5G is Rolling Out: Here’s How Cognitive Analytics Will Take Part in the Revolution

5G is here and is widely expected to be a transformative communications technology for the next decade. This new data network will enable never-before-seen data transfer speeds and high-performance remote computing capabilities. Such vast, fast networks will need dedicated tools and practices to be managed, including AI and machine learning processes that will ensure efficient management of network resources and flexibility to meet user demands.


The future of stock market analysis

Stock sales and trading play a huge role in the U.S. and global economy. Stock exchanges provide the backbone to the economic infrastructure of our nation, as they help companies to expand when they’re ready by offering the general public a chance to invest in company stock. However, investing in the stock market can be a gamble. You need to understand the market and know what you’re doing in order to receive a return on your investment, which is why many people go through stock brokers.


5 Artificial Intelligence Myths—Debunked

Artificial intelligence—you’ve read about it in science fiction novels, you’ve heard tech personalities talk optimistically about it, and you’ve seen headline after headline mention its potential and benefits. As a widely discussed concept, the technology is hard to miss, but how exactly does it work and what does it mean for businesses?

Deep Learning for Time Series Data (O'Reilly Artificial Intelligence Conference)

Arun Kejariwal and Ira Cohen, both thought leaders in the deep learning space, share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data. Present at the 2019 O'Reilly Artificial Intelligence Conference.

The Impact of AI on the Data Analyst

The introduction of AI, automation and data storytelling to the world of analytics has not only had an immediate impact on the end users of analytics but also the people that work in the field. While many analysts may fear they will be replaced by automation and AI, CEO of Yellowfin, Glen Rabie, believes that the role of the data analyst will increase in significance to the business and breadth of skills required.


Stop shackling your data-scientists: tap into the dark side of ML / AI models

Developing Artificial Intelligence and Machine Learning models comes with many challenges. One of those challenges is understanding why a model acts in a certain way. What’s really happening behind its ‘decision-making’ process? What causes unforeseen behavior in a model? To offer a suitable solution we must first understand the problem. Is it a bug in the code? A structural error within the model itself? Or, perhaps it’s a biased dataset?


Part 4: How machine learning, AI and automation could break the BI adoption barrier

In the last three parts of this four-part series, we have looked at: research on the state of analytics today and the lack of BI adoption; the history of BI and how we have arrived at the augmented era; and the four main blockers to BI adoption that is stunting the growth your business data culture. Today, let's take a look at how AI and machine learning (ML) can close that adoption gap.


Part 3: How machine learning, AI and automation could break the BI adoption barrier

In the first blog post of the series, we saw the dire state of analytics adoption. This problem feeds into the low usage and governance of data across organizations. Then, in the second post, we saw how the evolution of analytics has brought us to a prime position for augmented analytics. But will this new wave of augmented analytics break through the barriers to BI adoption?