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AIBotics Go-Digital Series 2020 Showcases AI and Robotic Innovations to Augment Human Potential

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Asia Pacific Assistive Robotics Association (APARA), a non-profit organization founded to facilitate adoption and augmentation of Artificial Intelligence (AI) and Robotics, today announced AIBotics Go-Digital Series 2020, an AI and Robotics event themed around'Augmenting the Human Potential', will be launched between August and November 2020, aiming to facilitate the increasing dependency on AI technology into improving human lives. An international event endorsed and supported by the International Alliance of Robotics Associations (IARA) and a number of global and regional partners including the University of Oxford, the ASEAN Smart Cities Network, Japan Science & Technology Agency, the Malaysian Artificial Intelligence and Robotics Association, among others, AIBotics Go-Digital Series 2020 reviews ethical and responsible AI and robotics innovations through webinars and a virtual exhibition 24 hours a day, seven days a week from August for four months, bringing together renowned industry experts as well as a number of projects and innovative solutions from all around the world. To enable a smart, seamless and sustainable digital conferencing experience, APARA is collaborating with Tencent Cloud, the official conferencing solution provider of AIBotics Go-Digital Series 2020, to bring visitors and delegates a series of power-packed webinars and a virtual exhibition through Tencent Cloud Conference (TCC) solutions which have been widely adopted by local and overseas organizations and enterprises at online and digital business conferences, annual meetings, road shows, lectures, industry forums, among others. "As we adjust to the'new normal' brought about by the COVID-19 pandemic, AI has also become much more mainstream while allowing gatherings and business meetings to be held amid current circumstances. We are excited to present AIBotics Go-Digital Series 2020, highlighting how AI and Robotics can truly augment human potential, which is a timely message in light of the virus-related disruptions globally," said Shanlynn Lee, President of APARA.


Artificial Intelligence in Outpatient Practice Today

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In the near future, MDs could get new helpful assistants, i.e. efficient multipurpose artificial intelligence (AI) algorithms that will assist them in examining and diagnosing patients, choosing the best treatment strategy, processing patients' requests, and keeping medical records. Currently, there are technologies that support physicians at every stage of treatment. Let's see how AI helps doctors in outpatient practice. One of the promising areas in outpatient practice is the introduction of chatbots. AI will quickly collect and analyze general symptoms of the condition, and then schedule an appointment with the right MD.


Global Big Data Conference

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One of the biggest challenges brands face today when utilizing social media is finding the right influencer to align with – and while a "wrong" choice likely won't hurt a brand in most cases, it could be more of a wasted effort. However, in a worst case scenario working with the wrong influencer can have a detrimental impact on a brand. According to a survey conducted by Salesforce Research last year, 92% of consumers surveyed report that trusting a brand makes them more likely to buy products and services. In addition, nearly a third – 32% – of consumers also said that the influencer's core values should also align with that of the consumer's. While human marketing teams can comb through the social media feeds of influencers for past posts to judge appropriateness as well as effectiveness, which can be a time consuming endeavor, and could still miss something important. It is important to find an influencer that is the right fit but is also actually going to provide a reasonable return on investment (ROI) in terms of sales or traffic.


Artificial Intelligence Finds Surprisingly Oxygen-Starved Early Galaxy

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A new galaxy, which is likely to be very young by cosmic standards, has been discovered thanks to the power of big data and machine learning. The galaxy, captured by an international team studying data from the Subaru Telescope in Hawaii, has broken the record for the lowest oxygen abundance in any galaxy observed from Earth. The galaxy, called HSC J1631 4426, has an extremely low oxygen abundance of 1.6% solar abundance, meaning it breaks the previous record of the lowest known oxygen abundance in a galaxy. This, the researchers explained in a press release, means that the stars in the galaxy likely formed very recently. As galaxies that are still in the early stages of formation in the modern Universe are rare, the international team behind the new discovery searched for them using wide-field imaging data taken with the Subaru Telescope.


Global Big Data Conference

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Computers are getting cleverer and increasingly inventive, offering terrific prospects to improve the human condition. There's a call to rethink Artificial Intelligence as Augmented Intelligence, to stress the capability of people working with AI instead of being replaced by AI. In present times, AI specialists, data scientists, designers, and engineers explore how we can function close by machines, when we should enable machines to make psychological decisions, and how all of society can gain from the productive and monetary benefits guaranteed by innovation in AI. Augmented Intelligence is a technological approach dependent on big data that joins techniques of machine learning, natural language processing, and data analytics intending to produce relevant data for targeted choices. It is a development of artificial intelligence that includes human association in a procedure of constant inclusion, focused on improving the results obtained.


Proposed voice analysis framework preserves both accuracy and privacy

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Imperial College London researchers claim they've developed a voice analysis method that supports applications like speech recognition and identification while removing sensitive attributes such as emotion, gender, and health status. Their framework receives voice data and privacy preferences as auxiliary information and uses the preferences to filter out sensitive attributes which could otherwise be extracted from recorded speech. Voice signals are a rich source of data, containing linguistic and paralinguistic information including age, likely gender, health status, personality, mood, and emotional state. This raises concerns in cases where raw data is transmitted to servers; attacks like attribute inference can reveal attributes not intended to be shared. In fact, the researchers assert attackers could use a speech recognition model to learn further attributes from users, leveraging the model's outputs to train attribute-inferring classifiers. They posit such attackers could achieve attribute inference accuracy ranging from 40% to 99.4% -- three or four times better than guessing at random -- depending on the acoustic conditions of the inputs.


The state of AI in 2020 likely sees more adoption

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This year "is the year that AI is going to enter the enterprise mainstream adoption," said Jeff Loucks, executive director of The Center for Technology, Media & Telecommunications at Deloitte Services LP. Deloitte's 2020 edition of its annual "State of AI in the Enterprise" report, released in July, indicates that many enterprises are investing heavily in AI, and many are buying cloud-based AI products instead of building their own. The technology and consulting company surveyed 2,737 IT and line-of-business executives across nine countries. All of the respondents use some form of AI in their companies. The survey showed that 53% of the adopters spent more than $20 million over the past year on AI-related technology and talent, with 71% of them expecting to increase spending in the next fiscal year.


The Art of SEO: Mastering Search Engine Optimization, 3rd Edition - Programmer Books

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Three acknowledged experts in search engine optimization share guidelines and innovative techniques that will help you plan and execute a comprehensive SEO strategy. Novices will receive a thorough SEO education, while experienced SEO practitioners get an extensive reference to support ongoing engagements. Comprehend SEO's many intricacies and complexities Explore the underlying theory and inner workings of search engines Understand the role of social media, user data, and links Discover tools to track results and measure success Examine the effects of Google's Panda and Penguin algorithms Consider opportunities in mobile, local, and vertical SEO Build a competent SEO team with defined roles Glimpse the future of search and the SEO industry


AI algorithm for detecting prostate cancer shows more than 98% sensitivity, 97% specificity in study - MedCity News

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An Israeli startup developing a digital pathology system based around artificial intelligence has published what it calls "outstanding outcomes" in a clinical validation study. Tel Aviv-based Ibex Medical Analysis said Tuesday that it had published data on Galen Prostate, its AI-based system for use by pathologists to detect and measure prostate cancer, in The Lancet Digital Health. The company called it the first and only AI-based system used by pathologists in routine clinical practice. The study took place at the University of Pittsburgh Medical Center, led by Drs. According to the data, sensitivity measured for prostate cancer was 98.46%, and specificity was 97.33%, while the operating characteristic curve was 0.991.


Graph signal processing for machine learning: A review and new perspectives

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The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.