If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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).
AI Architect – Responsible for working out where AI can help a business, measuring performance and--crucially-- "sustaining the AI model over time." Lack of architects "is a big reason why companies cannot successfully sustain AI initiatives," KMPG notes. AI Product Manager – Liaises between teams, making sure ideas can be implemented, especially at scale. Works closely with architects, and with human resources departments to make sure humans and machines can all work effectively. Data Scientist – Manages the huge amounts of available data and designs algorithms to make it meaningful.
The future of work will be driven by artificial intelligence, and HR is woefully ill equipped to make it happen -- at least according to many reports about AI and HR. IBM, PWC and Deloitte (among others) have all done surveys on AI's impact on HR in the last 18 months, and the message is clear: companies want AI, but they don't have the talent, leadership or confidence in their human resources team to make it happen. IBM predicts that 120 million workers in the world's 10 largest economies will need to be reskilled in the next few years to adapt to an AI-driven marketplace -- and that if companies don't get started soon they will quickly risk losing their competitive edge. Yet its "Unplug from the past" report found that just 28 percent of CHROs expect their enterprise to address changing workforce demographics with new strategies. Even if companies are gearing up for an AI reskilling evolution, roughly half of their employees don't think they can pull it off.
The pace at which companies are investing in artificial intelligence (AI) continues to gain momentum and the financial sector is not immune to this trend. According to research by global management consultancy Accenture, banks that invest in AI and human-machine collaboration tools could boost their revenue by over a third (34 per cent) by 2022. AI is considered one of the most important disruptive technologies for today's banks, with a recent PwC survey revealing that 72 per cent of senior management see AI and machine learning (ML) as key sources of competitive advantage. The survey also highlighted that 52 per cent of companies in the financial services sector are already making substantial commitments to AI, with 66 per cent projecting significant investments by 2020. The finance sector has been using AI in very specific areas for some time, but we're now seeing a rapid growth in take-up due to increasing market competition, the need to reduce overheads and the benefits of harnessing increasing volumes of data.