You are probably familiar with (or have at least heard of) Big Data. What Big Data means, how it affects you, and what its existence means for the world around you, however, are less understood. One response to this is an approach to personal data control called data privatization. While this concept is not new, there is huge potential growth in the market for methods and programs that privatize data, and savvy consumers (along with those who market to them) should make themselves familiar with these concepts.

Investing in AI: Balancing Diversification with Specialization

There has been a ​tremendous amount of venture capital and M&A activity with AI startups. It’s a technology you won’t stop hearing about anytime soon and anyone with a checkbook likely wants a piece of the pie. However, like any new technology, ‘spraying-and-praying’ doesn’t work. Effective venture investment into AI requires a delicate balance of risk-tolerance and risk-aversion - fusing specialized, industry knowledge with traditional portfolio diversification.

The Brownie Simplex: A brief expose on the scientific method mixed with design thinking as applied to the active and ongoing area of brownie research.

What do Brownies, Machine Learning, and Artistic Expression have in common? Most people will assume little, if any, between the three topics. But why? Have you thought about how we separate our daily lives, analytical thought, and creative thinking? In this piece we explore the value of combinatory thought patterns giving rise (so to speak) to exceptional baked goods.

Through a Monitor, Darkly: Normalization & Machine Intelligence

Machine Intelligence is all around us and people are growing increasingly comfortable interfacing with its agents. So why are companies so effortful in maintaining the illusion that we are communicating with a human being? As a society, we need to weigh the value of honesty against the value of misdirection.

The Virtue of Explicit Bias: Why Your Chatbot Should Be a D**k

An exploration of the value in applying explicit bias to the machine intelligence we create, this piece examines three disparate M.I.s and their exposure to the real world. It considers whether the Turing Test is pointing us towards humanistic multidimensionality when that may not a profitable avenue. It consistently has a negative connotation, but is there a place for bias after all?

Welcome to the Fourth Industrial Revolution

Both consumers and organizations have long feared the concept of AI, worrying that advanced machines could create a dystopian future and force humanity into subservience. But today, the concept of AI is far friendlier than what was originally intended. This is the Fourth Industrial Revolution. It's not going to be defined solely by our technological advancements, but by our ability to meaningfully apply this technology to solve real problems.

A World Without AI is Scary. Progress Isn’t

The concern floating around tech communities about AI becoming an existential threat to humanity is an unwarranted waste of energy. At least for most of us. In fact, our most pressing concern should be delivering on the digital experiences we were promised. However, while we continue to improve the low-hanging fruit, businesses should continue to explore how AI will impact their bottom-line, because whether we like it or not, AI is here to stay and it's making it's way to industry fast and we should embrace it. Fundamentally, AI is progress and it isn't scary.

The Tension Between Anthropomorphization and Subservience in MI

As children and younger consumers find themselves interacting with a variety of digital “assistants” on their favorite devices (Alexa, Siri, Cortana, etc.), there’s a fear that we are allowing a new generation to interact with technology as if it was a servile class, creating a swatch of detrimental risks on society for the long-term. This piece discusses how MI solutions can and should be trained with respect and manners from the ground up.


A piece that challenges the assumption that all artificial intelligence solutions are true and rooted in fact. In actuality, intelligent systems aren’t always true because the people, processes, and algorithms that train them are inherently biased.

Visit full page