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) …
Machine Learning is a part of Artificial Intelligence, which consists of algorithms and improving automatically with time. In order to apply machine learning to different datasets, we need to clean the data and prepare it for the machine learning phase. Also, we need to identify the data or problem whether it is Regression, Classification, etc. There are many machine learning algorithms that we can use for our prediction, regression, classification, etc. problems. But we need to call them individually and pass our data into them as parameters.
Academics from the Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have demonstrated how neural networks can be trained to administer anesthetic during surgery. Over the past decade, machine learning (ML), artificial intelligence (AI), and deep learning algorithms have been developed and applied to a range of sectors and applications, including in the medical field. In healthcare, the potential of neural networks and deep learning has been demonstrated in the automatic analysis of large medical datasets to detect patterns and trends; improved diagnosis procedures, tumor detection based on radiology images, and more recently, an exploration into robotic surgery. Now, neural networking may have new, previously-unexplored applications in the surgical and drug administration areas. A team made up of MIT and Mass General scientists, as reported by Tech Xplore, have developed and trained a neural network to administrator Propofol, a drug commonly used as general anesthesia when patients are undergoing medical procedures.
Okay, You have decided to build your own machine learning model. You are using Sklearn that is popular machine learning libraries for modeling. But wait do you know the common machine learning modeling challenges faced by every data scientist. No, then you have come to the right place. Here You will know each modeling challenges you face while building the model. When you have a categorical target dataset.
Loads of research came out this week! But FYI, we couldn't fit every story in this newsletter for space-saving reasons, so if you want complete coverage, follow our twitter, and as always, if you enjoy the read, please give it a and share with your enemies. And….yesterday, another update was made to the Super Duper NLP Repo and the Big Bad NLP Database: we added 10 datasets and 5 new notebooks. Highlights include the DialogRE dataset which may be the first human-annotated dialogue-based relation extraction dataset. Legend has it there's a bitcoin wallet worth $690 million that hackers have been attempting to crack for the past 2 years according to cybersecurity expert Alon Gal.
The Radiological Society of North America (RSNA) has launched its fourth annual artificial intelligence (AI) challenge, a competition among researchers to create applications that perform a clearly defined clinical task according to specified performance measures. The challenge for competitors this year is to create machine-learning algorithms to detect and characterize instances of pulmonary embolism. RSNA collaborated with the Society of Thoracic Radiology (STR) to create a massive dataset for the challenge. The RSNA-STR Pulmonary Embolism CT (RSPECT) dataset is comprised of more than 12,000 CT scans collected from five international research centers. The dataset was labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists.
Tesla may be introducing machine-learning training as a web service with its upcoming'Dojo' supercomputer, CEO Elon Musk said on Twitter. Project Dojo was initially revealed by Musk last year and is a supercomputer which Tesla has been working on. The supercomputer has been designed to ingest massive amounts of video data and perform massive levels of unsupervised training on the visual data. The goal of Dojo will be to be able to take in vast amounts of data and train at a video level and do massive unsupervised training of vast amounts of video data. Dojo uses our own chips & a computer architecture optimized for neural net training, not a GPU cluster. Could be wrong, but I think it will be best in world.
Using those primitives, DeepMind generated a dataset known as Procedurally Generated Matrices(PGM) that consists of triplets [progression, shape, color]. The relationship between the attributes in a triplet represent an abstract challenge. For instance, if the first attribute is progression, the values of the other two attributes must along rows or columns in the matrix. In order to show signs of abstract reasoning using PGM, a neural network must be able to explicitly compute relatioships between different matrix images and evaluate the viability of each potential answer in parallel. To address this challenge, the DeepMind team created a new neural network architecture called Wild Relation Network(WReN) in recognition of John Rave's wife Mary Wild who was also a contributor to the original IQ Test. In the WReN architecture, a convolutional neural network(CNN) processes each context panel and an individual answer choice panel independently to produce 9 vector embeddings. This set of embeddings is then passed to an recurrent network, whose output is a single sigmoid unit encoding the "score" for the associated answer choice panel.
"Being good is easy, what is difficult is being just." "We need to defend the interests of those whom we've never met and never will." Note: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learning algorithms. As such, I have tried to explain concepts in an accessible way with minimal use of mathematics, in the hope that everyone can get something out of reading this. Supervised machine learning algorithms are inherently discriminatory. They are discriminatory in the sense that they use information embedded in the features of data to separate instances into distinct categories -- indeed, this is their designated purpose in life. This is reflected in the name for these algorithms which are often referred to as discriminative algorithms (splitting data into categories), in contrast to generative algorithms (generating data from a given category). When we use supervised machine learning, this "discrimination" is used as an aid to help us categorize our data into distinct categories within the data distribution, as illustrated below. Whilst this occurs when we apply discriminative algorithms -- such as support vector machines, forms of parametric regression (e.g.