Accenture AI Testing is part of a complete range of testing services aimed at helping engineering professionals to be catalysts of speed, agility and business performance, while achieving spikes in productivity. Accenture serves over 1,000 testing clients across more than 40 industries, and has already used its "Teach and Test" methodology to train a conversational virtual agent for a financial services company's website, so that it could engage in accurate, unbiased conversations and know when to refer conversations to a human. The agent was trained 80% faster than previously possible, and achieved an 85% accuracy rate on customer recommendations. "The adoption of AI is accelerating as businesses see it's transformational value to power new innovations and growth," Bhaskar Ghosh, Group Chief Executive for Accenture Technology Services said. "As organisations embrace AI, it is critical to find better ways to train and sustain these systems – securely and with quality – to avoid adverse effects on business performance, brand reputation, compliance and humans."

Shock waves went out a few weeks ago when Google CEO Sundar Pichai called artificial intelligence (AI) "more profound than electricity or fire."1 It's no surprise that his provocative statement grabbed headlines. But I think Pichai is right. AI is a major transformative force that will change so many aspects of our lives at work and at home. I've been thinking about how AI will shape the next generation of customer care in financial services.

NEW YORK, USA – Businesses will soon be able to access dedicated testing services for AI systems, Accenture revealed in a new product announcement today. Accenture's AI Testing will provide companies with a methodology to help companies build, monitor, and measure reliable AI systems within their own infrastructure or by the cloud. It will form part of Accenture's Testing Services suite, which also includes testing strategy, engineering, digital, and enterprise technology for organizations. "The adoption of AI is accelerating as businesses see its transformational value to power new innovations and growth," said Bhaskar Ghosh, group chief executive of Accenture Technology Services. "As organizations embrace AI, it is critical to find better ways to train and sustain these systems – securely and with quality – to avoid adverse effects on business performance, brand reputation, compliance and humans."

IT services and consulting company Accenture is launching new services for testing artificial intelligence systems to help companies build own AI-driven products and services based locally or on the cloud. "The adoption of AI is accelerating as businesses see its transformational value to power new innovations and growth," Bhaskar Ghosh, group chief executive, Accenture Technology Services, said in a statement. "As organisations embrace AI, it is critical to find better ways to train and sustain these systems – securely and with quality – to avoid adverse effects on business performance, brand reputation, compliance and humans," Ghosh said. The Dublin-headquartered company said the new testing services works in two phases. While the first phase helps companies focus on choice of data, models and algorithms to teach the machine learning engine, the second phase helps them compare results of the engine with key performance indicators and understand if the engine can explain the decision-making process.

Nearly 70 Percent of Taxpayers Support Use of AI to Improve Accuracy of Filings, Accenture Global Survey Finds'Digital tax assistant' could prove especially beneficial to 40 percent of taxpayers who reported making filing errors NEW YORK; Feb. 20, 2018 – Nearly 70 percent of taxpayers in 12 countries said they would use AI to improve the accuracy of tax filings, according to a new study by Accenture (NYSE: ACN), which also found that more than 40 percent of taxpayers reported making a filing error in the last 24 months. The Accenture Digital Taxpayers Research asked more than 6,500 taxpayers across Europe, Asia-Pacific and North America who interacted with their tax authority in the prior 12 months about their experiences with, attitudes about and expectations of revenue authorities. The findings indicate that in an era in which people around the world expect easy and simple consumer experiences, tax rules and regulations still confuse citizens. For instance, 38 percent of respondents said they are not confident they pay the right amount of tax, and 44 percent said they feel their tax knowledge could be improved. While most respondents said they have limited contact with their revenue authorities after filing a tax form, half (51 percent) reported contacting their revenue authority once or twice in the past year, with 20 percent reporting three or more contacts.

In our recent conversation with David Schatsky, managing director at Deloitte, he indicated that 2018 is the year AI talk will turn into action. For CIOs who are still early in their talks – or who haven't even had the conversation yet – this could bring up some key questions. Like, how do my plans stack up to others in my industry? What are the early adopters seeing? And what does this mean for jobs?

Accenture has announced the launch of new services for testing artificial intelligence (AI) systems, powered by a "Teach and Test" methodology designed to help companies build, monitor and measure reliable AI systems within their own infrastructure or in the cloud. Accenture's "Teach and Test" methodology ensures that AI systems are producing the right decisions in two phases. The "Teach" phase focuses on the choice of data, models and algorithms that are used to train machine learning. This phase experiments and statistically evaluates different models to select the best performing model to be deployed into production, while avoiding gender, ethnic and other biases, as well as ethical and compliance risks. During the "Test" phase, AI system outputs are compared to key performance indicators, and assessed for whether the system can explain how a decision or outcome was determined.

A global Deloitte survey shows that robotic process automation will be universally adopted in just five years. Deloitte's report on robotics adoption around the globe shows that overall, 53pc of more than 400 executives surveyed are implementing robotic process automation (RPA) in their organisations, with 33pc of Irish respondents introducing these processes. RPA is the use of robots or robotics on computer systems to support more effective processing of data, and includes programmes that replace the need for humans to perform repetitive, rules-based tasks. It is seen as an entry point to more complex and ambitious cognitive automation initiatives, which leverage natural language processing, machine learning, advanced analytics and cognitive chatbots. RPA is in line with the top priorities of businesses around the globe as well as in Ireland, with 76pc of all surveyed planning to increase investment in the area over the next three years.

Accenture Launches New Artificial Intelligence Testing Services Powered by a "Teach and Test" methodology, the new services help companies validate the safety, reliability and transparency of their artificial intelligence systems NEW YORK; Feb. 20, 2018 – Accenture (NYSE: ACN) has launched new services for testing artificial intelligence (AI) systems, powered by a unique "Teach and Test" methodology designed to help companies build, monitor and measure reliable AI systems within their own infrastructure or in the cloud. Accenture's "Teach and Test" methodology ensures that AI systems are producing the right decisions in two phases. The "Teach" phase focuses on the choice of data, models and algorithms that are used to train machine learning. This phase experiments and statistically evaluates different models to select the best performing model to be deployed into production, while avoiding gender, ethnic and other biases, as well as ethical and compliance risks. Accenture AI Testing Services from Accenture Technology During the "Test" phase, AI system outputs are compared to key performance indicators, and assessed for whether the system can explain how a decision or outcome was determined.

Seekers after the glitter of intelligence are misguided in trying to cast it in the base metal of computing. Artificial intelligence (AI) has emerged as a signature issue of our time, set to reshape business and society. The excitement is warranted, but so are concerns. At a business level, large "big data" and AI projects often fail to deliver. Many of the culprits are familiar and persistent: forcing technological square pegs into strategic round holes, overestimating the sufficiency of available data or underestimating the difficulty of wrangling it into usable shape, taking insufficient steps to ensure that algorithmic outputs result in the desired business outcomes. At a societal level, headlines are dominated by the issue of technological unemployment. Yet it is becoming increasingly clear that AI algorithms embedded in ubiquitous digital technology can encode societal biases, spread conspiracies and promulgate fake news, amplify echo chambers of public opinion, hijack our attention, and even impair our mental well-being.2 Effectively addressing such issues requires a realistic conception of AI, which is too often hyped as emerging "artificial minds" on an exponential path to generally out-thinking humans.3 In reality, today's AI applications result from the same classes of algorithms that have been under development for decades, but implemented on considerably more powerful computers and trained on larger data sets.