The fear of robots coming for your job is one of the many challenges confronting 21st-century workers, but the machines aren't ready to take on every industry just yet. Bridgewater Associates, the massive hedge fund founded by legendary investor Ray Dalio, just released a report on the changing relationship between labour and capital in the US. One of the big factors the Bridgewater authors highlighted was the ongoing rise in automation across industries, which they noted could be a support for corporate profits in the years to come as more efficient robots and software potentially replace slower and error-prone human labour. Bridgewater cited a 2016 report from consulting firm McKinsey & Company that looked at which industries in the US were most susceptible to being automated. The McKinsey report used data from the Department of Labour to estimate how much time workers in various industry sectors spent doing different types of tasks, and which of those tasks could, theoretically, be automated using present technology.
By enabling machines to perceive, learn from, abstract, and act on data, Artificial Intelligence (AI) researchers are building machines that can perform tasks humans do--ideally better than we do them. As a result, organizations like yours are implementing AI to accomplish their missions and better serve their clients while enabling employees to work on more complex problems.
The above explanation is of course simplified and AI and ML have many more cognitive advantages that deserve a more extensive explanation. One key aspect is that the aim of AI and ML is not to replace humans, but to augment their capabilities. As AI is able to tackle routine tasks and increasingly complex non-routine tasks, humans can concentrate their efforts on tasks that have the most added value – those that really need human judgement. For instance, staff deployed in operations do not need to go through every invoice and process it in the appropriate way for the supplier. Instead, they can focus on the more complex ones while the AI algorithm processes the great majority of the invoices – faster, cheaper and more accurately than humans.
We all see the potential of artificial intelligence (AI). After all, this is brand new territory. It's easy to get caught up in the hype and to forget all the groundwork and tactical steps it takes to effectively establish and use AI in an organization. Having witnessed adoption by many clients, I've developed a short checklist of what's needed to be successful, and I plan to devote a blog to each one. These are big buckets holding lots of detail.
It doesn't have to be perfect, but it needs to have enough quality and consistency for useful patterns to emerge. However, many companies are overwhelmed by the volume, velocity and variety of their data and find themselves unable to access data's fourth V: value. So how should we think about data preparation strategies to avoid potential data paralysis or over-ambition with your AI projects? The better the data, the better the AI. But for many companies, there's a problem: 85 percent of their data is either dark (whereby its value is unknown), redundant, obsolete or trivial.
Large firms are finding that poor-quality customer and business data may be keeping them from leveraging digital tools to cut costs, boost revenues, and remain competitive, according to a survey by PricewaterhouseCoopers. Poor-quality customer and business data may be keeping companies from leveraging artificial intelligence (AI) and other digital tools to reduce costs, increase revenue, and stay competitive, according to a recent PriceWaterhouseCoopers (PwC) survey of 300 executives at U.S. companies in a range of industries with revenue of $500 million or more. While 76% of survey respondents said their firms want to extract value from the data they already have, just 15% said they currently have the right kind of data needed to achieve that goal. Most of the respondents said their firms see tremendous upside opportunity in fully optimizing the data they already have, but face multiple obstacles to achieving that goal including the quality limitations of the data. Companies working with older, unreliable data need to first assess that data by identifying its source, gauging its accuracy, and standardizing data formats and labels, according to PwC.
The Northern Virginia Technology Council's inaugural Impact AI 2019 summit on March 21 will gather technologists in government and tech executives from companies in the region making advancements in artificial intelligence. The all-day event kicks off 7 a.m. at the Inova Center for Personalized Health Conference Center in Fairfax, Virginia, and will open with keynote speaker Toni Townes-Whitley, president of U.S. regulated industries at Microsoft. Midmorning keynote speaker Rumman Chowdhury, global lead for responsible AI at Accenture Applied Intelligence, will discuss building ethical, responsible AI. Impact AI will also feature Tech Talks -- NVTC's version of TED Talks. The MITRE Corp.'s Jay Crossler, chief engineer of operations, will present a cyber Tech Talk, and Booz Allen Hamilton's Kirk Borne, principal data scientist and executive adviser, will talk about the real power of AI and how it can help us better understand our data.
As the capabilities of Artificial Intelligence (AI) grow more powerful, we are concerned the data science community is unprepared for the power we now wield. To be clear, we're big believers in the far-reaching good AI can do. Every week we learn of new advances that will dramatically improve the world. Recently we've seen research that could improve the way we control prosthetic devices, detect pneumonia, understand long-term patient trajectories, and monitor ocean health. By the time you read this, there will be even more examples.
Accenture to Deliver 2017 RBS 6 Nations Insights to Fans via Machine Learning Official Technology Partner continues to innovate around fan experience, including new VR-based mixed reality experience demonstration LONDON; Jan. 23, 2017 – Accenture (NYSE: ACN), the Official Technology Partner of the RBS 6 Nations Rugby Championship for the sixth year, is bringing machine learning to international rugby. Its latest analytics dashboard will deliver improved player, match and Championship insight, which 20,000 people accessed via Twitter last year. Accenture has also developed an innovative mixed reality application for the Championship, using Virtual Reality (VR), with a twist. The one-person immersive VR experience is sharable, and broadcast as live action for others to watch. Insights from the dashboard will be fed to the Accenture Analysis Team, made up of former players and coaches.
Pedro Larrañaga is Full Professor in Computer Science and Artificial Intelligence at the Universidad Politécnica de Madrid (UPM) since 2007, where he co-leads the Computational Intelligence Group. He received the MSc degree in mathematics (statistics) from the University of Valladolid and the PhD degree in computer science from the University of the Basque Country (excellence award). Before moving to UPM, his academic career was developed at the University of the Basque Country (UPV-EHU) at several faculty ranks: Assistant Professor (1985-1998), Associate Professor (1998-2004) and Full Professor (2004-2007). He earned the habilitation qualification for Full Professor in 2003. Professor Larrañaga has served as Expert Manager of Computer Technology area at the Deputy Directorate of research projects of the Spanish Ministry of Science and Innovation (2007-2010).