Integration of mechanical, software and electronic systems technologies for vehicle systems了解行业应用
Supply chain collaboration in design, construction, maintenance and retirement of mission-critical assets了解行业应用
Construction, mining, and agricultural heavy equipment manufacturers striving for superior performanceExplore Industry
Integration of manufacturing process planning with design and engineering for today’s machine complexity了解行业应用
Insurance & Financial
Visibility, compliance and accountability for insurance and financial industriesExplore Industry
Media & Telecommunications
Siemens PLM Software, a leader in media and telecommunications software, delivers digital solutions for cutting-edge technology supporting complex products in a rapidly changing market.Explore Industry
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Small & Medium Business
Remove barriers and grow while maintaining your bottom line. We’re democratizing the most robust digital twins for your small and medium businesses.Explore Industry
Siemens Digital Industries Software Machine Learning
The crux of Machine Learning (ML) is based on the recognition that human intelligence hinges more on ‘probability’ than ‘reason’ or ‘logic’. Our brains unconsciously perform more estimation – calculating the fastest driving route based on prior experience, guessing an opponent’s move while playing a board game, plotting a strategy – than using the skill of reasoning and thinking. The human brain relies first and foremost on its ability to assess how likely something is; we are not consciously aware of this.
The machine is flexible in its learning and uses an iterative approach to independently adapt to new data. The models learn from previous computations to produce repeatable and reliable results and decisions. Machine Learning algorithms have been around since the 1950s. However, the ability to apply complex mathematical calculations to big data is a recent development. The growing volumes and varieties of available information, cheap and powerful computational processing and affordable data storage makes the comprehensive machine learning capabilities available.
All of these things mean it's easier than ever for organizations to benefit from this technology by identifying profitable opportunities or avoiding unknown risks, building precise models and analyzing them to deliver effective solutions.
One of the critical questions about machine learning is, how much of human intelligence can be approximated with statistics?