Is AI core to growing your business?

Artificial intelligence technology is powering big changes across all industries, but its tough to separate out the companies with truly transformative applications from marketing hype. Thats why Forbes is compiling a list of promising startups that are emerging as leaders in this space.

Is AI at the heart of what your company does, not just a driver for an auxiliary business or way to improve an existing product? We want to hear from you.

Nominations are now open for the second annual Forbes AI list, which seeks to highlight private companies that are applying artificial intelligence to solve problems in innovative ways.

Forbes, in partnership with Sequoia Capital and Meritech Capital, will evaluate hundreds of companies based on metrics including revenue, growth and valuation, with a panel of experts weighing in on how innovative and mission-critical each companys use of AI is (versus buzzwords thrown onto a slide-deck).

We welcome any U.S.-based private company to apply by filling out this form by Friday, April 10. The number of nominations wont influence our selection, so stick to just one per company, please.

We look forward to hearing from you!

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Is Your Company Using Artificial Intelligence To Transform An Industry? Nominations For The Forbes 2020 AI 50 List Are Now Open - Forbes

Very few subjects in science and technology have caused much excitement right now as artificial intelligence as some of the worlds brightest minds have said that its potential to revolutionise all aspects of our lives.

AI makes it practical for machines to understand from experience, act human-like jobs, and adapt to the latest inputs. The concept works by amalgamating enormous data with quick, smart algorithms, and iterative processing, enabling the software to decipher by analysing patterns in the data in an automatic way.

There is science and well thought algorithm behind all the artificial solutions, where you need to set up proper expectations and clarification to avoid any rumours and myths around the outputs.

While the notion of AI is turning into a massive component of business and consumer transformations, its execution is generally stagnated because of some misconceptions associated with it.

Myth 1: AI will deliver magical resultsimmediately

The path to AI success is hard and takes time, and not just because of the technology. You also need a strategic framework and an iterative approach to avoid delivering a random set of disconnected AI solutions. The temptation is to go for moonshots to deliver the magic, but such projects often fail to live up to expectations because you dont have the basics homework done.

AI is not a magic, it requires rigour, logical thinking and long term strategy with a patience to do multiple iteration to get to the result.

Myth 2: AI Will Replace Human Jobs

Most of the times, management look at AI solutions to replace human and reduce the operational cost, creating a sense of fear among the employees.

So, if you think that AI solutions might strip human from their jobs, then you are undeniably wrong.

Reality is, AI and human need each other. AI is at its most valuable when it augments peoples capabilities. It can remove the duplicate work, freeing people up for more strategic activities. That has the added benefit of making people more motivated, productive, and loyal. Enterprise AI also relies on people to feed it the right data and work with it the right way. Often, AI doesnt provide conclusive answers to issues, but rather highly informed recommendations that an actual human can weigh to make the final decision.

Myth 3: AI Implementation Needs Huge Investment

Artificial developments resolutions appear to be tremendously scientific and complicated. This inclination recommends that just a modern tech organisation, including Google, Amazon, or Apple, with an extended team of experts and billion-dollar budgets can pay for implementing AI. In reality, there are a lot of smart tools existing for an enormous variety of organisation, which can be utilised to implement AI in their business procedures.

Myth 4: AI Algorithms are Competent to Process Any Data

Most of you must believe that ML algorithms are one of the most crucial elements in the entire system. An algorithm might appear to be robust and linked with the human brain, which can make intellect of any untidy data.

It is not possible, for algorithms, to make decisions without human intervention as they dont have magic power. It requires a specific piece of data to get impeccable results.

Myth 5: AI will Conquer Humanity

Machines are powerless to imagine similar to people and will barely be taught to do so. In fact, computers are going to have an optimistic impact on the world by supporting people in a lot of fields, building innovative business models, communities, and skills. Its certainly true that the advent of AI and automation has the potential to seriously disrupt labour and in many situations it is already doing just that. However, seeing this as a straightforward transfer of labour from humans to machines is a vast over-simplification. In fact, a lot of AI focus has been on reducing the drudgery" of day-to-day aspects of the work. AI gives an opportunity to upgrade your skills and move up in your career ladder at the same time.

About the Author: A technology and product leader, Rahul Kumar is Group Chief Product Officer with HT Media Group. An alumni of BIT Mesra, who later on honed his technology management skills from IIT Delhi, has been leveraging AI, ML and IOT to solve business and consumer problems across technology led startups and conglomerate.

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Artificial intelligence myths: Reality check - Livemint

WASHINGTON: US health and technology specialists on March 16 said they had launched a new collaborative venture to assemble a dataset of tens of thousands of scientific papers and literature on the coronavirus, which would then be analysed by artificial intelligence programs to find patterns and answer questions raised by the World Health Organisation about the pandemic.

The dataset includes 29,000 articles, including 13,000 full-text pieces of medical literature, which will be made available on a special website allowing data scientists and artificial intelligence programmers to propose tools and software code that can unearth insights from the articles, White House officials and experts told reporters in a conference call.

The venture came together after the White House Office of Science and Technology Policy issued a call to tech companies and research groups to figure out how artificial intelligence tools could be used to sift through thousands of research articles being published worldwide on the pandemic, said Lynn Parker, deputy chief technology officer at the White House office.

With data scientists and machine language experts mining the literature compilation known as Covid-19 Open Research Dataset, experts and White House officials expect to get help developing vaccines, forming new guidelines on how long social distancing should be maintained and other insights, Michael Kratsios, the US chief technology officer said.

The venture includes the National Library of Medicine, which is part of the National Institutes of Health, Microsoft, Allen Institute of AI, Georgetown University's Center for Security and Emerging Technology, the Chan Zuckerberg Initiative (named for Mark Zuckerberg, Facebook's founder, and his wife Priscilla Chan), and Kaggle, which is a unit of Google.

The Allen Institute's Semantics Scholar website will host the database of scientific articles and add to the collection over time, while Kaggle's platform, which provides access to about 4 million artificial intelligence researchers, will receive suggestions from the experts on tools and codes to use to mine the database, experts from both organisations said.

Scientists have been working and publishing their findings on various strains of coronavirus over the years, including other variants such as SARS, MERS, and the latest, Covid-19. The application of artificial intelligence tools to look for commonalities and differences among the thousands of such published articles will help the scientists spot things they may have missed, Eric Horvitz, Microsoft's chief scientific officer said.

"It's difficult for people to manually go through more than 20,000 articles and synthesise their findings," Anthony Goldbloom, co-founder and CEO of Kaggle said. "Recent advances in technology can be helpful here. We're putting machine readable versions of these articles in front of our community of more than 4 million data scientists. Our hope is that AI can be used to help find answers to a key set of questions about Covid-19."

"Sharing vital information across scientific and medical communities is key to accelerating our ability to respond to the coronavirus pandemic," said Cori Bargmann, head of science at the Chan Zuckerberg Initiative. "The new Covid-19 Open Research Dataset will help researchers worldwide to access important information faster."

Publishers of scientific journals and literature have agreed to make their full articles available to researchers so that machine learning algorithms can look for key insights from them, the experts said. As scientists around the world continue to publish new research, journal publishers have agreed to provide those articles in electronic form ahead of their printed versions, they said. CQ-Roll Call/Tribune News Service

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Artificial intelligence recruited to find clues about Covid-19 - The Star Online

(Washington, D.C.) The Army Research Laboratory is exploring new applications of AI designed to better enable forward operating robot tanks to acquire targets, discern and organize war-crucial information, surveil combat zones and even fire weapons when directed by a human.

For the first time the Army will deploy manned tanks that are capable of controlling robotic vehicles able to adapt to the environment and act semi-independently. Manned vehicles will control a number of combat vehicles, not small ones but large ones. In the future we are going to be incorporating robotic systems that are larger, more like the size of a tanks, Dr. Brandon Perelman, Scientist and Engineer, Army Research Laboratory, Combat Capabilities Development Command, Army Futures Command, told Warrior in an interview, Aberdeen Proving Ground, Md.

The concept is aligned with ongoing research into new generations of AI being engineered to not only gather and organize information for human decision makers but also advance networking between humans and machines. Drawing upon advanced algorithms, computer technology can organize, and disseminate otherwise dis-aggregated pools of data in seconds -- or even milliseconds. AI-empowered sensors can bounce incoming images, video or data off a seemingly limitless existing database to assess comparisons, differences and perform near real-time analytics.

At the speed of the most advanced computer processing, various AI systems can simultaneously organize and share information, perform analyses and solve certain problems otherwise impossible for human address within any kind of comparable timeframe. At the same time, there are many key attributes, faculties and problem solving abilities unique to human cognition. The optimal approach is, according to Perelman, to simultaneously leverage the best of both.

We will use the power of human intelligence and the speed of AI to get novel interactions, Perelman added.

This blending, or synthesis of attributes between mind and machine is expected to evolve quickly in coming years, increasingly giving warzone commanders combat-sensitive information much faster and more efficiently. For instance, a forward operating robotic wingman vehicle could identify a target that might otherwise escape detection, and instantly analyze the data in relation to terrain, navigational details, previous missions in the area or a database of known threats.

You have an AI system that is not better than a human but different than a human. It might be faster and it might be more efficient at processing certain kinds of data. It will deal with threats in concert with human teammates that are completely different than the way we do things today, Perelman said.

With these goals in mind, the ARL is now working on mock up interfaces intended to go into the services emerging family of Next Generation Combat Vehicles. Smaller robots such as IED-clearing PackBots have been in existence for more than a decade; many of them have integrated software packages enabling various levels of semi-autonomy, able to increasingly perform a range of tasks without needing human intervention. Current ARL efforts now venture way beyond these advances to engineer much greater levels of autonomy and also engineer larger robots themselves such as those the size of tanks.

Army Research Lab Mock Up of Next-Gen Combat Vehicle AI-Enabled System

Bringing this kind of manned-unmanned teaming to fruition introduces new strategic and tactical nuances to combat, enabling war commanders a wider and more immediate sphere of options.

Commanders will be able to view a target through vehicle sensor packages, or if there is an aided target recognition technology or some kind of AI to spot targets, they might see battlespace target icons pop up on the map indicating the location of that target, Perelman said.

AI-oriented autonomous platforms can greatly shorten sensor-to-shooter time and enable war commanders to quickly respond to, and attack, fast emerging moving targets or incoming enemy fire.

Everything that a soldier does today. Shooting, moving, communicating.. Will be different in the future because you do not just have human to human teammates, you have humans working with AI-teammates, Perelman said.

Enabling robots to understand and properly analyze humans is yet another challenging element of this complex equation. When you have two humans, they know when the other is cold and tired, but when you bring in an AI system you dont necessarily have that shared understanding, Perelman said.

Various kinds of advanced autonomy, naturally, already exists, such as self-guiding aerial drones and the Navys emerging ghost fleet of coordinated unmanned surface vessels operating in tandem. Most kinds of air and sea autonomous vehicles confront fewer operational challenges when compared to ground autonomy. Ground warfare is of course known to incorporate many fast-changing variables, terrain and maneuvering enemy forces - at times to a greater degree than air and sea conditions - fostering a need for even more advanced algorithms in some cases. Nevertheless, the concepts and developmental trajectory between air, land and ground autonomy have distinct similarities; they are engineered to operate as part of a coordinated group of platforms able to share sensor information, gather targeting data and forward-position weapons -- all while remaining networked with human decision makers.

You can take risks you would never do with a manned platform. A robotic system with weapons does not need to account for crew protection, Perelman said.

Interestingly, the Army Research Lab current efforts with human-machine interface are reinforced by an interesting 2015 essay in the International Journal of Advanced Research in Artificial Intelligence, which points to networking, command and control and an ability to integrate with existing technologies as key to drone-human warfare.

They (drones) should effectively interact with manned components of the systems and operate within existing command and control infrastructures, to be integral parts of the system, in Military Robotics: Latest Trends and Spatial Grasp Solutions, by Peter Simon Sapaty - Institute of Mathematical Machines and Systems, National Academy of Sciences.

Increased use of networked drone warfare not only lowers risks to soldiers but also brings the decided advantage of being able to operate in more of a dis-aggregated, or less condensed formation, with each drone and soldier system operating as a node in a larger integrated network. Dispersed forces can not only enable longer-range connectivity and improved attack options but also reduce force vulnerability to enemy fire by virtue of being less aggregated.

Despite the diversity of sizes, shapes, and orientations, they (drones and humans) should all be capable of operating in distributed, often large, physical spaces, thus falling into the category of distributed systems, Sapaty writes in the essay.

Also of great significance, Army thinkers explain, is that greater integration of drone attack assets can streamline a mission, thereby lessening the amount of soldiers needed for certain high-risk operations.

When you are calling in artillery or air support, there is a minimum distance from where you are able to do that as a human being. You dont have the same restrictions with robotic systems, so it allows you to take certain risks, Perelman.

A paper in an Army University Press publication explains how drones can expand the battlefield. By utilizing drone systems for combatfewer warfighters are needed for a given mission, and the efficacy of each warfighter is greater. Next, advocates credit autonomous weapons systems with expanding the battlefield, allowing combat to reach into areas that were previously inaccessible, the essay states. (Amitai Etzioni, Phd, Oren Etzioni, Phd)

This article by Kris Osborn originally appeared in WarriorMaven in 2020.

Kris Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army - Acquisition, Logistics& Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.

Image: Reuters

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The Army Will Soon Be Able to Command Robot Tanks With Artificial Intelligence - The National Interest

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Applying artificial intelligence to everything were comfortable doing in banking is much easier than changing how we do things which would make the greatest use of AI.

Few in financial services would argue that the future belongs to those institutions that harness data-driven machine intelligence to do more, better and faster. The insights and efficiencies needed to compete and thrive will come from AI-driven service personalization and optimization.

But AI should do more than speed up a financial assembly line. As Ernst & Young stated in a report: AI-driven financial health systems will become personal financial operating systems. Consumer finance will unbundle products and rebundle personalized and holistic value propositions based on life events.

While that is a worthy goal, the retail banking industry will not come any closer to achieving that if it continues the way it is thinking about and implementing AI.

I call the current mindset for applying AI to financial services the Product Gun. Its the familiar banking model of manufacturing a product, targeting a market segment for distribution, ensuring everything complies, and then shooting it to potential consumers. Its worked well for many years, but its had its day.

Hopes of providing consumers with personal financial operating systems and solutions tailored to life events wont happen merely by blending AI with the same old thing. In fact, applying complexity and leverage to well-understood financial products and processes may produce unintended consequences.

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But rethinking from the ground up can be rare. Models and the basic process behind them often dont change because business typically likes to save energy. Take the Boeing 737. The jets design dates back to 1964. The first one flew in 1967. The latest iteration still flies today. This makes a perfect example of leveraging an old business model to sustain profits as the saying goes, if it aint broke

Because banking is a regulated industry that deals with heaps of money and risk, a control structure has evolved to organize competencies and lines of business. Risk and profit are put in little boxes for success. Boxes like manufacture, target, and comply all have executives, KPIs, spreadsheets and politics. On the whole, it has worked well.

The problem is, innovative tools like AI get shoved into the same old boxes. Instead of using this technology to reimagine traditional processes, we use AI to build a supercharged 737.

This has some benefits to financial institutions business lines. This could include improving the consumer credit process, reducing compliance exceptions or automating support desks. Each of these, and similar applications of AI, could benefit the industry and those it serves.

However, AI can produce missteps, such as unwittingly biased outcomes. At best staying trapped inside old processes with new AI insides will do no harm, but its still not going to bring us closer to a vision for personalized, holistic financial advice.

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I want to propose something radical: Financial institutions should be optimizing for their clients needs. This sounds extremely simple but the commitment required, and the roadmap to making this reality is serious, expensive and difficult when it can take a long time to deliver on expectations.

Jeff Bezos said: Put the customer first. Invent. And be patient. It took Amazon 20 years to be profitable, and during that time Bezos kept investing to optimize his understanding of and delivery for his customers. Amazons impressive margins came about relatively recently, and only after a long battle.

The alternative to the traditional Product Gun attitude is something I call Mother Mind. This goes beyond simply shooting products at people. It gathers intelligence about what and who people are and what they need. It understands deeply what customers are going through in their lives, then it guides them with strategies that are actually going to be useful in the context of their lives. Used in this way, AI can keep guiding an institution in ways to better serve people and businesses.

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3 Stages of Consumer Lending Maturity

In this live webinar, experts from Kasasa will discuss the 3 stages of consumer lending maturity and how to move your institution from a risk-averse program to a consumer-centric growth model.

tuesday, April 14th at 2pm EST

Change is hard for financial institutions, but it can happen. Here are three achievable pivots that can help put a bank or credit union on the path to success.

Gather: Move from system-centric data to human-centric data. Even under the progressive framework of Europes PSD2 open banking framework, financial institutions still store and access data in a system-centric way: transactions, products, accounts and balances. Data organized this way makes it very difficult to understand much about peoples individual circumstances.

Today data comes in the language of systems and ledgers. To do anything radically different requires shifting to the language of human lives. This means building interfaces to data that will allow financial institutions to ask questions about peoples behavior and needs. What are their financial personalities? What events in their lives offer the chance to be of assistance?

Understand: Move from products to journeys. The word customer-centric means nothing if products continue to be bankings foundation. How do we distribute the product for less? How do we recommend products to customers at the right time? such thinking is inverse, today.

Consumers needs change as their lives and circumstances change. At any point and time they have problems that need solutions and questions that need answers. Whether or not these journeys are successful is going to start meaning a lot more. Customer love or hate is going to be a profitability issue in a world where switching providers is easy. Focusing on understanding people will result in institutions working in a completely different way the measure wont be on sales but on problems resolved.

Guide: Move from selling to advising. By virtue of living in a product-centric world, financial institutions have become sales-driven. But when the barrier to entry to manufacturing and distributing products keeps lowering, traditional institutions increasingly find themselves fighting fintechs and others for turf they used to think they owned. Shiny objects may grab attention and move a sale once, but when thats over, if an institution hasnt built a meaningful relationship, people will leave.

People want to be understood, and they want to be cared for. In the context of financial services, this means people want advice. Advice is not about buying a product. Its about working towards goals, planning for transitions and hopefully creating an overarching, happy story of personal wealth.

Putting energy into human-centric data and focusing on understanding makes the aspiration of providing personalized holistic advice more possible.

The personal financial operating system wont happen overnight, but institutions can move towards it. Personal financial management offerings that keep people aware of their situation, tools that help them plan for retirement, and hybrid advice platforms that enable collaboration are all steps in the right direction.

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Rethinking Financial Services with Artificial Intelligence Tools - The Financial Brand

DUBLIN--(BUSINESS WIRE)--The "North America Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User Industry" report has been added to ResearchAndMarkets.com's offering.

The North America artificial intelligence in fashion market accounted for US$ 128.7 Mn in 2018 and is expected to grow at a CAGR of 37.9% over the forecast period 2019-2027, to account for US$ 2254.2 Mn in 2027.

The artificial intelligence in fashion market is fragmented in nature due to the presence of several end-user industries, and the competitive dynamics in the market are anticipated to change during the coming years. In addition to this, various initiatives are undertaken by governmental bodies to accelerate the artificial intelligence in fashion market further. The North American countries are developing various policies and outlining best practices to implement artificial intelligence for promoting innovation in various industry sectors.

Further, the political agendas for North American countries are aligned with the development of Machine Learning (ML) and Artificial Intelligence (AI). Artificial intelligence technologies such as self-adapting machine learning, deep learning or Natural language processing are expected to transform the way businesses work. Governments of various North American countries are working on drafting robust and comprehensive set of regulations and policies for a holistic development of artificial intelligence in this region.

Reasons to Buy

Key Topics Covered:

1. Introduction

2. Key Takeaways

3. Research Methodology

3.1 Coverage

3.2 Secondary Research

3.3 Primary Research

4. Artificial Intelligence in Fashion Market Landscape

4.1 Market Overview

4.2 PEST Analysis - North America

4.3 Ecosystem Analysis

4.4 Expert Opinions

5. Artificial Intelligence in Fashion Market - Key Market Dynamics

5.1 Key Market Drivers

5.1.1 Availability of a huge amount of data originating from different data sources

5.1.2 Increase in adoption of artificial intelligence in fashion industry to enhance operational efficiency and improve customer experiences

5.2 Key Market Restraints

5.2.1 Concerns related to data privacy and security

5.3 Key Market Opportunities

5.3.1 Huge investments in developing NLP enabled solutions are anticipated to flourish the market growth

5.4 Future Trend

5.4.1 Use of AI for predicting fashion trends

5.5 Impact Analysis of Drivers and Restraints

6. Artificial Intelligence in Fashion Market - North America Market Analysis

6.1 Overview

6.2 North America Artificial Intelligence in Fashion Market Forecast and Analysis

7. North America Artificial Intelligence in Fashion Market - By Offerings

7.1 Overview

7.2 North America Artificial Intelligence in Fashion Market Breakdown, by Offerings, 2018 & 2027

7.3 Solutions

7.4 Services

8. North America Artificial Intelligence in Fashion Market - By Deployment

8.1 Overview

8.2 North America Artificial Intelligence in Fashion Market Breakdown, by Deployment, 2018 & 2027

8.3 On-premise

8.4 Cloud

9. North America Artificial intelligence in fashion Market - By Application

9.1 Overview

9.2 North America Artificial intelligence in fashion Market Breakdown, By Application, 2018 & 2027

9.3 Product Recommendation

9.4 Virtual Assistant

9.5 Product Search and Discovery

9.6 Creative Designing and Trend Forecasting

9.7 Customer Relationship Management (CRM)

9.8 Others

10. North America Artificial intelligence in fashion Market Analysis - By End User Industry

10.1 Overview

10.2 North America Artificial intelligence in fashion Market Breakdown, By End User Industry, 2018 & 2027

10.3 Apparel

10.4 Accessories

10.5 Cosmetics

10.6 Others

11. North America Artificial Intelligence in Fashion Market - Country Analysis

11.1 Overview

11.1.1 North America Artificial Intelligence in Fashion Market Breakdown, by Key Countries

11.1.1.1 US Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

11.1.1.2 Canada Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

11.1.1.3 Mexico Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

12. Artificial Intelligence in Fashion Market - Industry Landscape

12.1 Overview

12.2 Market Initiative

12.3 New Development

12.4 Top Five Company Ranking

13. Company Profiles

13.1 Adobe Inc.

13.2 Alphabet Inc. (Google)

13.3 Amazon.com, Inc.

13.4 Catchoom

13.5 Facebook Inc.

13.6 Huawei Technologies Co., Ltd.

13.7 IBM Corporation

13.8 Microsoft Corporation

13.9 Oracle Corporation

13.10 SAP SE

For more information about this report visit https://www.researchandmarkets.com/r/mgbpsb

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Insights into the North America Artificial Intelligence in Fashion Market to 2027 - Drivers, Restraints, Opportunities and Trends -...

Using computers and machine learning to make sense of mountains of biomedical data is nothing new. But the team at the Massachusetts Institute of Technology, led by James Collins, who studies applications of systems biology to antibiotic resistance, and Regina Barzilay, an artificial-intelligence researcher, achieved success by developing a neural network that avoids scientists potentially limiting preconceptions about what to look for. Instead, the computer develops its own expertise.

Read: Antibiotic resistance is everyones problem

With this discovery platform, which has been made freely available, youre going to identify molecules that dont look like antibiotics youre used to seeing, Collins said. It really shows how you can use the emerging technology of deep learning in an innovative manner to discover new chemistries.

Ever since Alexander Fleming derived the first antibiotic from fungus, nature has been the font for our antibacterial drugs. But isolating, screening and synthesizing thousands of natural compounds for laboratory tests is extremely expensive and time-consuming.

To narrow the search, researchers have sought to understand how bacteria live and multiply, and then pursued compounds that attack those processes (such as by damaging bacterias cell walls, blocking their reproduction, or inhibiting their protein production). You start with the mechanisms, and then you reverse engineer the molecule, Barzilay said.

Even with the introduction of computer-assisted, high-throughput screening methods in the 1980s, however, progress in antibiotic development was virtually nonexistent in the decades that followed. Screening occasionally turned up drug candidates that were toxic to bacteria, but they were too similar to existing antibiotics to be effective against resistant bacteria. Pharmaceutical companies have since largely abandoned antibiotic development, despite the need, in favor of more lucrative drugs for chronic conditions.

Read: How antibiotic resistance could make common surgeries more dangerous

The new work by Barzilay, Collins, and their colleagues, however, takes a radically fresh, almost paradoxical approach to drug discovery: It ignores how the medicine works. Its an approach that can succeed only with the support of extremely powerful computing.

Behind the new antibiotic finding is a deep neural network, in which the nodes and connections of its learning architecture are inspired by the interconnected neurons in the brain. Neural networks, which are adept at recognizing patterns, are deployed across various industries and consumer technologies for uses such as image and speech recognition. Conventional computer programs might screen a library of molecules to find certain defined chemical structures, but neural networks can be trained to learn for themselves which structural signatures might be usefuland then find them.

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An Unexpected Ally in the War With Bacteria - The Atlantic

Since the first report of coronavirus (COVID-19) in Wuhan, China, it has spread to at least 100 other countries. As China initiated its response to the virus, it leaned on its strong technology sector and specifically artificial intelligence (AI), data science, and technology to track and fight the pandemic while tech leaders, including Alibaba, Baidu, Huawei and more accelerated their company's healthcare initiatives. As a result, tech startups are integrally involved with clinicians, academics, and government entities around the world to activate technology as the virus continues to spread to many other countries. Here are 10 ways artificial intelligence, data science, and technology are being used to manage and fight COVID-19.

Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic

1. AI to identify, track and forecast outbreaks

The better we can track the virus, the better we can fight it. By analyzing news reports, social media platforms, and government documents, AI can learn to detect an outbreak. Tracking infectious disease risks by using AI is exactly the service Canadian startup BlueDot provides. In fact, the BlueDots AI warned of the threat several days before the Centers for Disease Control and Prevention or the World Health Organization issued their public warnings.

2. AI to help diagnose the virus

Artificial intelligence company Infervision launched a coronavirus AI solution that helps front-line healthcare workers detect and monitor the disease efficiently. Imaging departments in healthcare facilities are being taxed with the increased workload created by the virus. This solution improves CT diagnosis speed. Chinese e-commerce giant Alibaba also built an AI-powered diagnosis system they claim is 96% accurate at diagnosing the virus in seconds.

3. Process healthcare claims

Its not only the clinical operations of healthcare systems that are being taxed but also the business and administrative divisions as they deal with the surge of patients. A blockchain platform offered by Ant Financial helps speed up claims processing and reduces the amount of face-to-face interaction between patients and hospital staff.

4. Drones deliver medical supplies

One of the safest and fastest ways to get medical supplies where they need to go during a disease outbreak is with drone delivery. Terra Drone is using its unmanned aerial vehicles to transport medical samples and quarantine material with minimal risk between Xinchang Countys disease control centre and the Peoples Hospital. Drones also are used to patrol public spaces, track non-compliance to quarantine mandates, and for thermal imaging.

5. Robots sterilize, deliver food and supplies and perform other tasks

Robots arent susceptible to the virus, so they are being deployed to complete many tasks such as cleaning and sterilizing and delivering food and medicine to reduce the amount of human-to-human contact. UVD robots from Blue Ocean Robotics use ultraviolet light to autonomously kill bacteria and viruses. In China, Pudu Technology deployed its robots that are typically used in the catering industry to more than 40 hospitals around the country.

6. Develop drugs

Googles DeepMind division used its latest AI algorithms and its computing power to understand the proteins that might make up the virus, and published the findings to help others develop treatments. BenevolentAI uses AI systems to build drugs that can fight the worlds toughest diseases and is now helping support the efforts to treat coronavirus, the first time the company focused its product on infectious diseases. Within weeks of the outbreak, it used its predictive capabilities to propose existing drugs that might be useful.

7. Advanced fabrics offer protection

Companies such as Israeli startup Sonovia hope to arm healthcare systems and others with face masks made from their anti-pathogen, anti-bacterial fabric that relies on metal-oxide nanoparticles.

8. AI to identify non-compliance or infected individuals

While certainly a controversial use of technology and AI, Chinas sophisticated surveillance system used facial recognition technology and temperature detection software from SenseTime to identify people who might have a fever and be more likely to have the virus. Similar technology powers "smart helmets" used by officials in Sichuan province to identify people with fevers. The Chinese government has also developed a monitoring system called Health Code that uses big data to identify and assesses the risk of each individual based on their travel history, how much time they have spent in virus hotspots, and potential exposure to people carrying the virus. Citizens are assigned a color code (red, yellow, or green), which they can access via the popular apps WeChat or Alipay to indicate if they should be quarantined or allowed in public.

9. Chatbots to share information

Tencent operates WeChat, and people can access free online health consultation services through it. Chatbots have also been essential communication tools for service providers in the travel and tourism industry to keep travelers updated on the latest travel procedures and disruptions.

10.Supercomputers working on a coronavirus vaccine

The cloud computing resources and supercomputers of several major tech companies such as Tencent, DiDi, and Huawei are being used by researchers to fast-track the development of a cure or vaccine for the virus. The speed these systems can run calculations and model solutions is much faster than standard computer processing.

In a global pandemic such as COVID-19, technology, artificial intelligence, and data science have become critical to helping societies effectively deal with the outbreak.

For more on AI and technology trends, see Bernard Marrs bookArtificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problemsand his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.

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Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic - Forbes