These eight books about artificial intelligence cover a range of topics, including ethical issues, how AI is affecting the job market, and how organizations can use AI to gain a competitive advantage.

Artificial intelligence (AI) is an ever-evolving technology. With several different uses, it's easy to understand why it's being implemented more and more frequently. These titles answer common questions about AI, discuss what current AI technologies businesses are using, how humans can lose control over AI, and more.

T-Minus AI: Humanity's Countdown to Artificial Intelligence and the New Pursuit of Global Power

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In T-Minus AI, author, national expert, and the US Air Force's first Chairperson for Artificial Intelligence Michael Kanaan explains a human-oriented perspective of AI. He offers his view on our history of innovation to illustrate what we should all know about modern computing, AI, and machine learning. Additionally, Kanaan discusses the global implications of AI by illuminating the cultural and national vulnerabilities already present as well as future pressing issues.

The Alignment Problem: Machine Learning and Human Values

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The "alignment problem," according to researchers, occurs when the tech systems that humans attempt to teach don't do what is wanted or expected. Best-selling author Brian Christian discusses the alignment problem's "first-responders," and their plans to solve the problem before it is out of human hands. Using a blend of history and on-the-ground reporting, Christian follows the growth of machine learning in the field and examines our current technology and culture.

Rise of the Robots: Technology and the Threat of a Jobless Future

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With the possibility of AI making jobs like paralegals, journalists, and even computer programmers obsolete, author Martin Ford looks at the future of the job market and how it will continue to transform. Rise of the Robots helps us understand how employment and society will have to adapt to the changing market.

Artificial Intelligence: A Guide for Thinking Humans

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In Artificial Intelligence, author Melanie Mitchell asks urgent questions concerning AI today: How intelligent are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Mitchell also covers the dominant models of modern AI and machine learning, cutting-edge AI programs, and human investors in AI.

AI Ethics (The MIT Press Essential Knowledge series)

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AI Ethics discusses the major ethical issues artificial intelligence raises and addresses several concrete questions. Author Mark Coeckelbergh uses narratives, relevant philosophical discussions, and describes different approaches to machine learning and data science. AI Ethics takes a look at privacy concerns, responsibility and the delegation of decision-making, transparency and bias as it arises at all stages of data science processes, and much more.

The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (Management on the Cutting Edge)

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In The AI Advantage,Thomas Davenport offers a practical guide to using AI in a business setting. Davenport not only explains what AI technologies are available, but also how companies can use them to gain a competitive advantage.

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity

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In her book, author Amy Webb looks at how the foundations of AI are broken--all the way from the people working on the system to the technology itself. Webb suggests that the big nine corporations (Amazon, Google, Facebook, Tencent, Baidu, Alibaba, Microsoft, IBM, and Apple), "may be inadvertently building and enabling vast arrays of intelligent systems that don't share our motivations, desires, or hopes for the future of humanity."

Artificial Intelligence: 101 Things You Must Know Today About Our Future

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Artificial Intelligence: 101 Things You Must Know Today About Our Futurecontains many timely topics related to AI, including: Self-driving cars, robots, chatbots, as well as how AI will impact the job market, business processes, and entire industries. As the title suggests, readers can learn the answers to 101 questions about artificial intelligence, and have access to a large number of resources, ideas, and tips.

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AI reading list: 8 interesting books about artificial intelligence to check out - TechRepublic

Artificial intelligence can drive a car, curate the films and documentaries that you watch, develop chess programmes capable of beating grandmasters and use your face to access your phone. And, one company claims, it can also predict when footballers are about to suffer an injury.

Off the field, football has gone through a huge transformation in the 21st century, with the emergence of GPS-driven player performance data in the early 2000s, followed in the 2010s by the advanced analytics that now form a major part of every top club's player recruitment strategy. Just last month, Manchester City announced the appointment of Laurie Shaw to a new post of lead AI scientist at the Etihad Stadium, taking him from his role as research scientist and lecturer at Harvard University.

Football has always searched out innovations to make small, but crucial, differences. Many have become staples of the game, including TechnoGym to improve biomechanics, IntelliGym to improve cognitive processing and cryogenic gym sessions to ease the strain on muscles. Others have fallen by the wayside. Anyone remember nasal strips or the ball-bending properties of Predator boots?

The use of AI to predict when players are on the brink of suffering an injury could prove to be the next game-changing innovation that becomes a key component at the elite end of the game.

In a game dominated by clubs wanting to discover the extra 1% in marginal gains, keeping a player fit is arguably the most important challenge facing any coach. A depleted squad can lead to negative results and, if a team suffers too many, the manager or coach is generally the one who pays the price. This season has been more challenging than most, with the COVID-19 pandemic leading to fixtures being crammed into a reduced time frame, and players being forced to play 2-3 games a week on a regular basis.

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The toll on players' fitness is borne out by the injury lists. Crystal Palace and Southampton fulfilled their midweek Premier League fixtures with 10 first-team squad members sidelined. Champions Liverpool lost to Brighton on Wednesday with eight absentees, including long-term injury victims Virgil van Dijk, Joe Gomez and Joel Matip. Research by premierinjuries.com shows that up to and including match-week 21 of the Premier League this season, there has been a five percent increase in time lost to injuries this season. At the same stage last season, there were 356 "time-loss absences" (a player missing at least one league game), but the number has jumped to 374 this time around. With COVID-related absences, the number is 435.

Liverpool had suffered 14 time-loss absences at this stage of last season, but they're now up to 29 in 2020-21. Their league position -- fourth place, seven points adrift of top spot -- suggests they are paying a price for their sharp increase in players lost to injury.

But finding reliable injury prevention technology is the holy grail of sports scientists and fitness coaches. By November, ESPN reported a 16% rise in muscle injuries in the Premier League compared to the same stage last season. So can AI successfully predict when players are about to be injured?

Since the start of the 2017-18 season, La Liga side Getafe have partnered with the California-based AI company Zone7 to break down performance data and predict when players are at risk of injury. In simple terms, clubs like Getafe in Spain, Scottish Premiership leaders Rangers and MLS sides Real Salt Lake and Toronto FC send their training and match data to Zone7, who analyze it using their algorithm and send back daily emails with information about players who may be straying close to the so-called "danger zone."

Between the start of the 2017-18 season and March 2020, when La Liga was suspended due to the COVID-19 pandemic, Getafe recorded a substantial reduction in injuries.

"Three seasons ago, during the first year with Zone7, we saw a reduction of 40% in injury volume," Javier Vidal, the Getafe's Head of Performance, said. "As the Zone7 engine became more reliable and we had access to more data in the second year, we saw a reduction of 66 percent in the volume of injuries.

"This means that of every three injuries we had two seasons ago, we now have only one."

Jordi Cruyff, the former Barcelona and Manchester United midfielder, told ESPN that he has become a "minor, minor investor" in Zone7 after trialling the AI tool during his time as sporting director at Maccabi Tel Aviv in 2017. But he admits that he was only convinced by the AI technology after monitoring the data, even though Maccabi's then-coach declined to use it.

"I presented the tool to our then-coach and he wasn't too interested." Cruyff told ESPN. "So for the four to five months the coach was in charge, he would follow his own plan, but we would still give our performance data to the company, which they would run through their algorithm. I would then receive an email before training each day with which players were at risk and it actually predicted five of seven injuries.

"I thought 'wow.' Once or twice could be a coincidence, but catching five out of seven muscular injuries is a different thing. I would wait until after training to be told if a player had been injured. I would then go back to look at my email and there was the name. We were lucky in some ways that the coach wasn't interested in it because it gave us the chance to test it.

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"It was the perfect test, although I wish the coach would have listened, because then we would have avoided some injuries."

Tal Brown, who founded Zone7 with Eyal Eliakim in 2017 having worked together in the Israeli Defense Forces Intelligence Corps, spoke to ESPN to explain how AI can be used to detect injury risk.

"Every single player is now using a GPS vest, they are being tested for strength and flexibility at their clubs, many teams distribute watches to their players to measure sleep, so the reality is that somebody working for a club needs to look at two dozen dashboards every day -- multiplied by 20 players, multiplied by six days a week," Brown said via Zoom. "It is becoming a puzzle that a human brain wasn't really meant to solve.

"We can use a chess metaphor. Chess programmes used to be pretty simplistic and the experts could beat them, but today, a Google chess programme is unbeatable. It's not because Google has taught that chess programme 10,000 equations manually, it is because the programme has automatically studied every recorded chess game played in the history of mankind and, using AI, has developed its own understanding and interpretation.

"We are not there yet as a company. We don't have access to every single football injury that ever occurred, but we are getting much better and there will be a point where a programme focused on injury risk will out-perform humans in interpreting data."

More than 50 clubs across the world now use Zone7's AI programme. Many wish to remain anonymous, in an effort to protect any competitive advantage that the tool may provide -- football clubs are notoriously protective of such proprietary data -- while others simply do not wish to discuss any pros or cons they have discovered while using it. Despite repeated attempts by ESPN to speak to Real Salt Lake and Toronto, neither MLS team responded to enquiries.

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Julien Laurens puts Eden Hazard's latest injury into context for Real Madrid.

Rangers, 23 points clear at the top of the Scottish Premiership and on course for a first domestic title since 2011, adopted Zone7's AI tool last summer and, while keen to make a broader assessment after a full season of use, they believe it's been a valuable addition to their injury prevention strategy.

"I believe AI, coupled with the experience levels of those using it, will eventually become a bedrock within clubs' decision-making as data and technology advances," Jordan Milsom, Rangers' head of performance told ESPN. "Given our players had been exposed to one of the longest lockdowns of all [93 days] and the unknowns associated with such prolonged layoffs, we felt investing in such a system may well provide another layer of support for how we managed the players on what would clearly be a challenging season.

"We haven't used the system long enough compare season-to-season analysis, and it's important to understand we are a department that is data-informed and not data-driven. But it is my opinion that if such systems are used in this way, it can have many positive benefits."

Rangers manager Steven Gerrard has praised the club's fitness and sports science department, saying in December that the team were enabling his players to "hit top numbers," and Milson says that the AI data is helping to inform player rotation, even to the extent of highlighting which players should be substituted during games.

"All of our GPS and heart rate training load data from sessions and games is uploaded automatically into the Zone7 system," Milsom said. "The platform digests this, performs its modelling and provides us with risk alerts each day for players.

"Generally, there would be 1-2 players who may be flagged [for further monitoring]. Sometimes, these flags relate to overload -- other times it's under-load. This allows us to have a deeper dive into why specifically they are at risk. This information will feed into our general staff discussions to determine if any further areas support this information. As we typically compete every 3-4 days, if risk is associated with overload, I can often use that information to help support in-game substitutions as a means of maximising player availability, whilst potentially reducing risk through reduced minutes if and when possible."

The key to the success of the AI tool is the amount of data Zone7 are able to upload and analyse. While Brown stresses that "nobody ever sees your data. We don't own it and we're not allowed to retain a copy of it, post-relationship, so it's very strict," the volume of information provided by each client club is used to create a huge database that then enables the programme to predict injury risk.

"We can use 200 million hours of football data because we are working with 50-60 clients," Brown said. "As a result, we have 50-60 times more data than a typical team has, so the data set is very large. But what is important is that it's not just the injury in the sense of the date it occurred and what happened, it is every single day of training and games and medical data leading to the injury, going back as much as a year prior.

"That amount of information gives us the ability to look at the daily data leading to an incident and, using AI and deep learning, to find patterns that repeat themselves before hamstring injuries or groin injuries or knee injuries happen. That's how it works.

"If you are trying to forecast an event, which is an injury, you need to have a big database of incidents. A typical team would have something like 30-40 incidents a year for a squad, so multiply that by several years of historical data."

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The Gab and Juls show analyse Liverpool's loss to Brighton and look forward to their next game against Man City.

ESPN has spoken to people in sports science who believe that AI is a positive innovation if used alongside existing methods. "Their results are impressive," said one sports scientist, who has worked with several Premier League clubs in the past and spoke on condition of not being named. "The issue is the level of individualisation with injury results is high, so lots of variant data only gives you a small answer. Therefore, it definitely has to be a blended approach."

Zone7's AI tool is not restricted to sports. In tandem with Garmin wearable devices and Zone7, medical staff in Israel are having their health and well-being monitored during the COVID-19 pandemic and there is a similar project with a major hospital in New York City. There are also projects ongoing with military and special forces. In football, however, Getafe are the best example of AI being used successfully to improve the fitness record of a team, as explained by head of performance Vidal.

"It would take 200 people all day to analyse the data, but with this, I get the recommendations within minutes." Vidal said. "We use our own high-quality ultrasound to clinically to evaluate players that show predefined risk indications. After starting to use Zone7, some players would report feeling fine despite the engine identifying immediate risk for them.

"In many cases, our ultrasound tests confirmed muscular damage, allowing us to address this before the injury occurred. These players could have sustained injury but for the AI detection."

Cruyff, now coaching in China with Shenzhen FC, believes AI can become a key component for teams, but he makes clear that AI alone cannot be regarded as the silver bullet to prevent all injuries.

"It's not a deciding tool," he said. "You can see a risk of injury and decide to take the risk or not. It's part of the modernisation of sport. You have so many things -- video analysts, GPS tracking devices -- and I think this is a part that maybe we missed, but it is coming, little by little."

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Soccer looks to AI for an edge: Could an algorithm really predict injuries? - ESPN

Sean McClain, Co-Founder and CEO of AbSci.

There has been a lot of recent attention on the challenges of delivering COVID-19 vaccines. But there are also challenges in making them. For some of the newer options like those from Johnson & Johnson and Oxford-AstraZeneca, the modified cells used in vaccine production are struggling under the scale of demand. But synthetic biology company AbScis recent acquisition of the artificial intelligence platform, Denovium, could help mitigate this type of challenge in the future.

Unlike mRNA vaccines, the Johnson & Johnson/Oxford-AstraZeneca class of vaccines rely on a type of virus called adenovirus which is known to cause colds in chimpanzees. To address COVID-19, the adenovirus is genetically altered to express the SARS-CoV-2 spike protein which is what ultimately triggers the bodys immune response. Like mRNA vaccines, adenovirus-based vaccines train the body to recognize and fight COVID-19, foregoing the need to inject a person with a weakened version of SARS-CoV-2.

But producing enough adenovirus cells has been a challenge. To make vaccine doses, large volumes of altered adenovirus are produced by replicating cells in bioreactors. But, the scale of production can also cause the cells to weaken. This can result in a reduced output of adenovirus copies. So while these new vaccines may represent a breakthrough in adenovirus-based therapeutics, the process also highlights some critical roadblocks.

One major issue is that drug discovery and drug manufacturing are often disconnected from one another. Drug discovery typically starts with screeningthe process of finding a set of compounds out of 100,000 combinations that can best neutralize a targeted weak point of a disease. But when a promising protein is identified, it often turns out to be difficult to scale effectively.

Once a therapeutic compound is identified, researchers must then determine if it works well with a group of similar cells called a cell-line. By inserting the compound into the cellswhich then divide and multiply in a bioreactorthe cells act like factories to produce greater volumes of the compound of choice. But, as in the case with adenovirus-producing cells, not all cells can maintain their functions at large volumes. If the protein compound doesnt work well in a scalable cell-line, researchers often have to go back to the drawing board to find a new compound and start again.

Many in the biopharma space are aware of this inefficient process. The synthetic biology company AbSci has spent years developing a platform solution that streamlines the workflow. [Our platform] is simultaneously a drug discovery and manufacturing platform that allows you to discover your drug and the cell line that can manufacture [it], says AbSci CEO, Sean McClain. Were finally uniting drug discovery and manufacturing the first time.

AbSci refers to their core process as their Protein Printing platform, not because it uses ink and paper to make proteins but as an analogy for ease and speed. The first technology [in our platform] is our SoluPro E. coli strain. It has been highly engineered to be more mammalian-like to be able to produce mammalian-like proteins that E. coli wasn't previously capable of doing, says McClain. AbSci also uses what the company calls a folding solution to precisely tailor how proteins fold and therefore function.

Imad Ajjawi, Co-Founder and CBO of Denovium

To find the most effective protein, AbSci alters its folding solutions to create as many protein varieties as possible, often to the order of 10s of millions. The more protein types available, which AbSci refers to as libraries, the higher the likelihood of success. But this also creates a challenge: so many options, but which to choose?

To address this, AbSci recently acquired artificial intelligence company, Denovium. By integrating Denoviums AI platform, AbSci can improve its data analysis via AI models. From there, the company can take the best candidates and find the most effective cell-line to produce the chosen compounds at scale. McClain explains that traditional drug discovery and manufacturing typically takes years. But AbScis platform can take that timeline down to weeks. Were actually able to manufacture [therapeutics] because the dirty secret in pharma is that so many drugs get shelved because [pharma companies] can't actually manufacture them, says McClain.

For McClain, acquiring Denovium is a big step forward for AbScis discovery process. Its going to change the paradigm. Its really a perfect marriage of both data and AI technology. If you don't have good data feeding into your AI model, it's worthless. But if you don't have an AI technology, you can't mine [the data] and get all the benefits, says McClain.

Denoviums co-founder and CBO, Imad Ajjawi, also sees the new collaboration as a significant opportunity. It's really exciting to be a part of AbSci because they have all the data, billions of points that the deep learning engine can now analyze, says Ajjawi. AbScis acquisition also comes on the heels of the companys $65 million Series E in late 2020.

Upgrading the union of biology and AI is important for advancing synthetic biology innovation. But the true potential beneficiaries of this advanced discovery platform are those in need of novel drug options.

AbScis main goal as a company is to bring therapeutics to market more quickly. This technology's impact on healthcare is profound because more drugs and biologics can now enter patients' hands faster, says McClain.

McClain believes that AbScis technology will help speed the process of clinically testing new medications. Faster clinical trial turnarounds could increase the number of drugs approved to address a range of diseases. This could be most impactful for patients with rare or difficult to treat conditions as drug discovery is often prioritized based on how long it takes to find a scalable cell-line.

But though AbSci is working to accelerate drug discovery, the process still takes time. Right now, we have six drugs that are in preclinical or clinical trials. And one of them is actually in phase three. So we could have an improved product here in the next couple of years, says McClain.

As Absci and Denovium finalize their technology integrations, McClain is also looking ahead to build as many partnerships as possible. The more partnerships we do, the more patients were able to affect that at the end of the day, says McClain.

In line with that goal, AbSci today announced a continuation of its partnership with Astellas and Xyphos. AbSci will take on screening and identifying an optimal cell-line for a leading variant of Xyphos MicAbody, a bispecific antibody-like adaptor molecule used in the company's immuno-oncology program.

McClain expects more partnership announcements will follow in the first quarter of 2021. We have some really exciting partnerships that are going to be coming out over this next quarter that I think speak to the [range] of the types of disease states we're working on and the breadth of how the technology can be used within biopharma, says McClain.

Im the founder of SynBioBeta, and some of the companies that I write about are sponsors of the SynBioBeta conference and weekly digest, including AbSci. Thank you to Fiona Mischel and Vinit Parekh for additional research and reporting in this article.

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Synthetic Biology Startup Acquires AI Platform To Disrupt The Drug Industry - Forbes

Revolutionary voice AI technology enhances customer engagement at scale

SINGAPORE, Feb. 5, 2021 /PRNewswire/ -- Singapore based start-up, Wiz.AI, is proud to announce its latest partnership with Mobile Virtual Network Operator (MVNO), Zero1. The implementation of Wiz.AI's conversational Talkbots has allowed Zero1 to not only automate outbound calls from their call centre, it has also enabled the telco to engage with its customer base at scale.

Now, Zero1's customers can interact with Wiz.AI's Talkbot to immediately address their queries at any time of the day.

Talkbots, or conversational voice artificial intelligence, are virtual customer service representatives, powered by Wiz.AI's proprietary artificial intelligence technology. The Talkbots can understand each unique conversation in the caller's natural spoken language, incorporate unique nuances from human speech and reply in a hyper-realistic human-like localised accent that ensures customer experience is not compromised. When further assistance is required, the Talkbots will redirect the calls to the next available human agent.

The automation of Zero1's outbound customer engagement has increased the response rate at nearly four times of a customer service representative, without compromising on the customer interaction.

"We are excited to be working with Zero1 to enhance their customer engagement. Our proprietary conversational voice AI framework helps organisations increase their efficiency by automating routine rule-based tasks, allowing the human agent to focus on more complex customer issues. Our Talkbots are continuously evolving and improving their accuracy in recognising the various consumers' needs with every call. In addition, our conversational AI framework can be deployed quickly and tailored according to different requirements in different industries. The possibilities are endless with conversational Talkbots," said Jennifer Zhang, CEO and co-founder of Wiz.AI.

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"Wiz.AI's in-depth customer engagement data has allowed us to proactively engage with our customer base with urgent queries that they might have particularly during recent uncertain times. With Wiz.AI's Outreach Talkbot, we can reach out and reassure our customers that we are constantly hearing their needs," added Stuart Tan, CEO and founder of Zero1.

Each call is recorded and automatically categorised according to the customers' call intention and interest levels. With this new depth of customer data, Zero1 is able to categorise customers into different groups and deliver hyper-personalised customer outreach based on their specific needs and levels of interest.

Wiz.AI's Talkbots are highly adaptable and customisable, allowing them to deliver automated conversations for a multitude of business applications across industries. Wiz.AI has also built a global competitive advantage by being able to localise its speech recognition to the language and accent of its users. The start-up's Talkbot system currently supports languages including English, Mandarin, Singlish and Bahasa Indonesia.

About Wiz.AI

Wiz.AI is revolutionizing the customer service industry by using Voice Artificial Intelligence to digitalise the process of inbound and outbound calls. Wiz.AI helps companies engage with their customers at scale with hyper-realistic Talkbots that can communicate effectively with customers using natural spoken language.

The company has a sizable portfolio of clients ranging from industries such as telecommunication and ecommerce to banks, insurance and finance. Wiz.AI's technologies have empowered clients to effectively engage with their customers at scale and to shift from a reactive customer engagement experience to a proactive one with clear returns on investment for their businesses.

About Zero1

Zero1 Pte Ltd was founded in 2017 as a Mobile Virtual Network Operator (MVNO) licensed by the Info-communications Media Development Authority of Singapore. Its vision is to become a major regional mobile service provider offering unparalleled value to its customers with its unlimited mobile data plan and competitive pricing. Zero1 aims to achieve this through strategic partnership as well as innovative and disruptive use of state-of-the-art technologies. In March 2018, Zero1 launched an unlimited mobile data service at only $19.00 -- the first truly unlimited data service in Singapore, thus setting the scene for more such services to follow. Today Zero1 has over 100,000 subscribers signed up in Singapore. It is establishing a regional presence in S.E. Asia.

SOURCE Wiz.AI

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Wiz.AI boosts virtual telco, Zero1, with its conversational AI technology - Yahoo Finance

The arm of global inequality is long, rendering itself visible particularly in the development of AI and machine learning systems. In a recent paper, researchers at Cornell, the Universite de Montreal, the National Institute of Statistical Sciences (U.S.), and Princeton argue that this inequality in the AI industry involves a concentration of profits and raises the danger of ignoring the contexts to which AI is applied.

As AI systems become increasingly ingrained in society, they said, those responsible for developing and implementing such systems stand to profit to a large extent. And if these players are predominantly located in economic powerhouses like the U.S., China, and the E.U., a disproportionate share of economic benefit will fall inside of these regions, exacerbating the inequality.

Whether explicitly in response to this inequality or not, calls have been made for broader inclusion in the development of AI. At the same time, some have acknowledged the limitations of inclusion. For example, in an analysis of publications at two major machine learning conference venues, NeurIPS 2020 and ICML 2020, none of the top 10 countries in terms of publication index were located in Latin America, Africa, or Southeast Asia, the coauthors of this new study note. Moreover, the full lists of the top 100 universities and top 100 companies by publication index included no companies or universities based in Africa or Latin America.

This inequality manifests in part in data collection. Previous research has found that ImageNet and OpenImages, two large, publicly available image datasets, are U.S.- and Euro-centric. Models trained on these datasets perform worse on images from Global South countries. For example, images of grooms are classified with lower accuracy when they come from Ethiopia and Pakistan, compared to images of grooms from the United States. Along this vein, because of how images of words like wedding or spices are presented in distinctly different cultures, publicly available object recognition systems fail to correctly classify many of these objects when they come from the Global South.

Labels, the annotations from which AI models learn relationships in data, also bear the hallmarks of inequality. A major venue for crowdsourcing labeling work is Amazon Mechanical Turk, but an estimated less than 2% of Mechanical Turk workers come from the Global South, with the vast majority originating from the U.S. and India. Not only are the tasks monotonous and the wages low on Samasource, another crowdsourcing workload platform, workers earn around $8 a day but a number of barriers exist to participation. A computer and reliable internet connection are required, and on Amazon Mechanical Turk, U.S. bank accounts and gift cards are the only forms of payment.

As the researchers point out, ImageNet, which has been essential to recent progress in computer vision, wouldnt have been possible without the work of data labelers. But the ImageNet workers themselves made a median wage of $2 per hour, with only 4% making more than the U.S. federal minimum wage of $7.25 per hour itself a far cry from a living wage.

As [a] significant part of the data collection pipeline, data labeling is an extremely low-paying job involving rote, repetitive tasks that offer no room for upward mobility, the coauthors wrote. Individuals may not require many technical skills to label data, but they do not develop any meaningful technical skills either. The anonymity of platforms like Amazons Mechanical Turk inhibit the formation of social relationships between the labeler and the client that could otherwise have led to further educational opportunities or better remuneration. Although data is central to the AI systems of today, data labelers receive only a disproportionately tiny portion of the profits of building these systems.

The coauthors also find inequality in the AI research labs established by tech giants like Google, Microsoft, Facebook, and others. Despite these centers presence throughout South and Latin America, they tend to be concentrated in certain countries, especially India, Brazil, Ghana, and Kenya. And the positions there often require technical expertise which the local population might not have, as illustrated by AI researchers and practitioners tendency to work and study in places outside of their home countries. The coauthors cite a recent report from Georgetown Universitys Center for Security and Emerging Technologies that found that while 42 of the 62 major AI labs are located outside of the U.S., 68% of the staff are located within the United States.

Even with long-term investment into regions in the Global South, the question remains of whether local residents are provided opportunities to join management and contribute to important strategic decisions, the coauthors wrote. True inclusion necessitates that underrepresented voices can be found in all ranks of a companys hierarchy, including in positions of upper management. Tech companies which are establishing a footprint in these regions are uniquely positioned to offer this opportunity to natives of the region.

The coauthors are encouraged by the efforts of organizations like Khipuand Black in AI, which have identified students, researchers, and practitioners in the field of AI and made improvements in increasing the number of Latin American and Black scholars attending and publishing at premiere AI conferences. Other communities based on the African continent, like Data Science Africa, Masakhane, and Deep Learning Indaba, have expanded their efforts with conferences, workshops, and dissertation awards and developed curricula for the wider African AI community.

But this being the case, the coauthors say a key component of future inclusion efforts should be to elevate the involvement and participation of those historically excluded from AI development. Currently, they argue, data labelers are often wholly detached from the rest of the machine learning pipeline, with workers oftentimes not knowing how their labor will be used nor for what purpose. The coauthors say these workers should be provided with education opportunities that allow them to contribute to the models they are building in ways beyond labeling.

Little sense of fulfillment comes from menial tasks [like labeling], and by exploiting these workers solely for their produced knowledge without bringing them into the fold of the product that they are helping to create, a deep chasm exists between workers and the downstream product, the coauthors wrote. Similarly, where participation in the form of model development is the norm, employers should seek to involve local residents in the ranks of management and in the process of strategic decision-making.

While acknowledging that it isnt an easy task, the coauthors suggest embracing AI development as a path forward for economic development. Rather than relying upon foreign spearheading of AI systems for domestic application, where returns from these systems often arent reinvested domestically, they encourage countries to create domestic AI development activity focused on high-productivity activities like model development, deployment, and research.

As the development of AI continues to progress across the world, the exclusion of those from communities most likely to bear the brunt of algorithmic inequity only stands to worsen, the coauthors wrote. We hope the actions we propose can help to begin the movement of communities in the Global South from being just beneficiaries or subjects of AI systems to being active, engaged participants. Having true agency over the AI systems integrated into the livelihoods of communities in the Global South will maximize the impact of these systems and lead the way for global inclusion of AI.

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The AI industry is built on geographic and social inequality, research shows - VentureBeat

Since 2019, government-sponsored initiatives around AI have proliferated across Asia Pacific. Such initiatives include the setting up of cross-domain AI ethics councils, guidelines and frameworks for the responsible use of AI, and other initiatives such as financial and technology support. The majority of these initiatives builds on the country's respective data privacy and protection acts. This is a clear sign that governments see the need to expand existing regulations when it comes to leveraging AI as a key driver for digital economies. All initiatives to date are voluntary in nature, but there are indications already that existing data privacy and protection laws will be updated and expanded to include AI. To anticipate this, data and technology governance initiatives must evolve now.

Traditionally, data governance and the governance of tech associated with data has focused on topics such as master data management, data quality, and data retention -- all primarily operational. With the rise of privacy laws and data protection acts such as the General Data Protection Regulation (GDPR) in the EU and the Personal Data Protection Act (PDPA) in Singapore, the scope of data governance has been expanded to include data privacy, personal data protection, and data sovereignty. This has shifted data governance out of the operational corner and into the spotlight of regulatory compliance and enforceable laws.

With AI being ready for prime time -- that means large-scale production deployments -- data and technology governance must step up again and include data and AI ethics and AI risk management.

Like cybersecurity risk before it, regulatory initiatives and consumer demand join forces to drive AI risk management to the top of the corporate agenda. Evaluate your data and technology governance initiatives now to identify gaps and maturity challenges when it comes to the responsible use of data and AI. Prepare for AI risk management to follow cybersecurity risk to the boardroom and kick off corporate collaborations and cross-functional initiatives, including governance, risk, corporate social responsibility, and ethics. Ultimately, understand how you can build trust with your customers, partners, and employees into your responsible use of data and AI -- and turn this trust into your competitive advantage!

For more business and technology trends critical for the year head, download Forrester's 2021Asia PacificPredictions Guidehere.

This post was written by Principal Analyst Achim Granzen, and it originally appearedhere.

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AI drives the evolution of technology and data governance - ZDNet

FILE PHOTO: The new Google Pixel 4 smartphone is displayed during a Google launch event in New York City, New York, U.S., October 15, 2019. REUTERS/Eduardo Munoz/File Photo

(Reuters) - Cameras on Google Pixel smartphones will be able to measure heart and breathing rates starting next month, in one of the first applications of Alphabet Incs artificial intelligence technology to its wellness services.

Health programs available on Google Plays store and Apple Incs App Store for years have provided the same functionality. But a study in 2017 found accuracy varied and adoption of the apps remains low.

Google Health leaders told reporters earlier this week they had advanced the AI powering the measurements and plan to detail its method and clinical trial in an academic paper in the coming weeks. The company expects to roll out the feature to other Android smartphones at an unspecified time, it said in a blog post on Thursday, but plans for iPhones are unclear.

Apples Watch, Googles Fitbit and other wearables have greatly expanded the reach of continuous heart rate sensing technologies to a much larger population.

The smartphone camera approach is more ad hoc - users who want to take a pulse place their finger over the lens, which catches subtle color changes that correspond to blood flow. Respiration is calculated from video of upper torso movements.

Google Health product manager Jack Po said that the company wanted to give an alternative to manual pulse checks for smartphone owners who only want to monitor their condition occasionally but cannot afford a wearable.

Po said the technology, which can mistake heart rates by about 2%, requires further testing before it could be used in medical settings.

The new feature will be available as an update to the Google Fit app.

Google consolidated its health services about two years ago, aiming to better compete with Apple, Samsung Electronics Co and other mobile technology companies that have invested heavily in marketing wellness offerings.

Reporting by Paresh Dave; Editing by Sam Holmes

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Google phone cameras will read heart, breathing rates with AI help - Reuters

The U.S. Food and Drug Administration has authorized the use of CLEW Medical's artificial intelligence tool to predict hemodynamic instability in adult patients inintensive care units, the company announced on Wednesday.

The tool, CLEWICU, uses AI-based algorithms and machine learning models to identify the likelihood of occurrence of significant clinical events for ICU patients.

CLEW says the clearance is the FDA's first for such a device.

"AI can be a powerful force for change in healthcare, enabling assessment of time-critical patient information and predictive warning of deterioration that could enable better informed clinical decisions and improved outcomes in the ICU," said Dr. David Bates, medical director of clinical and quality analysis in information systems at Mass General Brigham and CLEW Advisory Board member, in a statement.

WHY IT MATTERS

Hemodynamic instability is a common COVID-19 complication, so CLEWICU's predictive capabilities could prove especially useful during the ongoing pandemic particularly given ICUs' strained resources around the country.

By analyzing patient data from various sources, including electronic health records and medical devices, CLEWICU provides a picture of overall unit status and helps identify individuals whose conditions are likely to deteriorate.

According to the company, the system notifies users of clinical deterioration up to eight hours in advance, enabling early intervention. The system also identifies low-risk patients who are unlikely to deteriorate, thus potentially enabling better ICU resource management and optimization.

"CLEW's AI-based solution is a huge leap forward in ICU patient care, providing preemptive and potentially lifesaving information that enables early intervention, reduces alarm fatigue and can potentially significantly improve clinical outcomes," said Dr. Craig Lilly of University of Massachusetts Medical School in a statement.

THE LARGER TREND

The FDA granted emergency use authorization to CLEWICU back this past June. The tool was among several AI-powered technology innovations developed, or modified, in response to the ongoing pandemic.

Mayo Clinic Chief Information OfficerCris Ross said in December that AI has been crucial in understanding the pandemic. He noted the variety of COVID-19-specific use cases, while he also flaggedthe risk of algorithmic bias.

"We know that Black and Hispanic patients are infected and die at higher rates than other populations. So we need to be vigilant for the possibility that that fact about the genetic or other predisposition that might be present in those populations could cause us to develop triage algorithms that might cause us to reduce resources available to Black or Hispanic patients because of one of the biases introduced by algorithm development," said Ross.

ON THE RECORD

"We are proud to have received this landmark FDA clearance and deliver a first-of-its-kind product for the industry, giving healthcare providers the critical data that they need to prevent life-threatening situations," said Gal Salomon, CLEW CEO, in a statement.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

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FDA issues landmark clearance to AI-driven ICU predictive tool - Healthcare IT News

The D.AI.SY project powered by Endotech lets people get income upfront, as well as a residual income from trading profits

Let the business resources in our guide inspire you and help you achieve your goals in 2021.

February4, 20213 min read

Opinions expressed by Entrepreneur contributors are their own.

Blockchain technology is quickly becoming a popular method to create and sustain various businesses. It was first recognized when cryptocurrency went mainstream. However, it has not been heavily utilized in the financial industry until now.

A noteworthy example of emergent blockchain technology isD.AI.SY,a crowdfunding model that enables cryptocurrency holders to receive equity in various forms, as well as peer-to-peer rewards through a secure system of crowdfunding supported by blockchain technology.

D.AI.SYlets people get income upfront, as well as a residual income from trading profits. It also provides investors with stock equity through its proprietary PACESETTER equity bonus system.

The first undertaking ofD.AI.SYis a crowdfunding project with a major artificial intelligence company,Endotech. TheD.AI.SY-Endotech teamis looking to develop AI-powered investing to unlock high-risk/high-return alpha from aggregated financial data. This tactical investing technology can produce substantially increased probability and reduced risks while keeping high-returns potential for investors.

D.AI.SYis currently working on a Tron Smart Contract. There are many benefits to Smart Contract Technology, as it allows for safe interaction betweenD.AI.SYand its members, as the scaling of transaction capacity with low transaction fees. All Smart Contract transactions are transparent for verification on the blockchain. Further, the Smart Contract is also immutable and indestructible, having the ability to persist to the end of time after it is launched.

For context,Endotechhas specialized for years in developing fully-automated tactical investment platforms based on dynamic artificial intelligence modeling.

D.AI.SYis Endotechs newest project which is set to deliver a new standard of predicting the probability of success in various trading markets like Forex, cryptocurrency, commodities, among other traditional markets.

The team is spearheaded by CEO and co-founder Dr. Anna Becker, and COO and co-founder Dmitry Gooshchin. The co-founders possess immense knowledge of blockchain technology and artificial intelligence, and their ingenuity is set to be reflected in their project for years to come.

Dr. Becker has achieved a plethora of success in the fintech space of artificial intelligence. She founded Strategy Runner (acquired by MFGlobal), a trading software tool that provided full automation capabilities for over 50,000 clients with over 300 professional strategy developers. She has also worked with over 35 systematic funds and artificial intelligence technologies for brokers during her time with the Gilboa Fund-of-Funds.

Dr. Becker manages a team that oversees over 20 proprietary artificial intelligence systems that are currently operating inEndoTech. She has extensive knowledge and experience as she has worked with over 300 brokers, and served as a compliance officer working with regulatory entities, such as NFA and CFTC.

Endotechhas big plans for the development of theD.AI.SYsoftware technology, which aims to produce more predictable, stable and reduced risk investment gains in various trading markets.The Daisy solution aims to rebalance the investment ecosystem by harnessing technological developments for improved investment opportunities and sustainability while offering certified network participants access to automated investment.

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This Project Democratizes AI Investments On The Blockchain - Entrepreneur

Computer science professor Tina Eliassi-Rad says shes proud to be named on an industry list of 100 Brilliant Women in AI Ethics, which identifies her as one of the top thinkers in the male-dominated field of artificial intelligence. But shes even prouder of what the carefully-curated list represents.

Part of the issue in a field such as computer science is that women and other under-represented minorities arent always seen. Initiatives like this one show that there are a lot of women who are qualified to do this work, says Eliassi-Rad.

Mia Shah-Dand, the CEO of the Oakland, California-based research firm Lighthouse3, created the annual list in 2018. Shah-Dand says she wanted to provide a rebuttal to technology leaders who complained that they couldnt find accomplished, diverse women to hire.

I was a little frustrated with all the times I would hear, There just arent enough qualified women, says Shah-Dand. Its the same old excuse. Well, we have an entire directory of qualified women now. There is no excuse. At this point in 2021, if you have only men on your staff, its intentional.

According to recent research by the World Economic Forum, women hold only 26% of data and artificial intelligence jobs across the globe, and even fewer have senior roles.

Shah-Dand says she included Eliassi-Rad on her 2021 list because of the professors extensive research on racial, gender and other baked-in biases in artificial intelligence algorithms.

Her emphasis on algorithmic accountability and fairness was particularly interesting, says Shah-Dand.

Algorithms, which scan large amounts of data and find whatever information its creators want, are increasingly part of our everyday lives. For example, credit card fraud departments use algorithms to detect abnormal spending, while social media algorithms use viewer interests to determine which ads to run.

Eliasi-Rads research at Northeastern focuses on the unseen but overwhelming influence that artificial intelligence algorithms can make in peoples lives, especially in social media.

Part of the problem with algorithms is that they can impact life-altering decisions if theyre used in criminal justice or even your credit score, says Eliassi-Rad. Microlenders, or individuals who issue small loans, will often check a candidates Facebook and Twitter feeds when deciding whether to grant a loan. A chance connection with someone who has defaulted on a loan could trigger a denial, says Eliassa-Rad.

Sometimes if you dont get the right loan in life, you cant better yourself, she says.

Eliassi-Rads career in computer science was sparked by her fathers early work with autonomous vehicles. She avidly read the many magazines he brought home and decided computer science was the perfect balance between math and electrical engineering. Her focus recently sharpened as she learned about the different class, race, and gender biases in machine learning.

She likens the data used in algorithms to an iconic photo of a police officers German shepherd attacking a Black high school student during a 1963 civil rights event in Birmingham, Alabama.

The German shepherd isnt racist, its the people teaching the dog, Eliassa-Rad says. Even if the data used in an algorithm isnt biased, the algorithm may still produce biased findings.

As you are developing an algorithm you are making choices, and those choices have consequences, Eliassi-Rad says.

Eliassi-Rad and Shah-Dand say the list of top women in AI ethics does more than provide a roster of qualified computer science professionals who also happen to be female, LGTBQ, or women of color. It creates a community to foster networking and support while providing role models for future generations.

Its sort of like a sisterhood, says Eliassi-Rad, who received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010. I hope young women see this and think, I can be somebody like this person.

For media inquiries, please contact media@northeastern.edu.

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She was named one of the 100 most brilliant women in AI ethics - News@Northeastern

HealthTensor, an artificial intelligence company creating software to help augment medical decision-making, has raised a $5 million in a seed round of financing led by Calibrate Ventures, TenOneTen Ventures and Susa Ventures.

WHY IT MATTERS

The round also includes hospitals and physicians, including a medical officer at Amazon Health. Funds will be used to scale the company's software engineering and implementation team to keep up with demand from major health systems, the vendor said.

HealthTensor's software functions between physicians and the troves of raw medical data from any given patient, which often is more than any individual doctor can handle. The company uses advanced algorithms to do AI-enabled diagnosiswith the aim of ensuring no medical condition is overlooked. The software was designed with the physician workflow in mind, enabling frictionless adoption of the product by users, the company contended.

"HealthTensor makes me a better doctor because it allows me to spend less time in front of the computer and more time in front of the patient," said Dr. Tasneem Bholat, an early user of HealthTensor's software. "HealthTensor synthesizes all the data from the patient's chart, saving me from doing chart biopsy and surfacing diagnoses I might have otherwise missed."

The company's software currently is integrated within several hospitals and will expand to more in the coming months, the vendor reported.

THE LARGER TREND

The use of AI in healthcare has been on the rise throughout 2020. According to some experts, 2021 could be a big year for AI and machine learning.

"AI had become mythical, but 2021 looks set to be the year where it may come into its own in the health sector, along with the use of automation," said Dr. Sam Shah, chief medical strategy officer at Numan and former director of digital development at NHSX. "During the next year, we are likely to see more solutions that support, not only imaging, but also the quality of reporting,as well as the greater use of natural language processing.

"The combination of these technologies will help improve efficiency in health systems as they begin to recover from the pandemic," he said.

ON THE RECORD

"We think of HealthTensor as an AI-powered medical resident that is focused specifically on the tedious, data-driven aspects of medicine, which is what computers do best," said Eli Ben-Joseph, cofounder and CEO of HealthTensor.

"Many doctors are forced to spend a majority of their day focused on data aggregation from medical records, which leads to missed diagnoses, patient dissatisfaction and physician burnout. HealthTensor frees up the physician to focus on the conceptual and emotional aspects of medicine, which is what humans do best."

"HealthTensor makes doctors' lives easier and helps provide better patient care, ultimately generating revenue for hospitals, making it one of the rare startups that has massive global potential for both patients and healthcare providers," said Jason Schoettler, general partner at Calibrate Ventures.

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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HealthTensor raises $5M for its AI-based medical diagnosis tools - Healthcare IT News

Military intelligence: the DoD is now trying to make the leap to a software-intensive enterprise says the new report from the NSCAI.

The US Department of Defense (DoD) must set an ambitious goal to have the foundations for widespread integration of artificial intelligence (AI) across defence in place by 2025, according to a draft of the final report from the National Security Commission on Artificial Intelligence (NSCAI).

This should include a common digital infrastructure that is accessible to internal AI development teams and critical industry partners, a technically literate workforce, and modern AI-enabled business practices that improve efficiency.

The draft report was published last month; the final version will be released on 1 March 2021.

The Commission has advocated for greater investment and uptake in AI in the defence and security sectors. It frames the USs efforts in AI similarly to an arms race, as hostile actors develop their own capabilities in autonomous weaponry, cyber tools and disinformation.

The magnitude of the technological opportunity coincides with a moment of strategic vulnerability. China is a competitor possessing the might, talent, and ambition to challenge Americas technological leadership, military superiority, and its broader position in the world, the introduction notes.

AI is deepening the threat posed by cyber attacks and disinformation campaigns that Russia, China, and other state and non-state actors use to infiltrate our society, steal our data, and interfere in our democracy, it adds.

The NSCAI was established in August 2018 as part of the annual defence spending settlement, with a mission to scope out how to advance AI, machine learning and associated technologies in relation to US national security and defence needs.

It is chaired by former Google chief executive Dr Eric Schmidt. The vice chair is Robert Work, a former Deputy Secretary of Defense from 2014 to 2017, under both the Obama and Trump administrations.

Its fifteen commissioners, supported by a secretariat of 25 staff, have completed five interim reports and memos since July 2019, informed by submissions from a wide range of experts. The commission is scheduled to be wound up in October 2021.

Previous reports have urged policies such as creating a national digital corps, setting up a military cyber academy, and increasing the federal budget for research and development into AI and associated technologies, according to US government news website Fedscoop.

The draft final report is in two halves. The first, Defending America in the AI Era, focuses on the defence applications of AI, and what the US should do to respond to the spectrum of AI-related threats from state and non-state actors.

In the second part, Winning the Technology Competition, the commission looks at AI as part of a wider global competition around new technologies and recommends policies to promote innovation in AI and create a critical and competitive advantage for the US.

The introduction paints a picture of a nation at risk of slipping behind competitor states, which, in future, could include small nations and actors able to exploit affordable, off-the-shelf hardware and readily available algorithms.

The report is also blunt about Chinas capability. In some areas of research and applications, China is already an AI peer, and it is more technically advanced in some applications. Within the next decade, China could surpass the United States as the worlds AI superpower, it notes.

It warns that US citizens have also not recognised the assertive role the government will have to play in ensuring the United States wins this innovation competition or the public investment needed. Despite our private sector and university leadership in AI, the United States remains unprepared for the coming era, the commission writes.

On the other hand, capabilities in AI could ensure the US can respond with greater agility to new or emerging vulnerabilities. Global crises exemplified in the global pandemic and climate change are expanding the definition of national security and crying out for innovative solutions. AI can help us navigate many of these challenges, the introduction says.

The authors argue that AI development and implementation requires a stack of interconnected elements containing including talent, data, hardware, algorithms, applications, and integration.

We regard talent as the most essential requirement because it drives the creation and management of all the other elements, the report says, recommending a focus on improving the government technology talent pipeline, both through new recruiting practices and retraining current employees.

If government agencies do not have enough of the right talent, every AIinitiative will struggle and most will fail, said commissioner Dr Jos-Marie Griffiths, president of Dakota State University, according to Fedscoop.

While the US armed forces might already deploy, and be able to counter, drones and autonomous weapons, the NSCAI warns that rapidly advancing capabilities could change the dynamic within human-machine teams.

In the past, computers could only perform tasks that fell within a clearly defined set of parameters or rules programmed by a human. As AI becomes more capable, computers will be able to learn and perform tasks based on parameters that humans do not explicitly program, creating choices and taking actions at a volume and speed never before possible.

The report therefore sees the construction of an AI infrastructure as the first step to creating new defence capabilities. DoD has long been hardware-oriented toward ships, planes, and tanks. It is now trying to make the leap to a software-intensive enterprise, it notes.

Original post:

US defense must have foundations for AI integration by 2025, report says - Global Government Forum

by Dan Kobialka Feb 4, 2021

RSA has announced NetWitness Detect AI, a SaaS analytics and machine learning solution that provides threat detection and insights on data captured via the NetWitness Platform.

The launch comes roughly five months afterSymphony Technology Group (STG) acquired RSAfromDell Technologies for $2.08 billion. Under STGs ownership, RSA is focused on three business segments:

NetWitness Detect AI certainly aligns with those priorities. Positioned as a turnkey solution, RSA says the software helps security teams investigate cyber threats. More specifically, it provides continuous, high-fidelity threat detection and monitoring without rules, signatures or manual analysis, RSA asserts.

NetWitness Detect AI helps security teams assess threats at every stage of the attack lifecycle and prioritize critical incidents, RSA indicated.

Security teams can use NetWitness Detect AI to find, prioritize and resolve threats, RSA noted. They can leverage NetWitness Detect AIs cloud-scale processing for behavior analytics and its machine learning to detect and respond to threats without manual oversight.

In addition, security teams can utilize NetWitness Detect AI to speed up incident response, according to RSA. Security teams can leverage NetWitness Detect AIs unsupervised machine learning from the moment it is activated, so they can immediately use it for incident response.

NetWitness Detect AI is now available globally and can be integrated into the NetWitness Platform.

The NetWitness Platform lets security teams collect and analyze data across endpoints and computing platforms, RSA said. It adds threat intelligence and business context to this information to help security teams accelerate threat detection and response.

Also, the NetWitness Platform offers a variety of threat detection and response capabilities, including:

Along with the NetWitness Platform, RSA offers SecureID identity and access management (IAM), Archer integrated risk management and other security products.

More here:

RSA Unveils AI-Based Threat Detection Solution - MSSP Alert

A pause button at the bottom of the screen would play a 20-second video clip featuring tall green trees and sounds of birds chirping and is intended help users calm down if things get heated. It plays over the entire video chat, too, and everyone has to take a break. A Chat button on the right pulled up a window for us to interact via text with both Serenity and other participants. There were also options to turn off our mics and cameras. Serenity told us to mute ourselves when not speaking (which I was grateful for because the feedback from seven peoples mics was infuriating).

After the introduction, Serenity asked everyone What are you mirroring now? That was a confusing question to start with, but one of my fellow attendees rephrased it for us. He speculated that it meant what we were thinking about and reflecting on, and we all answered based on that interpretation. At this point, the experience may sound painfully familiar and borderline pointless.

But Serenity went on to ask truly thought-provoking questions, like what wed like to see more of in 2050 or what wed like to not be talking about in that year. Then, it continued prodding, asking about the types of new jobs that would need to be created to facilitate some of our groups declared values and the world we wanted to create. The questions also differ slightly across all the sessions, according to McCarthy. She said that Each session follows an arc and many of the questions are the same, but there's also variation in response to the group discussion and flow.

Beyond the Breakdown

Beyond the Breakdown is about more than just introspection and imagining the future, though. Its core focus is conversation and dialogue otherwise why have you answer these questions with a group of strangers? Whenever it seemed like not everyone had responded, Serenity asked if anybody had more to add.

Learning from others in the conversation was what made the experience illuminating and hopeful. When Serenity asked where we thought people would call home in 2050, my fellow participants answers surprised me. I was thinking of more straightforward answers like, Earth, for example, but others talked about communal living spaces. Some questions were pretty vague, though, like What does care look like in this world, and some members of my group chose to interpret it as healthcare while others took it to mean community care.

Still, seeing how people interpreted and responded to the questions was part of learning about various perspectives. Like Lee said, the sessions offer an opportunity to build something rather than just ingest. Had I only been speaking with Serenity, I would have missed out on the collaborative aspect.

But of course, the quality of your BTB experience hinges on the people you get to interact with. My session was filled with a somewhat biased, self-selecting sample Sundance attendees that had access to a computer and spoke English. That excludes people from different socio-economic backgrounds or other nationalities that didnt converse in English. And while I applaud BTBs built-in accessibility features like live closed-captioning and text-based support, there are plenty of other considerations that still have to be made.

Beyond the Breakdown

That said, the fact that I was speaking with intelligent, seemingly like-minded people was a huge part of why I enjoyed BTB. It left me hopeful that the world isnt filled with angry people who shut down rational discourse, and that there are people committed to building a better future through empathy, sympathy and by listening to others. But I can imagine how my experience would have been completely different had it been filled with people who disagreed on fundamental issues. Sure, theres always the Pause button to cool things down, and anyone who signs up for a session of BTB is most likely going to be open-minded and agreeable to begin with. But Im not sure a 20-second timeout would be enough to cool down a truly heated argument.

Patrick said one of the questions he wanted BTB to answer was, Is it possible for a browser to help us with communal and community care? McCarthy added, What if the browser or the video chat experience itself could be leading you through this process, and what happens if we start to bring AI into that?

I didnt see Serenity step in to calm down a tricky situation since my session mates were all respectful and agreeable. In retrospect, I wish someone in my group had at least pretended to get heated to see how Serenity would have handled things. I like the idea of a neutral AI moderator leading the conversations, since it could appear more objective to participants regardless of their ideological differences. But I do believe that Beyond The Breakdown has an inherent limit: reach. The people we need to be having open-minded and open-hearted conversations in safe spaces with might not be likely or willing to sign up for such a chat. What it does offer to those of us keen on speaking with people around the world though, is a glimmer of hope as we shake off the debris of 2020 and head into the rest of the decade.

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My AI-moderated video chat with strangers gave me hope - Engadget

Iteratively CEO Patrick Thompson. (Iteratively Photo)

Seattle startup Iteratively raised a $5.4 million round led by Gradient Ventures, Googles AI-focused venture fund.

Founded in 2019 by veterans of Atlassian and Microsoft, Iteratively sells software to data and product teams for customer analytics tracking. The idea is to help prevent data quality problems at the outset of entry and have standardized customer data in one place. Iteratively integrates with third-party data analytics tools such as Amplitude, Mixpanel, Segment, dbt, and more.

Box, Beekeeper, thredUP, Dribbble, and others are clients.

The 10-person company is led by CEO Patrick Thompson, who co-founded Iteratively with Ondrej Hrebicek. They previously worked together at Syncplicity, a file sharing startup co-founded by Hrebicek that was acquired by EMC in 2016.

We kept hearing the same thing from data and product teams that they have lost confidence in their analytics, Thompson said in a statement. We built a tool that helps them rebuild trust in their data and empowers them to collaborate on analytics. We believe data is a team sport and collaboration is key for cross-functional teams to succeed.

Fika Ventures and PSL Ventures also participated in the round. PSL Ventures led a previous round in 2019. Zach Bratun-Glennon, partner at Gradient Ventures, joined the board.

The Iteratively team possesses a relentless focus on finally creating the source of truth for analytics data, PSL wrote in a blog post. This trustworthy foundation unlocks countless new data use cases from personalization and recommendation engines to drive growth, churn prediction and prevention to improve retention, and new 1-1 marketing scenarios.

Google launched Gradient Ventures in 2017 as part of Alphabets continued investment in AI. Gradient portfolio companies get access to AI training from Google and help from Google engineers.

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Googles AI-focused venture fund leads $5.4M investment for Seattle analytics startup Iteratively - GeekWire

Feb. 4, 2021 BSC participates in the AI-SPRINTproject contributing its experience on the programming and parallelization of applications on distributed infrastructures. The work will be organized in two main contributions, the deployment ofCOMPSson the edge devices considered by the use cases and the implementation of the AI applications to be executed across distributed heterogeneous infrastructures (on premise, edge and public clouds). In particular,AI-SPRINTwill benefit from the recent developments on the adaptation ofCOMPSsfor Fog to Cloud platforms and will extend it to support the execution of serverless functions as a service.

On the other side,COMPSswill be adopted to develop AI and big data applications in support of the use cases also leveraging the recent enhancements to develop workflows that combine HPC compute engines with High Performance Data Analytics (HPDA) and machine learning methods. These ML implementations are available through thedislib librarythat is also part of theFujitsu-BSCcollaboration.

The technology developed within the project will be put to test by BSC on a personalized healthcare use case that will focus on privacy and security, much needed in healthcare scenarios since the information to be exchanged and processed involves medical data about patients.

More specifically, an automated system for personalized stroke risk assessment and prevention will be developed by using continuous, non-invasive monitoring of heart activity. The process will gather heart parameters collected from a wearable device, patients lifestyle information and biochemical blood indicators from a mobile application. All data will be anonymized, processed and used to train AI models cooperatively by local edge servers and cloud. At the same time, it will provide personalized notifications, alerts, and recommendations for stroke prevention.

AI-SPRINTdefines a novel framework for the design and operation of AI applications in computing continua leveraging theCOMPSsprogramming framework and supporting AI applications development by enabling the seamless design and partition of AI applications among the plethora of cloud-based solutions and AI-based sensor devices. Moreover it will generate impacts bringing together different European industrial end-users while and making available the software tools through a marketplace for AI start-ups, SMEs, system integrators, and European cloud providers statesDaniele Lezzi, Senior Researcher in theComputer Sciences department Workflows and Distributed Computingat BSC.

About COMPSs:

COMPSsis a task-based programming model known for notably improving the performance of large-scale applications by automatically parallelizing their execution. TheCOMPSsruntime has been recently extended within BSC projects:CLASSandELASTICto manage distribution, parallelism and heterogeneity in the edge resources transparently to the application programmer and to handle data regardless of persistency by supporting a single and unified data model.COMPSsis the base of the Design Tools of the project and it will support developers to easily compose AI/ML applications also leveraging the dislib library, helping end users to deal with big datasets on distributed resources and providing automatic parallelization of the code.

About Personalized Healthcare:

AI-SPRINTapplications will pave the way for an effective framework for personalized AI models preventing risks coupled with a lifestyle modificationmodification programmebenefiting people aged between 40 and 80, improving and extending human lives. The project addresses theUnited Nations strategic development goalsSDG3 (Good Health and Well Being) through the personalized healthcare pilot.

About AI-SPRINT

AI-SPRINTwill tackle the skill shortage and considerably reduce steep learning curves in the development of AI software on edge ecosystems through OSS (Operations Support System). The project addresses the followingUnited Nations strategic development goals(SDGs): SDG8 (Decent Work and Economic Growth) enabling novel AI applications running in computing continua, SDG9 (Industry, Innovation and Infrastructure) by fostering innovation in the maintenance and inspection use case and contributing OSS and SDG12 (Ensure sustainable consumption and production patterns) through farming 4.0 pilot.

For further information, visit AI-SPRINT website:https://www.ai-sprint-project.eu/

The AI-SPRINT project has received funding from the European Union Horizon 2020 research and innovation programme under Grant Agreement No. 101016577

Source: BSC

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BSC Working Towards Adoption of AI Applications to Capture Insights on Personalised Healthcare - HPCwire

Scientists are working on a way to use AI to create quantitative measurements for chronic pain.

Why it matters: Chronic pain is an epidemic in the U.S., but doctors can't measure discomfort as they can other vital signs. Building methods that can objectively measure pain can help ensure that the millions in need of palliative care aren't left to suffer.

What's happening: Late last month, scientists from IBM and Boston Scientific presented new research outlining a framework that uses machine learning and activity monitoring devices to capture and analyze biometric data that can correspond to the perception of pain.

What they're saying: "We want to use all the tools of predictive analytics and get to the point where we can predict where people's pain is going to be in the future, with enough time to give doctors the chance to intervene," says Jeff Rogers, senior manager for digital health at IBM Research.

Background: According to one estimate, more than 100 million Americans struggle with chronic pain, at an annual cost of as much as $635 billion in painkillers and lost productivity.

What's next: Rogers hopes the research can lead to medical devices that could predict chronic pain signals ahead of suffering and adjust their response accordingly.

Read more from the original source:

Scientists are working with AI to measure chronic pain - Axios

Dive Brief:

The pandemic has transformed trends in office design that may have once pointed in the direction of open formats at many organizations.

This is partly reflected in the recommendations of public health officials, including the Centers for Disease Control and Prevention (CDC). In June 2020, the agency said employers should make changes to ensure social distancing or use transparent barriers in cases where social distancing is not possible. CDC's guidance also called on employers to increase cleaning of common areas and improve ventilation.

Research from last year appeared to show most employers were heeding calls for increased safety measures. A June survey of organizations by WorldatWork found that a majority planned to implement policies such as additional cleanings, reduced meeting sizes, workspace modifications and mask and temperature screening requirements.

Employers that previously operated communal office spaces adjusted early on in the pandemic. During an August 2020 webinar, an official with biopharmaceutical firm Abbvie described the company's decision to install touchless water facilities as well as automated systems for coffee areas. Others, such as publishing company Wiley, have embraced fully remote or hybrid work arrangements to reduce the need for physical office space.

IBM's TRIRIGA announcement is geared toward ensuring a flexible future for modern workplaces, Kendra DeKeyrel, director of IBM TRIRIGA offering management, said in the statement; "Returning to the workplace after nearly a year at home is going to be a challenging transition, not only for employers who need to create new spaces and protocols but for workers who need assurances their workplaces are safe, and need help navigating a changed and potentially more confusing workspace."

The return to offices could provide employers an opportunity to replace outdated equipment, according to a June 2020 report from the International Association of IT Asset Managers. The organization said firms could seek ways to turn technology investments made at the beginning of the pandemic into long-term asset strategy.

Read the original post:

IBM adds AI-powered tools to support return-to-work operations - HR Dive