AI for business

Solving real world business problem with AI solutions require more than a software project.

Almost always we begin with Plan of Attack. That is composed of ESI (Environment, Solution & Implementation). We define Environment by SAR (States, Actions & Rewards). We define Solution by Intuition & Theory. We define Implementation in terms of which language to use, where to deploy, how data will be collected, processed and output is generated.

Example: Business process of optimizing warehouse

Environment: States, Actions, Rewards

Solution: Markov Decision Process, Temporal Difference & Q-Learning

Implementation: Build using Python, Try using Jupitor notebook & Deploy using Amazon AWS Lambda

Algorithms, Maths & Process

Reinforcement Learning: The Bellman Equation

Assume: s-State, a-Action, R-Reward, Y-Discount (Gamma is represented as Y – in other books it says ‘gamma’), V = value i.e, V(s) is value of a specific state, V(s’) is value of state s’

V(s) = max of a (R(s,a) + YV(s’))

Markov Decision Process (MDP)

A stochastic process has the Markov property if the conditional probability distribution of future states of the process (conditional on both past and present states) depends only upon the present state, not on the sequence of events that preceded it. A process with this property is called a Markov process

Resources and References:

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The Emerging Power of Data Analytics

With new and rapid advancements in technology, data gathering and visualization has become one of the most important tool in the field of business today. Data gathering, filtering, sorting and further processing are only the few basic steps that can lead to better effective marketing for your company/product. All of this can be encapsulated under the idea of Data Analytics. It is a scientific approach of analyzing raw data and making valuable conclusions from that data.

In order to better understand this idea, Data Analytics involves using many different techniques and developing new algorithms that can reveal those aspects of our raw data that may remain hidden otherwise. The field of Data Analytics can be further divided into different sections that includes “descriptive analytics” and ” diagnostic Analytics” etc.

To link it with the importance it holds for future prospects, Data Analytics tends to be the emerging power for all small-scale, rising and big companies. It single-handedly serves to be the tool that can be used to optimize marketing strategies and performances. By using Data Analytics, any company can significantly improve its Customer Relationship Management and produce new and latest products.

Can machines really think?

For this, Artificial Intelligence might be the answer. While Data Analytics continues to be on a rise, Artificial Intelligence simultaneously is on an upsurge area. To start with, it is a branch of computer science that is linked with creating apt machines that can carry out those task that require human intelligence. These systems are further powered by machine learning. Siri and Alexa might seem to be one of the most basic examples for Artificial Intelligence while Robo-advisors and conversational bots for effective customer service can classify as amongst the top.

Even with the ongoing debate on the 2020 digitalization, it is imperative to state that the field of Data Analytics and Artificial Intelligence can provide one of the most promising and rewarding prospects in the future.

Ripe.ai recognizes the importance and need of such tools in today’s era and continues to offer intelligent customer insights using remarkable intangible products like polls, surveys, forms and feedback.

Ripe.ai uses AI/ML to capture intelligent customer insights. Not only this, but keeping in view the need of business in today’s world. You can create ripe objects (surveys, forms, feedback, poll and others) to capture data and analyze it.

Join today: https://ripe.ai/2020/promo?rb=0mj03mms&c=july2020

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