Amy Barrett: So Girl Decoded was published earlier this year by Penguin Business. Can you tell me, what is your book about? Rana el Kaliouby: So my book is a memoir. It's a juxtaposition of my personal journey intertwined with my journey building emotional intelligence into technology. AB: What made you actually want to start writing it? ReK: So the initial idea was to talk about emotion A.I. or artificial emotional intelligence and kind of tease apart the different applications of the technology and the ethical and moral implications of building technology like that. But very early on, I remember meeting with the publisher Penguin, Random House, and the editor there said, you know, your story is really fascinating. I grew up in the Middle East, found my way to the US by way of studying in the UK, actually. Ane he said, that's the story, you got to interweave your personal stories. So it ended up being this, again, kind of inter woven mix of my personal background and how I went from what I call "a nice Egyptian girl" to a CEO of a tech company. AB: And what some of the biggest challenges you say you faced to getting where you are today? ReK: I think the biggest kind of challenge is that I was always kind of doing some… I'm a misfit. Like, I grew up in the Middle East, but I really wanted to be a computer scientist. I left home to do my PhD, which was quite unusual at the time because my husband at the time had to stay back in Cairo for work.

Anyone can tell an eagle from an ostrich. It takes a skilled birdwatcher to tell a chipping sparrow from a house sparrow from an American tree sparrow. Now researchers are using AI to take this to the next level -- identifying individual birds. André Ferreira, a Ph.D. student at France's Centre for Functional and Evolutionary Ecology, harnessed an NVIDIA GeForce RTX 2070 to train a powerful AI that identifies individual birds within the same species. It's the latest example of how deep learning has become a powerful tool for wildlife biologists studying a wide range of animals.

"We are standing on the beach, watching a fast-approaching tsunami, and all that we have around us are a few pieces of driftwood. We can continue trying to build a shelter from the driftwood in the few seconds that we have remaining, or we can figure out how we are going to use the driftwood to ride the ineluctable wave." I thought of this analogy when I was at a recent meeting held between some of the key players in the book production industry in South Africa. I heard many complaints of how "various philanthropists are bypassing the official library chain in South Africa by establishing libraries", "the number of books that were being registered on an official registry are lower than previous years" and so on. In many ways, I could picture myself sitting in a meeting with key players in the music industry about 12 years ago, around the same time that the likes of Spotify were gaining traction, and they would have been having similar discussions focused on the future of music.

Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous systems provide new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected integrated Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling.

Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Integrated soluble solid and nitrate content assessment of spinach plants using portable NIRS sensors along the supply chain" LINK "Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem" LINK "Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)"LINK "FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples" Foods LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer" LINK "EXPRESS: Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging" LINK ...

On the basis of regional segmentation, the market is bifurcated into major regions of North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The regional analysis further covers country-wise bifurcation of the market and key players. The research report offered by Market Research Intellect provides an updated insight into the global Data Science and Machine-Learning Platforms market. The report covers an in-depth analysis of the key trends and emerging drivers of the market likely to influence industry growth. Additionally, the report covers market characteristics, competitive landscape, market size and growth, regional breakdown, and strategies for this market.

The market is further segmented on the basis of types and end-user applications. The report also provides an estimation of the segment expected to lead the market in the forecast years. Detailed segmentation of the market based on types and applications along with historical data and forecast estimation is offered in the report. Furthermore, the report provides an extensive analysis of the regional segmentation of the market. The regional analysis covers product development, sales, consumption trends, regional market share, and size in each region.

The taxation of multi-national companies is a complex field, since it is influenced by the legislation of several states. Laws in different states may have unforeseen interaction effects, which can be exploited by allowing multinational companies to minimize taxes, a concept known as tax planning. In this paper, we present a knowledge graph of multinational companies and their relationships, comprising almost 1.5M business entities. We show that commonly known tax planning strategies can be formulated as subgraph queries to that graph, which allows for identifying companies using certain strategies. Moreover, we demonstrate that we can identify anomalies in the graph which hint at potential tax planning strategies, and we show how to enhance those analyses by incorporating information from Wikidata using federated queries.

This paper gives a detailed review of reinforcement learning in combinatorial optimization, introduces the history of combinatorial optimization starting in the 1960s, and compares with the reinforcement learning algorithms in recent years. We explicitly look at a famous combinatorial problem known as the Traveling Salesman Problem. We compare the approach of the modern reinforcement learning algorithms on Traveling Salesman Problem with the approach published in the 1970s. Then, we discuss the similarities between these algorithms and how the approach of reinforcement learning changes due to the evolution of machine learning techniques and computing power.

We show that a particular form of target propagation, i.e., relying on learned inverses of each layer, which is differential, i.e., where the target is a small perturbation of the forward propagation, gives rise to an update rule which corresponds to an approximate Gauss-Newton gradient-based optimization, without requiring the manipulation or inversion of large matrices. What is interesting is that this is more biologically plausible than back-propagation yet may turn out to implicitly provide a stronger optimization procedure. Extending difference target propagation, we consider several iterative calculations based on local auto-encoders at each layer in order to achieve more precise inversions for more accurate target propagation and we show that these iterative procedures converge exponentially fast if the auto-encoding function minus the identity function has a Lipschitz constant smaller than one, i.e., the auto-encoder is coarsely succeeding at performing an inversion. We also propose a way to normalize the changes at each layer to take into account the relative influence of each layer on the output, so that larger weight changes are done on more influential layers, like would happen in ordinary back-propagation with gradient descent.