This Filter Makes Your Photos Invisible to Facial Recognition

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In 2020, it's safe to assume that any photo uploaded and made public to the internet will be analyzed by facial recognition. Not only do companies like Google and Facebook apply facial recognition as a feature, but companies like Clearview AI have been discreetly scraping images from the public internet in order to sell facial recognition technology to police for years. Now, A.I. researchers are starting to think about how technology can solve the problem it created. These algorithms aren't the solution to privacy on the web -- and they don't claim to be. But they're tools that, if adopted by online platforms, could claw back a little of the privacy typically lost by posting images online.


What Happens When Tinder and AI 'Hook Up'?

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The developers of the dating app Tinder recently announced that new safety features would be added to its app throughout 2020. These updates include a means to connect users with emergency services when they feel unsafe and more safety information provided through the app. Given that many users, especially women, experience harassment, sexism and threatening behaviour on Tinder, these appear to be positive steps to addressing such issues. Tinder also mentioned app updates will incorporate artificial intelligence (AI) to validate profile photos. "The [AI] feature allows members to self-authenticate through a series of real-time posed selfies, which are compared to existing profile photos using human-assisted AI technology."


Global Big Data Conference

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Disease outbreaks like the coronavirus often unfold too quickly for scientists to find a cure. But in the future, artificial intelligence could help researchers do a better job. While it's probably too late for the fledgling technology to play a major role in the current epidemic, there's hope for the next outbreaks. AI is good at combing through mounds of data to find connections that make it easier to determine what kinds of treatments could work or which experiments to pursue next. The question is what Big Data will come up with when it only gets meager scraps of information on a newly emerged illness like Covid-19, which first emerged late last year in China and has sickened more than 75,000 people in about two months.


Going 'all in' on AI-powered recruitment is a risky business.

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Few topics in the recruitment sector have proved to be more controversial than the use of artificial intelligence (AI). On one hand, everywhere you turn there are endless statistics about how AI is infiltrating business processes. According to LinkedIn, an overwhelming majority of recruiters and hiring managers agree that AI accelerates their ability to source and screen candidates. What's more, 70 per cent of recruiters believe their current process would be more effective if it were more data-driven and used AI. However, on the other side of the fence, research commissioned by the Royal Society of Arts suggests candidates don't agree, with 60 per cent of the public stating they're opposed to the use of automated decision-making in recruitment.


Insilico Medicine taps GSK alum for a six-month AI sprint aimed at brain cancer

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Digital molecule designer Insilico Medicine has launched a preclinical research program focused on finding new treatments for brain cancer, and has brought on the former global program head of GlaxoSmithKline's computer-aided drug discovery unit to help run it. George Okafo will join Insilico as an entrepreneur-in-residence, as the company looks to wield its artificial intelligence networks and generative engines to uncover novel targets and molecules for glioblastoma multiforme, and potentially spin out the findings into a new business. "Brain cancers are the worst diseases anyone can ever get and the most rare cancers are often overlooked because the efforts are expensive, the market is small, and the probability of failure is high," Insilico co-founder and CEO Alex Zhavoronkov said in a statement. "We will try to change this with our AI-powered drug discovery pipeline." "We needed to have a seasoned big pharma veteran to drive this process to the point where it can be quickly developed and externalized," Zhavoronkov added, saying the company plans to use its AI to generate and evaluate several leads over the coming months, before hopefully setting the whole project off on its own by the end of August.


Human Intelligence and AI for Oncology advancement - Vatican News

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The increase in the number of cancer cases worldwide is a major cause for concern for the medical community. Doctor Alexandru Floares, a speaker at a 3-day workshop organized by the Pontifical Academy for Life on Ethics and Artificial Intelligence (AI), spoke to Vatican Radio on the potential for larger strides in the field of oncology and medical research through the efficiency that AI provides. Dr. Floares, a Neurologist, specialist in AI applications in Oncology, and President of Solutions of Artificial Intelligence Applications (SAIA), gave a presentation titled "AI in Oncology." In his interview with Vatican Radio, Dr. Floares spoke on issues bordering on access to data for medical research, solutions to the emerging issues surrounding the use of AI in healthcare, and the revolutionary role of AI in the field of medicine. "The problems related to applying AI to medicine and oncology can be solved relatively easily," he said.


Putin's Youngest Daughter to Run Artificial Intelligence Institute

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Moscow State University will receive 890 million rubles ($13.6 million) in state funding for its development program this year, according to Vedomosti newspaper, which first reported Tikhonova's new role earlier on Friday.


Small LiDAR ICs offer higher bandwidth for faster self-driving cars

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Designers of automotive self-driving systems can now allow higher-speed autonomous driving through what is claimed to be the industry's fastest and smallest LiDAR ICs from Maxim Integrated Products. When compared to the closest competitive solution, the MAX40026 high-speed comparator and the MAX40660/MAX40661 high-bandwidth transimpedance amplifiers allow 10mph (15km/h) faster autonomous driving at highway speeds by giving more than 2x higher bandwidth and accommodating 32 extra channels (128 vs 96) to a LiDAR module within the identical module size. With automotive self-driving systems developing from 35mph to 65mph and beyond, LiDAR sensors are performing an expanding role in the fusion of vehicle sensors for their capacity to produce accurate distance measurement of objects. With more than twice the bandwidth and the capability to accommodate 33% more channels within the same LiDAR module size compared to the closest competitor, the TIAs provide optical receiver designers with higher-resolution images that facilitate faster autonomous driving systems. These ICs satisfy the stringent safety demands of the automotive industry with AEC-Q100 qualification, enhanced ESD performance and FMEDA to support ISO 26262 certification at the system level.


Heather Lynch Chosen as AAAS Leshner Fellow to Educate Public on AI SBU News

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The American Association for the Advancement of Science (AAAS) has selected Stony Brook University Professor Heather Lynch as one of 12 researchers in the area of artificial intelligence (AI) to be a 2020-2021 AAAS Alan I. Leshner Leadership Institute Public Engagement Fellow. Lynch, Institute for Advanced Computational Science Endowed Chair for Ecology & Evolution, was chosen for having demonstrated leadership and excellence in her research career and for her interest in promoting meaningful dialogue between science and society. AI focuses on developing and studying machines and algorithms that augment or mimic human abilities, learn from and adjust to new situations, and perform tasks. As a quantitative ecologist whose research focuses on the population dynamics of Antarctic wildlife, Lynch uses AI to study penguins, among other species. "Communicating with the public about penguins is pretty easy, but explaining AI? That's quite a bit harder," said Lynch.


Top 10 Python Machine Learning Packages

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The field of data science relies heavily on the predictive capability of Machine Learning (ML) algorithms. Python offers an opportune playground for experimenting with these algorithms due to the readability and syntactical efficiency of the language. The vast availability of ML libraries accessible to Python users makes it an even more attractive solution to interpret the immense amount of data available today. This article explores the top 10 ML packages that you need to know, as well as the advantages and disadvantages of each. A Python framework is an interface or tool that allows developers to build ML models easily, without getting into the depth of the underlying algorithms.