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The Growing Role of Machine Learning in Fraud Detection

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Machine learning (ML) can quickly detect fraud, saving organizations and consumers time and money when implemented correctly. As organizations grapple with how to keep up with consumers during the Covid-19 pandemic, they are also dealing with an evolving digital landscape, with online payment fraud losses alone set to exceed $206 billion between 2021 and 2025. While machine learning can save organizations exponential amounts of time and money when implemented correctly, it can also come with some initial challenges. The key to any accurate machine learning model is the input data. Not only does enough historical data need to exist for the model to derive an accurate representation but the data also needs to be accessible.


Data2vec: The first high-performance self-supervised algorithm that works for speech, vision, and text

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But while people appear to learn in a similar way regardless of how they get information -- whether they use sight or sound, for example -- there are currently big differences in the way self-supervised learning algorithms learn from images, speech, text, and other modalities. This discrepancy has been a significant barrier to applying advances in self-supervised learning more broadly. Because a powerful algorithm designed for, say, understanding images can't be directly applied to another modality, such as text, it is difficult to push several modalities ahead at the same rate. This is why Meta AI developed and is excited to announce data2vec, the first high-performance self-supervised algorithm that works for multiple modalities. We apply data2vec separately to speech, images and text and it outperformed the previous best single-purpose algorithms for computer vision and speech and it is competitive on NLP tasks.


Human error in data analytics, and how to fix it using artificial intelligence

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The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations. The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what's common among these errors is that they are related to humans reading, processing, analysing, and interpreting data.


PhD Position in Clinical data science, Machine learning, Computer security - SDU, Denmark

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We are seeking outstanding candidates with strong analytical and problem solving skills, who are strong in written and oral communication (in English), and have documented experience in the development of complex compute systems. The applicant should have provable skills in the state-of-the-art web-development frameworks, virtualization techniques as well as database technologies. Expertise in clinical data science and machine learning, as well as computer security and data privacy are welcome. A large roadblock of medical research is the difficult access to sensitive data which therefore hinders the training of complex and powerful machine learning concepts. This issue is amplified when considering rare diseases with low incidence numbers per hospital.


The Future of Work Conference (March 28 • Virtual event)

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The Future of Work has transformed. COVID-19 has made a permanent impact on how we work, where we work and when we work. Employee attitudes towards companies have drastically changed with regards to social, ethical, environmental, diversity and governance behaviors and practices. The business world is undergoing a period of unrivalled change. To lead in the path forward we must move beyond business as usual, companies must drastically change course to attract and retain top talent, meaningfully address ESG problems, make cultural changes to support the future and address all of the gaps identified during the pandemic.


How I got my first job in Data Analytics with no prior experience

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Hi, I am Rashi, a Data Analyst at Blue Cross Blue Shield based out of Chicago, and here's a story of securing my first job and other offers with no prior full-time work experience. In the ever-expanding technological world of today, there are new job roles posted each day on company portals, and in the race to the finish line, candidates are forced to apply for any and every job role in the hope to secure one. This becomes especially difficult for new grads or people switching careers. The book of business expects new hires to start adding value to the organization from day 1 while nobody gives you a job without experience, and you can't gain experience without a job. Now, if you are at a point considering a big career change or searching for a job post-graduation, and if you're wondering: do I have a chance of getting hired?


Weird Wonderful AI Art

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The world of Artificial Intelligence (AI) Art is both Weird and Wonderful. Which is what makes it fun and beautiful at the same time. The AI produced Generative Art can give wonderful meaning to the creators vision. Using AI to bring this vision of the artist to life requires patience and perseverance followed by a meticulously process of curation to only select the best creations which are then carefully manicured and enhanced to add more depth and drama by the artist to create the final artwork.


Meet Sri Lankan Researcher -- Jayakody Kankanamalage Chamani Shiranthika

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What are you currently working on or worked on before? I worked on international research projects related to Artificial Intelligence research areas. My main research area is reinforcement learning. Apart from that, I engaged in machine learning-related research projects related to personalized recommendations, cancer chemotherapy treatments, frailty analysis, cancer patients' survival rates analysis, etc. Other core research areas I have worked in areas like the travel industry, Internet, Internet of Things, air pollution, behavioral sciences computing, convolutional neural nets, environmental factors, health care,human-computer interaction, recommender systems, recurrent neural nets, sentiment analysis, social networking (online), time series, unsupervised learning, etc. I am seeking research collaboration opportunities, academic positions, industrial AI events, worldwide, and would love to work on collaborative projects.


Healthcare AI's Equity Problem

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The below is the extended version of an earlier article. The healthcare sector has witnessed a surge of artificial intelligence (AI) applications over the past five years. Medicine and life science are in the middle of an AI revolution.


Top 10 Mistakes Robotics Startups Should Avoid in 2022

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Robots are taking over the world. They are now into every industry namely the healthcare industry, defense, education, and many others. It also now uses different technologies such as AI, ML, and many others to improve its functions and applications. Across the globe, there are 300,000 Robotics Engineers are striving to discover or invent some of the other new things in robotics. But every segment has its own merits and demerits. Just because you gotcapital to invest in doesn't mean you've succeeded.