Being able to model and forecast international migration as precisely as possible is crucial for policymaking. Recently Google Trends data in addition to other economic and demographic data have been shown to improve the forecasting quality of a gravity linear model for the one-year ahead forecasting. In this work, we replace the linear model with a long short-term memory (LSTM) approach and compare it with two existing approaches: the linear gravity model and an artificial neural network (ANN) model. Our LSTM approach combined with Google Trends data outperforms both these models on various metrics in the task of forecasting the one-year ahead incoming international migration to 35 Organization for Economic Co-operation and Development (OECD) countries: for example the root mean square error (RMSE) and the mean average error (MAE) have been divided by 5 and 4 on the test set. This positive result demonstrates that machine learning techniques constitute a serious alternative over traditional approaches for studying migration mechanisms.

AI technologies are catalysing the initial and most crucial step in the biopharmaceutical value chain. The process of drug discovery has been historically slow, labour-intensive, failure-prone, and costly. Its four main stages, as shown below, typically take around five to six years to attain completion. This is a huge amount of time, especially during crisis situations such as the COVID-19 pandemic and considering the fact that drug research and discovery is only the first step in the biopharmaceutical value chain -- all in all it would take about a decade to finish the entirety of this process. Looking back in the past, discoveries were made mostly due to accidents and unexpected observations, like that of penicillin.

Job Number: R0083391 Artificial Intelligence/Machine Learning Data Scientist The Challenge Are you excited at the prospect of unlocking the secrets held by a data set? Are you fascinated by the possibilities presented by the IoT or recent advances in machine learning and artificial intelligence? In an increasingly connected world, massive amounts of structured and unstructured data open up new opportunities. As a data scientist, you can turn these complex data sets into useful information to solve global challenges. Across private and public sectors -- from fraud detection to cancer research to national intelligence -- you know the answers are in the data.

Chinese Academy of Sciences and the City University of Hong Kong led a research team that developed an AI-powered machine learning approach capable of generating lifelike human portraits from simple sketches. Current deep image-to-image translation techniques may be faster at generating human face images from sketches, but they often overfit their inputs, or in other words, require a professional artist to sketch the face first. Other deep learning based solutions for sketch-to-image translation interpret input sketches as fixed constraints and then attempt to recreate the missing texture information between strokes. This new approach learns the space of plausible face sketches from real ones and finds the point in this space that best approximates the input sketch treating it as a'soft' constraint used to guide image synthesis. It consists of three modules: CE (Component Embedding), FM (Feature Mapping), and IS (Image Synthesis). Recent deep image-to-image translation techniques allow fast generation of face images from freehand sketches.

Visualizing your Keras model, whether it's the architecture, the training process, the layers or its internals, is becoming increasingly important as business requires explainability of AI models. But until recently, generating such visualizations was not so straight-forward. Fortunately, with respect to the Keras deep learning framework, many visualization toolkits have been developed in the past few years. This has led to a landscape that is scattered and contains many open source toolkits and other elements. That's at least what I found out when I wrote tutorials for many of these recently.

Business Analytics, Machine Learning, AI, Deep Learning, Robotics, and Cloud have revolutionized the way we look, absorb, and process information. While there are still ongoing developments happening in several of these advanced fields, business analytics has gained the status of being all-pervasive across functions and domains. There is no aspect of our lives untouched by Analytics. The mammoth wings of analytics are determining how we buy our product to how we choose partners to how we lead our lives. Let us learn about, what is Business Analytics, the types of business analytics, business intelligence and analytics, and read more!

AI should be built on rigorous knowledge... Note: This is a follow-up to an earlier article on causal machine learning, "AI Needs More Why". There's much to be excited about with artificial intelligence (AI) in healthcare: Google AI is improving the workflow of clinicians with predictive models for diabetic retinopathy [2], many new approaches are achieving expert-level performance in tasks such as classification of skin cancer [3], and others surpassing the capabilities of doctors -- notably the recent report of DeepMind's AI for predicting acute kidney disease, capable of detecting potentially fatal kidney injuries 48 hours before symptoms are recognized by doctors [4]. Yet medical practitioners and researchers at the intersection of machine learning (ML) and medicine are quick to point out these successes are not representative of the more nuanced, non-trivial challenges presented by medical research and clinical applications. These ML success stories (notably all deep learning) are disease prediction problems, learning patterns that map well-defined inputs to well-labeled outputs [5]. Domains where instinctive pattern recognition works powerfully are what psychologist Robin Hogarth termed "kind learning environments" [6].

A year ago, a few of us started working on Cortex, an open source platform for building machine learning APIs. At the outset, we assumed all of our users--and all of the companies actually applying ML in production, for that matter--would be large companies with mature data science teams. Over the last year, we've seen students, solo engineers, and small teams ship models to production. A team of two, for example, recently spun up a 500 GPU inference cluster to support their application's 10,000 concurrent users. Not long ago, this kind of thing only happened at companies with large budgets and lots of data.

This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

For the first time, I taught an AI for Cyber Security course at the University of Oxford. I referred to this paper from Johns Hopkins which covered Deep Neural networks for Cyber Security (A Survey of Deep Learning Methods for Cyber Security) – references below where you can download the full paper for free. Detecting and Classifying Malware: The number and variety of malware attacks are continually increasing, making it more difficult to defend against them using standard methods. DL provides an opportunity to build generalizable models to detect and classify malware autonomously. There are a number of ways to detect malware.