Skip to main content

Why is it Important for Executives to know about Artificial Intelligence?




Imagine you are an IT executive in a company. As per your role, you have to offer IT solutions to problems that your company is facing, be it related to Finance, Sales, HR, Administration, etc. Maybe you are already playing this role. You have successfully implemented IT solutions to challenging problems. You are praised by your CTO, have also received various awards for successful implementations. You have been promoted in the past for this particular skill. You are living in a perfect world, where most of the problems that you are solving can be solved with programming. You are living in a non-Artificial Intelligence world.

Now imagine a world, where the problems that industries are facing today are not solved by programming. Imagine your company is facing problems that look difficult to be solved by any number of lines of code, or any number of current software solutions that exist in the market. You are faced with a dilemma of not able to offer solutions to such problems. Your CTO is always questioned by your CEO as to why your department is failing to find IT solutions to such problems. 

What if many challenges or problems that the industry is facing can be solved by AI? What if there are technologies that can give you solutions to problems that your company is facing? Will you ignore them, just because you don’t know how AI works? Will you wait for someone else to implement AI? Even if you wait for someone else to implement AI, how will you know whether the solution is optimal? 

Today, AI is all around us. It is in your mobiles that you carry. It is used when you message someone through Whatsapp, or click a picture, or book a flight, or travel using Google maps. It is used when you use Google search to find answers to what you are looking for. It is used when you do online shopping on Flipkart or Amazon. 

When AI is all around us, why is it you have not yet thought over using AI in applications that you already have, or ones that you plan to implement? Maybe you have thought of implementing AI, but have not received proper guidance on how to proceed in this direction. 

The industry is going through a paradigm shift, when AI based solutions will take over non-AI based solutions. A search engine with AI in it will always give better results than one with no AI. A Sales Report generated by using AI will help you in decision making better than a report generated with no AI. A machine will perform optimally since AI is able to do predictive maintenance rather than preventive maintenance. A MRI technology with AI will be able to assist a doctor in decision making better than a non-AI based MRI technology. A factory will be able to create better quality products with AI compared to a factory with no AI. An e-commerce website is able to recommend products better when AI based recommendation engines. A cognitive anti-virus will perform a better job than a non-cognitive anti-virus. Automated testing software with cognition will identify bugs better than automated testing software without cognition.

The examples are countless. AI can be applied to all the industries, in fact it is already applied to all the industries. All major cloud players such as AWS and Azure are offering AI as a service. You can develop an application on cloud, and add AI with few lines of code, or sometimes with just drag and drop. Countless services such as Microsoft Cognitive Services, IBM Watson services, Amazon AI services, and Google Cloud Machine Learning Platform, will help you to use Artificial Intelligence in your applications.

There are different ways in which you can understand the current trends in Artificial Intelligence, and how it can be applied to your industry. Attending conferences, webinars, reading articles is one of the ways. You will find countless case studies that explain how AI is applied in different industries, and how it is helping to generate more revenue and save costs. 

Carl Sagan has said – “We live in a society exquisitely dependent on science and technology, in which hardly anyone knows anything about science and technology.” 

I will like to add that today we live in a society that is slowly starting to depend on Artificial Intelligence, but hardly anyone knows anything about Artificial Intelligence.” I hope you are not one of them.

Siddhesh Wagle,
Research Consultant,
Cere Labs. 

Comments

Post a Comment

Popular posts from this blog

Implement XOR in Tensorflow

XOR is considered as the 'Hello World' of Neural Networks. It seems like the best problem to try your first TensorFlow program.

Tensorflow makes it easy to build a neural network with few tweaks. All you have to do is make a graph and you have a neural network that learns the XOR function.

Why XOR? Well, XOR is the reason why backpropogation was invented in the first place. A single layer perceptron although quite successful in learning the AND and OR functions, can't learn XOR (Table 1) as it is just a linear classifier, and XOR is a linearly inseparable pattern (Figure 1). Thus the single layer perceptron goes into a panic mode while learning XOR – it can't just do that. 

Deep Propogation algorithm comes for the rescue. It learns an XOR by adding two lines L1 and L2 (Figure 2). This post assumes you know how the backpropogation algorithm works.



Following are the steps to implement the neural network in Figure 3 for XOR in Tensorflow:
1. Import necessary libraries
impo…

From Cats to Convolutional Neural Networks

Widely used in image recognition, Convolutional Neural Networks (CNNs) consist of multiple layers of neuron collection which look at small window of the input image, called receptive fields.
The history of Convolutional Neural Networks begins with a famous experiment “Receptive Fields of Single Neurons in the Cat’s Striate Cortex” conducted by Hubel and Wiesel. The experiment confirmed the long belief of neurobiologists and psychologists that the neurons in the brain act as feature detectors.
The first neural network model that drew inspiration from the hierarchy model of the visual nervous system proposed by Hubel and Wiesel was Neocognitron invented by Kunihiko Fukushima, and had the ability of performing unsupervised learning. Kunihiko Fukushima’s approach was commendable as it was the first neural network model having the capability of pattern recognition similar to human brain. The model gave a lot of insight and helped future understanding of the brain.
A successful advancement i…

Understanding Projection Pursuit Regression

The following article gives an overview of the paper "Projection Pursuit Regression” published by Friedman J. H and Stuetzle W. You will need basic background of Machine Learning and Regression before understanding this article. The algorithms and images are taken from the paper. (http://www.stat.washington.edu/courses/stat527/s13/readings/FriedmanStuetzle_JASA_1981.pdf
What is Regression? Regression is a machine learning technology used to predict a response variable given multiple predictor variables or features. The main distinction is that the response to be predicted is any real value and not just any class or cluster name. Hence though similar to Classification in terms of making a prediction, it is largely different given what it’s predicting. 
A simple to understand real world problem of regression would be predicting the sale price of a particular house based on it’s square footage, given that we have data of similar houses sold in that area in the past. The regression so…