Bequia The First Bitcoin Community – Sounds and Colours

By Sounds and Colours | 29 August, 2022

Measuring just 18 square kilometers, the beautiful Bequia is the second-largest island in the Grenadines. Known to locals as the island in the clouds, Bequia (pronounced beckway) has fewer than 6,000 inhabitants and has, until now, been off-grid to most visitors to the Caribbean.

With its beautiful white sandy beaches and luscious, green-filled mountains, Bequia has welcomed the odd A-list celeb to take a dip in the deep blue sea from their anchored yacht or to drink at one of its straw beach bars.

It has also played host to royalty, with the Queen and her late husband Prince Philip visiting in 1985. Princess Margaret Beach was actually named after the royal who swam in its waters back in the 1950s. Bequia has been content to keep a low profile in comparison with its neighbor Mustique, the celebrity playground nine miles away, across the dazzling blue ocean.

A young Caribbean real estate developer, Storm Gonsalves, is looking to change the idyllic islands horizon forever. He is planning to create the worlds first Bitcoin community, where cryptocurrency will be taken as payment for everyday essentials such as buying groceries, eating out at a restaurant or even buying a ticket to see a film at the communitys cinema.

The One Bequia development will see the building of 39 luxurious villas with pools, bars, shops, restaurants and a clubhouse, with its own private gym, spa and cinema. Buyers will be able to buy the two to five-bedroom villas with a price range from $1.1m to $2.3m in digital currency or US dollars.

Locals on Bequia will continue to use the Eastern Caribbean dollar, which has Queen Elizabeth II on both its notes and coins. Goods and services will also be offered in dollars, but its hoped that crypto-friendly residents will opt to use Bitcoin, Ethereum, Dogecoin or Bitcoin Cash.

Bitcoin is a decentralized digital currency, not beholden to any government or central bank but to a network of computers (blockchain) that regulate its production and value. Bitcoin kicked off the crypto boom when it completed its first transaction in 2009.

There are over 10,000 products on the cryptocurrency list but Bitcoin remains top of the chart for value. Its a well-known fact that the five most-valued cryptocurrencies make up over 75% of all cryptocurrency value these include Bitcoin, Ethereum, Dogecoin and Bitcoin Cash.

Investors keep a close eye on the cryptocurrency prices to inform trade, however, Bitcoin seems to be a sure bet due to its consistency in holding top spot. At the time of writing, as measured by market capitalization, Bitcoin holds top position with a value above $24,000.

Ethereum sits in second place with a price of just over $1,900. Cryptocurrency prices for Dogecoin and Bitcoin Cash are lower in value but its clear to see that Bitcoin is most certainly the market leader.

The One Bequia development team, headed up by Storm Gonsalves, the son of Ralph Gonsalves, Prime Minister of St Vincent and the Grenadines, strongly deny that this is a marketing gimmick. Instead, they insist that the acceptance of cryptocurrency payments is becoming increasingly more of a necessity than a ploy.

Storm Gonsalves argues that residents of small islands find it increasingly difficult to send and receive money internationally due to de-risking by large international banks. De-risking is when large institutions remove intermediary banking services from smaller, island-based community banks.

The One Bequia development manager also stated that if this trend is to continue it will result in small island nations becoming cut-off from international trade and commerce. For tourism-based economies, this would be devastating.

It is for this reason that many Caribbean island nations are adopting blockchain and other cryptocurrencies much quicker than other more-developed nations, as a solution to the growing issues. Storm Gonsalves believes that Bermuda, the Bahamas, Barbados and now St Vincent are leading the way.

In March 2021, the Eastern Caribbean established themselves as the first digital currency union in the world, launching DCash across four island nations: Antigua and Barbuda, Saint Kitts and Nevis, Grenada, and St Lucia.

One Bequia are anticipating development of the 39 villas, bars, restaurants and the clubhouse to be completed by 2024. Villas will be equipped with the latest in smart technology and the development team believe they are an attractive investment for those accustomed to investing and speculating on the cryptocurrency markets.

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Is This The Reason Dutch Authorities Arrested The Alleged Tornado Cash Developer? | Bitcoinist.com – Bitcoinist

The plot thickens. Did the supposed Tornado Cash developer arrested in The Netherlands have ties to a Russian security agency? Is a job in 2017 for a company with tenuous connections to Russia enough to justify the arrest? As Bitcoinist told you and reiterated, there had to be another reason that FIOD and the OFAC arrested a simple Tornado Cash coder. Is this one it? We dont have official confirmation yet, but it might be.

Apparently, the alleged Tornado Cash developer was Aleksey Pertsev, CEO of PepperSec and resident of The Netherlands. The information about his supposed ties to Russian intelligence comes courtesy of Kharon. The company provides data and analytic tools to optimize the core functions of financial crimes compliance programs, including KYC, screening, and investigations.

Their report states that Tornado Cash runs on software code developed by PepperSec, Inc. What and where is that company exactly?

PepperSec is a Delaware-registered corporation with its primary place of business in Seattle, Washington, according to a 2020 SEC filing. PepperSecs website describes the company as a security consulting firm of white hat hackers.

Currently, the website in question contains an About Us that says:

Were professional security engineers seasoned by many years of practical experience and deep understanding of technologies. Were ready to battle to make your project as secure as possible.

Fair enough, but

Apparently, Kharon reviewed personal and company profiles and determined that Aleksey is PepperSecs CEO. So far, so good. Wheres the smoking gun, though? Back to Kharons report:

In 2017, Pertsev was an information security specialist and developer of smart contracts for Digital Security OOO, according to an archived version of the companys website reviewed by Kharon. Digital Security OOO is a Russian entity designated by the U.S. Treasury Department in 2018 for providing material and technological support to the FSB, Russias primary security agency. Treasury alleged that, as of 2015, Digital Security worked on a project that would increase the offensive cyber capabilities of Russias intelligence services.

Thats tenuous, to say the least. And in no way related to Tornado Cash. However, a caveat is that the other two PepperSecs founders U.S.-based Roman Storm and Russia-based Roman Semenov havent been touched. So far. So, the case might be against Aleksey Pertsev alone.

For this part, well turn to quotes from notorious bitcoin enemy Fortune. The magazine interviewed Nick Grothaus, vice president of research at Kharon, who said:

You had this guy working for [Digital Security OOO] and doing pen testing himself, and then Treasury designated the company for helping the FSBs hacking capabilities.

Ok, we didnt know about the pen testing part. However, how does this relate to Tornado Cash? To answer that question the magazine resorts to Alex Zerden, an adjunct senior fellow at the Center for a New American Security, who said:

This opens up a lot of credibility issues for the developers of Tornado Cash. This is pretty profound information that informs why the U.S. government and Dutch authorities have taken certain actions.

Does it, though? There seems to be a more complex and complicated picture that takes more time to unravel, Zerden added. And we agree. Thats why the EFF asked for clarity around the Tornado Cash situation. As Bitcoinist already said, maybe the OFAC has a better case and the developer is guilty of something else. If thats the case, with clarifying information and reducing the ambiguity the OFAC would have avoided this whole situation.

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The Top Privacy Coins Saw Fewer Percentage Losses Than Most Tokens This Week Privacy Bitcoin News – Bitcoin News

The privacy coins monero and zcash managed to see fewer percentage losses against the U.S. dollar this week, in contrast to crypto assets like bitcoin, ethereum, and solana. Seven-day statistics indicate zcash has lost 5.6% against the USD, while monero dropped by 6.1%.

At the time of writing, the entire market capitalization of all the privacy coins in existence is roughly $5.55 billion. Monero (XMR) leads the pack with a market valuation of around $2.64 billion or 47.5% of the entire privacy coin economy. Zcash (ZEC) is the second largest privacy coin in terms of market capitalization as ZECs overall market valuation today is $789 million.

Behind XMR and ZEC, are privacy tokens such as decred (DCR), nucypher (NU), secret (SCRT), horizen (ZEN), ergo (ERG), digibyte (DGB), and beldex (BDX), respectively. Top privacy coin double-digit gainers this week include deeponion (ONION), litecash (CASH), pivx (PIVX), and masari (MSR). The weeks top privacy coin losers in terms of percentage losses include tokens like zclassic (ZCL), lethean (LTHN), and phore (PHR).

The top five privacy coin crypto assets make up most of the $5.55 billion in privacy coin value, and each token offers different types of privacy techniques. XMR is a Cryptonote token with a blockchain protocol that was not forked from Bitcoin. XMR uses ring signatures, ring confidential transactions, stealth addresses, bulletproofs, and Dandelion++. The ZEC network can shield transactions by leveraging a zero-knowledge proof called zk-SNARKs.

Decred (DCR) utilizes a Coinjoin mixing scheme called Coinshuffle++ (CSPP) to obfuscate transactions. Nucypher (NU) deploys Proxy Re-Encryption (PRE), a technology that allows the owner of the private key to encrypt data. Similar to NU, the Secret (SCRT) blockchain provides key management techniques, Trusted Execution Environment (TEE) schemes combined with encryption to enhance privacy.

While privacy coins have taken less of a beating this week compared to ETH or BTC, they still have lost quite a bit of value during the last year. Four months ago the privacy crypto coin economy was worth a whole lot more, at $10.7 billion. XMRs overall market valuation was $4.13 billion and ZECs was $1.84 billion on April 28, 2022. Nine months ago on November 6, 2021, the privacy token economy was worth $14.9 billion.

At that time in November 2021, NU was not in the top five, as horizen (ZEN) held the fifth position in terms of privacy coins by market cap. XMRs overall market valuation was $4.69 billion. ZECs market capitalization on November 6, 2021 was slightly higher than the April 28, 2022 record with $1.94 billion. In November 2021, XMRs and ZECs market caps combined were larger than todays privacy coin economy value.

What do you think about the privacy coin market action in recent times? Let us know what you think about this subject in the comments section below.

Jamie Redman is the News Lead at Bitcoin.com News and a financial tech journalist living in Florida. Redman has been an active member of the cryptocurrency community since 2011. He has a passion for Bitcoin, open-source code, and decentralized applications. Since September 2015, Redman has written more than 5,700 articles for Bitcoin.com News about the disruptive protocols emerging today.

Image Credits: Shutterstock, Pixabay, Wiki Commons

Disclaimer: This article is for informational purposes only. It is not a direct offer or solicitation of an offer to buy or sell, or a recommendation or endorsement of any products, services, or companies. Bitcoin.com does not provide investment, tax, legal, or accounting advice. Neither the company nor the author is responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods or services mentioned in this article.

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Bank of Russia Eases Restrictions on Purchases of Dollar and Euro Cash Finance Bitcoin News – Bitcoin News

The Central Bank of Russia has relaxed some limitations for Russian banks selling U.S. dollars and euros to the public. The increased supply of foreign cash may affect the crypto market in the country as currency restrictions have been a driver of increased demand for digital coins.

The Central Bank of the Russian Federation (CBR) has lifted one of the restrictions on the sale of U.S. dollars and euros in cash to private individuals imposed amid Western sanctions over the war in Ukraine, the Interfax news agency reported.

Until recently, Russians could buy only dollars and euros sold to the banks at their cash desks after April 9, 2022, by other physical persons. Now the CBR has allowed Russian lenders to sell the two convertible currencies if they are also obtained from other sources.

The regulator explained that these may include transactions with non-resident banks as well as foreign cash deposited by Russian legal entities. The adjustment will allow banks to increase the supply of cash dollars and euros, its press service said, noting that other restrictive measures will remain in place until March 9, 2023, as announced earlier this year.

In August, the Bank of Russia extended restrictions on U.S. dollar and euro cash withdrawals for another six months. At the moment, Russian banks are not limited in the sale of other foreign fiat currencies, the report notes.

Moscows decision to invade Ukraine in late February was met with harsh economic and financial sanctions introduced by the West. They have limited Russias access to global finances, including its foreign currency reserves.

Currency restrictions enforced by the CBR led to a spike in demand for crypto. Many Russians have been buying bitcoin, other cryptocurrencies, and stablecoins to use them for money transfers abroad, among other purposes. It remains to be seen how their loosening now will affect the local crypto market. A recent poll showed that close to a third of Russians are ready to buy coins in the next six months.

Do you think the easing of foreign cash restrictions will influence crypto demand in Russia? Share your thoughts on the subject in the comments section below.

Lubomir Tassev is a journalist from tech-savvy Eastern Europe who likes Hitchenss quote: Being a writer is what I am, rather than what I do. Besides crypto, blockchain and fintech, international politics and economics are two other sources of inspiration.

Image Credits: Shutterstock, Pixabay, Wiki Commons, diy13

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Nonprofits, not Silicon Valley startups, are creating AI apps for the greater good – Recode

Predictions for the potential of artificial intelligence wax poetic solutions from climate change to curing disease but the everyday applications make it seem far more mundane, like a glorified clock radio.

Thankfully, the future may be closer than we think. And the miraculous feats are not happening in Silicon Valley X-Labs in a plot twist, nonprofits are leading the charge in creating human-centered applications of the hottest AI technologies. From the simplest automated communications to contextual learnings based on analysis of deep data, these technologies have the potential to rapidly scale and improve the lives of our most underserved communities.

Take chatbots for example, a new spin on mobile messaging that has historically been human-powered. Organizations like TalkingPoints and mRelief have for years used simple mobile messaging to meet users where theyre at. Recently, tech nonprofits are taking a new approach. Raheem.ai, a Facebook Messenger bot for reporting and rating experiences with police officers, engages with users to walk them through reporting police incidents and provide follow-on support. The interactions are simple, but powerful. Do Not Pay, the worlds first robot lawyer, started out as a bot to repeal parking tickets and now helps fight landlords in negligent housing situations, and even helps the homeless find and apply for social services. These chatbots eliminate the friction of traditional reporting and serve as legal empowerment in your pocket.

Crisis Text Line still implements a human-to-human volunteer model, but the tech nonprofit has the largest open source database of youth crisis behavior in the country, and has been able to use AI to dramatically shorten response time for high-risk texters from 120 seconds to 39. Crisis Text Line leveraged machine learning to identify the term ibuprofen as 16 times more likely to predict the need for emergency aid than the word suicide. Now using AI, messages containing the word ibuprofen are prioritized in the queue.

Machine learning even allows you to select the energy source that powers your home appliances. WattTime creates software that enables smart hardware devices to prioritize clean energy with a simple flip of a switch. Their product relies on machine learning to detect when to tell smart devices like thermostats to pull from the power grid, based on surges in clean energy. This means your A/C may turn on five minutes earlier or later than it typically would, because the algorithms instruct your utilities to capitalize upon instances of excess clean energy from sources like windmills, thus minimizing the use of dirty power.

Quill, a free online tool that helps students measurably improve grammar and writing, discovered that natural-language processing was essential to remedy students struggles with sentence fragmentation. Using open source tools and online training programs, Quills technical team built its own fragment detection algorithm powered by a combination of machine learning and natural-language processing. Quills methodology is exemplary for resource-constrained tech nonprofits. It leveraged Wikipedia to amass a dataset of 100,000 high-quality sentences, integrated the natural-language processing tool Spacy.io to break the sentences down, and incorporated Tensorflow for data classification.

The result? Quills fragment-detection algorithm accurately detected sentence fragments 84 percent of the time, and this will only continue to improve. Other tech nonprofits, like Dost Education, forecast using natural-language processing down the line to monitor their impact assessments with teachers and parents.

While many instances of AI pool internally sourced data, data mining allows organizations to execute deep research faster, or to scrape mass information on their target market to make product decisions based on behaviors and trends. The Pulitzer Prize-winning reporting on the Panama Papers conveys the growing importance of data mining in investigative journalism. With 261 gigabytes of data, data mining was essential if the team of 100 journalists were to dig through the largest mass of leaked data in the history of journalism.

Transparency Toolkit, a Berlin-based tech nonprofit, launched its first tool, ICWatch, which implements data mining to scrape information from publicly available profiles and resumes to identify individuals involved in activities ranging from government surveillance to drone strikes. The organization runs several different tools and projects designed to democratize the big data playing field for human rights activists and journalists.

Yes. As the cost of AI implementation drops, it will become ubiquitous across software. The AI use case for a nonprofit is significant because incentives are well aligned to collect and open source the collected data. Effective implementation of AI requires massive data. Profit motives can restrain a companys incentive to open its data, but this is not so for nonprofits. Open data serves the broader purpose of public education and knowledge sharing. As tech nonprofits deploy these technologies and open source their findings, they can deepen the capacity of all AI applications.

As tech nonprofits deploy these technologies and open source their findings, they can deepen the capacity of all AI applications.

However, corporations have a role to play, too. Businesses like Google and Accenture are leveraging their internal AI talent to build tools for positive impact. Google.org is working with Pratham Books StoryWeaver, a platform that connects readers, authors, illustrators and translators to massively expand the number of childrens e-books available in mother tongues. Through an integration with the AI-powered Google Translate API, StoryWeaver is expanding its library to 200,000 titles in 60 languages.

Accenture sees Responsible AI as both an opportunity and a responsibility for business, government and technology leaders to apply the technology in the right way, using human-centric design principles such as accountability, transparency and fairness. Accenture Labs in Bangalore is developing workforce accessibility solutions called Drishti, using Responsible AI to empower the visually impaired, in collaboration with the National Association for the Blind.

The tech for good use cases for AI are endless, ranging from refugee aid, to bankruptcy filings, to predictive solutions in child welfare. We are still in the early days of true implementation of AI, but in the tech nonprofit sector, the future looks bright.

Shannon Farley is the co-founder and executive director of Fast Forward, the first and only accelerator exclusively for tech nonprofits. Through her work at Fast Forward, Farley has accelerated 23 tech nonprofits that are now impacting over 18 million lives around the world. Previously, she was the founding executive director of Spark, the world's largest network of millennial philanthropists; she also co-founded The W. Haywood Burns Institute, a MacArthur Award-winning juvenile-justice reform organization. Reach her @Shannon_Farley.

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Startup Paves Easier Path to AI – Multichannel News

Implementing artificial intelligence systems can be technically challenging and expensive, but it doesnt have to be.

So says DimensionalMechanics, a startup based in Bellevue, Wash., that claims to have a developed a platform that can put A.I. within reach of a wide range of companies, with an initial focus on those in the media and entertainment industry.

The goal is to lower that technology and economic bar in a way that makes A.I. more accessible to organizations without requiring them to have a technical background in areas such as deep learning and machine learning, company CEO and co-founder Rajeev Dutt, said, noting that many are also looking for A.I. solutions that are not just affordable but customizable as well.

To help achieve some of those goals, DimensionalMechanics has introduced NeoPulse AI Studio, a set of applications based on the companys underlying framework that, it says, can help businesses and other organizations rapidly create and design customized A.I. solutions. That product complements the companys pre-built AI models in areas such as image and video analysis and recommendations systems.

The company, which has raised $6.7 million and intends to raise a B round this fall, is also getting a boost into the media and entertainment world through a strategic alliance with GrayMeta, a company that specializes in automated metadata collection, curation and search.

GrayMeta, which counts ABC, AMC, CBS, Deluxe, DirecTV, Disney, HBO, NBCUniversal and Showtime among its clients, is also the first to offer NeoPulse AI to the media and entertainment sector, DimensionalMechanics said.

Dutt said the media, entertainment and advertising industries are among the biggest producers and consumers of data, providing a proving ground for a lot of machine learning technologies.

Some use-case examples include a photo-ranking system that was trained using 2 million images to determine which ones might make an ad or news article more likely to grab attention or drive and maximize traffic. The technology is also being used to help editors analyze and write headlines that can improve click rates.

On the video side, the company also provides A.I. solutions to drive recommendations.

DimensionalMechanics has carved out a set of business models, including cloud software for independent developers, on-premises solutions that can simulate the cloud-based system while keeping a companys data close to the vest, as well as a way for partners to resell and monetize their A.I. models through the NeoPulse AI Store.

Theres a fairly broad range of applications, Dutt said.

Founded in 2015, DimensionalMechanics currently has 11 employees.

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AMP Robotics Named to Forbes AI 50 | RoboticsTomorrow – Robotics Tomorrow

Company recognized among rising stars of artificial intelligence for its AI-guided robots transforming the recycling industry

Forbes has named AMP Robotics Corp. ("AMP"), a pioneer and leader in artificial intelligence (AI) and robotics for the recycling industry, one of America's most promising AI companies. The publication's annual "AI 50" list distinguishes private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so. To be included on the list, companies needed to show that techniques like machine learning, natural language processing, or computer vision are a core part of their business model and future success.

AMP's technology recovers plastics, cardboard, paper, metals, cartons, cups, and many other recyclables that are reclaimed for raw material processing. AMP's AI platform uses computer vision to visually identify different types of materials with high accuracy, then guides high-speed robots to pick out and recover recyclables at superhuman speeds for extended periods of time. The AI platform transforms images into data to recognize patterns, using machine learning to train itself by processing millions of material images within an ever-expanding neural network of robotic installations.

"We consider AMP a category-defining business and believe its artificial intelligence and robotics technology are poised to solve many of the central challenges of recycling," said Shaun Maguire, partner at Sequoia Capital and AMP board member. "The opportunity for modernization in the industry is robust as the demand for recycled materials continues to swell, from consumers and the growing circular economy."

AMP's "AI 50" recognition comes on the heels of receiving a 2020 RBR50 Innovation Award from Robotics Business Review for the company's Cortex Dual-Robot System. Earlier this year, Fast Company named AMP to its "World's Most Innovative Companies" list for 2020, and the company captured a "Rising Star" Company of the Year Award in the 2020 Global Cleantech 100.

Since its Series A fundraising in November, AMP has been on a major growth trajectory as it scales its business to meet demand. The company announced a 50% increase in revenue in the first quarter of 2020, a rapidly growing project pipeline, a facility expansion in its Colorado headquarters, and a new lease program that makes its AI and robotics technology even more attainable for recycling businesses.

About AMP Robotics Corp.

AMP Robotics is applying AI and robotics to help modernize recycling, enabling a world without waste. The AMP Cortex high-speed robotics system automates the identification and sorting of recyclables from mixed material streams. The AMP Neuron AI platform continuously trains itself by recognizing different colors, textures, shapes, sizes, patterns, and even brand labels to identify materials and their recyclability. Neuron then guides robots to pick and place the material to be recycled. Designed to run 24/7, all of this happens at superhuman speed with extremely high accuracy. With deployments across the United States, Canada, Japan, and now expanding into Europe, AMP's technology recycles municipal waste, e-waste, and construction and demolition debris. Headquartered and with manufacturing operations in Colorado, AMP is backed by Sequoia Capital, Closed Loop Partners, Congruent Ventures, and Sidewalk Infrastructure Partners ("SIP"), an Alphabet Inc. (NASDAQ: GOOGL) company.

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Doctors are burdened by documentation, are AI scribes the answer? – MobiHealthNews

Before Dr. Matthew Fradkin was a pediatrician he played in a punk rock band. Aside from the years of training to become a physician, he said there were actually some similarities to the two namely the search for human connection.

They have the same core, the in-the-moment human connection is really important to both. Bringing back the human connection in medicine is where I see digital andAI advancing in helping to turn back the clock in terms of the patient provider experience, Fradkin, a pediatric and Swedish Primary Care and Providence St. Josephs, said during aHIMSS20 Digital event.

With more and more documentation piling up in the medical world, Fradkin said that connection with the patient is in jeopardy and so is provider burnout.

I want to find ways to make caregiving easier for our providers, and by finding ways for our providers to care for patients the way they want to in a large healthcare system, pushing towards standardization for population health advances. Or basically how do we prevent provider burnout using various digital tools and technology.

Physician burnout is associated with work, personal life balance, and organizational factors, he said.

The only thing I can really make an impact on are the work factors. Im probably not going to change a 50-year old primary care doctors personality, and Im not on the C-suite, so Im pretty sure I cant make structural change overnight so work factors for me is the obvious category to start addressing.

He decided to start with a notorious pain point in carethe documentation. He noted that, while EMRs provide doctors with a plethora of data points,they shoulder the bulk of the input burden. That was when he began to look at AI and machine learning scribes to help ease this issue.

We want ambient technology to provide an accurate note where providers dont change a thing in how they deal with patients in the clinical room or in their head, he said.

He started to work with the digital innovation team at his health system to look into pilots for fixing this issue.

Part of the digital innovation core of Swedish and Providence St. Joe's is the accelerated pilot process. This is a set framework that allows providers to investigate possible new technologies to help with their clinic experience, he said. This pilot allowed us to follow that framework and to evaluate possible vendors in a virtual arena, come up with KPIs or key performance indicators and streamlining the path through IT, security, legal or any other red tape that prevents pilots from occurring in large health system.

The tech that Fradkin decided to pilot was an AI-based medical scribe that is able to train and learn a providers individual style and preferences over time.

Initially, so the provider does not have to do heavy lifting in training the ML, there is a real-person reviewer offsite, reviewing what the ML is coming up with after a visit, and correcting it and making adjustments in the background based on my personal and organization-wide templates already in the system, he said.

The system lets providers choose how they would like to use it. Some just used it for the simple transaction notes. Others use the snip-it mode, where they say a phrase that helps the system choose a certain path. Lastly there was an ambient mode, which listens to the whole encounter and is able to do the bulk of the charting.

During and after the pilot I was able to go from seeing 16 to 17 patients a dayto 23, with a range of 22 to 30 with the entirety of the pilot where, for return on investment with the particular product we used, I would have to see one patient extra a week, four extra patients a month, to pay for the product, he said.

But Fradkin said that this type of system would do more than just boost the number of patients he could see.It would also help burnout.

Venturing into this new realm of AI-driven scribes is an exciting one, and one that needs to provide caregivers flexibility and responsivenessthe same factors parents expect from their providers in this new digital age of medicine, he said. Yes, there is a cost to this, never mind the fact that most of these solutions pay for themselves in the short run.But, if we have the same urgency to finding an answer to 'the cost of losing physician to burnout'from the EMRas we do to any other disease, we wouldnt even be having this discussion about cost. It makes care better for the providers and patients.

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Coronavirus will finally give artificial intelligence its moment – Bryan-College Station Eagle

For years, artificial intelligence seemed on the cusp of becoming the next big thing in technology - but the reality never matched the hype. Now, the changes caused by the covid-19 pandemic may mean AI's moment is finally upon us.

Over the past couple of months, many technology executives have shared a refrain: Companies need to rejigger their operations for a remote-working world. That's why they have dramatically increased their spending on powerful cloud-computing technologies and migrated more of their work and communications online.

With fewer people in the office, these changes will certainly help companies run more nimbly and reliably. But the centralization of more corporate data in the cloud is also precisely what's needed for companies to develop the AI capabilities - from better predictive algorithms to increased robotic automation - we've been hearing about for so long. If business leaders invest aggressively in the right areas, it could be a pivotal moment for the future of innovation.

To understand all the fuss around artificial intelligence, some quick background might be useful: AI is based on computer science research that looks at how to imitate the workings of human intelligence. It uses powerful algorithms that digest large amounts of data to identify patterns. These can be used to anticipate, say, what consumers will buy next or offer other important insights. Machine learning - essentially, algorithms that can improve at recognizing patterns on their own, without being explicitly programmed to do so - is one subset of AI that can enable applications like providing real-time protection against fraudulent financial transactions.

Historically, AI hasn't fully lived up to its hype. We're still a ways off from being able to have natural, life-like conversations with a computer, or getting truly safe self-driving cars. Even when it comes to improving less advanced algorithms, researchers have struggled with limited datasets and a lack of scaleable computing power.

Still, Silicon Valley's AI-startup ecosystem has been vibrant. Crunchbase says there are 5,751 private-held AI companies in the U.S. and that the industry received $17.4 billion in new funding last year. International Data Corporation (IDC) recently forecast that global AI spending will rise to $96.3 billion in 2023 from $38.4 billion in 2019. A Gartner survey of chief information officers and IT leaders, conducted in February, found that enterprises are projecting to double their number of AI projects, with over 40% planning to deploy at least one by the end of 2020.

As the pandemic accelerates the need for AI, these estimates will most likely prove to be understated. Big Tech has already demonstrated how useful AI can be in fighting covid-19. For instance, Amazon.com partnered with researchers to identify vulnerable populations and act as an "early warning" system for future outbreaks. BlueDot, an Amazon Web Services startup customer, used machine learning to sift through massive amounts of online data and anticipate the spread of the virus in China.

Pandemic lockdowns have also affected consumer behavior in ways that will spur AI's growth and development. Take a look at the soaring e-commerce industry: As consumers buy more online to avoid the new risks of shopping in stores, they are giving sellers more data on preferences and shopping habits. Bank of America's internal card-spending data for e-commerce points to rising year-over-year revenue growth rates of 13% for January, 17% for February, 24% for March, 73% for April and 80% for May. The data these transactions generate is a goldmine for retailers and AI companies, allowing them to improve the algorithms that provide personalized recommendations and generate more sales.

The growth in online activity also makes a compelling case for the adoption of virtual customer-service agents. International Business Machines Corporation estimates that only about 20% of companies use such AI-powered technology today. But they predict that almost all enterprises will adopt it in the coming years. By allowing computers to handle the easier questions, human representatives can focus on the more difficult interactions, thereby improving customer service and satisfaction.

Another area of opportunity comes from the increase in remote working. As companies struggle with the challenge of bringing employees back to the office, they may be more receptive to AI-based process automation software, which can handle mundane tasks like data entry. Its ability to read invoices and update databases without human intervention can reduce the need for some types of office work while also improving its accuracy. UiPath, Automation Anywhere and Blue Prism are the three leading vendors in this space, according to Goldman Sachs, accounting for about 36% of the roughly $850 million market last year. More imaginative AI projects are on the horizon. Graphics semiconductor-maker NVIDIA Corporation and luxury automaker BMW Group recently announced a deal where AI-powered logistics robots will be used to manufacture customized vehicles. In mid-May, Facebook said it was working on an AI lifestyle assistant that can recommend clothes or pick out furniture based on your personal taste and the configuration of your room.

As with the mass adoption of any new technology, there will be winners and losers. Among the winners, cloud-computing vendors will thrive as they capture more and more data. According to IDC, Amazon Web Services was number one in infrastructure cloud-computing services, with a 47% market share last year, followed by Microsoft at 13%.

But NVIDIA may be at an even better intersection of cloud and AI tech right now: Its graphic chip technology, once used primarily for video games, has morphed into the preeminent platform for AI applications. NVIDIA also makes the most powerful graphic processing units, so it dominates the AI-chip market used by cloud-computing companies. And it recently launched new data center chips that use its next-generation "Ampere" architecture, providing developers with a step-function increase in machine-learning capabilities.

On the other hand, the legacy vendors that provide computing equipment and software for in-office environments are most at risk of losing out in this technological shift. This category includes server sellers like Hewlett Packard Enterprise Company and router-maker Cisco Systems, Inc.

We must not ignore the more insidious consequences of an AI renaissance, either. There are a lot of ethical hurdles and complications ahead involving job loss, privacy and bias. Any increased automation may lead to job reductions, as software and robots replace tasks performed by humans. As more data becomes centrally stored on the cloud, the risk of larger data breaches will increase. Top-notch security has to become another key area of focus for technology and business executives. They also need to be vigilant in preventing algorithms from discriminating against minority groups, starting with monitoring their current technology and compiling more accurate datasets.

But the upside of greater computing power, better business insights and cost efficiencies from AI is too big to ignore. So long as companies proceed responsibly, years from now, the advances in AI catalyzed by the coronavirus crisis may be one of the silver linings we remember from 2020.

This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners. Kim is a Bloomberg Opinion columnist covering technology.

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Coronavirus will finally give artificial intelligence its moment - Bryan-College Station Eagle

Why impartiality has to be a key part of your AI game plan – E&T Magazine

A few simple principles can help businesses avoid the potentially disastrous implications of unleashing artificial-intelligence-based agents that develop their own unconscious bias.

With 60 per cent of UK companies already using or planning to implement artificial intelligence, the debate around its ethical challenges is gaining momentum. Earlier this year, for example, the European Commission drafted a white paper on AI outlining a new approach to excellence and trust a clear indication that Europe is seriously considering stricter measures its use.

According to Gartner, a quarter of employees who already use technology in their work will have an AI-powered digital colleague that they interact with on a daily basis by 2021. While the opportunity to create new efficiencies and offer an elevated level of service to consumers is immense, so are the risks.

For organisations just getting started, there are some key considerations to apply in the process of defining a responsible AI game plan.

First, you need to plan for the unexpected. Remember what happened when Microsoft deployed its customer-facing chatbot, Tay, on Twitter? Within 24 hours, Tay had become a racist and sexist spokesperson by re-posting content learned from her online interactions. This illustrates that while you cannot predict every possible interaction, your implementation strategy must be carefully planned. Common roll-out strategy safety checks should include testing with representative users before roll-out, using human oversight as an extra layer of analysis and safety, and designing the way the digital employee responds to unplanned and out-of-scope interactions.

Companies using intelligent digital employees must also think about how their brand fits in; the style and tone of the dialogue, the level to which the agent can execute tasks or not and the audiences they interact with. Whether an agent is used internally or in customer service, the AI solution inevitably becomes a brand ambassador. This understanding will help define appropriate and consistent guidelines, which will ensure the AI is trained to represent the values, goals, and culture of the business.

The collection and provisioning of adequate training data is another big challenge for AI developers. Amazon, for example, has been scrutinised for the bias of its automated recruitment too, trained with data from CVs submitted over a 10-year period, which reflected the gender imbalance within the tech industry and therefore unfairly disadvantaged female applicants. The impartiality of training data and modelling is only as impartial as the design team. It is of critical importance for companies to reflect on the potential gaps in their data objectivity, use test methods that explore the potential impacts and operate with transparency on the progress of these issues.

To build a diverse team from a list of qualified applicants, consider using techniques that mitigate unconscious bias. Three tactics that can help are: removing names from the applicant review processes, augmenting candidate evaluation with a skills test to evaluate potential, and introducing structure to the interview process in order to focus on the same set of questions and answers for every possible hire. Lastly, consider setting diversity goals for your AI team that emphasise the importance of representation and balance of decision-making power for successful AI projects.

One way to leverage the potential of machine learning without risking a Tay-like disaster is through managed self-learning. This means that a digital employee is first trained via a defined data set and then learns through user interactions a process that is overseen by a supervisory body to check and approve the newly acquired knowledge. This approach ensures that learned behaviours correspond to the organisations functional and ethical vision for its AI implementation, and are based on accurate and unbiased data.

Before embarking on any AI implementation, you need to think hard about what your ideal digital employee would look like. Ensuring its ethical development requires comprehensive and thoughtful planning: reviewing the make-up of the team, the representativeness of datasets, and how it will be managed on an ongoing basis.

Considering the potentially disastrous repercussions of unconscious bias in this field, its clear that organisations need to make an enormous effort to ensure their AI solutions are designed and set up to act impartially. By adhering to the principles described here, companies can not only minimise the risk of reputational damage, but also benefit from better outcomes of their AI investments in the medium and long term.

Esther Mahr is a conversational experience designer with IPsoft. Noelle Langston is the companys director of experience design.

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Why impartiality has to be a key part of your AI game plan - E&T Magazine

See how old Amazon’s AI thinks you are – The Verge

Amazons latest artificial intelligence tool is a piece of image recognition software that can learn to guess a humans age. The feature is powered by Amazons Rekognition platform, which is a developer toolkit that exists as part of the companys AWS cloud computing service. So long as youre willing to go through the process of signing up for a basic AWS account that entails putting in credit card info but Amazon wont charge you you can try the age-guessing software for yourself.

In what sounds like a smart move on Amazons end, the tool gives a wide range instead of trying to pinpoint a specific number, along with the likelihood that the subject of the image is smiling or wearing glasses. Microsoft tried the latter approach back in 2015 with its own AI tool, resulting in some hilariously bad estimates that exposed fundamental weaknesses in how these types of image recognition algorithms function. Still, these experiments are more for fun, and both companies cracks at age-guessing algorithms are a good way to mess around with AI if youre so inclined.

For instance, heres Amazons tool trying to digest an old photo of me in my early twenties:

Heres what it had to say about a more recent photo:

And heres what it has to say about a drastically different image of me from nearly ten years ago, sans glasses and short hair:

Needless to say, I am not 30, 47, or any age in between in any of those photos. Microsoft is equally guilty of thinking I am far older than I actually am perhaps a product of the beard, at least for the first two images. When giving both tools a photo of clean-shaven Microsoft CEO Satya Nadella, we get a slightly more accurate description: Amazon thinks Nadella is between 48 and 68 years old, while Microsofts tool thinks hes 67. (Nadella is 49 years old). Trying Bezos yields similar results that are only kinda, sorta on point, yet still within a range of acceptability.

The goal here of course is not to try and trick the software. After all, these tools are not supposed to 100 percent accurate all of the time, and purely for fun in Microsofts case. Amazon, on the other hand, offers Rekognition to developers who are interested in implementing general object recognition, labeling, and other likeminded features for their products and services.

In this case, Amazons Jeff Barr sees the age range feature as a way to power public safety applications, collect demographics, or to assemble a set of photos that span a desired time frame, he writes in a blog post. For those purposes, Amazons tool may be good enough. Even when it isnt, we know it will be getting better all the time, thanks to deep learning methods that train it using billions of publicly available images.

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See how old Amazon's AI thinks you are - The Verge

MWC: Completely superfluous ‘AI’ added to consumer items – Naked Security

Naked Security is reporting this week from Mobile World Congress in Barcelona

All of a sudden, Olay famous for its skincare products and cheering inducements to Love the skin youre in has an artificial intelligence (AI) capability. You heard it right: Olay, maker of your mums face cream, is now in the machine intelligence game.

Naked Security reported on a crop of smart devices that are anything but smart from the CES tech show in Las Vegas in January, but tech companies have moved on for MWC, focusing instead on adding AI and machine learning to products that would do just as well without them.

With the launch of Olay Skin Advisor, an app that claims to reveal womens true skin age based on a selfie, Olays new skinbot delivers personalised product information based on perceived improvement areas.

So now Olays answer to HAL 9000 wants you to know that your skin might not be quite so loveable after all. That blotch on your left cheek it could do with some work. The greasy patch on your nose theres a potion for that!

Olay claims to be the first company of its kind to use such deep learning technology, and has the entirely modest aim of transforming the way that women shop for beauty products. Indeed, so serious is it about these new opportunities that its is sending a whole team of scientists, researchers and AI experts to Mobile World Congress (MWC) in Barcelona in order to big up its credentials.

And Olay is far from being the only non-tech company at the event pushing a dubious AI message. Somewhat bizarrely, machine learning has also found its way into the dental hygiene segment, including through an AI-enabled toothbrush called Ara.

Designations such as smart toothbrush are clearly now entirely insufficient; this is a dental implement with actual intelligence! You can bet that Isaac Asimov never saw that one coming.

Ara apparently has AI embedded directly in the brush handle, enabling it to capture data about brushing efficiency and thereby remove more bacteria, reduce plaque and prevent gingivitis.

Kolibree, the products manufacturer, claims that it uses patent-pending M2M technology to provide a personalised, interactive tooth brushing experience; each time the user brushes, embedded algorithms in the toothbrush learn the users brushing pattern, meaning it can make personalised recommendations.

Of course, the toothbrush also syncs with the obligatory app, which serves as a personal and highly useful record of your brushing history. The possibilities are therefore endless. In future years, feeling nostalgic perhaps on a rainy Sunday afternoon sometime in the early 2020s, you might be tempted to access the record of that particularly vigorous session back in March 17 Such is the transformative power of technology.

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MWC: Completely superfluous 'AI' added to consumer items - Naked Security

How AI Is Transforming Healthcare – Forbes

Artificial intelligence is driving massive improvement and innovation in the healthcare and life sciences sectors. AI is expediting advances in drug research and discovery. Its allowing for better and faster diagnoses. And its enabling far greater efficiency in business processes.

Thats noteworthy considering so many of us stand to benefit from it. Healthcare is truly one of the industries in which AI stands to have the greatest impact.

Practitioners Can Leverage AI For Faster Diagnosis

AI allows for more accurate diagnoses by getting practitioners clean data quickly.

That clearly addresses a significant pain point in the healthcare arena. Misdiagnosis is estimated to cause up to 80,000 hospital deaths each year and results in billions of dollars in wasted medical spending.

Traditionally, a person would have to crunch terabytes of data to diagnose a patient. Now businesses can leverage AI for number crunching, and human beings can validate the results.

Scientists in China and the U.S. are experimenting with AI in medical diagnoses. My company did a test showing how our platform can be used to detect breast cancer. Bayer, Lunit and PathAI are applying AI to medical diagnosis, too.

Health Insurance Companies Can Employ AI To Automate Claims Processing

Insurance businesses can use AI to improve and automate their operations as well.

Lets say an insurance company reads the patient email and attached claim. Then the company opens its health claim system and inputs the patients policy number. That way the insurance company can see if the patient is eligible for the claim. The insurer then sends a patient email saying something like, "Thank you for your claim. Out of $2,000, you're eligible for $1,762.26. Your check will go out in 22 days." This would work in a similar matter in cases in which insurance companies work directly with healthcare providers.

This process can be automated thanks to AI-based integrated automation platforms (IAPs). They automate the claims process end to end. IAPs can ingest, extract and analyze data from claims. They can leverage business rules to understand patient eligibility and coverage. And they can provide that information to the insurer, its partners and the patients.

Aetna recently enlisted AI to settle health insurance claims. Cigna and Humana are using bots. And Mercer uses our IAP to optimize and personalize quotes for new policies and renewals.

Pharmaceutical Companies Can Use AI To Accelerate Development

AI is also accelerating the delivery of new findings in drug research. That will enable the pharmaceutical industry to provide doctors and patients with better treatments, faster.

The pharmaceutical industry generated $1.2 trillion in worldwide revenue in 2018, and its poised to grow by 160% between 2017 and 2030. However, reports indicate that drug discovery and development have declining success rates and a stagnant pipeline.

AI could help drive improvement on these fronts.

For example, in 2007 a robot identified the function of a yeast gene. A more advanced robot discovered that a common ingredient in toothpaste offered the potential to treat drug-resistant malaria parasites. A machine learning algorithm helped identify a new antibiotic compound. And AI created a drug used to treat patients with obsessive-compulsive disorder.

Broad AI Adoption Hinges On Data, Trust, Education and Ethical AI

Having the right platform is just part of the equation to get to widespread AI adoption. AI also needs to have the right data. In addition, AI must gain user trust, which will continue to be built with ethical and responsible use of the technology.

By the right data, I mean enough representative data to get accurate results.

But that doesnt necessarily mean you need giant datasets. Fractal technology uses relatively small data sets to train the AI engine. It is based on a deterministic science and has been proven by such organizations as NASA.

Imagine, however, that your doctor advised you to buy a $149 thermal camera on Amazon. She told you to take a selfie of your breast and run the image through our platform to get results. Youd probably prefer to go through the painful yet more familiar experience of having a mammogram. Choosing the mammogram might not provide a better experience or results, but its whats known.

Thats where the need for education comes in. Those providing and using AI need to educate patients and healthcare providers that they are in safe hands. They can do that by demonstrating this fact.

For example, for the first 250,000 patient cases, you could have the AI engine provide a result, and you could have a doctor do the diagnosis as well.

You could present both to the patient and/or practitioner, and they would then see the AI is just as good at diagnosing illness as humans.

In other words, the right data will yield accurate results. And when people learn about that accuracy, they will trust the technology. Adoption will increase, and more people will benefit.

Everyone also benefits from ethical AI, which allows for greater accountability, traceability and sustainability. Ethical AI can work to define which AI use cases are and are not acceptable. And it can set rules for specific application requirements. Thats important in healthcare, which can involve life-or-death decisions. The application requirements for AI in healthcare are obviously unique from banking requirements, for example.

Everybody Wants Faster, Better Results

Whatever the sector, reducing time and enhancing customer delight and outcomes are the goals of automation. Those goals are now achievable with AI, fractal science and IAPs.

The future for what this technology will bring to patient care and outcomes stands to be truly transformational. Weve barely scratched the surface of whats possible.

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How AI Is Transforming Healthcare - Forbes

A New Study Shows Basic Income Doesn’t Deter People From Working

Ya Basic!

If you’ve ever talked about basic income — that’s the idea that the government should give everyone a monthly stipend to cover their everyday needs odds are you’ve run into That Guy. Maybe it was the chap in your undergrad philosophy class who loved Ayn Rand and playing devil’s advocate. Maybe it was a high-profile investor on cable news.

No matter who it was, they share the same argument — if the government provides for people by giving out a universal basic income, those people will lose the motivation to work and become totally dependent.

Plot twist: there’s now compelling evidence showing that in at least one case, basic income didn’t discourage people from working.

Everybody Wins

The National Bureau of Economic Research (NBER) published research earlier this year showing that Alaska’s ongoing basic income program — started in 1982 — had no direct impact on full-time employment in the state.

The state has the highest unemployment levels in the country, but that’s totally unrelated to its basic income program and the payments actually helped boost part-time employment within the state by 17 percent. Take that, Kyle from Philosophy 101!

Ya Extra!

Basic income can be a great tool and safety net for those who need it, but some have argued that the government should go even further. Futurist Kai-Fu Lee argued in his new book “AI Superpowers: China, Silicon Valley, and the New World Order” that as more jobs are automated, basic income will only serve as a painkiller.

To truly serve people, the government should kick it up a notch and actively provide careers and salaries for those who serve their community in addition to providing that safety net. And given the NBER’s new findings, he may be on to something.

READ MORE: Critics of universal basic income argue giving people money for nothing discourages working — but a study of Alaska’s 36-year-old program suggests that’s not the case [Business Insider]

More on universal basic income: Finland’s Assessment of Basic Income Trial: Not Impressed

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A New Study Shows Basic Income Doesn’t Deter People From Working

How AI is helping scientists in the fight against COVID-19, from robots to predicting the future – GeekWire

Artificial intelligence is helping researchers through different stages of the COVID-19 pandemic. (NIST Illustration / N. Hanacek)

Artificial intelligence is playing a part in each stage of the COVID-19 pandemic, from predicting the spread of the novel coronavirus to powering robots that can replace humans in hospital wards.

Thats according to Oren Etzioni, CEO of Seattles Allen Institute for Artificial Intelligence (AI2) and a University of Washington computer science professor. Etzioni and AI2 senior assistant Nicole DeCario have boiled down AIs role in the current crisis to three immediate applications: Processing large amounts of data to find treatments, reducing spread, and treating ill patients.

AI is playing numerous roles, all of which are important based on where we are in the pandemic cycle, the two told GeekWire in an email. But what if the virus could have been contained?

Canadian health surveillance startup BlueDot was among the first in the world to accurately identify the spread of COVID-19 and its risk, according to CNBC. In late December, the startups AI software discovered a cluster of unusual pneumonia cases in Wuhan, China, and predicted where the virus might go next.

Imagine the number of lives that would have been saved if the virus spread was mitigated and the global response was triggered sooner, Etzioni and DeCario said.

Can AI bring researchers closer to a cure?

One of the best things artificial intelligence can do now is help researchers scour through the data to find potential treatments, the two added.

The COVID-19 Open Research Dataset (CORD-19), an initiative building on Seattles Allen Institute for Artificial Intelligence (AI2) Semantic Scholar project, uses natural language processing to analyze tens of thousands of scientific research papers at an unprecedented pace.

Semantic Scholar, the team behind the CORD-19 dataset at AI2, was created on the hypothesis that cures for many ills live buried in scientific literature, Oren and DeCario said.Literature-based discovery has tremendous potential to inform vaccine and treatment development, which is a critical next step in the COVID-19 pandemic.

The White House announced the initiative along with a coalition that includes the Chan Zuckerberg Initiative, Georgetown Universitys Center for Security and Emerging Technology, Microsoft Research, the National Library of Medicine, and Kaggle, the machine learning and data science community owned by Google.

Within four days of the datasets release on March 16, itreceived more than 594,000 views and 183 analyses.

Computer models map out infected cells

Coronaviruses invade cells through spike proteins, but they take on different shapes in different coronaviruses. Understanding the shape of the spike protein in SARS-Cov-2 that causes coronavirus is crucial to figuring out how to target the virus and develop therapies.

Dozens of research papers related to spike proteins are in the CORD-19 Explorer to better help people understand existing research efforts.

The University of Washingtons Institute for Protein Design mapped out 3D atomic-scale models of the SARS-CoV-2 spike protein that mirror those first discovered in a University of Texas Austin lab.

The team is now working to create new proteins to neutralize the coronavirus, according to David Baker, director of the Institute for Protein Design. These proteins would have to bind to the spike protein to prevent healthy cells from being infected.

Baker suggests that its a pretty small chance that artificial intelligence approaches will be used for vaccines.

However, he said, Asfar as drugs, I think theres more of a chance there.

It has been a few months since COVID-19 first appeared in a seafood-and-live-animal market in Wuhan, China. Now the virus has crossed borders, infecting more than one million people worldwide, and scientists are scrambling to find a vaccine.

This is one of those times where I wish I had a crystal ball to see the future, Etzioni said of the likelihood of AI bringing researchers closer to a vaccine. I imagine the vaccine developers are using all tools available to move as quickly as possible. This is, indeed, a race to save lives.

More than 40 organizations are developing a COVID-19 vaccine, including three that have made it to human testing.

Apart from vaccines, several scientists and pharmaceutical companies are partnering to develop therapies to combat the virus. Some treatments include using antiviral remdesivir, developed by Gilead Sciences, and the anti-malaria drug hydroxychloroquine.

AIs quest to limit human interaction

Limiting human interaction in tandem with Washington Gov. Jay Inslees mandatory stay-at-home order is one way AI can help fight the pandemic, according to Etzioni and DeCario.

People can order groceries through Alexa without stepping foot inside a store. Robots are replacing clinicians in hospitals, helping disinfect rooms, provide telehealth services, and process and analyze COVID-19 test samples.

Doctors even used a robot to treat the first person diagnosed with COVID-19 in Everett, Wash., according to the Guardian. Dr. George Diaz, the section chief of infectious diseases at Providence Regional Medical Center, told the Guardian he operated the robot while sitting outside the patients room.

The robot was equipped with a stethoscope to take the patients vitals and a camera for doctors to communicate with the patient through a large video screen.

Robots are one of many ways hospitals around the world continue to reduce risk of the virus spreading. AI systems are helping doctors identify COVID-19 cases through CT scans or x-rays at a rapid rate with high accuracy.

Bright.md is one of many startups in the Pacific Northwest using AI-powered virtual healthcare software to help physicians treat patients more quickly and efficiently without having them actually step foot inside an office.

Two Seattle startups, MDmetrix and TransformativeMed, are using their technologies to help hospitals across the nation, including University of Washington Medicine and Harborview Medical Center in Seattle. The companies software helps clinicians better understand how patients ages 20 to 45 respond to certain treatments versus older adults. It also gauges the average time period between person-to-person vs. community spread of the disease.

The Centers for Disease Control and Prevention uses Microsofts HealthCare Bot Service as a self-screening tool for people wondering whether they need treatment for COVID-19.

AI raises privacy and ethics concerns amid pandemic

Despite AIs positive role in fighting the pandemic, the privacy and ethical questions raised by it cannot be overlooked, according to Etzioni and DeCario.

Bellevue, Wash., residents are asked to report those in violation of Inslees stay home order to help clear up 911 lines for emergencies, Geekwire reported last month. Believe police then track suspected violations on the MyBellevue app, which shows hot spots of activity.

Bellevue is not the first. The U.S. government is using location data from smartphones to help track the spread of COVID-19. However, privacy advocates, like Jennifer Lee of Washingtons ACLU, are concerned about the long-term implications of Bellevues new tool.

Etzioni and DeCario also want people to consider the implications AI has on hospitals. Even though deploying robots to take over hospital wards helps reduce spread, it also displaces staff. Job loss because of automation is already at the forefront of many discussions.

Hear more from Oren Etzioni on this recent episode of the GeekWire Health Tech podcast.

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How AI is helping scientists in the fight against COVID-19, from robots to predicting the future - GeekWire

With a $16M Series A, Chorus.ai listens to your sales calls to help your team close deals – TechCrunch

Just about everyone can benefit from an extra ear listening in at the right time. And while an ear dedicated to helping me remembertheitems my housemate asked me topickup at the store last week has yet to be commercialized into a startup,Chorus.ai is riffing off the concept to deliver a solution to help sales teams close more deals. The Chorus team is announcing a $16 million Series A today led by Redpoint.

Taking a page from companies like Cogito and Deepgram,Chorus.ai is first and foremost a system for extracting insights from audio. But unlike Cogito that got its start servicing call centers,Chorus.ai is setting its sights on sales.

In the style of X.ai, Chorus simply joins conference calls, in the same way a human would, to record and transcribe content in real-time. The platform flags important action items and topics that came up over the duration of calls.

We have invested in algorithms that are tuned to sales, but evensome simple keyword matching adds a lot of value, explainsRoy Raanani, co-founder and CEO ofChorus.ai.

The platform that the Chorus team built broadly serves two functions. Because it transcribes calls, it serves as a valuable reference for sales reps when completing follow-ups on action items. But Chorus can also add enterprise value by acting as a training ground for reps to share best practices and closing strategies.

Chorus.ai is the latest example of an AI startup finding vitality through verticalization. ThoughRaanani was careful not tocommit to any numbers, he explained that Chorus is likely better than products like IBMs Watson at thespecialized task of sales support.

Intuitively, mastery of general speech recognition is a harder task than mastery of language commonly used in the domain of sales.Even today, with speech recognition mostly a solved problem, many systems still struggle to parse the complexities (or lack there of) in the speech of young children for example.

In just four months, the company transitioned through the gears of a seed stage startup. Its first institutional round, led by Emergence Capital, who also participated in todays round, closed in October of last year for $6.3 million. All the whileRaanani, and his co-founderMicha Breakstone, continuedpolishing off the Chorus platform. The team built out key integrations with a number of meeting and support platforms like Zoom, BlueJeans, WebEX and Salesforce. And they closed customers likeQualtrics andMarketo.

In the future,Raanani and his team want to double down on the real-time advantage of the Chorus platform. The idea being that sales reps pitching to potential clients could leverage the speed of machines to pull up content in real-time to help close deals. If a customer on the phone references a competitor, Chorus could flash an informational aid on screen with known differentiators andpast successful pitches to give the sales rep a smarterace card.

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With a $16M Series A, Chorus.ai listens to your sales calls to help your team close deals - TechCrunch

Caption Health AI Awarded FDA Clearance for Point-of-Care Ejection Fraction Evaluation – HIT Consultant

What You Should Know:

Caption Health AI is awarded FDA 510(k) clearance forits innovative point-of-care ejection fraction evaluation.

Latest AI ultrasound tool makes it even easier toautomatically assess ejection fraction, a key indicator of cardiac function, atthe bedsideincluding on the front lines of the COVID-19 pandemic.

Caption Health, a Brisbane,CA-based leader in medical AI technology, today announced it has received FDA510(k) clearance for an updated version of Caption Interpretation, whichenables clinicians to obtain quick, easy and accurate measurements of cardiacejection fraction (EF) at the point of care.

Impact of Left Ventricular Ejection Fraction

Left ventricular ejection fraction is one of the most widelyused cardiac measurements and is a key measurement in the assessment of cardiacfunction across a spectrum of cardiovascular conditions. Cardiovasculardiseases kill nearly 700,000 Americans annually, according to the Centers forDisease Control and Prevention; furthermore, considering EF as a new vital signmay shed light on determining cardiac involvement in the progression of COVID-19. Arecent global surveypublished inEuropean Heart Journal Cardiovascular Imagingreportedthat cardiac abnormalities were observed in half of all COVID-19 patientsundergoing ultrasound of the heart, and clinicalmanagement was changed inone-third of patients based on imaging.

How Caption Interpretation Works

Caption Interpretation applies end-to-end deep learning toautomatically select the best clips from ultrasound exams, perform qualityassurance and produce an accurate EF measurement. The technology incorporatesthree ultrasound views into its fully automated ejection fraction calculation:apical 4-chamber (AP4), apical 2-chamber (AP2) and the readily-obtainedparasternal long-axis (PLAX) viewan industry first. While ejection fraction iscommonly measured using the more challenging apical views, the PLAX view is ofteneasier to acquire at the point of care in situations where patients may not beable to turn on their sides, such as intensive care units, anesthesiapreoperative settings and emergency rooms. This software provides unprecedentedaccess for healthcare providers to bring specialized ultrasound techniques tothe bedside.

Developing artificial intelligence that mimics an expert physicians eye with comparable accuracy to automatically calculate EFincluding from the PLAX view, which has never been done beforeis a major breakthrough, saidRoberto M. Lang, MD, FASE, FACC, FESC, FAHA, FRCP, Professor of Medicine and Radiology andDirector of Noninvasive Cardiac Imaging Laboratories at theUniversity of ChicagoMedicine and past president of the American Society of Echocardiography. Whether you are assessing cardiac function rapidly, or looking to monitor changes in EF in patients with heart failure, Caption Interpretation produces a very reliable assessment.

Caption Interpretation Benefits

At the point of care, a less precise visual assessment of EFis frequently performed in lieu of a quantitative measurement due to resourceand time constraints. Using Caption Interpretation in these settings providesthe best of both worlds: it is as easy as performing a visual assessment, butwith comparable performance to an expert quantitative measurement.

Caption Interpretation was trained on millions of imageframes to correctly estimate ejection fraction, emulating the way an expertcardiologist learns by evaluating EF as part of their clinical practice. Whilevirtually all commercially available EF measurement software works by tracingendocardial borders, Caption Interpretation analyzes every pixel and frame in agiven clip to produce highly accurate EF measurements.

Caption Health broke new ground in 2018 when it received thefirst FDA clearance for a fully automated EF assessment software. Two yearslater, Caption Interpretation remains the only fully automated EF toolavailable to providers, and, with todays clearance, continues to be the pacesetterin ultrasound interpretation.

We are pleased to have received FDA clearance for our latest AI imaging advancementour third so far this year, said Randolph P. Martin, MD, FACC, FASE, FESC, Chief Medical Officer of Caption Health, Emeritus Professor of Cardiology atEmory University School of Medicine, and past president of the American Society of Echocardiography. An accurate EF measurement is an indispensable tool in a cardiac functional assessment, and this update to Caption Interpretation makes it easier for time-constrained clinicians to incorporate it into their practice.

Recent Traction/Milestones

Caption Interpretation works in tandem with CaptionGuidance,cleared by the FDA earlier this year,as part of the Caption AI platform. Caption Guidanceemulates the expertise of a sonographer by providing over 90 types of real-timeinstructions and feedback. These visual prompts direct users to make specifictransducer movements to optimize and capture a diagnostic-quality image. Incontrast, use of other ultrasound systems requires years of expertise torecognize anatomical structures and make fine movements, limiting access toclinicians with specialized training.

The company recently closedits Series B funding round with$53 millionto further develop andcommercialize this revolutionary ultrasound technology that expands patientaccess to high-quality and essential care.

See the article here:

Caption Health AI Awarded FDA Clearance for Point-of-Care Ejection Fraction Evaluation - HIT Consultant

Cities aren’t even close to being ready for the AI revolution – Axios

Globally, no city is even close to being prepared for the challenges brought by AI and automation. Of those ranking highest in terms of readiness, nearly 70% are outside the U.S., according to a report by Oliver Wyman.

Why it matters: Cities are ground zero for the 4th industrial revolution. 68% of the world's population will live in cities by 2050, per UN estimates. During the same period, AI is expected to upend most aspects of how those people live and work.

The big picture: Many cities are focused on leveraging technology to improve their own economies such as becoming more efficient and sustainable "smart cities" or attracting companies to compete with Silicon Valley.

What they found: No city or continent has a significant advantage when it comes to AI readiness, but some have parts of the recipe.

By the numbers: Here are the survey stats that stood out.

Cities to watch:

Reality check: Cities can't deal with the repercussions of AI on their own. National and regional governments will also have to step in with policy strategies in collaboration with businesses.

Go deeper: See how your city measures up

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Cities aren't even close to being ready for the AI revolution - Axios

Admiral Seguros Is The First Spanish Insurer To Use Artificial Intelligence To Assess Vehicle Damage – PRNewswire

To do this, Admiral Seguros is using an AI solution, developed by the technology company Tractable, which accurately evaluates vehicle damage with photos sent through a web application. The app, via the AI, completes the complex manual tasks that an advisor would normally perform and produces a damage assessment in seconds, often without the need for further review.

Upon receiving the assessment, Admiral Seguros will use it to make immediate payment offers to policyholders when appropriate, allowing them to resolve claims in minutes, even on the first call.

Jose Maria Perez de Vargas, Head of Customer Management at Admiral Seguros, said: "Admiral Seguros continues to advance in digitalisation as a means to provide a better service to our policyholders, providing them with an easy, secure and transparent means of evaluating damages without the need for travel, achieving compensation in a few hours. It's a simple, innovative and efficient claims management process that our clients will surely appreciate."

Adrien Cohen, co-founder and president of Tractable, said: "By using our AI to offer immediate payments, Admiral Seguros will resolve many claims almost instantly, to the delight of its customers. This is central to our mission of using Artificial Intelligence to accelerate recovery, converting the process from weeks to minutes."

Tractable's AI uses deep learning for computer vision, in addition to machine learning techniques. The AI is trained with many millions of photographs of vehicle damage, and the algorithms learn from experience by analyzing a wide variety of different examples. Tractable's technology can be applied globally to any vehicle.

The AI enables insurers to assess car damage, shares recommended repair operations, and guides the claims management process to ensure these are processed and settled as quickly as possible.

According to Admiral Seguros, the application of this technology in the insurance sector will be a great step in digitization and will offer a great improvement in the customer experience of Admiral's insurance brands in Spain, Qualitas Auto and Balumba.

About Tractable:

Tractable develops artificial intelligence for accident and disaster recovery. Its AI solutions have been deployed by leading insurers across Europe, North America and Asia to accelerate accident recovery for hundreds of thousands of households. Tractable is backed by $55m in venture capital and has offices in London, New York City and Tokyo.

About Admiral Seguros

In Spain, Admiral Group plc has been based in Seville since 2006 thanks to the creation of Admiral Seguros. More than 700 people work from there and for the entire national territory, cementing and marketing their two commercial brands: Qualitas Auto, and Balumba.

Recognized as the third best company to work for in Spain, the sixth in Europe and the eighteenth in the world by the consultancy Great Place to Work, Admiral Seguros is committed to a corporate culture focused on people.

SOURCE Tractable

https://tractable.ai

Originally posted here:

Admiral Seguros Is The First Spanish Insurer To Use Artificial Intelligence To Assess Vehicle Damage - PRNewswire

Pimloc gets $1.8M for its AI-based visual search and redaction tool – TechCrunch

U.K.-based Pimloc has closed a 1.4 million (~$1.8 million) seed funding round led by Amadeus Capital Partners. Existing investor Speedinvest and other unnamed shareholders also participated in the round.

The 2016-founded computer vision startup launched a AI-powered photo classifier service called Pholio in 2017 pitching the service as a way for smartphone users to reclaim agency over their digital memories without having to hand over their data to cloud giants like Google.

It has since pivoted to position Pholio as a specialist search and discovery platform for large image and video collections and live streams (such as those owned by art galleries or broadcasters) and also launched a second tool powered by its deep learning platform.This product, Secure Redact, offers privacy-focused content moderation tools enabling its users to find and redact personal data in visual content.

An example use case it gives is for law enforcement to anonymize bodycam footage so it can be repurposed for training videos or prepared for submitting as evidence.

Pimloc has been working with diverse image and video content for several years supporting businesses with a host of classification, moderation and data protection challenges (image libraries, art galleries, broadcasters and CCTV providers), CEO Simon Randall tells TechCrunch.

Through our work on the visual privacy side we identified a critical gap in the market for services that allow businesses and governments to manage visual data protection at scale on security footage. Pimloc has worked in this area for a couple of years building capability and product; as a result, Pimloc has now focused the business solely around this mission.

Secure Redact has two components: A first (automated) step that detects personal data (e.g. faces, heads, bodies) within video content. On top of that is what Randall calls a layer of intelligent tools letting users quickly review and edit results.

All detections and tracks are auditable and editable by users prior to accepting and redacting, he explains, adding: Personal data extends wider than just faces into other objects and scene content, including ID cards, tattoos, phone screens (body-worn cameras have a habit of picking up messages on the wearers phone screen as they are typing, or sensitive notes on their laptop or notebook).

One specific user of redaction with the tool he mentions is the University of Bristol. There, a research group, led by Dr Dima Damen, an associate professor in computer vision, is participating in an international consortium of 12 universities which is aiming to amass the largest data set on egocentric vision and needs to be able to anonymise the video data set before making it available for academic/open source use.

On the legal side, Randall says Pimloc offers a range of data processing models thereby catering to differences in how/where data can be processed. Some customers are happy for Pimloc to act as data processor and use the Secure Redact SaaS solution they manage their account, they upload footage and can review/edit/update detections prior to redaction and usage. Some customers run the Secure Redact system on their servers where they are both data controller and processor, he notes.

We have over 100 users signed up for the SaaS service covering mobility, entertainment, insurance, health and security. We are also in the process of setting up a host of on-premise implementations, he adds.

Asked which sectors Pimloc sees driving the most growth for its platform in the coming years, he lists the following:smart cities/mobility platforms (with safety/analytics demand coming from the likes of councils, retailers, AVs); the insurance industry, which he notes is capturing and using an increasing amount of visual data for claims and risk monitoring and thus looking at responsible systems for data management and processing; video/telehealth, with traditional consultations moving into video and driving demand for visual diagnosis; and law enforcement, where security goals need to be supported by visual privacy designed in by default (at least where forces are subject to European data protection law).

On the competitive front, he notes that startups are increasingly focusing on specialist application areas for AI arguing they have an opportunity to build compelling end-to-end propositions which are harder for larger tech companies to focus on.

For Pimlock specifically he argues it has an edge in its particular security-focused niche given deep expertise and specific domain experience.

There are low barriers to entry to create a low-quality product but very high technical barriers to create a service that is good enough to use at scale with real in the wild footage, he argues, adding: The generalist services of the larger tech players do not match up with domain specific provisions of Pimloc/Secure Redact. Video security footage is a difficult domain for AI, systems trained on lifestyle/celebrity or other general data sets perform poorly on real security footage.

Commenting on the seed funding in a statement, Alex van Someren, MD of Amadeus Capital Partners, said: There is a critical need for privacy by design and large-scale solutions, as video grows as a data source for mobility, insurance, commerce and smart cities, while our reliance on video for remote working increases. We are very excited about the potential of Pimlocs products to meet this challenge.

Consumers around the world are rightfully concerned with how enterprises are handling the growing volume of visual data being captured 24/7. We believe Pimloc has developed an industry leading approach to visual security and privacy that will allow businesses and governments to manage the usage of visual data whilst protecting consumers. We are excited to support their vision as they expand into the wider Enterprise and SaaS markets, added Rick Hao, principal at Speedinvest, in another supporting statement.

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Pimloc gets $1.8M for its AI-based visual search and redaction tool - TechCrunch