Reinforcement Learning

• Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence.

• It allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behaviour; this is known as the reinforcement signal.

• Reinforcement learning, in the context of artificial intelligence, is a type of dynamic programming that trains algorithms using a system of reward and punishment.

• A reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly.

• The agent learns without intervention from a human by maximizing its reward and minimizing its penalty.

• Reinforcement learning is an approach to machine learning that is inspired by behaviorist psychology. It is similar to how a child learns to perform a new task.

• As an agent, which could be a self-driving car or a program playing chess, interacts with its environment, receives a reward state depending on how it performs, such as driving to destination safely or winning a game. 

• Conversely, the agent receives a penalty for performing incorrectly, such as going off the road or being checkmated.

• The agent over time makes decisions to maximize its reward and minimize its penalty using dynamic programming. 

• The advantage of this approach to artificial intelligence is that it allows an AI program to learn without a programmer spelling out how an agent should perform the task.

        

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Self Driving Cars

• A self-driving car (also known as an autonomous car or a driverless car) is a vehicle that is capable of sensing its environment and navigating without human input.
• Autonomous cars combine a variety of techniques to perceive their surroundings, including radar, laser light, GPS, odometry, and computer vision. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage.
• The potential benefits of autonomous cars include reduced mobility and infrastructure, increased safety, increased mobility, increased customer satisfaction, and reduced crime. These benefits also include a potentially significant reduction in traffic collisions.

• Including less need for insurance. Automated cars are predicted to increase traffic flow,provide enhanced mobility for children, the elderly,disabled, and the poor; relieve travelers from driving and navigation chores; lower fuel consumption; significantly reduce needs for parking space,reduce crime

• Facilitate business models for transportation as a service, especially via the sharing economy.

Levels of driving automation:

• Level 1 ("hands on"): The driver and the automated system share control of the vehicle.
• Level 2 ("hands off"): The automated system takes full control of the vehicle (accelerating, braking, and steering). The driver must monitor the driving and be prepared to intervene immediately at any time if the automated system fails to respond properly.
• Level 3 ("eyes off"): The driver can safely turn their attention away from the driving tasks, e.g. the driver can text or read a book. The vehicle will handle situations that call for an immediate response, like emergency braking. The driver must still be prepared to intervene within some limited time, specified by the manufacturer, when called upon by the vehicle to do so. 
• Level 4 ("mind off"): As level 3, but no driver attention is ever required for safety, i.e. the driver may safely go to sleep or leave the driver's seat.
• Level 5 ("steering wheel optional"): No human intervention is required at all. An example would be a robotic taxi.

        This shows the vast disruptive potential of the emerging technology.

        with the power of Artificial Intelligence!

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Robotic Process Automation

• Robotic process automation (or RPA) is an emerging form of business process automation technology based on the notion of software robots or artificial intelligence(AI) workers.
• Robotic process automation (RPA) is the use of software with artificial intelligence (AI)and machine learning capabilities to handle high-volume, repeatable tasks that previously required humans to perform. These tasks can include queries, calculations and maintenance of records and transactions.
• RPA technology, sometimes called a software robot or bot,mimics a human worker, logging into applications, entering data, calculating and completing tasks, and logging out.

• As a form of automation, the same concept has been around for a long time in the form of screenscraping but RPA is considered to be a significant technological evolution of this technique in the sense that new software platforms are emerging which are sufficiently mature, resilient, scalable and reliable to make this approach viable for use in large enterprises.

• The hosting of RPA services also aligns with the metaphor of a software robot, with each robotic instance having its own virtual workstation, much like a human worker. The robot uses keyboard and mouse controls to take actions and execute automations.

• Normally all of these actions take place in a virtual environment and not on screen; the robot does not need a physical screen to operate, rather it interprets the screen display electronically.

• The scalability of modern solutions based on architectures such as these owes much to the advent of virtualization technology, without which the scalability of large deployments would be limited by available capacity to manage physical hardware and by the associated costs.

• Implementation of RPA in business enterprises has shown dramatic cost savings when compared to traditional non-RPA solutions.

     Benefits of RPA:

    Enabling better customer service

• Ensuring business operations and processes comply with regulations and standards.

• Allowing processes to be completed much more rapidly.

• Providing improved efficiency by digitizing and auditing process data.

      

       

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Face Recognition

• Facial recognition is a Biometric Artificial Intelligence based application that can uniquely identify a person by analysing patterns based on the person's facial textures and shape. 

• While initially a form of computer application, it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics.

• It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems.Recently, it has also become popular as a commercial identification and marketing tool.

• A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.

• There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from given image with faces within a database.

Facial Recognition Applications

Based on our assessment of the applications in the field today, the a majority of facial recognition use-cases appear to fall into three major categories:

• Security: Companies are training deep learning algorithms to recognize fraud detection, reduce the need for traditional passwords, and to improve the ability to distinguish between a human face and a photograph.

• Healthcare: Machine learning is being combined with computer vision to more accurately track patient medication consumption and support pain management procedures.

• Marketing: Fraught with ethical considerations, marketing is a burgeoning domain of facial recognition innovation, and it’s one we can expect to see more of as facial recognition becomes ubiquitous.

Uses of facial recognition technology:

•  A research team at Carnegie Mellon has developed a proof-of-concept iPhone app that can take a picture of an individual and -- within seconds -- return the individual's name, date of birth and social security number.

• The Google Arts & Culture app uses facial recognition to identify museum doppelgangers by matching a real person's faceprint with portrait's faceprint.

• Professor Shen Hao of the Communications University of China uses facial recognition technology to track students’ attendance.

• Amazon, MasterCard and Alibaba have rolled out facial recognition payment methods  commonly referred to as selfie pay.

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Virtual Agents

• A virtual agent or chatbot is used to describe a program based in artificial intelligence (AI) that provides automated customer service. 

• Virtual agent can also refer to a human customer service agent who works remotely from his employer's location.

• Virtual agent software has improved over the past five years with advances in AI and cognitive computing programs.

• Virtual agents are designed to provide customer services, product information, marketing, support, sales, order placing, reservations or other custom services.

• Virtual Agents are powered by a knowledge base, which includes an extensive list of possible different questions, responses and gestures, allowing the bot to react and respond to human input in a relatively human way.

• This is enormously useful for sales and marketing teams, as they typically only focus on leads deemed "high quality." With a virtual agent, all leads can be followed up on, which could result in higher sales. In addition, virtual agents cost significantly less than human employees.

• An intelligent virtual agent serves as a company representative and is built around a specific task, such as answering customer questions on a website's homepage.

• Companies interested in adopting virtual agent software through a cloud service provider or software vendor must invest time and resources into "training" the virtual agent

• Virtual agents are based on machine learning technology, which improves over time as the system ingests more data and "learns" through continued use.

• There are a number of cloud-based virtual agent platforms that are pretrained for customer service tasks. These programs require no coding or machine learning knowledge; instead, users configure the virtual agent to suit their business needs and branding.

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AI Medical Field

• Artificial Intelligence(AI) in healthcare is the use of algorithms and software to approximate human cognition in the analysis of complex medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input.

• The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes.AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine,and patient monitoring and care. 

• Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, Massachusetts General Hospital, and National Health Service, have developed AI algorithms for their departments. Large technology companies such as IBM and Google, and startups such as Welltok and Ayasdi, have also developed AI algorithms for healthcare.

• Artificial intelligence in medicine may be characterized as the scientific discipline pertaining to research studies, projects, and applications that aim at supporting decision-based medical tasks through knowledge- and data-intensive computer-based solutions that ultimately support and improve the performance of a human care provider.

• The purpose of this special issue is to demonstrate the potential of several intelligent approaches exploited in medical informatics technologies and applications.

• While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues. These challenges of the clinical use of AI has brought upon potential need for regulations.

Medical and technological advancements occurring over this half-century period that have simultaneously enabled the growth healthcare-related applications of AI include:

• Improvements in computing power resulting in faster data collection and data processing.

• Increased volume and availability of health-related data from personal and healthcare-related devices.

• Growth of genomic sequencing databases

• Widespread implementation of electronic health record systems.

• Improvements in natural language processing and computer vision, enabling machines to replicate human perceptual processes.

• Enhanced the precision of robot-assisted surgery.

        

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CrowdSourcing

• Crowdsourcing is a sourcing model in which individuals or organizations obtain goods and services. These services include ideas and finances, from a large, relatively open and often rapidly evolving group of internet users; it divides work between participants to achieve a cumulative result.
• This phenomenon can provide organizations with access to new ideas and solutions, deeper consumer engagement, opportunities for co-creation, optimization of tasks, and reduced costs. The Internet and social media have brought organizations closer to their stakeholders, laying the groundwork for new ways of collaborating and creating value together like never before. The approach is being embraced.
• There are major differences between crowdsourcing and outsourcing.Crowdsourcing comes from a less-specific, more public group, whereas outsourcing is commissioned from a specific, named group, and includes a mix of bottom-up and top-down processes.Advantages of using crowdsourcing may include improved costs, speed, quality, flexibility, scalability, or diversity.

• Currently, crowdsourcing has transferred mainly to the Internet, which provides a particularly beneficial venue for crowdsourcing since individuals tend to be more open in web-based projects where they are not being physically judged or scrutinized, and thus can feel more comfortable sharing.
• This approach ultimately allows for well-designed artistic projects because individuals are less conscious, or maybe even less aware, of scrutiny towards their work. In an online atmosphere, more attention can be given to the specific needs of a project, rather than spending as much time in communication with other individuals.

• Crowdsourcing touches across all social and business interactions. It is changing the way we work, hire, research, make and market. Governments are applying crowdsourcing to empower citizens and give a greater voice to the people. 
• In science and health care, crowdsourcing can democratize problem solving and accelerate innovation. With education, it has the potential to revolutionize the system, just as crowdfunding is currently challenging traditional banking and investing processes. It’s a 21st-century mindset and approach that can be applied in many areas and many ways.

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