Graph Databases are the (not-so-) new must-have in your tech stack. Over the past few decades, more and more tech giants (Facebook, Twitter, and Google leading the way) have started migrating data into graph databases to not only store and retrieve data in a highly scalable way, but also incorporate models on graph structures to produce highly effective recommendations. What makes graph databases so enticing to these companies with gargantuan data? Relational Database Management Systems (RDBMS's) refers to databases that store their data in a structured format with rows and columns, and have a schema that demonstrates the linkage between different tables within a database. RDBMS's are generally queried with SQL (Structured Query Language) or a similar equivalent, and are great for fairly static (non-changing) and normalized data structures.

This is an early and tentative document, intended to roughly summarize a line of thinking and to spur discussion and action among relevant individuals, organizations and communities. Many particulars discussed here are expected to evolve as more and more of the concepts described here move to practical realization. This is a living, evolving body of ideas. The introduction of AI tools and agents into all sectors of the economy, from factory robots to highly specialized electronic scientific brains, and the transition from narrow AI (domain specific, at best weakly autonomous) toward Artificial General Intelligence (broadly intelligent and strongly autonomous), are likely to be the biggest story of the next few decades. The tremendous promise and peril of these developments, which are already well underway, have been much discussed in fictional, media and intellectual spheres.

While deepfakes have now been around for a number of years, deepfake casting and acting are relatively new. Early deepfake technologies weren't very good, used primarily in dark corners of the internet to swap celebrities into porn videos without their consent. But as deepfakes have grown increasingly realistic, more and more artists and filmmakers have begun using them in broadcast-quality productions and TV ads. This means hiring real actors for one aspect of the performance or another. Some jobs require an actor to provide "base" footage; others need a voice.

Tim Bandos from Digital Guardian says technology always requires human consciousness to strive forward. It has and will require human effort to counter cyberattacks and stop them. According to Tim, Experts and Analysts are the real heroes, and AI is just a sidekick. How are hackers using AI to attack cybersecurity? In some cyberattacks, hackers exploit the data which is used to train machine learning models.

You may not realize it, but artificial intelligence (AI) is already enhancing our lives in a multitude of ways. AI systems already man our call centers, drive our cars, and take orders through kiosks at local fast food restaurants. In the days ahead, AI and machine learning will become a more prominent fixture, disrupting industries and extracting tediousness from our everyday lives. As we hand over larger chunks of our lives to the machines, we need to lift the hood to see what kind of ethics are driving them, and who is defining the rules of the road. Many CIOs have begun experimenting with AI in areas that may not be very visible to end users, such as automating warehouses.

A London firm specialising in regulatory technology for businesses has announced plans for further international expansion following a period of growth. TAINA Technology has grown its workforce by 46 per cent in 2020, despite the disruption caused by COVID-19 and the subsequent lockdown. Founded by tax lawyer Maria Scott, TAINA uses machine learning to allow businesses to streamline the validation of tax forms. The firm, which has already established a presence in both the UK and US, has plans to expand globally into new markets. TAINA Technology founder, Maria Scott, explained: "As a tax lawyer, I know from first-hand experience the burden that regulatory compliance procedures place on businesses. "TAINA helps automate some of the most painful elements of compliance (FATCA and CRS), reducing cost and risk, while improving customers' experience and streamlining their onboarding.

For more than four decades, University of California, San Diego, Professor of Physics Patrick H. Diamond and his research group have been advancing our understanding of fundamental concepts in plasma physics. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Extreme Science and Engineering Discovery Environment (XSEDE)-allocated Comet supercomputer at the San Diego Supercomputer Center at UC San Diego to showcase how machine learning produced a new model for plasma turbulence. Plasmas have many applications, including fusion energy. When light nuclei fuse together, the mass of the products is less than that of the reactants, and the missing mass becomes energy – hence Albert Einstein's famous E mc2 equation. In order for this to occur, temperatures must literally reach astronomical levels, such as those found in the Sun's core.

Product risk assessment is the overall process of determining whether a product, which could be anything from a type of washing machine to a type of teddy bear, is judged safe for consumers to use. There are several methods used for product risk assessment, including RAPEX, which is the primary method used by regulators in the UK and EU. However, despite its widespread use, we identify several limitations of RAPEX including a limited approach to handling uncertainty and the inability to incorporate causal explanations for using and interpreting test data. In contrast, Bayesian Networks (BNs) are a rigorous, normative method for modelling uncertainty and causality which are already used for risk assessment in domains such as medicine and finance, as well as critical systems generally. This article proposes a BN model that provides an improved systematic method for product risk assessment that resolves the identified limitations with RAPEX. We use our proposed method to demonstrate risk assessments for a teddy bear and a new uncertified kettle for which there is no testing data and the number of product instances is unknown. We show that, while we can replicate the results of the RAPEX method, the BN approach is more powerful and flexible.

Using the attention map based probing frame-work from (Clark et al., 2019), we observe that, on the RAMS dataset (Ebner et al., 2020), BERT's attention heads have modest but well above-chance ability to spot event arguments sans any training or domain finetuning, vary-ing from a low of 17.77% for Place to a high of 51.61% for Artifact. Next, we find that linear combinations of these heads, estimated with approx 11% of available total event argument detection supervision, can push performance well-higher for some roles - highest two being Victim (68.29% Accuracy) and Artifact(58.82% Accuracy). Furthermore, we investigate how well our methods do for cross-sentence event arguments. We propose a procedure to isolate "best heads" for cross-sentence argument detection separately of those for intra-sentence arguments. The heads thus estimated have superior cross-sentence performance compared to their jointly estimated equivalents, albeit only under the unrealistic assumption that we already know the argument is present in an-other sentence. Lastly, we seek to isolate to what extent our numbers stem from lexical frequency based associations between gold arguments and roles. We propose NONCE, a scheme to create adversarial test examples by replacing gold arguments with randomly generated "nonce" words. We find that learnt linear combinations are robust to NONCE, though individual best heads can be more sensitive.

Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task. Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong generalization. To investigate this question, we propose a taxonomic hierarchy of categories that are relevant for the NLI task. We introduce TAXINLI, a new dataset, that has 10k examples from the MNLI dataset (Williams et al., 2018) with these taxonomic labels. Through various experiments on TAXINLI, we observe that whereas for certain taxonomic categories SOTA neural models have achieved near perfect accuracies - a large jump over the previous models - some categories still remain difficult. Our work adds to the growing body of literature that shows the gaps in the current NLI systems and datasets through a systematic presentation and analysis of reasoning categories.