KW 32: Researchers use AI to unlock the secrets of ancient texts, State of AI applied to quality engineering report, Amazon Transcribe Call Analytics API for conversation insight


Researchers use AI to unlock the secrets of ancient texts: Researchers at University of Notre Dame are developing an artificial neural network to read complex ancient handwriting based on human perception to improve capabilities of deep learning transcription. In research published in the Institute of Electrical and Electronics Engineers journal Transactions on Pattern Analysis and Machine Intelligence, Walter Scheirer, the Dennis O. Doughty Collegiate Associate Professor in the Department of Computer Science and Engineering at Notre Dame, outlines how his team combined traditional methods of machine learning with visual psychophysics — a method of measuring the connections between physical stimuli and mental phenomena, such as the amount of time it takes for an expert reader to recognize a specific character, gauge the quality of the handwriting or identify the use of certain abbreviations. The team was able to adjust the program to transcribe Ethiopian texts, adapting it to a language with a completely different set of characters — a first step toward developing a program with the capability to transcribe and translate information for users.

State of AI applied to quality engineering report: Sogeti joins other industry experts to analyze how AI and machine learning can improve the quality testing process and formulate real-world advice for business leaders on how to apply AI to quality engineering across several key focus areas including design, automation, performance, data management, security and operations. The first of 10 sections released thares insights and emerging trends on how to get started applying AI to quality engineering practices. Sogeti will introduce each follow-on section of the full report every two weeks from September to the end of January 2022.

Amazon Transcribe Call Analytics API for conversation insights: Amazon Transcribe Call Analytics is a new machine learning powered conversation insights API that enables businesses to improve their customer experience and agent productivity. Using Transcribe Call Analytics API, you can analyze call recordings to get turn-by-turn call transcripts and actionable insights. Businesses can better understand customer-agent interactions, identify trending issues, and track performance metrics. The API combines speech-to-text and natural language processing models that are trained specifically to understand customer service and sales calls.

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Twitter announces first algorithmic bias bounty challenge: Twitter has announced its first algorithmic bias bounty challenge, offering cash prices ranging from $500 to $3,500 for those who can help the social media giant identify a range of issues. After significant backlash last year, the company admitted in May that its automatic cropping algorithm repeatedly cropped out Black faces in favor of White ones. It also favored men over women, according to research from Twitter. Rumman Chowdhury, director of Twitter META, explained that the company decided to change the algorithm and admitted that companies like Twitter often „find out about unintended ethical harms once they’ve already reached the public.“ On Friday, Chowdhury and Twitter META product manager Jutta Williams unveiled the algorithmic bias bounty competition, which they said was part of this year’s DEF CON AI Village. „In May, we shared our approach to identifying bias in our saliency algorithm (also known as our image cropping algorithm), and we made our code available for others to reproduce our work. We want to take this work a step further by inviting and incentivizing the community to help identify potential harms of this algorithm beyond what we identified ourselves,“ the two said.

IT decision makers bet on flash-based object storage to fuel AI/ML: Scality has announced the results of an independent survey completed by IT decision makers across the UK, France and Germany. It revealed a fundamental shift in the industry towards flash-based object storage to fuel artificial intelligence (AI), machine learning (ML), automation, and big data analytics in place of other forms of primary storage. All-flash object storage provides comprehensive data protection of and rapid access to massive volumes of unstructured data, powering the heavy workloads of these digital business initiatives.

Whitepaper – how AI can be integrated into energy ecosystems: For the AI-Energy whitepaper, standardization and AI experts worked closely together to optimize supply security in the energy ecosystem with AI. The whitepaper relates potential applications from the energy sector to AI and discusses possible fields of application. The experts identified and classified over 300 standards that could be relevant for AI solutions in the energy sector. They then created an architecture in which standards and norms ensure the interoperability of systems and processes. The AI developers contributed their ideas and applications to extend the architectures.

Company history: From AI high-flyer to million-dollar flop: What actually happened at Zeitgold?
Book review: Three books that take a critical look at artificial intelligence
E-commerce business: Using AI responsibly in human resources
Social media: New way to train AI could put a stop to sexist remarks
Industry: „We want to increase knowledge about artificial intelligence“

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Artificial intelligence, AI for short, has celebrated its 65th birthday.


Why China doesn’t dominate the AI race: A data analysis suggests that China’s power in AI is overestimated and that of other geopolitical powers, including Europe, is underestimated. In terms of, for example, the number of AI-related patents filed, the European Union is comparable to China. According to the study conducted, only the United States seems to dominate all others in the AI race. „We see no reason to believe that US companies should need the European Union’s help or even fear regulation by the European Union,“ conclude the authors of the scientific article („Is There an AI Cold War?“) Joanna J. Bryson and Helena Malikova.,

Opportunities and challenges of AI: Artificial intelligence and machine learning offer great potential for the medtech industry. TÜV SÜD has published a new white paper under the title of “Artificial Intelligence in Medical Devices – Verifying and validating AI-based medical devices”. The publication discusses the opportunities and challenges faced by device manufacturers seeking to bring new technologies to market, including medical devices incorporating artificial intelligence, that are safe for both patients and health professionals. The white paper also provides an overview of the essential criteria to be considered by manufacturers when developing and evaluating innovative designs that incorporate these advanced technologies.


Sedimentum builds AI fall detector: Sedimentum has developed a contactless solution for fall and emergency detection and fall prevention in healthcare. A single sensor device is installed in each room (e.g. bedroom, bathroom) of the person in need of protection, for example, on the ceiling. The sensor device contactlessly collects different measurements, like human motion activities. All data is anonymized by the sensor device, encrypted and transmitted in real time to Sedimentum’s AI software. The AI software learns the „reference state“ of the room based on the transmitted data. If an irregularity in the data subsequently occurs, e.g. triggered by a fall, third parties (e.g. nursing staff in retirement homes) are notified by an alert sent in real time.


„Artificial intelligence will continue to gain traction in the music industry in the coming years, as many branches of the industry can benefit from the technology.“
Klaus Böhm, Head of Media & Entertainment at Deloitte, on the various applications of AI in the music industry.


Australian federal court rules that AI can be a patent inventor: In a possible world-first decision, an Australian court has ruled that artificial intelligence can be named as the inventor of a patent. Federal Court Justice Jonathan Beach ruled in Thaler v. Commissioner of Patents that under Australian patent law, inventors don’t necessarily have to be human. The Deputy Commissioner of Patents said that the applicant, Dr Stephen Thaler, could not name an inventor because the inventor he had named, the AI system DABUS, simply cannot be an inventor under the Australian Patents Act. But Justice Beach said “that position confuses the question of ownership and control of a patentable invention including who can be a patentee, on the one hand, with the question of who can be an inventor, on the other hand.”,

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