Researchers from Estonia and Italy developed an innovative method by combining neural networks and statistics. Using this newly developed method, they refined the "Out of Africa" scenario. The researchers claimed that the African dynamics around the time of the Out of Africa expansion are more complex than previously thought. Archaeologists and geneticists agree that all modern humans originated somewhere in Africa around 300 thousand years ago. The population movement that colonized the rest of the globe occurred approximately 60-70 thousand years ago.

On the 12th of October, the world will celebrate Ada Lovelace Day to honor the achievements of women in science, technology, engineering and maths (STEM). In Finding Ada (the main network supporting Ada Lovelace Day), there will be three free webinars that you can enjoy in the comfort of your own home. There will also be loads of events happening around the world, so you have a wide range of content to celebrate Ada Lovelace Day 2021! Engineering is the science of problem solving, and we have some pretty big problems in front of us. So how are engineers tackling the COVID-19 pandemic and climate change?

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. After a 2019 research paper demonstrated that commercially available facial analysis tools fail to work for women with dark skin, AWS executives went on the attack. Instead of offering up more equitable performance results or allowing the federal government to assess their algorithm like other companies with facial recognition tech have done, AWS executives attempted to discredit study coauthors Joy Buolamwini and Deb Raji in multiple blog posts. More than 70 respected AI researchers rebuked this attack, defended the study, and called on Amazon to stop selling the technology to police, a position the company temporarily adopted last year after the death of George Floyd. But according to the Abuse and Misogynoir Playbook, published earlier this year by a trio of MIT researchers, Amazon's attempt to smear two Black women AI researchers and discredit their work follows a set of tactics that have been used against Black women for centuries.

I've always found the fact that the acronym for artificial intelligence in English, AI, is surprisingly similar to the first two characters for that word in both simplified Chinese -- '人工智能'. The first two characters together, 人工, mean'people' and'work' individually, but when put together mean'artificial' while '智能' means'intelligent.' This is quite a fascinating linguistic experiment, and it's interesting that the two most widely used languages in the world came up a similar acronym or character for one of the most important technologies ever invented by man. Perhaps there is some weird universal synergy going on or maybe there's an easy answer hidden somewhere deep within the linguistic annals of these two languages. Either way, this got me thinking about language.

A reflection of the Corona pandemic highlights the need for more sustainable production systems using automation. The goal is to retain automation of repetitive tasks while allowing complex parts to come together. We recognize the fragility and how hard it is to create traditional automation. We introduce a method which converts one really hard problem of producing sustainable production code into three simpler problems being data, patterns and working prototypes. We use developer seniority as a metric to measure whether the proposed method is easier. By using agent-based simulation and NanoVC repos for agent arbitration, we are able to create a simulated environment where patterns developed by people are used to transform working prototypes into templates that data can be fed through to create the robots that create the production code. Having two layers of robots allow early implementation choices to be replaced as we gather more feedback from the working system. Several benefits of this approach have been discovered, with the most notable being that the Robot of Robots encodes a legacy of the person that designed it in the form of the 3 ingredients (data, patterns and working prototypes). This method allows us to achieve our goal of reducing the fragility of the production code while removing the difficulty of getting there.

False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.

The use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP). Here, we explore the idea of using a batch-softmax contrastive loss when fine-tuning large-scale pre-trained transformer models to learn better task-specific sentence embeddings for pairwise sentence scoring tasks. We introduce and study a number of variations in the calculation of the loss as well as in the overall training procedure; in particular, we find that data shuffling can be quite important. Our experimental results show sizable improvements on a number of datasets and pairwise sentence scoring tasks including classification, ranking, and regression. Finally, we offer detailed analysis and discussion, which should be useful for researchers aiming to explore the utility of contrastive loss in NLP. Recent years have seen a revolution in Natural Language Processing (NLP) thanks to the advances in machine learning. While a lot of attention has been paid to the architectures, especially for deep learning, there has been less focus on studying loss functions. At the same time, loss functions based on similar or on the same ideas were reinvented multiple times under different names. This can cause difficulties when solving new problems or when designing new experiments based on previous results. To a greater extent, this applies to "universal" loss functions, which can be applied in different machine learning areas and tasks such as Computer Vision (CV), Recommendation Systems, and NLP.

We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the continuous limit of the classical momentum accelerated gradient descent, to improve neural ODEs (NODEs) training and inference. HBNODEs have two properties that imply practical advantages over NODEs: (i) The adjoint state of an HBNODE also satisfies an HBNODE, accelerating both forward and backward ODE solvers, thus significantly reducing the number of function evaluations (NFEs) and improving the utility of the trained models. (ii) The spectrum of HBNODEs is well structured, enabling effective learning of long-term dependencies from complex sequential data. We verify the advantages of HBNODEs over NODEs on benchmark tasks, including image classification, learning complex dynamics, and sequential modeling. Our method requires remarkably fewer forward and backward NFEs, is more accurate, and learns long-term dependencies more effectively than the other ODE-based neural network models. Code is available at \url{https://github.com/hedixia/HeavyBallNODE}.

Learning tasks with sequential data as inputs (and possibly outputs) arise in a wide variety of contexts, including computer vision, text and speech recognition, natural language processing, and time series analysis in the sciences and engineering. While recurrent gradient-based models have been successfully used in processing sequential data sets, it is well-known that training these models to process (very) long sequential inputs is extremely challenging on account of the so-called exploding and vanishing gradients problem [32]. This arises as calculating hidden state gradients entails the computation of an iterative product of gradients over a large number of steps. Consequently, this (long) product can easily grow or decay exponentially in the number of recurrent interactions. Mitigation of the exploding and vanishing gradients problem has received considerable attention in the literature. A classical approach, used in Long Short-Term Memory (LSTM) [18] and Gated Recurrent Units (GRUs) [11], relies on gating mechanisms and leverages the resulting additive structure to ensure that gradients do not vanish.

The Cognitive/Artificial Intelligence Systems Market report includes a comprehensive analysis of the global market. This includes investigating past progress, on-going market scenarios, and future prospects. Accurate data on the products, strategies and market share of leading companies in this particular market are mentioned. This report provides a 360-degree overview of the global market's competitive landscape. The report further predicts the size and valuation of the global market during the forecast period.