Climate change has emerged as the biggest threat to humanity, with devastating consequences such as extreme weather events, climate migration, and a sharp decline in biodiversity. While the brunt of climate action is shouldered by green parties and public activists like the young Greta Thunberg, in recent years many industries have stepped up innovation to try and do their bit. The informatics industry in particular has been flexing its R&D muscle to propose bleeding-edge solutions. A recent paper published by a group of high-profile AI experts and IT professionals explores the potential that can be found "at the nexus of climate change and machine learning". Headed by David Rolnick, a Postdoctoral Research Fellow at the University of Pennsylvania, the paper puts a spotlight on "high-impact opportunities for real-world change" present in such ML fields as artificial intelligence, computer vision, unsupervised learning, and more.

In any normal year, Cambridge Analytica would have been the biggest story. Facebook alone had a royal flush of scandals, including a huge data breach in September, becoming the subject of multiple class action lawsuits for discrimination, accusations of inciting ethnic cleansing in Myanmar, potential violations of the Fair Housing Act, and hosting masses of fake Russian accounts. Throughout the year, Facebook executives were frequently summoned to testify, with Mark Zuckerberg himself facing the US Senate in April and the European Parliament in May. News broke in March that Google was building AI systems for the Department of Defense's drone surveillance program, Project Maven. The news kicked off an unprecedented wave of tech worker organizing and dissent.

Artificial intelligence (AI) is a megatrend that the industry will continue to talk about for the next 20 years. So said Brian Burke, chief of research at Gartner, who presented the top 10 strategic technology trends for 2019 on day three of the Gartner Symposium/ITxpo in Cape Town this morning. Burke said technology is continuing to advance. It is these advancements that underpin Gartner's "Continuous Next" operating philosophy. "AI is going to underlie pretty much everything that we do in technology." Every year, Gartner puts together a list of what it considers to be the most impactful technology trends for organisations.

In the age of digital transformation, global insurance provider AXA has adopted a decentralised approach to innovation. AXA International and New Markets (AXA INM) takes charge of AXA's operations in emerging and developing markets, covering Eastern European territories, Latin America, the GCC (Gulf Cooperation Council), Africa, India, Singapore, Malaysia and more. "There's a significant amount of Transformation to deliver across the 20-25 entities," says Kuldeep Kaushik, Chief Operating and Transformation Officer at AXA INM. "We have very different maturity levels across the businesses and very different technology landscapes as well. Part of my role is evaluating each of those entities and defining programmes which are specific to their maturity, business strategy, and needs and capabilities."

Machine learning is hard but moving your ML model to your embedded device can be even harder. Here, we'll discuss a few pain points in this process, and some up-front Addressing these issues early in the design process is key to getting your new gadget out the door. Most likely you will develop and train your machine-learning models using one of the big four (Google, Amazon, Microsoft, IBM) service stacks, one of the many MLaaS platforms (C3, BigML, WandB, Databricks, Algorithmia, OpenML, Paperspace, PredictionIO, DeepAI, DataRobot, etc.), or you'll roll your own using some variant of Anaconda/Jupyter and ML frameworks such as Keras, TensorFlow, PyTorch, Caffe, MXNet, Theano, CNTK, Chainer, or Scikit-Learn. How do you get from this set of tools, code and data using many different formats, sources, licenses and execution environments into something that you can execute entirely inside some little box--one that may (or may not) be connected to the internet ever again? The initial code for your model will be written in Python, R, MATLAB, Lua, Java, Scala, C, or C .

With successful adoption of AntWorks' IAP solution, businesses will stand to save millions and realise increased performance and efficiency by automating and processing business data, including unstructured data, which will make up 80% of the world's data by 2025. The partnership will help the GCC become a blueprint for the AI economy in the rest of the Middle East, Turkey and Africa, especially as governments look to diversify and drive revenue from non-oil and gas sectors. "We are deeply honored to partner with The Private Office of Sheikh Saeed bin Ahmed Al Maktoum and SEED Group expanding our reach into the Middle East," said Asheesh Mehra, AntWorks Co-Founder and Group CEO. "We see our partnership with SEED Group as an incredible opportunity to bring AntWorks' leading expertise in artificial intelligence to the GCC - helping the UAE's Ministry of AI realise its 2031 Artificial Intelligence Strategy. This is a market that thrives on innovation and has taken some of the most ambitious steps in the world in adopting the use of AI across government and business as they seek to create new economic, social, and educational opportunities for citizens. We look forward to a powerful and productive relationship that will make straight-through processing a reality across the GCC."

We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model differentiates itself from other approaches by modeling both the event correlation within each individual document as well as across the corpus. To facilitate this, we introduce the concept of a proximity-network, a novel space-efficient data structure to facilitate scalable event mining. This proximity network captures the corpus-level co-occurence statistics for candidate event descriptors, event attributes, as well as their connections. We probabilistically model the proximity network as a generative process with sparsity-inducing regularization. This allows us to efficiently and effectively extract high-quality and interpretable news events. Experiments on three different news corpora demonstrate that the proposed method is effective and robust at generating high-quality event descriptors and attributes. We briefly detail many interesting applications from our proposed framework such as news summarization, event tracking and multi-dimensional analysis on news. Finally, we explore a case study on visualizing the events for a Japan Tsunami news corpus and demonstrate ProxiModel's ability to automatically summarize emerging news events.

FPGAs have become a popular choice for deploying deep learning architectures (DLA). There are many researchers that have explored the deployment and mapping of DLA on FPGA. However, there has been a growing need to do design-time hardware-software co-verification of these deployments. To the best of our knowledge this is the first work that proposes a 2-Level 3-Way (2L-3W) hardware-software co-verification methodology and provides a step-by-step guide for the successful mapping, deployment and verification of DLA on FPGA boards. The 2-Level verification is to make sure the implementation in each stage (software and hardware) are following the desired behavior. The 3-Way co-verification provides a cross-paradigm (software, design and hardware) layer-by-layer parameter check to assure the correct implementation and mapping of the DLA onto FPGA boards. The proposed 2L-3W co-verification methodology has been evaluated over several test cases. In each case, the prediction and layer-by-layer output of the DLA deployed on PYNQ FPGA board (hardware) alongside with the intermediate design results of the layer-by-layer output of the DLA implemented on Vivado HLS and the prediction and layer-by-layer output of the software level (Caffe deep learning framework) are compared to obtain a layer-by-layer similarity score. The comparison is achieved using a completely automated Python script. The comparison provides a layer-by-layer similarity score that informs us the degree of success of the DLA mapping to the FPGA or help identify in design time the layer to be debugged in the case of unsuccessful mapping. We demonstrated our technique on LeNet DLA and Caffe inspired Cifar-10 DLA and the co-verification results yielded layer-by-layer similarity scores of 99\% accuracy.

Social activities play an important role in people's daily life since they interact. For recommendations based on social activities, it is vital to have not only the activity information but also individuals' social relations. Thanks to the geo-social networks and widespread use of location-aware mobile devices, massive geo-social data is now readily available for exploitation by the recommendation system. In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities. We present an attention mechanism to model the influence of the target user $u_T$ in candidate user groups that satisfy the social constraints. It helps to retrieve the optimal user group and activity topic candidates, as well as explains the group decision-making process. Once the user group and topics are retrieved, a novel efficient spatial query algorithm SPA-DF is employed to determine the activity location under the constraints of the given user group and activity topic candidates. The proposed method is evaluated in real-world datasets and the experimental results show that the proposed model significantly outperforms baseline methods.

Fall armyworm, the larval life stage of a fall armyworm moth, impacts maize crops worldwide but particularly in countries like Uganda, where agricultural businesses employ 70% of the population. Studies show the potential impact is between 8.3 and 20.6 million tons per year, with the fallout amounting to between $2.48 million and $6.19 million per year. The threat of devastating losses prompted developers participating in a Google Developer Group in Mbale to create an Android app -- FlatButter -- that identifies signs of fall armyworm damage in maize crops. It's been featured on a national TV station in Uganda and highlighted by the Food Agricultural Organization of the United Nations, as well as by Google in a short film published today. "The vast damage and yield losses in maize production, due to FAW, got the attention of global organizations, who are calling for innovators to help," wrote Hansu Mobile and Intelligent Innovations CEO Nsubuga Hassan, who led the team that developed the app.