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Nuh Gedik and Pablo Jarillo-Herrero are 2020 Moore Experimental Investigators in Quantum Materials – MIT News

Physics professorsNuh GedikandPablo Jarillo-Herrerohave been named Experimental Investigators in Quantum Materials by theGordon and Betty Moore Foundation.

The two are among 20 winners nationwide of the foundation's Emergent Phenomena in Quantum Systems (EPiQS) Initiative. Each will receive a five-year, $1.6 million unrestricted grant to support their research in quantum materials.

Gediks research centers on using advanced optical techniques for probing and controlling properties of quantum materials. He will use his grant to search for novel, light-induced phases in these systems.

These materials display fascinating but poorly understood properties, such as high-temperature superconductivity or topological protection, says Gedik. We use ultrafast laser pulses to make femtosecond movies of electrons and atoms inside these systems to understand the mechanism behind their exotic behavior. Our ultimate goal isto use light as a controllable tuning parameter (just as magnetic field orpressure) to switch between equilibrium phases and to engineer newlight-induced stateswith no equilibrium counterparts.

Jarillo-Herrero, theCecil and Ida Green Professor of Physics,leads a laboratory that uses quantum electronic transport and optoelectronic techniques to investigate novel 2D materials and heterostructures, with a focus on emergent correlated and topological phenomena/phases resulting from the interplay between unusual electronic structures and electron interaction effects.

This Moore Foundation award will allow my group to focus on a novel experimental platform called twistronics, where a new degree of freedom, namely the twist angle between two stacked 2D crystalline lattices, enables the exploration of a plethora of intriguing quantum mechanical effects, such as superconductivity. This emergent platform may provide important clues about the origin of many of the most fascinating phases of matter present in the universe, as well as the potential engineering of these phases to create new quantum technologies.

The EPiQS Initiative of the Gordon and Betty Moore Foundation aims to stimulate experimental research in the physics of quantum materials by providing some of the fields most creative scientists with freedom to take risks and flexibility for agile change of research direction. The collective impact of these investigators will produce a more comprehensive understanding of the fundamental organizing principles of complex quantum matter in solids.

The Experimental Investigator awards are the largest grant portfolio within the EPiQS initiative, says Amalia Fernandez-Paella, program officer of the EPiQS Initiative. We expect that such substantial, stable, and flexible support will propel quantum materials research forward and unleash the creativity of the investigators.

The cohorts research will cover a broad spectrum of research questions, types of materials systems, and complementary experimental approaches. The investigators will advance experimental probes of quantum states in materials; elucidate emergent phenomena observed in systems with strong electron interactions; investigate light-induced states of matter; explore the vast space of two-dimensional layered structures; and illuminate the role of quantum entanglement in exotic systems such as quantum spin liquids. In addition, the investigators will participate in EPiQS community-building activities, which include investigator symposia, topical workshops, and theQuantEmX scientist exchange program.

Since 2013, EPiQS has supported an integrated research program that includes materials synthesis, experiment, and theory, and that crosses the boundaries between physics, chemistry, and materials science. Thesecond phaseof the initiative was kicked off earlier this year with the launch of two major grant portfolios:Materials Synthesis Investigators and Theory Centers. The 20 newly inaugurated experimental investigators will join these grantees to form a vibrant, collaborative community that strives to push the entire field toward a new frontier.

The first cohort of EPiQS Experimental Investigators made advances that changed the landscape of quantum materials, and I expect no less from this second cohort. Emergent phenomena appear when a large number of constituents interact strongly, whether these constituents are electrons in materials, or the brilliant scientists trying to crack the mysteries of materials. says Duan Pejakovi, director of the EPiQS Initiative. Gedik and Jarillo-Herrero were also part of the first cohort of EPIQS awardees.

The Gordon and Betty Moore Foundation fosters pathbreaking scientific discovery, environmental conservation, patient care improvements, and preservation of the special character of the San Francisco Bay Area.

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Quantum-safe security firm evolutionQ awarded contribution from Canada Space Agency for Quantum Key Distribution (QKD) Network Research and…

KITCHENER, Ontario (PRWEB) August 10, 2020

evolutionQ was awarded a Space Technology Development Program (STDP) contribution by the CSA to develop solutions to advance satellite-based secure quantum communication services and tools to address challenges related to satellite-based Quantum Key Distribution (QKD) networks.

Cryptography underpins the secure communications required for the digital, network-based social and financial interactions that are at the heart of modern society and the economy, including banking, the sharing of confidential healthcare data, and the exchange of sensitive information between governmental institutions. However, rapid advancements in quantum computing threaten current encryption methods because quantum computers, when built, will be able to break commonly used cybersecurity systems. It is important to develop tools, like QKD, that will be resistant to such quantum threats.

QKD technologies leverage the fundamental laws of quantum physics to distribute confidential cryptographic keys between two users, while detecting the attempts of malicious third-parties to intercept such keys. Unfortunately, typical terrestrial methods to establish such direct secure connection between locations are limited to relatively short distances, of the order of at most 200 km. This is clearly a challenge for a country as vast as Canada. Satellite-based QKD will enable secure, reliable, and economical key-sharing across Canada.

A powerful quantum computer has the power to decimate todays cryptography. As key quantum computing milestones are achieved, the need for quantum-safe solutions intensifies, said Dr. Michele Mosca, President and CEO of evolutionQ. Robust cryptography is absolutely necessary for our safety and the proper functioning of our digital economy. We must adopt quantum-safe solutions to secure and safeguard our critical infrastructures, financial services and intellectual property."

Quantum Key Distribution is an important tool in addressing the quantum threat. QKD uses the fundamental laws of physics to protect information shared between two parties. CTO of evolutionQ, Dr. Norbert Ltkenhaus remarked. Satellite-based QKD is essential for a vast country like Canada and will help secure communications from coast to coast. evolutionQ is poised to utilize its expertise and develop solutions to help establish satellite QKD, and to integrate it with existing terrestrial solutions.

evolutionQ will develop tools to address the challenges unique to satellite-based QKD. This will be accomplished by modelling the role and performance of QKD satellites, and by designing optimization algorithms to integrate QKD satellites with terrestrial networks. The software solutions will be designed to be integrated with existing and planned satellite hardware. The project is expected to last 24 months.

The initiative will also help Canada safeguard sovereignty in the quantum age and strengthen Canadian leadership in the space and quantum sectors. The initiative aligns with the new Space Strategy for Canada, the safety and security principle in Canadas Digital Charter and the Government of Canadas Innovations and Skills Plan.

This project is undertaken with the financial support of the Canadian Space Agency.

About evolutionQ:evolutionQ is a leading quantum-safe cybersecurity company led by world-renowned quantum computing experts Dr. Michele Mosca and Dr. Norbert Ltkenhaus. evolutionQ delivers quantum-risk management strategy and advisory services along with robust cybersecurity products designed to be safe against quantum computers.

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What Is The Artificial Intelligence Revolution And Why Does It Matter To Your Business? – Forbes

As a species, humanity has witnessed three previous industrial revolutions: first came steam/water power, followed by electricity, then computing. Now, were in the midst of a fourth industrial revolution, one driven by artificial intelligence and big data.

What Is The Artificial Intelligence Revolution And Why Does It Matter To Your Business?

I like to refer to this as the Intelligence Revolution." But whatever we call it the fourth industrial revolution, Industry 4.0 or the Intelligence Revolution one thing is clear: this latest revolution is going to transform our world, just as the three previous industrial revolutions did.

What makes AI so impactful, and why now?

AI gives intelligent machines (be they computers, robots, drones, or whatever) the ability to think and act in a way that previously only humans could. This means they can interpret the world around them, digest and learn from information, make decisions based on what theyve learned, and then take appropriate action often without human intervention. Its this ability to learn from and act upon data that is so critical to the Intelligence Revolution, especially when you consider the sheer volume of data that surrounds us today. AI needs data, and lots of it, in order to learn and make smart decisions. This gives us a clue as to why the Intelligence Revolution is happening now.

After all, AI isnt a new concept. The idea of creating intelligent machines has been around for decades. So why is AI suddenly so transformative? The answer to that question is two-fold:

We have more data than ever before. Almost everything we do (both in the online world and the offline world) creates data. Thanks to the increasing digitization of our world, we now have access to more data than ever before, which means AI has been able to grow much smarter, faster, and more accurate in a very short space of time. In other words, the more data intelligent machines have access to, the faster they can learn, and the more accurate they become at interpreting the information. As a very simple example, think of Spotify recommendations. The more music (or podcasts) you listen to via Spotify, the better able Spotify is to recommend other content that you might enjoy. Netflix and Amazon recommendations work on the same principle, of course.

Impressive leaps in computing power make it possible to process and make sense of all that data. Thanks to advances like cloud computing and distributed computing, we now have the ability to store, process, and analyze data on an unprecedented scale. Without this, data would be worthless.

What the Intelligence Revolution means for your business

I guarantee your business is going to have to get smarter. In fact, every business is going to have to get smarter from small startups to global corporations, from digital-native companies to more traditional businesses. Organizations of all shapes and sizes will be impacted by the Intelligence Revolution.

Take a seemingly traditional sector like farming. Agriculture is undergoing huge changes, in which technology is being used to intelligently plan what crops to plant, where and when, in order to maximize harvests and run more efficient farms. Data and AI can help farmers monitor soil and weather conditions, and the health of crops. Data is even being gathered from farming equipment, in order to improve the efficiency of machine maintenance. Intelligent machines are being developed that can identify and delicately pick soft ripe fruits, sort cucumbers, and pinpoint pests and diseases. The image of a bucolic, traditional farm is almost a thing of the past. Farms that refuse to evolve risk being left behind.

This is the impact of the Intelligence Revolution. All industries are evolving rapidly. Innovation and change is the new norm.Those who cant harness AI and data to improve their business whatever the business will struggle to compete.

Just as in each of the previous industrial revolutions, the Intelligence Revolution will utterly transform the way we do business. For your company, this may mean you have to rethink the way you create products and bring them to market, rethink your service offering, rethink your everyday business processes, or perhaps even rethink your entire business model.

Forget the good vs bad AI debate

In my experience, people fall into one of two camps when it comes to AI. Theyre either excited at the prospect of a better society, in which intelligent machines help to solve humanitys biggest challenges, make the world a better place, and generally make our everyday lives easier. Then there are those who think AI heralds the beginning of the end, the dawning of a new era in which intelligent machines supersede humans as the dominant lifeform on Earth.

Personally, I sit somewhere in the middle. Im certainly fascinated and amazed by the incredible things that technology can achieve. But Im also nervous about the implications, particularly the potential for AI to be used in unethical, nefarious ways.

But in a way, the debate is pointless. Whether youre a fan of AI or not, the Intelligence Revolution is coming your way. Technology is only going in one direction forwards, into an ever-more intelligent future. Theres no going back.

Thats not to say we shouldnt consider the implications of AI or work hard to ensure AI is used in an ethical, fair way one that benefits society as well as the bottom line. Of course, we should do that. But it's important to understand that; however, you feel about it, AI cannot be ignored. Every business leader needs to come to terms with this fact and take action to prepare their company accordingly. This means working out how and where AI will make the biggest difference to your business, and developing a robust AI strategy that ensures AI delivers maximum value.

AI is going to impact businesses of all shapes and sizes, across all industries. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.

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The Guardian view on artificial intelligence’s revolution: learning but not as we know it – The Guardian

Bosses dont often play down their products. Sam Altman, the CEO of artificial intelligence company OpenAI, did just that when people went gaga over his companys latest software: the Generative Pretrained Transformer 3 (GPT-3). For some, GPT-3 represented a moment in which one scientific era ends and another is born. Mr Altman rightly lowered expectations. The GPT-3 hype is way too much, he tweeted last month. Its impressive but it still has serious weaknesses and sometimes makes very silly mistakes.

OpenAIs software is spookily good at playing human, which explains the hoopla. Whether penning poetry, dabbling in philosophy or knocking out comedy scripts, the general agreement is that the GPT-3 is probably the best non-human writer ever. Given a sentence and asked to write another like it, the software can do the task flawlessly. But this is a souped up version of the auto-complete function that most email users are familiar with.

GPT-3 stands out because it has been trained on more information about 45TB worth than anything else. Because the software can remember each and every combination of words it has read, it can work out through lightning-fast trial-and-error attempts of its 175bn settings where thoughts are likely to go. Remarkably it can transfer its skills: trained as a language translator, GPT-3 worked out it could convert English to Javascript as easily as it does English to French. Its learning, but not as we know it.

But this is not intelligence or creativity. GPT-3 doesnt know what it is doing; it is unable to say how or why it has decided to complete sentences; it has no grasp of human experience; and cannot tell if it is making sense or nonsense. What GPT-3 represents is a triumph of one scientific paradigm over another. Once machines were taught to think like humans. They struggled to beat chess grandmasters. Then they began to be trained with data to, as one observer pointed out, discover like we can rather than contain what we have discovered. Grandmasters started getting beaten. These days they cannot win.

The reason is Moores law, the exponentially falling cost of number-crunching. AIs bitter lesson is that the more data that can be consumed, and the more models can be scaled up, the more a machine can emulate or surpass humans in quantitative terms. If scale truly is the solution to human-like intelligence then GPT-3 is still about 1,000 times smaller than the brains 100 trillion-plus synapses. Human beings can learn a new task by being shown how to do it only a few times. That ability to learn complex tasks from only a few examples, or no examples at all, has so far eluded machines. GPT-3 is no exception.

All this raises big questions that seldom get answered. Training GPT-3s neural nets is costly. A $1bn investment by Microsoft last year was doubtless needed to run and cool GPT-3s massive server farms. The bill for the carbon footprint a large neural net is equal to the lifetime emissions of five cars is due.

Fundamental is the regulation of a for-profit OpenAI. The company initially delayed the launch of its earlier GPT-2, with a mere 1.5bn parameters, because the company fretted over its implications. It had every reason to be concerned; such AI will emulate the racist and sexist biases of the data it swallows. In an era of deepfakes and fake news, GPT-style devices could become weapons of mass destruction: engaging and swamping political opponents with divisive disinformation. Worried? If you arent then remember that Dominic Cummings wore an OpenAI T-shirt on his first day in Downing Street.

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3 Daunting Ways Artificial Intelligence Will Transform The World Of Work – Forbes

Each industrial revolution has brought with it new ways of working think of the impact computers and digital technology (the third industrial revolution) have had on how we work.

3 Daunting Ways AI Will Transform The World Of Work

But this fourth industrial revolution what I call the intelligence revolution, because it is being driven by AI and data feels unprecedented in terms of the sheer pace of change. The crucial difference between this and the previous industrial revolutions is were no longer talking about generational change; were talking about enormous transformations that are going to take place within the next five, 10 or 20 years.

Here are the three biggest ways I see AI fundamentally changing the work that humans do, within a very short space of time.

1. More tasks and roles will become automated

Increasing automation is an obvious place to start since a common narrative surrounding AI is robots are going to take all our jobs. In many ways, this narrative is completely understandable in a lot of industries and jobs, the impact of automation will be keenly felt.

To understand the impact of automation, PricewaterhouseCoopers analyzed more than 200,000 jobs in 29 countries and found:

By the early 2020s, 3 percent of jobs will be at risk of automation.

That rises to almost 20 percent by the late 2020s.

By the mid-2030s, 30 percent of jobs will be at the potential risk of automation. For workers with low education, this rises to 44 percent.

These are stark figures. But there is a positive side to increasing automation. The same study found that, while automation will no doubt displace many existing jobs, it will also generate demand for new jobs. In fact, AI, robotics, and automation could provide a potential $15 trillion boost to global GDP by 2030.

This is borne out by previous industrial revolutions, which ultimately created more jobs than they displaced. Consider the rise of the internet as an example. Sure, the internet had a negative impact on some jobs (I dont know about you but I now routinely book flights and hotels online, instead of popping to my local travel agent), but just look at how many jobs the internet has created and how its enabled businesses to branch into new markets and reach new customers.

Automation will also lead to better jobs for humans. If were honest with ourselves, the tasks that are most likely to be automated by AI are not the tasks best suited to humans or the tasks that humans should even want to do. Machines are great at automating the boring, mundane, and repetitive stuff, leaving humans to focus on more creative, empathetic, and interpersonal work. Which brings me to

2. Human jobs will change

When parts of jobs are automated by machines, that frees up humans for work that is generally more creative and people-oriented, requiring skills such as problem-solving, empathy, listening, communication, interpretation, and collaboration all skills that humans are generally better at than machines. In other words, the jobs of the future will focus more and more on the human element and soft skills.

According to Deloitte, this will lead to new categories of work:

Standard jobs:Generally focusing on repeatable tasks and standardized processes, standard jobs use a specified and narrow skill set.

Hybrid jobs:These roles require a combination of technical and soft skills which traditionally havent been combined in the same job.

Superjobs:These are roles that combine work and responsibilities from multiple traditional jobs, where technology is used to both augment and widen the scope of the work, involving a more complex combination of technical and human skills.

For me, this emphasizes how employees and organizations will need to develop both the technical and softer human skills to succeed in the age of AI.

3. The employee experience will change, too

Even in seemingly non-tech companies (if there is such a thing in the future), the employee experience will change dramatically. For one thing, robots and cobots will have an increasing presence in many workplaces, particularly in manufacturing and warehousing environments.

But even in office environments, workers will have to get used to AI tools as co-workers. From how people are recruited, to how they learn and develop in the job, to their everyday working activities, AI technology and smart machines will play an increasingly prominent role in the average person's working life. Just as we've all got used to tools like email, we'll also get used to routinely using tools that monitor workflows and processes and make intelligent suggestions about how things could be done more efficiently. Tools will emerge to carry out more and more repetitive admin tasks, such as arranging meetings and managing a diary. And, very likely, new tools will monitor how employees are working and flag up when someone is having trouble with a task or not following procedures correctly.

On top of this, workforces will become decentralized (a trend likely to be accelerated by the coronavirus pandemic) which means the workers of the future can choose to live anywhere, rather than going where the work is.

Preparing for the AI revolution

AI, and particularly automation, is going to transform the way we work. But rather than fear this development, we should embrace this new way of working. We should embrace the opportunities AI provides to make work better.

No doubt, this will require something of a cultural shift for organizations just one of the many ways in which organizations will have to adapt for the intelligence revolution. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.

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Digitalized Discrimination: COVID-19 and the Impact of Bias in Artificial Intelligence – JD Supra

[co-author: Jordan Rhodes]

As the world grapples with the impacts of the COVID-19 pandemic, we have become increasingly reliant on artificial intelligence (AI) technology. Experts have used AI to test potential treatments, diagnose individuals, and analyze other public health impacts. Even before the pandemic, businesses were increasingly turning to AI to improve efficiency and overall profit. Between 2015 and 2019, the adoption of AI technology by businesses grew more than 270 percent.

The growing reliance on AIand other machine learning systemsis to be expected considering the technologys ability to help streamline business processes and tackle difficult computational problems. But as weve discussed previously, the technology is hardly the neutral and infallible resource that so many view it to be, often sharing the same biases and flaws as the humans who create it.

Recent research continues to point out these potential flaws. One particularly important flaw is algorithm bias, which is the discriminatory treatment of individuals by a machine learning system. This treatment can come in various forms but often leads to the discrimination of one group of people based on specific categorical distinctions. The reason for this bias is simpler than you may think. Computer scientists have to teach an AI system how to respond to data. To do this, the technology is trained on datasetsdatasets that are both created and influenced by humans. As such, it is necessary to understand and account for potential sources of bias, both explicit and inherent, in the collection and creation of a dataset. Failure to do so can result in bias seeping into a dataset and ultimately into the results and determinations made by an AI system or product that utilizes that dataset. In other words, bias in, bias out.

Examining AI-driven hiring systems expose this flaw in action. An AI system can sift through hundreds, if not thousands, of rsums in short periods of time, evaluate candidates answers to written questions, and even conduct video interviews. However, when these AI hiring systems are trained on biased datasets, the output reflects that exact bias. For example, imagine a rsum-screening machine learning tool that is trained on a companys historical employee data (such as rsums collected from a companys previously hired candidates). This tool will inherit both the conscious and unconscious preferences of the hiring managers who previously made all of those selections. In other words, if a company historically hired predominantly white men to fill key leadership positions, the AI system will reflect that preferential bias for selecting white men for other similar leadership positions. As a result, such a system discriminates against women and people of color who may otherwise be qualified for these roles. Furthermore, it can embed a tendency to discriminate within the companys systems in a manner that makes it more difficult to identify and address. And as the countrys unemployment rate skyrockets in response to the pandemic, some have taken issue with companies relying on AI to make pivotal employment decisionslike reviewing employee surveys and evaluations to determine who to fire.

Congress has expressed specific concerns regarding the increase in AI dependency during the pandemic. In May, some members of Congress addressed a letter to House and Senate Leadership, urging that the next stimulus package include protections against federal funding of biased AI technology. If the letters recommendations are adopted, certain businesses that receive federal funding from the upcoming stimulus package will have to provide a statement certifying that bias tests were performed on any algorithms the business uses to automate or partially automate activities. Specifically, this testing requirement would apply to companies using AI to make employment and lending determinations. Although the proposals future is uncertain, companies invested in promoting equality do not have to wait for Congress to act.

In recent months, many companies have publicly announced initiatives to address how they can strive to reduce racial inequalities and disparities. For companies considering such initiatives, one potential actionable step could be a strategic review of the AI technology that a company utilizes. Such a review could include verifying whether the AI technology utilized by the company is bias-tested and consideration of the AI technologys overall potential for automated discriminatory effects given the context of its specific use.

Only time will reveal the large-scale impacts of AI on our society and whether weve used AI in a responsible manner. However, in many ways, the pandemic demonstrates that these concerns are only just beginning.

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Adopting IT Advances: Artificial Intelligence and Real Challenges – CIO Applications

By coming together, we are able to select and strengthen a business process supported by advanced analytics, which local teams can embrace and deploy across their business units.

In addition to the benefits of forming a cross functional, multi-national team, its been exciting to watch the collaborative process evolve as Baby Boomers, Gen X, Gen Y and Gen Z colleagues work to solve business critical challenges. Weve found that by bringing these generations together, we can leverage the necessary experiences and skillsets to create a balanced vision that forms the strategy as the work streams begin to develop their actions. Pairing the multi-generational workforce with our focus on inclusion and diversity also fosters internal ownership. This participation yield steam unity and pride through clearly understood program goals, objectives and--ultimately--improved adoption deep across all business regions.

Build confidence

Even with a global, inter-generational team building advanced applications, theres still a question of confidence in the information delivered through AI and ML techniques. Can the information being provided actually be used to create a better, more reliable experience for our customers?

A recent article by Towards Data Science, an online organization for data scientists and ML engineers, put it best: At the end of the day, one of the most important jobs any data scientist has is to help people trust an algorithm that they most likely dont completely understand.

To build that trust, the heavy lifting done early in the process must contain algorithms and mathematical calculations that deliver correct information while being agile enough to also capture the changes experienced on a very dynamic basis in our business. This step begins further upstream in the process by first establishing a cross-functional group that owns, validates and organizes the data sets needed for accurate outputs. This team also holds the responsibility for all modifications made post-implementation as continuous improvement steps are added into the data driven process. While deploying this step may delay time to market delivery, the benefits gained by providing a dependable output decreases the need for rework and increases user reliability.

Time matters

How flexible is your business? It takes time and dedication to successfully incorporate AI and ML into an organization since it requires the ability to respond quickly.

Business complexity has evolved over the years along customers increasing expectations for excellence. Our organization continues reaching new heights by deploying AI and ML techniques that include an integration that: Creates a diverse pool of talented external candidates Leads to stronger training and development processes and programs for our employees Localizes a global application Bridges technological enhancements with business processes Drives business value from delivering reliable information

By putting the right processes in place now, forward-thinking businesses are better prepared for a quicker response when tackling IT challenges and on the path to finding very real solutions.

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Stanford Center for Health Education Launches Online Program in Artificial Intelligence in Healthcare to Improve Patient Outcomes – PRNewswire

STANFORD, Calif., Aug. 10, 2020 /PRNewswire/ --TheStanford Center for Health Education launched an online program in AI and Healthcare this week. The program aims to advance the delivery of patient care and improve global health outcomes through artificial intelligence and machine learning.

The online program, taught by faculty from Stanford Medicine, is designed for healthcare providers, technology professionals, and computer scientists. The goal is to foster a common understanding of the potential for AI to safely and ethically improve patient care.

Stanford University is a leader in AI research and applications in healthcare, with expertise in health economics, clinical informatics, computer science, medical practice, and ethics.

"Effective use of AI in healthcare requires knowing more than just the algorithms and how they work," said Nigam Shah, associate professor of medicine and biomedical data science, the faculty director of the new program. "Stanford's AI in Healthcare program will equip participants to design solutions that help patients and transform our healthcare system. The program will provide a multifaceted perspective on what it takes to bring AI to the clinic safely, cost-effectively, and ethically."

AI has the potential to enable personalized care and predictive analytics, using patient data. Computer system analyses of large patient data sets can help providers personalize optimal care. And data-driven patient risk assessment canbetter enable physicians to take the right action, at the right time. Participants in the four-course program will learn about: the current state, trends and implications of artificial intelligence in healthcare; the ethics of AI in healthcare; how AI affects patient care safety, quality, and research; how AI relates to the science, practice and business of medicine; practical applications of AI in healthcare; and how to apply the building blocks of AI to innovate patient care and understand emerging technologies.

The Stanford Center for Health Education (SCHE), which created the AI in Healthcare program, develops online education programs to extend Stanford's reach to learners around the world. SCHE aims to shape the future of health and healthcare through the timely sharing of knowledge derived from medical research and advances. By facilitating interdisciplinary collaboration across medicine and technology, and introducing professionals to new disciplines, the AI in Healthcare program is intended to advance the field.

"In keeping with the mission of the Stanford Center for Health Education to expand knowledge and improve health on a global scale, we are excited to launch this online certificate program on Artificial Intelligence in Healthcare," said Dr. Charles G. Prober, founding executive director of SCHE. "This program features several of Stanford's leading thinkers in this emerging field a discipline that will have a profound effect on human health and disease in the 21st century."

The Stanford Center for Health Education is a university-wide program supported by Stanford Medicine. The AI in Healthcare program is available for enrollment through Stanford Online, and hosted on the Coursera online learning platform. The program consists of four online courses, and upon completion, participants can earn a Stanford Online specialization certificate through the Coursera platform. The four courses comprising the AI in Healthcare specialization are: Introduction to Healthcare, Introduction to Clinical Data, Fundamentals of Machine Learning for Healthcare, and Evaluations of AI Applications in Healthcare.

SOURCE Stanford Center for Health Education

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The costs and benefits of artificial intelligence – The Japan Times

New York The robots are no longer coming; they are here. The COVID-19 pandemic is hastening the spread of artificial intelligence, but few have fully considered the short- and long-run consequences.

In thinking about AI, it is natural to start from the perspective of welfare economics productivity and distribution. What are the economic effects of robots that can replicate human labor? Such concerns are not new. In the 19th century, many feared that new mechanical and industrial innovations would replace workers. The same concerns are being echoed today.

Consider a model of a national economy in which labor performed by robots matches that performed by humans. The total volume of labor robotic and human will reflect the number of human workers, H, plus the number of robots, R. Here, the robots are additive they add to the labor force rather than multiplying human productivity. To complete the model in the simplest way, suppose the economy has just one sector, and that aggregate output is produced by capital and total labor, human and robotic. This output provides for the countrys consumption, with the rest going toward investment, thus increasing the capital stock.

What is the initial economic impact when these additive robots arrive? Elementary economics shows that an increase in total labor relative to initial capital a drop in the capital-labor ratio causes wages to drop and profits to rise.

There are three points to add. First, the results would be magnified if the additive robots were created from refashioned capital goods. That would yield the same increase in total labor, with a commensurate reduction in the capital stock, but the drop in the wage rate and the increase in the rate of profit would be greater.

Second, nothing would change if we adopted the Austrian Schools two-sector framework in which labor produces the capital good and the capital good produces the consumer good. The arrival of robots still would decrease the capital-labor ratio, as it did in the one-sector scenario.

Third, there is a striking parallel between the models additive robots and newly arrived immigrants in their impact on native workers. By pushing down the capital-labor ratio, immigrants, too, initially cause wages to drop and profits to rise. But it should be noted that with the rate of profit elevated, the rate of investment will rise. Owing to the law of diminishing returns, that additional investment will drive down the profit rate until it has fallen back to normal. At this point, the capital-labor ratio will be back to where it was before the robots arrived, and the wage rate will be pulled back up.

To be sure, the general public tends to assume that robotization (and automation generally) leads to a permanent disappearance of jobs, and thus to the immiseration of the working class. But such fears are exaggerated. The two models described above abstract from the familiar technological progress that drives up productivity and wages, making it reasonable to anticipate that the global economy will sustain some level of growth in labor productivity and compensation per worker.

True, sustained robotization would leave wages on a lower path than they otherwise would have taken, which would create social and political problems. It may prove desirable, as Bill Gates once suggested, to levy taxes on income from robot labor, just as countries levy taxes on income from human labor. This idea deserves careful consideration. But fears of prolonged robotization appear unrealistic. If robotic labor increased at a non-vanishing pace, it would run into limits of space, atmosphere, and so on.

Moreover, AI has brought not just additive robots but also multiplicative robots that enhance workers productivity. Some multiplicative robots enable people to work faster or more effectively (as in AI-assisted surgery), while others help people complete tasks they otherwise could not perform.

The arrival of multiplicative robots need not lead to a lengthy recession of aggregate employment and wages. Yet, like additive robots, they have their downsides. Many AI applications are not entirely safe. The obvious example is self-driving cars, which can (and have) run into pedestrians or other cars. But, of course, so do human drivers.

A society is not wrong, in principle, to deploy robots that are prone to occasional mistakes, just as we tolerate airplane pilots who are not perfect. We must judge costs and benefits. For efficiency, people ought to have the right to sue robots owners for damages. Inevitably, a society will feel uncomfortable with new methods that introduce uncertainty.

From the perspective of ethics, the interface with AI involves imperfect and asymmetric information. As Wendy Hall of the University of Southampton says, amplifying Nicholas Beale, We cant just rely on AI systems to act ethically because their objectives seem ethically neutral.

Indeed, some new devices can cause serious harm. Implantable chips for cognitive enhancement, for example, can cause irreversible tissue damage in the brain. The question, then, is whether laws and procedures can be instituted to protect people from a reasonable degree of harm. Barring that, many are calling on Silicon Valley companies to establish their own ethics committees.

All of this reminds me of the criticism leveled at innovations throughout the history of free-market capitalism. One such critique, the book Gemeinschaft und Gesellschaft by the sociologist Ferdinand Tonnies, ultimately became influential in Germany in the 1920s and led to the corporatism arising there and in Italy in the interwar period thus bringing an end to the market economy in those countries.

Clearly, how we address the problems raised by AI will be highly consequential. But they are not yet present on a wide scale, and they are not the main cause of the dissatisfaction and resulting polarization that have gripped the West.

Edmund S. Phelps, the 2006 Nobel laureate in economics and director of the Center on Capitalism and Society at Columbia University, is author of Mass Flourishing and co-author of Dynamism. 2020, Project Syndicate

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Analysis Covid 19: Artificial Intelligence in Healthcare Market Scenario 2020 Current Trends, Size, Share and Future Opportunities by 2026 – The…

Impact Analysis of Covid-19

The complete version of the Report will include the impact of the COVID-19, and anticipated change on the future outlook of the industry, by taking into the account the political, economic, social, and technological parameters.

Artificial Intelligence in Healthcare Market Research Study provides detailed information about the key factors influencing the growth of the industry which include drivers, restraints, opportunities, and industry-specific challenges, strategically profile key players and comprehensively analyze their market share and core competencies. This report includes analytical assessment of the prime challenges faced by the Artificial Intelligence in Healthcare industry currently and in the coming years, which helps Market participants in understanding the problems they may face while operating in this market over a longer period of time.

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These research report also provides an overall analysis of the market share, size, segmentation, revenue forecasts and geographic regions of the Artificial Intelligence in Healthcare Market along with industry-leading players are studied with respect to their company profile, product portfolio, capacity, price, cost, and revenue. The research report also provides detail analysis on the Artificial Intelligence in Healthcare market current applications and comparative analysis with more focused on the pros and cons of Artificial Intelligence in Healthcare and competitive analysis of major companies.

Major Players Operating in this market include IBM Corporation, Google, Inc., NVIDIA Corporation, Microsoft Corporation, iCarbonX, Next IT Corp., CloudMex Inc., Carescore, Atomwise Inc., Zephyr Health Inc., Deep Genomics Inc., Medtronic Plc., Koninkiljke Philips N.V., and Oncora Medical, Inc.

The key players are highly focusing on innovation in production technologies to improve efficiency and shelf life. The best long-term growth opportunities for this sector can be captured by ensuring ongoing process improvements and financial flexibility to invest in optimal strategies. Company profile section of players includes its basic information like legal name, website, headquarters, its market position, historical background, and top 5 closest competitors by Market capitalization/revenue along with contact information. Each player/ manufacturer revenue figures, growth rate, and the gross profit margin is provided in easy to understand tabular format for past 5 years and a separate section on recent development like mergers, acquisition or any new product/service launch, etc.

In the end, the report makes some important proposals for a new project of Artificial Intelligence in Healthcare Industry before evaluating its feasibility. Overall, the report provides an in-depth insight into the global market covering all important parameters.

Artificial Intelligence in Healthcare Driver Artificial Intelligence in Healthcare Challenge Artificial Intelligence in Healthcare Trend

The report includes chapters which deeply display the following deliverable about the industry:

Research Objective and Assumption

Market Overview Report Description, Executive Summary, and Coherent Opportunity Map (COM)

Market Dynamics, Regulations, and Trends Analysis Market Dynamics, Regulatory Scenario, Industry Trend, Mergerand Acquisitions, New system Launch/Approvals, Value Chain Analysis, Porters Analysis, and PEST Analysis

Global Artificial Intelligence in Healthcare Market, By Regions

Artificial Intelligence in Healthcare Market Competition by Manufacturers including Production, Share, Revenue, Average Price, Manufacturing Base Distribution, Sales Area, and Product Type.

Manufacturers Profiles/Analysis including Company Basic Information, Manufacturing Base, and Its Competitors.

Artificial Intelligence in Healthcare Market Manufacturing Cost Analysis including Key Raw Materials and Key Suppliers of Raw Materials.

Industrial Chain, Sourcing Strategy and Downstream Buyers including Upstream Raw Materials Sourcing and Downstream Buyers

Marketing Strategy Analysis, Distributors/Traders including Marketing Channel, Market Positioning, and Distributors/Traders List.

Market Effect Factors Analysis including Technology Progress/Risk, Consumer Needs/Customer Preference Change, and Economic/Political Environmental Change.

Artificial Intelligence in Healthcare Market Forecast including Production, Consumption, Import, and Export Forecast by Type, Applications, and Region.

Research Findings and Conclusion

Why This Report is Useful? It helps:

1. The report will include the qualitative and quantitative analysis with Artificial Intelligence in Healthcare market estimation and compound annual growth rate (CAGR) between 2020 and 2026

2. Assess the Artificial Intelligence in Healthcare production processes, major issues, and solutions to mitigate the development risk.

3. Comprehensive analysis of market dynamics including factors and opportunities of the global Artificial Intelligence in Healthcare Market will be provided in the report

4. Insights from this report will allow marketers and management authorities of companies to make informed decisions with respect to their future product launch, technology upgrades, market expansion, and marketing tactics.

In this study, the years considered to estimate the market size of 2020-2026 Artificial Intelligence in Healthcare Market are as follows:

History Year: 2016-2018Base Year: 2018Estimated Year: 2019Forecast Year 2020 to 2026

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Analysis Covid 19: Artificial Intelligence in Healthcare Market Scenario 2020 Current Trends, Size, Share and Future Opportunities by 2026 - The...

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