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Five EU FinTechs Using AI to Support Consumers

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From accountancy to anti-fraud measures, the various technologies that exist under the FinTech umbrella have always deployed the latest innovations. When it comes to the application of artificial intelligence (AI) in finance, startups are proving to be every bit as important as the tech giants that dominate the AI space. In Europe, startups up and down the continent are turning to Big Data and machine learning techniques to answer challenges as diverse as the FinTech ecosystem itself. With the global market for AI in FinTech expected to be worth $54 billion by 2032, PYMNTS looks at five European startups that are paving the way. In the realm of accounting software, machine learning is used to automate routine data entry and analysis.


Senior Data Scientist

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Must have a Bachelor's degree in Computer Science or a related field plus 4 years of experience in metrics definition and tracking, machine learning models, and data infrastructure and tools integration; or a Master's degree in Computer Science or a related field plus 2 years of experience in metrics definition and tracking, machine learning models, and data infrastructure and tools integration. Of the required experience, must have 2 years of experience in each of the following (which may be gained concurrently): statistical modeling (Python, R, or SQL); and, data visualization. Of the required experience, must have 1 year of experience in two or more of the following (which may be gained concurrently): production machine learning models; new feature launch experimentation; user growth and retention strategy optimization; blockchain analytics; financial modeling; time series analysis; or, predictive modeling. To apply, please email resume to: jobpostings@ripple.com Ripple is flexible-first: Ripplers have the option to work remotely, from our offices, or a combination.


Operational Decisioning with AIoT and Intelligent Assets at Enterprise-Scale? IoTPractitioner.com The IoT Portal Platform

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Every day, small and large companies alike make strategic and operational decisions that influences the bottom line. Strategic decisions are typically made by the C-suite, and these generally are one-off decisions that are made over time and only after careful study of curated information from several sources and consultation with experts. Examples of strategic decisions include mergers and acquisitions and large capital expenditures. Operational decisions on the other hand are made every day by workers and operations personnel. For small organizations, this can mean dozens, if not hundreds of decisions every day.


Grid.ai rebrands as Lightning AI, raises $40M for AI dev tools – TechCrunch

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Lightning AI, the startup behind the open source PyTorch Lightning framework, today announced that it raised $40 million in a Series B round led by Coatue with participation from Index Ventures, Bain, the Chainsmokers' Mantis VC and First Minute Capital. CEO William Falcon told TechCrunch that the new money will be used to expand Lightning AI's 60-person team while supporting the community around PyTorch Lightning development. Lightning AI, formerly Grid.ai, is the culmination of work that began in 2018 at the New York University Computational Intelligence, Learning, Vision, and Robotics (NYU CILVR) Lab and Facebook AI Research (now Meta AI Research). After Falcon started developing PyTorch Lightning as an undergrad at Columbia in 2015, he founded Lightning AI in 2019 with Luis Capelo, the former head of data products at Forbes. While working on his PhD at NYU and Facebook AI Research, Falcon open sourced PyTorch Lightning and -- according to him -- the project quickly gained traction.


Top 9 Technology Trends In The Next 5 Years

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Maiyro is a platform where writers create publications, take part in discussions, build their professional profiles, and engage in writing opportunities. We value supportive and constructive dialogue in the pursuit of great publications and career growth for all members. The ecosystem spans from beginner to advanced writers, and all are welcome to find their place within our community.


Council Post: Using Artificial Intelligence To Improve Business Decisions

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CFO CFO of Sandline Global & author of Deep Finance, Glenn has spent the past two decades helping startups prepare for funding or acquisition. Artificial intelligence (AI) is to business what telescopes are to star gazing--an incredible technological boost that magnifies, clarifies and illuminates business decisions. AI-enabled technology drives everything from algorithms that filter spam emails to complex systems that can drive cars without human intervention. Advances in AI over the past decade have been nothing short of astounding. Thanks to advances in computing power and the ever-increasing data available to train models, growth in AI and machine learning (ML) has been exponential. Machines can now teach themselves to play and beat the best players in the world in skilled games like chess, Go and countless other digital strategy games.


Blockchain, Artificial Intelligence & Machine Learning: Hype or Help?

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The mortgage industry has been talking about ending its'Paper-Palooza' for at least 20 years as we linger behind other industries like healthcare and insurance. Digital technology opportunities span the entire ecosystem, bound only by the willingness of its participants. Artificial intelligence (AI) and machine learning (ML) are the most understood and deployed, thereby leading the way in these early stages. Blockchain, on the other hand, is more "fuzzy" to many, yet has a persuasive cast of evangelists. There are companies, and even countries, being built on blockchain tech, such as Figure and Liquid Mortgage.


Exclusive Interview with Dmitry Petrov, Co-founder, and CEO, Iterative

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As the machine learning market catches up with the competition, the ML engineers would need tools that can evolve beyond catering to the basic needs of an ML team, to make it easier and faster to develop models and enable collaboration. Iterative develops open-source tools for developers to build and deploy models to specialized software that can speed up the training process. Analytics Insight has engaged in an exclusive interview with Dmitry Petrov, Co-founder, and CEO of Iterative. Iterative's mission is to deliver the best developer experience for machine learning teams by creating an ecosystem of open, modular ML tools. Our tools are Git-native to bridge the gap between software engineering and machine learning so that these two sides of the ML to production pipeline can happen collaboratively, efficiently, and reproducibly.


Building Transparency Into AI Projects - AI Summary

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That means communicating why an AI solution was chosen, how it was designed and developed, on what grounds it was deployed, how it's monitored and updated, and the conditions under which it may be retired. There are four specific effects of building in transparency: 1) it decreases the risk of error and misuse, 2) it distributes responsibility, 3) it enables internal and external oversight, and 4) it expresses respect for people. In 2018, one of the largest tech companies in the world premiered an AI that called restaurants and impersonated a human to make reservations. To "prove" it was human, the company trained the AI to insert "umms" and "ahhs" into its request: for instance, "When would I like the reservation? If the product team doesn't explain how to properly handle the outputs of the model, introducing AI can be counterproductive in high-stakes situations. In designing the model, the data scientists reasonably thought that erroneously marking an x-ray as negative when in fact, the x-ray does show a cancerous tumor can have very dangerous consequences and so they set a low tolerance for false negatives and, thus, a high tolerance for false positives. Had they been properly informed -- had the design decision been made transparent to the end-user -- the radiologists may have thought, I really don't see anything here and I know the AI is overly sensitive, so I'm going to move on. By being transparent from start to finish, genuine accountability can be distributed among all as they are given the knowledge they need to make responsible decisions. Consider, for instance, a financial advisor who hides the existence of some investment opportunities and emphasizes the potential upsides of others because he gets a larger commission when he sells the latter. The more general point is that AI can undermine people's autonomy -- their ability to see the options available to them and to choose among them without undue influence or manipulation. That means communicating why an AI solution was chosen, how it was designed and developed, on what grounds it was deployed, how it's monitored and updated, and the conditions under which it may be retired. There are four specific effects of building in transparency: 1) it decreases the risk of error and misuse, 2) it distributes responsibility, 3) it enables internal and external oversight, and 4) it expresses respect for people. In 2018, one of the largest tech companies in the world premiered an AI that called restaurants and impersonated a human to make reservations. To "prove" it was human, the company trained the AI to insert "umms" and "ahhs" into its request: for instance, "When would I like the reservation?


BrainChip Partners with Prophesee Optimizing Computer Vision AI Performance and Efficiency

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LAGUNA HILLS, CA / ACCESSWIRE / June 19, 2022 /BrainChip Holdings Ltd (ASX:BRN)(OTCQX:BRCHF)(ADR:BCHPY), the world's first commercial producer of neuromorphic AI IP, and Prophesee, the inventor of the world's most advanced neuromorphic vision systems, today announced a technology partnership that delivers next-generation platforms for OEMs looking to integrate event-based vision systems with high levels of AI performance coupled with ultra-low power technologies. Inspired by human vision, Prophesee's technology uses a patented sensor design and AI algorithms that mimic the eye and brain to reveal what was invisible until now using standard frame-based technology. BrainChip's first-to-market neuromorphic processor, Akida, mimics the human brain to analyze only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Keeping AI/ML local to the chip, independent of the cloud, also dramatically reduces latency. "We've successfully ported the data from Prophesee's neuromorphic-based camera sensor to process inference on Akida with impressive performance," said Anil Mankar, Co-Founder and CDO of BrainChip.