Google has introduced a change in how it presents image search results yesterday. Google has removed the convenient "view image" button from image search which allowed users to open the image alone instead of opening the website where the image was originally published. "Today we're launching some changes on Google Images to help connect users and useful websites. This will include removing the View Image button," Google said on its SearchLiaison Twitter page. "The Visit button remains, so users can see images in the context of the webpages they're on."

Say you're training an image recognition system to identify U.S. presidents. The historical data reveals a pattern of males, so the algorithm concludes that only men are presidents. It won't recognize a female in that role, even though it's a probable outcome in future elections. This latent bias is one of the many types of biases that challenge data scientists today. If the machine learning data set they use in an AI project isn't neutral -- and it's safe to say almost no data is -- the outcomes can actually amplify bias and discrimination that's present in the machine learning data set.

The media service at Grubhub ingests and manages images for every menu item currently available on the Grubhub platform. These images need to be moderated for prohibited content and quality before they are presented to our diners. Manual moderation of millions of pre-existing images on the platform along with the ones constantly being added everyday, is a tedious task. Automating this process saves time of the manual moderators allowing them to focus only on moderating images that cannot be approved by the automated process. Owing to the increase in computational power due to the advent of Graphic Processing Units (GPUs), usage of Neural Networks to identify objects in images has become feasible.

Danger patterns on microbes or damaged host cells bind and activate C1, inducing innate immune responses and clearance through the complement cascade. How these patterns trigger complement initiation remains elusive. Here, we present cryo–electron microscopy analyses of C1 bound to monoclonal antibodies in which we observed heterogeneous structures of single and clustered C1–immunoglobulin G1 (IgG1) hexamer complexes. Distinct C1q binding sites are observed on the two Fc-CH2 domains of each IgG molecule. These are consistent with known interactions and also reveal additional interactions, which are supported by functional IgG1-mutant analysis.

The woman in the photo seems familiar. She looks like Jennifer Aniston, the "Friends" actress, or Selena Gomez, the child star turned pop singer. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. She was created by a machine. The image is one of the faux celebrity photos generated by software under development at Nvidia, the big-name computer chip maker that is investing heavily in research involving artificial intelligence. At a lab in Finland, a small team of Nvidia researchers recently built a system that can analyze thousands of (real) celebrity snapshots, recognize common patterns, and create new images that look much the same -- but are still a little different.

AI is a considerably massive field. In recent years, with the extensive on-going research, generation of massive data sets and availability of massive computing power, Deep Learning has become one of most exciting fields of this era. Lets have a look at one of the foremost and supreme applications of Deep Learning which at the forefront of innovation and technology. Image Recognition is at the sweet intersection b/w Deep Learning and Computer Vision. I have seen a lot of people using these two terms interchangeably.

Annual worldwide AI revenue is projected to grow from $644 million in 2016 to $37 billion by 2025, with top use cases including algorithmic trading strategy performance improvement; static image recognition, classification, and tagging; efficient, scalable processing of patient data; predictive maintenance; content distribution on social media; and more. The financial services industry is no stranger to machine learning – a number of large institutions continue to successfully implement the technology across such areas as risk analytics and regulation, customer segmentation, cross-selling and upselling, sales and marketing campaign management, creditworthiness evaluation. Among institutions that are applying machine learning are BBVA, JPMorgan Chase, HSBC, OCBC, and many more. "Credit applications and underwriting are the key areas where machine learning, and data analytics in general, will have an initial impact. The outcomes will include cost reductions, increased efficiency, and less onerous customer experiences," experts suggest.

Amazon builds a lot of its business on machine-learning systems (as a subset of AI) and defines AI as "the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition." Machine learning is so important to Amazon, they stated, "Without ML, Amazon.com While some of the major tech companies haven't published a dictionary-type definition for AI, we can extrapolate how they define the importance of AI by reviewing their research areas. Machine and deep learning are the priority for Google AI and its tools to "create smarter, more useful technology and help as many people as possible" from translations to healthcare to making our smartphones even smarter. Facebook AI Research is committed to "advancing the file of machine intelligence and are creating new technologies to give people better ways to communicate."

Artificial intelligence and machine learning promise to radically transform many industries, but they also pose significant risks -- many of which are yet to be discovered, given that the technology is only now beginning to be rolled out in force. There have already been a number of public, and embarrassing, examples of AI gone bad. Microsoft's Tay went from innocent chatbot to a crazed racist in just a day, corrupted by Twitter trolls. Two years ago, Google had to censor image searches for keywords like "gorilla" and "chimp" because it returned photos of African-Americans -- and the problem still hasn't been fully fixed in its Google Photos app. As businesses increasingly embrace AI, the stakes will only get higher.

It will also pull "view image" links for pictures to reduce the number of direct downloads. Google has long had an option to filter photos by licensing rights, but that only helps if you already intend to honor image permissions. The cost of the deal isn't known. It's no surprise that a deal exists at all, mind you. Google already has its plate full with EU matters, including a shopping-related antitrust fine and tax disputes.