Computational Biology

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• Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, ecological, behavioral, and social systems.

• The field is broadly defined and includes foundations in biology, applied mathematics, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, computer science and evolution.

• Computational biology is different from biological computing, which is a subfield of computer science and computer engineering using bioengineering and biology to build computers, but is similar to bioinformatics, which is an interdisciplinary science using computers to store and process biological data.

• Computational Biology, which includes many aspects of bioinformatics, is the science of using biological data to develop algorithms or models to understand biological systems and relationships. Until recently, biologists did not have access to very large amounts of data.

• This data has now become commonplace, particularly in molecular biology and genomics. Researchers were able to develop analytical methods for interpreting biological information, but were unable to share them quickly among colleagues.

• Bioinformatics began to develop in the early 1970s. It was considered the science of analyzing informatics processes of various biological systems. At this time, research in artificial intelligence was using network models of the human brain in order to generate new algorithms.

• This use of biological data to develop other fields pushed biological researchers to revisit the idea of using computers to evaluate and compare large data sets. By 1982, information was being shared among researchers through the use of punch cards.

• Once the problem has been framed, the second major task of computational biologists begins.  This is to borrow, refine, or invent methods to solve the problem.Current computational biology research can be divided into a number of broad areas, mainly based on the type of experimental data that is analyzed or modeled.

• Among these are analysis of protein and nucleic acid structure and function, gene and protein sequence, evolutionary genomics and proteomics, population genomics, regulatory and metabolic networks, biomedical image analysis and modeling, gene-disease associations, and development and spread of disease.

         

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Expert Systems

• An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.

• Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s.

• Expert systems were among the first truly successful forms of artificial intelligence software. However, some experts point out that expert systems were not part of true artificial intelligence since they lack the ability to learn autonomously from external data.

• Expert systems have played a large role in many industries including in financial services, telecommunications, healthcare, customer service, transportation, video games, manufacturing, aviation and written communication.

• Expert system incorporates a knowledge base containing accumulated experience and an inference or rules engine a set of rules for applying the knowledge base to each particular situation that is described to the program. 

• The system's capabilities can be enhanced with additions to the knowledge base or to the set of rules.

• Expert systems are part of a general category of computer applications known as artificial intelligence. To design an expert system, one needs a knowledge engineer, an individual who studies how human experts make decisions and translates the rules into terms that a computer can understand.

         

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Fuzzy Systems

• Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 inclusive. 

• It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.

• Fuzzy Logic System was invented by Lotfi Zadeh. Also, he observed, unlike other computers, it includes a range of possibilities between YES and NO, in a human decision.

• Can be implemented in systems with various sizes and capabilities. That should be range from mall micro-controllers to large. Also, it can be implemented in hardware, software, or a combination of both in artificial intelligence.

• Meaning of the expressions cold, warm, and hot are represented by functions mapping a temperature scale. A point on that scale has three "truth values"—one for each of the three functions. 

• The vertical line in the image represents a particular temperature that the three arrows gauge. Since the red arrow points to zero, this temperature may be interpreted as "not hot". The orange arrow (pointing at 0.2) may describe it as "slightly warm" and the blue arrow (pointing at 0.8) "fairly cold".

           

          Fuzzy Systems Applications:

• Automotive Systems
• Consumer Electronic Goods
• Domestic Goods
• Environment Control

        

          Advantages of Fuzzy Systems:

• Generally, in this system, we can take imprecise, distorted, noisy input information.
• Also, these logics are easy to construct and understand.
• Basically, it’s solution to complex problems. Such as medicine.
• Also, we can relate math in concept within fuzzy logic. Also, these concepts are very simple.
• Due to the flexibility of fuzzy logic, we can add and delete rules in FLS system.

         

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Neuromorphic Computing

• Neuromorphic computing is a concept developed by Carver Mead  in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.

• In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). 

• The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors.

• A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.

• Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.

• Neuromorphic computing utilizes an engineering approach or method based on the activity of the biological brain.

• This type of approach can make technologies more versatile and adaptable, and promote more vibrant results than other types of traditional architectures, for instance, the von Neumann architecture that is so useful in traditional hardware design.

• Neuromorphic computing has been around for a while, but it is now beginning to be applied in new and different ways. A prime example is the proposal to create neuromorphic chips which are more complex in nature than traditional microprocessors.

• Neuromorphic chips would have architectures more like the neurons of the human brain, allowing them to process information in more specialized ways.

              
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Recommender Sytems

• A Recommender Systems is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item.
• Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.
• There are also recommender systems for experts, collaborators, restaurants, garments, financial services, life insurance and twitter pages.

• Most recommender systems focus on the task of information filtering, which deals with the delivery of items selected from a large collection that the user is likely to find interesting or useful.
• Recommender systems are special types of information filtering systems that suggest items to users. Some of the largest e-commerce sites are using recommender systems and apply a marketing strategy that is referred to as mass customization.
• Some web sites present users with personalized information by letting them choose from a set of predefined topics of interest. 

• Users however do not always know what they are interested in beforehand and their interests may change overtime which would require them to change their selection frequently.
• Recommender systems provide personalized information by learning the user’s interests from traces of interaction with that user.
• Recommender systems typically produce a list of recommendations in one of two ways – through collaborative filtering or through content-based filtering.
• Collaborative filtering approaches build a model from a user's past behaviour (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items  that the user may have an interest in.
• Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.

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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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