What happens when a computer algorithm merges with a form of artificial curiosity – to solve precarious problems?
Meticulous researchers at the University of California, Berkeley framed an “intrinsic curiosity model” to make their learning algorithm function even when there is a lack of strong feedback signal. The pioneering model developed by this team visions the AI software controlling a virtual agent in video games in pursuit of maximising its understanding of its environment and related aspects affecting that environment. Previously, there have been numerous attempts to render AI agents’ curiosity, but this time the trick is simpler and rewarding.
Two decades ago, if someone asked me to write a computer program that played tic-tac-toe, I would have failed horribly. Now being an accomplished computer programmer, I know the desirable tricks to solve tic-tac-toe with the help of “Minimax Algorithm”, and what it takes is just about an hour to jot down the program. No doubt, my coding skills have evolved over the period of time, but also computer science technology has reached unattainable heights.
When the whole world is going crazy about business analytics, you might be sitting in a corner and wondering what does it all mean? With so many explanations, notions run a gamut of options.
It’s TIME to be acquainted with all the imperceptible jargons of data science; let’s get things moving with these elementary FAQs.
The saying goes – ‘necessity is the mother of all inventions’ and with the advent of globalization we have witnessed this aphorism in its sincerest form. A new wave of competition and profit generation owing to the advent of the internet, within the labyrinth of our society has led to the creation of Data at a scale previously unthinkable. To capture the essence of this huge amount of data, a new term Big Data, was coined which meant extremely large data sets, which are to be analyzed to reveal patterns that lie within.
Today, technology has become the backbone of the society and data is its vertebrae. The technological boom began and became common around year 2000; this is when data monetization became apparent.
The job of a data analyst or scientist revolves around gathering a bunch of disorganized data, and then using them to build a case through deduction and logic. Finally, following that you will reach a conclusion after analysis.
Below quote from Sherlock Holmes is relevant –
“When you have eliminated the impossible whatever remains, no matter how Improbable it is must be the truth.”
Appalling forces are re-establishing the relationship between humans and water.
In the past, communities developed slowly, while the weather remained constant. Water sources never depleted at tumultuous rates as it has today. Water is no longer a dependable resource. That’s why many countries and cities are embracing smart technologies to manage water efficiently and preserve it for the coming generations.
As we observe the United Nations World Water Day on Wednesday, 22nd March, it is apt to assess the development being made in conserving this diminishing resource.
Do you follow your instincts instead of data and insights?
Do you prefer storing data in different databases, in separate formats with varying values?
Habits are subject to change. Though it may take some time, but eventually it evolves. Good and bad habits make a person. Good habits don’t demand attention, but bad habits often need to be looked into.
At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.
In the slides he made, one can see the following:
A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.
One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.
Ever wondered why many organizations often find it hard to implement Big Data? The reason often is poor or non-existent data management strategies which works counterproductive.
Data cannot be delivered or analysed without proper technology systems and procedural flows data can never be analysed or delivered. And without an expert team to manage and maintain the setup, errors, and backlogs will be frequent.
When Moore’s Law meets with modern day Business Intelligence, what happens? Disruption and then wider adoption!
With costs of implementing BI tools lowering, more and more enterprises are keen on jumping on-board the homebrewed variety of custom BI solution to help drive their business. The result of these efforts is that these days several organizations are pursuing data driven intelligent decision-making, at a cost, which is almost fractional compared to yesteryear’s Business Intelligence budgets.
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