Goto

Collaborating Authors

Why AI, Machine Learning, and Bots? Better Experiences.

#artificialintelligence

In the year 2016 bots, AI, and machine learning seemed to be all the rage, with all of these topics getting mixed into one big bundle. Many people seemed to forget that they are not the same but only related to each other. Artificial Intelligence (AI) is an umbrella term that describes the part of computer science that deals with making computers emulate human intelligence. Machine learning is the part of AI that makes a computer appear intelligent. Bots are an application -- an application being most helpful if it is based upon a minimal level of (artificial) intelligence, and that particularly serves interaction purposes.


Why AI, Machine Learning, and Bots? Better Experiences.

#artificialintelligence

In the year 2016 bots, AI, and machine learning seemed to be all the rage, with all of these topics getting mixed into one big bundle. Many people seemed to forget that they are not the same but only related to each other. Artificial Intelligence (AI) is an umbrella term that describes the part of computer science that deals with making computers emulate human intelligence. Machine learning is the part of AI that makes a computer appear intelligent. Bots are an application -- an application being most helpful if it is based upon a minimal level of (artificial) intelligence, and that particularly serves interaction purposes.


Predictive analytics are the future of big data

#artificialintelligence

Analysing big data has been on the tip of many a technologist's tongue for the past couple of years. This analysis is described as the future for enterprises looking to gain insights into business operations and find patterns between sales and marketing activity against revenue. Many organisations have used it to good effect. Camden Council uses IBM big data analytics to create a database that consolidates residents' data to reduce fraud and costs, while Expedia consumes big data to better understand what its customers are buying. Open source frameworks like Hadoop make the storage of data more cost effective and, with numerous analytics tools on offer, the promised big data future is here.


Users tap mix of tools to mine big data analytics architecture

#artificialintelligence

Before it deployed a Hadoop cluster five years ago, retailer Macy's Inc. had big problems analyzing all of the sales and marketing data its systems were generating. And the problems were only getting bigger as Macy's pushed aggressively to increase its online business, further ratcheting up the data volumes it was looking to explore. The company's traditional data warehouse architecture had severe processing limitations and couldn't handle unstructured information, such as text. Historical data was also largely inaccessible, typically having been archived on tapes that were shipped to off-site storage facilities. Data scientists and other analysts "could only run so many queries at particular times of the day," said Seetha Chakrapany, director of marketing analytics and customer relationship management (CRM) systems at Macy's.


The Power to Predict

#artificialintelligence

This is one of four articles in a special report about the use of predictive analytics. Advanced analytics goes beyond hindsight and insight, providing CFOs with the ability to see the future. While no one can look into the future, a smart CFO can use predictive analytics to understand the market and use that insight to generate growth. More and more CFOs -- and companies -- are applying predictive analytics to boost planning and forecasting accuracy and solve an ever-increasing range of business problems. One reason: The barriers to using predictive analytics tools, which employ statistical models to make forecasts and projections and uncover key business drivers, are being lowered.