DoT Blog

Image By Seanbatty (Pixabay) According to Encyclopedia Britannica , artificial intelligence (AI) can be defined as: "The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, like the ability to reason, discover meaning, generalize, or learn from previous experiences." By now, we have all heard about how AI can make it possible for computers, machines and other electronic devices to perform increasingly complex and human-like tasks. As all this sounds almost like magic, with machines performing increasingly complex tasks —from new gaming computers to self-driving cars – in reality most of AI technologies rely on the blend of software methods and technologies that imply collecting, processing and recognizing patterns within large amounts of data. So, how does AI W...

Following on my previous WTF post on Machine Learning, it just make sense to continue in this line of thought to address another of many popular and trendy concepts. We are talking about: Deep Learning. So without further due, lets explain WTF is deep learning shall we? Simply put, and as inferred from the previous post mentioned, deep learning is one of  now many approaches to machine learning we can find out there, along the lines of other approaches like decision tree learning, association rule learning, or Bayesian networks. While deep learning is not new, was introduced by Dr. Rina Dechter in 1986, its until recent years that this approach have gained fame and popularity among users and particularly among software companies adopting it within their analytics arsenals. Deep learning enables to train the computer to perform tasks including recognizing speech, identifying images or making predictions by, instead of organizing data to run through predefined equations, s...

As the need for gathering data continues, organizations keep dealing with increasing amounts of information that need to be stored, processed, and analyzed faster and better, stimulating the growth and evolution of the high performance computing (HPC) market. One key segment in this market the continues to grow, especially in recent years, is the high performance data analytics (HPDA) as organizations continue to adopt and evolve their big data and data lake initiatives, to the point that IDC forecasts that in 2018, the HPDA server market will reach $2.6 billion (23.5% CAGR) and the HPDA external storage market will add $1.6 billion (26.5% CAGR). Is not strange then, that software companies like IBM are keen to develop solutions capable to address the specific HPDA market segment, and IBM haws been working specifically on it with its Spectrum software line. IBM Evolves Spectrum to Keep the Pace with the HPDA Market Segment Late in November of last year, IBM announced a bra...

I have to say that I attended this year’s Cloudera analyst event in San Francisco with a mix of excitement, expectation and a grain of salt also. My excitement and expectation were fuelled with all that has been said about Cloudera and its close competitors in the last couple of years, and also by the fact that I am currently focusing my own research on big data and “New Data Platforms” . Moreover, when it comes to events hosted by vendors, I always recommend taking its statements with a grain of salt, because logically the information might be biased. However, in the end, the event resulted in an enriching learning experience, full of surprises and discoveries. I learnt a lot about a company that is certainly collaborating big time in the transformation of the enterprise software industry. The event certainly fulfilled many of my “want-to-know-more” expectations about Cloudera and its offering stack; the path the company has taken; and their view of the enterprise data mana...