Articles about AI in the media are often accompanied by images of blue brains, white robots, and flying maths, sometimes only tangentially related to the content being reported. Due to these poor image choices, communications from media sources and marketing materials risk misinforming or misleading the public about how AI works and the impact it can have. However, finding images that better represent the research and technologies is difficult. A recent project has focussed on providing people with the sources and knowledge necessary to create their own images. The Archival Images of AI project has been exploring how existing images – especially those from digital heritage collections – can be remixed and reused to create new images, particularly to represent AI in more compelling ways.

Are you looking to improve the analysis and management of images and videos? The Computer Vision API provides access to advanced algorithms for processing media and returning information. By uploading a media asset or specifying a media asset's URL, Azure's Computer Vision algorithms can analyze visual content in different ways based on inputs and user choices, tailored to your business. Want to try out this service with samples that return data in a quick, straightforward manner, without technical support? We are happy to introduce Vision Studio in preview, a platform of UI-based tools that lets you explore, demo and evaluate features from Computer Vision, regardless of your coding experience.

With ML.NET and related NuGet packages for TensorFlow you can currently do the following: Here's a Getting started sample on scoring a TensorFlow model which is using the Inception pre-trained TensorFlow model. Transfer Learning on top of a pre-trained TensorFlow model: You can re-use part of an already pre-trained TensorFlow model (such as the Inception pre-trained TensorFlow model) to build a new model trained with additional samples for the final layer, such as trained with new images. For instance, see this Tutorial on how to use Transfer Learning with ML.NET by using an already trained Image Classifier TensorFlow model to build a new custom model to classify images into different categories. However, in the scenario where you want to train with your own images, the Transfer Learning approach can be a bit complex because even without taking into account the code implementation for transfer learning you'll need to find a base TensorFlow model to train on top of it which was originally trained with similar image types to your new images. Here's some specific examples to understand that statement: For instance, the TensorFlow Inception model was trained with photos of may objects, animals, vegetables and people, so you could train the final layer, let's say with photos of'super heroes', and the model will clasify properly images of specific'super heroes'.

In 2002, a couple of Japanese visitors to Australia swapped passports with each other before walking through an automatic biometric border control gate being tested at Sydney airport. The facial recognition algorithm falsely matched each of them to the others' passport photo. These gentlemen were in fact part of an international aviation industry study group and were in the habit of trying to fool biometric systems then being trialed round the world. When I heard about this successful prank, I quipped that the algorithms were probably written by white people - because we think all Asians look the same. Colleagues thought I was making a typical sick joke, but actually I was half-serious.

Image classification and object detection in images are hot topics these days, thanks to a combination of improvements in algorithms, datasets, frameworks, and hardware. These improvements democratized the technology and gave us the ingredients for creating our own solution for image classification. The state-of-the-art technologies for image classification and object detection are based on deep learning (DL). DL is a subarea of machine learning (ML) that is focused on algorithms for handling neural networks (NN) with many layers, or deep neural networks. ML, in turn, is a subarea of artificial intelligence (AI), a computer-science discipline.

Google Cloud Platform now provides machine learning images designed for deep learning practitioners. This article will cover the fundamentals of the Google Deep Learning Images, how they benefit the developer, creating a deep learning instance, and common workflows. The Google Deep Learning images are a set of prepackaged VM images with a deep learning framework ready to be run out of the box. Currently, there are images supporting TensorFlow, PyTorch, and generic high-performance computing, with versions for both CPU-only and GPU-enabled workflows. To understand which set of images is right for your use case, consult the graph below.

Slowly but surely, robots are entering the mainstream consumer market. Softbank's chatty Pepper is already dealing with customers at Pizza Hut and Californian clothing store The Ave, Nadine has been working as a receptionist at Singapore's Nanyang Technological University (NTU), and online grocery leaders Ocado are building their own robot mechanic to help human employees. Scarlett Johansson even makes for a particularly effective killing machine in this year's sci-fi action film Ghost in the Shell. Whilst these bots are all used for various different applications within a range of industries, there's one thing they all have in common. In many ways, it seems to make sense that many advanced robots have been given a human design.

Artificial Intelligence is a branch of computer science that probes into the possibility of simulating human intelligence into a machine. John McCarthy, An American computer scientist & cognitive scientist, considered to be the father of AI coined the term "artificial intelligence" in 1956 at the Dartmouth conference. Since the beginning of computers or machines era, technology has exponentially evolved. Machines and technology have considerably reduced human efforts and helped in increasing efficiency and speed with respect to time and money. Though machines today are not only assisting humans in executing tasks but completely surrogating them, they can understand, think, learn and behave like humans, thanks to Artificial Intelligence or AI.

Every revolution brings about change. The Next Economy will be driven by 10 key technologies that intersect to create one massive meta trend, underpinned by artificial intelligence. If A.I. reaches the point were it is truly autonomous, it might be the last invention humanity will create. As awesome as that sounds, most people are afraid of the implications for a world dominated by A.I.: we'll lose our jobs. Technology has always eliminated and displaced jobs, but its also created new ones.

Recently, Google added Try the API boxes on the product pages of each of its Cloud Machine Learning APIs: Cloud Vision API, Speech API and Natural Language API. Now anyone can instantly experience the power of Google's machine intelligence on their own images, voice and text. Let's see how it works. Using the label detection method of the API, Cloud Vision executes image content analysis on the uploaded image. Looks like Cloud Vision's machine intelligence is smart enough to understand not just the object, but also the context ("halloween," "holiday," "carving").