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5 predictions for 2016 on data, analytics and machine learning

@machinelearnbot

We are able to confidently predict that in 2016 more and more applications for analysing data will require less technical expertise. It is an easy prediction but, more and more data sets will be blended from different sources allowing more insights, this will be a noticeable trend that will emerge during 2016. We predict that in 2016 a new data centric semiotic, a visual language for communicating data derived information, will become stronger, grow in importance and be the engine of informatics .


How Can We Trust Machine Learning? - insideBIGDATA

@machinelearnbot

Exploration, Evaluation and Explanation for ML Models: Machine learning technologies are at the core of a new generation of intelligent applications that differentiate disruptive businesses from established players. Today, business tasks like product recommendation, image tagging, sentiment analysis, churn prediction, fraud detection and lead scoring can only be achieved using machine learning (ML). To build these applications at scale, companies are fast adopting tools such as Dato's GraphLab Create and Predictive Services, enabling developers to accelerate the innovation cycle, and quickly take their ideas from inspiration to production.


4 Predictions for Supercomputing in 2017

#artificialintelligence

The growing competitiveness of China and shifting political landscapes mean that 2017 holds some uncertainties for supercomputing. Yet familiar technologies remain strong and provide a stable foundation with fewer surprises. Here are four predictions of where the industry is headed in 2017. Despite gaining ground as a marketing term, and being a rich field for basic and applied research, it's highly doubtful we'll see the emergence of AI as a dominant force in the next 12 months. We're still far from the "singularity" that so many of us tech-geeks fear, so don't expect AI to jump out of marketing copy and begin hunting us down a la The Terminator by next Christmas.


Challenges in operationalizing a machine learning system

#artificialintelligence

The goal of this blog is to cover the key topics to consider in operationalizing machine learning and to provide a practical guide for navigating the modern tools available along the way. To that end, the subsequent blogs will include further detailed architecture concepts and help you apply them to your own model pipelines. This blog series will not explain machine learning concepts but rather to tackle the auxiliary challenges like dealing with large data sets, computational requirements and optimizations, and the deployment of models and data to large software systems. Most classical software applications are deterministic where the developer writes explicit lines of code that encapsulate the logic for the desired behavior. Whereas, the ML software applications are probabilistic where the developer writes a more abstract code and lets the computer write the code in a human unfriendly language i.e. the weights or parameters required for the ML model.


what is machine learning ?

#artificialintelligence

What are the basics of machine learning? Everything you need to know -- this section clarifies machine learning and its similarities to artificial intelligence, why it functions and the significance of it. Machine learning is a branch of artificial intelligence that seeks to learn from data and make predictions using these methods. Machine learning is most often used as a computer algorithm, but can also be viewed as machine software, rather than software that runs on computers. The automated conclusion or prediction drawn from the analysis of data by machine learning algorithms is called the "analytical result".