Telemedicine enables health care practitioners to assess, diagnose and treat patients at a distance using telecommunications technologies. In the last decade, the policy has undergone a striking transformation and it is becoming an increasingly important part of the American healthcare system.
“Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
As articulated by many researchers in the field, the above description encapsulates the ideal goal or ultimate purpose of machine learning. The aim of this article is to give an expert perspective to a business-minded reader on how machine learning is described and how it works. However, in the minds of many, machine learning and artificial intelligence share the same meaning.
What is Deep Learning?
Deep Learning, is based on neural networks, the foundations have existed since the early 1940s. Due to the growing computing power and the simplified access to huge amounts of data (Big Data) the attention increased and enabled the use of neural networks on a completely new level. Today we use them to convert structured and unstructured information from images, text and other sources into numerical values and interpret them.
How can Deep Learning be classified?
The digital data that accumulates today in everyday life and in companies can be divided into unstructured and structured data. While structured data have a normalized form and can be stored in row- and column-oriented databases, unstructured data has an unidentifiable data structure and cannot be stored easily in conventional, SQL-based databases. Examples of unstructured data are text files, presentations, videos or images and other types of data. Big Data applications provide functions that enable processing, storage and analysis of unstructured data.
And this is where the term deep learning comes in.
Deep Learning is a subset of Machine Learning and Artificial Intelligence, but there are differences.
The main difference between Deep Learning and the “classical” Machine Learning is the ability to process unstructured data by artificial neural networks (ANN). The “classical” machine learning is primarily related to the processing of static data and methods that do not use artificial neural networks, in contrast to deep learning methods, where data processing is realized exclusively by the algorithm of the ANN.
How does a ANN work?
The so-called deep learning is thus created by the complexity of the ANN and its function can be broken down as follows:
The nodes of the given network structure, the neurons, are initially assigned a random initial weight, which can be varied and adapted in the course of the bias. The obtained input data gets weighted by the neurons with their individual weight. The results of the calculation are passed on layer by layer to further neurons via the respective network connections and get reweighted. At the output layer, the overall result is calculated and displayed.
At the beginning of the learning process, as with any machine learning method, this overall result will not always provide correct information and errors will occur. However, these are necessary to enable learning, because the errors and the share of each neuron in the learning process can be calculated. The goal is to minimize the error in each run piece by piece. This is achieved by adjusting the weight of each neuron via the bias.
Academics and practitioners alike believe that continual learning (CL) is a fundamental step towards artificial intelligence. Continual learning is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a model to autonomously learn and adapt in production as new data comes in. Some may know it as auto-adaptive learning, or continual AutoML. The idea of CL is to mimic humans ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. Its known that in machine learning, the goal is to deploy models through a production environment. With continual learning, we want to use the data that is coming into the production environment and retrain the model based on the new activity.
In our approach, we use a convolutional network (CNN) as the task network to illustrate how RCL adaptively expands this CNN to prevent forgetting, though our method can not only adapt to convolutional networks, but also to fully-connected networks. Our method searches for the best neural architecture for coming task by reinforcement learning, which increases its capacity when necessary and effectively prevents semantic drift. We aim to implement both fully connected and convolutional neural networks as our task networks, and validate them on different datasets. Firstly, we will develop new strategies for RCL to facilitate backward transfer, i.e. improve previous tasks’ performance by learning new tasks. Moreover, how to reduce the training time of RCL is particularly important for large networks with more layers.
On our platform only verified health specialists can help in fine-tuning the models by interacting with software and give feedback on the test results, so the software continues learning and optimise its predictions automatically. Our goal is to use collective human intelligence in order to make state of the art Artificial Intelligence.
In order to make predictions, conventional machine learning requires a data pipeline that uses a central server (on-prem or cloud) that hosts the trained model. The downside to this architecture is that all the information obtained by local devices and sensors is sent back for processing to the central server and then returned back to the devices.
This round-trip restricts the capacity of a model to learn in real-time. Federated learning (FL), by comparison, is a method that uses local data to download the current model and calculate a modified model on the computer itself.
These locally trained models are then sent back to the central server from the devices where they are aggregated, i.e. average weights, and then a single unified and enhanced global model is sent back to the devices.