Cognitive Computing Vs Conventional Computing

By -

           AI brings a new era of computing, cognitive computing. Cognitive Computing is radically a new type of computing that provides a very different form of the programmable system than the previous generation of its kind.

         From 1940 the Conventional Computing worked based on the mathematical principle to bring out a solution. Conventional Computing is a program based on rules and logic intended to derive mathematically precise answers, often followed by a rigid decision tree approach. 


         Nowadays, with the increase in the wealth of data and information which requires more complex evidence-based decisions, rigid approaches like Conventional Computing often break or fail to coordinate with the available large amount of data(Big Data).Cognitive Computing gives power to people to create a profoundly new kind of value, finding answers and insights locked away in volumes of data. Cognitive computing is the implementation of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain.

Cognitive Computing focuses on mimicking human behavior and reasoning to solve complex problems. Whether We consider a mechanic fixing a car or bike, trader investing in the stocks, or even an architect designing a new building, they all need new approaches to put into context the volume of information they deal with on a daily basis in order to derive value from it.

These all processes required human expertise. Cognitive Computing replicate some of the key cognitive element of human expertise, system that brings out solution to the problem like human does. We as a human when we need to understand something and make a decision out of it we follow four key steps.

1. We observe visible phenomena and bodies of evidence.

2. We draw on what we need to interpret What we are seeing to generate hypotheses about what it means.

3. We evaluate which hypotheses are right or wrong.

4. Finally, We will decide the option which seems best and act accordingly.


        Humans become experts at anything by just going through the process of observation, evaluation and decision-making, similarly cognitive computing systems use similar processes to learn about the information they read, and they can do this at massive scale and speed.


  Conventional Computing systems can interpret and handle only neatly organized structured data stored in the database. Unlike Conventional Computing systems, Cognitive Computing systems can handle and interpret unstructured data which is around 80 percent of the total data present today. Cognitive Computing is the technology typically specialized in processing and analyzing the large unstructured database. Cognitive Computing systems can use machine learning models to adapt to different contexts with minimal human supervision. Cognitive Computing systems are equipped with a chatbot or search assistant that understands queries, explains data insights and interacts with humans in natural language.


Application of Cognitive Computing:

1. Speech Recognition

2. Computer Vision

3. Sentimental Analysis

4. Behavioral recommendations

5. Face Detection

6. Risk Assessment

Comments

Post a Comment

Please do not use foul language while commenting. We will appreciate your suggestions and reviews in the comments.

Read Also

Deep Neural Networks for ADMET properties' prediction

By -
Image
Introduction The absorption, distribution, metabolism, excretion and toxicity properties of any compound contributes to its potential as an orally administered drug. A set of criteria defined by Rule of Five or the Pfizer's Rule of Five stats that any compound meeting these criteria are more likely to have ADMET properties. The importance of these properties has increased in modern medicinal chemistry and new machine learning techniques are employed to predict these properties. The predictive ADMET models have helped in discovery of small molecules with improved safety and dose. Deep Neural Networks are showing usefulness in such predictions due to improved computational efficiency, larger datasets and adaption of image processing in the chemistry. Early Models The early DNNs employed showed improved prediction performance. Many researchers raised concerned that this improvement is in fact has no relation with better computational capacity, rather it is result of mere memorizing of
Read more

Marine eDNA Analysis using DL techniques

By -
Image
Introduction Environmental DNA (eDNA) is the genetic information or material released by an organism into its surroundings through activities like skin shedding, mucus, urine or other secretions. These materials contain DNA fragments that can be found in soil, water or air samples. The concept of eDNA is based on the fact that organisms constantly leave organic traces behind them. By analysing the eDNA, researchers can detect the presence of any species in that area. This method is a valuable technique for biodiversity monitoring and ecological research.    Unlike traditional methods that involve direct observation or capturing of organisms, eDNA analysis allows researchers to study biodiversity without directly interacting with the species. This non-invasive approach is particularly useful for studying elusive or endangered species. eDNA analysis has been applied in various ecosystems, including freshwater, marine, and terrestrial environments. It is used to study aquatic organisms, t
Read more

Assisting Neuroimaging through DL

By -
Image
Introduction: In recent years, neuroimaging has been a research attraction, especially due to fMRI (Functional Magnetic Resonance Imaging) advancements. The fMRI data comprises of often several individuals. This data is used by researchers to study the association between the cognitive states of an individual and the underlying brain activities. The fMRI data used for neuroimaging is ideally suited for deep learning applications. The large, structured datasets of fMRI can be used for representation-learning methods of the DL. Generally, DL can be defined as a learning method with multiple levels of abstraction, where at each level the input data is transformed by a simple non-linear function which enables the model to recognize complex patterns. With higher-level representation, DL methods can associate a target variable with variable patterns in input data. Also, DL techniques can independently acquire these transformations from the data, eliminating the need for a comprehensive preex
Read more

What is Terraforming?

By -
Image
  Humans are working more and more on extraterrestrial habitations. A few decades back we were planning to settle on the Moon and now we are planning the same on Mars. Many researchers also proposed many theories on such habitations as O'Neill Cylinder, McKendree Cylinder, and many more. One such famous hypothetical theory is Terraforming. But what does the term terraforming mean? Terraforming is the process of transforming the atmosphere, topography, temperature and other factors of any moon or planet to make it habitable and similar to the Earth. It was Carl Sagan who first proposed the theory of terraforming. He proposed the terraforming of Venus. He thought of seeding Venus with algae. The algae will convert nitrogen and carbon dioxide to organic matter and eventually, the greenhouse effect will reduce to a comfortable, habitual level.  However, later studies revealed that the clouds of Venus are full of sulphuric acid. Also, the atmosphere of Venus is very thick, any carbon co
Read more