Moving up the charts with AI

There is increasing awareness that the greatest problems with artificial intelligence are not primarily technical, but rather how to achieve value from the technology. This was a growing problem even in the booming economy of the last several years, but a much more important issue in the current pandemic-driven recessionary economic climate.

Older AI technologies like natural language processing, and newer ones like deep learning, work well for the most part and are capable of providing considerable value to organizations that implement them. The challenges are with large-scale implementation and deployment of AI, which are necessary to achieve value. There is substantial evidence of this in surveys.

In an MIT Sloan Management Review/BCG survey, seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years.This number improves to 3 out of 5 when we include companies that have made significant investments in AI. Even so, this means 40% of organizations making significant investments in AI do not report business gains from AI.

NewVantage Partners 2019 Big Data and AI Executive surveyFirms report ongoing interest and an active embrace of AI technologies and solutions, with 91.5% of firms reporting ongoing investment in AI. But only 14.6% of firms report that they have deployed AI capabilities into widespread production. Perhaps as a result, the percentage of respondents agreeing that their pace of investment in AI and big data was accelerating fell from 92% in 2018 to 52% in 2019.

Deloitte 2018 State of Enterprise AI surveyThe top 3 challenges with AI were implementation issues, integrating AI into the companys roles and functions, and data issuesall factors involved in large-scale deployment.

In a 2018 McKinsey Global Survey of AI, most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions.

In short, AI has not yet achieved much return on investment. It has yet to substantially improve the lives of workers, the productivity and performance of organizations, or the effective functions of societies. It is capable of doing all these things, but is being held back from its potential impact by a series of factors I will describe below.

Whats Holding AI Back

Ill describe the factors that are preventing AI from having a substantial return in terms of the letters of our new organization: the ROAI Institute. Although it primarily stands for return on artificial intelligence, it also works to describe the missing or critical ingredients for a successful return:

ReengineeringThe business process reengineering movement of the 1980s and early 90s, in which I wrote the first article and book (admittedly by only a few weeks in both cases) described an opportunity for substantial change in broad business processes based on the capabilities of information technology. Then the technology catalyst was enterprise systems and the Internet; now its artificial intelligence and business analytics.

There is a great opportunitythus far only rarely pursuedto redesign business processes and tasks around AI. Since AI thus far is a relatively narrow technology, task redesign is more feasible now, and essential if organizations are to derive value from AI. Process and task design has become a question of what machines will do vs. what tasks are best suited to humans.

We are not condemned to narrow task redesign forever, however. Combinations of multiple AI technologies can lead to change in entire end to end processesnew product and service development, customer service, order management, procure to pay, and the like.

Organizations need to embrace this new form of reengineering while avoiding the problems that derailed the movement in the past; I called it The Fad that Forgot People. Forgetting people, and their interactions with AI, would also lead to the derailing of AI technology as a vehicle for positive change.

Organization and CultureAI is the child of big data and analytics, and is likely to be subject to the same organization and culture issues as the parent. Unfortunately, there are plenty of survey results suggesting that firms are struggling to achieve data-driven cultures.

The 2019 NewVantage Partners survey of large U.S. firms I cite above found that only 31.0% of companies say they are data-driven. This number has declined from 37.1% in 2017 and 32.4% in 2018. 28% said in 2019 that they have a data culture. 77% reported that business adoption of big data and AI initiatives remains a major challenge. Executives cited multiple factors (organizational alignment, agility, resistance), with 95% stemming from cultural challenges (people and process), and only 5% relating to technology.

A 2019 Deloitte survey of US executives on their perspectives on analytical insights found that most executives63%do not believe their companies are analytics-driven. 37% say their companies are either analytical competitors (10%) or analytical companies (27%). 67% of executives say they are not comfortable accessing or using data from their tools and resources; even 37% of companies with strong data-driven cultures express discomfort.

The absence of a data-driven culture affects AI as much as any technology. It means that the company and its leaders are unlikely to be motivated or knowledgeable about AI, and hence unlikely to build the necessary AI capabilities to succeed. Even if AI applications are successfully developed, they may not be broadly implemented or adopted by users. In addition to culture, AI systems may be a poor fit with an organization for reasons of organizational structure, strategy, or badly-executed change management. In short, the organizational and cultural dimension is critical for any firm seeking to achieve return on AI.

Algorithms and DataAlgorithms are, of course, the key technical feature of most AI systemsat least those based on machine learning. And its impossible to separate data from algorithms, since machine learning algorithms learn from data. In fact, the greatest impediment to effective algorithms is insufficient, poor quality, or unlabeled data. Other algorithm-related challenges for AI implementation include:

InvestmentOne key driver of lack of return from AI is the simple failure to invest enough. Survey data suggest most companies dont invest much yet, and I mentioned one above suggesting that investment levels have peaked in many large firms. And the issue is not just the level of investment, but also how the investments are being managed. Few companies are demanding ROI analysis both before and after implementation; they apparently view AI as experimental, even though the most common version of it (supervised machine learning) has been available for over fifty years. The same companies may not plan for increased investment at the deployment stagetypically one or two orders of magnitude more than a pilotonly focusing on pre-deployment AI applications.

Of course, with any technology it can be difficult to attribute revenue or profit gains to the application. Smart companies seek intermediate measures of effectiveness, including user behavior changes, task performance, process changes, and so forththat would precede improvements in financial outcomes. But its rare for these to be measured by companies either.

A Program of Research and Structured Action

Along with several other veterans of big data and AI, I am forming the Return on AI Institute, which will carry out programs of research and structured action, including surveys, case studies, workshops, methodologies, and guidelines for projects and programs. The ROAI Institute is a benefit corporation that will be supported by companies and organizations who desire to get more value out of their AI investments

Our focus will be less on AI technology-though technological breakthroughs and trends will be considered for their potential to improve returnsand more on the factors defined in this article that improve deployment, organizational change, and financial and social returns. We will focus on the important social dimension of AI in our work as wellis it improving work or the quality of life, solving social or healthcare problems, or making government bodies more responsive? Those types of benefits will be described in our work in addition to the financial ones.

Our research and recommendations will address topics such as:

Please contact me at tdavenport@babson.edu if you care about these issues with regard to your own organization and are interested in approaches to them. AI is a powerful and potentially beneficial technology, but its benefits wont be realized without considerable attention to ROAI.

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Return On Artificial Intelligence: The Challenge And The Opportunity - Forbes

In the heart of Silicon Valley, Stanford clinicians and researchers are exploring whether artificial intelligence could help manage a potential surge of Covid-19 patients and identify patients who will need intensive care before their condition rapidly deteriorates.

The challenge is not to build the algorithm the Stanford team simply picked an off-the-shelf tool already on the market but rather to determine how to carefully integrate it into already-frenzied clinical operations.

The hardest part, the most important part of this work is not the model development. But its the workflow design, the change management, figuring out how do you develop that system the model enables, said Ron Li, a Stanford physician and clinical informaticist leading the effort. Li will present the work on Wednesday at a virtual conference hosted by Stanfords Institute for Human-Centered Artificial Intelligence.

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The effort is primed to be an accelerated test of whether hospitals can smoothly incorporate AI tools into their workflows. That process, typically slow and halting, is being sped up at hospitals all over the world in the face of the coronavirus pandemic.

The machine learning model Lis team is working with analyzes patients data and assigns them a score based on how sick they are and how likely they are to need escalated care. If the algorithm can be validated, Stanford plans to start using it to trigger clinical steps such as prompting a nurse to check in more frequently or order tests that would ultimately help physicians make decisions about a Covid-19 patients care.

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The model known as the Deterioration Index was built and is marketed by Epic, the big electronic health records vendor.Li and his team picked that particular algorithm out of convenience, because its already integrated into their EHR, Li said. Epic trained the model on data from hospitalized patients who did not have Covid-19 a limitation that raises questions about whether it will be generalizable for patients with a novel disease whose data it was never intended to analyze.

Nearly 50 health systems which cover hundreds of hospitals have been using the model to identify hospitalized patients with a wide range of medical conditions who are at the highest risk of deterioration, according to a spokesperson for Epic. The company recently built an update to help hospitals measure how well the model works specifically for Covid-19 patients. The spokesperson said that work showed the model performed well and didnt need to be altered. Some hospitals are already using it with confidence, according to the spokesperson. But others, including Stanford, are now evaluating the model in their own Covid-19 patients.

In the months before the coronavirus pandemic, Li and his team had been working to validate the model on data from Stanfords general population of hospitalized patients. Now, theyve switched their focus to test it on data from dozens of Covid-19 patients that have been hospitalized at Stanford a cohort that, at least for now, may be too small to fully validate the model.

Were essentially waiting as we get more and more Covid patients to see how well this works, Li said. He added that the model does not have to be completely accurate in order to prove useful in the way its being deployed: to help inform high-stakes care decisions, not to automatically trigger them.

As of Tuesday afternoon, Stanfords main hospital was treating 19 confirmed Covid-19 patients, nine of whom were in the intensive care unit; another 22 people were under investigation for possible Covid-19, according to Stanford spokesperson Julie Greicius. The branch of Stanfords health system serving communities east of the San Francisco Bay had five confirmed Covid-19 patients, plus one person under investigation. And Stanfords hospital for children had one confirmed Covid-19 patient, plus seven people under investigation, Greicius said.

Stanfords hospitalization numbers are very fluid. Many people under investigation may turn out to not be infected, and many confirmed Covid-19 patients who have relatively mild symptoms may be quickly cleared for discharge to go home.

The model is meant to be used in patients who are hospitalized, but not yet in the ICU. It analyzes patients data including their vital signs, lab test results, medications, and medical history and spits out a score on a scale from 0 to 100, with a higher number signaling elevated concern that the patients condition is deteriorating.

Already, Li and his team have started to realize that a patients score may be less important than how quickly and dramatically that score changes, he said.

If a patients score is 70, which is pretty high, but its been 70 for the last 24 hours thats actually a less concerning situation than if a patient scores 20 and then jumps up to 80 within 10 hours, he said.

Li and his colleagues are adamant that they will not set a specific score threshold that would automatically trigger a transfer to the ICU or prompt a patient to be intubated. Rather, theyre trying to decide which scores or changes in scores should set off alarm bells that a clinician might need to gather more data or take a closer look at how a patient is doing.

At the end of the day, it will still be the human experts who will make the call regarding whether or not the patient needs to go to the ICU or get intubated except that this will now be augmented by a system that is smarter, more automated, more efficient, Li said.

Using an algorithm in this way has potential to minimize the time that clinicians spend manually reviewing charts, so they can focus on the work that most urgently demands their direct expertise, Li said. That could be especially important if Stanfords hospital sees a flood of Covid-19 patients in the coming weeks. Santa Clara County, where Stanford is located, had confirmed 890 cases of Covid-19 as of Monday afternoon. Its not clear how many of them have needed hospitalization, though San Francisco Bay Area hospitals have not so far faced the crush of Covid-19 patients that New York City hospitals are experiencing.

That could change. And if it does, Li said, the model will have to be integrated into operations in a way that will work if Stanford has several hundred Covid-19 patients in its hospital.

This is part of a yearlong series of articles exploring the use of artificial intelligence in health care that is partly funded by a grant from the Commonwealth Fund.

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Stanford launches an accelerated test of AI to help with Covid-19 care - STAT

Spurred by China's gains in this area, other nations can unite to share expertise in order to expand AI's current capability and ensure that AI can replicate its role in helping China deal with the novel coronavirus pandemic.

March30, 20208 min read

Opinions expressed by Entrepreneur contributors are their own.

You're reading Entrepreneur Middle East, an international franchise of Entrepreneur Media.

From its epicenter in China, the novel coronavirus has spread to infect 414,179 people and cause no less than 18,440 deaths in at least 160 countries across a three-month span from January 2020 till date. These figures are according to the World Health Organization (WHO) Situation report as of March 25th. Accompanying the tragic loss of life that the virus has caused is the impact to the global economy, which has reeled from the effects of the pandemic.

Due to the lockdown measures imposed by several governments, economic activity has slowed around the world, and the Organization for Economic Cooperation and Development (OECD) has stated that the global economy could be hit by its worst growth rate since 2009. The OECD have alerted that the growth rate could be as slow as 2.4%, potentially dragging many countries into recession. COVID-19 has, in a short period of time, emerged as one of the biggest challenges to face the 21st century world. Further complicating the response to this challenge are the grey areas surrounding the virus itself, in terms of its spread and how to treat it.

Related:We're In This Together: Business Resources, Offers, And More For MENA Entrepreneurs To Get Through The Coronavirus Pandemic

As research details emerge, the data pool grows exponentially, beyond the capacity of human intelligence alone to handle. Artificial intelligence (AI) is adept at identifying patterns from big data, and this piece will elucidate how it has become one of humanitys ace cards in handling this crisis. Using China as a case-study, Chinas success with AI as a crisis management tool demonstrates its utility, and justifies the financial investment the technology has required to evolve over the last few years.

Advancements in AI application such as natural language processing, speech recognition, data analytics, machine learning, deep learning, and others such as chatbots and facial recognition have not only been utilized for diagnosis but also for contact tracing and vaccine development. AI has no doubt aided the control of the COVID-19 pandemic and helped to curb its worst effects.

Related:Here's What Your Business Should Focus On As It Navigates The Coronavirus Pandemic

Spurred by Chinas gains in this area, other nations can unite to share expertise in order to expand AIs current capability and ensure that AI can replicate its role in helping China deal with the novel coronavirus pandemic. AI has been deployed in several ways so far, and the following are just seven of the ways in which AI has been applied as a measure to solve the pandemic:

1. DISEASE SURVEILLANCE AI With an infectious disease like COVID-19, surveillance is crucial. Human activity -especially migration- has been responsible for the spread of the virus around the world. Canada based BlueDot has leveraged machine learning and natural language processing to track, recognize, and report the spread of the virus quicker than the World Health Organization and the US Centre for Disease Control and Prevention (CDC). In the near and distant future, technology like this may be used to predict zoonotic infection risk to humans considering variables such as climate change and human activity. The combined analysis of personal, clinical, travel and social data including family history and lifestyle habits obtained from sources like social media would enable more accurate and precise predictions of individual risk profiles and healthcare results. While concerns may exist about the potential infringement to civil liberties of individuals, policy regulations that other AI applications have faced will ensure that this technology is used responsibly.

2. VIRTUAL HEALTHCARE ASSISTANTS (CHATBOTS) The number of COVID-19 cases has shown that healthcare systems and response measures can be overwhelmed. Canada-based Stallion.AI has leveraged its natural language processing capabilities to build a multi-lingual virtual healthcare agent that can answer questions related to COVID-19, provide reliable information and clear guidelines, recommend protection measures, check and monitor symptoms, and advise individuals whether they need hospital screening or self-isolation at their homes.

Related:The Coronavirus Pandemic Versus The Digital Economy: The Pitfalls And The Opportunities

3. DIAGNOSTIC AI Immediate diagnosis means that response measures such as quarantine can be employed quickly to curb further spread of the infection. An impediment to rapid diagnosis is the relative shortage of clinical expertise required to interpret diagnostic results due to the volume of cases. AI has improved diagnostic time in the COVID-19 crisis through technology such as that developed by LinkingMed, a Beijing-based oncology data platform and medical data analysis company. Pneumonia, a common complication of COVID-19 infection, can now be diagnosed from analysis of a CT scan in less than sixty seconds with accuracy as high as 92% and a recall rate of 97% on test data sets. This was made possible by an open-source AI model that analyzed CT images and not only identified lesions but also quantified in terms of number, volume and proportion. This platform, novel in China, was powered by Paddle Paddle, Baidus open-source deep learning platform.

4. FACIAL RECOGNITION AND FEVER DETECTOR AI Thermal cameras have been used for some time now for detecting people with fever. The drawback to the technology is the need for a human operator. Now, however, cameras possessing AI-based multisensory technology have been deployed in airports, hospitals, nursing homes, etc. The technology automatically detects individuals with fever and tracks their movements, recognize their faces, and detect whether the person is wearing a face mask.

5. INTELLIGENT DRONES & ROBOTS The public deployment of drones and robots has been accelerated due to the strict social distancing measures required to contain the virus spread. To ensure compliance, some drones are used to track individuals not using facemasks in public, while others are used to broadcast information to larger audiences and also disinfect public spaces. MicroMultiCopter, a Shenzhen-based technology company, has helped to lessen the virus transmission risk involved with city-wide transport of medical samples and quarantine materials through the deployment of their drones. Patient care, without risk to healthcare workers, has also benefited as robots are used for food and medication delivery. The role of room cleaning and sterilization of isolation wards has also been filled by robots. Catering-industry centred Pudu Technology have extended their reach to the healthcare sector by deploying their robots in over 40 hospitals for these purposes.

Related:How Managers Can Weather The Impact Of The Coronavirus Pandemic On Their Businesses

6. CURATIVE RESEARCH AI Part of what has troubled the scientific community is the absence of a definitive cure for the virus. AI can potentially be a game changer as companies such as the British startup, Exscienta, has shown. Earlier this year, they became the first company to present an AI designed drug molecule that has gone to human trials. A year is all it took the algorithm to develop the molecular structure compared with the five-year average time that it takes traditional research methods.

In the same vein, AI can lead the charge for the development of antibodies and vaccines for the novel coronavirus, either entirely designed from scratch or through drug repurposing. For instance, using its AlphaFold system, Googles AI company, DeepMind, is creating structure models of proteins that have been linked with the virus in a bid to aid the science worlds comprehension of the virus. Although the results have not been experimentally verified, it represents a step in the right direction.

7. INFORMATION VERIFICATION AI The uncertainty of the pandemic has unavoidably resulted in the propagation of myths on social media platforms. While no quantitative assessment has been done to evaluate how much misinformation is out there already, it is certainly a significant figure. Technology giants like Google and Facebook are battling to combat the waves of conspiracy theories, phishing, misinformation and malware. A search for coronavirus/COVID-19 yields an alert sign coupled with links to verified sources of information. YouTube, on the other hand, directly links users to the WHO and similar credible organizations for information. Videos that misinform are scoured for and taken down as soon as they are uploaded.

While the world continues to grapple with the effects of COVID-19, positives can be drawn from the expertise and bravery of healthcare workers, as well as the complementary efforts of AI technology to their endeavors in the above listed ways. As the AI world partners with other sectors for solutions, the light at the end of this tunnel shines brighter, creating the much-needed hope the world needs in these uncertain times.

Related:Work In The Time Of Coronavirus: Here's How You Can Do Your Job From Home (Like A Pro)

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How Artificial Intelligence Is Helping Fight The COVID-19 Pandemic - Entrepreneur

Artificial intelligence can enable its productive tools to be employed to fight against COVID-19. Here's how

April1, 20204 min read

Opinions expressed by Entrepreneur contributors are their own.

You're reading Entrepreneur India, an international franchise of Entrepreneur Media.

In the fight against COVID-19, enterprise artificial intelligence (AI) is not getting the same attention as teleconferencing and telehealth technologies.

A massive shift to working from home to avoid spreading the virus means virtual collaboration companies like Zoom Video Communications are in the headlines. The same dynamic is playing out in healthcare as hospitals attempt to prioritize physical care for the coronavirus patients and are trying telehealth product suites like TelaDoc to manage everyone else and scale up.

AIs role in getting us through this remains less intuitive that is because not enough AI solutions can be plugged into an organization on the run like teleconferencing and telehealth can today. In addition to that, it served up as point solutions that just help with a single task do not do sufficient good fast enough. What is needed is its suite that quickly makes entire workflows easier just like teleconferencing and telehealth can.

Its worth listening to Benchmark Capitals Chetan Puttagunta on the first point as the pandemic accelerated in the U.S. as he reminded us that during previous downturns, the companies that could deliver their solutions fastest and easiest rose to the top. If it takes too long to implement a technology, you are now in a holding pattern.

The weakness of point solutions may not be as clear in times like these. The Pentagons former head of information technology in the 1990s, Paul Strassmann, articulated it best. He called it managerial productivity versus operational productivity. Steve Jobs showed Strassmanns managerial productivity helps you do a few things well and then its value tapers off. Operational productivity lifts everything the enterprise does.

AI has to show up in the form of an operational productivity solution that helps everything work better in the industry it serves. It cannot just be a tool that is floating out of context of an industrys workflows. It has to feel purpose built, have the power of the kind of solution suite that demonstrates a grasp of the unique problems of a specific industry.

The current pandemic is likely to winnow the pack of AI companies down to a smaller group of enterprise suites as some run out of cash and others realize they need to get back to the drawing board.

The same was true for teleconferencing solutions during the Great Recession. Industry veterans like myself saw the field of companies reduced down to enterprises that learned how to grow in that environment and refine their suite to be at the forefront today.

Taking a page from that period, tech leaders are looking for early lessons from COVID-19s new world to see what the future will look like. Fundamentally, this crisis means utilizing integrated digital systems to recognize and respond to emerging risks and consumer demands. The key is responding, not just automating todays activities and workflows. Changing events need to be anticipated, recognized and reacted to. That is the test AI faces.

Until now, most predictive technologies have been based on assessing two variables retrospectively. AI can be deployed to explore multiple variables and how they change through time in relation to each other. Make this easy to plug in and deploy as a suite that delivers operational productivity and a new game becomes possible.

In the insurance industry, it can mean an active claim intake processes and alerts to emerge threats in a multi-variable world. In the financial services industry, it can mean real-time understanding of liquidity, capital reserves, when best to utilize these reserves and when best to increase them. In healthcare, it can mean empowering front-line clinicians with tools that pull in data for collective use and also help them make more informed treatment decisions.

There is a lot at stake. Strassman may now be in his 90s but he still speaks to local groups at his hometown in Connecticut. At such a gathering a few weeks ago he pointed out the tensions in the global economy that could be pushed too far by todays events. His small book on the subject has already been sold out on Amazon. A perfect example of more panic buying in pivotal times.

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Enterprise Artificial Intelligence Along With Telehealth And Teleconferences Can Help In Fighting COVID-19 - Entrepreneur

MADRID, SPAIN - MARCH 28: Health personnel are seen outside the emergency entrance of the Severo ... [+] Ochoa Hospital on March 28, 2020 in Madrid, Spain. Spain plans to continue its quarantine measures at least through April 11. The Coronavirus (COVID-19) pandemic has spread to many countries across the world, claiming over 20,000 lives and infecting hundreds of thousands more. (Photo by Carlos Alvarez/Getty Images)

AI (Artificial Intelligence) has a long history, going back to the 1950s when the computer industry started. Its interesting to note that much of the innovation came from government programs, not private industry.This was all about how to leverage technologies to fight the Cold War and put a man on the moon.

The impact of these program would certainly be far-reaching.They would lead to the creation of the Internet and the PC revolution.

So fast forward to today: Could the COVID-19 pandemic have a similar impact? Might it be our generations Space Race?

I think so. And of course, its just not the US this time. This is about a worldwide effort.

Wide-scale availability of data will be key.The White House Office of Science and Technology has formed the Covid-19 Open Research Dataset, which has over 24,000 papers and is constantly being updated.This includes the support of the National Library of Medicine (NLM), National Institutes of Health (NIH), Microsoft and the Allen Institute for Artificial Intelligence.

This database helps scientists and doctors create personalized, curated lists of articles that might help them, and allows data scientists to apply text mining to sift through this prohibitive volume of information efficiently with state-of-the-art AI methods, said Noah Giansiracusa, who is the Assistant Professor at Bentley University.

Yet there needs to be an organized effort to galvanize AI experts to action.The good news is that there are already groups emerging.For example, there is the C3.ai Digital Transformation Institute, which is a new consortium of research universities, C3.ai (a top AI company) and Microsoft.The organization will be focused on using AI to fight pandemics.

There are even competitions being setup to stir innovation.One is Kaggles COVID-19 Open Research Dataset Challenge, which is a collaboration with the NIH and White House.This will be about leveraging Kaggles 4+ million community of data scientists.The first contest was to help provide better forecasts of the spread of COVID-19 across the world.

Next, the Decentralized Artificial Intelligence Alliance is putting together Covidathon, an AI hackathon to fight the pandemic coordinated by SingularityNET and Ocean Protocol.The organization has more than 50 companies, labs and nonprofits.

And then there is MIT Solve, which is a marketplace for social impact innovation.It has established the Global Health Security & Pandemics Challenge.In fact, a member of this organization, Ada Health, has developed an AI-powered COVID-19 personalized screening test.

AI tools and infrastructure services can be costly.This is especially the case for models that target complex areas like medical research.

But AI companies have stepped upthat is, by eliminating their fees:

DarwinAI's COVID-19 neural network

Patient care is an area where AI could be essential.An example of this is Biofourmis.In a two-week period, this startup created a remote monitoring system that has a biosensor for a patients arm and an AI application to help with the diagnosis.In other words, this can help reduce infection rates for doctors and medical support personnel.Keep in mind thatin Chinaabout 29% of COVID-19 deaths were healthcare workers.

Another promising innovation to help patients is from Vital. The founders are Aaron Patzer, who is the creator of Mint.com, and Justin Schrager, an ER doc.Their company uses AI and NLP (Natural Language Processing) to manage overloaded hospitals.

Vital is now devoting all its resources to create C19check.com.The app, which was built in a partnership with Emory Department of Emergency Medicine's Health DesignED Center and the Emory Office of Critical Event Preparedness and Response, provides guidance to the public for self-triage before going to the hospital.So far, its been used by 400,000 people.

And here are some other interesting patient care innovations:

While drug discovery has made many advances over the years, the process can still be slow and onerous.But AI can help out.

For example, a startup that is using AI to accelerate drug development is Gero Pte. It has used the technology to better isolate compounds for COVID-19 by testing treatments that are already used in humans.

Mapping the virus genome has seemed to happen very quickly since the outbreak, said Vadim Tabakman, who is the Director of technical evangelism at Nintex.Leveraging that information with Machine Learning to explore different scenarios and learn from those results could be a game changer in finding a set of drugs to fight this type of outbreak.Since the world is more connected than ever, having different researchers, hospitals and countries, providing data into the datasets that get processed, could also speed up the results tremendously.

Tom (@ttaulli) is the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems.

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AI (Artificial Intelligence) Companies That Are Combating The COVID-19 Pandemic - Forbes

How Artificial Intelligence is Going to Make Your Analytics Better Than Ever | 2020-03-31 | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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How Artificial Intelligence is Going to Make Your Analytics Better Than Ever - Security Magazine

The coronavirus outbreak has rapidly accelerated the nations slow-moving effort to incorporate artificial intelligence into medical care, as hospitals grasp onto experimental technologies to relieve an unprecedented strain on their resources.

AI has become one of the first lines of defense in the pandemic. Hospitals are using it to help screen and triage patients and identify those most likely to develop severe symptoms. Theyre scanning faces to check temperatures and harnessing fitness tracker data, to zero in on individual cases and potential clusters. They are also using AI to keep tabs on the virus in their own communities. They need to know who has the disease, who is likely to get it, and what supplies are going to run out tomorrow, two weeks from now, and further down the road.

Just weeks ago, some of those efforts might have stirred a privacy backlash. Other AI tools were months from deployment because clinicians were still studying their impacts on patients. But as Covid-19 has snowballed into a global crisis, health cares normally methodical approach to new technology has been hijacked by demands that are plainly more pressing.

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Theres a crucial caveat: Its not clear if these AI tools are going to work. Many are based on drips of data, often from patients in China with severe disease. Those data might not be applicable to people in other places or with milder disease. Hospitals are testing models for Covid-19 care that were never intended to be used in such a scenario. Some AI systems could also be susceptible to overfitting, meaning that theyve modeled their training data so well that they have trouble analyzing new data which is coming in constantly as cases rise.

The uptake of new technologies is moving so fast that its hard to keep track of which AI tools are being deployed and how they are affecting care and hospital operations. STAT has developed a comprehensive guide to that work, broken down by how the tools are being used.

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This list focuses only on AI systems being used and developed to directly aid hospitals, clinicians, and patients. It doesnt cover the flurry of efforts to use AI to identify drug and vaccine candidates, or to track and forecast the spread of the virus.

This is one of the earliest and most common uses of AI. Hospitals have deployed an array of automated tools to allow patients to check their symptoms and get advice on what precautions to take and whether to seek care.

Some health systems, including Cleveland Clinic and OSF HealthCare of Illinois, have customized their own chatbots, while others are relying on symptom checkers built in partnership with Microsoft or startups such as Boston-based Buoy Health. Apple has also released its own Covid-19 screening system, created after consultation with the White House Coronavirus Task Force and public health authorities.

Developers code knowledge into those tools to deliver recommendations to patients. While nearly all of them are built using the CDCs guidelines, they vary widely in the questions they ask and the advice they deliver.

STAT reporters recently drilled eight different chatbots about the same set of symptoms. They produced confusing patchwork of responses. Some experts on AI have cautioned that these tools while well-intentioned are a poor substitute for a more detailed conversation with a clinician. And given the shifting knowledge-base surrounding Covid-19, these chatbots also require regular updates.

If you dont really know how good the tool is, its hard to understand if youre actually helping or hurting from a public health perspective.

Andrew Beam, artificial intelligence researcher

If you dont really know how good the tool is, its hard to understand if youre actually helping or hurting from a public health perspective, said Andrew Beam, an artificial intelligence researcher in the epidemiology department at Harvard T.H. Chan School of Public Health.

Clover, a San Francisco-based health insurance startup, is using an algorithm to identify its patients most at risk of contracting Covid-19 so that it can reach out to them proactively about potential symptoms and concerns. The algorithm uses three main sources of data: an existing algorithm the company uses to flag people at risk of hospital readmission, patients scores on a frailty index, and information on whether a patient has an existing condition puts them at a higher risk of dying from Covid-19.

AI could also be used to catch early symptoms of the illness in health care workers, who are at particularly high risk of contracting the virus. In San Francisco, researchers at the University of California are using wearable rings made by health tech company Oura to track health care workers vital signs for early indications of Covid-19. If those signs including elevated heart rate and increased temperature show up reliably on the rings, they could be fed into an algorithm that would give hospitals a heads-up about workers who need to be isolated or receive medical care.

Covid-19 testing is currently done by taking a sample from a throat or nasal swab and then looking for tiny snippets of the genetic code of the virus. But given severe shortages of those tests in many parts of the country, some AI researchers believe that algorithms could be used as an alternative.

Theyre using chest images, captured via X-rays or computed tomography (CT) scans, to build AI models. Some systems aim simply to recognize Covid-19; others aim to distinguish, say, a case of Covid-19-induced pneumonia from a case caused by other viruses or bacteria. However, those models rely on patients to be scanned with imaging equipment, which creates a contamination risk.

Other efforts to detect Covid-19 are sourcing training data in creative ways including by collecting the sound of coughs. An effort called Cough for the Cure led by a group of San Francisco-based researchers and engineers is asking people who have tested either negative or positive for Covid-19 to upload audio samples of their cough. Theyre trying to train a model to tell the difference, though its not clear yet that a Covid-19 cough has unique features.

Among the most urgent questions facing hospitals right now: Which of their Covid-19 patients are going to get worse, and how quickly will that happen? Researchers are racing to develop and validate predictive models that can answer those questions as rapidly as possible.

The latest algorithm comes from researchers at NYU Grossman School of Medicine, Columbia University, and two hospitals in Wenzou, China. In an article published in a computer science journal on Monday, the researchers reported that they had developed a model to predict whether patients would go on to develop acute respiratory distress syndrome or ARDS, a potentially deadly accumulation of fluid in the lungs. The researchers trained their model using data from 53 Covid-19 patients who were admitted to the Wenzhou hospitals. They found that the model was between 70% and 80% accurate in predicting whether the patients developed ARDS.

At Stanford, researchers are trying to validate an off-the-shelf AI tool to see if it can help identify which hospitalized patients may soon need to be transferred to the ICU. The model, built by the electronic health records vendor Epic, analyzes patients data and assigns them a score based on how sick they are and how likely they are to need escalated care. Stanford researchers are trying to validate the model which was trained on data from patients hospitalized for other conditions in dozens of Covid-19 patients. If it works, Stanford plans to use it as a decision-support tool in its network of hospitals and clinics.

Similar efforts are underway around the globe. In a paper posted to a preprint server that has not yet been peer-reviewed, researchers in Wuhan, China, reported that they had built models to try to predict which patients with mild Covid-19 would ultimately deteriorate. They trained their algorithms using data from 133 patients who were admitted to a hospital in Wuhan at the height of its outbreak earlier this year. And in Israel, the countrys largest hospital has deployed an AI model developed by the Israeli company EarlySense, which aims to predict which Covid-19 patients may experience respiratory failure or sepsis within the next six to eight hours.

AI is also helping to answer pressing questions about when hospitals might run out of beds, ventilators, and other resources. Definitive Healthcare and Esri, which makes mapping and spatial analytics software, have built a tool that measures hospital bed capacity across the U.S. It tracks the location and number of licensed beds and intensive care (ICU) beds, and shows the average utilization rate.

Using a flu surge model created by the CDC, Qventus is working with health systems around the country to predict when they will reach their breaking point. It has published a data visualization tracking how several metrics will change from week to week, including the number of patients on ventilators and in ICUs.

Its current projection: At peak, there will be a shortage of 9,100 ICU beds and 115,000 beds used for routine care.

To focus in-person resources on the sickest patients, many hospitals are deploying AI-driven technologies designed to monitor patients with Covid-19 and chronic conditions that require careful management. Some of these tools simply track symptoms and vital signs, and make limited use of AI. But others are designed to pull out trends in data to predict when patients are heading toward a potential crisis.

Mayo Clinic and the University of Pittsburgh Medical Center are working with Eko, the maker of a digital stethoscope and mobile EKG technology whose products can flag dangerous heart rhythm abnormalities and symptoms of Covid-19. Mayo is also teaming up with another mobile EKG company, AliveCor, to identify patients at risk of a potentially deadly heart problem associated with the use of hydroxychloroquine, a drug being evaluated for use in Covid-19.

Many developers of remote monitoring tools are scrambling to deploy them after the Food and Drug Administration published a new policy indicating it will not object to minor modifications in the use or functionality of approved products during the outbreak. That covers products such as electronic thermometers, pulse oximeters, and products designed to monitor blood pressure and respiration.

Among them is Biofourmis, a Boston-based company that developed a wearable that uses AI to flag physiological changes associated with the infection. Its product is being used to monitor Covid-19 patients in Hong Kong and three hospitals in the U.S. Current Health, which makes a similar technology, said orders from hospitals jumped 50% in a five-day span after the coronavirus began to spread widely in the U.S.

Several companies are exploring the use of AI-powered temperature monitors to remotely detect people with fevers and block them from entering public spaces. Tampa General Hospital in Florida recently implemented a screening system that includes thermal-scanning face cameras made by Orlando, Fla.-based company Care.ai. The cameras look for fevers, sweating, and discoloration. In Singapore, the nations health tech agency recently partnered with a startup called KroniKare to pilot the use of a similar device at its headquarters and at St. Andrews Community Hospital.

As experimental therapies are increasingly tested in Covid-19 patients, monitoring how theyre faring on those drugs may be the next frontier for AI systems.

A model could be trained to analyze the lung scans of patients enrolled in drug studies and determine whether those images show potential signs of improvement. That could be helpful for researchers and clinicians desperate for signal on whether a treatment is working. Its not clear yet, however, whether imaging is the most appropriate way to measure response to drugs that are being tried for the first time on patients.

This is part of a yearlong series of articles exploring the use of artificial intelligence in health care that is partly funded by a grant from the Commonwealth Fund.

Read more from the original source:

STAT's guide to how hospitals are using AI to fight Covid-19 - STAT

6 Visions of How Artificial Intelligence will Change Architecture

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In his book "Life 3.0", MIT professor Max Tegmark says "we are all the guardians of the future of life now as we shape the age of AI." Artificial Intelligence remains a Pandora's Box of possibilities, with the potential to enhance the safety, efficiency, and sustainability of cities, or destroy the potential for humans to work, interact, and live a private life. The question of how Artificial Intelligence will impact the cities of the future has also captured the imagination of architects and designers, and formed a central question to the 2019 Shenzhen Biennale, the world's most visited architecture event.

As part of the "Eyes of the City" section of the Biennial, curated by Carlo Ratti, designers were asked to put forth their visions and concerns of how artificial intelligence will impact the future of architecture. Below, we have selected six visions, where designers reflect in their own words on aspects from ecology and the environment to social isolation. For further reading on AI and the Shenzhen Biennial, see our interview with Carlo Ratti and Winy Maas on the subject, and visit our dedicated landing page of content here.

The advance of AI technologies can make it feel as if we know everything about our citiesas if all city dwellers are counted and accounted for, our urban existence fully monitored, mapped, and predicted.

But what happens when we train our attention and technologies on the non-human beings with whom we share our urban environments? How can our notion of urban life, and the possibilities to design for it, expand when we use technology to visualize more than just the relationship between humans and human-made structures?

There is much we have yet to discover about our evolving urban environments. As new technologies are developed, deployed, and appropriated, it is critical to ask how they can help us see both the city and our discipline differently. Can architecture and urban design become a multi-species, collaborative practice? The first step is opening our eyes to all of our fellow city dwellers.

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For all of their history, the machines around us have stood silent, but when the city acquires the ability to see, to listen and to talk back to us, what might constitute a meaningful reciprocal interaction? Is it possible to have a productive dialogue with an autonomous shipping crane loading containers into the hull of a ship at a Chinese mega port; or, how do we ask a question of a warehouse filled with a million objects or talk to a city managing itself based on aggregated data sets from an infinite network of media feeds? Consumer-facing AIs like Amazons Alexa, Microsofts Cortana, Google Assistant or Apples Siri repeat biases and forms of interactions which are a legacy of human to human relationships. If you ask Microsofts personal digital assistant Cortana if she is a woman she replies Well, technically I'm a cloud of infinitesimal data computation. It is unclear if Cortana is a she or an it or a they. Deborah Harrison, the lead writer for Cortana, uses the pronoun she when referring to Cortana but is also explicit in stating that this does not mean she is female, or that she is human or that a gender construct could even apply in this context. We are very clear that Cortana is not only not a person, but there is no overlay of personhood that we ascribe, with the exception of the gender pronoun, Harrison explains. We felt that it was going to convey something impersonal and while we didnt want Cortana to be thought of as human, we dont want her to be impersonal or feel unfamiliar either.

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AI (artificial intelligence) can transform the environment we live in. Cities are facing the rise of UI (urban intelligence). Micro sensors and smart handheld electronics can gather large amounts of information. Mobile sensors, referred to as urban tech, allow cars, buses, bicycles, and even citizens to collect information about air quality, noise pollution, and the urban infrastructure at large. For example, noise data can be captured, archived, and made accessible. In an effort to contribute toward urban noise mitigation, citizens will be able to measure urban soundscapes, and urban planners and city councils can react to the data. How will our lives change intellectually, physically, and emotionally as the Internet of Things migrates into urban environments? How does technology intersect with society?

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Thanks to the development of the digital world, cities can be part of natural history. This is our great challenge for the next few decades, The digital revolution should allow us to promote an advanced, ecological and human world. Being digital was never the goalit was a means to reinvent the world. But what kind of world?

In many cases, digital allows us to continue doing everything we invented with the industrial revolution in a more efficient way. Thats why many of the problems that arose with industrial life have been exacerbated with the introduction of new digital technologies. Our cities are still machines that import goods and generate waste. We import hydrocarbons extracted from the subsoil of the earth to make plastics or fuels, which allow us to consume or move effectively while polluting the environment. Cities are also the recipients of the millions of containers filled with products that move around the world, and where we produce waste that creates mountains of garbage.

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We may imagine that one day, when a city was full of sensors to give it the ability of watching and hearing, data could be collected and analyzed as much as possible to make the city run more efficiently. Public space would be better managed to avoid any offense and crime, traffic flows be better monitored to avoid any traffic jam or traffic accident, public services be more evenly distributed to achieve social equity in space, land use be more reasonably zoned or rezoned to achieve a land value as high as possible, and so on. The city would function as a giant machine of high efficiency and rationality that would treat everyone and everything in the city as an element on the giant machine, under the supervision and in line with the values of the hidden eyes and ears. But, the city is not a machine, it is an organism composed of first of all numerous men who are often different one from another, and then the physical environment they create and shape in a collective way. Before the appearance of the city full of sensors, man needs to first work out a complete set of regulations on the utilization of sensors and the data they collect to deal with the issues of privacy and diversity.

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In his bookThe Second Digital Turn, Mario Carpo provides an incisive definition of the difference between artificial intelligence and "human" intelligence. Through the slogan "search, don't sort", he well describes how our way of using email has changed after the spread of Gmail:

We used to think that sorting saves time. It did; but it doesnt any more, because Google searches (in this instance, Gmail searches) now work faster and better. So taxonomies, at least in their more practical, utilitarian modeas an information retrieval toolare now useless. And of course computers do not have queries on the meaning of life, so they do not need taxonomies to make sense of the world, eitheras we do, or did.[Mario Carpo,The Second Digital Turn. Design Beyond Intelligence, MIT Press, Cambridge MA, 2017, p. 25.]

Machine-intelligence is an infinite search based on a finite request: Carpo's machine, which announces the second digital turn (or revolution?), is able to find a needle in a haystack - so long as someone asks it to look for a needle, for reasons that are still human. There is no longer any need for shelves, drawers, or taxonomies to narrow down the search-terms into increasingly coherent sets (as was the case with "sorting"). The machine will find the needle wherever it is, in the chaos of the pseudo-infinite space of the World Wide Web or, in a more general sense, of the "Big Data". It will do so in an instant. And herein lies its intelligence: it can look for a needle in a pseudo-infinite haystack (Big Data) at a very high speed (Big Calcula).

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6 Visions of How Artificial Intelligence will Change Architecture - ArchDaily

Artificial intelligence (AI) is already deeply embedded in so many areas of our lives. Societys reliance on AI is set to increase at a pace that is hard to comprehend.

AI isnt the kind of technology that is confined to futuristic science fiction movies the robots youve seen on the big screen that learn how to think, feel, fall in love, and subsequently take over humanity. No, AI right now is much less dramatic and often much harder to identify.

Artificial intelligence is simply machine learning. And our devices do this all the time. Every time you input data into your phone, your phone learns more about you and adjusts how it responds to you. Apps and computer programmes work the same way too.

Any digital programmes that display learning, reasoning or problem solving, are displaying artificial intelligence. So, even something as simple as a game of chess on your desktop counts as artificial intelligence.

The problem is that the starting point for artificial intelligence always has to be human intelligence. Humans programme the machines to learn and develop in a certain way which means they are passing on their unconscious biases.

The tech and computer industry is still overwhelmingly dominated by white men. In 2016, there were ten large tech companies in Silicon Valley the global epicentre for technological innovation that did not employ a single black woman. Three companies had no black employees at all.

When there is no diversity in the room, it means the machines are learning the same biases and internal prejudices of the majority white workforces that are developing them.

And, with a starting point that is grounded in inequality, machines are destined to develop in ways that perpetuate the mistreatment of and discrimination against people of colour. In fact, we are already seeing it happen.

In 2017, a video went viral of social media of a soap dispenser that would only automatically release soap onto white hands.

The dispenser was created by a companycalledTechnical Concepts, and the flaw occurred because no one on the development team thought to test their product on dark skin.

A study in March last year found that driverless cars are more likely to drive into black pedestrians, again because their technology has been designed to detect white skin, so they are less likely to stop for black people crossing the road.

It would be easy to chalk these high-profile viral incidents up as individual errors, but data and AI specialist Mike Bugembe, says it would be a mistake to think of these problems in isolation. He says they are indicative of a much wider issue with racism in technology, one that is likely to spiral in the next few years.

I can give you so many examples of where AI has been prejudiced or racist or sexist, Mike tells Metro.co.uk.

The danger now is that we are actually listening and accepting the decisions of machines. When computer says no, we increasingly accept that as gospel. So, were listening now to something that is perpetuating, or even accentuating the biases that already exist in society.

Mike says the growth of AI can have much bigger, systemic ramifications for the lives of people of colour in the UK. The implications of racist technology go far beyond who does and who doesnt get to use hand soap.

AI is involved in decisions about where to deploy police officers, in deciding who is likely to take part in criminial activity and reoffend. He says in the future we will increasingly see AI playing a part in things like hospital admissions, school exclusions and HR hiring processes.

Perpetuating racism in these areas has the potential to cause serious, long-lasting harm to minorities. Mike says its vital that more black and minority people enter this sector to diversify the pool of talent and help to eradicate the problematic biases.

If we dont have a system that can see us and give us the same opportunities, the impact will be huge. If we dont get involved in this industry, our long-term livelihoods will be impacted, explains Mike.

Its no secret that within six years, pretty much 98% of human consumer transactions will go through machines. And if these machines dont see us, minorities, then everything will be affected for us. Everything.

An immediate concern for many campaigners, equality activists and academics is the deployment and roll out of facial recognition as a power for the police.

In February, the Metropolitan Police began operational use of facial recognition CCTV, with vans stationed outside a large shopping centre in east London, despite widespread criticism about the methods.

A paper last year found that using artificial intelligence to fight crime could raise the risk of profiling bias. The research warned that algorithms might judge people from disadvantaged backgrounds as a greater risk.

Outside of China, the Metropolitan police is the largest police force outside of China to roll it out, explains Kimberly McIntosh, senior policy officer at Runnymede Trust. We all want to stay safe but giving the green light to letting dodgy tech turn our public spaces into surveillance zones should be treated cautiously.

Kimberly points to research that shows that facial recognition software has trouble identifying the faces of women and black people.

Yet roll outs in areas like Stratford have significant black populations, she says.There is currently no law regulating facial recognition in the UK. What is happening to all that data?

93% of the Mets matches have wrongly flagged innocent people. The Equality and Human Rights Commission is right the use of this technology should be paused. It is not fit for purpose.

Kimberlys example shows how the inaccuracies and inherent biases of artificial intelligence can have real-world consequences for people of colour in this case, it is already contibuting to their disproportionate criminalisation.

The ways in which technological racism could personally and systemically harm people of colour are numerous and wildly varied.

Racial bias in technology already exists in society, even in the smaller, more innocuous ways that you might not even notice.

There was a time where if you typed black girl into Google, all it would bring up was porn, explains Mike.

Google is a trusted source of information, so we cant overstate the impact that search results like these have on how people percieve the world and minorities. Is it any wonder that black women are persistantly hypersexualised when online search results are backing up these ideas?

Right now, if you Google cute baby, you will only see white babies in the results. So again, there are these more pervasive messages being pushed out there that speak volumes about the worth and value of minorities in society.

Mike is now raising money to gather data scientists together for a new project. His aim is to train a machine that will be able to make sure other machines arent racist.

We need diversity in the people creating the algorithms. We need diversity in the data. And we need approaches to make sure that those biases dont carry on, says Mike. So, how do you teach a kid not to be racist? The same way you will teach a machine not to be racist, right?

Some companies say to be well, we dont put race in our feature set which is the data used to train the algorithms. So they think it doesnt apply to them. But that is just as meaningless and unhelpful as saying they dont see race.

Just as humans have to acknoweldge race and racism in order to beat it, so too do machines, algorithm and artificial intelligence.

If we are teaching a machine about human behaviour, it has got to include our prejudices, and strategies that spot them and fight against them.

Mike says that discussing racism and existing biases can be hard for people with power, particuarly when their companies have a distinct lack of employees with relevant lived experiences. But he says making it less personal can actually make it easier for companies to address.

The current definition of racism is very individual and very easy to shrug off people can so easily say, Well, thats not me, Im not racist, and thats the end of that conversation, says Mike.

If you change the definition of racism to a pattern of behaviour like an algorithm itself thats a whole different story. You can see what is recurring, the patterns than pop up. Suddenly, its not just me thats racist, its everything. And thats the way it needs to be addressed on a wider scale.

All of us are increasingly dependent on technology to get through our lives. Its how we connect with friends, pay for food, order new clothes. And on a wider scale, technology already governs so many of our social systems.

Technology companies must ensure that in this race towards a more digital-led world, ethnic minorities are not being ignored or treated as collateral damage.

Technological advancements are meaningless if their systems only serve to uphold archaic prejudices.

This series is an in-depth look at racism in the UK in 2020.

We aim to look at how, where and why racist attitudes and biases impact people of colour from all walks of life.

It's vital to improve the language we have to talk about racism and start the difficult conversations about inequality.

We want to hear from you - if you have a personal story or experience of racism that you would like to share get in touch: metrolifestyleteam@metro.co.uk

MORE: Muslims are scared of going to therapy in case theyre linked to terrorism

MORE: How the word woke was hijacked to silence people of colour

MORE: Black women are being targeted with disgusting misogynoir in online gaming forums

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The race problem with AI: Machines are learning to be racist' - Metro.co.uk

The "Artificial Intelligence in Retail Market by Product (Chatbot, Customer Relationship Management), Application (Programmatic Advertising), Technology (Machine Learning, Natural Language Processing), Retail (E-commerce and Direct Retail)- Forecast to 2025" report has been added to ResearchAndMarkets.com's offering.

The artificial intelligence in retail market is expected to grow at a CAGR of 35.9% from 2019 to 2025 to reach $15.3 billion by 2025.

The growth in the artificial intelligence in retail market is driven by several factors such as the rising number of internet users, increasing adoption of smart devices, rapid adoption of advances in technology across retail chain, and increasing adoption of the multi-channel or omnichannel retailing strategy. Besides, the factors such as increasing awareness about AI and big data & analytics, consistent proliferation of Internet of Things, and enhanced end-user experience is also contributing to the market growth. However, high cost of transformation and lack of infrastructure are the major factors hindering the market growth during the forecast period.

The study offers a comprehensive analysis of the global artificial intelligence in retail market with respect to various types.

The global artificial intelligence in retail market is segmented on the basis of product (chatbot, customer relationship management, inventory management), application (programmatic advertising, market forecasting), technology (machine learning, natural language processing, computer vision), retail (e-commerce and direct retail), and geography

The predictive merchandising segment accounted for the largest share of the overall artificial intelligence in retail market in 2019, mainly due to growing demand for the customer behavior tracking solutions among the retailers. However, the in-store visual monitoring and surveillance segment is expected to witness rapid growth during the forecast period, as it helps in plummeting the issue of shoplifting in retail, which is one of the major reasons to incur financial loss in the stores.

An in-depth analysis of the geographical scenario of the market provides detailed qualitative and quantitative insights about the five regions including North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. In 2019, North America commanded the largest share of the global artificial intelligence in retail market, followed by Europe and Asia Pacific. The large share of this region is mainly attributed to its open-minded approach towards smart technologies and high technology adoption rate, presence of key players & start-ups, and increased internet access. However, the factors such as speedy growth in spending power, presence of young population, and government initiatives supporting digitalization is helping Asia Pacific to register the fastest growth in the global artificial intelligence in retail market.

Key Topics Covered:

1. Introduction

1.1. Market Definition

1.2. Market Ecosystem

1.3. Currency and Limitations

1.3.1. Currency

1.3.2. Limitations

1.4. Key Stakeholders

2. Research Methodology

2.1. Research Approach

2.2. Data Collection & Validation

2.2.1. Secondary Research

2.2.2. Primary Research

2.3. Market Assessment

2.3.1. Market Size Estimation

2.3.2. Bottom-Up Approach

2.3.3. Top-Down Approach

2.3.4. Growth Forecast

2.4. Assumptions for the Study

3. Executive Summary

3.1. Overview

3.2. Market Analysis, by Product Offering

3.3. Market Analysis, by Application

3.4. Market Analysis, by Learning Technology

3.5. Market Analysis, by Type

3.6. Market Analysis, by End-User

3.7. Market Analysis, by Deployment Type

3.8. Market Analysis, by Geography

3.9. Competitive Analysis

4. Market insights

4.1. Introduction

4.2. Market Dynamics

4.2.1. Drivers

4.2.2. Restraints

4.2.3. Opportunities

4.2.4. Challenges

4.2.5. Trends

5. Artificial Intelligence in Retail Market, by Product Type

5.1. Introduction

5.2. Solutions

5.2.1. Chatbot

5.2.2. Recommendation Engines

5.2.3. Customer Behaviour Tracking

5.2.4. Visual Search

5.2.5. Customer Relationship Management

5.2.6. Price Optimization

5.2.7. Supply Chain Management

5.2.8. inventory Management

5.3. Services

5.3.1. Managed Services

5.3.2. Professional Services

6. Artificial Intelligence in Retail Market, by Application

Story continues

6.1. Introduction

6.2. Predictive Merchandising

6.3. Programmatic Advertising

6.4. In-Store Visual Monitoring & Surveillance

6.5. Market Forecasting

6.6. Location-Based Marketing

7. Artificial Intelligence in Retail Market, by Learning Technology

7.1. Introduction

7.2. Machine Learning

7.3. Natural Language Processing

7.4. Computer Vision

8. Artificial Intelligence in Retail Market, by Type

8.1. Introduction

8.2. Offline Retail

8.2.1. Brick & Mortar Stores

8.2.2. Supermarkets & Hypermarket

8.2.3. Specialty Stores

8.3. Online Retail

9. Artificial Intelligence in Retail Market, by End-User

9.1. Introduction

9.2. Food & Groceries

9.3. Health & Wellness

9.4. Automotive

9.5. Electronics & White Goods

9.6. Fashion & Clothing

9.7. Other

10. Artificial Intelligence in Retail Market, by Deployment Type

10.1. Introduction

10.2. Cloud

10.3. On-Premise

11. Global Artificial Intelligence in Retail Market, by Geography

11.1. Introduction

11.2. North America

11.3. Europe

11.4. Asia-Pacific

11.5. Latin America

11.6. Middle East & Africa

12. Competitive Landscape

12.1. Competitive Growth Strategies

12.1.1. New Product Launches

Read the rest here:

Artificial Intelligence in Retail Market Projected to Grow with a CAGR of 35.9% Over the Forecast Period, 2019-2025 - ResearchAndMarkets.com - Yahoo...

Artificial intelligence (AI) is informing just about every facet of society, from detecting fraud and surveillanceto helping countries battle the current COVID-19 pandemic. But AI is a thorny subject, fraught with complex terminology, contradictory information, and general confusion about what it is at its most fundamental level. This is why the Oxford Internet Institute (OII), the University of Oxfords research and teaching department specializing in the social science of the internet, has partnered with Google to launch a portal with a series of explainers outlining what AI actually is including the fundamentals, ethics, its impact on society, and how its created.

The Oxford Internet Institute is a multidisciplinary research and teaching department of the University of Oxford, dedicated to the social science of the Internet.

At launch, the A-Z of AI covers 26 topics, including bias and how AI is used in climate science, ethics, machine learning, human-in-the-loop, and Generative adversarial networks (GANs).

Googles People and AI Research team (PAIR) worked with Gina Neff, a senior research fellow and associate professor at OII, and her team to select the subjects they felt were pivotal to understanding AI and its role today.

The 26 topics chosen are by no means an exhaustive list, but they are a great place for first-timers to start, the guides FAQ section explains. The team carefully balanced their selections across a spectrum of technical understanding, production techniques, use cases, societal implications, and ethical considerations.

For example, bias in data sets is a well-documented issue in the development of AI algorithms, and the guide briefly explains how the problem is created and how it can be addressed.

Typically, AI forms a bias when the data its given to learn from isnt fully comprehensive and, therefore, starts leading it toward certain outcomes, the guide reads. Because data is an AI systems only means of learning, it could end up reproducing any imbalances or biases found within the original information. For example, if you were teaching AI to recognize shoes and only showed it imagery of sneakers, it wouldnt learn to recognize high heels, sandals, or boots as shoes.

You can peruse the guide in its full A-Z form or filter content by one of four categories: AI fundamentals, Making AI, Society and AI, and Using AI.

Those with a decent background in AI will find this guide simplistic, but its a good starting point for anyone looking to grasp the key points they will be hearing about as AI continues to shape society in the years to come.

Its also worth noting that this isnt a static resource the plan is to update it as AI evolves.

The A-Z will be refreshed periodically as new technologies come into play and existing technologies evolve, the guide explains.

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Google and the Oxford Internet Institute explain artificial intelligence basics with the A-Z of AI - VentureBeat

Hi Seth, you are a pioneer in the AI and Emerging tech realm. How did you start with Infosys?

I joined Infosys in June of 2019. The reason I came here is that we have the unique intersection of being able to build an executable strategy. Many services firms love to do strategy work and then fail at execution. Some are great at execution but make everything about price.

What drew me to Infosys is that we make it about realized value. Our clients choose whom they want to work with, and we find they want to work with us because we can flawlessly execute the strategies that we build for them.

A great joke I recently heard is that ML is written in Python and AI is written in Powerpoint. What excited me is we are at the beginning of the next great generational shift in technology. The last one was the Internet and the PC before that. We see tectonic shifts every 20 years or so. We are at the beginning of the next great journey and that is exciting.

In AI/ML what excites me is doing simple things. It is about predicting what employee may get injured on the job and preventing that from happening. It is about looking at how to evaluate why something happened and using that knowledge to drive predictions on next best action. These are things that we couldnt have done two years ago and now we can. Someone will go home from work today that may not have two years ago because of the work we do.

What we find across our client base is a similarity of problems that they are looking to solve. All of our clients are trying to figure out how to improve the customer experience while driving employee engagement. We are lucky to be able to serve the worlds best clients and leverage our experience to help them get to answers faster.

Read More: AiThority Interview with Frans van Hulle, CEO and Co-Founder at PX

The first focus of our team is having a diverse talent pool. Unconscious bias is part of AI. The way we make sure our models are more effective is to consciously build diverse talents across all of the aspects necessary to achieve the right talent mix.

We build strategies and solutions that solve the most vexing issues our clients have. Our favorite engagement is when a client has tried to achieve success and needs our experience to complete their success.

Python, R, Juptyr, TensorFlow, Keras, Basically everything AWS, Google Cloud, Azure; none of those things existed in the form we use today 24-36 months ago. The three major players in the Automation space werent companies 4 years ago.

Can you imagine running a company today and not having a built out RPA strategy? These technologies exist because the intersection of aspiration and technology crossed paths.

The best feedback loop is a continuous feedback loop. Products that are built well are built are ethnographic research. What is the problem your customer is trying to solve? Observe how a customer is trying to solve something and you will learn how to build a better mousetrap.

Digital shelf is about taking all of the lessons learned in the physical world and bringing them to the digital world. How do you ensure that your products are what comes up when someone uses a Voice search? What can a company do to have their product be featured on the digital end cap? What techniques do you use to measure the promotion effectiveness of digital product placement?

Treating your digital shelf with the same rigor that you place on your physical one is what we help our clients solve.

Ever since the day after the first CIO was hired, she has been hearing cut budgets to be more efficient. For almost 20 years all anyone has heard is that IT is not a competitive differentiator. Any CIO that still believes that wont be CIO for much longer.

The CIO must be part of the enterprise change journey. That journey includes getting the human capital an organization has to reskill themselves and let go that ownership of a stack is where your importance comes from. Stacks are becoming irrelevant. Your expertise in showing how to drive customer interactions, improved financial performance and employee engagement is what is most valued now.

Our secret sauce is to add value to every interaction that we have with our clients. We discuss problems that we observe that they have. We dont wait to respond to a RFP to show what we are capable of.

Our team proactively goes to our clients as advisors and tell them where we think they can have differentiated performance. That is how we ensure that we are memorable with our clients.

Read More: AiThority Interview with Nicole Silver, Vice President of Marketing at Button

There are two types of companies. There are companies have developed their RPA strategy, implemented it and are seeing benefits. The other company will be their peers that havent done that yet, which are now at a competitive disadvantage.

The importance of Intelligent Automation, which is the next wave of RPA, cannot be overstated. Name the #1 company in the world 50 years ago, it was General Motors. Now it isnt in the top 10. Companies transform over time and RPA will drive whoever is the next #1 company.

AI is not a destination, it is a once in a generation transformation that will take time. We need to not pay attention to hypecycles that exist around things like Blockchain. All technology has a viable use case for some clients. What is most important is that we dont treat everything like the newest shiny object and run to it.

Add value in everything you do. Be memorable for adding value, not for speaking more than everyone else.

Yeesh, tough question. I would have to say the best superhuman I feel connected to is my wife. After 20 years of living with me, she has to be superhuman.

Jim Fowler, CIO at Nationwide

Thank you, Seth! That was fun and hope to see you back on AiThority soon.

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AiThority Interview with Seth Siegel, AI Consulting at Infosys - AiThority

Scientists have developed an artificial intelligence system that can translate a persons thoughts into text by analysing their brain activity. Researchers at the University of California developed the AI to decipher up to 250 words in real-time from a set of between 30 and 50 sentences.The algorithm was trained using the neural signals of four women with electrodes implanted in their brains, which were already in place to monitor epileptic seizures. The volunteers repeatedly read sentences aloud while the researchers fed the brain data to the AI to unpick patterns that could be associated with individual words. The average word error rate across a repeated set was as low as 3%.'; var randomNumber = Math.random(); var isIndia = (window.geoinfo && window.geoinfo.CountryCode === 'IN') && (window.location.href.indexOf('outsideindia') === -1 ); console.log(isIndia && randomNumber A decade after speech was first decoded from human brain signals, accuracy and speed remain far below that of natural speech, states a paper detailing the research, published in the journal Nature Neuroscience. We trained a recurrent neural network to encode each sentence-length sequence of neural activity into an abstract representation, and then to decode this representation, word by word, into an English sentence, the report states.The system is, however, still a long way off being able to understand regular speech. People could become telepathic to some degree, able to converse not only without speaking but without words, the report stated.

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Artificial Intelligence turns a persons thoughts into text - Times of India

Written by AZoRoboticsApr 1 2020

Businesses are often tempted to employ a range of technologies, including artificial intelligence (AI), to enhance performance, reduce labor costs, and improve the bottom linea fact that is logical.

Image Credit: Rensselaer Polytechnic Institute.

However, before opting for automation that can potentially risk the jobs of humans, business owners should carefully assess their operations.

According to Chris Meyer, a professor of practice and the director of undergraduate education at the Lally School of Management at Rensselaer Polytechnic Institute, the same method should not be used when applying AI to each business.

Meyer had studied this topic and has now detailed this in a recent conceptual paperpublishedin an exclusive issue of the Journal of Service Management on AI and Machine Learning in Service Management.

AI has the potential to upend our ideas about what tasks are uniquely suited to humans, but poorly implemented or strategically inappropriate service automation can alienate customers, and that will hurt businesses in the long term.

Chris Meyer, Professor of Practice and Director of Undergraduate Education, The Lally School of Management, Rensselaer Polytechnic Institute

Based on Meyers findings, the option to utilize AI or automation has to be a strategic decision. For example, if a companys business competes by providing an array of service offerings that shift from one client to another, or by offering a considerable amount of human interaction, then its business will experience a lower success rate if human experts are replaced with AI technologies.

Meyer further observed that the reverse is also true: Businesses that restrict customer interaction and choice will witness better success if they decide to automate.

Business leaders planning to migrate to automation should cautiously assess their strategies for handling knowledge resources. Before investing in AI, companies should first understand whether it is a strategically viable option to use algorithms and digital technologies in the place of human interaction and judgment.

The ideas are of use to managers, as they suggest where and how to use automation or human service workers based on ideas that are both sound and practical. Managers need guidance. Like any form of knowledge, AI and all forms of service automation have their place, but managers need good models to know where that place is.

Chris Meyer, Professor of Practice and Director of Undergraduate Education, The Lally School of Management, Rensselaer Polytechnic Institute

Meyer also established that in businesses where reputation and trust are vital factors in fostering and sustaining a client base, individuals will probably be effective than that of automated technologies.

On the other hand, in businesses where human biases are specifically dangerous to the service provision, AI will serve as a comparatively better tool for companies.

Meyer further stressed that several businesses will eventually be utilizing a combination of automation and peoples skills to compete effectively. Even AI, which can manage highly complicated jobs, works optimally alongside humansand the other way round.

Automation and human workers can and should be used together. But the extent of automation must fit with the businesss strategic approach to customers.

Chris Meyer, Professor of Practice and Director of Undergraduate Education, The Lally School of Management, Rensselaer Polytechnic Institute

Source: https://rpi.edu/

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The Limitations of Artificial Intelligence in Businesses - AZoRobotics

Credit: Vascular Development Laboratory and EM Unit

A blood test that can detect over 50 cancer types is big news this week.

Theres a lot of excitement around the latest research, published in Annals of Oncology. And its easy to see why.

Scientists have used machine learning to help identify if someone has cancer based on tiny bits of tumour DNA floating in their blood. Which could open the door to a blood test that can detect and identify multiple types of cancer.

But its not there yet. And in the blood test buzz, some news articles have missed out crucial details.

The team looked for differences in the DNA shed from cancer cells and healthy cells into the blood.

They focused on differences in a chemical tag that sit on top of DNA in cells, called methyl groups. These groups are usually spread evenly across the DNA in cells, but in cancer cells they tend to cluster at different points. And its this distinction scientists wanted to exploit.

They trained a machine learning algorithm a type of artificial intelligence that pick up patterns and signals to detect differences between methylation patterns in DNA from cancer and non-cancer cells.

The algorithm was trained on 3,052 samples from people with and without cancer from two large databases.

And once the program was fired up and ready to go, the team tested its cancer-spotting ability on a different set of 1,264 samples f, half of which were from people with cancer.

Any test with the goal of being able to detect cancers at their earliest stages in people without symptoms must strike the right balance between picking up cancer (sensitivity) and not giving false positives (specificity). Weve blogged before about what makes a good cancer test, as well as the efforts to develop a cancer blood test.

How do you assess a cancer test?

Researchers look at 3 main things when assessing a new diagnostic test.

Firstly the good news: fewer than 1% of people without cancer were wrongly identified as having the disease. Which is a good sign for the specificity of this test.

And when it came to detecting cancer, across all types of cancer, the test correctly identified the disease in 55% of cases. This is a measure of the tests sensitivity.

But there was a huge variation in sensitivity depending on the type of cancer and how advanced the disease was. The test was better at picking up more advanced cancer, which makes sense more advanced cancers typically shed more DNA into the bloodstream.

If we look at the numbers, across all cancer types the test correctly detected the disease in 93% of those with stage 4 cancer, but only 18% of early, stage 1 cancers.

An important consideration is that the study was only testing if the algorithm could detect cancer in patients who were already known to have cancer. According to the researchers, these figures may change if the test was used on a wider, general population.

Encouragingly for a multicancer test, when the researchers looked at a smaller number of samples to explore if the test helped them identify where the cancer was growing, the algorithm was able to predict the location in 96% of samples, and it was accurate in 93%.

First things first, although the samples numbers are big, they become a lot smaller when you break them down by cancer type and cancer stage. Some cancer types were particularly poorly represented, with only 1 or 2 samples included in the final analysis so theres more work to do there. Based on this, its a bit too soon to say that the test can pick up 50 cancer types.

And if the plan is to use this as a screening tool, then the researchers will need to do more to study people who didnt have symptoms when they were diagnosed. The current study included people who were symptomatic as well as people without symptoms.

And the participant data lacked variation in age, race and ethnicity. Between 83 and 87% of all the samples used to train and test the algorithm were Caucasian.

The big conclusion is that these results are encouraging and should be taken forward into bigger studies. But its important to put the results in context theyre a step in the right direction. There are a lot of steps between this study and a fully-fledged cancer test.

According to the research team, they plan to validate the results using samples from US and UK studies, and well as to begin to examine if the test could be used to screen for cancer. We look forward to seeing the results.

Our head of early detection research, Dr David Crosby, sums it up nicely: Although this test is still at an early stage of development, the initial results are encouraging. And if the test can be fine-tuned to be more efficient at catching cancers in their earliest stages, it could become a tool for early detection.

But more research is needed to improve the tests ability to catch early cancers and we still need to explore how it might work in a real cancer screening scenario.

Lilly

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New blood test study uses artificial intelligence to identify cancer. But its not ready for patients yet. - Cancer Research UK - Science Blog

The agency is looking at how to best apply curated data sets to new use cases.

The Department of Veterans Affairs is closer to expanding its use of artificial intelligence and developing novel use cases.

In looking back on the early stages of the VAs newly launched artificial intelligence program, the department's Director of AI Gil Alterovitz noted ongoing questions about how to best leverage AI data sets for secondary uses.

One of the interesting challenges is often that data is collected for maybe one reason, and it may be used for analyzing and finding results for that one particular reason. But there may be other uses for that data as well. So when you get to secondary uses you have to examine a number of challenges, he said at AFCEA's Automation Transformation conference.

Some of the most pressing concerns the VAs AI program hasencountered include questions of how to best apply curated data sets to newfound use cases, as well as how to properly navigate consent of use for proprietary medical data.

Considering the specificity of use cases, particularly for advanced medical diagnostics and predictive analytics, Alterovitz has proposed releasing broader ecosystems of data sets that can be chosen and applied depending on the demands of specific AI projects.

Theres a lot to think about data sets and how they work together. Rather than release one data set, consider releasing an ecosystem of data sets that are related," he said."Imagine, for example, someone is searching for a trial you have information about. Consider the patient looking for the trial, the physician, the demographics, pieces of information about the trial itself, where its located. Having all that put together makes for an efficient use case and allows us to better work together."

Alterovitz also discussed the value of combining structured and unstructured data sets in AI projects, a methodology that Veterans Affairs has found to provide stronger results than using structured data alone.

When you look at unstructured data, there have been a number of studies in health care looking at medical records where if you look at only structured data or only unstructured data individually, you dont get as much of a predictive capability whether it be for diagnostics or prognostics as by combining them, he said.

Beyond refining and expanding these data applications methodologies, the VA also appears attentive to how to best leverage proprietary medical data while protecting personally identifying information.

The solution appears to lie in creating synthetic data sets that mimic the statistical parameters and overall metrics of a given data set while obscuring the particularities of the original data set it was sourced from.

How do you make data available considering privacy and other concerns?" Alterovitz said."One area is synthetic data, essentially looking at the statistics of the underlying data and creating a new data set that has the same statistics, but cant be identified because it generates at the individual level a completely different data set that has similar statistics."

Similarly, creating select variation within a given data set can serve to remove the possibility of identifying the patient source, You can take the data, and then vary that information so that its not the exact same information you received, but is maybe 20% different. This makes it so you can show its statistically not possible to identify that given patient with confidence.

Going forward, the VA appears intent on solving these quandaries so as to best inform expanded AI research.

A lot of the data we have wasnt originally designed for AI. How you make it designed and ready for use in AI is a challenge and one that has a number of different potential avenues, Alterovitz concluded

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VA Looking to Expand Usage of Artificial Intelligence Data - GovernmentCIO Media