If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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
It is reported that 85% of internet users watch online video content on any of their devices in the United States. This means, there are more possibilities for watching online video content than ever before. At the same time, videos are still one of the most impactful ways to communicate your message and engage more audiences around the world. As marketers deploy their own unique video content creation and marketing strategies to tailor their brand and to boost ROI, the world of online video is progressing and changing all the time. In this article, we are going to highlight some of the most recent advancement with online video content creation and three of the best tips to help you to make better videos and content.
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.
You may have heard of 3D movies and paintings, but would you dare walk on a 3D steel printed bridge? Amsterdam has just installed the world's first, built to withstand heavy pedestrian traffic. The bridge, which is now open to pedestrians and cyclists, was created by the Imperial College London and took over four years to build, according to a press release. The bridge was publicly revealed by Her Majesty Queen Máxima of the Netherlands. The almost 40-foot structure weights 4.9 tons and will be carefully monitored using installed sensors.
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 . 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.