Tensorflight, an artificial intelligence and imaging-based insurtech, has announced a Series A funding round to accelerate market penetration and product optimisation across the multi-billion-dollar property insurance data analytics market. The investment was led by QBE Ventures and includes new investors Tareyton Venture Partners and ff Venture Capital Poland. Participating prior investors include ff Venture Capital and HSCM Bermuda. Tensorflight combines superior satellite, aerial and street level imagery understanding technology, with a proprietary Artificial Intelligence engine to automate commercial property inspections. Founded by ex-Googlers Zbigniew Wojna, PhD and Robert Kozikowski, Tensorflight's team includes technical expertise from tech leaders such as Google, Facebook, Nvidia and DeepMind.

HSCM Bermuda has increased its investment in Tensorflight, an artificial intelligence and imaging-based insurtech. The investment was part of a Series A funding round by Tensorflight, which it said is aimed at accelerating market penetration and product optimisation across the multi-billion-dollar property insurance data analytics market. Tensorflight said that the investment was led by QBE Ventures and includes new investors Tareyton Venture Partners and ff Venture Capital Poland. Participating prior investors included ff Venture Capital in addition to HSCM Bermuda. The local operation is the reinsurance and ILS investment management business arm of Hudson Structured Capital Management Ltd, the asset manager with a focus on the re/insurance and transportation sectors.

Most graph neural network architectures work by message-passing node vector embeddings over the adjacency matrix, and it is assumed that they capture graph topology by doing that. We design two synthetic tasks, focusing purely on topological problems - triangle detection and clique distance - on which graph neural networks perform surprisingly badly, failing to detect those "bermuda" triangles. Many tasks need to handle the graph representation of data in areas such as chemistry (Wale & Karypis, Method Triangles Clique 2006), social networks (Fan et al., 2019), and transportation GCN 50.0 50.0 (Zhao et al., 2019). Furthermore, it is not GCN D 75.7 83.2 limited to these graph tasks but also includes images GCN D ID 80.4 83.4 (Chen et al., 2019) and 3D polygons (Shi & Rajkumar, GIN 74.1 97 2020) that are possible to convert to graph data GIN D 75.0 99.4 formats. Because of these broad applications, Graph GIN D ID 70.5 100.0 Deep Learning is an important field in machine learning GAT 50.0 50.0 research. GAT D 88.5 99.9 Graph neural networks (GNNs, (Scarselli et al., 2008)) GAT D ID 94.1 100.0 is a common approach to perform machine learning SVM WL 67.2 73.1 with graphs. Most graph neural networks update SVM Graphlets 99.6 60.3 the graph node vector embeddings using the message passing. Node vector embeddings are usually initialized FCNN 55.6 54.6 with data features and local graph features like TF 100.0 70.0 node degrees. Then, for a (n 1)-th stacked layer, the TF AM 100.0 100.0 new node state is computed from the node vector representation TF-IS AM 86.7 100.0 of the previous layer (n).

For millennia, religious thinkers and philosophers have cited humanity's self-awareness—that is, our ability to think about our own mind and character—as being key to the uniqueness of our species. Carl Linnaeus's groundbreaking biological taxonomy ([ 1 ][1]) likewise characterized our genus by the words “Homo. Nosce te ipsum” (“Man. Know thyself”). Given our long-standing interest in selfawareness, it is surprising how little science has traditionally had to say about it. What features of our brains enable us to think about ourselves? What are our strengths and weaknesses in this respect and how do they influence how we decide, learn, and interact? Can we train self-awareness, and how does this improve our performance? In the past three decades, however, research addressing such questions has been picking up speed. In Know Thyself , cognitive neuroscientist Stephen Fleming synthesizes this multifaceted research into an admirably coherent narrative and outlines how the resulting knowledge may be applied to solve societal problems. Writing about self-awareness is challenging because concepts such as “self” and “awareness”—let alone the combination thereof—are hard to define. The book does not get lost in this epistemological Bermuda triangle but rather conceptualizes self-awareness as the set of mental and brain processes that keep track of our percepts, thoughts, and actions. Not all of these metacognitive processes concern the self in a philosophical sense, Fleming notes, and not all of them need to be conscious. A helpful metaphor in the book compares the human brain to a flying plane that is largely guided by autopilot technology but can be flexibly controlled by the pilot whenever needed. For our behavior, the autopilot is the unconscious, “implicit” part of metacognition, and the pilot is the “explicit” metacognition that we can consciously report. Fleming begins by summarizing the psychology and neuroscience of these metacognitive processes. Implicit metacognition, he notes, is evident in many seemingly low-level brain processes, ranging from the sensory brain cells that signal the uncertainty associated with particular percepts, to brain cells that activate when we commit action errors (think: mistyping on a keyboard). All of these implicit signals can be read out in the service of explicit metacognition, when, for example, we need to judge our confidence in having chosen the right action. This latter ability depends on specific brain areas in the prefrontal cortex and is independent of the basic perceptual and motor abilities it serves to monitor. Explicit metacognition, meanwhile, depends on our ability to think about the mental states of others—an ironic twist nicely summarized by the caption of a cartoon that appears in the book: “Of course I care about how you imagined I thought you perceived I wanted you to feel.” It is eye-opening to realize how many fields of human endeavor depend not just on our skills and knowledge but also on our ability to estimate our competence. Obvious examples can be found in education, politics, the legal system, corporate decision-making and leadership, news and social media, and, indeed, any collaboration in which people pool their expertise. The book illustrates the role of metacognition in these diverse fields with elegant combinations of philosophical considerations, basic science findings, and more applied examples. Fleming even ventures into the near future, sketching how artificial intelligence with superhuman computational abilities but no self-awareness may become disconnected from humanity at best and outright catastrophic at worst. Emerging ideas on how we may address this problem—for example, by endowing intelligent machines with coarse self-awareness or by ensuring that self-aware humans remain at the helm—only serve to prove how little we have appreciated our own prodigious metacognitive abilities. In the end, the book makes a convincing case that self-awareness is a key feature of human existence and that our growing knowledge about it will be important for addressing many of our societal problems. One may quibble that the book somewhat understates this point, because it focuses on metacognition and does not cover our ability to monitor our emotions, another key aspect of self-awareness that has major implications for health and well-being. However, the literature on this topic is so diverse that doing it justice would likely require several additional volumes. As it stands, Fleming's book finally heaves metacognition into a long-deserved place in the scientific spotlight. 1. [↵][2]1. C. Linnaeus , Systema Naturae (Haak, 1735). [1]: #ref-1 [2]: #xref-ref-1-1 "View reference 1 in text"

This forecast is part of the Revolut Stock Trading Package, one of I Know First's algorithmic trading tools. The full investment universe includes the most promising stocks presented on Revolut trading platform. Package Name: Revolut Stock Trading Recommended Positions: Long Forecast Length: 3 Months (1/19/21 – 4/19/21) I Know First Average: 17.09% This Revolut Stock Trading Package forecast had correctly predicted 10 out of 10 stock movements. The highest trade return came from IVZ, at 32.4%.

Say what you will about Kim Kardashian--at least she's a human. The next generation of the famous-for-being-famous are being engineered from scratch. They're synthetic stars--algorithmically generated characters who have millions of Instagram followers, show up in glossy magazines, and have songs on Spotify. She models for the likes of Prada and Calvin Klein, her first single came out last year, and she has sponsorship deals with companies like Samsung. Among her pals: Bermuda, a rule-breaking bad girl who models and touts brands, and Blawko, an L.A.-based Gen-Zer who likes fast cars and Absolut vodka, and who is never seen without his trademark scarf covering his nose and mouth.

The use of machine learning technology is spreading across all areas of modern organizations, and its predictive capabilities suit the finance function's forward-looking needs. Understanding how to work with machine learning models is crucial for making informed investment decisions. Yet, for many finance professionals, successfully employing them is the equivalent of navigating the Bermuda Triangle. Machine learning is a subset of artificial intelligence that's focused on training computers to use algorithms for making predictions or classifications based on observed data. Finance functions typically use "supervised" machine learning, where an analyst provides data that includes the outcomes and asks the machine to make a prediction or classification based on similar data.

The use of machine learning technology is spreading across all areas of modern organizations, and its predictive capabilities suit the finance function's forward-looking needs. Understanding how to work with machine learning models is crucial for making informed investment decisions. Yet, for many finance professionals, successfully employing them is the equivalent of navigating the Bermuda Triangle. Does this project match the characteristics of a typical machine learning problem? Is there a solid foundation of data and experienced analysts?

Blockchain and artificial intelligence will reshape much of the insurance industry, but there should also be far more gender and race diversity across the sector. Those were two of the points raised during a discussion on --technology shaping industry-- at ILS Convergence 2018. Kara Swisher, an American technology journalist and cofounder of Recode, the technology news website, said the likes of artificial intelligence would significantly impact the insurance and reinsurance landscape. So every part of your business is going to be affected by AI, or whatever. Every part of your business could be replaced and affected,-- she told delegates at the two-day conference.

Applicant tracking systems (ATS) should be used to communicate to candidates through each stage of the recruitment process so that they know their status is. This will alleviate the dozens of calls recruiters get from candidates inquiring about their status and will create a favorable candidate experience since they won't feel like their resume just fell into the Bermuda Triangle of recruiting. As much as I would love to see AI reduce bias in the hiring process, the latest machine learning news reveals that AI mirrors human biases that exist. If that could be resolved, it would be a gamechanger. Considering this, my second choice would be talent sourcing replaced by AI.