European Government Organizations Are Enthusiastic About Artificial Intelligence but Face Challenges Adopting It, According to Accenture Study DUBLIN; Oct. 23, 2019 – Public-service executives in Europe are optimistic and enthusiastic about the impact of artificial intelligence (AI) on government operations and services but face challenges implementing the technology, according to a study issued today by Accenture (NYSE: ACN). The study -- based on a survey of 300 government leaders and senior information technology (IT) decision-makers in Finland, France Germany, Norway and the U.K.-- found that the vast majority (90%) of respondents believe that AI will have a high impact on their organizations over the coming years. In addition, nearly the same number (86%) said that their organization plans to increase its spending on AI next year. Customer service and fraud & risk management are the two operational areas favored most for public service AI deployments, cited by 25% and 23% of respondents, respectively. In addition, respondents most often cited increased efficiencies, cost or time savings, and enhanced productivity as the greatest anticipated benefits from their AI investments.

There are expected to be 7.7 million more high-net-worth-individuals (HNWIs) in the world by 2023, according to a report by wealth data business, Wealth-X. This equates to $20.9trn of newly created wealth in the next five years, presenting a huge growth opportunity for the global wealth management industry. As such, we've decided to compile a short list of interesting statistics on the global wealth management industry – taking into account the current state of the industry, future growth prospects and what HNWIs expect from their advisors. We also take a look at how wealth management firms are using technology, and how they plan to do so in the future, considering aspects such as the use of AI and robo-advisors. The strongest-growing region in 2017 was Asia, which posted a 19% increase.

ReTV (http://retv-project.eu/) is a Horizon 2020 funded research project that gathers together computer scientists, broadcasters, interactive TV companies and audiovisual archives from across Europe with the goal to unlock the potential of audiovisual media assets in the digital realm and bring the legacy of broadcaster archives to the 21st-century audiences. ReTV is building an innovative infrastructure that uses machine learning techniques to enhance and tailor audiovisual content for online publication and repurposing. The project is currently developing tools that segment video materials from media management systems into multiple granularities and automatically annotate them with concepts depicted in the footage. During our presentation, we will demonstrate these AI-based video annotation algorithms, more specifically a web-based concept-based video annotation service running on real video examples. The service segments any submitted video to its constituent parts, i.e. shots and scenes, and evaluates a few hundred visual concept detectors for each video shot, generating shot-level concept-based annotations.

In this blog, we'll thoroughly discuss the several use-cases available for WhatsApp Chatbots for Insurance. Let's begin with some context. In the 2009 American sports drama The Blind Side, protagonist Sandra Bullock delivers an oddly specific analogy. She talks about why defenders are an integral part of any sport. As every housewife knows, the first check you write is for the mortgage, but the second is for the insurance.

What technologies are on your business radar right now? When I put this question to colleagues working in different industries, the answers usually tend to be AI or AR. We tend to agree but what about voice? While emerging technologies like Artificial Intelligence, or VR have the potential to (literally) change the world around us, they're not yet part of our daily lives. According to Google, 20% of all searches are voice.

A fundamental truth about AI systems is that training the system with biased data creates biased results. This can be especially dangerous when the systems are being used to predict crime or select sentences for criminals, since they can hinge on unrelated traits such as race or gender to make determinations. A group of researchers from the Massachusetts Institute of Technology (MIT) CSAIL is working on a solution to "de-bias" data by resampling it to be more balanced. The paper published by PhD students [Alexander Amini] and [Ava Soleimany] describes an algorithm that can learn a specific task – such as facial recognition – as well as the structure of the training data, which allows it to identify and minimize any hidden biases. Testing showed that the algorithm minimized "categorical bias" by over 60% compared against other widely cited facial detection models, all while maintaining the same precision of detection.

Farmers have always collected and evaluated a large amount of data each growing season. It started in ledger books. Then it was moved to spreadsheets, which were eventually saved on USB drives. Now, we have real-time reports enabled by field monitoring equipment, enriched by artificial intelligence (AI), and available to farmers on tablets and smartphones. New digital tools offer farmers customized insights with a few taps on a screen.

You've likely discovered that artificial intelligence can be used to perform plenty of remarkable tasks and tasks. AI can help musicians and designers create fast alterations to the artwork. AI may also help investigators identify "bogus" pictures or link sense and touch. AI is used to program sites and programs by blending symbolic reasoning and profound learning. Fundamentally, artificial intelligence extends beyond profound learning.

Munich Re says developments in data analytics and artificial intelligence have enabled it to discover hidden loss drivers and respond accordingly, for example by introducing loss-prevention measures or adjusting pricing. Data analytics and artificial intelligence open up new possibilities for managing and covering risk, says Munich Re. The company's experts combine primary insurers' portfolio data with external, public sources, for example about site geography, building construction, weather or socio-economic status. By applying machine-learning principles, Munich Re can discover hidden loss drivers and respond accordingly, for example by introducing loss-prevention measures or adjusting pricing. Taking the example of motor insurance, "The Box" supports cedants in managing their portfolios, by using machine learning to precisely predict expected damage.

France's aggressive push to develop its AI ecosystem seems to be paying dividends as the number of startups continues to soar. A new report released today identified 432 AI-related startups in France, up from 312 last year and 180 back in 2016. The report was produced by Roland Berger, a global consulting firm, and France Digitale, an association that represents venture capitalist and entrepreneurs. "It's fantastic news," said Nicholas Brien, CEO of France Digitale. The report was released on the eve of the 4th annual France is AI conference that is being held Wednesday at the Station F startup campus in Paris.