"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Let's say you're looking to buy a new PC from an online store (and you're most interested in how much RAM it has) and you see on their first page some PCs with 4GB at $100, then some with 16 GB at $1000. So, you estimate in your head that given the prices you saw so far, a PC with 8 GB RAM should be around $400. This will fit your budget and decide to buy one such PC with 8 GB RAM. This kind of estimations can happen almost automatically in your head without knowing it's called linear regression and without explicitly computing a regression equation in your head (in our case: y 75x – 200). So, what is linear regression? Linear regression is just the process of estimating an unknown quantity based on some known ones (this is the regression part) with the condition that the unknown quantity can be obtained from the known ones by using only 2 operations: scalar multiplication and addition (this is the linear part).
Tesla plans to offer machine-learning training as a web service with its new'Dojo' supercomputer, according to new comments from CEO Elon Musk. Project "Dojo" was first announced by Musk at Tesla's Autonomy Day last year: We do have a major program at Tesla which we don't have enough time to talk about today called "Dojo." The goal of Dojo will be to be able to take in vast amounts of data and train at a video level and do unsupervised massive training of vast amounts of video with the Dojo program -- or Dojo computer. Dojo means "place of the Way" in Japanese and the term is often used for a place to practice meditation or martial arts. In this case, the Dojo supercomputer will be a place for Tesla to train its Full Self-Driving AI. Last month, Musk revealed that Tesla's Dojo supercomputer will be capable of an exaFLOP, one quintillion (1018) floating-point operations per second, or 1,000 petaFLOPS.
I know for sure that human behavior could be predicted with data science and machine learning. Taking a look at human behavior from a sales data analysis perspective, we can get more valuable insights than from social surveys. In this article, I want to show how machine learning approaches can help with customer demand forecasting. Since I have experience in building forecasting models for retail field products, I'll use a retail business as an example. Moreover, considering uncertainties related to the COVID-19 pandemic, I'll also describe how to enhance forecasting accuracy.
On 1st January 2019, we (Fabin Rasheed and I) had introduced to the world, a side project we've been working on for months. An artificial poet-artist, who doesn't physically exist in this world but writes a poem, draws an abstract art based on the poem and finally color the art based on emotion. We called "her" Auria Kathi -- an anagram for "AI Haiku Art". Auria has an artificial face along with her artificial poetry and art. Everything about Auria was built using artificial neural networks.
With a ROC curve, you're trying to find a good model that optimizes the trade off between the False Positive Rate (FPR) and True Positive Rate (TPR). What counts here is how much area is under the curve (Area under the Curve AuC). The ideal curve in the left image fills in 100%, which means that you're going to be able to distinguish between negative results and positive results 100% of the time (which is almost impossible in real life). The further you go to the right, the worse the detection. The ROC curve to the far right does a worse job than chance, mixing up the negatives and positives (which means you likely have an error in your setup).
Artificial intelligence (AI) is swiftly fueling the development of a more dynamic world. AI, a subfield of computer science that is interconnected with other disciplines, promises greater efficiency and higher levels of automation and autonomy. Simply put, it is a dual-use technology at the heart of the fourth industrial revolution. Together with machine learning (ML) -- a subfield of AI that analyzes large volumes of data to find patterns via algorithms -- enterprises, organizations, and governments are able to perform impressive feats that ultimately drive innovation and better business. The use of both AI and ML in business is rampant.
This is a TensorFlow implementation of the stereo matching algorithm described in the paper "Efficient Deep Learning for Stereo Matching". The code is tested using Tensorflow r1.4 under Ubuntu 14.04 with Python 2.7. The KITTI 2015 dataset has been used for training. This dataset consists of total of 200 scenes for training and of 200 scenes for testing. For more details, please check the KITTI website.
I would like to share paper/code of our latest work entitled "Self-Supervised Relational Reasoning for Representation Learning" that has been accepted at NeurIPS 2020. There are three key technical differences with contrastive methods like SimCLR: (i) the replacement of the reprojection head with a relation module, (ii) the use of a Binary Cross Entropy loss (BCE) instead of a contrastive loss, and (iii) the use of multiple augmentations instead of just two. In the GitHub repository we have also released some pretrained models, minimalistic code of the method, a step-by-step notebook, and code to reproduce the experiments. Abstract: In self-supervised learning, a system is tasked with achieving a surrogate objective by defining alternative targets on a set of unlabeled data. The aim is to build useful representations that can be used in downstream tasks, without costly manual annotation.
This essay provides a broad overview of the sub-field of machine learning interpretability. While not exhaustive, my goal is to review conceptual frameworks, existing research, and future directions. I follow the categorizations used in Lipton et al.'s Mythos of Model Interpretability, which I think is the best paper for understanding the different definitions of interpretability. We'll go over many ways to formalize what "interpretability" means. Broadly, interpretability focuses on the how. It's focused on getting some notion of an explanation for the decisions made by our models. Below, each section is operationalized by a concrete question we can ask of our machine learning model using a specific definition of interpretability. If you're new to all this, we'll first briefly explain why we might care about interpretability at all.
Artificial intelligence (AI) coding can be used to improve medical websites in various ways, from custom personalised content presentations to the integration of unique medical AI features. These include medical appointment scheduling software, healthcare cost estimators, design medical website, prescribe medication, and answer questions. By developing more targeted and custom AI solutions, some artificial intelligence healthcare platforms aim to usher in the more widespread adoption of medical AI technologies, benefitting organisations, practices, clinicians, and patients alike. AI medical websites can use AI tools to present targeted information specific to the consumer. AI medical websites use IP addresses to locate the user and present information specific to physicians and practices local to their area.