What an AI Development Company Is Seeking in the Current Landscape

Technology can be seen as both, Yin and Yang, it can be beneficent and can be malignant and machine learning gives us enormous power to solve complex problems that cannot be possible with writing rule for every specific case.Machine learning has been disrupting Education Technology and has been prolific across most of the EdTech startup.

Be it provision of recommendation to students about the courses or assisting students with feedback or summarizing the content so the reader can comprehend the useful part of the content rather than getting into nitty-gritty details of every text.We thought of building an application in a similar domain, assisting students with feedback and self-evaluation.

In the age of information and technology where data is available in abundance, it’s difficult as well as imperative for a student to learn and understand the concept behind the content; regular assessment and keeping the user progress can be a better way to improvise the learning.Let’s start our Journey — Building a student-centred Feedback systemThere are a couple of entities a person needs to keep in mind while building the Text processing and Natural language system.

A complex Natural language system contains a substantial number of algorithm depending on the use case, be it a key phrase extraction using Tf-if, Text-rank for Text summarization, Bayes Theorem for Sentiment analysis, POS tagging, NER extraction using Naïve Bayes etc.

these are some of the simple algorithms which can be useful while building a simple text processing engine.With the advent of deep learning, it is feasible to replenish the area of Natural language understanding which opens a broader scope of understanding the emotion behind the text the user conveyed, such as sarcasm, humour, disgust, excitement etc.This technology can help us build a complex natural language system and general-purpose Artificial Intelligence.The components we need for our purpose are:1.

To measure the similarity of the answer with the actual answer while taking the assessment we’ll be using the consine similarity matrix.

Enhancing model performance can be challenging at times.

The problem is most prevalent among providers of machine learning development services who diligently apply multiple algorithms to optimize the model performance.

Yet, developers fail at improving the accuracy of the model.

You feel helpless and stuck.

And, this is where 90% of data scientists give up.

But, this is where the real story begins!

Artificial Intelligence today, is probably the most amazing and detailed creations of humanity to date, disregarding the fact that the area is still largely unexplored.

AI’s powerful capabilities and fast growth have made humans paranoid regarding the proximity and certainty of an AI’s acceptance.

Since AI research aims in making machines imitating human-like respondings, the extent to which an AI system can clone human capabilities is used as a standard for determining the various types of AI.Types of AI can be labeled under one, among the multiple types of AI, depending on how a machine compares to humans in terms of flexibility and execution.1.

Reactive MachinesThese are the oldest forms of Artificial Intelligence systems that have very limited capability.

Features of Reactive Machines :They correspond to the human mind’s capacity to react to various kinds of stimuli.Such machines are incapable of “learning” as they cannot use previously acquired experiences to guide the present actions.These machines can be only used to react to situations that exist presently and that too for a limited set of inputs.An example of such machines would be IBM’ Deep Blue, i.e.

Limited MemoryLimited Memory machines are those machines that have some additional capabilities than Reactive Machines.