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Government Deep Tech 2022 Top Funding Focus Explainable AI, Photonics, Quantum

#artificialintelligence

DARPA, In-Q-Tel, US National Laboratories (examples: Argonne, Oak Ridge) are famous government funding agencies for deep tech on the forward boundaries, the near impossible, that have globally transformative solutions. The Internet is a prime example where more than 70% of the 7.8 billion population are online in 2022, closing in on 7 hours daily mobile usage, and global wealth of $500 Trillion is powered by the Internet. There is convergence between the early bets led by government funding agencies and the largest corporations and their investments. An example is from 2015, where I was invited to help the top 100 CEOs, representing nearly $100 Trillion in assets under management, to look ten years into the future for their investments. The resulting working groups, and private summits resulted in the member companies investing in all the areas identified: quantum computing, block chain, cybersecurity, big data, privacy and data, AI/ML, future in fintech, financial inclusion, ...


AI/ML, Data Science Jobs #hiring

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StormGain is a crypto trading platform for everyone. It's a convenient solution for those who want to profit from either the growth or decline of the cryptocurrency market and from long-term investments in crypto assets.


Analyst / Sr. Analyst (Data Scientist), Credit Risk Management

#artificialintelligence

Upgrade is a fintech unicorn founded in 2017. We are the fastest-growing company in the Americas (Financial Times). In the last five years, over 15 million people have applied for an Upgrade card or loan, and we have delivered over $10 billion in affordable and responsible credit. Our innovative Upgrade Card is the fastest growing credit card in America (Nilson Report). Combining the flexibility of a credit card with the low cost of an installment loan helps us redefine banking.


Principal Machine Learning Engineer

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Figure is transforming the trillion dollar financial services industry using blockchain technology. In three short years, Figure has unveiled a series of fintech firsts using the Provenance blockchain for loan origination, equity management, private fund services, banking and payments sectors - bringing speed, efficiency and savings to both consumers and institutions. Today, Figure is one of less than a thousand companies considered a unicorn, globally. Our mission requires us to have a creative, team-oriented, and supportive environment where everyone can do their absolute best. The team is composed of driven, innovative, collaborative, and curious people who love architecting ground-breaking technologies.


Machine Learning Engineer - Recommender Systems

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Coinbase has built the world's leading compliant cryptocurrency platform serving over 73 million accounts in more than 100 countries. With multiple successful products, and our vocal advocacy for blockchain technology, we have played a major part in mainstream awareness and adoption of cryptocurrency. We are proud to offer an entire suite of products that are helping build the cryptoeconomy and increase economic freedom around the world. There are a few things we look for across all hires we make at Coinbase, regardless of role or team. First, we look for signals that a candidate will thrive in a culture like ours, where we default to trust, embrace feedback, disrupt ourselves, and expect sustained high performance because we play as a championship team.


Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System

arXiv.org Artificial Intelligence

This is the accepted version of an article with the same name, published in the Special Issue "Federated Learning and Blockchain Supported Smart Networking in Beyond 5G (B5G) Wireless Communication" in Computer Networks. Abstract Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regression illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system. A Blockchain blockchain eliminates the need for a centralized authority, provides transparency, enforces the federated learning protocol, and provides a decentralized infrastructure for the collection of fees and the distribution of rewards. The reward payment is calculated based on the client's clients' Federated learning enables multiple clients FIM Research Center 1. Introduction The application of machine learning (ML) promises far-reaching potentials across industries [1]. ML has already proven successful in many areas, such as web search or recommender systems in e-commerce, in which a lot of high-quality data exists [2]. While researchers address ML's growing demand for compute power and use of data with, e.g., distributed ML approaches where multiple computing nodes share their resources [3, 4, 5] and quality issues with data processing, access to data is not only a technical issue. Both traditional ML and distributed ML approaches assume that their training data is centralized by nature, preventing the applicability of ML approaches to domains in which data is sensitive and distributed at the same time. To avoid that ML approaches must rely on data to which only a centralized organization or individual has full access, federated machine learning (FL) can aggregate the less sensitive ML models that were independently and locally trained by individual clients [6, 7].


Blockchain-based Federated Learning: A Comprehensive Survey

arXiv.org Artificial Intelligence

With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and scalability will constrain the development of machine learning. Federated learning (FL) can prevent privacy leakage by assigning training tasks to multiple clients, thus separating the central server from the local devices. However, FL still suffers from shortcomings such as single-point-failure and malicious data. The emergence of blockchain provides a secure and efficient solution for the deployment of FL. In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL). First, we investigate how blockchain can be applied to federal learning from the perspective of system composition. Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL. We also survey the applications of BCFL in reality. Finally, we discuss some challenges and future research directions.


Marketing Manager - AI Technology

#artificialintelligence

Excelsior has been engaged exclusively to conduct a search for this innovative technology company growing their New York operation. This really is a unique career opportunity to join a fast-growing and innovative Artificial Intelligence (AI) fintech company that is pioneering the deployment of next generation process automation solutions for banks through the use of artificial intelligence / machine learning technologies. This international firm has already had a lot of US success with refence-able banks as clients, and following Series A investment they're taking their US operation to the next level. Reporting to the Head of Marketing based in Europe, this is a newly created NYC position for a marketing professional to take the lead role in execution of the company's US marketing, with go-to-market strategies for new products and market areas. As their first marketing professional in the US working closely with the sales team this means taking the marketing plan from the strategic – business drivers, positioning, solution messaging and value propositions, through to tactical operations – content, campaigns, demand generation, events and PR etc.


AI Trustworthiness in the Aerospace, Fintech, Automotive, and Healthcare Industries

#artificialintelligence

Many industries are utilizing AI. However, in this paper, we look at its applications in the aerospace, fintech, autonomous vehicles, and health care industries, where better AI hardware, software, solutions, and services are creating many opportunities. Data integrity, privacy policies, decision system guidelines, and holistic regulations are continuously evolving in these industries. This ecosystem is now ripe for service providers and system integrators to play their parts, with AI adoption achieving appreciable return on investment. Key applications of AI in this space include optimizing operational efficiencies, assuring robustness of systems, data and image interpretation, and human augmented decision-making. Other applications include automation of processes and workflows, better compliance, improved performance, and reliability platforms, unmanned derivative systems (in finance) and digital and virtual assistants. Figure 1 summarizes AI's importance across the four industries discussed in this paper.1-36 The primary drivers of AI are data privacy, security, cost, risk, authenticity, guarantee and improved decision systems. Each driver has its own specific impact and relevance from a business adoption and operations perspective. The driver ensures that applications will have business significance and are attuned to regulations, while having close association with global and geography-specific ecosystems. Also, the drivers ensure quicker adoption to enhance operational efficiency, without compromising on the end-user experience. Regulatory and government bodies play a vital role in assessing and formulating guidelines for adopting AI in the business value chain.


Data-driven Smart Ponzi Scheme Detection

arXiv.org Artificial Intelligence

Tóm tắt nội dung--A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection methods have the problems of requiring many human resources in feature engineering and poor model portability. To solve these problems, we propose a datadriven smart Ponzi scheme detection system in this paper. The system uses dynamic graph embedding technology to automatically learn the representation of an account based on multi-source and multi-modal data related to account transactions. Compared with traditional methods, the proposed system requires very limited human-computer interaction. To the best of our knowledge, this is the first work to implement smart Ponzi scheme detection through dynamic graph embedding. Ponzi schemes require a constant flow of funds from new investors. The detection method based on source contributed by new investors to pay off the returns of existing code inspection detects the smart Ponzi scheme by manually investors (Figure 1).