How can Machine Learning Enhance Insurance Underwriting

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The day-to-day work of an Underwriter ranges from research, to data entry, to pricing a risk, to ultimately negotiating that premium value with an agent. At the core, they need to accurately gauge risk, on a case by case basis. But their job doesn’t stop there. Even if we were to codify all the significant risk factors (as actuarial tables do), this doesn’t translate directly to how much the insurance firm ultimately charges for a given premium. Underwriters need to create an offer that they can justify to their customers, and keep an eye on the prevailing market dynamics.

Underwriters navigate a sea of data, software, and file formats to reach a premium for each policy. It’s challenging work, with many uncertainties. In our experience, Machine Learning can be used to enhance the insurance underwriting process in a number of ways.

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Big Progress in Brain-computer interfaces in 2021

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Eight months in, 2021 has already become a record year in brain-computer interface (BCI) funding, tripling the $97 million raised in 2019. BCIs translate human brainwaves into machine-understandable commands, allowing people to operate a computer, for example, with their mind. Just during the last couple of weeks, Elon Musk’s BCI company, Neuralink, announced a $205 million in Series C funding, with Paradromics, another BCI firm, announcing a $20 million Seed round a few days earlier.

Pattern recognition and transfer learning

The ability to translate brain activity into actions was achieved decades ago. The main challenge for private companies today is building commercial products for the masses that can find common signals across different brains that translate to similar actions, such as a brain wave pattern that means “move my right arm.”

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Top 10 Data Science Use Cases in Insurance

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The insurance industry is regarded as one of the most competitive and less predictable business spheres. It is instantly related to risk. Therefore, it has always been dependent on statistics. Nowadays, data science has changed this dependence forever.

Now, insurance companies have a wider range of information sources for the relevant risk assessment. Big Data technologies are applied to predict risks and claims, to monitor and to analyse them in order to develop effective strategies for customers attraction and retention. Undoubtedly, the insurance companies benefit from data science application within the spheres of their great interest. Therefore, we have prepared the top 10 data science use cases in the insurance industry, which cover many various activities.

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Popular NLP applications

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Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence, which is concerned about how human interact with computers using human language. It aims at enabling computers to understand the contexts of documents, including the contextual nuances of the language within them, by processing, analysing and developing insights from large amounts of natural language data.

In other word, NLP enables computers to read, edit and summarise text as well as generate their own “speech.” Consequently, one can accurately extract useful information and insights contained in documents and then also present the knowledge into human languages for a variety of applications, such as recognition of specific concepts mentioned in the text, categorisation of information of the documents and emulate speech of human being.

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Next-Generation Pose Detection with MoveNet

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Human pose estimation has come a long way in the last five years, but surprisingly hasn’t surfaced in many applications just yet. This is because more focus has been placed on making pose models larger and more accurate, rather than doing the engineering work to make them fast and deployable everywhere.

To solve the problems, Google research launched their novel pose detection model, MoveNet, and its new pose-detection API as a javascript library in TensorFlow.js recently. The MoveNet is an ultra fast and accurate model that detects 17 keypoints of a body. MoveNet leverages the best estimation performance of state-of-the-art methods, while keeping inference times as low as possible. It delivers accurate keypoints across a wide variety of poses, environments, and hardware setups.

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LEAPS, BOUNDS, AND BACKFLIPS – Inside the lab: How does Atlas work?

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By training Atlas to maneuver its way through complex parkour courses, Boston Dynamics engineers develop new movements inspired by human behaviors and push the humanoid robot to its limits.

For the first time today, both Atlas robots have completed the complex obstacle course flawlessly. Or, almost flawlessly.

The first of the two robots ran up a series of banked plywood panels, broad jumped a gap, and ran up and down stairs in the course set up on the second floor of the Boston Dynamics headquarters. The second robot leapt onto a balance beam and followed the same steps in reverse, and then the first robot vaulted over the beam. Both landed two perfectly synchronized backflips, and the video team has captured every move. 

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2020: A Year Full of Amazing AI Papers - A Review

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A curated list of the latest breakthroughs in AI in 2020 by release date with a clear video explanation, link to a more in-depth article, and code.

Even with everything that happened in the world this year, we still had the chance to see a lot of amazing research come out. Especially in the field of artificial intelligence. More, many important aspects were highlighted this year, like the ethical aspects, important biases, and much more. Artificial intelligence and our understanding of the human brain and its link to AI is constantly evolving, showing promising applications in the soon future.

Here are the most interesting research papers of the year, in case you missed any of them. In short, it is basically a curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation, link to a more in-depth article, and code (if applicable). Enjoy the read, and let me know if I missed any important papers in the comments, or by contacting me directly on LinkedIn!

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10 Insightful AI Books To Read in 2021

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Want to improve your understanding and skills in the AI/ML domain this year? Check out these 10 best books published over the past two years that offer deep insights into the fundamentals and applications of AI.

Over the past two years, we’ve seen the release of many books that provide deep insights about the fundamental concepts, technical process, and applications of artificial intelligence. This list highlights books authored by renowned computer scientists and practitioners who are entrenched in the AI industry. No matter you are a researcher, an engineer, or a business professional in the AI/ML domain, your are bound to find a few interesting books to add to your reading list this year!

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10 Most Notable AI Papers of 2020

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The year 2020 has presented many challenges, but it did not stop new AI research breakthroughs from the global community. Here is a list of 10 best papers and their presentations from this year’s top AI conferences across machine learning, computer vision, NLP, robotics, and more.

The pandemic in 2020 has caused all major AI conferences to go virtual and thus made the latest research discussions much more accessible to the global community. Here, we put together a list of 10 notable AI papers from this year’s top conferences and publications, covering new research in computer vision, natural language processing, reinforcement learning, recommendation systems, robotics, and more. For each paper, we provide a short summary and link to the original conference talk or explanatory video for a quick grasp of the research highlights.

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10 Must-read AI Papers

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From AlexNet to GPT-3, we curate a list of 10 papers that mark significant research advancements in machine learning, deep learning, computer vision, NLP, and reinforcement learning over the past 10 years. Author presentation and detailed paper reviews are also included.

We have put together a list of 10 most cited and discussed research papers in machine learning that published over the past 10 years, from AlexNet to GPT-3. These are great readings for researchers new to this field and freshers for experienced researchers. For each paper, we provide links to the short overview, author presentations and detailed paper walkthrough for readers with different levels of expertise.

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