Akbar Solo Researchers in Moscow and America have discovered how to use machine learning to grow artificial organs, especially to tackle blindness Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. The study was published in Frontiers in Cellular Neuroscience. How would this enable easier organ growth? This would allow to expand the applications of the technology for multiple fields including the drug discovery and development of cell replacement therapies to treat blindnessIn multicellular organisms, the cells making up different organs and tissues are not the same.
Google today announced that it has signed up Verizon as the newest customer of its Google Cloud Contact Center AI service, which aims to bring natural language recognition to the often inscrutable phone menus that many companies still use today (disclaimer: TechCrunch is part of the Verizon Media Group). For Google, that's a major win, but it's also a chance for the Google Cloud team to highlight some of the work it has done in this area. It's also worth noting that the Contact Center AI product is a good example of Google Cloud's strategy of packaging up many of its disparate technologies into products that solve specific problems. "A big part of our approach is that machine learning has enormous power but it's hard for people," Google Cloud CEO Thomas Kurian told me in an interview ahead of today's announcement. "Instead of telling people, 'well, here's our natural language processing tools, here is speech recognition, here is text-to-speech and speech-to-text -- and why don't you just write a big neural network of your own to process all that?' Very few companies can do that well. We thought that we can take the collection of these things and bring that as a solution to people to solve a business problem. And it's much easier for them when we do that and […] that it's a big part of our strategy to take our expertise in machine intelligence and artificial intelligence and build domain-specific solutions for a number of customers."
Artificial intelligence is the major buzzword in federal IT these days, the way that cloud once was. It's easy to see why. There is booming investment in AI in the private sector, and various agencies across the government are experimenting with AI to achieve their missions. The National Oceanic and Atmospheric Administration is working with Microsoft to use AI and cloud technology to more easily and accurately identify animals and population counts of endangered species. NASA is ramping up the use of AI throughout its operations, from conducting basic financial operations to finding extra radio frequencies aboard the International Space Station.
The term'covidiot' is a coronavirus-era slang term for someone who ignores recommendations to limit the spread of the deadly disease – and a new study reveals what makes these people dismiss the warnings. Researchers found that whether or not an individual decides to follow social distancing depends on how much information their working memory can store, which determines mental abilities such as intelligence. Following a survey of 850 Americans, the team discovered that those with more working memory capacity were more likely to comply with recommendations during the early stage of the outbreak. The findings suggest that policy makers need promote compliance behaviors, such as wearing a mask, based on individuals' general cognitive abilities to avoid effortful decisions. The coronavirus began spread across the US earlier this year and when it gained more traction, the Centers for Disease Control and Prevention (CDC) released a list of recommendations aimed at limiting the spread of the virus.
The first five months of 2020 sent a parade of "wicked problems" around the globe, including a plague of locusts in Asia and Africa, bushfires in Australia and, of course, the COVID-19 pandemic. Wicked problems can be defined as problems that no one knows how to solve without creating further problems. We struggle to mitigate them because they transcend borders and generations. During and after World War II, policymakers also confronted significant problems, such as how to keep the peace, encourage recovery and prevent starvation. They tackled these problems by creating collaborative institutions and rules, and by providing generous aid and technical assistance.
Over the last several months, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a global pandemic, resulting in nearly 480,000 COVID-19 related deaths as of June 25, 2020 . While the disease can manifest in a variety of ways--ranging from asymptomatic conditions or flu-like symptoms to acute respiratory distress syndrome--the most common presentation associated with morbidity and mortality is the presence of opacities and consolidation in a patient's lungs. Upon inhalation, the virus attacks and inhibits the lungs' alveoli, which are responsible for oxygen exchange. This opacification is visible on computed tomography (CT) scans. Due to their increased densities, these areas appear as partially opaque regions with increased attenuation, which is known as a ground-glass opacity (GGO).
From drones for food delivery and robots for automation to COVID-19 contact tracing apps, and online education learning platforms, we've seen a great acceleration in adoption of different technologies in the past few months. Technology has been a great pillar of strength during the pandemic and it's also going to help redefine the post COVID-19 world. Now, different businesses and industries will benefit from different technologies, but there are some common ones that are likely to dominate the world after COVID-19. Nuff said, let's take a look at some of the tech trends that are likely to see a surge in adoption post COVID-19. We know this one's too obvious but that's for a reason - AI is playing a massive role in helping us all get through the pandemic and it will see a greater adoption after the pandemic is over.
As we've talked about in the past, the focus on data – how much is being generated, where it's being created, the tools needed to take advantage of it, the shortage of skilled talent to manage it, and so on – is rapidly changing the way enterprises are operating both in the datacenter and in the cloud and dictating many of the product roadmaps being developed by tech vendors. Automation, analytics, artificial intelligence (AI) and machine learning, and the ability to easily move applications and data between on-premises and cloud environments are the focus of much of what OEMs and other tech players are doing. And all of this is being accelerated by the COVID-19 pandemic, which is speeding up enterprise movement to the cloud and forcing them to adapt to a suddenly widely distributed workforce, trends that won't be changing any time soon as the coronavirus outbreak tightens its grip, particularly in the United States. OEMs over the past several months have been particularly aggressive in expanding their offerings in the storage sector, which is playing a central role in help enterprises bridge the space between the datacenter, the cloud and the network edge and to deal with the vast amounts of structured and – in particular – unstructured data being created. That can be seen in announcements that some of the larger vendors have made over the past few months.
Automated recognition of faces in images has become a much-debated topic in recent times. To enable an informed discussion about this topic, it is important to know some background about this technology and its current use. Facial recognition is a very active branch of computer vision that pursues the goal of identifying an individual based on an image of their face. The rationale behind this is that people's faces are unique to them, like other biometrics, such as fingerprints, DNA, or, curiously, ear shape . An obvious advantage of identifying one's face, rather than, for example, DNA, is that it is very accessible – all it takes is a picture.
Robotic process automation startup UiPath today announced it has closed a $225 million funding round, bringing its total raised to over $1.2 billion. While the new round is roughly half the $568 million UiPath raised last April, it catapults the New York-based company's post-money valuation to $10.2 billion, up from $7 billion in 2019 and $3 billion in 2018. CEO Daniel Dines says the funding will be used to scale UiPath's platform and deepen its investments in "AI-powered innovation" as it expands its cloud software-as-a-service (SaaS) offerings. The round will also likely lay the groundwork for future strategic deals, following UiPath's acquisition of startups StepShot and ProcessGold last October. RPA -- technology that automates monotonous, repetitive chores traditionally performed by human workers -- is big business.