Recently, self-attention based models have achieved state-of-the-art performance in sequential recommendation task. Following the custom from language processing, most of these models rely on a simple positional embedding to exploit the sequential nature of the user's history. However, there are some limitations regarding the current approaches. First, sequential recommendation is different from language processing in that timestamp information is available. Previous models have not made good use of it to extract additional contextual information. Second, using a simple embedding scheme can lead to information bottleneck since the same embedding has to represent all possible contextual biases. Third, since previous models use the same positional embedding in each attention head, they can wastefully learn overlapping patterns. To address these limitations, we propose MEANTIME (MixturE of AtteNTIon mechanisms with Multi-temporal Embeddings) which employs multiple types of temporal embeddings designed to capture various patterns from the user's behavior sequence, and an attention structure that fully leverages such diversity. Experiments on real-world data show that our proposed method outperforms current state-of-the-art sequential recommendation methods, and we provide an extensive ablation study to analyze how the model gains from the diverse positional information.

IMAGE: Cover for "What is Artificial Intelligence: A Conversation between an AI Engineer and a Humanities Researcher " view more What do the words'artificial' and'intelligence' mean? And what are the consequences of developing AI? Instead of reiterating received definitions or surveying the field from a disciplinary perspective, Peter and Suman put two differing standpoints into conversation in their new book What is Artificial Intelligence? to engage with these questions and more. Peter is an AI engineer: with his applied approach, he focuses on how to make AI work. Suman is a humanities researcher: his approach is conceptual and so he concentrates on what people and academics mean when they say'AI'. Covering issues such as the meaning of'automation' and'language', What is Artificial Intelligence?

The world of artificial intelligence is expanding at a rapid rate, and its massive potential is perhaps most clear in the automotive industry. When we think of AI's use in transportation, we often cast our minds to self-driving cars and getting shuttled around in autonomous Ubers from location to location. But the possible applications of AI in vehicles extends way beyond simply becoming a backseat driver. According to the United Nations' Office at Geneva, AI is expanding in the transportation sector at a rapid rate. In fact, in terms of patent filings, artificial intelligence within transportation is growing at over four times the rate of that in life and medical sciences.

Lawyers were trained to think that practicing law is a zero-sum game, with no space for collaboration. Some recent events gave me hope that this is no longer the case. I know I am biased, because in the past I've been an open innovation manager in a global corporation, but even now that I'm working on legal technology adoption for a big law firm I think we might have a good chance. I recently attended a working breakfast in Milan (Italy), organized by Luminance, a leading contract review company, which was the first in a sequence of similar events. On that occasion, several partners and innovation heads from big Italian law firms openly discussed ideas and best practices on the matter.

"Machine Learning in Finance Market 2020" report share informative Covid-19 Outbreak data figures as well as important insights regarding some of the market component which is considered to be future course architects for the market. This includes factors such as market size, market share, market segmentation, significant growth drivers, market competition, different aspects impacting economic cycles in the market, demand, expected business up-downs, changing customer sentiments, key companies operating in the Machine Learning in Finance Market, etc. In order to deliver a complete understanding of the global market, the report also shares some of the useful details regarding regional as well as significant domestic markets. The report presents a 360-degree overview and SWOT analysis of the competitive landscape of the industries. The report also incorporates premium quality data figures associated with financial figures of the industry including market size (in USD), expected market size growth (in percentage), sales data, revenue figures and more.

Global AI and Machine Learning Operationalization Software Market provides in-depth analysis of parent market trends, macro-economic indicators and governing factors along with market attractiveness as per segments. Global AI and Machine Learning Operationalization Software Market research report presentation demonstrates and presents an easily understandable market depiction, lending crucial insights on market size, market share as well as latest market developments and notable trends that collectively harness growth in the Global AI and Machine Learning Operationalization Software Market. Global AI and Machine Learning Operationalization Software Market research report presentation demonstrates and presents an easily understandable market depiction, lending crucial insights on market size, market share as well as latest market developments and notable trends that collectively harness growth in the Global AI and Machine Learning Operationalization Software Market. This detailed and meticulously composed market research report on the Global AI and Machine Learning Operationalization Software Market discussed the various market growth tactics and techniques that are leveraged by industry players to make maximum profits in the Global AI and Machine Learning Operationalization Software Market even amidst pandemic situation such as COVID-19. Regional Analysis of the Global AI and Machine Learning Operationalization Software Market Further in the subsequent sections of the report, readers can get an overview and complete picture of all major company players, covering also upstream and downstream market developments such as raw material supply and equipment profiles as well as downstream demand prospects.

We argue that the next frontier in natural language understanding (NLU) and generation (NLG) will include models that can efficiently access external structured knowledge repositories. In order to support the development of such models, we release the VisualSem knowledge graph (KG) which includes nodes with multilingual glosses and multiple illustrative images and visually relevant relations. We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG. This multi-modal retrieval model can be integrated into any (neural network) model pipeline and we encourage the research community to use VisualSem for data augmentation and/or as a source of grounding, among other possible uses. VisualSem as well as the multi-modal retrieval model are publicly available and can be downloaded in: https://github.com/iacercalixto/visualsem.

Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because fouling increases the drag on vessels as they move through the water, resulting in higher fuel costs, and presents a biosecurity risk by providing a pathway for marine non-indigenous species (NIS) to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply so-called deep learning to automate the classification of images from in-water inspections for the presence and severity of biofouling. We combined images collected from in-water surveys conducted by the Australian Department of Agriculture, Water and the Environment, the New Zealand Ministry for Primary Industries and the California State Lands Commission, and annotated them using the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We compared the annotations from three biofouling experts on a 120-sample subset of these images, and found that for two tasks, identifying images containing fouling, and identifying images containing heavy fouling, they showed 89% agreement (95% CI: 87-92%). It was found that the MTurk labelling approach achieved similar agreement with experts, which we defined as performing at most 5% worse than experts (p=0.004-0.020). Our deep learning model trained with the MTurk annotations also showed reasonable performance in comparison to expert agreement, although at a lower significance level (p=0.071-0.093). We also demonstrate that significantly better performance than expert agreement can be achieved if a classifier with high recall or precision was required.

By Santiago Viquez, Physicist turned Data Scientist. Not long ago, I started thinking if I had to start learning machine learning and data science all over again, where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting. I'm aware that we all learn in different ways. Some prefer videos, others are OK with just books, and a lot of people need to pay for a course to feel more pressure.

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