KUALA LUMPUR, March 10 (Bernama) -- Bursa Malaysia rebounded to end higher today with the benchmark FBM KLCI reclaiming the 1,700 psychological level, supported by improved global sentiment after US President Donald Trump signalled a potential de-escalation of the Iran conflict, alongside Malaysia’s stronger Industrial Production Index (IPI) data. At 5 pm, the FTSE Bursa Malaysia KLCI (FBM KLCI) increased 27.51 points, or 1.64 per cent, to 1,701.68 from yesterday’s close of 1,674.17. The benchmark index opened 10.68 points higher at 1,684.85, its lowest point today, and hit a high of 1,703.61 in the late afternoon session. Market breadth was positive, with gainers thumping losers 929 to 382. A total of 361 counters were unchanged, 982 untraded and 19 suspended. Turnover declined to 3.60 billion units worth RM3.75 billion from yesterday’s 5.52 billion units worth RM5.87 billion.
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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