Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-in-graph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.

Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.

It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".

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The graph represents a network of 2,995 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 20 September 2020 at 23:42 UTC. The requested start date was Sunday, 20 September 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 21-hour, 20-minute period from Sunday, 06 September 2020 at 01:03 UTC to Saturday, 19 September 2020 at 22:24 UTC.

"A world perfectly fair in some dimensions would be horribly unfair in others." "Fairness" in Artificial Intelligence (AI) applications -- both as a concept and a practice -- is the focus of many organisations as they deploy new technologies for greater effectiveness and efficiencies. That machines are faster at processing large amounts of information and the notion that they are'more objective' than humans, appear to make them an obvious choice for progressivity and seemingly impartial actors in'fairer' decision-making. Yet, algorithmic based decisions have not come without their share of controversies -- Australia's recent'robo-debt' government intervention which wrongly pursued thousands of welfare recipients; the UK's'A-Levels fiasco' of downgrading graduating grades based on historical data, its controversial visa application streaming tool; and concerns about Clearview AI's facial recognition software for policing are raising new questions on the role of these technologies in society. Risk assessments are part of the fabric of modern society, but what we are dealing with here is not just'scaling up' human capacity for decision-making without the unwanted human biases and errors -- we are also extolling the'virtues of objectivity' under the guise of'fairness' (which is inherently subjective!) and failing to recognise the many inter-relationships that are being unraveled through the use of these algorithms in our daily lives.

Last week, on September 15 and 16, the Pentagon's Joint Artificial Intelligence Center (JAIC) held a meeting with officials from 13 countries, including but not only U.S. allies, around the ethical military uses of artificial intelligence, the first of its kind. Breaking Defense quotes Mark Beall, the JAIC's head of strategy and policy, who called the meeting "historic," as saying, "This group of … countries, to my knowledge, has never been brought together under one banner before." Earlier this year, the Pentagon adopted a set of ethics guidelines around AI use. At a time when China and Russia's pursuit of military AI has raised considerable alarm in Western capitals, Beall noted that the meeting was not about creating a coalition against specific countries. Rather, "we're really focused on, right now, rallying around [shared] core values like digital liberty and human rights… international humanitarian law," Beall said.

Throughout the Covid-19 pandemic, cyber attackers have taken advantage of uncertainty to relentlessly target businesses and individuals. Interpol reported in its Covid-19 cybercrime report in August that two-thirds of EU member countries witnessed a significant increase in malicious domains registered with the keywords'Covid' or'Corona' aiming to take advantage of the growing number of people searching for information about Covid-19 online. Opportunistic cyber criminals are also taking advantage of the global crisis by deploying ransomware against institutions and businesses associated with responding to the pandemic. But attacks were on the rise even before the pandemic came along. Businesses have been facing unprecedented demands on their networks, people and finances, but attacks are particularly devastating for small businesses, with the cost of recovery often insurmountable.

Spam filtering, face recognition, recommendation engines -- when you have a large data set on which you'd like to perform predictive analysis or pattern recognition, machine learning is the way to go. The proliferation of free open source software has made machine learning easier to implement both on single machines and at scale, and in most popular programming languages. These open source tools include libraries for the likes of Python, R, C, Java, Scala, Clojure, JavaScript, and Go. Apache Mahout provides a way to build environments for hosting machine learning applications that can be scaled quickly and efficiently to meet demand. Mahout works mainly with another well-known Apache project, Spark, and was originally devised to work with Hadoop for the sake of running distributed applications, but has been extended to work with other distributed back ends like Flink and H2O. Mahout uses a domain specific language in Scala.

We propose a simple and effective embedding model, named QuatRE, to learn quaternion embeddings for entities and relations in knowledge graphs. QuatRE aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product. QuatRE achieves this by associating each relation with two quaternion vectors which are used to rotate the quaternion embeddings of the head and tail entities, respectively. To obtain the triple score, QuatRE rotates the rotated embedding of the head entity using the normalized quaternion embedding of the relation, followed by a quaternion-inner product with the rotated embedding of the tail entity. Experimental results show that our QuatRE outperforms up-to-date embedding models on well-known benchmark datasets for knowledge graph completion.