You may wonder why?
In theory, it should be simple to come up with an idea, gather feedback and prototype to get a demo and validate the concept.
However, in reality, the majority of data science and machine learning professionals fail at every step of the process and as a result deliver solutions that rarely achieve production stage or post launch and are of no value whatsoever (do not increase productivity, revenue or simply do not entertain).
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If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.
AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.
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Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.
Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.
While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.
On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.
However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.
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In a previous post, I have written about how I spoon-feed management step by step. Once they understand the gist of AI, and I have an AI use case to pursue internally. It is time for setting up that first meeting to introduce my use case. In this meeting, I keep it simple, I have four key messages I use to explain the use case.
I write down the solution in one sentence. What is it exactly that my solution will do? Going through the process of writing it down one sentence helps me to find the right explanation and make it easier to get them on board. And I make sure to focus on the business side of things.
A good example: “Automatic acceptance/rejection of credit applications”
A bad example: “A logistic regression for prediction of paying back loans”
Just think of what will happen in the mind of your manager. In the first example (s)he will think “Great. I can save costs”. In the second example (s)he will think “Mm, interesting”.
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In this post let us talk about the types of agents and challenges of data set for the agents.
All agents have the same skeletal structure. They get percepts as inputs from the sensors and the actions are performed through the actuators. Now the agent can either just act on a percept as a reflex for example if you throw a ball at me and I try to catch it (or duck from it given that I am bad at baseball) than that is a quick reaction to the percept. On the other hand if you throw a ball at me and tell me to arrange it by color or count the number different colors that you are throwing at me then I would have to maintain a state to do the counts correctly. So this would involve some state but is still ok. Now, if you want to trouble me further and you tell me to jump twice if you throw red ball at me and do a burpee if you throw a green ball at me apart from catching and counting then you got me for sure
This would involve a complex logic of me maintaining a mind table of what needs to be done on what percept and this is called** condition-action rule**. Now if all these percepts were to be indexed then this would become a significant data set.
Consider the automated taxi: the visual input from a single camera (eight cameras is typical) comes in at the rate of roughly 70 megabytes per second (30 frames per second, 1080 × 720 pixels with 24 bits of color information). This gives a lookup table with over 10 600,000,000,000 entries for an hour’s driving. Even the lookup table for chess—a tiny, well-behaved fragment of the real world—has (it turns out) at least 10 150 entries.
This can become a lot of information.
The key challenge for AI is to find out how to write programs that, to the extent possible, produce rational behavior from a smallish program rather than from a vast table.
So this brings us to 4 documented types of agent programs
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