The lack of skilled IT workers is hurting the deployment of emerging technology, according to a new survey from Gartner. In areas from cloud to cybersecurity, this crisis is expected to last for years to come.
AI is seeping into just about everything, from consumer products to industrial equipment. As enterprises utilize AI to become more competitive, more of them are taking advantage of machine learning to accomplish more in less time, reduce costs and discover something whether a drug or a latent market desire.
While there's no need for non-data scientists to understand how machine learning (ML) works, they should understand enough to use basic terminology correctly.
Although the scope of ML is vast, following are some of the fundamentals.
Before one can grasp machine learning concepts, they need to understand what machine learning terms mean. Some of the commonly used terms include:
Deep learning is a subset of machine learning that utilizes multiple layers of algorithms. The algorithms form neural network nodes that are arranged in three basic layers: input layer, hidden layer, and output layer. If the network has more than one hidden layer, it is considered a deep neural network.
"Deep learning is just a series of matrix multiplications and nonlinear transformations," said Brooke Wenig, machine learning practice lead at cloud data platform provider Databricks. "You do a bunch of matrix multiplications to your input features; each has a corresponding weight and then you add nonlinear transformations."
There are many different types of neural network architectures available today, and the list keeps growing.
One of the things to keep in mind with deep learning is its expense because it requires a lot of data and therefore storage. It also requires a lot of compute power. This can not only be expensive from a resource point of view but also from an environmental (carbon footprint) point of view. There are also other considerations.
"People should be minimizing their models, not based on some error criteria, but based upon some kind of economic impact of the model," said Wayne Thompson, chief data scientist at analytics software provider SAS.
"The problem is, we don't know what numbers to put in for the economic aspect. When I talk to some customers, they can't tell me the price of acquiring a customer or the revenue associated with keeping them once acquired."
Which type of ML technique(s) data scientists use depends on several factors including the business problem that needs to be solved, the data available, the level of accuracy required, time, efficiency, etc? Sometimes, the most elegant solution is the simplest, not the most sophisticated or complex.
There are many different types of neural network architectures, all of which have an input layer, an output layer and one or more hidden layers. Generative adversarial networks (GANs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are just three examples.
Cybersecurity and games use GANs because in both cases an adversary is involved. GANs involve two networks, one of which is adversarial.
"We've tried just about everything with GANs. They work really well, but they're a little problematic, because they're hard to train," said Cameron Fen, co-founder and head of research at venture capital firm AI Capital Management. "People are trying to replace GANs with another generative model that works just as well or better because they don't like training GANs."
Convolutional Neural Networks (ConvNets or CNNs) are modeled after the visual cortex of animals so not surprisingly, they're used for image recognition. The purpose of a CNN is to reduce the image size for processing without sacrificing the features necessary for a good prediction.
CNNs are used for a variety of use cases including advertising, climate change, natural disaster prediction and self-driving cars.
Recurrent Neural Networks (RNNs) use sequential or time series data. They are called "recurrent" because they perform the same task on every step of the sequence. Practically speaking, RNNs are used for handwriting and speech recognition, time series prediction, time series anomaly detection and even robot control.
Originally Published On: https://www.informationweek.com/ai-or-machine-learning/machine-learning-basics-everyone-should-know