Healthcare innovation has helped healthcare providers offer better care and unlock new ways to enhanced treatment for larger population groups. Technology advancements such as Artificial Intelligence and machine learning can offer innovative solutions to the healthcare sector by improving care delivery options and automating tasks that can reduce administrative burden. The Healthcare Innovation Forum discusses how machine learning and AI have revolutionized healthcare through efficient data analysis which has facilitated the decision-making process. By integrating the power of AI and machine learning the healthcare ecosystem can benefit greatly through automation of manual tasks, analyzing large data to improve health outcome levels, and lowering healthcare costs. According to Business Insider, 30% of healthcare costs are related to administrative and operational tasks.

Entering the 22nd of 150 epochs after 10 hours of training, I realized the 3000 wav file dataset was a bit tough to swallow for my 5 year old MacBook Pro. The Free Spoken Digit Dataset contains recordings from 6 speakers and 50 of each digit per speaker in 8kHz .wav As I was following along the outstanding video series on Sound Generation With Neural Networks by Valerio Velardo, I found myself stuck in an endless training phase. The goal is to train a custom-made Variational Auto-Encoder to generate sound digits. The preprocessing of the FSDD wav files was performed locally and generated a training dataset of 3000 spectrograms in .npy

If the power of logical reasoning is able to optimize the resources needed to reach quality AI solutions in a nonconventional way, then the AI industry should prepare for a major upcoming change. It is a change that is built on creativity; regardless of application titles or goals, no two applications will have the same results. Companies strive to transform their ideas into working plans to achieve their tactical goals. They do have highly specialized teams to make this happen, but not many companies in the AI realm have the strategic view of what may soon emerge in the industry. Having a highly specialized crew is indeed crucial to achieve tactical objectives.

The five reasoning methods are also called the five tribes. They help to solve the Master Algorithm. Each of the five tribes has a different technique and strategy for solving problems that result in unique algorithms. If we are successful to combine these algorithms, then it will lead us to (theoretically) the master algorithm. These are defined by the Portugues author, Pedro Domingos in his book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.

Do you understand how your machine learning model works? Despite the ever-increasing usage of machine learning (ML) and deep learning (DL) techniques, the majority of companies say they can't explain the decisions of their ML algorithms [1]. This is, at least in part, due to the increasing complexity of both the data and models used. It's not easy to find a nice, stable aggregation over 100 decision trees in a random forest to say which features were most important or how the model came to the conclusion it did. This problem grows even more complex in application domains such as computer vision (CV) or natural language processing (NLP), where we no longer have the same high-level, understandable features to help us understand the model's failures.

In a recent post on BERT, we discussed BERT transformers and how they work on a basic level. The article covers BERT architecture, training data, and training tasks. However, we don't really understand something before we implement it ourselves. So in this post, we will implement a Question Answering Neural Network using BERT and a Hugging Face Library. In this task, we are given a question and a paragraph in which the answer lies to our BERT Architecture and the objective is to determine the start and end span for the answer in the paragraph.

AI systems are becoming increasingly popular and central in many industries. They decide who might get a loan from the bank, whether an individual should be convicted, and we may even entrust them with our lives when using systems such as autonomous vehicles in the near future. Thus, there is a growing need for mechanisms to harness and control these systems so that we may ensure that they behave as desired. One important issue that has been gaining popularity in the last few years is fairness. While usually ML models are evaluated based on metrics such as accuracy, the idea of fairness is that we must ensure that our models are unbiased with regard to attributes such as gender, race and other selected attributes.

If you're a deep learning enthusiast you're probably already familiar with some of the basic mathematical primitives that have been driving the impressive capabilities of what we call deep neural networks. Although we like to think of a basic artificial neural network as some nodes with some weighted connections, it's more efficient computationally to think of neural networks as matrix multiplication all the way down. We might draw a cartoon of an artificial neural network like the figure below, with information traveling in from left to right from inputs to outputs (ignoring recurrent networks for now). This type of neural network is a feed-forward multilayer perceptron (MLP). If we want a computer to compute the forward pass for this model, it's going to use a string of matrix multiplies and some sort of non-linearity (here represented by the Greek letter sigma) in the hidden layer: MLPs are well-suited for data that can be naturally shaped as 1D vectors.

The authors concluded that a 178MB AlexNet model can have up to 36.9MB of malware embedded into its structure without being detected using a technique called steganography. Neural networks could be the next frontier for malware campaigns as they become more widely used, according to a new study. According to the study, which was posted to the arXiv preprint server on Monday, malware can be embedded directly into the artificial neurons that make up machine learning models in a way that keeps them from being detected. The neural network would even be able to continue performing its set tasks normally. "As neural networks become more widely used, this method will be universal in delivering malware in the future," the authors, from the University of the Chinese Academy of Sciences, write.

The human mediator complex has long been one of the most challenging multi-protein systems for structural biologists to understand.Credit: Yuan He The human genome holds the instructions for more than 20,000 proteins. But only about one-third of those have had their 3D structures determined experimentally. And in many cases, those structures are only partially known. Now, a transformative artificial intelligence (AI) tool called AlphaFold, which has been developed by Google's sister company DeepMind in London, has predicted the structure of nearly the entire human proteome (the full complement of proteins expressed by an organism). In addition, the tool has predicted almost complete proteomes for various other organisms, ranging from mice and maize (corn) to the malaria parasite (see'Folding options').