WNS (Holdings) Limited a leading provider of global Business Process Management (BPM) services, today announced it has acquired Vuram, a global leader in enterprise automation services. Vuram helps companies accelerate digital transformation by aligning, automating, and optimizing processes using a combination of low-code software applications and intelligent automation platforms. By integrating these technologies into core business operations, Vuram is able to drive end-to-end enterprise automation and the creation of custom, scalable BPM solutions. These solutions include the ability to extract, collect, and categorize data using OCR and AI-based document processing, develop rule-based processing engines and ML-based augmentation, and leverage advanced analytics to improve decision-making. Vuram has also created customizable, low-code, "plug and play" solutions across front, middle, and back-office functions, including industry-specific solutions for the Banking/Financial Services, Insurance, and Healthcare verticals.

We've written a policy research paper identifying four strategies that can be used today to improve the likelihood of long-term industry cooperation on safety norms in AI: communicating risks and benefits, technical collaboration, increased transparency, and incentivizing standards. Our analysis shows that industry cooperation on safety will be instrumental in ensuring that AI systems are safe and beneficial, but competitive pressures could lead to a collective action problem, potentially causing AI companies to under-invest in safety. We hope these strategies will encourage greater cooperation on the safe development of AI and lead to better global outcomes of AI. It's important to ensure that it's in the economic interest of companies to build and release AI systems that are safe, secure, and socially beneficial. This is true even if we think AI companies and their employees have an independent desire to do this, since AI systems are more likely to be safe and beneficial if the economic interests of AI companies are not in tension with their desire to build their systems responsibly.

Anti-money laundering (AML) and know-your-customer (KYC) compliance might be transformed by artificial intelligence (AI). AI is beneficial when completing repetitive activities since it saves time, effort, and resources that can be redirected to higher-value client tasks. Natural language processing (NLP) and machine learning (ML) are two AI technologies that, when combined, can generate leapfrog automation possibilities in areas of client life cycle management (CLM) that are currently labor-intensive, time-consuming, and error-prone. Artificial intelligence in KYC can intelligently extract risk-relevant information from a large volume of data in the anti-money laundering AML space, making identifying high-risk customers much easier in the battle against financial crime. It can follow legislative changes worldwide, discover gaps in client information maintained by the financial institution, and send out know your customer (KYC) warnings to customers to gather missing information.

Deep image classification models are typically trained in a supervised manner over a large, annotated dataset. Although a model's performance will improve as more annotated data becomes available, large-scale datasets for supervised learning are often difficult and expensive to obtain, requiring numerous hours of effort from expert annotators. With this in mind, one may begin to wonder if cheaper sources of supervision exist. Put simply, is it possible learn high-quality image classification models from data this is already publicly available? The proposal of Contrastive Language-Image Pre-Training (CLIP) model [1] -- recently re-popularized due to its use in the DALLE-2 model--by OpenAI answered this question in a positive fashion.

On June 28, 2022, Deep Longevity was granted a patent for an aging clock to estimate the age of a person based on their gut bacteria. This is the first patent granted for a microbiomic aging clock. The method uses neural networks to interpret gut metagenomic information. Scientists at Deep Longevity plan to use the technology to identify pro-aging bacteria to help scientists develop treatments to promote healthy longevity. Deep Longevity plans to develop commercial products based on the patent in 2023.

Frequent instances of enterprise downtime can derail the growth trajectory of your organization. To avoid that fate, one of the more effective solutions to prevent downtime involves incorporating AI in the workplace. Downtime of any kind, whether it is driven by cyber-attacks, malfunctioning devices, erratically-working applications or maintenance work, is lossmaking for your organization. Unplanned network outages, device breakdown and other events that cause downtime--a loose term used to denote the cumulative "productive company time" lost during repairs--can incur losses of up to US$5 million for organizations, and that figure excludes legal fees, compensation and penalties of any kind. Let's face it, events such as the ones listed above are inevitable for organizations in any sector.

The smallest mention of a heatwave in the UK leads to ice creams selling out, barbecues heating up and shorts being dusted off as the nation celebrates. In June this year, air temperatures in parts of the country soared to over 90 F (33 C), while sharp increases were also felt across Europe, the US and Asia. Air temperatures were recorded in excess of 18 F (10 C) above the average for the time of year in many cities, according to the World Meteorological Organisation. But new heat maps released by the European Space Agency (ESA) show that this might not be such a cause for celebration. They reveal that heat dissipated more slowly in urban areas creating'heat islands' and make life more of a struggle. Experts are worried that this effect will only be exacerbated as climate change continues to take hold.

Researchers in the eastern Chinese province of Anhui say they have developed a device that can determine loyalty to the ruling Chinese Communist Party (CCP) using facial scans. A short video uploaded to the Weibo account of the Hefei Comprehensive National Science Center on June 30 said the project was an example of "artificial intelligence empowering party-building." The Weibo post was later deleted, but a text summary of the video, produced in honor of the CCP's July 1 anniversary, remained available on the Internet Archive on Monday. "Guaranteeing the quality of party-member activities is turning into a problem in need of coordination," the text said. "This equipment is a kind of smart ideology, using AI technology to extract and integrate facial expressions, EEG readings and skin conductivity ... making it possible to ascertain the levels of concentration, recognition and mastery of ideological and political education so as to better understand its effectiveness," the description said.

Carlos Gaitan, the CEO and Co-founder of Benchmark Labs a leading provider of AI & IoT-driven weather forecasting solutions for the agriculture, energy, and insurance sectors joins Enterprise Radio. Dr. Gaitan is the Co-founder and CEO of Benchmark Labs. He did his doctoral studies at the University of British Columbia (Vancouver, Canada) working with William Hsieh in machine learning applications in the environmental sciences. He also holds a Bachelor degree in Civil Engineering and a Master degree in Hydrosystems from the Pontificia Universidad Javeriana (Bogota, Colombia). He is an elected member of the American Meteorological Society's (AMS) Artificial Intelligence Committee.

Bhasha Daan: An crowdsourcing initiative for Indian languages that will be as Indian, as you and I. We invite you to contribute data to develop Speech Recognition, Text-to-Speech, Machine Translation and Optical Character Recognition for Indian languages.