Coupled dynamical systems are frequently observed in nature, but often not well understood in terms of their causal structure without additional domain knowledge about the system. Especially when analyzing observational time series data of dynamical systems where it is not possible to conduct controlled experiments, for example time series of climate variables, it can be challenging to determine how features causally influence each other. There are many techniques available to recover causal relationships from data, such as Granger causality, convergent cross mapping, and causal graph structure learning approaches such as PCMCI. Path signatures and their associated signed areas provide a new way to approach the analysis of causally linked dynamical systems, particularly in informing a model-free, data-driven approach to algorithmic causal discovery. With this paper, we explore the use of path signatures in causal discovery and propose the application of confidence sequences to analyze the significance of the magnitude of the signed area between two variables. These confidence sequence regions converge with greater sampling length, and in conjunction with analyzing pairwise signed areas across time-shifted versions of the time series, can help identify the presence of lag/lead causal relationships. This approach provides a new way to define the confidence of a causal link existing between two time series, and ultimately may provide a framework for hypothesis testing to define whether one time series causes another.

We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization of captured events, search for the maximum of the intensity function and learning level sets of the intensity function. We model the intensity function as a sample from a truncated Gaussian process, represented in a specially constructed positive basis. In this basis, the positivity constraint on the intensity function has a simple form. We show how an minimal description positive basis can be adapted to the covariance kernel, non-stationarity and make connections to common positive bases from prior works. Our adaptive sensing algorithms use Langevin dynamics and are based on posterior sampling (\textsc{Cox-Thompson}) and top-two posterior sampling (\textsc{Top2}) principles. With latter, the difference between samples serves as a surrogate to the uncertainty. We demonstrate the approach using examples from environmental monitoring and crime rate modeling, and compare it to the classical Bayesian experimental design approach.

From 2012 to 2018, for instance, the computational cost of advanced AI applications that use deep-learning models increased by 300,000 times, causing a significant rise in electric power consumption and resource utilization. The emerging green AI, or environmentally friendly AI, on the other hand, addresses the issue by minimizing ML's computational demand and reducing its carbon footprint.

OpenAI's text generating system GPT-3 has captured mainstream attention. GPT-3 is essentially an auto-complete bot whose underlying Machine Learning (ML) model has been trained on vast quantities of text available on the Internet. The output produced from this autocomplete bot can be used to manipulate people on social media and spew political propaganda, argue about the meaning of life (or lack thereof), disagree with the notion of what differentiates a hot-dog from a sandwich, take upon the persona of the Buddha or Hitler or a dead family member, write fake news articles that are indistinguishable from human written articles, and also produce computer code on the fly. There have also been colorful conversations about whether GPT-3 can pass the Turing test, or whether it has achieved a notional understanding of consciousness, even amongst AI scientists who know the technical mechanics. The chatter on perceived consciousness does have merit–it's quite probable that the underlying mechanism of our brain is a giant autocomplete bot that has learnt from 3 billion years of evolutionary data that bubbles up to our collective selves, and we ultimately give ourselves too much credit for being original authors of our own thoughts (ahem, free will).

I ulearn/learn stuff full time for a paradigm shift in art/design. Went to National Institute of Design, India. 'The Endangered' was born as an urge to spread message to the wider world about our great seas and rivers through my art. The art is a representation of the natural beauty (texture/color) of the endangered species enlisted by WWF and IUCN Red list. Process briefly explained below how AI/Machine learning was used to bring in power to represent the heavy dataset in the way the artist intents.

Knowledge Graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large-scale Knowledge Graphs (KGs). Recently Neural Machine Translation based approaches are gaining momentum that translates natural language queries to structured query languages thereby solving the KGQA task. However, most of these methods struggle with out-of-vocabulary words where test entities and relations are not seen during training time. In this work, we propose a modular two-stage neural architecture to solve the KGQA task. The first stage generates a sketch of the target SPARQL called SPARQL silhouette for the input question. This comprises of (1) Noise simulator to facilitate out-of-vocabulary words and to reduce vocabulary size (2) seq2seq model for text to SPARQL silhouette generation. The second stage is a Neural Graph Search Module. SPARQL silhouette generated in the first stage is distilled in the second stage by substituting precise relation in the predicted structure. We simulate ideal and realistic scenarios by designing a noise simulator. Experimental results show that the quality of generated SPARQL silhouette in the first stage is outstanding for the ideal scenarios but for realistic scenarios (i.e. noisy linker), the quality of the resulting SPARQL silhouette drops drastically. However, our neural graph search module recovers it considerably. We show that our method can achieve reasonable performance improving the state-of-art by a margin of 3.72% F1 for the LC-QuAD-1 dataset. We believe, our proposed approach is novel and will lead to dynamic KGQA solutions that are suited for practical applications.

This transcript has been edited for clarity. This is Eric Topol with Medicine and the Machine, with my co-host, Abraham Verghese. This is a special edition for us, to speak with one of the leading lights of artificial intelligence (AI) in the world, Jeff Dean, who heads up Google AI. Jeff Dean, PhD: Thank you for having me. Topol: You have now been at Google for 22 years. In a recent book by Cade Metz (a New York Times tech journalist) called Genius Makers, you are one of the protagonists. I didn't know this about you, but you grew up across the globe. Your parents took you from Hawaii, where you were born, to Somalia, where you helped run a refugee camp during your middle school years. As a high school senior in Georgia where your father worked at the CDC, you built a software tool for them that helped researchers collect disease data, and nearly four decades later it remains a staple of epidemiology across the developing world.

Deep streams of data from Earth-imaging satellites arrive in databases every day, but advanced technology and expertise are required to access and analyze the data. Now a new system, developed in research based at the University of California, Berkeley, uses machine learning to drive low-cost, easy-to-use technology that one person could run on a laptop, without advanced training, to address their local problems. Berkeley-based project could support action worldwide on climate, health, and poverty. More than 700 imaging satellites are orbiting the earth, and every day they beam vast oceans of information -- including data that reflects climate change, health, and poverty -- to databases on the ground. There's just one problem: While the geospatial data could help researchers and policymakers address critical challenges, only those with considerable wealth and expertise can access it.

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I don't think it's a spoiler to say that the machines are trying to take over in the Netflix show The Mitchells vs. the Machines. I mean, there's obviously some type of conflict. But in case you haven't seen it yet, here is your official warning: I'm going to use the movie to do some fun estimation problems about the robot apocalypse. Maybe I've already said too much. Let's get right to the important stuff: The machines have decided that they would be better off without all those pesky humans, so they are gathering up all the people and putting them into seven giant 128-story rockets.