Automation and AI

Automated machines collate data; AI systems ‘understand’ it.

 

Automation has a single purpose: To let machines perform repetitive, monotonous tasks. This frees up time for fleshy humans to focus on more important tasks that require the personal touch. The end result is a more efficient, cost-effective business and a more productive workforce.

 

 

 AI is intensely bad at simply following orders. That’s not what it’s designed to do; it’s designed to constantly seek patterns (like humans), learn from experience (like humans) and self-select the appropriate responses in situations based on that (like humans).

 

coupling machines capable of automatically collecting incredible amounts of data with systems that can intelligently make sense of that information.

 

 

Which kinda, sorta sounds like what AI is for, right?

So, crucially, the big difference here is that automated machines are all driven by the manual configuration — which is just a fancy way of saying, you have to set up the way you want your automated system to work using Workflows and the like.

With automation, it’s a case of ‘If X, then Y.’ Essentially, you define the ‘X’, which OK’s the automated system to perform the ‘Y’.

An obedient worker that never calls in sick or takes a holiday and always gets the job done perfectly every time? It’s no wonder that businesses so readily embrace automation. Now, you’ll find automation, in some or other form, in just about every serious business on the planet.

Essentially, it’s a machine that’s smart enough to follow orders.

 

 

 AI is intensely bad at simply following orders. That’s not what it’s designed to do; it’s designed to constantly seek patterns (like humans), learn from experience (like humans) and self-select the appropriate responses in situations based on that (like humans).

 

coupling machines capable of automatically collecting incredible amounts of data with systems that can intelligently make sense of that information.

 Bayesian and neuroevolutionary methods being used in conjunction with deep learning. 

Bayesian Machine Learning Reinforcement Learning Neuroevolution Optimal Control Other Machine Learning techniques 

what will happen? 
Predictive What happened? Analytics Descriptive Supervised Analytics Learning Unsupervised Learning 
How can we make it happen? 
Prescriptive Analytics 
Reinforcement Learning 


deep learning, reinforcement learning (RL) stands out as a topic gaining interest among companies. Reinforcement learning has played a critical role in many prominent AI systems. Depending on the context, an AI system might be asked to solve different types of problems: reinforcement learning excels at problems that fall outside the realm of unsupervised and supervised machine learning. One way to think of RL is in the context of an agent learning how to behave within a given environment—through repeatedly exploring a given environment, one attempts to learn a policy for how an agent should behave under certain conditions. The fact that prominent examples of “self-learning” systems rely on reinforcement learning has made it a hot topic among AI researchers.

However, reinforcement learning is not without challenges:

    •    First, teaching an agent how to act in a given environment requires a lot of data. That’s why many of the initial applications are in areas where you have access to simulations.

    •    Second, it’s challenging to reproduce the results found in research papers let alone to translate them into working systems. This may change as new open source systems, particularly RISE Lab’s Rayand RLlib, get used by more researchers, and we see less custom or one-off code. Coincidentally, in recent weeks I’ve come across some major companies that are already using Ray in production as part of their infrastructure.
Despite these challenges, reinforcement learning is beginning to see real-world usage in areas like industrial automation. Mark Hammond of Bonsai has described many examples of how companies are using RL—including how to manage wind turbines or operate expensive machines. Google’s DeepMind has reportedly developed an RL-based system to help improve power consumption in its data centers. Hammond describes the process of training RL models as “machine teaching”: using domain experts to train an RL-based system that can then enable automation:

[You want] to enable your subject matter experts (a chemical engineer or a mechanical engineer, someone who is very, very well versed in whatever their domain is but not necessarily in machine learning or data science) to take that expertise and use it as the foundation for describing what to teach and then automating the underlying pieces.
Automation
Machine learning and AI will enable automation across many domains and professions. We sometimes think of automation as binary: either we have full automation or we have no automation. The truth is, automation occurs on a spectrum. 

Automation Building Blocks 
Sensory Perception 
Natural Language Generation Natural Language Understanding 
Physical Capabilities 
Cognitive Capabilities 
Social & Emotional Capabilities 
 

 

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