Earth observation analytics have the potential to serve many time-sensitive applications. However, due to limited bandwidth and duration of ground-satellite connections, it takes hours or even days to download and analyze data from existing Earth observation satellites, making real-time demands like timely disaster response impossible. Toward real-time analytics, we introduce OrbitChain, a collaborative analytics framework that orchestrates computational resources across multiple satellites in an Earth observation constellation. OrbitChain decomposes analytics applications into microservices and allocates computational resources for time-constrained analysis. A traffic routing algorithm is devised to minimize the inter-satellite communication overhead. OrbitChain adopts a pipeline workflow that completes Earth observation tasks in real-time, facilitates time-sensitive applications and inter-constellation collaborations such as tip-and-cue. To evaluate OrbitChain, we implement a hardware-in-the-loop orbital computing testbed. Experiments show that our system can complete up to 60% analytics workload than existing Earth observation analytics framework while reducing the communication overhead by up to 72%.

One of the main reasons for Alzheimer's disease (AD) is the disorder of some neural circuits. Existing methods for AD prediction have achieved great success, however, detecting abnormal neural circuits from the perspective of brain networks is still a big challenge. In this work, a novel decoupling generative adversarial network (DecGAN) is proposed to detect abnormal neural circuits for AD. Concretely, a decoupling module is designed to decompose a brain network into two parts: one part is composed of a few sparse graphs which represent the neural circuits largely determining the development of AD; the other part is a supplement graph, whose influence on AD can be ignored. Furthermore, the adversarial strategy is utilized to guide the decoupling module to extract the feature more related to AD. Meanwhile, by encoding the detected neural circuits to hypergraph data, an analytic module associated with the hyperedge neurons algorithm is designed to identify the neural circuits. More importantly, a novel sparse capacity loss based on the spatial-spectral hypergraph similarity is developed to minimize the intrinsic topological distribution of neural circuits, which can significantly improve the accuracy and robustness of the proposed model. Experimental results demonstrate that the proposed model can effectively detect the abnormal neural circuits at different stages of AD, which is helpful for pathological study and early treatment.

When I was the Vice President of Advertiser Analytics at Yahoo, this became a key focus guiding the analytics that we were delivering to advertisers to help them optimize their spend across the Yahoo ad network. Advertisers had significant untapped advertising and marketing spend into which we were not tapping because we could not deliver audience, content and campaign insights to help them spend that money with us. And the MOTT was huge. Now here I am again, and I'm again noticing this massive "Money on the Table" (MOTT) economic opportunity across all companies – orphaned analytics. Orphaned Analytics are one-off analytics developed to address a specific use case but never "operationalized" or packaged for re-use across other organizational use cases.

Summary: The results are in. There is only one demonstrably successful strategy for creating big wins for AI-first companies. We'll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner. For the last three years we've been close observers of exactly what makes a successful AI/ML strategy. In addition to our own observations we've been listening closely to VCs and how they describe their internal process for deciding who to fund.

What does Google know that the other 99.99% of organizations don't? I don't roam the hallowed hallways of Google or have access to any insider secrets as to their business plans and future business models, but it sure does make one contemplate why they would give away the very engine that fuels their obscenely-lucrative search business. You see, Google uses Machine Learning and Deep Learning to fuel its Personal Photo App (it auto-magically groups your photos into storyboards or collages), recognize spoken words (natural language processing), translate foreign languages (handy for me as I frequently travel internationally), and serve as the basis for its search engine. Arguably, Machine Learning and Deep Learning are the foundation for everything that makes Google money, and TensorFlow is the foundation for those Machine Learning and Deep Learning efforts. So why would Google open source TensorFlow and make it accessible to everyone – researchers, scientists, machine learning experts, students, and even its competitors?

What is the Intelligence Revolution equivalent to the 1/4" bolt? One of the key capabilities of the Industrial and Information revolutions was the transition from labor-intensive, hand-crafted to mass manufactured solutions. In the Information Revolution, it was the creation of standardized database management systems, middleware and operating systems. For the Industrial Revolution, it was the creation of standardized parts – like the ¼" bolt – that could be used to assemble versus hand-craft solutions. So, what is the ¼" bolt equivalent for the AI Revolution?

What is the Intelligence Revolution equivalent to the 1/4" bolt? I asked this question in the blog "How History Can Prepare Us for Upcoming AI Revolution?" when trying to understand what history can teach us about technology-induced revolutions. One of the key capabilities of the Industrial and Information revolutions was the transition from labor-intensive, hand-crafted to mass manufactured solutions. In the Information Revolution, it was the creation of standardized database management systems, middleware and operating systems. For the Industrial Revolution, it was the creation of standardized parts – like the ¼" bolt – that could be used to assemble versus hand-craft solutions.

Machine Learning today tends to be "open-loop" – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called "In-Stream Analytics (ISA)". Here, the data is processed as soon as it arrives and insights are generated quickly. However, action may be taken offline and the effects of the actions are not immediately incorporated back into the learning process. If we did, it is an example of a "closed-loop" system – we will call this approach "Adaptive Machine Learning" or AML.

Machine Learning today tends to be "open-loop" – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called "In-Stream Analytics (ISA)". Here, the data is processed as soon as it arrives and insights are generated quickly. However, action may be taken offline and the effects of the actions are not immediately incorporated back into the learning process. If we did, it is an example of a "closed-loop" system – we will call this approach "Adaptive Machine Learning" or AML.