The third annual Modern Artificial Intelligence (AI) seminar series at NYU Tandon, bringing together students and experts to discuss recent advances in the field, wrapped up on December 6 with a presentation by Raia Hadsell, Head of Robotics Research at DeepMind. In the final presentation of the series, sponsored by the Department of Electrical and Computer Engineering and organized by Professor Anna Choromanska, Hadsell explored ways in which human learning can inform machine learning systems to develop highly sophisticated AI to solve complex real-world tasks. The Fall roster kicked off in early October with a lecture by Facebook AI Research's Leon Bottou. The researcher, who harbors the long-term ambition of replicating human-level intelligence, examined causal inference, or finding the relationship between existing facts and objects. Next, on November 14, Francis Bach, researcher at Institut National de Recherche en Informatique et en Automatique (INRIA) in France, spoke about a new generation of "distributed optimization" schemes that are critically needed to scale algorithms to massive data.

To thwart sophisticated methods of altering photos and video, researchers at the NYU Tandon School of Engineering have demonstrated an experimental technique to authenticate images throughout the entire pipeline, from acquisition to delivery, using artificial intelligence (AI). In tests, this prototype imaging pipeline increased the chances of detecting manipulation from approximately 45 percent to more than 90 percent without sacrificing image quality. Determining whether a photo or video is authentic is becoming increasingly problematic. Sophisticated techniques for altering photos and videos have become so accessible that so-called "deep fakes"--manipulated photos or videos that are remarkably convincing and often include celebrities or political figures--have become commonplace. Pawel Korus, a research assistant professor in the Department of Computer Science and Engineering at NYU Tandon, pioneered this approach.

To frustrate complex strategies for modifying photographs and video, analysts at the NYU Tandon School of Engineering have shown an exploratory procedure to validate pictures all through the whole pipeline, from obtaining to conveyance, utilizing man-made brainpower (AI). In tests, this model imaging pipeline expanded the odds of identifying control from roughly 45 percent to more than 90 percent without giving up picture quality. Deciding if a photograph or video is valid is ending up progressively hazardous. Advanced strategies for changing photographs and recordings have turned out to be accessible to the point that purported "profound fakes" -- controlled photographs or recordings that are surprisingly persuading and frequently incorporate VIPs or political figures -- have turned out to be ordinary. Pawel Korus, an exploration partner educator in the Department of Computer Science and Engineering at NYU Tandon, spearheaded this methodology.

Artificial intelligence and shopping -- does that mean robots that'll stock the shelves? We'll have robots that will use A.I. to check inventory, help customers find the items they're shopping for, ferry supplies from one part of the warehouse to another, aid with shipping, you name it. But the real revolution for A.I. and shopping will be invisible because the technology will create better experiences for consumers while helping employees and shopkeepers run operations more effectively. Let's take any big department store that sells thousands of home, fashion, and beauty products -- more specifically, let's say that there are exactly 100,000 products. Because customers will buy up to 150 percent more and be happier with their purchases if they're shown the items in context, merchants will create outfits, design window displays, and produce splashy catalogs and digital lookbooks to help customers imagine how to wear the latest fashion trend, how to arrange their living rooms to show off their new velvet sectional, or how to install an outdoor shower.

Artificial intelligence and shopping -- does that mean robots that'll stock the shelves? We'll have robots that will use A.I. to check inventory, help customers find the items they're shopping for, ferry supplies from one part of the warehouse to another, aid with shipping, you name it. But the real revolution for A.I. and shopping will be invisible because the technology will create better experiences for consumers while helping employees and shopkeepers run operations more effectively. Let's take any big department store that sells thousands of home, fashion, and beauty products -- more specifically, let's say that there are exactly 100,000 products. Because customers will buy up to 150 percent more and be happier with their purchases if they're shown the items in context, merchants will create outfits, design window displays, and produce splashy catalogs and digital lookbooks to help customers imagine how to wear the latest fashion trend, how to arrange their living rooms to show off their new velvet sectional, or how to install an outdoor shower.