In this incredibly fast paced and noisy world, it's getting increasingly more difficult to get and retain people's attention. We're on 24 hour news overload, social media demands our attention, and the barrage of email and messages continues without losing steam. It's a relative wonder that companies can get their messages across and communicate their offerings to prospective customers. Leaping into the foray are AI solutions that are aiming to help identify potential customers, hyperpersonalize and tailor messages to their specific needs, and identify the most effective means to communicate the message. In fact, AI is particularly good at all those things: identifying potential customers through clustering and pattern matching, tailoring messages through AI-enabled hyperpersonalization, and finding the best times and means to communicate through pattern identification.
Currently, the digital media is in a transitional phase, where the format of the medium is changing from text-based to one with visuals. Due to this significant shift, advertising has to play catch up, to stay up-to-date with the latest trends the industry. On top of that, the marketing industry has to deal with ad-blockers, which blocks out intrusive advertisements. According to a study done by PageFair, there are at least 615 million devices that use Adblock regularly. As you can imagine, getting through these ad-blockers is an uphill task, because they keep disruptive advertisements at bay.
Perceptual ad-blocking is a novel approach that uses visual cues to detect online advertisements. Compared to classical filter lists, perceptual ad-blocking is believed to be less prone to an arms race with web publishers and ad-networks. In this work we use techniques from adversarial machine learning to demonstrate that this may not be the case. We show that perceptual ad-blocking engenders a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks. We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker's full visual-detection pipeline, that enable publishers or ad-networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries. Our attacks exploit the unreasonably strong threat model that perceptual ad-blockers must survive. Finally, we evaluate a concrete set of attacks on an ad-blocker's internal ad-classifier by instantiating adversarial examples for visual systems in a real web-security context. For six ad-detection techniques, we create perturbed ads, ad-disclosures, and native web content that misleads perceptual ad-blocking with 100% success rates. For example, we demonstrate how a malicious user can upload adversarial content (e.g., a perturbed image in a Facebook post) that fools the ad-blocker into removing other users' non-ad content.
Adobe is pursuing a different tack: dishing out $50,000 no-strings-attached grants to professors and doctoral students working on projects of joint interest. "What academia provides is more the advanced mathematical algorithms and the advanced research that's gone into other related areas but hasn't been applied to our field," said Anil Kamath, Adobe's VP of technology. Adobe was not an early promoter of AI products as were other major technology players, like Google with its Automated Insights pattern-recognition tool, IBM with Watson and Einstein from Salesforce. But Adobe's research grant program, which has dished out 40 grants for a total of $2 million in the past four years, is bringing algorithmic AI into the company through academic work. Adobe is also doing outreach at events.
Artificial intelligence is one of the most buzzed-about terms in technology. The AI market is estimated to reach $5.05 billion USD by 2020, up from $419.7 million USD in 2014 – a 53% increase. With the launch of Facebook's chatbots, Amazon's Echo, and IBM's Watson, companies in many fields are considering how they can use new AI tools to their advantage. Advertising agencies that use AI, machine learning, and image recognition are hyper-targeting consumers by learning their interests and tastes. An everyday example is Facebook's targeted ads, which use artificial intelligence to narrow target segments down in a matter of hours.
Imagine that you are searching for a brown leather sandal online. You know what it should look like, but don't know how to describe it. You search "brown sandal" in Google, which serves up many results--but none of them are it. In 2015, GE inaugurated a new, Multi-Modal manufacturing facility in Chakan, India. If the company's ambitions for the space are realized, it could drive a massive change in global manufacturing.
The platform will be the integral part of Image Search Engine for Image Referral Network and Image Ad Network, to automate generation and placement of highly-relevant targeted ads based on images in a large scale for the first time in the industry. ZAC's AI Discovery platform can also be used for other types of images, data, or objects, e.g., clothing, purse, accessories, medical images, satellite images, and biometrics. ZAC has an impressive team of scientists and developers. The software development is headed by Saied Tadayon, a veteran software developer and scientist, who got PhD from Cornell at age 23. One of ZAC's inventors is Prof. Lotfi A. Zadeh ("The Father of Fuzzy Logic"), a pioneer computer scientist at U.C. Berkeley.