neural network



We were awestruck by two projects. The first allows sketches of objects to become rendered images using generative neural networks. We've shared similar versions of this idea -- from Google's open source library of 3D rendered models to 3D gestures that map onto a space of virtual objects -- but this particular application shows how simple it is to go from concept to realistic (ish) environment. Yes, it's still ugly and messy, but for how long?  

The second project does an even more impressive trick. It takes the visual environments rendered in the 3D bubbles (or "360 video") of Google Maps and generates background sound for the environment. Note that this isn't the actual recorded sound, but a neural network hallucinated auditory experience that is correlated to the image mathematically. Listen to the video for full effect.

The melding of physical and digital spaces requires steps like this to become scalable and repeatable. We believe that once this type of technology is polished around the edges, augmented reality experiences and commerce will become profound.

ARTIFICIAL INTELLIGENCE: Neural Networks Managing Money at Man Group

Source:  Soul Machines

Source: Soul Machines

We still need humans to figure out how to value new assets like crypto tokens. Or do we? In what seems like an incredible story, $96 billion asset manager Man Group outlined exactly how it is already using artificial intelligence to help trade its portfolios at scale (on some products, not all). To quote directly: "By 2015 artificial intelligence was contributing roughly half the profits in one of Man’s biggest funds, the AHL Dimension Programme that now manages $5.1 billion, even though AI had control over only a small proportion of overall assets." The firm has since decided to use AI as a cornerstone across trading and investment selection, running neural networks on massive data sets in both supervised and unsupervised learning approaches. This requires a big infrastructure: terabytes of data worth of financial information, weather forecasts and global shipping schedules on specialized computers running deep learning software.

Investment management product manufacturing is a particularly thorny problem for AI. Unlike computer vision (concerned with finding a cat photo in a sea of dog photos) or even lending/insurance underwriting (allowing new data to proxy for risk), figuring out what variables to solve around or even what data to use is much more nebulous. As the article describes, data is noisy and outcomes are uncertain. Yet we are likely to see more of this type of machine intelligence, not less. For example, Wells Fargo is augmenting its equity research analysts with a AI bot of their own. Earnings are a narrower problem and MiFID II will push prices of humans down.

How will we visualize these artificial intelligences? While they live inside voice interfaces in the current platforms, that may not be sufficient to actually trust Man Group or Wells Fargo's automated investment philosophy. Even Millennials still like to have a human face on their roboadvisor. Perhaps something like this smart hologram from VNTANA or this virtually rendered baby from Soul Machines? These AIs will need to manufacture some empathy before selling us mutual funds!