Bringing Legacy Airline Planning Up to Speed with Machine Intelligence

Legacy airline planning has ingrained certain procedures so far into the core of the industry that affecting change can truly feel impossible. We, of course, know that is not true. The integration of intelligence as a technology into core, organizational structures can dramatically improve the health and stability of a manufacturer, lessor, creditor, or airline when user needs and experience design are taken into consideration. Luckily enough, the application of machine intelligence continues to quantify even the most intangible of goals – think customer satisfaction – long enough for drastic innovation to present a solid ROI to those calling the shots.


By applying a type of feature engineering known as backwards construction, data scientists can actively recover the features originally used to construct a piece of information. Some of the new, recommended tools for doing so are graph databases or knowledge graphs, both able to model how often datapoint features interact or how a single feature interacts across a database. The goal? To utilize the secondary digraph – and, potentially, parts of the primary digraph – to extract useful information and features about the primary digraph that were previously unknown.

AI-Enabled Computer Vision Paves the Future of Food, Marketing & Health

As intelligence becomes an increasingly important technology and machines are better equipped to comprehend their surroundings, the ability of a machine to see what humans cannot becomes paramount to the success of artificial-human integration. This translates directly into core performance indicators such as customer satisfaction, revenue growth, cost and waste reduction, and overall optimization. In addition, investing in computer vision is critical to the future success of all sectors as some contemporary technologies – even those that came with high hopes – reveal their inability to truly communicate not only with each other, but with established organizational processes and needs.

Machine Intelligence: The Hypergiant Edition

Today, the enhancing technology that is machine learning has matured to the point where its commoditization allows us to apply intelligence horizontally across an organization. Oil and gas companies, consumer packaged goods brands, airlines, space technologists, and information officers from all industries are invested in the continuous development of these applications and, so, bring them more and more into business discussions and large-scale solutions. Curious how we do it here at Hypergiant? Flip through our playbook, delve into our ethos, and embrace the improvements brought on by machine intelligence.


Machine intelligence offers a transformational approach to problem solving, one that prepares business models for unexpected changes and simultaneously takes action against the labor and market forces close at hand. With AI at the core, an ecosystem of knowledge distribution can be built to service business units across the board, with unique and agile interactions weaved throughout. It can even cater to different personas in the workforce, enabling both the increasing storage of veterans’ know-how and the personalized experiences required for newcomers to effectively interface with and absorb that information.


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?

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