Malaysia’s benchmark index retreated as profit-taking in key heavyweights weighed on sentiment, while overall market activity remained active. Summary FBM KLCI fell 0.83% to 1,684.93 , dragged by losses in banking and selected large-cap names, despite steady trading participation. Market Performance FBM KLCI : 1,684.93 (-0.83%) FBM Mid 70: -0.00% (flat) FBM Small Cap: -0.23% FBM ACE: +0.20% Broad market was mixed , with weakness concentrated in large caps. Market Breadth & Trading Activity Total volume: 3.54 billion shares Total value: RM4.19 billion Gainers: 456 Losers: 678 Unchanged: 550 Market breadth turned negative , reflecting cautious sentiment. Top Movers – KLCI Gainers Axiata (6888.MY) +1.54% Petronas Gas (6033.MY) +1.18% Sunway (5211.MY) +1.15% Losers Hong Leong Bank (5819.MY) -3.29% Maybank (1155.MY) -3.02% CIMB (1023.MY) -2.47% Banking sector weakness was the main ...
If you think Microsoft's new CEO is making another bold statement without action, think again. Microsoft's new CEO, Satya Nadella is serious about cloud computing and he has a strategy.
A supposedly comprehensive predictive analysis service — and all you have to do is store your data in Azure, the Microsoft cloud.
The service will be known as Microsoft Azure Machine Learning (ML) was announced on Monday but will only be available in June. This is the first time where Microsoft combines their very own software with publicly available open source software, so that it's much more easier for usage than most of the available arcane strategies that are available now.
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| Machine Learning - fixing today's problem yesterday |
The VP for ML at Microsoft proudly said, "This is drag-and-drop software."
This is a big step forward in popularizing what is currently a difficult process in increasingly high demand. It would also further the ambitions of Satya Nadella, Microsoft’s chief executive, of making Azure the center of Microsoft’s future.
Machine learning computers examine historical data through different algorithms and programming languages to make predictions. The process is commonly used in Internet search, fraud detection, product recommendations and digital personal assistants, among other things.
As more data is automatically stored online, there are opportunities to use machine learning for performing maintenance, scheduling hospital services, and anticipating disease outbreaks and crime, among other things. The methods have to become easier and cheaper to be popular, however.
That is the goal of Azure Machine Learning.
Here is a video posted by Microsoft on Youtube:
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