Though it seems like something out of a sci-fi movie, machine learning (ML) is part of our day-to-day lives. So often, in fact, that we may not always notice it. For example, social networks and mobile applications use ML to assess user patterns and interactions to deliver a more personalized experience. However, AWS services provide many options for the integration of ML. In this post, we will show you some use cases that can enhance your platforms and integrate ML into your production systems.

Last Tuesday, I had the privilege of leading an insightful conversation at the Gartner IT Symposium with a badass panel of AI technologists – Hillery Hunter, Hilary Mason and Margaret Dawson – about the obstacles that organizations need to overcome if they are to successfully implement AI. The foundation of our panel discussion was based on a study that we recently commissioned to understand how enterprise executives are approaching AI. I will share more of the research in coming weeks, but one big takeaway is that companies that see results from AI are keeping AI capabilities at the core of the business. While most organizations are still experimental, we are seeing a strong correlation between organizations with highly dedicated on-premise AI capabilities to high performance, measurable ROI and less failure. Net-net, on-prem AI capabilities and solutions are fulfilling the AI hype.

Funneling incoming data into a data store is the first step of any ML workflow. The key point is that data is persisted without undertaking any transformation at all, to allow us to have an immutable record of the original dataset. Data can be fed from various data sources; either obtained by request (pub/sub) or streamed from other services. NoSQL document databases are ideal for storing large volumes of rapidly changing structured and/or unstructured data since they are schema-less. They also offer a distributed, scalable, replicated data storage.

The Transhuman House, ZS and more… what is in store for 2019? The past year has had a lot of ups and downs. From the success of the AI research at the AGI Laboratory, or the opening and building out of the Transhuman House 2.0, to the Foundation Retreat, a lot has happened this last year. It will be an interesting ride to see what happens this coming year. I hope one theme that I've embraced in life the past year will follow me through the next and that is the archetype "the Architect" and how that will apply to a cohesive plan for the year.

A general intelligence possesses the abilities, given any goals and environment, to iteratively evaluate, plan, discover or learn and build or gain competencies, tools and resources to succeed at those goals. The only known examples of general intelligence are the obligatorily gregarious, conscious “selves” designated homo sapiens that currently dominate our planet. We argue that humans are reasonably deep in a safe and effective attractor in the state space of intelligence and that adhering as closely as possible to the human model of an emotion-driven conscious moral mind, has the advantages of safety, effectiveness, comfort and ease of transition due to a known and explored state space. Most concerns about AI safety are due to expected differences from humans – which seems unnecessary when, not only can we choose to make them more humanlike but the history of AI research clearly shows that we are unlikely to succeed unless we do so. We therefore propose a human-like emotion-driven consciousness-based architecture to solve these problems. We rely upon the Attention Schema Theory of consciousness and the social psychologists’ functional definition of morality to create entities that are reliably safe, stable, self-correcting and sensitive to current human intuitions, emotions and desires.

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