Image Classification is a pivotal pillar when it comes to the healthy functioning of Social Media. Classifying content on the basis of certain tags are in lieu of various laws and regulations. It becomes important so as to hide content from a certain set of audiences. I regularly encounter posts with a "Sensitive Content" on some of the images while scrolling on my Instagram feed. I am sure you must have too.

Injecting malicious backdoors into deep neural networks is easier than previously thought, a new study by researchers at Texas A&M University shows. There's growing concern about the security implications of deep learning algorithms, which are becoming an integral part of applications across different sectors. Vulnerabilities in deep neural networks (DNN), the main technology behind deep learning, has become a growing area of interest in recent years. Trojan attacks are hidden triggers embedded in neural networks that can cause an AI model to act erratically at the whim of a malicious actor. For instance, an attacker can fool the image processor of a self-driving car into bypassing a stop sign or mistaking it for a speed limit sign.

In 1829, an event took place that unleashed a technological revolution. At the Rainhill Trials a group of steam locomotives squared off to determine which one could win a series of tests of speed, strength and reliability. The winning machine, Rocket, not only blew away its competition at the trials, it also set the direction for steam locomotive development for the following century. What does all this have to do with GPT-3, the transformer language model that OpenAI made available in a limited beta starting in June? Some reviewers have heralded GPT-3 as the first glimpse of artificial general intelligence, while others are calling it a massive lookup table.

These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know! Welcome back to deep learning!

At the start of the week, Liam Porr had only heard of GPT-3. By the end, the college student had used the AI model to produce an entirely fake blog under a fake name. It was meant as a fun experiment. But then one of his posts found its way to the number-one spot on Hacker News. Few people noticed that his blog was completely AI-generated.

The most advanced artificial intelligence model named the Generative Pre-Training-3 (GPT-3) was released a few weeks back. And users were left awestruck.Get latest Technology online at cnbctv18.com

Recently I came across an interesting Paper named, "Deep Ensembles: A Loss Landscape Perspective" by a Laxshminarayan et al.In this article, I will break down the paper, summarise it's findings and delve into some of the techniques and strategies they used that will be useful for delving into understanding models and their learning process. It will also go over some possible extensions to the paper. You can also find my annotations on the paper down below. The authors conjectured (correctly) that Deep Ensembles (an ensemble of Deep learning models) outperform Bayesian Neural Networks because "popular scalable variational Bayesian methods tend to focus on a single mode, whereas deep ensembles tend to explore diverse modes in function space." In simple words, when running a Bayesian Network at a single initialization it will reach one of the peaks and stop.

This is the second post devoted to Deep Q-Network (DQN), in the "Deep Reinforcement Learning Explained" series, in which we will analyse some challenges that appear when we apply Deep Learning to Reinforcement Learning. We will also present in detail the code that solves the OpenAI Gym Pong game using the DQN network introduced in the previous post. Unfortunately, reinforcement learning is more unstable when neural networks are used to represent the action-values, despite applying the wrappers introduced in the previous section. Training such a network requires a lot of data, but even then, it is not guaranteed to converge on the optimal value function. In fact, there are situations where the network weights can oscillate or diverge, due to the high correlation between actions and states.

An app by University of Melbourne researchers uses AI to analyse and interpret the facial expressions of your pet. And, yes, dogs are easier to read than cats.

Machine Learning has been an outstanding achievement in the field of Artificial Intelligence. The algorithms behind the success of Machine Learning are Deep Neural Networks which were made after research of years through the expert researchers. The only difficulties the researchers faced was the discovery of AutoMl. AutoML is that field that aims to automate the style of Machine Learning Algorithms. Till now the AutoML has only aimed at building solutions by joining the sensitive hand-designed components. The only alternative method to using the hand-designed components in AutoML is the search for the whole algorithm from the zero levels.