Table of Contents 64q43
A artificial intelligence and machine learning are in vogue in the tech industry in recent years, but what exactly do they mean? Here you can check out a post we made explaining in detail what machine learning is, but the question we want to address here is how can we differentiate these two . 2j2yg
digital learning 6l4s25
The two are often confused and incorrectly used by companies looking to make their technology sophisticated. In fact, artificial intelligence and machine learning are very different, with different implications for what computers can do and how they interact with us.
O machine learning is the computing paradigm that drives the growth of "Big Data" e IA. It is based on the development of neural networks and deep learning. This is usually described as mimicking the way humans learn, but this is incorrect. Machine learning really relates to statistical analysis and iterative learning.
Instead of building a traditional program composed of logical statements e decision trees, One neural network is built specifically for training and learning using a parallel network of neurons, each configured for a specific purpose.
The nature of any particular neural network can be very complicated, but the key to the way they work is by applying weights (or factors of importance) to some attribute of the input. Using networks of various weights and layers, it is possible to produce a probability or estimate that their input matches one or more of the defined outputs.
The problem with this type of computation, as with regular programming, is that it depends on how the human programmer configures it, and readjusting all these weights to refine the accuracy of the output can take many man-hours to be feasible. A neural network transitions into the machine learning domain once a corrective loop is introduced.
“Training” the Machine w6450
By monitoring the output, comparing it to the input, and gradually reducing the neuron weights, a network can train itself to improve accuracy. The important part here is that a machine learning algorithm is able to learn and act without programmers, specifying all possibilities within the dataset.
Training a network can be done in many different ways, but they all involve an iterative brute-force approach to maximize output accuracy and train optimal paths through the network. However, this self-training is still a more efficient process than optimizing an algorithm manually and allows algorithms to change and sort much larger amounts of data in much faster times than would otherwise be possible.
Once trained, a machine learning algorithm is capable of classifying new inputs across the network with great speed and real-time accuracy. This makes it an essential technology for computer vision, speech recognition, language processing and scientific research projects.
What is and isn't AI 326w20
Machine learning is an intelligent processing technique, but it lacks any real intelligence. An algorithm doesn't need to understand exactly why it self-corrects, just how it might be more accurate in the future.
A machine learning algorithm that can sift through a database of images and identify the main object in the image doesn't really look smart, because it's not applying that information in a "humane" way.
Artificial intelligences can be divided into two large groups, applied ou general. applied artificial intelligence it's much more viable now. It is more closely tied to the machine learning examples above and designed to perform specific tasks. This could be commercial inventory, managing traffic in a smart city, or helping diagnose patients.
A general artificial intelligence it is, as the name implies, wider and more capable. It is capable of handling a wider range of tasks, understands virtually any set of data, and therefore appears to think more broadly, just like humans. General AI, theoretically, could learn outside of its original knowledge set, potentially leading to rampant growth in its skills.
Looking to the future 65701z
For all the scientific jargon and technical talk, machine learning and artificial intelligence applications are already here. We're still a long way from living alongside general AI, but if you're using Google Assistant or the Amazon Alexa, you are already interacting with an applied AI form.
Machine learning used for language processing is one of the key enablers of today's smart devices, although they are certainly not smart enough to answer all your questions.
The smart home is just the last use case. Machine learning has been employed in the realm of big data for a while now, and these use cases are increasingly encroaching on AI territory. O Google uses it for search engine tools. O Facebook uses to optimize advertising.
There is a big difference between machine learning and artificial intelligence, although the former is a very important component of the latter. We will certainly continue to hear many conversations about the two throughout 2018 and beyond.
