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The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning
We’re all familiar with the term “Artificial Intelligence.” After all, it’s been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine).
Arthur Samuel coined the phrase not too long after AI, in 1959, defining it as, “the ability to learn without being explicitly programmed.” You see, you can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.
So instead of hard coding software routines with specific instructions to accomplish a particular task, machine learning is a way of “training” an algorithm so that it can learnhow.
To give an example, machine learning has been used to make drastic improvements to computer vision (the ability of a machine to recognize an object in an image or video).
Other approaches include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others.
It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer.
Our brains take that data and makes sense of it, turning light into recognizable objects and turning sounds into understandable speech.
As mentioned above, machine learning and deep learning require massive amounts of data to work, and this data is being collected by the billions of sensors that are continuing to come online in the Internet of Things.
On the industrial side, AI can be applied to predict when machines will need maintenance or analyze manufacturing processes to make big efficiency gains, saving millions of dollars.
We might ask for information like the weather or for an action like preparing the house for bedtime (turning down the thermostat, locking the doors, turning off the lights, etc.).
Wireless connectivity, driven by the advent of smartphones, means that data can be sent in high volume at cheap rates, allowing all those sensors to send data to the cloud.
Machine Learning Vs. Artificial Intelligence: How Are They Different?
Artificial intelligence and machines have become a part of everyday life, but that doesn't mean we understand them well.
Machine learning is based on the idea that we can build machines to process data and learn on their own, without our constant supervision.
The concepts stretch back to certain imaginative individuals from decades, centuries and even millennia ago.
Instead, people are seeking to create machines that can make decisions in similar ways to humans and use those decisions to complete tasks.
The first breakthrough involved realizing that it was more efficient to teach computers how to learn than to teach them how to perform every possible taskand give them the information needed to complete those tasks.
Machines could now look at amounts of data that they'd never been able to access before due to storage limitations.
These two breakthroughs made it clear that instead of teaching machines to do things, a better goal was to design them to 'think' for themselves and then allow them access to the mass of data available online so they could learn.
The Role Of Neural Networks The advent of neural networks became essential for this process of teaching computers to think like humans.
Neural networks allow computers to more closely mimic human brains while still being faster, more accurate and less biased.
For example, a neural network can look at pictures, recognize the elements in them and classify them according to what they show.
The machine can find out whether or not its decisions were right, and then change its approach to do better next time.
This will someday allow companies to offer automated customer service that's just as useful as human customer support.
These systems have many great applications to offer, but ML has gotten much more publicity lately, so many companies have focused on that source of solutions.
Machine Learning Made Simple with Cisco, Google and NVIDIA
The world is being transformed by the recent and rapid proliferation in Artificial Intelligence (AI) and Machine Learning (ML) from directly improving our personal lives to enterprise applications around security, business intelligence, automation, analytics, spam filtering, conversational interfaces etc.
is this image a malware image?). This is different from traditional computing paradigms where one encodes a set of deterministic/procedural steps to directly process the data without the insight from prior data.
These teams have several choices when they design their infrastructure, ranging from leveraging public clouds to investing in private datacenter in order to support their AI lifecycle needs.
Within our AI team, our focus is to devise new methods for moving the needle in areas ranging from core technology to applications of AI in security, collaboration and IoT.
Our natural choice was Google’s TensorFlow for our learning tasks, due to the maturity of the toolchains, its production battle hardiness, and the fact that we can run this seamlessly, either on bare metal or on virtualized infrastructure.
We have also run our internal training workloads on the same setup on a wide variety of workloads, and we have been very satisfied by the performance. This gave us the confidence to consider UCS as a viable long-term investment for our own long-term AI/ML training workloads, in addition to our public cloud spend (for our elastic needs).
For now, our infrastructure choices are clear – in the multicloud world we live in, leveraging a combination of public clouds and managed hyperconverged infrastructure (HyperFlex) is a very balanced option.
- On 15. januar 2021
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