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With the coronavirus growing more deadly in China, artificial intelligence researchers are applying machine-learning techniques to social media, web, and other data for subtle signs that the disease may be spreading elsewhere.

The new virus emerged in Wuhan, China, in December, triggering a global health emergency. It remains uncertain how deadly or contagious the virus is, and how widely it might have already spread. Infections and deaths continue to rise. More than 31,000 people have now contracted the disease in China, and 630 people have died, according to figures released by authorities there Friday.

John Brownstein, chief innovation officer at Harvard Medical School and an expert on mining social media information for health trends, is part of an international team using machine learning to comb through social media posts, news reports, data from official public health channels, and information supplied by doctors for warning signs the virus is taking hold in countries outside of China.

The program is looking for social media posts that mention specific symptoms, like respiratory problems and fever, from a geographic area where doctors have reported potential cases. Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel. A company called BlueDot used a similar approachminus the social media sourcesto spot the coronavirus in late December, before Chinese authorities acknowledged the emergency.

We are moving to surveillance efforts in the US, Brownstein says. It is critical to determine where the virus may surface if the authorities are to allocate resources and block its spread effectively. Were trying to understand whats happening in the population at large, he says.

The rate of new infections has slowed slightly in recent days, from 3,900 new cases on Wednesday to 3,700 cases on Thursday to 3,200 cases on Friday, according to the World Health Organization. Yet it isnt clear if the spread is really slowing or if new infections are simply becoming more difficult to track.

So far, other countries have reported far fewer cases of coronavirus. But there is still widespread concern about the virus spreading. The US has imposed a travel ban on China even though experts question the effectiveness and ethics of such a move. Researchers at Johns Hopkins University have created a visualization of the viruss progress around the world based on official numbers and confirmed cases.

Health experts did not have access to such quantities of social, web, and mobile data when seeking to track previous outbreaks such as severe acute respiratory syndrome (SARS). But finding signs of the new virus in a vast soup of speculation, rumor, and posts about ordinary cold and flu symptoms is a formidable challenge. The models have to be retrained to think about the terms people will use and the slightly different symptom set, Brownstein says.

Even so, the approach has proven capable of spotting a coronavirus needle in a haystack of big data. Brownstein says colleagues tracking Chinese social media and news sources were alerted to a cluster of reports about a flu-like outbreak on December 30. This was shared with the WHO, but it took time to confirm the seriousness of the situation.

Beyond identifying new cases, Brownstein says the technique could help experts learn how the virus behaves. It may be possible to determine the age, gender, and location of those most at risk more quickly than using official medical sources.

Alessandro Vespignani, a professor at Northeastern University who specializes in modeling contagion in large populations, says it will be particularly challenging to identify new instances of the coronavirus from social media posts, even using the most advanced AI tools, because its characteristics still arent entirely clear. Its something new. We dont have historical data, Vespignani says. There are very few cases in the US, and most of the activity is driven by the media, by peoples curiosity.

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How AI Is Tracking the Coronavirus Outbreak - WIRED

The editors at Solutions Review curated this list of the best AI and machine learning TED talks for practitioners in the field.

TED Talks are influential videos from expert speakers in a variety of verticals. TED began in 1984 as a conference where Technology, Entertainment and Design converged, and today covers almost all topics from business to technology to global issues in more than 110 languages. TED is building a clearinghouse of free knowledge from the worlds top thinkers, and their library of videos is expansive and rapidly growing.

Solutions Review has curated this list of AI and machine learning TED talks to watch if you are a practitioner in the field. Talks were selected based on relevance, ability to add business value, and individual speaker expertise. Weve also curated TED talk lists for topics like data visualization and big data.

Erik Brynjolfsson is the director of the MIT Center for Digital Business and a research associate at the National Bureau of Economic Research. He asks how IT affects organizations, markets and the economy. His books include Wired for Innovation and Race Against the Machine. Brynjolfsson was among the first researchers to measure the productivity contributions of information and community technology (ICT) and the complementary role of organizational capital and other intangibles.

In this talk, Brynjolfsson argues that machine learning and intelligence are not the end of growth its simply the growing pains of a radically reorganized economy. A riveting case for why big innovations are ahead of us if we think of computers as our teammates. Be sure to watch the opposing viewpoint from Robert Gordon.

Jeremy Howard is the CEO ofEnlitic, an advanced machine learning company in San Francisco. Previously, he was the president and chief scientist atKaggle, a community and competition platform of over 200,000 data scientists. Howard is a faculty member atSingularity University, where he teaches data science. He is also a Young Global Leader with the World Economic Forum, and spoke at the World Economic Forum Annual Meeting 2014 on Jobs for the Machines.

Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis.

Nick Bostrom is a professor at the Oxford University, where he heads theFuture of Humanity Institute, a research group of mathematicians, philosophers and scientists tasked with investigating the big picture for the human condition and its future. Bostrom was honored as one ofForeign Policys 2015Global Thinkers. His bookSuperintelligenceadvances the ominous idea that the first ultraintelligent machine is the last invention that man need ever make.

In this talk, Nick Bostrom calls machine intelligence the last invention that humanity will ever need to make. Bostrom asks us to think hard about the world were building right now, driven by thinking machines. Will our smart machines help to preserve humanity and our values or will they have values of their own?

Lis work with neural networks and computer vision (with Stanfords Vision Lab) marks a significant step forward for AI research, and could lead to applications ranging from more intuitive image searches to robots able to make autonomous decisions in unfamiliar situations. Fei-Fei was honored as one ofForeign Policys 2015Global Thinkers.

This talk digs into how computers are getting smart enough to identify simple elements. Computer vision expert Fei-Fei Li describes the state of the art including the database of 15 million photos her team built to teach a computer to understand pictures and the key insights yet to come.

Anthony Goldbloom is the co-founder and CEO ofKaggle. Kaggle hosts machine learning competitions, where data scientists download data and upload solutions to difficult problems. Kaggle has a community of over 600,000 data scientists. In 2011 and 2012,Forbesnamed Anthony one of the 30 under 30 in technology; in 2013 theMIT Tech Reviewnamed him one of top 35 innovators under the age of 35, and the University of Melbourne awarded him an Alumni of Distinction Award.

This talk by Anthony Goldbloom describes some of the current use cases for machine learning, far beyond simple tasks like assessing credit risk and sorting mail.

Tufekci is a contributing opinion writer at theNew York Times, an associate professor at the School of Information and Library Science at University of North Carolina, Chapel Hill, and a faculty associate at Harvards Berkman Klein Center for Internet and Society. Her book,Twitter and Tear Gas was published in 2017 by Yale University Press.

Machine intelligence is here, and were already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control. In this cautionary talk, techno-sociologist Zeynep Tufekci explains how intelligent machines can fail in ways that dont fit human error patterns and in ways we wont expect or be prepared for.

In his bookThe Business Romantic, Tim Leberecht invites us to rediscover romance, beauty and serendipity by designing products, experiences, and organizations that make us fall back in love with our work and our life. The book inspired the creation of the Business Romantic Society, a global collective of artists, developers, designers and researchers who share the mission of bringing beauty to business.

In this talk, Tim Leberecht makes the case for a new radical humanism in a time of artificial intelligence and machine learning. For the self-described business romantic, this means designing organizations and workplaces that celebrate authenticity instead of efficiency and questions instead of answers. Leberecht proposes four principles for building beautiful organizations.

Grady Booch is Chief Scientist for Software Engineering as well as Chief Scientist for Watson/M at IBM Research, where he leads IBMs research and development for embodied cognition. Having originated the term and the practice of object-oriented design, he is best known for his work in advancing the fields of software engineering and software architecture.

Grady Booch allays our worst (sci-fi induced) fears about superintelligent computers by explaining how well teach, not program, them to share our human values. Rather than worry about an unlikely existential threat, he urges us to consider how artificial intelligence will enhance human life.

Tom Gruberis a product designer, entrepreneur, and AI thought leader who uses technology to augment human intelligence. He was co-founder, CTO, and head of design for the team that created theSiri virtual assistant. At Apple for over 8 years, Tom led the Advanced Development Group that designed and prototyped new capabilities for products that bring intelligence to the interface.

This talk introduces the idea of Humanistic AI. He shares his vision for a future where AI helps us achieve superhuman performance in perception, creativity and cognitive function from turbocharging our design skills to helping us remember everything weve ever read. The idea of an AI-powered personal memory also extends to relationships, with the machine helping us reflect on our interactions with people over time.

Stuart Russell is a professor (and formerly chair) of Electrical Engineering and Computer Sciences at University of California at Berkeley. His bookArtificial Intelligence: A Modern Approach (with Peter Norvig) is the standard text in AI; it has been translated into 13 languages and is used in more than 1,300 universities in 118 countries. He also works for the United Nations, developing a new global seismic monitoring system for the nuclear-test-ban treaty.

His talk centers around the question of whether we can harness the power of superintelligent AI while also preventing the catastrophe of robotic takeover. As we move closer toward creating all-knowing machines, AI pioneer Stuart Russell is working on something a bit different: robots with uncertainty. Hear his vision for human-compatible AI that can solve problems using common sense, altruism and other human values.

Dr. Pratik Shahs research creates novel intersections between engineering, medical imaging, machine learning, and medicine to improve health and diagnose and cure diseases. Research topics include: medical imaging technologies using unorthodox artificial intelligence for early disease diagnoses; novel ethical, secure and explainable artificial intelligence based digital medicines and treatments; and point-of-care medical technologies for real world data and evidence generation to improve public health.

TED Fellow Pratik Shah is working on a clever system to do just that. Using an unorthodox AI approach, Shah has developed a technology that requires as few as 50 images to develop a working algorithm and can even use photos taken on doctors cell phones to provide a diagnosis. Learn more about how this new way to analyze medical information could lead to earlier detection of life-threatening illnesses and bring AI-assisted diagnosis to more health care settings worldwide.

Margaret Mitchells research involves vision-language and grounded language generation, focusing on how to evolve artificial intelligence towards positive goals. Her work combines computer vision, natural language processing, social media as well as many statistical methods and insights from cognitive science. Before Google, Mitchell was a founding member of Microsoft Researchs Cognition group, focused on advancing artificial intelligence, and a researcher in Microsoft Researchs Natural Language Processing group.

Margaret Mitchell helps develop computers that can communicate about what they see and understand. She tells a cautionary tale about the gaps, blind spots and biases we subconsciously encode into AI and asks us to consider what the technology we create today will mean for tomorrow.

Kriti Sharma is the Founder of AI for Good, an organization focused on building scalable technology solutions for social good. Sharma was recently named in theForbes 30 Under 30 list for advancements in AI. She was appointed a United Nations Young Leader in 2018 and is an advisor to both the United Nations Technology Innovation Labs and to the UK Governments Centre for Data Ethics and Innovation.

AI algorithms make important decisions about you all the time like how much you should pay for car insurance or whether or not you get that job interview. But what happens when these machines are built with human bias coded into their systems? Technologist Kriti Sharma explores how the lack of diversity in tech is creeping into our AI, offering three ways we can start making more ethical algorithms.

Matt Beane does field research on work involving robots to help us understand the implications of intelligent machines for the broader world of work. Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MITs Institute for the Digital Economy. He received his PhD from the MIT Sloan School of Management.

The path to skill around the globe has been the same for thousands of years: train under an expert and take on small, easy tasks before progressing to riskier, harder ones. But right now, were handling AI in a way that blocks that path and sacrificing learning in our quest for productivity, says organizational ethnographer Matt Beane. Beane shares a vision that flips the current story into one of distributed, machine-enhanced mentorship that takes full advantage of AIs amazing capabilities while enhancing our skills at the same time.

Leila Pirhaji is the founder ofReviveMed, an AI platform that can quickly and inexpensively characterize large numbers of metabolites from the blood, urine and tissues of patients. This allows for the detection of molecular mechanisms that lead to disease and the discovery of drugs that target these disease mechanisms.

Biotech entrepreneur and TED Fellow Leila Pirhaji shares her plan to build an AI-based network to characterize metabolite patterns, better understand how disease develops and discover more effective treatments.

Janelle Shane is the owner of AIweirdness.com. Her book, You Look Like a Thing and I Love Youuses cartoons and humorous pop-culture experiments to look inside the minds of the algorithms that run our world, making artificial intelligence and machine learning both accessible and entertaining.

The danger of artificial intelligence isnt that its going to rebel against us, but that its going to do exactly what we ask it to do, says AI researcher Janelle Shane. Sharing the weird, sometimes alarming antics of AI algorithms as they try to solve human problems like creating new ice cream flavors or recognizing cars on the road Shane shows why AI doesnt yet measure up to real brains.

Sylvain Duranton is the global leader of BCG GAMMA, a unit dedicated to applying data science and advanced analytics to business. He manages a team of more than 800 data scientists and has implemented more than 50 custom AI and analytics solutions for companies across the globe.

In this talk, business technologist Sylvain Duranton advocates for a Human plus AI approach using AI systems alongside humans, not instead of them and shares the specific formula companies can adopt to successfully employ AI while keeping humans in the loop.

For more AI and machine learning TED talks, browse TEDs complete topic collection.

Timothy is Solutions Review's Senior Editor. He is a recognized thought leader and influencer in enterprise BI and data analytics. Timothy has been named a top global business journalist by Richtopia. Scoop? First initial, last name at solutionsreview dot com.

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The 17 Best AI and Machine Learning TED Talks for Practitioners - Solutions Review

ATHENS, Ga., Feb. 7, 2020 /PRNewswire/ -- LinkShadow,Next-Generation Cybersecurity Analytics, announces its presence at the prestigious RSA Conference 2020 in San Francisco from February 24-28.

LinkShadow offers a wide spectrum of cybersecurity solutions that focuses on how to overcome the critical challenges in this smart cyberattacks era.These products include ThreatScore Quadrant, Identity Intelligence, Asset AutoDiscovery, TrafficScene Visualizer & AttackScape Viewer, CXO Dashboards and Threat Shadow. When combined with state-of-art machine-learning capabilities, LinkShadow delivers supreme solutions which include Behavioral Analytics, Threat Intelligence, Insider Threat Management, Privileged Users Analytics, Network Security Optimization, Application Security Visibility, Risk Scoring and Prioritization, Machine Learning and Statistical Analysis and, finally, Anomaly Detection and Predictive Analytics.

At RSA Conference, LinkShadow expert teams will be sharing valuable insights on how this dynamic platform can empower organizations and help improve their defenses against advanced cyberattacks.

Duncan Hume, Vice President USA, LinkShadow, commented that "Undoubtedly RSA Conference is the perfect platform to showcase this unique technology, and we plan to make the best of this opportunity.While you are there, meet the technical teams for a demo session and learn how LinkShadow's best-in-class threat hunting capabilities powered by intense and extensive machine learning algorithms can help organizations become cyber-resilient."

To schedule a personalized demo or fix a meeting at LinkShadow - Booth No. 5487, North Hall, register now:https://www.linkshadow.com/events/RSA-Conference

About LinkShadow

LinkShadow is a U.S.-registered company with regional offices in the Middle East.It is pioneered by a team of highly skilled solution architects, product specialists and programmers with a vision to formulate a next-generation cybersecurity solution that provides unparalleled detection of even the most sophisticated threats. LinkShadow was built with the vision of enhancing organizations' defenses against advanced cyberattacks, zero-day malware and ransomware, while simultaneously gaining rapid insight into the effectiveness of their existing security investments.For more information, visit http://www.linkshadow.com.

Raji John | Head of Client ServiceseMediaLinkT: +971 4 279 4091E: raji@emedialinkme.net

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LinkShadow to Showcase Machine Learning Based Threat Analytics Technology at RSA Conference 2020 - Yahoo Finance

Research Fellow in Machine Learning for ConstructionFaculty of Engineering and Environment

Applications are invited for a Research Fellow in Machine Learning for Construction to contribute to an exciting industry-led project for the development of an AI-driven and real-time command and control centre for site equipment in infrastructure projects. This project follows a successfully completed feasibility project hat tested the concept of improving site equipment in through the application IoT, AI, and BIM. This position is funded by Innovate UK and you will work with a leading team of academics and award-winning digital construction businesses.

As a successful candidate, you will lead the development and the implementation of machine learning and data analytics capabilities of the platform. In particular, you will implement AI techniques for the estimation of the productivity of site equipment, development of predictive schedules, and generation of benchmark data for earthwork.

The ideal candidate will have experience in developing data pipelines within cloud-based platforms such as AWS, Google Cloud or Azure, particularly in the area of IoT, scalable database design, data processing and machine learning. Experience in developing machine learning applications using high level programming languages such as Tensorflow, Python and R within open-source platforms such as Jupyter or Conda is essential.

For an informal discussion about the post, please contact Assoc. Prof Dr. Mohamad Kassem by e-mail mohamad.kassem@northumbria.ac.uk

To apply for this vacancy please click 'Apply Now', and submit a Covering Letter and your CV, including a full list of publications where relevant and any documents specifically requested in the Role Description and Person Specification, such as a sample of written work or journal article.

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Please note this vacancy will close on 08/03/2020

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Research Fellow in Machine Learning for Construction job with NORTHUMBRIA UNIVERSITY | 195895 - Times Higher Education (THE)

It may be that time of the year when new year resolutions start to fizzle, but Google seems to be just getting started.The tech giant has been building tools and services to bring in the benefits of artificial intelligence (AI) to its users. The company has begun upping its arsenal of AI-powered products with a string of new releases this month alone.

Here is a list of the top products launched by Google in January 2020.

Although first introduced in 2014, the latest iterations of sequence-to-sequence (seq2seq) AI models have strengthened the capability of key text-generating tasks including sentence formation and grammar correction. Googles LaserTagger, which the company has open-sourced, speeds up the text generation process and reduces the chances of errors

Compared to traditional seq2seq methods, LaserTagger computes predictions up to 100 times faster, making it suitable for real-time applications. Furthermore, it can be plugged into an existing technology stack without adding any noticeable latency on the user side because of its high inference speed. These advantages become even more pronounced when applied at a large scale.

The company has expanded its Coral lineup by unveiling two new Coral AI products Coral Dev Board Mini and Coral Accelerator Module. Announced ahead of the Consumer Electronics Show (CES) this year, the latest addition to the Coral family followed a successful beta run of the platform in October 2019.

The Coral Accelerator Module is a multi-chip package that encapsulates the companys custom-designed Edge Tensor Processing Unit (TPU). The chip inside the Coral Dev Board is designed to execute multiple computer vision models at 30 frames per second or a single model at over 100fps. Users of this technology have said that it is easy to integrate into custom PCB designs.

Coral Accelerator Module, a new multi-chip module with Google Edge TPU.

Google has also released the Coral Dev Board Mini which provides a smaller form-factor, lower-power, and a cost-effective alternative to the Coral Dev Board.

Caption: The Coral Dev Board Mini is a cheaper, smaller and lower power version of the Coral Dev Board

Officially announced in March 2019, the Coral products were intended to help developers work more efficiently by reducing their reliance on connections to cloud-based systems by creating AI that works locally.

Chatbots are one of the hottest trends in AI owing to its tremendous growth in applications. Google has added to the mix with its human-like multi-turn open-domain version. Meena has been trained in an end-to-end fashion on data mined from social media conversations held in the public domain with a totalling 300GB+ text data. Furthermore, it is massive in size with 2.6B parameter neural network and has been trained to minimize perplexity of the next token.

Furthermore, Googles human evaluation metric called Sensibleness and Specificity Average (SSA) also captures the key elements of a human-like multi-turn conversation, making this chatbot even more versatile. In a blog post, Google had claimed that Meena can conduct conversations that are more sensible and specific than existing state-of-the-art chatbots.

Plugged as an important development of Googles Transformer the novel neural network architecture for language understanding Reformer is intended to handle context windows of up to 1 million words, all on a single AI accelerator using only 16GB of memory.

Google had first mooted the idea of a new transformer model in a research paper in collaboration with UC Berkeley in 2019. The core idea behind this model was self-attention, and the ability to attend to different positions of an input sequence to compute a representation of that sequence elaborated in one of our articles.

Today, Reformer can process whole books concurrently and that too on a single gadget, thereby exhibiting great potential.

Google has time and again reiterated its commitment to the development of AI. Seeing it as more profound than fire or electricity, it firmly believes that this technology can eliminate many of the constraints we face today.

The company has also delved into research anchored around AI that is spread across a host of sectors, whether it be detecting breast cancer or protecting whales or other endangered species.

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Top Machine Learning Projects Launched By Google In 2020 (Till Date) - Analytics India Magazine