Machine Intelligence

 

 

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Machine intelligence is a somewhat obscure term for specific kinds of artificial intelligence that are being noticing as the field advances.

 

A definition put forth by some artificial intelligence companies is that machine intelligence “enables a machine to interact with an environment in an intelligent way.”

 

To understand machine intelligence better, it is good to look at this term within the context of two other terms that are proliferating in today’s tech world – “artificial intelligence” and “machine learning.”

 

Artificial intelligence is composed of systems that allow computers to imitate human cognitive processes or perform tasks that used to be done by humans.

 

Machine learning is defined as systems that enable a computer system to learn from inputs, rather than being directed only by linear programming.

 

In this context, another way to explain “machine intelligence” is that through a basis of machine learning and artificial intelligence, the machine learns to work proactively.

 

Theoretically, if a machine learns to extract various kinds of data to put together its own processes and arrive at its own conclusions, you could say that that constitutes machine intelligence based on both machine learning and artificial intelligence functionalities.

 

 

 

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Smart machines

 

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A smart machine is a device embedded with machine-to-machine (M2M) and/or cognitive computing technologies such as artificial intelligence (AI), machine learning or deep learning, all of which it uses to reason, problem-solve, make decisions and even, ultimately, take action.

 

Smart machines include robots, self-driving cars and other cognitive computing systems that are designed to work through tasks without human intervention.

 

Smart machines are digital disruptors because of the positive and negative impact they have, and will continue to have, on society. In business, the competitive advantages these technologies are capable of providing are expected to bring higher profit margins and lead to more efficient manufacturing processes.

 

However, smart machines are also expected to displace workers and dramatically change the nature of work and other societal norms.

 

However, smart machines are the next step in a long history of incremental advancements in machines and computing. Indeed, smart machines could trace their roots back to early mechanization and the first Industrial Revolution, when, in the 18th century, rudimentary machines were used to automate some human tasks.

 

The advent of computers in the 20th century laid the modern groundwork for smart machines. Related technological advancements such as the internet, data storage systems and sensors, gave computer developers the ability to collect and analyze an unprecedented volume of data toward the turn of the century, further speeding the rise of smart machines.

 

Those capabilities led to business intelligence (BI) and advanced analytics, whereby computers run algorithms to analyze data to identify patterns and then use those patterns to generate insights into past and current events and, later, offer insights on what would happen and what could happen if certain future actions were taken.

 

 

 

 

 

 

 

 

 

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Smart manufacturing (SM)

 

 

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Smart manufacturing (SM) is a technology-driven approach that utilizes Internet-connected machinery to monitor the production process.

 

The goal of SM is to identify opportunities for automating operations and use data analytics to improve manufacturing performance.

 

SM is a specific application of the Industrial Internet of Things (IIoT).

 

Deployments involve embedding sensors in manufacturing machines to collect data on their operational status and performance. In the past, that information typically was kept in local databases on individual devices and used only to assess the cause of equipment failures after they occurred.

 

Now, by analyzing the data streaming off an entire factory's worth of machines, or even across multiple facilities, manufacturing engineers and data analysts can look for signs that particular parts may fail, enabling preventive maintenance to avoid unplanned downtime on devices.

 

Manufacturers can also analyze trends in the data to try to spot steps in their processes where production slows down or is inefficient in their use of materials. In addition, data scientists and other analysts can use the data to run simulations of different processes in an effort to identify the most efficient ways of doing things.

 

A lack of standards and interoperability are the biggest challenges holding back greater adoption of smart manufacturing. Technical standards for sensor data have yet to be broadly adopted, which inhibits different kinds of machines from sharing data and communicating with each other effectively.

 

 

 

 

 

 

 

 

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Virtual manufacturing (VM)

 

 

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Virtual manufacturing (VM) is the use of computers to model, simulate and optimize the critical operations and entities in a factory plant. Virtual manufacturing started as a way to design and test machine tools but has since expanded to encompass production processes and the products themselves.

 

The main technologies used in VM include computer-aided design (CAD), 3D modeling and simulation software, product lifecycle management (PLM), virtual reality, high-speed networking and rapid prototyping.

 

Virtual manufacturing provides an organization with the ability to analyze the manufacturability of a part or product as well as evaluate and validate production processes and machinery and train managers, operators and technicians on production systems. There are three main subcategories of VM:

 

Design-centered VM provides information about the manufacturing process to engineers and designers so they can optimize products for production purposes or learn how production issues might impact product design. They can also save money by testing 3D product models and processes instead creating of physical prototypes.

 

VM can be extended to multiple manufacturers and suppliers, creating in effect a virtual manufacturing network for collaborating on production and sharing models and other types of information. It can also be used to assess business risks and identify potential breakdowns in machine tools and other equipment.

 

The market for specialized VM software consists mostly of niche vendors that often focus on one aspect, such as robotics simulation.

 

However, many vendors of CAD, 3D modeling and PLM software support the modeling and simulation of a "virtual" product, process or machine -- sometimes called a digital twin -- that is central to virtual manufacturing.

 

 

 

 

 

 

 

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