Atlassian today announced that it has acquired Percept.AI, an AI company from Y Combinator's summer 2017 batch, that offers an automated virtual agent support solution -- a chatbot, basically -- based on a proprietary AI engine for natural language understanding. Atlassian plans to integrate this virtual agent technology into Jira Service Management, its tool for helping IT teams provide better service to employees and customers. Ahead of today's acquisition, Percept had raised a seed round for an undisclosed amount from the likes of Hike Ventures, Builders VC, Cherubic Ventures, Amino Captial, Tribe Capital and Y Combinator, according to Crunchbase. The two companies did not disclose the financial details of today's acquisition. There can be little doubt that Atlassian is investing heavily in Jira Service Management.
AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.
Zhang, Daniel, Mishra, Saurabh, Brynjolfsson, Erik, Etchemendy, John, Ganguli, Deep, Grosz, Barbara, Lyons, Terah, Manyika, James, Niebles, Juan Carlos, Sellitto, Michael, Shoham, Yoav, Clark, Jack, Perrault, Raymond
Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.
Intelligent systems for the annotation of media content are increasingly being used for the automation of parts of social science research. In this domain the problem of integrating various Artificial Intelligence (AI) algorithms into a single intelligent system arises spontaneously. As part of our ongoing effort in automating media content analysis for the social sciences, we have built a modular system by combining multiple AI modules into a flexible framework in which they can cooperate in complex tasks. Our system combines data gathering, machine translation, topic classification, extraction and annotation of entities and social networks, as well as many other tasks that have been perfected over the past years of AI research. Over the last few years, it has allowed us to realise a series of scientific studies over a vast range of applications including comparative studies between news outlets and media content in different countries, modelling of user preferences, and monitoring public mood. The framework is flexible and allows the design and implementation of modular agents, where simple modules cooperate in the annotation of a large dataset without central coordination.
Augment today announced it has raised $5 million for an AI platform that assists customer service agents at large companies. The startup had operated in stealth for 10 months prior to launch. The company joins competitors like Mattersight, DigitalGenius, LivePerson, and others in its efforts to train AI using conversations between customers and businesses in order to better guide customer service agents. The money will be used to bolster the Augment AI platform, which is trained by an aggregated dataset made up of 100 million conversational interactions at large companies, including Dyson. Augment makes no attempt to replace human agents, only to make them more efficient.
NEW YORK CITY - 15 Jun 2017: LivePerson, Inc. (Nasdaq: LPSN), a leading provider of cloud mobile and online business messaging solutions, and IBM (NYSE: IBM) have announced LiveEngage with Watson, the first global, enterprise-scale, out-of-the-box integration of Watson-powered bots with human agents. The new offering combines IBM's Watson Virtual Agent technology with LivePerson's LiveEngage platform, allowing brands to rapidly and easily deploy conversational bots that get smarter with each interaction, and lets consumers message those brands from their smartphone - via the brand's app, SMS, Facebook Messenger, or even the brand's mobile site - instead of having to call an 800 number. This legacy approach has not kept pace with the consumer move to smartphones and messaging apps, now the dominant way consumers communicate digitally. Forrester's 2017 Customer Service Trends report revealed that "Customers of all ages are moving away from using the phone to using self-service -- web and mobile self-service, communities, virtual agents, automated chat dialogs, or chatbots -- as a first point of contact with a company" and, according to Dimension Data, while there has been a 12 percent decline in phone volume, there has been growth in every digital channel. LiveEngage with Watson helps meet that demand - allowing consumers to message large brands from their smartphones and instantly get answers from AI-powered bots, with human care representatives brought in seamlessly, in real-time, if a bot is not able to resolve an issue satisfactorily.
Overall investment in automation technologies – including robotic process automation (RPA), autonomics, virtual customer service agents and personal assistants, natural language processing and machine learning – is expected to double in the next two years, the survey finds, as enterprises look to harness technologies that have the flexibility to solve more than one business problem. "Automation and artificial intelligence are top of mind for business executives and service providers alike – and with good reason," said Todd Lavieri, partner and president of ISG Americas. "Robotic process automation, autonomic systems and cognitive agents are making employees more productive by taking over routine, process-oriented tasks. At the same time, data scientists are using machine learning to find patterns and make predictions on vast troves of structured and unstructured data. These technologies, taken together, promise to usher in the next wave of enterprise growth and profitability."
Deloitte and IPsoft have signed an agreement to further develop IPsoft's cognitive platform Amelia in Australia. In July, Deloitte announced a partnership with IPsoft to roll out two of IPsoft's products to its US clients: Amelia, an AI platform similar to Amazon's Alexa, and IPcenter, an autonomic IT management platform. The companies will now be customising the Amelia platform for the Australian market. Often compared to Apple's Siri and Amazon's Alexa, Amelia is is touted as more expressive than her AI peers. Alan Marshall, Deloitte's Analytics and Cognitive Technology practice leader, said Amelia has a unique natural language processing capability that means she can engage in dialogues just like humans.
The Council encouraged Science and Engineering Fair, to be sometimes after an appropriate the Conference Committee to gather held May 8-10 in San Jose. Carol asked waiting period agreeable to our copublisher, extensive feedback after the 2002 conference for a volunteer to replace Mel Montemerlo The MIT Press. The Council voted to gauge how well this new as the coordinator of the judging in favor of reaffirming this policy format was received.
The conference will be held July 18-22, 1999, at the Omni Rosen Hotel and the Orange County Convention Center in Orlando, Florida. National Conference on Artificial by two keynote addresses: (1) AAAI is pleased to announce the Intelligence. This award will honor the author(s) of of AI in other organizations (for example, AAAI is happy to announce its sponsorship paper(s) deemed most influential, CRA, ACM, IEEE); or influential of the CHIKids program during chosen from a specific conference service as a government agency contract AAAI-99. The 1999 award will be given to monitor or program director, provides child care for conference the most influential paper(s) from the resulting in positive effects on the attendees' children, first started two First National Conference on Artificial field of AI. Nominees must be current years ago at the SIGCHI-96.