Friday, 26 May 2017

Machine Learning (ML) and Artificial Intelligence (AI): Healthcare – Part Six by Dr. RGS Asthana Senior Member IEEE

Machine Learning (ML) and Artificial Intelligence (AI): Healthcare – Part Six

Dr. RGS Asthana

Senior Member IEEE

Figure 1: ML and AI in Healthcare [11]

Summary

ML, AI, mobile technology, Big Data and Robotics and many other technologies are being applied to Healthcare in a major way and are showing their impact almost in all areas related to Healthcare. We have reviewed this field and divide the applications in three major areas: first Tele-medicine and Tele-health apps. These apps came first and are really low hanging fruits. The second category was for improving service delivery and exploiting Robotics and 3D printing technologies for benefits of the human being. The third category included apps for personal genetics, drug discovery and disease identification and management. All three categories used AI and ML to some degree. The third used AI/ML in a major way and exploited Big Data techniques too. We expect that multi-disciplinary approach to healthcare will grow further in coming years and will show amazing results.

Keywords

Machine Learning (ML) Tools, Artificial Intelligence (AI), Neural Networks, Internet of Things (IoT), Deep Mind, IBM’s Watson, Deep Genomics

Prelude

ML, AI, Mobile Technology, Big Data, 3D Printing and Robotics are playing very important role in healthcare (see figure 1). The primary goal of healthcare is to help people live longer and live better and peaceful lives and this goal has led to the most innovative startups in the world. We do describe here a few apps which are established but need to be revamped with application of ML and AI techniques. We also cover some of the important apps developed using AI and other upcoming technologies.

The improvements in service delivery is obtained by following multi- disciplinary approach and by automating manual processes by digitizing data to make it usable by the software and also exploiting smart phone technology available ubiquitously. Further, we refer to a few apps which use 3D printing and robotics with AI to further improve healthcare for the benefit of patients by making prosthetics and human body parts available on a customized basis hopefully in very near future at affordable rates.

The use of ML and AI techniques—are accelerating the pace of innovation in healthcare and motivating growth also in the following areas:

· Personal genetics

· Drug discovery

· Disease identification and management

Biotech companies such as Cloud Pharmaceuticals [32] and Berg [24] are combining AI and big data to identify new drug compounds. Johnson & Johnson [33] and Sanofi [31] are using IBM’s Watson [20] to find new targets for FDA approved drugs. Johnson & Johnson’s [30] 3D Printing Center of Excellence is working to change healthcare through 3D printing innovations. In fact, companies are competing among themselves to gain the first movers advantage to market with a sustainable product.

Healthcare Apps

Tele-medicine and tele-health apps

These are making healthcare more beneficial to individual wellness, are less expensive to use and cater mainly for preventive aspects. These applications need to be further enhanced using new AI techniques particularly using NLP.

Doctor on Demand

It is a medically certified mobile application which permits a Certified professionals {read doctor} to treat non-emergency routine medical cases through video consultation such as cold and flu symptoms, sports injuries, heartburn, respiratory infections and allergies, urinary tract infections as well as many pediatric issues. The doctor can prescribe medicine for travel purposes and refill existing prescriptions.

Microsoft HealthVault [34]

This healthcare service allows facility to store personal health information and let customers participate in new ways which are safe and up to date. The health information can only be shared with healthcare bodies, e.g., hospitals, pharmacies, and lab testing companies

You can either enter your health data manually or have it done automatically by simply connecting HealthVault to any medical device, such as, fitness tracker, Wi-Fi bathroom scales, and other apps. Using ML and analytics, HealthVault is designed to generate new comprehensions about patient health, ensures adherence to care plans, and encourages patient commitment.

Epocrates

This is a mobile application for pharmacists and is HIPPA compliant. It has comprehensive medical information, including drug interaction checker and a pill identification tool, texting service that allows physicians and other hospital personnel to text each other with convenience. It keeps doctors up to date on important news stories, essential resources, clinical guidelines, ICD-10 codes, disease information and other important information.

PingMD

It is basically a messaging app. It allows medical professionals to connect with other specialists as well as with their patients. It is HIPAA compliant and also lets a patient connect with his/her physicians the same way one might communicate with a friend via text messaging or image sharing.

Apps Developed using AI

Heart Attack Forecast

It is very difficult to predict Heart attacks. An AI system [10] based on routine medical data can forecast which patients will have heart attacks within 10 years.

Figure 2: Heart Attack Prediction using AI [15]

During a new study, Stephen Weng, epidemiologist at the University of Nottingham, U. K. (see figure 2) compared four machine learning algorithms (viz. random forest, gradient boosting, neural networks, and logistic regression) to the ACC/AHA guidelines. The training of the AI system was done by searching through nearly 300.000 medical records from 2005 to not only find patterns associated with cardiovascular events leading to heart failures but also to predict which patients would suffer their first cardiovascular event within the next decade and then compare answers to the 2015 records. Each of the four AI methods executed better than the ACC/AHA guidelines, however, the best results came by using neural networks as it correctly predicted 7.6% more events and raised 1.6% less false alarms than the ACC/AHA guidelines. However, no AI technique (out of the four used) projected diabetes as the reason although the guidelines show this as one reason.

Lung Cancer Identification [12]

A team of experts from the University of Warwick, Intel Corporation, the Alan Turing Institute and University Hospitals Coventry & Warwickshire NHS Trust (UHCW) is formed to use AI and examine pathology images of lung and arrive at computer-assisted diagnosis and grading of cancer, if found. This project is likely to increase both the accuracy and reliability of analysis of cancerous tissue specimen.

Brain-computer interfaces (BCI) [13]

These are making brain control possible. Today, a paralyzed person can control Exoskeletons with their thoughts. Developed at the University of Melbourne, the device connects into the brain without need to perform any surgery on the skull. In fact, interface uses stents and slides through an incision made in the jugular vein up to a blood vessel near the motor cortex in the brain. At the end of the stent, a metallic mesh with electrodes is used to pick up brain activity and relays this information to a recording device in the wearer’s chest, which wirelessly transmits it to an external computer that will control the exoskeleton. When a wearer wishes to move in a particular direction say, left — the brain fires and generates a pattern. By detecting the pattern from the brain, BCI and the system not only reads the thoughts of the person but also translates those into actions which can be followed by the Exoskeleton (see figure 3).

Figure 3: Exoskeleton controlled by brain of a paralyzed person

Prosthetics

Biomedical engineers at Newcastle University in the UK [14] have developed a prosthetic hand with an incredible new skill: the ability to "see" by using camera to assess shape and size by taking picture of any object in front of the hand and then taking appropriate action by activating a series of movements in the hand.

3D printing is one technology which is boon for the patients who need prosthetics as one can print them customized for each and every patient.

3D printing in healthcare [17, 30]

Scientists are finding the best way to 3D print human organs for transplant. Northwestern University's Feinberg School of Medicine and McCormick School of Engineering, joint team is developing 3D-printed ovaries that can boost hormone production and restore fertility and successfully tested it on mice that not only produced healthy pups but mothers also nursed their young.

The ovaries were 3D printed using gelatin scaffolding. The team loaded the structures with immature egg cells before implanting them into their test subjects. The main cause of the success lies in the temperature used while 3D printing the structure but the team's biosynthetic ovaries can even be considered for use in humans will take long time. 3D printing technologies use all type of materials from metals to polymers to biomaterials—materials that mimic living tissue—to create objects.

3D printing is used to customize instruments used by orthopedic surgeon specifically for each patient, so he does not bother about so many different sizes into surgery.

Figure 4: 3D printing of mouse ovaries at Northwestern University

Personal genetics

The most significant application of AI and ML in genetics understands how DNA impacts life and also what influences life and biology; we need to first understand the language that is DNA. This is where ML algorithms play a big role and big players, such as; Google’s Deep Mind [19] and IBM’s Watson [20] are in the fray. Technologies like big data detect patterns from enormous amounts of data (e.g. patient records, clinical notes, diagnostic images, treatment plans).

Deep Genomics [21] is developing the capability to interpret DNA by creating a system that predicts the molecular effects of genetic variation. Their database is able to explain how hundreds of millions of genetic variations can impact a genetic code. This will provide personalized insights to individuals based on their biological dispositions. This trend is indicative of a new era of “personalized genetics,” whereby individuals are able to take full control of their health through access to unprecedented information about their own bodies. As is the case with any application of AI/ML and also Big Data, the technology must have access to vast amounts of data in order to better curate lifestyle changes for individuals.

Develop drugs of the future

Application of AI/ML in healthcare is not only reshaping the industry and making what was once was considered impossible task but also helping in the reduction of both cost and time in drug discovery.

Atomwise [22]—a San Francisco-based startup—is considering to use supercomputers instead of test tubes in the drug development procedure. The company uses ML and 3D neural networks that search through a database of molecular structures to find out therapies, discover the effectiveness of new chemical compounds on diseases and identifying existing medications that can be used to cure another ailment.

Berg Health [24]—a Boston-based Bio-pharma company—uses a different approach for drug discovery and works on patient biological data using AI to determine why some people survive diseases, and then applies this insight to improve current therapies or create new ones.

Figure 5: Cloud technology used for drug discovery

Cloud Pharmaceuticals [32] is using AI design innovations to discover and generate new novel drugs using cloud technology (see figure 5).

There are many new companies working on drug discovery exploiting ML/AI to the hilt to discover drugs and the future of this field is encouraging. It may be worth mentioning that the cost and time of drug development in the usual way varies between USD 0.5 to 3 Billion and may take more than a decade to reach the user.

Discovering new diseases

Most diseases are far more than just a simple gene mutation. Although the healthcare system generated enormous amounts of (unstructured) data—which is gradually refining and improving in quality—but we did not possess the necessary hardware and software in place to investigate it and produce telling results.

Disease diagnosis involves a number of factors, like the texture of a patient’s skin to the amount of sugar level in his blood. So far, we have worked on symptomatic detection, e.g. if one has a fever and stuffy nose, he is diagnosed most likely to have the flu. Systems can now predict the molecular effects of genetic variation [21], opening a new and exciting path to discovery for disease diagnostics and therapies.

But often the arrival of detectable symptoms is too late, especially when dealing with diseases such as cancer and Alzheimer’s. With ML, the hope is that faint signatures of diseases can be discovered well in advance of detectable symptoms, increasing the probability of survival and/or treatment options.

Freenome [25, 28]—a San Francisco-based startup—is a technology company developing proprietary algorithms and novel methods to enable accurate diagnosis of clinical conditions. Freenome platform—an Adaptive Genomics Engine—dynamically detects disease signatures in your blood. The company uses your Freenome—the dynamic collection of genetic material floating in your blood and is constantly changing over time and it’s like a genomic thermometer of who you are as you grow, live and age. As per CEO of the company, ‘our aim is to bring accurate, accessible, and non – invasive screenings to doctor to proactively treat cancer and other diseases at their most manageable stages’.

Enlitic [26] uses deep learning techniques to empower doctors with necessary data to not only become faster but also accurate. This is possible as doctors look at disease diagnosis and treatment plans by coupling deep learning with medical data and extract actionable insights from billions of clinical cases. IBM’s Watson is working with Memorial Sloan Kettering in New York to analyze data on cancer patients and treatments used over decades to present and suggest treatment options to doctors in dealing with unique cancer cases.

Google’s Deep Mind will scan 1 million medical records of Moorfields Eye Hospital [27] and will analyze these digital scans of the eye will be used to train an AI system to help doctors better understand and diagnose eye disease. Google has estimated that up to 98% of sight loss happens due to diabetes and can be prevented with timely and early treatment.

Way forward

The data Scientist today is playing a major role in preprocessing large amount of data and making it meaningful for the ML algorithm to show good and acceptable results. It is well known fact that ML algorithms give better results with more and more of the training and test data. The rule for maintaining good ratio of training and test data is amid 65:35 to 75:25. The combination of Big Data and ML/AI techniques play an interesting and decisive role in analyzing and handling enormous amount of data (which medical fraternity possesses) and deriving meaningful and more accurate results.

While robots and computers will never completely replace doctors and nurses, machine learning/deep learning and AI are changing the healthcare industry, improving not only results but also discovering new medicines to attack diseases, and changing the very mode doctors think today of providing care. ML/AI is improving diagnostics, predicting results and tomorrow doctors may be in position to give even personalized care.

IBM’s Watson [20] helps oncologist to make the best care decisions for their patients. The Care team helps doctors devise and understand the best care protocols for cancer patients.

AiCure is using mobile technology and facial recognition technologies to determine if a patient is taking the right medications at the right time to help doctors confirm that the patient is taking their medications and alert them if something goes wrong [23] (see figure 6). This approach may help doctors to face biggest hurdles in healthcare, i.e., patient come back to hospital after discharge and doctor don’t know why?

Figure 6: Amalgamation of mobile and AI tech for doctor feedback

Healthcare start-ups are using ML/AI, Big-data and even deep learning to make it flexible for all the development phases and areas for all kind of users in healthcare. Industry analysts forecast that 30 percent of health providers will use cognitive analytics with patient data by 2018. In near future [29] robots will help children with autism and diseases will be diagnosed by a breathalyzer.

References

[1] Progress and Perils of Artificial Intelligence (AI)

http://newblogrgs10.blogspot.in/2017/04/progress-and-perils-of-artificial_5.html

[2] Invited Chapter 6 - Evolutionary Algorithms and Neural Networks, Pages 111-136, R.G.S. Asthana in book, Soft Computing and Intelligent Systems (Theory and Applications), Academic Press Series in Engineering, Edited by:Naresh K. Sinha, Madan M. Gupta and Lotfi A. Zadeh ISBN: 978-0-12-646490-0

http://www.sciencedirect.com/science/book/9780126464900

[3] Future 2030 by Dr. RGS Asthana, Senior Member IEEE

https://www.linkedin.com/pulse/future-2030-dr-rgs-asthana-senior-member-ieee-r-g-s-asthana

[4] Machine Learning (ML) and Artificial Intelligence (AI) – Part 1, by Dr. RGS Asthana, Senior Member IEEE

https://plus.google.com/113904112609362672954/posts/4ti2gyQVPZ7

[5] Machine Learning (ML) and Artificial Intelligence (AI) – Part Two, by Dr. RGS Asthana, Senior Member IEEE

https://plus.google.com/113904112609362672954/posts/X5wB7baJLwt

[6] Internet of Things (IoT)

http://www.cisco.com/c/en_in/solutions/internet-of-things/overview.html

[7] Machine Learning (ML) and Artificial Intelligence (AI): Cognitive Services and Robotics – Part Three by Dr. RGS Asthana, Senior Member IEEE

https://plus.google.com/113904112609362672954/posts/EA9hZcY6AwL

[8] Machine Learning (ML) and Artificial Intelligence (AI): Big Data and 3 D Printing – Part four by Dr. RGS Asthana, Senior Member, IEEE.

https://plus.google.com/113904112609362672954/posts/agAa6pY9ekA

[9] Machine Learning (ML) and Artificial Intelligence (AI): Drones and Self-driving Cars– Part Five by, Dr. RGS Asthana, Senior Member IEEE

http://newblogrgs10.blogspot.in/2017/05/machine-learning-ml-and-artificial_17.html

[10] Confirmed: AI Can Predict Heart Attacks and Strokes More Accurately Than Doctors

https://futurism.com/confirmed-ai-can-predict-heart-attacks-and-strokes-more-accurately-than-doctors/

[11] Google Image

https://www.google.co.in/search?q=ML+and+AI+and+Healthcare&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjc9oKVmP7TAhWLuY8KHUUPDqYQ_AUIBygC&biw=1280&bih=709#imgrc=S7GeXvSZ1dX1oM:

[12] The Huffington Post: Using AI to spot lung cancer

https://flipboard.com/@huffingtonpost/-using-ai-to-spot-lung-cancer/f-a31b491418%2Fhuffingtonpost.com

[13] Brain implant allows paralyzed people to control Exoskeleton with their mind

https://www.digitaltrends.com/cool-tech/brain-stent-exoskeleton-control/

[14] UK Engineers Develop Prosthetic Hand That Can 'See'

http://www.pcmag.com/news/353532/uk-engineers-develop-prosthetic-hand-that-can-see

[15] Self-Taught Artificial Intelligent Increases Heart Attack Prediction Rate Significantly

http://trendintech.com/2017/05/06/self-taught-artificial-intelligent-increases-heart-attack-prediction-rate-significantly/

[16] 5 apps changing how the healthcare industry works

https://www.airsassociation.org/services-new/airs-knowledge-network-n/airs-articles/item/19318-5-apps-changing-how-the-healthcare-industry-works

[17] 3D-printed ovaries successfully produce healthy mice pups

https://www.engadget.com/2017/05/18/3d-printed-ovaries-mice/

[18] Advances in AI and ML are reshaping healthcare

https://techcrunch.com/2017/03/16/advances-in-ai-and-ml-are-reshaping-healthcare/

[19] Deep mind website

https://deepmind.com/

[20] IBM Watson Website

https://www.ibm.com/watson/

[21] Changing the course of genomic medicine

https://www.deepgenomics.com/

[22] Atomwise website: Artificial Intelligence for Drug Discovery

http://www.atomwise.com/

[23] How ML, Big Data and AI are changing Healthcare forever

https://www.forbes.com/sites/bernardmarr/2016/09/23/how-machine-learning-big-data-and-ai-are-changing-healthcare-forever/#1a1c020c1a1c

[24] Berghealth website

http://berghealth.com/

[25] Freenome website

https://www.freenome.com/

[26] Enlitic website

http://www.enlitic.com/

[27] Google's DeepMind to peek at NHS eye scans for disease analysis

http://www.bbc.com/news/technology-36713308

[28] Freenome

https://www.facebook.com/freenome/posts/908794832474557

[29] The Future of Artificial Intelligence & Machine Learning In Healthcare

https://www.eventbrite.co.uk/e/the-future-of-artificial-intelligence-machine-learning-in-healthcare-tickets-32955561973#

[30] The power of 3D Printing: How this technology is blazing new medical frontiers

https://www.jnj.com/healthcare-products/medical-devices

[31] AI is using in dynamic phases of Healthcare start-ups

https://www.linkedin.com/pulse/dynamic-phases-artificial-intelligence-health-care-start-up-alam-mo

[32] Cloud Pharmaceuticals website

http://www.cloudpharmaceuticals.com/

[33] Johnson & Johnson Looks to IBM’s Watson to Predict Patient Outcomes

https://blogs.wsj.com/cio/2015/09/25/johnson-johnson-looks-to-ibms-watson-to-predict-patient-outcomes/

[34] Microsoft Health Vault website

https://www.healthvault.com/en-us/

Wednesday, 17 May 2017

Machine Learning (ML) and Artificial Intelligence (AI): Drones and Self-driving Cars– Part Five by Dr. RGS Asthana Senior Member IEEE

Machine Learning (ML) and Artificial Intelligence (AI): Drones and Self-driving Cars– Part Five

Dr. RGS Asthana

Senior Member IEEE

Figure 1: Amazon delivery drone [11]

Summary

This article describes ML and AI based technology mainly related to Drones and SDCs. The apps in Industries such as agriculture and construction, insurance and inspection, smart cities & IoTs are described in some detail. It also briefly mentions topics related to drones and SDCs, e.g., drones offered as a service, disaster management, monitoring use of unauthorized drones in restricted areas; and how to avoid as well as manage hacking & hijacking, Human error, environmental (weather, birds, collision) issues and technical errors. The areas, such as, mob control using drones and smooth and safe communications as well as ethical issues involved in operation of SDCs are also mentioned.

The future of ML and AI based drone and SDC is also discussed in ‘way forward’.

Keywords

Machine Learning (ML) Tools, Artificial Intelligence (AI), Neural Networks, Internet of Things (IoT),

Prelude

It is assumed that one has seen [1-8] before reading this article. Multi-disciplinary tech has made way in our daily lives now, e.g., unmanned aerial vehicles (UAV or drones or smart flying robots) or self-driving-cars (SDCs) are, in fact, product of a combo of robotics, artificial intelligence (AI) and many more technologies. A huge business potential is emerging with application of Multi-disciplinary technology in diverse areas - such as logistics, supply chain management, transportation, search and rescue, military, and scientific studies. Estimates suggest that the global market for commercial drone technology applications alone, which currently stands at about USD 2 billion, will increase to about USD 120 billion by 2020 as a result of regulatory progress for UAV operations, effective legislation, lower costs as well as new, innovative business models [9]. Though not directly related to drones or even SDCs the app described below is of immense value for enhancing crop yield and falls in category which describes apps which really are useful to the humans. A smartphone app from Climate Corporation – Silicon Valley based developer called Climate Basic – divides the entire continental U.S. into plots that are 10 meters square. The app then gets local temperature and erosion records, precipitation expected, soil quality, and other agricultural data to determine how to maximize yields for each plot. This is an example of how AI is creating new methods to business models, operations, and the deployment of people that are likely to fundamentally change the way business operates.

Amazon recently delivered items [11] to a customer near Cambridge, U.K. (in a drone test-flight zone) shortly after it had received the online order. It could not be done in US as US government has not given necessary permissions at that time. Other drone applications include aerial photography and filming, 3D imagery, surveying/mapping, crop control, inspections of offshore systems, pipelines, & wind towers, buildings and verifying insurance claims.

The prototypes of driverless cars or SDCs built by companies, such as, Google [12] (see figure 2) and Tesla incorporate a lot of AI to free drivers from routine tasks like cruise control, keeping in lane, and braking when the car gets too close to the vehicle in front. GM acquired Cruise and partnered with Lyft [31]. Ford acquired Chariot and Israeli ML and computer vision startup SAIPS. Drive.ai [10] - a Silicon Valley startup - is creating AI software for autonomous vehicles using deep learning, which we believe is the key to the future of transportation. Google launched a car recently which is programmed to handle self-driving uncertainties. It uses deep learning — a type of ML techniques to power everything in the car from the sensors and cameras, to the vehicle’s decision-making, to the way the car communicates with people and things around it. Apple is also working and may launch its own SDC soon.

Figure 2: Google's fully driverless car [12]

But what happens when a SDC is involved in an accident, or when a drone violates privacy rights? This is where ethicists, insurers, lawyers, policymakers, transport specialists and business planners play a role. There is need to sensitize people to the regulatory and ethical-moral issues associated with these new technologies, e.g., when an accident is about to occur whether a SDC will have priority to save its passenger or the pedestrian?

Drone and SDCs: Applications

When a tornado or hurricane comes, not only it hits infrastructure severely but also damages it. Rescue cannot be undertaken till situation becomes safe for human workers. In such a scenario, a group of Drones could not only be sent to the site to carry out survey of the damage and drones can be programmed to send photos to a central point but can also make available temporary shelter on the spot using AI based technologies such as Additive Building Manufacturing assisted by 3D printing. All this can be done while it’s too dangerous for the human teams to provide even an iota of help. If people need to be picked up SDCs may also play a role if roads are serviceable else Helicopter service may be used.

Agriculture and construction

Agriculture [14] and construction use Unmanned Aerial Vehicles (UAVs) as these industries may involve large or unapproachable areas of land. Drones are extensively used in agricultural. Drones can suggest adequate measures to formers on drought handling and also pesticide use. Smart drones not only monitor herds of livestock grazing in the fields but can also act as part of a coordinated team and offer services to not only keep track of the livestock but also find lost animals in the herds. Drones equipped with thermal imaging can cover vast distances in a short time and easily locate dangerous animals.

High-quality video shot by a drone can show farmers their crops or livestock without requiring the farmer to go to the fields. Today farmers use some time even excessive pesticides, herbicides and fungicides on the crop irrespective of infection caused by a particular pest or not. With the availability of precise imagery of the crops from drone; a former may use pest control only at locations where it is required thus not only resulting in savings but also health crops.

One can use different cameras to compute crop yield, or a thermal camera to detect the insufficient irrigation issue. With proper cameras one can even get the height of a plant from the air.

Similarly one can use drones to measure progress in construction and client could see the progress without visiting the site.

Construction Industry [15] will be affected by the use of AI based technologies and will automate purchase of construction materials and hiring engineering companies. Autonomous TMA Truck (ATMA) is a vehicle where no driver is needed. Such vehicles may be used in various dangerous situations. ATMA enables leader/follower truck capability that allows the ATMA to follow a lead vehicle which is completely unmanned. Its NAV module communicates with the GPS and collects position data called ‘eCrumbs’ [16] and passes it on to the follower vehicle, this enables follower vehicle to move on the exact path and speed of the leader vehicle at each point along the route. This technology is being used by US military in the highway construction industry in difficult terrain.

ML is used to estimate and predict building A/C energy consumption to assist in optimizing and automating air conditioning systems. Amount of energy consumed in commercial buildings and offices is maintained by monitoring and controlling the temperature at a desirable level through use of artificial neural networks. This controls the cost of air conditioning.

Earthquakes cause widespread damage and ongoing aftershocks result in losses in billions of dollars. With help of AI and low cost remote sensing data one can detect building collapse in post-earthquake environment. Precise plotting of damage can help in decision making that when people can return safely to their home. One can also know about delayed building collapses and save lives.

Insurance and inspections

In insurance industry drones can play a major role. Insurance companies get a lot of requests for roof inspections due to damage from natural disasters like hail, wind or ice. Instead of having a person climb up on the roof, which is dangerous, they can now send a drone to capture images of the damage and decide on the claim by analyzing the imagery. AI software can play a major role here.

Claims management [14] can be augmented using ML and AI techniques in different stages of the claim handling process. By leveraging AI insurers can not only fast-track certain claims by reducing the processing time but also identify patterns in the data leading to identification of fraudulent claims in the process. This in turn will reduce the handling costs and also enhance the customer experience. AI systems, with their self-learning abilities over time can improve with experience and locate new unseen cases and further improve the fraud detection. Furthermore, machine learning models can automatically assess the severity of damages and predict the repair costs from historical data, sensors, and images.

With AI, there will be a change in the skills needed to work in the insurance industry. Tech savvy experts must train for the future and learn to work together with AI to show their true innovative potential.

Smart cities and IoT

The infrastructure may include smart communities, connected homes, intelligent transport systems, e-health, e-government, e-education, smart grids and smart energy solutions. Smart cities [21] will use latest AI based technologies and may also have Electric Vehicles, SDCs, Mobile applications, Drones, Wearable and Smart devices and so on are just some of the key developments to watch. The data may be collected from IoT [6] intelligent devices and then decisions taken and solutions implemented automatically by AI or ML based algorithms, e.g., a drone may be sent not only to monitor status of power line but also to replace fused bulbs.

Disaster Management: Rope bridges

A drone or group of drones may not only provide data, but use AI and ML techniques for disaster management by physically augmenting the infrastructure around us, that will be the most cutting-edge usage of UAV technology. One such example could be that Drone sets up a rope bridge say across a river on a stormy weather day. It will however need special hardware, software, battery life, and also FAA regulation (in USA) to implement. Drones may also become a part of IoT [6].

Drones offered as a service

The Skymatics app [18] is available on Google Playstore and clients can book UAV services and jobs instantly on their mobile device.

Figure 3 describes architecture of a Drone which works on voice instructions as well as can identify faces. This Drone was built at a cost of less than USD 200 [19]. The Azure Face API [20] lets you upload pictures of your friends and it recognizes them. It also gives face attribute like age, emotion, gender, pose, smile, and facial hair along with 27 landmarks for each face in the image.

Monitoring unauthorized Drones [24]

A weatherproof device called DroneTracker, has number of sensors including a near-infrared cameras, acoustical sensors and video camera to provide a 120-degree arc of airspace coverage at a range of up to 100 meters. It sends data in real-time to a paired device for viewing and to create a temporary drone-detecting perimeter at public events or in restricted and sensitive areas.

Figure 3: Architecture of Drone which takes voice commands and also recognizes faces

Avoid remote hacking and hijacking [25]

Jonathan Andersson – a Security researcher - has developed a tiny gadget called, ‘Icarus’ [26] - a name based on Greek mythology - to enable hijacking nearly any drone mid-flight. This device allows attackers to lock the owner out and give them complete control over the device. This device [26] can, in fact, affect any radio controlled device using the popular DSMx radio platform.

DSMx is an evolution of DSM2 2.4 GHz technology [28], destined to deliver the most robust, reliable, and efficient level of RF link possible in the modeling world today. The hack works against any drone that communicates over DSMx, a widely used remote control protocol [27] for operating hobbyist drones, planes, helicopters, cars, and boats.

Human error, environmental (weather, birds, collision) issues and technical errors

It is expected that there will be more than 25 billion IoT [6] connections in the next 10 years and drone technology will be part of the connected world. Although this is an interesting technology but it has all risk factors of aviation.

Luckily there are established norms for the aircrafts which has humans; the norms for Drones will also evolve with time as it offers an unmanned scenario. Human errors as well as natural disasters have played a major role for flying safely in the skies. We can minimize technical as well as human errors with training but natural disasters are beyond our control.

Monitoring Mob control using Drone Technology

The police in the capital of the U.P. have been using drones to take aerial photographs since 2014, but new drones ordered [29] by the force have the capability to control crowds using pepper spray and also fire up to 20 paintballs per second while simultaneously dispersing tear gas pellets onto people. The drones are also fitted with onboard speakers so authorities can communicate with crowds, as well as bright strobe lights and “eye safe” lasers to disorient and disperse a gathering.

Dubai [32] police is now using drones with 60 minutes flight time to monitor crowds at soccer matches.

How to manage safe and smooth communications

Drone technology is exciting but very challenging and complex. Time will tell to what extent is its wider applications are realistic? The issues related to the management of cellular connectivity for coordinated operation and control of drone [30] as well as SDCs enabling a growing set of use cases within and beyond the operator’s visual line of sight. In fact, connectivity is important for safe operation of both drone and SDC.

SDCs and ethics [23]

Driverless cars will be fully accepted someday. The ethical questions need be answered before that day could be:

1) Who will decide who lives or who dies during an accident scenario say, between a passenger in the car or a pedestrian on the road?

2) Whether a driverless vehicle could choose to ram a school bus full of kids or sacrifice the driverless vehicle's occupants during a mishap?

3) Should police be given control to remotely control a SDC to pull it over?

Way forward

The wave of progress encompasses all aspects of computer technology, software engineering and is applied to business [17]. Companies today have to decide what type of AI role they should play. The ML and AI based technology used in a Drone, robot, SDC or a truck is, particularly, fascinating. AI progress, in fact, will affect all technologies described above but will surely be used to further improve navigation of drones and SDCs.

Drive.ai was founded to develop technology with the potential to save lives and transform industries. The Google car [12] - a SDC - is round, white and gray car. It is without permanent driving setup like a gas pedal or wheel. However, to comply with California state law, there are still removable, temporary controls for the required "safety driver" -- a real person who needs to be in the car and ready to take over in an emergency. The goal is to eventually remove any interior controls so that passengers can take a nap or knit while the car does all the work.

Connected and autonomous vehicles in general are viewed as the future of surface transportation, and this technology may be one of the first ways in which it gets commercialized.

A recent report from the World Economic Forum (WEF) found that the digitalization of telecommunications could unlock $2 trillion of value over the next decade – value for the telecommunications industry and society as a whole.

AI, Big data and IoT [6] are transforming how future wars will be fought. Drones [33] in military are used for a variety of purposes including conducting surveillance, for attacking hostile targets, spotting submarines and mines, and for delivering aid to places where ii is unsafe for human convoys. This all is possible as drones are small, can be stealthier and safer for the troops employing them. The likelihood of seeing a drone from the ground is very remote. Drones are activated and operated from a computer station located in friendly territory.

Major automakers like Tesla, BMW, Ford, Audi, and even Google are in the SDC game in some way or other [34]. While many have only started hearing about autonomous technology recently, research is going now for decades. In fact, AI is the brain of SDCs. Each autonomous vehicle has advanced tools to gather information, including long-range radar, LIDAR, cameras, short/medium-range radar, and ultrasound. Apple Inc. U.S.A. is strongly rumored to be in the race for SDCs. AI can assist a Chauffeured car in the following tasks:

· Directing the car to a gas station or recharge station as applicable;

· Finding the fastest route;

· Incorporating speech recognition for advanced communication with passengers;

· Locating parking space and park by itself or provide assistance in parking;

· Eye tracking for improved Chauffeur monitoring; and

· Provide intelligent user interface by using NLP and/or virtual assistance technologies.

Future of autonomous cars as well as SDCs will depend on acceptance of advanced AI algorithms by the government as well as public to allow them on public roads permanently.

References

[1] Progress and Perils of Artificial Intelligence (AI)

http://newblogrgs10.blogspot.in/2017/04/progress-and-perils-of-artificial_5.html