WhatsApp appears to be working on a new feature to help users identify whether an image they receive is legitimate or not. While picking apart update 2.19.73, WABetaInfo discovered a "search by image" function that will let you upload a received image directly to Google to reveal "similar or equal" images on the web. With this info, you should be able to more accurately judge whether the picture is real, or fake news. The feature isn't available yet, and there's no official word on when it will be.

It's common knowledge that neural networks are really good at one narrow task, but they fail at handling multiple tasks. This is unlike the human brain which is able to use the same concepts at amazingly diverse tasks. For example, if you have never seen a fractal before and I show you one right now. After seeing the image of a fractal, you'll be able to handle multiple tasks related to it: How are you able to do all these tasks? Are there dedicated neural networks in your brain specializing in all these tasks?

Combine Python & TensorFlow powers to build projects. In this course, you will learn how to code in Python, calculate linear regression with TensorFlow, and use AI for automation. Together with a professional you will perform CIFAR 10 image data and recognition and analyze credit card fraud by building practical projects. We explain everything in a straightforward teaching style that is easy to understand. Join Mammoth Interactive in this course, where we blend theoretical knowledge with hands-on coding projects to teach you everything you need to know as a beginner to credit card fraud detection What you'll learn Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram.

Summary: In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable. C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list. For data scientists these are miraculous times. We tend to think of miracles as something that occurs instantaneously but in our world that's not quite so. Still the rate of change in deep learning, particularly in image recognition is mind boggling and way up there on the miraculous scale.

Google is borrowing a few cues from Instagram and Pinterest to encourage more shopping in its search results. The internet giant is testing shoppable ads within image searches -- find a picture of your ideal desk and you can tap a shopping tag button to see basic details as well as a link to buy it. This only applies to sponsored ads, thankfully, so you don't have to worry about ads covering the images you want to see. The test is currently visible only to a "small percentage" of users who search for certain broader topics, such as home offices and abstract art. Whether or not it expands will likely depend on early results.

In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he's speaking about AI and machine learning. Tamim, one of the things you've talked about is the notion of five different developments of AI. Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don't make the same mistakes we do, because they capture things the way they are, and they don't see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera. So there are a lot of business problems [that image technology can help].

Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast. These are just a few of many examples of how image classification will ultimately shape the future of the world we live in. So, let's take a look at an example of how we can build our own image classifier. VGG16 is a built-in neural network in Keras that is pre-trained for image recognition.

ViSenze, an image recognition startup that delivers visual search tools designed for online retailers such as ASOS and Rakuten, recently revealed that it had secured $20 million in a Series C funding round. Both Sonae IM and Gobi Ventures co-led the financing round, which also included the participation of other investors, including returning backers WI Harper and Rakuten. Established back in 2012, ViSenze has so far raised a total of 34.5 million dollars (its previous funding round was a Series B held in September 2016). ViSenze customers include the likes of Uniqlo, Zalora, and Urban Outfitters, who bill the Singapore-based company's software portfolio as a "personal shopping concierge" that helps shoppers in finding or discovering products based on automatic photo tagging, visual search, and suggestions based on their browser history. The company's verticals include intellectual property, furniture, jewelry, and fashion.

Clarifai's API is another image recognition tool that doesn't require any machine learning knowledge prior to implementation. It can recognize images and also perform thorough video analysis. A user can start to make image or video predictions with the Clarifai API after they specify a parameter. For example, if you input a "color" model, the system will provide predictions about the dominant colors in an image. You can either use Clarifai's pre-built models or train your own one.

Amgen's drug discovery group is a few blocks beyond that. Until recently, Barzilay, one of the world's leading researchers in artificial intelligence, hadn't given much thought to these nearby buildings full of chemists and biologists. But as AI and machine learning began to perform ever more impressive feats in image recognition and language comprehension, she began to wonder: could it also transform the task of finding new drugs? The problem is that human researchers can explore only a tiny slice of what is possible. It's estimated that there are as many as 1060 potentially drug-like molecules--more than the number of atoms in the solar system. But traversing seemingly unlimited possibilities is what machine learning is good at. Trained on large databases of existing molecules and their properties, the programs can explore all possible related molecules.