Cloud content management company Box has unveiled Box Skills, a framework for applying machine learning tools such as computer vision, video indexing, and sentiment analysis to stored content. Box Skills will facilitate businesses to re-imagine the business processes considered as impractical to digitise or automate or too expensive. Audio Intelligence: Uses audio files to create and index a text transcript that can be easily searched and manipulated in a variety of use cases; powered by IBM Watson technology. Video Intelligence: Provides transcription, topic detection and detects people to allow users to quickly look up the information they need in a video; powered by Microsoft Cognitive Services. Image Intelligence: Detects individual objects and concepts in image files, captures text through optical character recognition (OCR), and automatically adds keyword labels to images to easily build metadata on image catalogues; powered by Google Cloud Platform. David Kenny, Senior Vice President, IBM Watson and Cloud Platform, said: "Box Skills is an extension of our strategic partnership with Box aimed at helping businesses work more efficiently, solve challenges and seize opportunities for innovation."

Task instruction quality is widely presumed to affect outcomes, such as accuracy, throughput, trust, and worker satisfaction. Best practices guides written by experienced requesters share their advice about how to craft task interfaces. However, there is little evidence of how specific task design attributes affect actual outcomes. This paper presents a set of studies that expose the relationship between three sets of measures: (a) workers’ perceptions of task quality, (b) adherence to popular best practices, and (c) actual outcomes when tasks are posted (including accuracy, throughput, trust, and worker satisfaction). These were investigated using collected task interfaces, along with a model task that we systematically mutated to test the effects of specific task design guidelines.

How can we make dividing bills easier when visiting a restaurant with friends or family? The answer is this app that uses machine learning for optical character recognition of the prices. It makes any calculator obsolete and there is also no need anymore to ask the waiter to split the bill for you at the cash register. With our app you can split any bill, not just from restaurants alone. So go ahead and have a go with our app when you organise your next party, have a lunch with colleagues, visit a fancy restaurant with some of your best friends, or ...

The rapid growth of technology is transforming businesses every day, and no technology is more poised to revolutionize nearly every industry in the next decade than artificial intelligence (AI). Since 1956, when it was introduced as an academic discipline, AI has generated both optimism and disappointment. However, it has been on a relatively upward projection since 2000, finding particular vigor in leveraging statistical approaches to machine learning, which in turn has rendered many previously used tools and schools of thought obsolete. As the field of AI continues to innovate, and machines and systems become more capable, technological solutions that used to be considered as futuristic AI, like optical character recognition, have become routine -- effectively losing their "AI" status. Other technologies yet to be conquered -- like driverless cars, and the artificial re-creation of human speech -- are still being developed as AI.

FARMERS can now zap their crops with a handheld scanner to instantly determine nutritional content, which could prove crucial in mitigating the effects of climate change on food quality. It also brings similar consumer gadgets a step closer – so we can find out what is in our food for ourselves. The device, called GrainSense, analyses wheat, oats, rye and barley by scanning a sample with various frequencies of near-infrared light. The amount of each type of light that is absorbed allows it to precisely determine the levels of protein, moisture, oil and carbohydrate in the grain. This technique has been used for decades in the lab, but this is the first time it has been available instantly on a handheld device.

Bank of America Merrill Lynch has launched a new fintech solution that brings together artificial intelligence, machine learning and optical character recognition to help companies match incoming payments with invoices. Under the name Intelligent Receivables, the solution is developed by HighRadius, a US-based fintech company. It is targeted at large or complex companies where the remittance information is either missing or received separately from the payment, which according to the bank is a source of big frustration to its clients. Using AI and other new technologies, Intelligent Receivables can help these companies improve their straight through reconciliation (STR) of incoming payments and post their receivables faster. It does so in four steps: first, the solution identifies payers and associates their payments to remittances that are received separately. Third, it uses this enriched remittance data to match payments to open receivables.

This story was delivered to BI Intelligence "Fintech Briefing" subscribers. To learn more and subscribe, please click here. Bank of America Merrill Lynch (BAML) has revealed that it is implementing enterprise software fintech HighRadius' artificial intelligence (AI) solution to speed up receivables reconciliation for the bank's large business clients. Large companies with numerous customers often receive payments without accompanying contextual information, like which customer or debtor it's come from, or precisely what the payment is for, which makes balancing a company's books, i.e. reconciling, a lengthy and resource-intensive task. HighRadius' solution uses AI, machine learning, and optical character recognition to identify a payer, match them to an uncontextualized payment, and match that to an open receivable.

You may have heard about machine learning from interesting applications like spam filtering, optical character recognition, and computer vision. Getting started with machine learning is long process that involves going through several resources. There are books for newbies, academic papers, guided exercises, and standalone projects. It's easy to lose track of what you need to learn among all these options. So in today's post, I'll list seven steps (and 50 resources) that can help you get started in this exciting field of Computer Science, and ramp up toward becoming a machine learning hero.

Trade finance giants HSBC is working with IBM to develop a cognitive intelligence solution combining optical character recognition with advanced robotics to make global trade safer and more efficient for thousands of businesses. HSBC's Global Trade and Receivables Finance (GTRF) team facilitates over $500bn of documentary trade for customers every year, and in doing so must manually review and process up to 100m pages of documents, ranging from invoices to packing lists and insurance certificates. Newsweek is hosting an AI and Data Science in Capital Markets conference on December 6-7 in New York. The new solution uses IBM's analytics technology, including intelligent segmentation and text analytics, to identify, digitise and extract key data within these documents before feeding it into the bank's transaction processing systems; boosting accuracy whilst freeing up staff for more value-adding activities, said a statement. Natalie Blyth, HSBC's Global Head of GTRF, said: "The average trade transaction requires 65 data fields to be extracted from 15 different documents, with 40 pages to be reviewed. By digitising this process we will make transactions quicker and safer for both buyers and suppliers, leading our industry forwards, and we will reduce compliance risks through an enhanced ability to manage huge volumes of data."

Bank of America Merrill Lynch (BAML) is launching a new solution – intelligent receivables – that uses artificial intelligence (AI) and other software to help companies "vastly improve" their straight-through reconciliation (STR) of incoming payments to help them post their receivables faster, reports Banking Technology's sister publication Paybefore. Intelligent receivables is designed for large or complex companies that are seeking to reduce costs, decrease days-sales-outstanding, and improve cash forecasting and their end-customer experience, the bank says. The service is "ideally suited" for companies that manage a large volume of payments where the remittance information is either missing or received separately from the payment. Incomplete remittance information typically leads to an arduous and costly reconciliation process, says Rodney Gardner, head of global receivables in global transaction services at BAML. "Our solution brings together AI, machine learning and optical character recognition, setting a new bar in accounts receivable reconciliation and payment matching," adds Gardner. Intelligent receivables is currently available in the US and Canada.