One of the strangest slogan created by the AI robot is'Chicken Fries For Creep King' Burger King's advertising team has enlisted the help of an AI robot to write the slogans for its latest TV adverts. The fast-food brand is testing a beta version of a new deep learning algorithm to develop a series of adverts. Researchers trained the algorithm to write slogans by feeding it thousands of fast-food commercials, as well as reports from industry research. Using this data, the algorithm, was able to identify key patterns of slogans that worked and didn't work, and develop its own. However, the results are somewhat questionable.

Sign in to report inappropriate content. Docturnal is a point-of-care screening and diagnostics provider enabling non-invasive and proactive detection of diseases. Docturnal products are designed to make detection of diseases easy and hassle-free. In this #CASESTUDY, we have Rahul Pathri, Founder & CEO, talk about how Docturnal creates point-of-care, non-invasive, real-time results driven solutions leveraging AI/Deep Learning under the umbrella of telemedicine.

Artificial intelligence is learning to decipher damaged ancient Greek engravings. The AI seems to be better than humans at filling in missing words, but may be most useful as a collaborative tool, where researchers use it to narrow down the options. There are thousands of ancient inscriptions we already know about, with dozens more discovered every year. Unfortunately, many have become eroded or damaged over the centuries, resulting in segments of text being lost. Figuring out what the gaps could be is a difficult task, involving looking at the rest of the inscription and other similar texts.

With Hands-On Neural Networks with TensorFlow 2.0 - 1st Edition, Understand the basics of machine learning and discover the power of neural networks and deep learning, Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0, Solve any deep learning problem by developing neural network-based solutions using TF 2.0 Description TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. Book Details Author(s): Paolo Galeone Publisher: Packt Publishing Published: September 18, 2019 Language: English ISBN-10: 1789615550 ISBN-13: 9781789615555 Number of pages: 358 Get This Book Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks 1st Edition

GPUs are famously expensive – high end Nvidia Teslas can be priced well above $10,000. Now a New York startup, Paperspace, has announced a free cloud GPU service for machine/deep learning development on the company's cloud computing and deep learning platform. Designed for students and professional learning how to build, train and deploy machine learning models, the service can be thought of as an ML/DL starter kit that helps developers expand their skills and try out new ideas without financial risk. Utilizing Nvidia Quadro M4000 and P5000 GPU's and called "Gradient Community Notebooks," the service is based on Jupyter notebooks and enables developers working with widely used deep learning frameworks, such as PyTorch, TensorFlow, Keras and OpenCV, to launch and collaborate on their ML projects. Similar to GitHub, Gradient Community Notebooks can be shared and "forked" into a user's own account while providing pre-loaded templates with various libraries, dependencies and drivers, the company said.

Untrained dragons can cause a lot of damage. Likewise, as AI systems spread further and have more influence over our lives, it's getting far more important to make sure they're properly trained. Bias can creep into the reasoning of AI very easily, either via datasets that are not diverse enough or through irrelevant data attached to viable data points, leading to flawed results and in some cases prejudiced or dangerous conclusions. Despite regulations like GDPR to protect the privacy of our data, personal consumer data is increasingly being used by companies to improve services or to gain customer insight. Ironically, these regulations also make it more difficult for companies to gather enough data to train an AI system or to prove how their AI reaches its decisions (an impossible task for many deep learning systems).

Algorithms are often grouped by similarity in terms of their function (how they work). For example, tree-based methods, and neural network inspired methods. I think this is the most useful way to group algorithms and it is the approach we will use here. This is a useful grouping method, but it is not perfect. There are still algorithms that could just as easily fit into multiple categories like Learning Vector Quantization that is both a neural network inspired method and an instance-based method.

Facebook Inc. today updated its popular artificial intelligence software framework PyTorch with support for new features that enable a more seamless AI model deployment to mobile devices. PyTorch is used by developers to research and build AI models for software applications, and then move those apps straight to production thanks to its integration with leading public cloud platforms. PyTorch was first built by Facebook's AI research group as a machine learning library of functions for the programming language Python. It's primarily designed for use with deep learning, which is a branch of machine learning that attempts to emulate the way the human brain functions. It has led to major breakthroughs in areas such as language translation and image and voice recognition.

As you know a perceptron serves as a basic building block for creating a deep neural network therefore, it is quite obvious that we should begin our journey of mastering Deep Learning with perceptron and learn how to implement it using TensorFlow to solve different problems. In case you are completely new to deep learning, I would suggest you to go through the previous blog of this Deep Learning Tutorial series to avoid any confusion. Basically, a problem is said to be linearly separable if you can classify the data set into two categories or classes using a single line. On the contrary, in case of a non-linearly separable problems, the data set contains multiple classes and requires non-linear line for separating them into their respective classes. Let us visualize the difference between the two by plotting the graph of a linearly separable problem and non-linearly problem data set:Since, you all are familiar with AND Gates, I will be using it as an example to explain how a perceptron works as a linear classifier.

Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake. An adversarial attacker could target autonomous vehicles by using stickers or paint to create an adversarial stop sign that the vehicle would interpret as a'yield' or other sign. A confused car on a busy day is a potential catastrophe packed in a 2000 pound metal box. So far, the majority of adversarial attacks, the attacker designed few perturbations to produce an output specific to a given input. The attacks consisted of untargeted attacks that aim to degrade the performance of a model.