LG today announced an updated version of the V30 smartphone and it comes with a supercharged camera powered by a third-party AI system. LG turned to EyeEm to add new features to the camera. Now, thanks to the EyeEm platform, the LG V30S ThinQ and soon the original V30 can automatically detect the subject of the picture -- being a hamburger or dachshund -- the camera will adjust the settings to produce the best results. EyeEm's AI platform runs locally on the device and does not need to pull AI information from the cloud to help with identification. According to a meeting with LG, even though this platform runs locally, it still has the ability to improve its capabilities as its used more.

AI attracts a lot of attention from many different industries, and marketing is no exception. There are a lot of discussions going on about how AI can help companies make their marketing efforts more efficient. When speaking about AI in marketing, the focus is usually on how AI can effectively process big data to make predictions, help marketers save time on repetitive tasks, or drive conclusions from big chunks of data. AI already helps us with automation and making better marketing decisions, so it's safe to say the technology can execute complex analytical tasks. But we all know marketing is not only about crunching data -- it is also about creativity.

The way we create and consume visual content has changed significantly with the advent of both digital and mobile cameras. Today, algorithms bring on the next wave of innovation and evolution in the space. AI technologies are increasingly good at classifying, organizing and understanding images, changing the way both brands and creators think about visuals. Cheaper and faster hardware, along with an abundance of rich data sets, has led to the visual computing revolution this past decade. The gap between mobile cameras and professional gear is quickly decreasing, giving everyone the ability to create photos and get their photos discovered.

AI research has long been the domain of universities, public institutions, and large corporations. Thanks to some amazing developments in the field over the past few years (and a whole lot of hype), every startup, VC, agency, and hot dog cart is scrambling to find a way to get on that bandwagon -- to be AI-powered, AI-adjacent, or just faking-until-we-make-it-AI. The typical VC is not set up for long-term research; any research really worth doing is typically a multi-year effort that could last longer than a fund's lifespan. Raising future rounds requires clear traction on core KPIs, and I'm not sure most VCs count p-value and f-score as such. Revenue growth is not easy to show when you need years to bring a product to market.

Can a machine learn aesthetics in a way a human would? Could it then look at a set of photos, and draw on those same aesthetics to reproduce a different set? It's a big question because it has long-term implications for how AI is going to develop. Is it just what you "like"? How does it all work? When you as a human find it hard to express what you like do you think a machine going to find it easy?

Visual aesthetics are very personal, often subconscious, and hard to express. In a world with an overload of photographic content, a lot of time and effort is spent manually curating photographs, and it's often hard to separate the good images from the visual noise. The question we put forward at EyeEm is: can a machine learn personalized aesthetics embodied in a set of chosen photos, and recreate them in a different set? The incapacity to name is a good symptom of disturbance. Does this photograph draw your attention?

EyeEm is a photography community and marketplace of over 18 million photographers. It also publishes a magazine, also called EyeEm. For its fourth issue, Machina: A Curation of Real Photography by a Machine, the company turned to an artificial intelligence powered by computer vision, EyeEm Vision, to curate the magazine, selecting the photographs it feels are the best aesthetically and most impactful. Now, before the inner smartphone photographer in you rolls your eyes, understand that it is pretty neat that a machine can, in some ways, learn to identify photographic aesthetics like a human. Sure, an A.I. cannot truly exercise a similar series of complex calculations of why an image might be great or resonant, but it's certainly intriguing to see where humans are in imbuing machines with mental processes.

Long ago computers and machines began to replace blue collar jobs but as creatives we thought we were safe. Have we now reached the tipping point when computers can replace even the photo editor? In recent months, EyeEm, an image-sharing platform, has been working on algorithms that, it says, will "augment" the work of photo editors. One algorithm analyzes pictures to determine what is in them, using deep learning to recognize thousands of concepts – from objects to colors and even emotions. The other algorithm references a database of millions of curated images to determine the quality of a photograph and give each one an "Aesthetic Score."

EyeEm's Head of R&D Appu Shaji explains how his team developed a deep learning technology that understands aesthetic taste and applies it to your photos. As a child I waited anxiously for the arrival of each new issue of National Geographic Magazine. The magazine had amazing stories from around the world, but it was the stunning photographs that really stood out to me. The colors, shadows and composition intrigued me, as well as a union of visual arrangement and storytelling. This childhood fascination with photographs sparked a curiosity to understand their behavior, nuances and semantics. Ultimately, this curiosity drove me to study computer vision, which has empowered me to develop systems for understanding images from a computational and scientific perspective.

To me, photography is the simultaneous recognition, in a fraction of a second, of the significance of an event. As a child I waited anxiously for the arrival of each new issue of National Geographic Magazine. The magazine had amazing stories from around the world, but the stunningly beautiful photographs were more important to me. The colors, shadows and composition intrigued and wowed me, and there was a cohesion of visual arrangement and storytelling. This childhood fascination with photographs aroused in me a curiosity to understand the behavior, nuances and semantics embedded inside them.