Category Archives: Artificial Intelligence

RwHealth's Data Science Platform combines Artificial Intelligence (AI)andMachine Learning to give healthcare providers in-depth data to make better decisions and improve patient outcomes. By using RwHealth's analytical capabilitiesto make predictions, model treatment options, improve safety and increase efficiency, clinicians can deliver better, more widespread care.

One important capability driving the company's significant growth is its ability to anticipate hospital patient flows. Being able to model patient numbers has been vital during Covid, and the RwHealth platform has helped UK hospitals to anticipate demand, combat bed shortages and tackle worsening waiting list issues.

RwHealth's success mirrors the wider growth of AI in healthcare, as stakeholders across the health ecosystem find new ways to increase efficiency, save money and deliver optimised clinical outcomes. In September 2020, NHSX, the organisation driving digital transformation inhealth and social care, announced a 250m investment into AI in UK healthcare.

Orlando Agrippa, CEO and Founder of RwHealth, said: "We've grown at an extraordinary rate, as healthcare providers realise how AI can improve patient outcomes, while helping to ease the wider pressures that the healthcare industry faces. It's importantto tackle backlogs and bed capacity issues so that healthcare remains safe andsteady as we attempt to recover post-Covid."

RwHealth client, Chief Medical Officer (interim) and Responsible OfficerAndrew McLaren, adds: "RwHealth's platform enables us to solve bottlenecks before they become a problem. Faster treatment leads to better outcomes, so every moment the solution helps us save no matter how small has a tangibleimpact on patient care."

The protection of patient data is at the heart of the RwHealth proposition. While its primary customers are NHS Trusts and private healthcare organisations, the companydoes nothold any private patient information, nor any personally identifiable hospital data.With a dedicated Data Protection Officer, RwHealth uses suitable safeguards to protect all information from unauthorised access.

Today, RwHealth works with more than 70 UKand international providers, its AI technology having processed more than 10m UKpatients and 5.5 million across the Middle East and Australia. Founded in 2017, RwHealth is headquartered in London's Canary Wharf.

Photo - https://mma.prnewswire.com/media/1626850/Orlando_Agrippa.jpg

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NHS Artificial Intelligence provider reports 160% growth, promising to transform healthcare with better data - PR Newswire UK

Iowa State University researchers are growing two kinds of corn plants.

If you drive past the many fields near the universitys campus in Ames, you can see row after row of the first. But the second exists in a location that hasnt been completely explored yet: cyberspace.

The researchers, part of the AI Institute for Resilient Agriculture, are using photos, sensor data and artificial intelligence to create digital twins of corn plants that, through analysis, can lead to a better understanding of their real-life counterparts. They hope the resulting software and techniques will lead to better management, improved breeding, and ultimately, smarter crops.

We need to use lots of real-time, high-resolution data to make decisions, Patrick Schnable, an agronomy professor and director of Iowa States Plant Sciences Institute,told Agri-Pulse. Just collecting data for data's sake is not something that production ag wants. But data which is then linked to statistical models or other kinds of mathematical models that advise farmers on what to do has a lot of value.

The idea of machine learning systems that can improve or take over typical human tasks has been seeing increased attention over the past couple of years in many industries, including agriculture. In 2019, the National Science Foundation and several partner agencies, including the USDA, began establishing and funding AI institutes to research and advance artificial intelligence in fields like agriculture.

In their call for proposals, the organizations said AI could spur the next revolution in food and feed production.

The Green Revolution of the 1960s greatly enhanced food production and resulted in positive impacts on food security, human health, employment, and overall quality of life for many, the solicitation said. There were also unintended consequences on natural resource use, water and soil quality, and pest population expansion. An AI-based approach to agriculture can go much further by addressing whole food systems, inputs and outputs, internal and external consequences, and issues and challenges at micro, meso, and macro scales that include meeting policy requirements of ecosystem health.

Among the seven inaugural institutes established in 2020 were two focusing on agriculture: the AI Institute for Future Agricultural Resilience, Management and Sustainability at the University of Illinois at Urbana-Champaign, and the AI Institute for Next Generation Food Systems at the University of California, Davis. The 2021 lineup includesthe AIIRA and the Institute for Agricultural AI for Transformation Workforce and Decision Support (AgAID) at Washington State University.

Lakshmi Attigala, a senior scientist and lab manager at Iowa State University, prepares a corn plant to be photographed.

The AIIRA, which received $20 million in funding from these governmental organizations, plans to pool the expertise of researchers at Iowa State, Carnegie Mellon University, the University of Arizona, New York University, George Mason University, the Iowa Soybean Association, the University of Nebraska-Lincoln and the University of Missouri to study the intersection of plant science, agronomics and AI.

The institute hopes to develop AI algorithms that can take all of the collectible data from a field whether by ground robots, drones, or satellites and analyze it to create tools farmers can use to improve production of crops for resilience to the pressures brought about by climate change.

This is a game-changer, Baskar Ganapathysubramian, the director of the institute, told Agri-Pulse as he walked toward a nondescript white shed tucked between crop fields on the Iowa State University campus.

Scouting is based on the visual, he said. By using multimodal things, you can actually go beyond the visual and do early detection and early mitigation. That's not only sustainable, because you're going to use less of the chemicals needed, but also amazingly profitable.

Ganapathysubramian opened the door to reveal a flurry of activity. Directly inside, genetics graduate student Yawei Li held a protractor up to a corn plant in various positions, trying to measure the angles of its leaves.

Across the room, Lakshmi Attigala, a senior scientist and lab manager, grabbed a fully headed corn plant from a gray tote and walked it over to the labs makeshift photography studio, where a sheet of blue cloth hanging from the ceiling served as a backdrop.

She placed the corn plant in a small, rotating green vase ringed by light stands and adjusted its leaves, preparing it for a photo shoot. She gave it a unique number, 21-3N3125-1, which was printed on a piece of paper she attached to the front of it.

As the vase rotated, she used two cameras one hanging from the ceiling and the other sitting atop a tripod in front of the corn plant to take shots of the plant.

On the north side of the building two researchers senior staff member Zaki Jubery and graduate student Koushik Nagasubramanian placed eight more corn plants in a ring surrounding a terrestrial laser scanner. The scanner sends out a signal to detect point clouds, or find the exact dimensions of these plants based on which points the lasers bounce off.

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All three of these actions, while happening separately and in different parts of the room, feed data from the 80 corn plants scanned that day to a computer learning program that can study their features to learn what the plants look like. If cameras, lasers and sensors can collect enough data on corn plants, the software should be able to create near-identical models of them when fully developed.

The idea is that we perfect something from here and then we do that on a higher scale in the field, said Nagasubramanian. Thats a more complicated thing if you have plants in the background and you have changing light intensities and clouds.

The institute, which collaborates with the Genomes to Fields Initiative to phenotype corn hybrid varieties across 162 environments in North America, also monitors a corn field lined with cameras mounted on poles. The solar-powered cameras sit above the corn plants and take photos every 15 minutes to watch each one develop over time.

The resulting data can be fed to AI programs to get a better understanding of how these plants grow and what genetic traits they share.

Certainly it is going to help us understand for example, with the photography what is the genetic control of leaf angle. And then that would allow us to develop varieties with different leaf angles more readily, Schnable said.

Schnable said its too soon for the developing technology to be widely deployed in fields or used for breeding purposes and that for now, the research funding is limited. But he believes private companies will use AI technology to develop their own products.

These things do have significant impacts out there in the world, he said.

For more news, go to http://www.Agri-Pulse.com.

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New institute aims to unlock the secrets of corn using artificial intelligence - Agri-Pulse

Artificial intelligence (AI) is sometimes referred to as a herald of the fourth industrial revolution. That revolution is already here. Whenever you say Hey Siri or glance at your phone in order to unlock it, youre using AI. Its current and potential applications are numerous, including medical diagnosis and predictive technologies that enhance user interactions.

As chairwoman of the U.S. House Committee on Science, Space, and Technology, I am particularly interested in the potential for AI to accelerate innovation and discovery across the science and engineering disciplines. Just last year, DeepMind announced that its AI system AlphaFold had solved a protein-folding challenge that had stumped biologists for half a century. It is clear that not only will AI technologies be integral to improving the lives of Americans, but they will also help determine Americas standing in the world in the decades to come.

However, the vision of AIs role in humanitys future isnt all rosy. Increasingly autonomous devices and growing amounts of data will exacerbate traditional concerns, such as privacy and cybersecurity.Other potential dangers of AI have also arrived, appearing as patterns of algorithmic bias that often reflect our societys systemic racial and gender-based biases. We have seen discriminatory outcomes in AI systems that predict credit scores, health care risks, and recruitment potential. These are domains where we must mitigate the risk of bias in our decision-making, and the tools we use to augment that decision-making.

Technological progress does not have to come at the expense of safety, security, fairness, or transparency. In fact, embedding our values into technological development is central to our economic competitiveness and national security. Our federal government has the responsibility to work with private industry to ensure that we are able to maximize the benefits of AI technology for society while simultaneously managing its emerging risks.

To this end, the Science Committee has engaged in efforts to promote trustworthy AI. Last year, one of our signature achievements was passing the bipartisan National Artificial Intelligence Initiative Act, which directs the Department of Commerces National Institute of Standards and Technology (NIST) to develop a process for managing AI risks.

NIST may not be the most well-known government institution, but it has long conducted critical work on standard-setting and measurement research that is used by federal agencies and private industry. Over the past year, NIST has conducted a series of workshops examining topics like AI trustworthiness, bias, explainability, and evaluation. These workshops are geared at helping industry professionals understand how to detect, catalogue, and ultimately prevent the harmful outcomes that erode public trust in AI technology.

Most recently, NIST has been working to construct a voluntary Risk Management Framework that is intended to support the development and deployment of safe and trustworthy AI. This framework will be important for informing the work of both public and private sector AI researchers as they pursue their game-changing research. NIST is soliciting public comments until Sept. 15, 2021 and will develop the framework in several iterations, allowing for continued input. Interested stakeholders should submit comments and/or participate in the ongoing processes at NIST.

We know that AI has the potential to benefit society and make the world a better place. In order for the U.S. to be a true global leader in this technology, we have to ensure that the AI we create does just that.

Eddie Bernice JohnsonEddie Bernice JohnsonUS must not only lead in artificial intelligence, but also in its ethical application Our approach to schizophrenia is failing House passes bills to boost science competitiveness with China MORE represents the 30th District of Texas and is chairwoman of the House Committee on Science, Space, and Technology.

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US must not only lead in artificial intelligence, but also in its ethical application | TheHill - The Hill

Healthcare system survival pivots on many metrics, but the ability to generate revenue and to evidence high quality of care are two of the most essential.

At the center of both metrics is the clinical documentation process, where an accurate representation of every patients clinical experience while in a providers care must be recorded.

As simple as it may sound, achieving that accurate reflection of diagnoses, interventions and the clinical picture is anything but simple. Medicine is as much science as it is art, and complex definitions, levels of specificity and complex medical terminology mean that most hospitals struggle to document everything properly, leading to significant lost revenues and under-reporting on quality metrics.

Health systems have answered this challenge by standing up clinical documentation integrity (CDI) programs, staffed with clinicians. As more healthcare revenue is tied to achieving specific quality metrics, the role of CDI has become even more critical.

However, ensuring integrity and completeness of documentation would require health systems to staff CDI teams with an incredible amount of highly trained clinicians to review and correct documentation on every record, every day. The cost and complexity of such an operation is unimaginable, and no healthcare system has the resources to either employ that many people or even find a supply of that many highly specialized staff.

As a result, many health systems are turning to software to support CDI with technology that scales clinical staff abilities and provides intelligent automation. Unfortunately, the challenge that many have run into is how to identify the right technology for their operation.

RELATED:Industry VoicesWhy the COVID-19 pandemic was a watershed moment for machine learning

All the work CDI specialists perform requires clinical knowledgethe sort of knowledge that is gained only after decades of academic study and real work experience. Automating that work means that the technology must mirror the same level of clinical thinking that any one of these specialists employs every day.

The challenge is immense. Emulating clinical thinking with software is among the loftiest goals of artificial intelligence in healthcare and requires the most sophisticated, cutting-edge technologies availablenot to mention years of training. Even with the most advanced technology, AI has sometimes failed to impress the critics, as weve seen multiple reports call out the stumbles of larger ambitioned (but similarly conceptualized)efforts like IBM Watson.

But, while there are still areas for improvement, the truth is that AI still is making a significant impact across the healthcare landscapeand especially within CDI, where success is well documented.

While CDI is an excellent and proven use case for AI in healthcare, providers should understand that not all AI is the same. In fact, many legacy systems that deploy the wrong type of AI to CDI are unable to see all the gains possible with the correct deployment.

The key to leveraging AI in CDI is to utilize technology that can truly emulate the way clinicians think. It must read, digest, understandand make statistical predictions on the entirety of the clinical record similarly to how physicians look at all the evidence to assess and diagnose to appropriately provide patient care.

Thats where machine learning holds the key. Machine-learning is, at its heart, a pattern-recognition engine that can digest a plethora of individual pieces of data, recognize patterns and then use those patterns to make statistical predictions. If properly applied to clinical information, it is a very powerful technology.Fed over time with millions of patient encounters, machine learning begins to emulate the way clinicians think, automating numerous tasks or challenges that otherwise would only be solvable by a human. While it does not replace clinicians, it does reduce clinical staff burden, providing more time to be spent on patient care.

Additionally, by automatically the review of every patient record in real-time every day, cases can be prioritized so a CDI specialist knows what to look atversus wasting time on those with no documentation irregularities.This type of machine learning interprets the clinical evidence, compares it to the existing documentationand highlights and prioritizes which cases have discrepancies automatically.

RELATED:Healthcare AI investment will shift to these 5 areas in the next 2 years: survey

Many legacy applications attempt to use another AI technology, natural language processing (NLP), to automate complex clinical tasks. While NLP has some useful applications for tasks like clinical narrationwhere the dictionary-like look up function of NLP suggests a better or more accurate wordNLP is only a partial solution for CDI.

For example, NLP can translate the narrative documentation from the clinician into text understoodby a computer. However, unless its paired with a machine learning solution that simultaneously reads and emulates clinical decision-making (thus enabling a comparison between what was written and what the clinical evidence says), its an inadequate solution to solving the core challenges in CDI.

Additionally, rules-based technology solutions that utilize rules or markers to automate clinical tasks fail entirely to emulate the way that clinicians think. As a result, they cannot reflect the many permutations of the way clinical conditions are presented.

Robotic process automation (RPA) is another buzzword in healthcare that has been cited as a tool for handling repeatable basic tasks. However, within the mid-revenue cycle (and thus CDI), nearly all tasks have a clinical element, requiring clinical understanding to complete.That means RPA definitionally is not suited for more complex tasks that require higher-level thinking.

Instead, intelligent process automation (IPA) is the right solution, as IPA applies machine learning to RPA to automate complex tasks that require human judgment (much like the work of CDI).Thus, to apply IPA in the revenue cycle, not only is machine learning critical, it also is the only technology available today that specifically emulates clinical thinking and judgment.

RELATED: Iodine Software acquires competitor ChartWise Medical Systems to expand its reach to more hospitals

As technology gets better at emulating a clinicians mind, increasingly powerful AI engines will soon be able to capture documentation and coding instantaneously. By accurately automating clinical condition documentation directly into EMRs and identifying the final code set, the process will become even more efficient and will have fewer translation errors.

Ultimately, that means smaller teams will be able to support the entire documentation process, which reduces costs for providers and stress on clinicians.

There is no doubt that managing a health system has become increasingly complex, and thats especially true for CDI teams that must capture data accurately and efficiently. However, AI has become a critical tool that is truly making an impact in the mid-revenue cycle, and there is much more innovation to come in the next few years. But, while we wait for that larger revolution, its important that health systems implement a stable and efficient CDI program now, powered by the right technology.

William Chan is theco-founder and CEO ofIodine Software.

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Industry VoicesNot all automation is created equally for clinical documentation improvement - FierceHealthcare

On 14 September 2021, eight international organisations joined forces to launch a new portal promoting global co-operation on artificial intelligence (AI). The portal is a one-stop shop for data, research findings and good practices in AI policy.

The objective of the portal is to help policymakers and the wider public navigate the international AI governance landscape. It provides access to the necessary tools and information, such as projects, research and reports to promote trustworthy and responsible AI that is aligned with human rights at global, national and local level.

Key partners in this joint effort include the Council of Europe, the European Commission, the European Union Agency for Fundamental Rights, the Inter-American Development Bank, the Organisation for Economic Co-operation and Development (OECD), the United Nations (UN), the United Nations Educational, Scientific and Cultural Organization (UNESCO), and the World Bank Group.

Access the website: https://globalpolicy.ai

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Artificial Intelligence A New Portal to Promote Global Cooperation Launched with 8 International Organisations - Council of Europe

Yan Cui, PhD, associate professor in the UTHSCDepartment of Genetics, Genomics, and Informatics,recently received a $1.7 million grant from the National Cancer Institute for a study titled Algorithm-based prevention and reduction of cancer health disparity arising from data inequality.

Dr. Cuis project aims to prevent and reduce health disparities caused by ethnically-biased data in cancer-related genomic and clinical omics studies. His objective is to establish a new machine learning paradigm for use with multiethnic clinical omics data.

For nearly 20 years, scientists have been using genome-wide association studies, known as GWAS, and clinical omics studies to detect the molecular basis of diseases. But statistics show that over 80% percent of data used in GWAS come from people of predominantly European descent.

As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, this European-centric skew is set to exacerbate long-standing disparities in health. With less than 20% of genomic samples coming from people of non-European descent, underrepresented populations are at a severe disadvantage in data-driven, algorithm-based biomedical research and health care.

Biomedical data-disadvantage has become a significant health risk for the vast majority of the worlds population, Dr. Cui said. AI-powered precision medicine is set to be less precise for the data-disadvantaged populations including all the ethnic minority groups in the U.S. We are committed to addressing the health disparities arising from data inequality.

The project is innovative in the type of machine learning technique it will use. Multiethnic machine learning normally uses mixture learning and independent learning schemes. Dr. Cuis project will instead be using a transfer learning process.

Transfer learning works much the same way as human learning. When faced with a new task, instead of starting the learning process from scratch, the algorithm leverages patterns learned from solving a related task. This approach greatly reduces the resources and amount of data required for developing new models.

Using large-scale cancer clinical omics data and genotype-phenotype data, Dr. Cuis lab will examine how and to what extent transfer learning improves machine learning on data-disadvantaged cohorts. In tandem with this, the team aims to create an open resource system for unbiased multiethnic machine learning to prevent or reduce new health disparities.

Neil Hayes, MD, MPH, assistant dean for Cancer Reesearch in the UTHSC College of Medicine and director of the UTHSC Center for Cancer Research, and Athena Starlard-Davenport, PhD, associate professor in the Department of Genetics, Genomics, and Informatics, are co-Investigators on the grant. Yan Gao, PhD, a postdoctoral scholar working with Dr. Cui, is a machine learning expert in the team. A pilot study for this project, funded by the UT Center for Integrative and Translational Genomics and UTHSC Office of Research, has been published in Nature Communications.

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Yan Cui and Team Are Innovating Artificial Intelligence Approach to Address Biomedical Data Inequality - UTHSC News

Data is the lifeblood of business it drives innovation and enhances competitiveness. However, its importance was brought to the fore by the pandemic as lockdowns and social distancing drove digital transformation like never before.

About the author

Andrew Brown, General Manager, Technology Group, IBM United Kingdom & Ireland.

Forward-thinking businesses have started to grasp the importance of their data; they understand the consequences of not fully mobilizing it, but many are sat at the start of their journey.

Even the best organizations are failing to extract the maximum benefits from their data while keeping it safe. This is where artificial intelligence (AI) comes into play it can benefit enterprises with their data in three fundamental ways.

First, without the right tools it is impossible to unlock datas hidden value. For that to happen businesses need to deploy AI because of its ability to analyze complex datasets and produce actionable insights. These can significantly enhance business agility and improve the foresight of enterprises of all sizes.

The success of any move to adopt AI will depend on a robust IT infrastructure being in place. Transforming data into useful information is only possible with this solid foundation, which in turn allows advanced AI applications to extract the real value locked inside the data.

During the first wave of the pandemic, IBM worked with The Royal Marsden, a world-leading cancer hospital, to launch an AI virtual assistant to alleviate some of the pressures and uncertainty for staff associated with COVID-19. The system depended on fast access to trusted information from various diverse sources, such as the hospitals official policy handbook as well as data from NHS England. By tapping into these rich knowledge sources, staff were able to get quicker answers to workplace queries while the HR team had more time to handle complex requests.

Another issue is that far too many businesses simply dont know how much data they own. Split up into silos, it can be impossible to gain a clear view of not only what data is available but also where it resides. Removing this bottleneck can also be achieved through the implementation of AI. This is important because incomplete data will result in incomplete insights.

Businesses should prioritize making all data sources as simple and accessible as possible. Cloud computing technologies, such as hybrid data management, have a vital role to play here. Adoption makes it possible to manage all data types across multiple sources and locations, effectively breaking down these silos and a major barrier to AI adoption.

IBM has partnered with Wimbledon for more than 30 years, helping the worlds leading tennis tournament get the most from its data. Tapping into a wealth of new and archived footage, player data and historical records, fans can now benefit from personalized recommendations and highlights reels. Created through a rules-based recommendation engine integrated across Wimbledons digital platforms, this personalized content allows fans to track their favorite players through the tournament as well as receive suggestions on emerging talent to follow.

This is all made possible by the hybrid cloud the data spans a combination of on-premises systems, private clouds, and public cloud. Breaking down these silos has allowed Wimbledon to innovate at pace to attract new global audiences.

While extracting value from data is undoubtedly beneficial for organizations, it also creates risks. Criminals are increasingly aware of the potential to exploit vulnerabilities to disrupt operations or cause reputational issues through leaking sensitive data. The threat landscape is evolving and rising data breach costs are a growing problem for businesses in the wake of the rapid technology shifts triggered by the pandemic.

Over the last year businesses were forced to quickly adapt their technology approaches, with many companies encouraging or requiring employees to work from home, and 60% of organizations moved further into cloud-based activities during the pandemic.

According to the latest annual Cost of a Data Breach report, conducted by Ponemon Institute and analyzed by IBM Security, serious security incidents now cost UK-based organizations an average of $4.67 million (around 3.4 million) per incident, the highest cost in the 17-year history of the report. This is higher than the global average of $4.24 million per incident, highlighting the importance of protecting data for British businesses.

AI has a role to play here, and the study revealed encouraging signs about the impact of intelligent and automated security tools. While data breach costs reached a record high over the past year, the report also showed positive signs about the impact of modern cybersecurity tactics, such as AI and automation which may pay off by reducing the cost of these incidents further down the line.

The adoption of AI and security analytics were in the top five mitigating factors shown to reduce the cost of a breach. On average, organizations with a fully deployed security automation strategy faced data breach costs of less than half of those with no automation technology in place.

The sector in which a business operates also has a direct impact on the overall cost of a security breach. The report identified that the average cost of each compromised record containing sensitive data was highest for UK organizations in Services (191 per record), Financial (188) and Pharmaceuticals (147). This highlights how quickly the costs of a breach can escalate if a large number of records are compromised.

The Cost of a Data Breach report highlights a number of trends and best practices that were consistent with an effective response to security incidents. These can be adopted by organizations of all types and sizes and contribute to form the basis of a data management and governance strategy:

1. Invest in security orchestration, automation and response (SOAR). Security AI and automation significantly reduce the time to identify and respond to a data breach. By deploying SOAR solutions alongside your existing security tools, its possible to accelerate incident response and reduce overall costs associated with breaches.

2. Adopt a zero trust security model to help prevent unauthorized access to sensitive data. Organizations with mature zero trust deployments have far lower breach costs than those without. As businesses move to remote working and hybrid cloud environments, a zero trust strategy can help protect data by only making it accessible in the right context.

3. Stress test incident response plans to improve resilience. Forming an Incident Response team, developing a plan and putting it to the test are crucial steps to responding quickly and effectively to attacks.

4. Invest in governance, risk management and compliance. Evaluating risk and tracking compliance can help quantify the cost of a potential breach in real terms. In turn this can expedite the decision-making process and resource allocation.

5. Protect sensitive data in the cloud using policy and encryption. Data classification schema and retention policies should help minimize the volume of the sensitive information that is vulnerable to a breach. Advanced data encryption techniques should be deployed for everything that remains.

So how should a business bring its AI strategy to life? First, organizations must ensure their infrastructure is equipped to handle all the data, processing and performance requirements needed to effectively run AI. If you use your existing storage arrangement without modernizing it, you greatly increase your risk of failure. A hybrid cloud implementation is likely to be the best solution in most instances as it offers the optimum flexibility.

Enterprises should also directly embed AI into their data management and security systems, which should have clearly defined data policies to ensure appropriate levels of access and resilience. The data management system and the data architecture should be optimized for added agility and ease of operation.

A fully featured AI implementation doesnt just aggregate data and perform largescale analytics, it also enhances security and governance. Together they enable companies to create valuable business insights that fuel innovation. AI will also help ensure that data if used more efficiently and minimize data duplication. But above all, properly managed data is the lifeblood of enterprise a resource that needs to be identified and protected. Only then can companies start to climb the AI ladder.

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Unlocking the power of data with artificial intelligence - TechRadar

The Museum of Wild and Newfangled Art (mowna) will open their final show of the year "This Show is Curated by a Machine" on September 23, 2021.

The Artificial Intelligence curated exhibition opens with a talk on the development of the AI model followed by a Q&A with the AI Team: IV (Ivan Pravdin) and museum co-founders cari ann shim sham* and Joey Zaza. "This Show is Curated by a Machine" runs September 23, 2021 through January 31, 2022 tickets bought prior to opening day, September 23rd, include entrance to AI talk and are available at: https://www.mowna.org/museum/this-show-is-curated-by-a-machine

Earlier this year, The Whitney Museum of American Art commissioned and exhibited the work "The Next Biennial Should Be Curated by a Machine" for their online artport. In response the Museum of Wild and Newfangled Art has designed an artificial intelligence curator that will not only redefine how we look at curation and AI but will also underscore the need to move forward with AI curation in an ethical way.

The artificial intelligence model was trained on image sets from various sources, including the Museum of Modern Art, the Art Institute of Chicago, and the mowna Biennial submissions, an exhibit of around 88 International Artists from 44 countries.

"Curation is very subjective. It's my hope through the development of an AI curator that we can allow for equity and diversity, and eliminate some biases," says cari ann shim sham*.

Artists in the show include Alice Prum, a London based artist whose work explores the invisible relationships between space, the body, and technology. Bridget DeFranco is an east coast media artist working against the high-stimulation nature of the screen. Avideh Salmanpour is an Iranian artist whose paintings explore the bewilderment of contemporary man and the attempt to find a new way.

The artificial intelligence curator was created by multiple artists. IV is a post-contemporary artist working with various artificial intelligence and neural networking techniques. cari ann shim sham* is the co-founder and curator of mowna, a wild artist working at the intersection of dance and technology, and an associate arts professor of dance and technology at NYU Tisch School of the Arts. Joey Zaza is the co-founder and curator of mowna, and works in photography, software, video, sound, and installation. They combined forces to explore the potential of using artificial intelligence in art curation. The team's initial thoughts, strategies and questions in the development of the AI model can be found on mowna's blog.

Human curation is also included alongside the AI curation for "This Show is Curated by a Machine" to offer a comparison. Text written by the team will explain why or why not they think the AI chose the work. This show is a successful completion of phase one of mowna's AI model which ranks and curates a show using image based files. mowna will release a paper with its phase one research and findings to the public. With this data the team will enter into phase two development for the AI's ability to curate sound and video files.

"This Show is Curated by a Machine" will be installed and available for viewing on September 23, 2021 and marks the third online art exhibition by mowna. The second, the 2021 mowna Biennial, showcases art of all mediums and focuses on exhibiting art that might have otherwise gone unseen due to gaps in the post-pandemic art world. It is currently still on exhibit until September 22, 2021 and can be viewed on the mowna website. Tickets are a sliding scale of pay-what-you-wish.

mowna makes it their priority to showcase a broad range of art and is committed to diversity in every way. It provides an international online platform for the most timely, diverse, and preeminent artists. At the center of the constantly changing and expanding art world, mowna showcases a mixture of the familiar and unfamiliar. Members will have the opportunity to see artists who have been curated by the MoMA or the Whitney alongside artists available only on mowna.

As the global landscape shifts towards a more technological way of being, mowna is there to meet the needs of an ever-changing art world. The Museum of Wild and Newfangled Art was formed to feature the newest art developments and make art of all mediums accessible to everyone. And it unmistakably builds on that foundation with the upcoming exhibition "This Show is Curated by a Machine".

For more information on current and upcoming exhibitions and events, please visit mowna's new pages on Instagram and Facebook (below) as well as the museum's official website.

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Museum Of Wild And Newfangled Art's Opening Exhibition Curated By Artificial Intelligence - Broadway World

Having spent more than 25 years working with industry partners to educate and prepare the future workforce, it is not surprising to see that Florida has experienced growth in the technology sector.

Across the nation, the U.S. Bureau of Labor Statistics estimates that computer and information technology occupations are projected to grow 11 percent from 2019 to 2029, much faster than the average for all occupations. Additionally, demand for skilled professionals in robotics and artificial intelligence is growing. The World Economic Forum estimates that while 85 million jobs will be displaced, 97 million new jobs will be created across 26 countries by 2025 due to the growth of artificial intelligence technology.

From my conversations with industry leaders to the research and data Ive studied, all signs lead me to believe that robotics and artificial intelligence will be a significant economic driver, surpassing the impact of Christopher Columbus exploration of the New World in 1492.

While Columbus used sophisticated technology that was highly advanced for his time, he was still required to convince Queen Isabella that his trip and tools had value. His technology included the compass, maps, and charts that helped him navigate what many considered a nearly unthinkable journey.

Today, few in our modern world need to be convinced that computing and other advanced technologies, including robotics and artificial intelligence, have value.

While certainly some people fear technology will impact us negatively with the loss of jobs or human touch, others see technologies like robotic surgery or manufacturing as protections that can help heal people faster or make work more effective. Today, robots are largely sophisticated tools that are as amazing and mindboggling as the compass and quadrant were in Columbus time.

While Columbus trip changed the world, it took hundreds of years for its impact to be understood and capitalized upon. Robotics, as a field of practice and study, rapidly will change the future for graduates, and all of us, with new technologies being employed each year.

The idea of a robot may bring to mind images of Commander Data from Star Trek, or more frighteningly, the robots featured in The Terminator, but the field of robotics is much broader than those perceptions.

According to the Institute of Electrical and Electronics Engineers, there are many types of robots from those in aerospace, to consumer products, disaster response, drones, autonomous vehicles, and exoskeletons, to industrial robots, and medical robots, among others. In 2019, an article in Oxford Economics revealed that the number of robots in use worldwide multiplied three-fold over the past two decades, to 2.25 million. In many cases, robots are simply machines that are programmed to perform tasks or take actions. They are able to do things in anticipation of needs, based on artificial intelligence coding.

A final point to consider is the impact on the economy. After Columbus journey, trade between nations became prevalent and a new economic system was born. Likewise, demand for robotics and artificial intelligence technology will grow and create new efficiencies. PriceWaterhouseCoopers Global Artificial Intelligence Study predicts that by 2030, growth of artificial intelligence will lead to an estimated $15.7 trillion, or 26 percent increase, in global gross domestic product.

Demand for robotics engineers and technicians also will grow, given the need for designing and maintaining robots. There also will be strong demand for application developers for robotic systems and solutions. So, while some fear that robots and artificial intelligence will take away jobs from humans, they will create many more jobs and careers.

With what I now know today, if I could go back and change my college major, I would select robotics. There are many opportunities in this growing field. It is multidisciplinary, creative, impactful, and would allow me to innovate. It is and will be the next big discovery in our world.

Jeffrey D. Senese, PhD, is the president of Saint Leo University, a private, nonprofit Catholic university based in Pasco County, FL. Saint Leo is the largest Benedictine Catholic university in the world, educating more than 18,000 students each year. This fall, the university is launching a bachelors degree in robotics and artificial intelligence and opening a new college dedicated to the growing field.

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Why robotics and artificial intelligence will be bigger than the discovery of the New World | Column - Tampa Bay Times

Microsoft has chosen Spain as the base of operations for its new R&D hub specialized in the application of Artificial Intelligence technologies to improve user experience on the web. This facility will be one of eight research centers worldwide and part of Microsofts WebXT (Web Experiences Team) division. Chaired by Mikhail Parakhin, WebXT is an organization of more than 5,000 people focused on the development of advanced user experiences that leverage Artificial Intelligence and deep learning technologies.

Located in Barcelona, the hub seeks to attract talent from all European countries and is part of the Search & AI team led by Jordi Ribas, corporate vice president of Microsoft Corporation in the United States. This team that specializes in AI applications is the largest group in WebXT and contributes to the development of multiple Microsoft products, including Windows, Azure and, of course, Bing.

I am honored to announce the launch of this R&D hub in Spain. After creating Search & AI hubs in London, Munich and Paris, and having been able to learn first-hand about the requirements, needs and, above all, opportunities for attracting talent, I am convinced that implementing this hub in Spain will add much value to Microsofts R&D footprint worldwide,says Jordi Ribas.

In the first phase, the current investment includes the recruitment of 30 Software Engineers and Data Scientists specialized in advanced areas of software engineering including Artificial Intelligence, Machine Learning and Deep Learning, and in the coming years, the hub may exceed 100 employees. (*)

In the words of Alberto Granados, GM at Microsoft Spain, Microsofts investment in our country is further proof of the high qualifications of our professionals and our ability to become a pole of attraction for talent.Around this hub we want to generate a vector of innovation in Artificial Intelligence in collaboration with universities, research centers and technology companies, reinforcing our efforts to boost digital talent in Spain and training in machine learning technologies.

According to Nadia Calvio, First Vice-President of the Government of Spain and Minister for the Economy and Digital Transformation, We celebrate the announcement of this R&D center in artificial intelligence models and Microsofts commitment to our country. The government of Spain is promoting an ambitious digital agenda, supported by the Recovery Plan, which is already mobilizing important investments in the sectors of the most future.

In words of Jordi Puigner, Vice Presidentand Minister of Digital Policies and Public Administration of Catalonia,Catalonia has the capabilities to lead the development of Artificial Intelligence in Europe and has become a global technological pole in this area. The Microsoft R&D Hub reinforces the ecosystem driven by the Artificial Intelligence Strategy of Catalonia (Catalonia.ai) and the joint commitment of the Govern and Microsoft to the CIDAI (Center for Innovation in Data Tech and Artificial Intelligence) as an initiative of reference for the adoption of Artificial Intelligence in Catalonia.

According to Ada Colau, Major of Barcelona,It is excellent news that Microsoft has chosen Barcelona for a strategic project linked to Artificial Intelligence: it confirms that we are a leading city in the field of innovation and new technologies, and a pole of attraction in this area. This choice reinforces the city as one of the best in the world to live and work, with a large scientific and technological ecosystem. Companies that want to attract the best talent know that if they are in Barcelona they have it easier.And she adds:All the international rankings place us among the most attractive cities for the technology sector, and Microsofts decision is further proof of this. In this final stretch of the pandemic, it also shows that Barcelona is recovering well and confirms that the work done in recent years is contributing to improving Barcelonas attractiveness and its international competitiveness.

WebXT work areas

The WebXT organization operates as a start-up environment, where it encourages teams to take on challenges that create high impact web experiences to consumers, such as Microsoft Bing, Edge, and MS News, while collaborating with other Microsoft product groups, including Azure, Windows, and Office, among others. The hub in Spain will start with teams from Search & AI, which is part of WebXT, and will focus on four main areas:

This article was first published on September 13 by Microsoft.

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Microsoft creates an R&D hub in Spain focused on Artificial Intelligence models to improve user experience - Science Business