The reality is that AI is still heavily-reliant upon smart, willing and trained humans in order for AI to behave in a manner that we would expect. Humans are needed to scope the problems, identify relevant examples and verify the results. Without humans as a guide, current AI is no more capable
Fundamentally, machine learning is a productivity tool for data scientists. As the heart of systems that can learn from data, machine learning allows data scientists to train a model on an example data set and then leverage algorithms that automatically generalize and learn both from that example
The need for information is paramount to our need to excel and succeed. Businesses rely on information for strategic planning and driving growth. Individuals rely on information to make decisions and gain understanding of things. All information is driven by knowledge, and for knowledge to be an
A new Harvard Business Review Analytic Services and IBM study shows 72% of line of business leaders believe they are susceptible to disruption within three years but only 14% are prepared to respond. This presentation provides a lens into the thinking of 600 business executives and examines the
January marked the release of the long awaited Hidden Figures movie featuring an all-star cast and highlighting the contributions of both women and IBM's technology to history. Hidden Figures tells the true story of three African-American female mathematicians, Katherine Johnson, Mary Jackson, and
Dinesh Nirmal is Vice President, IBM Analytics Platform Development. In this podcast, he discusses the role that machine learning plays in enterprise cognitive analytics initiatives. He will be speaking on this topic on February 15, 2017 at the IBM Machine Learning Launch Event.
When the data lake is deployed as an infrastructure to be exploited by different users in various departments with their own needs, their own different requirements and often their own dialects in terms of a business language, then a universal translator can become very useful. Especially with the
There is so much talk about data as a new natural resource. The amount of data organizations and citizens across the globe produce, is authored in many systems and consumed by various organizations and users in different formats. This begs the following questions: Who owns this data? And why it is
Jeff Josten is IBM Distinguished Engineer for DB2 for z/OS Development, IBM Analytics, Platform Development. In this podcast, he discusses how the value of machine learning in enterprise applications of hybrid transaction/analytics processing. He will be speaking on this topic on February 15, 2017
CIOs are saddled with the incredible responsibility of ensuring all things IT are not just functioning, but are meeting the high demands of both internal enterprise users as well as those customers that rely on that enterprise as part of their own business. Though CIOs have an incredible
This is the first in a sequence of blogs that takes a peek at what is driving analytics onto the cloud, what are the challenges that will need to be overcome over the next 5 years and how they will be tackled.
J White Bear is a data scientist and software engineer at IBM. In this podcast, White Bear discusses simultaneous localization and mapping, an ongoing research area in robotics for autonomous vehicles and well-recognized as a nontrivial problem space in both industry and research.
In cognitive computing era, new revenue generation stream has emerged with data at center of the modern digital business model. One of the key capabilities cognitive computing enables for an organization is the ability to generate additional revenue streams by using data effectively. In the big