According to a recent Teradata study, 80% of IT and business decision-makers have already implemented some form of artificial intelligence (AI) in their business. The study also found that companies have a desire to increase AI spending. Forty-two percent of respondents to the Teradata study said they thought there was more room for AI implementation across the business, and 30% said their organizations weren't investing enough in AI. Forrester recently released their 2018 Predictions and also found that firms have an interest investing in AI. Fifty-one percent of their 2017 respondents said their firms were investing in AI, up from 40% in 2016, and 70% of respondents said their firms will have implemented AI within the next 12 months.
As the tail end of the Millennial generation enters the workforce, considers having children, and cements itself in the world at large, it is without question that Millennials are going to experience a unique future compared to Generation X or any other generation that came before them for that matter. The generation that people love to hate is known for being early adopters of new technologies; however, it remains to be seen exactly how AI will shape the post-adolescent lives of 80 million Millennials which has become the largest age grouping in American history. Unlike Robotic Process Automation (RPA), AI is self-learning, meaning it adapts and learns as it goes. RPA, however, performs the tasks it was programmed to do and does not adapt to changes by itself. Automation software replaces humans for repetitive or predictable tasks, where AI can use reason to mimic the thoughts of a human.
A common problem for any scaling business is bringing on the right help at the right time. Small-business owners often find that the people they can afford don't have the skills or experience to do the job well, and the people they want cost too much relative to the business's current cash flow. Most of the work is labor intensive, repetitive, boring and must be done correctly. As luck would have it, that's exactly the kind of tasks we build computers to do. In the past 15 or so years, outsourcing to international assistants and part-time workers has been the go-to solution for entrepreneurs looking to grow on a budget.
NVIDIA's (NASDAQ: NVDA) graphics processing unit (GPU)-based approach to high-performance computing and deep learning, a category of artificial intelligence (AI) in which machines are trained to make inferences from data the way humans do, has begun making inroads into the global oil and gas industry. This is great news for investors, as this is a multitrillion-dollar industry that forms the foundation of the global economy. While renewable forms of energy have been steadily displacing fossil fuels to generate electricity and electric vehicles (EVs) have begun lessening the transportation industry's ravenous appetite for petroleum products, full transformations of these realms will take decades. Moreover, beyond being used to produce just about everything, oil derivatives are key ingredients in products ranging from plastics and fertilizers to the asphalt that paves our roads and the synthetic fibers that clothe many of us. In 2018, NVIDIA has announced two wins in the oil and gas space.
The company is an early mover in its industry in using high-performance computing, and in 2014 it won an award from the HPC community's leading publication, HPCwire, for the best use of the tech in the oil and gas industry. "NVIDIA's Tesla GPU-accelerated computing platforms have been instrumental in supporting Eni's exploration activity, improving our ability to turn around advanced seismic imaging tasks in a shorter time and with a higher accuracy," an NVIDIA blog quoted Luca Bertelli, Eni's chief exploration officer, as saying.
"To me, it's not just about automating the rig, it's about automating everything upstream of the rig," says Ahmed Hashmi, head of upstream technology for BP Plc." Using deep learning to listen for early warning signs that a car might be nearing a breakdown. "Jeff Dean, who leads the Google Brain research group, mused last week that some of the work of such workers could be supplanted by software. He described what he termed "automated machine learning" as one of the most promising research avenues his team was exploring." Andrew Ng demonstrates Baidu's new office entrance!
You've probably been told that the singularity is coming. It is that long-awaited point in time -- likely, a point in our very near future -- when advances in artificial intelligence lead to the creation of a machine (a technological form of life?) smarter than humans. If Ray Kurzweil is to be believed, the singularity will happen in 2045. If we throw our hats in with Louis Rosenberg, then the day will be arriving a little sooner, likely sometime in 2030. MIT's Patrick Winston would have you believe that it will likely be a little closer to Kurzweil's prediction, though he puts the date at 2040, specifically.
Tech giants Apple (AAPL), Alphabet (GOOGL), Facebook (FB), and Microsoft (MSFT) have raced to apply artificial intelligence to their businesses, and the oil industry is starting to seize on AI's benefits too. The reason interest is surging now is because artificial intelligence is "actually doable," he said in an interview with IBD at CERAWeek, explaining that advancements in cloud computing and infrastructure have made AI more affordable and accessible. "The industrial world is waking up to best practices," he said. "They are all waking up to it." Several heavyweights in the energy industry are already investors in his company, including General Electric (GE), Chevron (CVX), Royal Dutch Shell (RDSA) and Saudi Aramco.
Natural human flaws can have severe impacts on business with lasting damage – 82% of operational asset failures are attributed to human performance. Indeed, a recent study by ARC Advisory Group found that the global process industry loses up to $20 billion a year due to unscheduled downtime – or $12,500 hourly, on average. However, machine learning is helping eliminate these costly flaws and is helping transform the manufacturing industry. This technology, along with others like big data analytics, are able to predict if and when something will break – cancelling the possibility of costly downtime. See also: Anticipating downtime will be business' next competitive advantage Seth Page is a cognitive computing veteran and industrial IoT pioneer based in Washington DC, and is CEO and co-founder of DataRPM, a Progress company.
If tech experts are to be believed, artificial intelligence (AI) has the potential to transform the world. But those same experts don't agree on what kind of effect that transformation will have on the average person. Some believe that humans will be much better off in the hands of advanced AI systems, while others think it will lead to our inevitable downfall. How could a single technology evoke such vastly different responses from people within the tech community? Artificial intelligence is software built to learn or problem solve -- processes typically performed in the human brain.