments as well as led and conducted research in the area of robotics and automation in Mechanical Engineering, Petroleum Engineering and the Nuclear Engineering Teaching Laboratory. He has worked for numerous research sponsors including, NASA, DARPA, DOE, INL, LANL, ORNL, Y-12, and many industrial partners.
I was on the phone recently with a large multinational corporate investor discussing the applications for robotics in the energy market. He expressed his frustration about the lack of products to inspect and repair active oil and gas pipelines, citing too many catastrophic accidents. His point was further endorsed by a Huffington Post article that reported in a twenty-year period such tragedies have led to 534 deaths, more than 2,400 injuries, and more than $7.5 billion in damages. The study concluded that an incident occurs every 30 hours across America's vast transcontinental pipelines. The global market for pipeline inspection robots is estimated to exceed $2 billion in the next six years, more than tripling today's $600 million in sales.
Broken water pipes can lead to millions of dollars in damages. When it comes to building construction, all sorts of things can go wrong. The most common problem is water damage, according to insurance claim records. "It's the silent killer," says Yaron Dycian, chief product and strategy officer for WINT Water Intelligence. Water is also an increasingly scarce, and valuable, resource – a reality that is not lost on many big companies who are embracing sustainable practices.
Baker Hughes, a GE Company (BHGE) has entered into a joint venture with Artificial Intelligence (AI) software provider C3.ai to accelerate the digital transformation in the oil and gas industry. C3.ai's AI suite and applications will be deployed into BHGE's business and leverage the oilfield services company's existing digital portfolio. "The oil and gas industry is rapidly evolving, and digital technology is critical to achieving increased levels of productivity, efficiency and safety for ourselves and for our customers," BHGE CEO Lorenzo Simonelli said in a company statement. "This agreement is a mutual recognition of the technology leadership we each bring to the table and a willingness to work in new ways … integrating our strong digital capabilities and oil and gas industry expertise with C3.ai's unique AI solutions, we will accelerate the overall digital transformation of this industry." Under the terms of the joint venture agreement, BHGE will take a minority equity position in C3.ai and will have a seat on C3.ai's board of directors.