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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

Artificial intelligence (AI) has the potential to help us tackle the pressing issues raised by the COVID-19 pandemic. It is not the technology itself, though, that will make the difference but rather the knowledge and creativity of the humans who use it.

Indeed, the COVID-19 crisis will likely expose some of the key shortfalls of AI. Machine learning, the current form of AI, works by identifying patterns in historical training data. When used wisely, AI has the potential to exceed humans not only through speed but also by detecting patterns in that training data that humans have overlooked.

However, AI systems need a lot of data, with relevant examples in that data, in order to find these patterns. Machine learning also implicitly assumes that conditions today are the same as the conditions represented in the training data. In other words, AI systems implicitly assume that what has worked in the past will still work in the future.

A new strain of Coronavirus, COVID 19, is spreading around the world, causing deaths and major disruption to the global economy.

Responding to this crisis requires global cooperation among governments, international organizations and the business community, which is at the centre of the World Economic Forums mission as the International Organization for Public-Private Cooperation.

The Forum has created the COVID Action Platform, a global platform to convene the business community for collective action, protect peoples livelihoods and facilitate business continuity, and mobilize support for the COVID-19 response. The platform is created with the support of the World Health Organization and is open to all businesses and industry groups, as well as other stakeholders, aiming to integrate and inform joint action.

As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the coronavirus.

What does this have to do with the current crisis? We are facing unprecedented times. Our situation is jarringly different from that of just a few weeks ago. Some of what we need to try today will have never been tried before. Similarly, what has worked in the past may very well not work today.

Humans are not that different from AI in these limitations, which partly explains why our current situation is so daunting. Without previous examples to draw on, we cannot know for sure the best course of action. Our traditional assumptions about cause and effect may no longer hold true.

Humans have an advantage over AI, though. We are able to learn lessons from one setting and apply them to novel situations, drawing on our abstract knowledge to make best guesses on what might work or what might happen. AI systems, in contrast, have to learn from scratch whenever the setting or task changes even slightly.

The COVID-19 crisis, therefore, will highlight something that has always been true about AI: it is a tool, and the value of its use in any situation is determined by the humans who design it and use it. In the current crisis, human action and innovation will be particularly critical in leveraging the power of what AI can do.

One approach to the novel situation problem is to gather new training data under current conditions. For both human decision-makers and AI systems alike, each new piece of information about our current situation is particularly valuable in informing our decisions going forward. The more effective we are at sharing information, the more quickly our situation is no longer novel and we can begin to see a path forward.

Projects such as the COVID-19 Open Research Dataset, which provides the text of over 24,000 research papers, the COVID-net open-access neural network, which is working to collaboratively develop a system to identify COVID-19 in lung scans, and an initiative asking individuals to donate their anonymized data, represent important efforts by humans to pool data so that AI systems can then sift through this information to identify patterns.

Global spread of COVID-19

Image: World Economic Forum

A second approach is to use human knowledge and creativity to undertake the abstraction that the AI systems cannot do. Humans can discern between places where algorithms are likely to fail and situations in which historical training data is likely still relevant to address critical and timely issues, at least until more current data becomes available.

Such systems might include algorithms that predict the spread of the virus using data from previous pandemics or tools that help job seekers identify opportunities that match their skillsets. Even though the particular nature of COVID-19 is unique and many of the fundamental rules of the labour market are not operating, it is still possible to identify valuable, although perhaps carefully circumscribed, avenues for applying AI tools.

Efforts to leverage AI tools in the time of COVID-19 will be most effective when they involve the input and collaboration of humans in several different roles. The data scientists who code AI systems play an important role because they know what AI can do and, just as importantly, what it cant. We also need domain experts who understand the nature of the problem and can identify where past training data might still be relevant today. Finally, we need out-of-the-box thinkers who push us to move beyond our assumptions and can see surprising connections.

Toronto-based startup Bluedot is an example of such a collaboration. In December it was one of the first to identify the emergence of a new outbreak in China. Its system relies on the vision of its founder, who believed that predicting outbreaks was possible, and combines the power several different AI tools with the knowledge of epidemiologists who identified where and how to look for evidence of emerging diseases. These epidemiologists also verify the results at the end.

Reinventing the rules is different from breaking the rules, though. As we work to address our current needs, we must also keep our eye on the long-term consequences. All of the humans involved in developing AI systems need to maintain ethical standards and consider possible unintended consequences of the technologies they create. While our current crisis is very pressing, we cannot sacrifice our fundamental principles to address it.

The key takeaway is this: Despite the hype, there are many ways that humans in which still surpass the capabilities of AI. The stunning advances that AI has made in recent years are not an inherent quality of the technology, but rather a testament to the humans who have been incredibly creative in how they use a tool that is mathematically and computationally complex and yet at its foundation still quite simple and limited.

As we seek to move rapidly to address our current problems, therefore, we need to continue to draw on this human creativity from all corners, not just the technology experts but also those with knowledge of the settings, as well as those who challenge our assumptions and see new connections. It is this human collaboration that will enable AI to be the powerful tool for good that it has the potential to be.

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Written by

Matissa Hollister, Assistant Professor of Organizational Behaviour, McGill University

The views expressed in this article are those of the author alone and not the World Economic Forum.

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COVID-19: AI can help - but the right human input is key - World Economic Forum

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

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

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.

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