An interesting article on AI. 

What are the Difference between Artificial Intelligence, Machine Learning, and Deep Learning?

Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavour of AI you are referring to.

For example, when Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. But they are not the same things.

The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion —  fitting inside both. Read more .......

• Dramatic advancements in technology are driving the Internet of Things, creating more tools for industrial automation
• IoT is starting to create a world that is more connected, including customers, manufacturing processes, and supply chains.
• The danger with new, “shiny” technology is not staying focused on goals to achieve competitive manufacturing.

Read the InTech Magazine › 2016 / Nov-Dec › Cover Story: IOT impact on manufacturing. This article describe the current state of IoT and associated technology.

IoT impact on manufacturing

Technology shifts have worldwide implications, enabling more manufacturers to compete

• Cloud computing: Cloud computing leverages shared resources and economies of scale similar to an electric utility. The cloud deliverscomputing power and massive storage on demand. This model transforms a traditional capital expenditure of investing in dedicated hardware to an operating expenditure where you "pay as you go" or pay for use. One advantage is enabling users to focus on projects instead of infrastructure and software administration details. Cloud storage, for example, can be used as a plant historian. The constraint on cloud computing communications is latency, because it is remote.
• Cloud analytics: Cloud analytics for predictive maintenance, optimization, and other use cases is a way to provide powerful analysis without having to invest in local computing.
• IoT: High-volume Internet of Things products and applications are driving the creation of low-cost, high-power computer chips with integrated communications and low-cost sensors to be the bedrock of cyber-physical systems.
• Edge computing: Edge-of-network devices, including Ethernet switches, sensors, actuators, and video cameras, are incorporating open, embedded computing platforms that allow users to download executable applications.
• Embedded analytics/rules: Embedded computing in edge devices, sensors, and actuators enables analytics and rules engines running distributed in the field (e.g., wireless analyzers attached to an electric motor frame that relays operational and predictive maintenance information).

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