As the global epidemic of the COVID -19 plagues the world, we are in danger of no learning from many of our past disasters and to really utilize and re-emphasize the many fields where AI and machine learning are beginning to make an impact. Implemented in the right way, AI can assist and inform first responders, as well as communities affected, AI can be leveraged to save lives in these turbulent times.

AI can support with the enrichment of optimization strategies, which are greatly needed as the turmoil around the world reaches epidemic proportions. Research on the use of machine learning to evaluate and optimize strategies for social distancing between communities, cities, and countries to control the spread of epidemics, detecting pattern, forecasting and prediction, while robotic use is already I practice.

 

AI in Detecting Patterns

AI can pinpoint patterns in a passel of data and make predictions, and the hope is these tools could identify drug prospects to test on humans within months. As coronaviruses such as COVID-19 mutate, a drug candidate will have to be effective against a broad spectrum of possible forms. Work is now underway worldwide, to use AI in pursuit of a vaccine.

AI will without a doubt, speed up vaccine development, the technologies rely on large quantities of accurate training data. A new, highly infectious disease for which there is limited data poses challenges for even the more sophisticated AI techniques.

AI can also be used to identify and locate commonalities within localized outbreaks of the virus, or with micro-scale adverse health events that are out of the ordinary. The insights from these events can help answer many of the unknowns about the nature of the virus.

 

AI In Forecasting

It’s primary strength is the way it increases our capacity to predict, and therefore plan for, events and circumstances. Considering that one of the most important ways to save lives in disasters is to have early warning, there’s a lot of good the technology can do.

The mere potential of AI isn’t just in predicting that a disaster will happen but in; forecasting where it will have the most impact, which systems are likely to fail, and what communities are in the most danger. This data can be used to improve decision-making about the issuing of building permits and insurance.

AI could prove invaluable to tracking and reporting the efforts as well as providing critique in the maintenance of critical equipment’s and systems.

 

AI On the Ground During a Disaster

Over the years where a natural disaster has occurred, people on the frontline have often turned to social media and ad-hoc volunteer groups in addition to, and sometimes instead of, relying on aid from the government or traditional charitable organizations. Local and wider communities have proven that they’re capable of coming together in the face of a disaster when aid doesn’t come quick enough.

Nearly all social media platforms already rely on machine learning algorithms for advertising, but additional AI functionality could be of great assistance during disasters, helping both ordinary people and first responders keep up to date and organized. Deployed in the right way, existing timeline algorithms could be used to deliver and distribute information where it’s most needed. Or, AI could be used to scrape information from millions of social media posts and clue rescue workers in to the hardest hit areas and people in the most need.

 

AI is needed to keep up

With unmanned robots being used to great effect in every aspect of disaster response, it is now time to make the best use of robots and our ability to gather more data, our data collection and analysis techniques must keep up. As big data grows, we need to ensure the capability to draw conclusions on it, to increase response time. Autonomous machines and AI algorithms, when combined, act as a significant force multiplier in our ability to protect people and property in the face of disaster.

Recently, a robot helped doctors treat an American man diagnosed with the novel coronavirus. The robot, which carried a stethoscope, helped the patient communicate with medical staff while limiting their own exposure to the illness. We see AI is currently being leveraged in diagnosing the illness. Several hospitals in China are using AI-based software to scan through CT images of patients’ lungs to look for signs of Covid-19, the infection caused by the novel coronavirus.

We are also seeing in this time, the coronavirus epidemic has also inspired several drug companies to use artificial intelligence-powered drug discovery platforms to search for possible treatments. That process can involve using AI to find entirely new molecules that might be capable of treating the pneumonia-like illness, or mining through databases of already-approved drugs (for other illnesses) that might also work against Covid-19.

Importantly, while AI drug discovery might speed up the process of finding candidates for new drugs and treatments, there’s no guarantee that the technology will come up with anything better than what human scientists could find on their own.

 

AI in Classrooms for distance study during the pendimic

  • Optional Lessons

By end of the pandemic and In the future, online courses would have advanced from omni-channel where students get the option to get the lesson anytime, anywhere and from any medium of choice  to omni-choice whereby students would have the options to customize and configure the education journey based on their individual needs and interest.

The classroom will be in a remote setting -home, office or during commute – and students would be able to continue their lesson on any device they have on hand for any duration and format.

The AI-driven personalization would enable students to focus on the content of the learning module, as opposed to the mechanics of the lesson delivery.

  • Wearables

Simulated classes with a limited number of possible scenarios will be a thing of a past and will be replaced instead with interactive, technology-based learning experience which will allow students to learn by doing.

The traditional delivery model will be abandoned, and borrowing digital strategies and innovations from the healthcare industry, the future classroom will feature IoT devices such as wearables which will allow for continual monitoring and trackings.

Data derived from these devices will then be paired with for example AI with cognitive capabilities to customize lesson plans based on predefined metrics.

  • Future counselors

Advisory and counseling in future will be carried out via various futuristic virtual interactive tools, such as voice calls, wearables, augmented reality and virtual reality to guide students through their education process.

The AI-driven virtual counselors will advise students on from course selection, lesson personalizations, degree completion to career planning based on in-depth data analysis and advanced algorithms, for each unique student, moving away from the current one size fits all education syllabi.

These virtual counselors will free up the workload of human counselors so that they focus more meaningful and strategic work functions.

 

Using Artificial Intelligence for development & grading

Course development for the institutions will soon be done by AI, based on the real-time data and up-to-date information.

The above will enable students to get only the most relevant and impactful lesson that will prepare them for the ever-changing job market as well as reducing the world load of course developers and administrators.

Beyond that, AI course grading would also have advanced, which will result in increased fairness and accuracy in assigning grades to students.

In conclusion, the education sector upon fully embracing digital transformation will be completely different from what it is today.

However, the success of these innovative technology adoptions hinges primarily in the ability to deliver on the set objectives and core functions of the educational institutions.

Hence, it is imperative that schools and universities of the future focus on developing a robust learning experience for their students while having a purpose to change the world for the better.

 

AI in Call Centres

Customer service centers are experiencing an unprecedented uptick in overall call volume. Airlines, Banks and credit card companies, including Capital One, are seeing longer-than-average hold times, with some customers reporting disconnections. Some companies warned their customers to expect “longer than usual” wait times as a result of precautionary health measures that have the company operating with a limited team.

As customer representatives are increasingly ordered to work from home in Manila, some companies are turning to AI to bridge the resulting gaps in service. The solutions aren’t perfect — there’s always going to be a need for human teams, even where chatbots are deployed  but COVID-19 has accelerated the need for AI-powered contact center messaging.

On the one hand, injecting more AI and automation into customer service is business as usual. Even before the pandemic, autonomous agents were on the way to becoming the rule rather than the exception, partly because consumers prefer it that way. According to research published last year, 25% of people prefer to have their queries handled by a chatbot or other self-service alternative. And Salesforce says roughly 69% of consumers choose chatbots for quick communication with brands.

But AI isn’t likely to replace human agents entirely — as recent developments have shown, it’s far from a perfect science. Twitter and YouTube said recently that as they increase their reliance on AI moderation, content might be flagged or taken down by mistake, and Facebook’s decision to more widely deploy its content-moderating AI almost immediately resulted in the blocking of legitimate posts and links.

Google’s AI call center management solution that launched in general availability last November. It offers virtual AI-powered agents that automate basic customer interactions, but it also provides seamless handoffs to human agents through real-time call transcription. Additionally, its Agent Assist feature furnishes live agents with support during calls, including the aforementioned transcriptions, as well as customer intent identification and recommended articles and workflows.

 

Facial Recognition

The COVID-19 has also inspired facial recognition developers to integrate their tech with thermal imaging. This type of scanning is being used to sense whether people might have elevated temperatures, which might indicate whether they’ve been infected with the coronavirus and help verify their identity.

What’s more, facial recognition sellers are also using coronavirus to push the idea that touch-free biometric systems are safer than, say, using a key or a fingerprint to enter a building. This concept isn’t necessarily incorrect, as stated it may be possible that the coronavirus could be spread by contact with infected surfaces, like a fingerprint scanner.