Daily Archives: October 4, 2022

The World Health Organization, with support from the Qatar Ministry of Health, today launched the AI-powered WHO Digital Health Worker, Florence version 2.0, offering an innovative and interactive platform to share a myriad of health topics in seven languages at the World Innovation Summit for Health (WISH) in Qatar.

Florence can share advice on mental health, give tips to destress, provide guidance on how to eat right, be more active, and quit tobacco and e-cigarettes. She can also offer information on COVID-19 vaccines and more. Florence 2.0 is now available in English with Arabic, French, Spanish, Chinese, Hindi and Russian to follow.

Florence has helped fight misinformation around COVID-19 since the beginning of the pandemic. The pandemic has had a significant effect on mental health. It is estimated that 1 in every 8 people in the world lives with a mental disorder. Her topics like tobacco and unhealthy diet kill 16 million people every year, while physical inactivity kills an estimated 830 000. These deaths are due to diseases like cancer, heart disease, lung disease, and diabetes that can be prevented and controlled with the right support.

Digital technology plays a critical role in helping people worldwide lead healthier lives, said Andy Pattison, WHOs Team Lead for Digital Channels. The AI health worker Florence is a shining example of the potential to harness technology to promote and protect peoples physical and mental health. At WISH, we aim to meet with visionary partners to continue to improve this cutting-edge technology. AI can help fill gaps in health information that exist in many communities around the world.

We are pleased to partner with the WHO for the development of Florence and are very excited about the opportunities this technology can offer to raise awareness of key health issues, said Dr Yousuf Al Maslamani, Official Healthcare Spokesperson for the FIFA World Cup Qatar 2022, Ministry of Public Health.

We know that providing advice on Florences key health topics, including mental health, nutrition and tobacco cessation is an important tool in our commitment to support people to make healthy lifestyle choices, added Dr. Al Maslamani.

At the WISH conference, WHO released the beta version of Florence 2.0 to interact with scientists, public health organizations, entrepreneurs, and policy-makers and plans to continue to develop the digital health worker to help meet major health issues facing the world today.

We are pleased to have Florence 2.0 launched at the WISH conference. This is a place for global actors to come together to find solutions for public health. WHO is demonstrating incredible innovation leadership by using groundbreaking empathetic AI, said Nick Bradshaw, Director of Partnerships and Outreach at WISH.

The digital health worker is a prominent feature of the Sport For Health partnership between WHO and the Qatar Ministry of Public Health, which has been established to help make this years FIFA World Cup Qatar 2022 a beacon for health and safety.

The project is supported by technology company Soul Machines, which brings avatars to life in the form of autonomously animated Digital People. Through this collaboration, we have created a personality for the frontline responder that is empathetic, informative, and understanding, says Greg Cross, CEO and Co-Founder of Soul Machines. Our Digital People operate and respond in real time, providing users with a unique and emotionally engaging experience. We look forward to continuing our work on Florence as we aim to positively reshape and transform the health-care industry.

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WHO and partners launch world's most extensive freely accessible AI health worker - World Health Organization

New tools from Google's cloud unit will help healthcare organizations analyze and store medical images.

Google on Tuesday announced a new set of artificial intelligence tools aimed at letting healthcare organizations use the search giants software and servers to read, store and label X-rays, MRIs and other medical imaging.

The tools, from Googles cloud unit, allow hospitals and medical companies to search through imaging metadata or develop software to quickly analyze images for diagnoses. Called the Medical Imaging Suite, the tools can also help healthcare professionals to automatically annotate medical images and build machine learning models for research.

With the advancements in medical imaging technology, there's been an increase in the size and complexity of these images, Alissa Hsu Lynch, Google Clouds global lead for health tech strategy and solutions, said in an interview. We know that AI can enable faster, more accurate diagnosis and therefore help improve productivity for healthcare workers.

Based on Google's other forays into healthcare, privacy advocates may raise concerns that the tech giant, which makes the majority of its $257 billion annual revenue from personalized ads based on user data, would use patient information to feed its vast advertising machine.

Lynch says Google doesnt have any access to patients protected health information, and none of the data from the service would be used for the companys advertising efforts. Google claims the service is compliant with the Health Insurance Portability and Accountability Act, or HIPAA, a federal law that regulates the use of patient data.

The tech giant is working with a handful of medical organizations as early partners for the imaging software. One partner, a company called Hologic, is using the Google suite for cloud storage, as well as developing tech to help improve cervical cancer diagnostics. Another partner called Hackensack Meridian Health, a network of healthcare providers in New Jersey, is using the tools to scrub identifying information from millions of gigabytes of X-rays. The company will also use the software to help build an algorithm for predicting the metastasis of prostate cancer.

Google's software tools will help healthcare organizations to view and search through imaging data.

The new tools come as Google and its parent company Alphabet invest more heavily in health-related initiatives. In the early days of the pandemic, Alphabets Verily unit, which focuses on life-sciences and med tech, partnered with the Trump administration to provide online screening for Covid tests. Google also partnered with Apple to create a system for contract tracing on smartphones. Last year the company dissolved its Google Health unit, restructuring its health efforts so they werent housed in one central division.

Google has stirred controversy in the past for its healthcare efforts. In 2019, Google drew blowback for an initiative called Project Nightingale, in which the company partnered with Ascension, the second-largest healthcare system in the country, to collect the personal health information of millions of people. The data included lab results, diagnoses and hospitalization records, including names and birthdays, according to the Wall Street Journal, though Google at the time said the project complied with federal law. Google had reportedly been using the data in part to design new software.

Two years earlier, the tech giant partnered with the National Institute of Health to publicly post more than 100,000 images of human chest X-rays. The goal there was to showcase the companys cloud storage capabilities and make the data available to researchers. But two days before the images were to be posted, the NIH told Google its software had not properly removed data from the X-rays that could identify patients, according to The Washington Post, which would potentially violate federal law. In response, Google canceled its project with NIH.

Asked about Googles past fumble with de-identifying information, Sameer Sethi, SVP and chief data and analytics officer at Hackensack Meridian Health, says the company has safeguards in place to prevent such mishaps.

You never actually trust the tool, he told Forbes. He adds Hackensack Meridian Health works with a third-party company to certify that the images are de-identified, even after using Googles tools. We will not bring anything to use without expert determination.

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Google Will Let Healthcare Organizations Use Its AI To Analyze And Store X-Rays - Forbes

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The artificial intelligence (AI) boom began in earnest in 2012 when Alex Krizhevsky,in collaboration with Ilya SutskeverandGeoffrey Hinton (who was Krizhevskys Ph.D. advisor), created AlexNet, which then won the ImageNet Large Scale Visual Recognition Challenge. The goal of that annual competition, which had begun in 1996, was to classify the 1.3 million high-resolution photographs in the ImageNet training set into 1,000 different classes. In other words, to correctly identify a dog and a cat.

AlexNet consisted of a deep learning neural network and was the first entrant to break 75% accuracy in the competition. Perhaps more impressively, it halved the existing error rate on ImageNet visual recognition to15.3%.It also established, arguably for the first time, that deep learning had substantive real-world capabilities. Among other applications, this paved the way for the visual recognition systems used across industries from agriculture to manufacturing.

This deep learning breakthrough triggered accelerated use of AI. But beyond the unquestioned genius of these and other early practitioners of deep learning, it was the confluence of several major technology trends that boosted AI. The internet, mobile phones and social media led to a data explosion, which is the fuel for AI. Computing continued its metronome-like Moores Law advance of doubling performance about every 18 months, enabling the processing of vast amounts of data. The cloud provided ready access to data from anywhere and lowered the cost of large-scale computing. Software advances, largely open-source, led to a flourishing of AI code libraries available to anyone.

All of this led to an exponential increase in AI adoption and a gold rush mentality. Research from management consulting firm PwC shows global GDP could be up to 14% higher in 2030 as a result of AI, the equivalent of an additional$15.7 trillion making it the biggest commercial opportunity in todays economy. According to Statista, global AI startup company funding has grown exponentially from $670 million in 2011 to $36 billion in 2020. Tortoise Intelligence reported that this more than doubled to $77 billion in 2021. In the past year alone, there have been over 50 million online mentions of AI in news and social media.

MetaBeat 2022

MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.

All of that is indicative of the groundswell of AI development and implementation. Already present in many consumer applications, AI is now gaining broad adoption in the enterprise. According to Gartner,75%of businesses are expected to shift from piloting to operationalizing AI by 2024.

It is not only deep learning that is driving this. Deep learning is a subset of machine learning (ML), some of which has existed for several decades. There are a large variety of ML algorithms in use, for everything from email spam filters to predictive maintenance for industrial and military equipment. ML has benefitted from the same technology trends that are driving AI development and adoption.

With a rush to adoption have come some notable missteps. AI systems are essentially pattern recognition technologies that scour existing data, most of which has been collected over many years. If the datasets upon which AI acts contain biased data, the output from the algorithms can reflect that bias. As a consequence, there have been chatbots that have gone terribly awry, hiring systems that reinforce gender stereotypes, inaccurate and possibly biased facial recognition systems that lead to wrongful arrests, and historical bias that leads to loan rejections.

These and other problems have prompted legitimate concerns and led to the field of AI Ethics. There is a clear need for Responsible AI, which is essentially a quest to do no harm with AI algorithms. To do this requires that bias be eliminated from the datasets or otherwise mitigated. It is also possible that bias is unconsciously introduced into the algorithms themselves by those who develop them and needs to be identified and countered. And it requires that the operation of AI systems be explainable so that there is transparency in how the insights and decisions are reached.

The goal of these endeavors is to ensure that AI systems not only do no specific harm but are trustworthy. As Forrester Research notes in a recent blog, this is critical for business, as it cannot afford to ignore the ethical debt that AI technology has accrued.

Responsible AI is not easy, but is critically important to the future of the AI industry. There are new applications using AI coming online all the time where this could be an issue, such as determining which U.S. Army candidates are deserving of promotion. Recognizing that the problem exists has focused considerable efforts over the last few years on developing corrective measures.

There is good news on this front, as techniques and tools have been developed to mitigate algorithm bias and other problems at different points in AI development and implementation, whether in the original design, in deployment or after it is in production. These capabilities are leading to the emerging field of algorithmic auditing and assurance which will build trust in AI systems.

Besides bias, there are other issues in building Trustworthy AI, including the ability to explain how an algorithm reaches its recommendations and whether the results are replicable and accurate, ensure privacy and data protection, and secure against adversarial attack. The auditing and assurance field will address all these issues, as found in research done by Infosys and the University College of London. The purpose is to provide standards, practical codes and regulations to assure users of the safety and legality of algorithmic systems.

There are four primary activities involved.

Development: An audit will have to account for the process of development and documentation of an algorithmic system.

Assessment: An audit will have to evaluate an algorithmic systems behaviors and capacities.

Mitigation: An audit will have to recommend service and improvement processes for addressing high-risk features of algorithmic systems.

Assurance: An audit will be aimed at providing a formal declaration that an algorithmic system conforms to a defined set of standards, codes of practice or regulations.

Ideally, a business would include these concepts from the beginning of an AI project to protect itself and its customers. If this is widely implemented, the result would produce an ecosystem of Trustworthy AI and Responsible AI. In doing so, algorithmic systems would be properly appraised, all plausible measures for reducing or eliminating risk would be undertaken, and users, providers and third parties would be assured of the systems safety.

Only a decade ago, AI was practiced mostly by a small group of academics. The development and adoption of these technologies has since expanded dramatically. For all the considerable advances, there have been shortcomings. Many of these can be addressed and resolved with algorithmic auditing and assurance. With the wild ride of AI over the last 10 years, this is no small accomplishment.

Bali (Balakrishna) DR is senior vice president, service offering head ECS, AI and automation atInfosys.

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Trustworthy AI is now within reach - VentureBeat

Video content is a very powerful source of information, essential for analysis, said TVCONAL founder Masoumeh Izadi.

Her company has developed an AI and machine learning powered platform that rapidly analyses sports footage, with a current focus on cricket.

Data plays a central role in sports, she said, Put together to create game semantics and metadata labels, and that would make your content searchable, customisable, and you can extract value and monetise it.

Read moreArtificial intelligence in broadcasting

TVCONAL has analysed 168 cricket matches over an eight-month period. The end result in each case is a content platform that can be searched for game events such as specific batting or bowling techniques.

Each match needs to be sliced into what we call units of analysis, which is different in every sport, said Izadi. For example, this could be shots or pitches, or delivery or a stroke depending on the sport At the heart of this learning model is learning to slice.

The platform uses machine learning and computer vision algorithms to recognise these game events based solely on the content in the video itself.

The solution we are proposing is to use video analytics, which at the moment is very, very advanced, to the point you can understand and discover what is in the content. In sport content, this would mean identifying and locating different kinds of objects, being able to track those objects, detect players, the type of player, track their movements, etc - whether thats their position or the key points of their body just from the content of the video.

AI recognises batting techniques based on the movements of the player, or a 6-point shot by analysing when the ball crosses the boundary, with upwards of 95% accuracy. TVCONAL has developed the system recording top-tier multi-cam cricket productions, basic 3-camera shoots and single camera recordings, where accuracy with a more limited analysis model can reach above 99%.

This form of AI-powered video analysis takes much of the cost and effort out of content tagging and categorisation.

Even in top tier productions this is very time consuming, labour intensive, and prone to human error. Plus for archive content accumulated over the years, it is a nightmare to go through, said Izadi.

There are numerous uses for this form of AI content analysis, and TVCONAL is currently focused on applications within the sport itself. It is in discussions with six pro cricket teams in SE Asia and 10 cricket centres of excellence in Malaysia about the use of its platform as a training tool.

Izadi calls it a move to democratise sport in the digital age.

[AI technology can] give any sport team the privilege the big sport teams have. Saving cost and saving time from productions, empowering the production team in their operation, and unlocking their ability to produce more engaging, more interesting sport content.

TVCONAL used more than 20,000 samples to train its cricket machine learning algorithms, and is planning to branch out into other sports in future. Were looking into racquet spots, tennis and table tennis, said Izadi.

She also demonstrated experimental auto-generated commentary during IBC2002, building upon game event analysis with automatically generated sports presenter-style speech to match the on-screen action.

However, the true cutting edge of speech synthesis was demonstrated by Kiyoshi Kurihara from NHK, the Japan Broadcasting Corporation.

NHK currently uses text-to-speech generation to accompany news reports on the NHK 1 TV channel, live sports commentary online and for weather forecasts on local radio stations. It provides a professional line read, through an AI anchor, but the actual input is typed or automatically generated rather than spoken by an actual person.

Kiyoshi Kurihara explained the process as breaking down the written words into graphemes, recognising their respective sound, or phoneme, and then converting these phonemes into a waveform. This is the audio clip of generated speech that can be broadcast over the airwaves or synced to a video.

The AI model that can produce realistic line readings requires 20 hours of speech data per person, according to Kunihara. Its every hard work, he added.

This is particularly true using a more traditional method. Training is difficult for two reasons. First, text-to-speech requires high quality speech [recordings], and this requires four people. An anchor, engineer, director and data annotator. Second, regarding quality it is important to produce high quality speech synthesis, because noise will also be re-generated, he explained.

The leading edge component of NHKs process removes much of this heavy workload. This manual process can be eliminated, said Kunihara, using a supervised learning approach.

Read moreBroadcast trends: From AI and D2C to NFTs and 8K

NHKs AI Anchor model is created using real speech from radio already broadcast. One difficulty here is that a radio programme may feature a mix of music and speech and, naturally, only the speech can be used to build the tech-to-speech profile.

We have developed a method to automatically retrieve one sentence clips using state of the art speech processing, said Kunihara. The broadcast is broken down into small segments, cutting out music and other extraneous sections, which become the units of the units used to train the AI voice synthesiser.

Wave form synthesis uses deep learning and converts phoneme to waveform, explained Kunihara. And by automating some of the most challenging parts of the process, NHK is able to affordably and efficiently develop virtual radio and TV presenters.

Local radio provides a great example of when this can not just reduce costs, but increase the usefulness of the broadcast. There are 54 [radio] stations in the local area and this was costly for them to provide local weather, said Kunihara. NHK automatically generates scripts using weather report information for each local station, and then employs its TTS (text-to-speech) system to create ready-to-broadcast bespoke weather report audio.

NHK used similar techniques for mass sporting events too. In 2018 and 2021 we provided live sports commentary using TTS during the Olympic and Paralympic Games and distributed them over the internet, said Kunihara. The team used metadata from the official Olympic Data Feed to auto-generate a script for each feed.

This ties into the metadata creation TVCONAL creates in its cricket footage analysis, demonstrating how AI technologies can often work hand-in-hand in this field.

Kiyoshi Kurihara of NHK and TVCONAL founder Masoumeh Izadi were speaking at an IBC2022 session titled Technical Papers: How AI is Advancing Media Production. It was moderated by

Nick Lodge, director of Logical Media. For more content from the IBC2022 Show check out the latest IBC2022 video and the full coverage on 365.

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How AI can lower costs and increase efficiency in content creation - IBC365

TORONTO, Oct. 4, 2022 /PRNewswire/ - UN-Habitat, the United Nations organization working for sustainable urbanization in over 90 countries, and Mila, the world's largest academic deep learning research centre, announce the launch of their collaborative White Paper offering insights and recommendations on how Artificial Intelligence (AI) systems could be harnessed to support the development of socially and environmentally sustainable cities and human settlements.

Aimed at supporting both urban managers and practitioners, the White Paper, part of UN-Habitat's strategy to promote a people-centered approach to digital transformation,outlines the current value add and challenges of AI in cities and provides a set of practical recommendations to improve the way in which AI is used and promotes sustainable cities and inclusive inclusive communities.

The document covers urban applications of AI across a wide range of sectors (Energy, Mobility, Public Safety, Healthcare, etc.), and covers important themes including the trad-offs and risks of AI, the specific approaches and applications of urban AI in urban governance.

The paper will be unveiled during a panel discussion at the 2022 Urban Economy Forum (UEF) in Toronto.

"AI, like any other technology, can be a force that widens or reduces social gaps depending on how it is applied. We have a collective responsibility to appropriately use and integrate AI solutions and ensure that there are no unintended consequences." says Maimunah Mohd Sharif, Under-Secretary-General and Executive Director at UN-Habitat.

The goal of the White Paper is to provide local authorities with the necessary guidance they need to weigh up the pros and cons of AI in fostering sustainable local communities. Local authorities regularly make decisions about AI that impact people's lives, and need the tools to assess where and for whom AI brings added value and whether it is appropriate in a given context. The paper serves as a knowledge resource to help guide decision making with respect to the positive engagement with and use of AI for smart and people centered sustainable cities and human settlements.

"Like any other transformative opportunity, integrating AI into urban environments comes with challenges and risks that must be taken and tackled seriously for AI to benefit societies," says Valrie Pisano, President and CEO of Mila. "This White Paper could be a very useful tool for local authorities who, like UN-Habitat and Mila, are committed to the responsible development of AI."

Visit https://unhabitat.org/ to access the full White Paper.

White Paper main authors:

More details on the Launch event:

About UN-Habitat

UN-Habitat is the United Nations programme working towards a better urban future. Its mission is to promote socially and environmentally sustainable human settlements development and the achievement of adequate housing for all. It works in over 90 countries to promote transformative change in cities and human settlements through knowledge, policy advice, technical assistance and collaborative action. It works with partners to build inclusive, safe, resilient and sustainable cities and communities while promoting urbanization as a positive transformative force for people and communities, reducing inequality, discrimination and poverty. For more information, visit UN-Habitat - A Better Urban Future | UN-Habitat (unhabitat.org)

About Mila

Founded by Professor Yoshua Bengio of the University of Montreal, Mila is an artificial intelligence research institute that brings together over 1,000 researchers specializing in machine learning. Based in Montreal, Mila's mission is to be a global hub for scientific advances that inspire innovation and development of AI for the benefit of all. Mila is a non-profit organization recognized worldwide for its significant contributions to the field of deep learning, particularly in language modeling, machine translation, object recognition, and generative models. For more information, visit mila.quebec

SOURCE Mila - Quebec AI Institute

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AI and Cities: UN-Habitat and Mila launch a collaborative White Paper on the use and potential challenges of AI to support the development of...