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Israeli Startup AI21 Labs Raises $34.5M For AI-Based Language Tech

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AI21 Labs, a startup formed by veterans from an elite tech unit in the IDF to build AI systems, announced on Thursday it raised $34.5 million in total equity capital to work on its AI-based writing solutions and offerings. The funding includes a seed round of $9.5 million in January 2019 and the latest round of $25 million led by Pitango First, the seed and early-stage fund of Pitango's investment platform. Pitango is Israel's largest venture capital fund and was co-founded by Chemi Peres, the son of former president Shimon Peres. The VC focuses on core technologies like deep tech, AI, and machine learning. Other investors in the latest funding round included TPY Capital, another VC headquartered in Tel Aviv.


AI smartphone apps use camera to test for urinary tract infections

Daily Mail - Science & tech

Two new artificial intelligence apps use your smartphone camera to screen for urinary tract infections (UTIs) or possible signs of chronic kidney disease. Designed to cater for people in lockdown, the apps from Israel-based health technology company Healthy.io With the UTI app, called Velieve, users order a UTI test kit to be delivered to their home, submit their results through the app, and then receive an in-app diagnosis within 30 minutes. The kidney disease app, meanwhile, will be'prescribed' by GPs to patients who are at high risk of chronic kidney disease. This test, which detects albumin to creatinine ratio (ACR) – a key marker of kidney disease – in urine samples, will also be delivered via post and analysed through the app, with the result delivered to directly to the GP.


Facebook's New AI System Can Pass Multiple-Choice Intelligence Tests

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Recently, a team of researchers from Facebook AI and Tel Aviv University proposed an AI system that solves the multiple-choice intelligence test, Raven's Progressive Matrices. The proposed AI system is a neural network model that combines multiple advances in generative models, including employing multiple pathways through the same network. Raven's Progressive Matrices, also known as Raven's Matrices, are multiple-choice intelligence tests. The test is used to measure abstract reasoning and is regarded as a non-verbal estimate of fluid intelligence. In this test, a person tries to finish the missing location in a 3X3 grid of abstract images.


Researchers develop AI that solves a matrix-based visual cognitive test

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Multiple choice tests provide test-takers the ability to compare answers to eliminate choices (or guess the correct one). Each choice can be compared with the question to infer patterns that might have been missed; it's arguably the ability to narrow down the right answer from sets of answers that's the test of true comprehension. Inspired by this, researchers at Tel Aviv University and Facebook developed a machine learning model that generates answers to the Raven Progressive Matrix (RPM), a type of intelligence test where the goal is to complete the location in a grid of abstract images. The coauthors claim that their algorithm is not only able to generate a plausible set of answers competitive with state-of-the-art methods, but that it could be used to build an automatic tutoring system that adjusts to the proficiencies of individual students. RPM is a nonverbal test typically used in educational settings like schools.


Consider This: Theomorphic Robots; Not Losing Our Religion?

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As icons and rituals adapt to newer technologies, the rise of robotics and AI can change the way we practice and experience spirituality. Some 100,000 years ago, fifteen people, eight of them children, were buried on the flank of Mount Precipice, just outside the southern edge of Nazareth in today's Israel. One of the boys still held the antlers of a large red deer clasped to his chest, while a teenager lay next to a necklace of seashells painted with ochre and brought from the Mediterranean Sea shore 35 km away. The bodies of Qafzeh are some of the earliest evidence we have of grave offerings, possibly associated with religious practice. Although some type of belief has likely accompanied us from the beginning, it's not until 50,000–13,000 BCE that we see clear religious ideas take shape in paintings, offerings, and objects. This is a period filled with Venus figurines, statuettes made of stone, bone, ivory and clay, portraying women with small heads, wide hips, and exaggerated breasts.


The 'deep fake' scare is more dangerous than AI-tech behind it

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Recognizing them is increasingly hard if not impossible to the untrained human eye. Overall, as most journalistic coverage of the topic tells us, deepfakes -- alongside other AI technologies, machine learning, and online neural networks in general -- are here and will serve to cast a shadow of technological terror over society. As part of media coverage on this topic, our future is deemed dystopian -- humankind has lost the battles to machines and episodes of the TV series "Black Mirror" will pale in comparison with the havoc sowed by technology. In fact, research I conducted with a colleague from the University of Haifa (Yael Oppenheim) has found that most images and narratives that journalists worldwide use to cover these technologies tend to stress destruction, loss, crisis, and fear regarding the future of humanity. From Israel to the U.S., deepfake videos are becoming a major threat to democracy'Every woman on Instagram is exposed': New AI creates nude photos of clothed women It is, however, important to contextualize this alarmist media frenzy.


Deci Raises $9.1M in Seed Funding to Build AI that Crafts Next Generation of AI – IAM Network

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Deci, the deep learning company dedicated to transforming the AI lifecycle, today announced it has raised $9.1 million in a seed round led by Israel-based VC firm Emerge and global VC fund Square Peg. The company is building an AI-based platform that can automatically craft robust, scalable, and efficient deep neural network solutions ready for production at scale. Deci aims to help AI practitioners build the next generation of deep learning models. Advancements in AI, mainly powered by deep learning, have triggered groundbreaking innovations in medicine, manufacturing, transportation, communication, and retail. But, prolonged development cycles, high computing costs, and unsatisfying inference performance are making it nearly impossible for enterprises to productize AI.


Square Peg aims for the AI sweet spot with latest pick

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"When we met with the [Deci] team we found they were able to help companies, especially at the junction between training the data on deployment and deploying the models into production, there's so much pain at that junction and this company really helps close that gap." Square Peg has made several investments in the field like radiology AI startup Aidoc and has seen portfolio companies like weather forecasting startup ClimaCell increasingly use AI models. "It's very much in our sweet spot," Mr Schwartz said. "We are pretty much focused on, Australia, New Zealand Israel and Southeast Asia, so it fits our geographic purpose, and the size of the company... it's at the early stage of its commercialisation, it's pre-revenue or early revenue and beginning (to get) commercial traction, so all that fits pretty well with us." Mr Geifman said Deci was working to make AI more accessible for more companies.


Global Big Data Conference

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Deep learning startup Deci today announced that it raised $9.1 million in a seed funding round led by Israel-based Emerge. According to a spokesperson, the company plans to devote the proceeds to customer acquisition efforts as it expands its Tel Aviv workforce. Machine learning deployments have historically been constrained by the size and speed of algorithms and the need for costly hardware. In fact, a report from MIT found that machine learning might be approaching computational limits. A separate Synced study estimated that the University of Washington's Grover fake news detection model cost $25,000 to train in about two weeks.