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How can Technology and AI help fight Coronavirus better?

The spread of coronavirus shows no sign of ending soon. 

Many countries have been affected by the virus but at the moment, there is no vaccine to treat it. As Coronavirus continues to spread, the most important task is to contain the outbreak as much as possible. 

Amidst the global pandemic, technology can be used to help and save lives.

How can technology and AI (artificial intelligence) help people in the fight against COVID19?

AI to Track Coronavirus Outbreaks

AI (Artificial Intelligence) can help us to track Coronavirus outbreaks. 
Today, we can track data via the live dashboard – it pulls data from the World Health Organization (WHO) to better locate possible outbreaks and provide safety measures to minimize the spread.

Source: John Hopkins University

A Canadian health monitoring company BlueDot uses AI to scrap foreign news reports, forums and announcements from public health officials to protect people around the world from infectious diseases. BlueDot’s early-warn system uses AI (ML or Machine Learning and NLP or Natural Language Processing) to track and analyze data which helps them to understand when to notify about the spread

Speed matters the most in a pandemic situation and AI can spot the outbreaks quickly and intervene to keep them to a minimum. AI surveillance uses tons of posts about Coronavirus to get more accurate data and predict how fast the outbreaks may be. 

More and more countries have started deploying AI surveillance to monitor and track citizens to achieve a wide range of purposes. Although it raises serious questions about the future of privacy, AI surveillance has proven a useful tool to monitor and respond to the global outbreak such as Coronavirus. 

AI and Wearables to Track People 

Some countries, like China, use AI and ML based tools to detect Coronavirus cases – not only detect but also to track people that have been put to quarantine or self-isolation. 

Face recognition can help with detecting people’s faces, especially with people who have been diagnosed with the virus and if, in any case, cannot be located. 

Many applications have also been developed to help people track if they’ve been in places as the confirmed virus patients. 

Source: austincountynewsonlne.com

Wearable technology is another great way that may help to fight the virus – it enables healthcare professionals to monitor vital signs with patients without physical contact. 

Some hospitals in China have used a continuous temperature sensor to help reduce the spread of the virus – the sensor is applied to the patient and sends real-time information to the health institution. 

Wearables have proven beneficial and can be used to monitor heart and respiratory rates among patients.  

Source:rateittoday.com

Drones Assistance

Drones’ assistance have become essential in minimizing the spread – people use drones to carry out tasks like spraying disinfectant, dispersing public gatherings or tracking people in quarantine.

Usually lighter drones are used for detection and monitoring while some types of drones can also be used for food and face masks delivery which helps prevent the virus spread.  

Souce: wetalkuav.com

Drones can be upgraded for Aerial Thermography – usage of drones equipped with thermal camera scanners to detect temperatures of people in a crowd where they can identify persons with the highest body temperature.

Source: thedronegirl.com

Robots and Tech Tools for Contactless Communication

Robots have become more important than ever during the pandemic outbreak as it’s extremely difficult for medical staff to remain safe while providing treatment to patients. 

Shortage of medical workers has become a top problem as they are in direct contact with the patients and can get infected by the virus. 

Using robots can minimize the exposure of medical staff to the infected patients – the fewer people in contact with infected patients, the better. 

Robots can help perform tasks such as delivering food and medicines and cleaning the patients’ rooms enabling contactless assistance and preventing further spread. 

Source: news.panasonic.com

Healthcare applications can provide real-time consultations – patients that suffer from symptoms similar to Coronavirus can consult a doctor via a video call instead of coming into close contact with patients. 

Virtual consultations can provide doctors with a person’s current state along with healthcare data like blood pressure, heart rate, etc. 

Source: smarthealth.nl

In China, Ecommerce giants have gone further and deployed robots to deliver food to prevent the spread of the virus. Contactless delivery systems try to minimize the human contact resulting in decreased spread of the virus itself. Autonomous vehicles can carry up to 100kg of goods and deliver 3-4 rounds per trip. 

Self-driving robots can be used for disinfection of hospitals, for example, UVD robots release concentrated UV-C light which has a germicidal effect and can kill bacteria and airborne viruses on the surface. 

One of the most popular robotic ‘disinfectants’ is developed by Blue Ocean Robotics that uses ultraviolet light to kill viruses and germs. 

Source: blue-ocean-robotics.com

Thermal Imaging Cameras to Detect Temperature

Although Coronavirus is a current danger, people still need to work and travel if necessary. 

Many thermal screening cameras have been installed at airports across many countries – they use thermal imaging equipment to detect a person’s body temperature if above normal range and react accordingly without spreading panic.

Thermal cameras create images using infrared radiation – the heat sensors record the heat of people’s bodies and display 2D images with temperature levels.

Thermal sensors can scan the large crowd of people and instantly pinpoint the ones with high temperatures or fever as opposed to checking everyone’s temperature and creating massive delays. 

Source: gizmodo.com

As already mentioned, another great usage of thermal cameras is called Aerial Thermography which includes an unmanned aerial device (drone) and an infrared camera scanning. 

Drones show higher safety levels as they can complete the task with less or no contact and they can be obtained at affordable costs.

Such data can be sent to health institutions so that the treatments can start immediately and have the biggest effect. 

AI to Help Develop Medicine

Coronavirus is similar to EBola and SARS as they’re RNA viruses which means they are responsive to mutation and more complex to develop vaccines.

Source: blockbox.io

Many companies have already deployed their resources in attempts to develop medicines – Google’s DeepMind has used its AlphaFold system to release structure predictions associated with the virus. Although these haven’t been experimentally proven, they hope it will lead to better understanding how the virus functions

Baidu Research has opened its LinearFold algorithm for free to the scientific community and epidemic prevention centres so that all research on Covid19 structure can be continuously expedited – the algorithm takes only 27 seconds to recognize and solve the RNA structure of Covid19 which is over 100 times faster than classic algorithms. 

The most important thing for these algorithms is access to data so it’s vital to release all details crucial for any algorithm searching for a cure. 

Tech Tools Provide Online Education Programs

Due to the Coronavirus outbreak, many schools and universities are closed while schooling is organized via online education programs.

Simple technology tools and apps enable many educational institutions to organize pre-recorded classes or live streaming where teachers can hold their classes with help of a whiteboard or PowerPoint presentation thus continuing the school year. 

In Serbia, school and university programs have been adopted via national TV channels or digital channels so that it is accessible to everyone.

Source: politika.rs

A great number of online teaching and educational platforms also provide their services for free during the outbreak helping students not to be so much affected by Coronavirus.

The crisis evoked by Coronavirus triggered an increased demand for online education, courses and content. 

AI to Fight Fake Information

Global pandemic affected the world population and it’s been noticed that some social media networks started posting misleading content related to Coronavirus and its prevention – from the origin of the virus to various claims about miracle cures. 

Spreading panic in a pandemic situation is extremely dangerous and an increased number of fake news can just make the situation worse. 

Source: blo88er.com

AI can be used to analyze words or word patterns to try to spotlight fake stories. 

For example, Facebook uses AI to address the issue by quickly learning behaviour through pattern recognition – power of AI can detect false stories on the Internet with great accuracy. 

That is the reason why the biggest internet and social media companies put extra effort to detect fake information and filter information that can be sent over their networks. 

AI to Help Production of Face Masks

As the Coronavirus outbreak rapidly spread in some countries, lack of medical equipment has become a common issue within many health institutions worldwide. 

Lack of masks, gloves, protective scrubs etc. is a huge problem and we need to address it if we intend to keep our medical workers healthy and minimize the spread of virus with those who are the first in the frontline. 

Source: sonoviatech.com

One of the companies that contribute to the issue of shortage of medical products is Sonovia, an Israeli startup that works on producing fabrics that will be immune to the virus until we develop vaccines. The most important thing is that they use technology that can cover fabrics with antivirus without using any chemicals. 

In Serbia, a Facebook group ‘Vizionari Srbije’ was established on 20 March 2020 by a group of enthusiasts with a goal to help their citizens at risk of getting infected by Coronavirus. The first project was to create 3D printed safety face visors to protect healthcare professionals and medical staff from direct transmission of the virus. The project achieved a huge response – the group created visors for more than 22.000 people and still counting. 

Source: https://www.facebook.com/groups/vizionarisrbije

Final Word

As the Coronavirus crisis is growing, we can see how some of the technologies can be helpful in the fight against infectious diseases in the future. 

Technology can be our ally and help save lives, but let us not forget that humanity and solidarity among people enable the same technology to show its full potential and keep us together during the outbreak. 

I believe technology still needs time before being able to stop such disease outbreaks so we need to invest in it proactively. The human experts and technology are the best combination for the future times.

AI and the Powerful Impact on Mobile Technology

We’ve all heard about Artificial Intelligence (AI) but only few of us know exactly what it means and how does it impact our everyday life.

When thinking about AI, many Baby Boomers and X Gens think of the old sci-fi films and scenes where machines come alive and take over the world. But that’s just a funny representation how humans used to perceive the unknown.

If you remember the old TV show ‘Beyond 2000’, you may recall that their ideas and inventions were outstanding at the time, which only shows the potential of the technology.

What is in fact AI and what are the examples we can see on mobile?

Artificial Intelligence (AI) is present in mobile phones for some time now, but in the prior generation of phones, it was cloud-based and required Internet to be accessed. The new generation of smartphones integrate the cloud-based AI with built-in AI on hardware – this innovation was announced by tech giants such as Google, Apple and Huawei.

Built-in AI hardware

AI’s been dominant in app development for several years already and has a potential to grow much more in the coming years.

Devices are now offering a number of features to build up AI performance – combining AI with these built-in elements makes apps more relevant and personalized.

Some of the examples are Apple’s iPhone XS (pronounced Ten-s), XR and iPhone XS Max (S-Max) which power various advanced features including Face ID, Animoji and augmented reality apps.

Immediate follower is Google’s  Pixel 3 XL which is said to have the best camera phone according to TechRadar. You can blur the background with a single camera called dense dual-pixel autofocus – using the depth map, the Portrait Mode software replaces each pixel in the image with enticing blurry background known as bokeh. The result is a high quality image that matches the professional quality with just a quick tap.

The third big player Huawei released Huawei Mate 20, Huawei Mate 20 Pro and Mate 20 X. Mate 20 and Mate 20 Pro are both powered by Huawei’s newest in-house processor the Kirin 980 chipset and have triple rear cameras – the phones’ AI chip offers a number of features, including ‘4D predictive focus’ (tracking the main object in the photo so to keep in focus) and more.

Apart from those two, Huawei Mate 20 X is intended mostly for gaming audience. Its large screen can display more information thus reducing amount of scrolling.  

All three brands also paid attention to a better battery performance on the new generation phones which is partly due to the in-device AI.

Use of AI in Mobile Software

Tensor Flow Services

TensorFlow was created to be a reliable deep learning (DL) solution for mobile platforms.There are two solutions for deploying machine learning (ML) applications on mobile and embedded devices: TensorFlow for Mobile and TensorFlow Lite.

TensorFlow for Mobile has a fuller set of supported functionalities and you should use it to cover production cases while TensorFlow Lite allows targeting accelerators through the Neural Networks API.

Source: tensorflow.com

Some common use cases for on-device deep learning:

  • Speech Recognition (small neural network running on-device listening out for a particular keyword and transmitting the conversation to the server for further processing);
  • Image Recognition (helps the camera to apply appropriate filters, label photos to be easily findable, uses image sensors to detect all sorts of interesting conditions);
  • Object Localization (augmented reality use cases, TensorFlow offers pre-trained model along with tracking code – the tracking is important for apps where you’re trying to count how many objects are present over time – it gives you a good idea when a new object enters or leaves the scene);
  • Gesture Recognition (effective way of deploying apps with hand or other gestures, either recognized from images or through analyzing accelerometer sensor data);
  • Optical Character Recognition OCR (Google Translate’s live camera view is a great example – the simplest way is to segment the line of text into individual letters, and then apply a simple neural network to the bounding box of each);
  • Translation (these are often sequence-to-sequence recurrent models where you’re able to run a single graph to do the whole translation, without needing to run separate parsing stages);
  • Text classification (if you want to suggest relevant prompts to users based on their previous readings, you need to understand the meaning of the text and this is where text classification comes in. Text classification is an umbrella term that covers everything from sentiment analysis to topic discovery, example like Skip-Thoughts)
  • Voice Synthesis (a synthesized voice can be a great way of giving users feedback or helping accessibility, and recent advances such as WaveNet show that deep learning can offer very natural-sounding speech).

Image Recognition Features

The technology of facial recognition is nothing new but it’s expected to witness new growth opportunities in coming years.

Mobile app creators try out new ways to apply the technology in an unconventional way since camera phones became a focal point for communication. Set of techniques that serves as a groundwork for such applications are ego-motion estimation, enhancement, feature extraction, perspective correction, object detection and document retrieval.

Since retail giants such as Amazon, Target and Macy offer image recognition with their mobile apps, the technology will likely become a must-have. Scan-to-buy options enable customers to shop directly from a retailer’s catalogue and in-store signage increased in demand and became a standard offer today.

Some retailers are employing image recognition that allows consumers to point their phone at any object and receive suggestions for the similar products. Direct example of this is IKEA Place app which they developed for iOS – the users can place the IKEA furniture into their homes with the help of AR and rotate around as if in realistic world.

Source: ikea.com

Visual Search on Mobile

Mobile visual search is a great potential to create the new profit opportunities – brands are trying to utilize the smartphone camera’s increasing sophistication so to activate consumers and drive sales. In some cases, visual search is faster and more accurate than text or voice and smartphone is the perfect device for the visual search technology.

Leading Internet search companies such as Google and Baidu are racing to capture mobile visual search market as it begins to replace traditional forms of search.

Let’s say you saw something you really liked but you don’t know how to find it or how it’s called – visual search lets you find all those things you don’t have the words to describe. Google Lens is a perfect example – in 2017 Google Lens was introduced in Google Photos and the Assistant. As of 2018, Google announced three major updates: first, smart text selection that connects the words you see with the answers and actions you need.

Second update is a style match, e.g. if you like a specific outfit you can open Lens and see things in a similar style that fit the look you like.

Third update is that Lens now works in real time – it allows you to browse the world around you just by pointing your camera.

With a snap of camera, companies can use technology as a tool to determine the elements of their inventory, publishers can use it to source quality visual content from their photo libraries and Digital Asset Management (DAM) software can include visual search to organize and curate their customers’ content – visually.

Visual Search can help businesses in E-commerce to increase catalogue discovery, customer engagement and conversion rates.

Source: slyce.it

Image Recognition API

Image recognition APIs train computers to analyze, classify and alter different types of pictures.

Let’s list some of them:

  • Clarifai independent team built system that accurately recognize most entities. Unlike any other APIs on the list, it’s offered scene recognition with a bonus of video analysis. For images, Clarifai can perform sentiment analysis, text recognition, logo and face detection, as well as more robust version of Resemble’s image attribute detection: brightness, colour and a dominant colour.
  • Cloud Vision by Google enables developers to understand the content of an image by covering ML models – it includes many of Clarifai’s key features and some add ons like: landmark detection and a simple REST API. You can’t make your own models to test against but you have the access to an API backed by Google which is constantly improved. Furthermore, you can build metadata on your image catalog, easily detect broad sets of objects in your images and moderate offensive content from your crowd-sourced images which is powered by Google SafeSearch. Optical Character Recognition (OCR) allows you to detect text within your images as well as automatic language identification.
  • On the other hand, Amazon Rekognition prides itself with a more robust suite of facial analysis tools, including facial recognition (not offered by Google or Clarifai) across images, and detailed information like beard recognition (yes/no), and facial comparison (how likely is it that two faces are the same person?). It also pledges integration with AWS services (S3 and Lamba).
Source: aws.amazon.com
  • We still have The IBM Watson™ Visual Recognition service which uses DL algorithms to analyze images for scenes, objects, faces and other content. You can make and train your custom image classifiers using your own image collections – use cases include manufacturing, visual auditing, insurance, social listening, social commerce, retail and e-commerce. As visual recognition understands visual data, it can turn piles of images into organized information. With the IBM Watson Visual Recognition service, building mobile apps that can accurately detect and analyze objects in images is easier than ever.

Natural Language Processing

Your first contact with  Natural Language Processing (NLP) might involved a GPS navigation app which allows you to verbally request directions to a destination – they are far more sophisticated than they used to be.

The best-known mobile app with NLP is SIRI, a virtual assistant (VA) technology followed by other VAs including Alexa, Cortana and Google Assistant.

NLP became more common in the medical and healthcare sector as the use here is wide. This is especially true when it comes to the apps for wearable health apps that allow you to use verbal input as this field has an increased need for hands-free communication.

Another usage is within detecting spam messages where NLP can be extremely useful. Spam filtering algorithms ‘read’ the content of blog comments, social media or email messages etc. Then, they compare it to the known spam messages and text patterns to identify the spam.

Also, there’s a huge potential in creating and pulling the data from the information stores – a user can give verbal input to search plethora of ebooks, websites, videos, footages etc.

There will be improvements within language translation apps and mobile apps that include talk-to-type functionalities.

Natural Language Understanding

Natural Language Understanding (NLU) handles machine ‘reading comprehension’.

It converts text pieces into more formal illustrations such as first-order logic structures that are easier for a computer program to manipulate.

NLU identifies the intended semantic from the multiple possible semantics which can be extracted from a NL expression and which usually takes the form of organized notations of NL concepts.

Regardless of the approach used, most NLU systems share certain common elements – the system needs lexicon of the language, a parser and the grammar rules to break sentences into an internal representation. The umbrella term ‘NLU’ can be applied to a different set of computer applications – from simple tasks like short commands issued to robots to highly complex ones such as full comprehension of the newspaper articles.

Text-To-Speech (TTS) Systems

TTS is a high fidelity speech synthesis which gives better user experience for some specific groups like people with learning disabilities, literacy difficulties, people who speak language but cannot read it, people with visual impairment and different learning styles, people who multitask or that access content from mobile phones.

Making your digital content audible helps online population to understand the text better and as people are increasingly going mobile, TTS can turn any digital content into a multimedia experience that people can listen to the content.

Some of the best text-to-speech software are:

  • Amazon Polly – Besides Alexa, Amazon created an intelligent TTS system called Polly. Polly turns text into lifelike speech. It supports an API that lets you easily incorporate speech synthesis capabilities into ebooks, articles and other media. It is easy to use – you just need to send the text through the API and it’ll send an audio stream straight back to your app.
  • Voice Reader Home 15 – Linguatec created Voice Reader that can quickly convert text (Word docs, emails, EPUBs and PDFs) into audio files. You can listen to those files on a PC or a mobile device.
  • Capti Voice – Speech synthesis apps are popular in education world as they improve comprehension among other things. Capti Voice lets you to listen to anything you want to read. You can customize learning or teaching as well as overcome language barriers.
Source: aws.amazon.com
  • The new cutting-edge TTS service launched by Google is Cloud Text-to-Speech powered by WaveNet, a software created by DeepMind AI. It analyzes the waveforms from a vast database of human speech and re-creates them at a rate of 24,000 samples per second. The final result includes voice with subtleties like a lip smack or accents. Google advises the new service provides 32 different voices capable of speaking 12 languages and users are able to customize factors like pitch and speed.

Speech-To-Text (STT) Systems

If you’re at the conference or a lecture, it can be quite hard to write down every word the speaker says and this is where speech recognition comes in to solve the problem.

As it is dependant on computational linguistics, it identifies spoken language and turns it into text.

These systems can differ in capabilities where simple ones can recognize only a selection of words while the most advanced ones can understand the natural speech.

Some of the best STT apps are:

  • Evernote for AndroidEvernote allows you to record audio notes and turn those into the text. Unlike Dragon Dictation (see below), Evernote saves both the audio and the text file together so you can record what’s on your mind and sort the data later. The app is free, but since it uses Google Android text transcription service, it requires Internet connection.
  • Dragon Dictation – this app has only one button – just tap it and start talking and Dragon Dictation will take care of the rest. The text shows after you’re done with dictating and once the app finished transcribing your speech, you can send it out via email, or copy and paste to another app. You can also post directly to Facebook or Twitter or just save your text and use it later on. The app is for free for iPhone and iPad but it requires Internet connection.
  • Voice Assistant – this redesigned app has a fast access feature that makes it easy to post on Twitter, Facebook or email. With Voice Assistant, you can utilize auto copy feature to send your recordings to other apps such as Google Search, YouTube etc or straight to a wireless printer. It also has grammar correction and on-screen editing with suggestion for corrections.
  • Transcribe – this is a popular dictation app that’s powered by AI where you can import files from Dropbox. Transcribe any video or voice memo automatically, supporting 80 languages from across the world. Once the file is transcribed, you can export raw text to a word processor to edit. The app is free to download yet you’ll have to make an in-app purchase if you want most of these features.
  • Speechnotes –  Speechnotes doesn’t require to create an account – just open the app and press on the microphone icon and you’re ready to go. When recording a note, you can easily dictate punctuation marks through voice commands. You can quickly add names, signatures, greetings, etc. by using custom keys on the built-in keyboard. Speechnotes app allows you to access plenty of fonts and text sizes – the app is free to download from Google Play Store but you have to make in-app purchase to access all features.
Source: evernote.com

Chat Bots

Chat Bots for mobile apps are classified as ‘recent’ sensation. But their beginning and development started in 1966 with Eliza – a medical chatbot which can be considered as the mother of all chatbots.

Chatbots are great for specific tasks – from simple ones, like answering basic customer questions to complex ones like helping with customer service questions. Chatbots won’t replace websites or apps but they work great when integrated with the same apps and websites to boost interaction with customers.

For companies, it’s essential to engage with their customers on a regular basis – mobile apps are the best platform for this engagement. Today, almost everybody would rather communicate with a company through their app than through email.

Chatbots can assist with privacy issues – that is the reason why many banks are building their own Chatbot platform like Erica from Bank of America. Using native chatbot helps to avoid privacy issues.

Some great examples of chatbots are: Duolingo, Erika by Bank of America (still in beta stage), Lemonade Maya (replacing brokers and bureaucracy), Operator by Intercom (customer service chatbot that handles simple tasks). I would also like to mention Messenger platform for chatbots (Facebook) which currently dominates the Web.  

Source: chatbotsmagazine.com

Benefits of bots in mobile are massive – customer interaction will be more lively and engaging, you won’t need to download an app for a task, chatbots will be your calculator, booking agent etc., they will recommend new things to you, help you with repetitive tasks and will save you a lot of space on your phone as they will be a number of apps in one.

Developers will see the benefits through seamless deployment of the chatbots for messaging or other instances, integration of chatbot with other apps – with an intelligent chatbot you can have easy-to-see features and additional functionality added to your mobile app.

Chatbots are adding quality to your mobile app esp with the intelligence support it gets from AI since it will help you increase conversions.

How Will Mobile AI Impact Businesses?

The major tech companies are incorporating AI algorithms into various devices to strategically retain users – it helps business to deeply engage users and provide more incentives to use their services.

Many devices and apps will be written with algorithms that adapt based on the learned behavior – the algorithms will be able to filter the data, find trends and adjust the apps themselves to create more meaningful opportunities for engaging the users. Forward-thinking enterprises will prosper on the advantages AI provides, as it continues to connect users to brands.

What Will Happen

The most obvious changes AI will bring are processing speed and efficiency — doing things faster and without multiple charges of your phone. The whole point of AI is to create more personalized and user-friendly relationship with our smartphone.

Google’s $400 million acquisition of DeepMind is a prime example of mainstream AI application. A study conducted by the Mckinsey Global Institute revealed that tech giants such as Baidu and Google spent between $20 billion to $30 billion on AI in 2017, with 90% of this spent on R&D and deployment, and only 10% on AI acquisitions.

Based on the progress in technology and the growing demand for smart applications, AI and mobile are the PERFECT match.