Humans learn from past experiences, Machines follow the instructions given by humans but, what if humans can train the machines to learn from the past experiences (data) and can do act much faster, here comes the concept of Machine Learning. Machine learning is the field of study that gives computers the capability to learn without being explicitly programmed. Machine learning algorithms build a mathematical model based on the data, known as training data, in order to make predictions or decisions. Machine learning is not only about learning, but also about understanding and reasoning. Machine Learning is not programmed, it is taught with data.

Adobe Inc said on Monday that it has put a new set of artificial intelligence tools into its digital marketing software with the aim of helping companies sharpen their marketing campaigns. Once known for applications like Photoshop, Adobe has become one of the biggest providers of software for running such campaigns, which businesses use to decide which of thousands of images and pieces of writing to content to show to potential customers. Growth in its marketing software division has helped send shares up nearly 50 per cent this year. The artificial intelligence features released on Monday aid that effort by, for example, scanning and labelling thousand of product images by colour and shape, or using natural language processing technology to read an article to determine its subject. That makes it easier for marketing campaigns to make a recommendation, whether that means showing a person browsing an e-commerce site a pair of shoes similar to ones they have previously viewed or a news website suggesting a story on a similar subject to the one just read.

A growing body of recent evidence has highlighted the limitations of natural language processing (NLP) datasets and classifiers. These include the presence of annotation artifacts in datasets, classifiers relying on shallow features like a single word (e.g., if a movie review has the word "romantic", the review tends to be positive), or unnecessary words (e.g., learning a proper noun to classify a movie as positive or negative). The presence of such artifacts has subsequently led to the development of challenging datasets to force the model to generalize better. While a variety of heuristic strategies, such as counterfactual examples and contrast sets, have been proposed, the theoretical justification about what makes these examples difficult is often lacking or unclear. In this paper, using tools from information geometry, we propose a theoretical way to quantify the difficulty of an example in NLP. Using our approach, we explore difficult examples for two popular NLP architectures. We discover that both BERT and CNN are susceptible to single word substitutions in high difficulty examples. Consequently, examples with low difficulty scores tend to be robust to multiple word substitutions. Our analysis shows that perturbations like contrast sets and counterfactual examples are not necessarily difficult for the model, and they may not be accomplishing the intended goal. Our approach is simple, architecture agnostic, and easily extendable to other datasets. All the code used will be made publicly available, including a tool to explore the difficult examples for other datasets.

Abstract: Sample efficiency remains a fundamental issue of reinforcement learning. Model-based algorithms try to make better use of data by simulating the environment with a model. We propose a new neural network architecture for world models based on a vector quantized-variational autoencoder (VQ-VAE) to encode observations and a convolutional LSTM to predict the next embedding indices. A model-free PPO agent is trained purely on simulated experience from the world model. We adopt the setup introduced by Kaiser et al. (2020), which only allows 100K interactions with the real environment, and show that we reach better performance than their SimPLe algorithm in five out of six randomly selected Atari environments, while our model is significantly smaller.

A recommendation system generates a compiled list of items in which a user might be interested, in the reciprocity of their current selection of item(s). It expands users' suggestions without any disturbance or monotony, and it does not recommend items that the user already knows. For instance, the Netflix recommendation system offers recommendations by matching and searching similar users' habits and suggesting movies that share characteristics with films that users have rated highly. In this tutorial, we will dive into building a recommendation system for Netflix. This tutorial's code is available on Github and its full implementation as well on Google Colab.

Jesus Diaz wrote the following for Fast Company: "I don't care how efficient the company says the algorithm is -- from my personal experience, it doesn't work. A machine can never fully replace personal taste and exploration based on human interaction." He continued in the op-ed: "We're sick of algorithms telling us where to go, who to listen to, and what to watch. Your machine predicts a 98% chance that I would like to watch Frozen. In fact, I have yet to find an instance of any algorithm surprising me with a smart suggestion."

Sign up here to get the day's top stories delivered straight to your inbox. The routine is familiar now. Off from the day, lounging on a couch, ice cream at the ready, the remote or mouse clicks onto the preferred streaming site, perhaps to watch the show everyone is talking about or to be reacquainted with an old favorite. Streaming television and movie services are so deeply ingrained in the quotidian now, binge-watching is a common weekend activity and there are colloquial dating terms that invoke them -- "Netflix and chill." People log onto Netflix, Hulu, and other streaming services at all times of the day: during their commute; in the morning while getting ready; and when curling up at night.

Artificial intelligence has been the topic of countless Sci-Fi movies. There have been nearly ten Terminator movies or television shows so far, all using the same theory: AI will mean the demise of our race. While some have been saying that is a possibility, others have been quick to dismiss such claims. What is pretty obvious though, is that the advances made in the field cannot be ignored anymore, there are real concerns and we need to be prepared for whatever the future holds. Thankfully, some companies working with AI are taking the concerns surrounding AI seriously.

Prime Day 2020 is now underway, with thousands of deals available across the site, and as always, some of the best discounts can be found on Amazon own devices. The mega-site is kicking off the shopping extravaganza with a host of incredible deals, including top savings on the popular Echo Dot 3rd Gen, Echo Show 5 and Kindle. Whether you're shopping for Christmas or treating yourself, now is a great time to buy. Discounts will continue for just 48 hours up until October 14, so to ensure you don't miss a deal we've scoured the site and selected the very best savings on Amazon devices right now. Now £39.99 (that's a saving of £40), the Echo Show 5 is its lowest price in this Prime Day deal.

RoadRunner will use the capital to expand into new markets, increase hiring and further develop its artificial intelligence/machine learning technology.