RoomCloud (ATL StartupWeekend)
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Created over the course of a single weekend, RoomCloud aims to be the Airbnb of storage by connecting people with too much stuff to people with too much space.
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A N N U A L R E P O R T
To our shareowners:
Something strange and remarkable has happened over the last 20 years. Take a look at these numbers:
1999 3%
2000 3%
2001 6%
2002 17%
2003 22%
2004 25%
2005 28%
2006 28%
2007 29%
2008 30%
2009 31%
2010 34%
2011 38%
2012 42%
2013 46%
2014 49%
2015 51%
2016 54%
2017 56%
2018 58%
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party sellers – mostly small- and medium-sized businesses – as opposed to Amazon retail’s own first party sales.
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And it’s a high bar too because our first-party business has grown dramatically over that period, from $1.6 billion
in 1999 to $117 billion this past year. The compound annual growth rate for our first-party business in that time
period is 25%. But in that same time, third-party sales have grown from $0.1 billion to $160 billion – a
compound annual growth rate of 52%. To provide an external benchmark, eBay’s gross merchandise sales in that
period have grown at a compound rate of 20%, from $2.8 billion to $95 billion.
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inventory, process payments, track shipments, create reports, and sell across borders – and we’re inventing more
every year. But of great importance are Fulfillment by Amazon and the Prime membership program. In
combination, these two programs meaningfully improved the customer experience of buying from independent
sellers. With the success of these two programs now so well established, it’s difficult for most people to fully
appreciate today just how radical those two offerings were at the time we launched them. We invested in both of
these programs at significant financial risk and after much internal debate. We had to continue investing
significantly over time as we experimented with different ideas and iterations. We could not foresee with
certainty what those programs would eventually look like, let alone whether they would succeed, but they were
pushed forward with intuition and heart, and nourished with optimism.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
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Overview
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Key Topics Covered
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2. Understanding Edge (IoT)
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3. What is ArgoCD?
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4. Deployment Using ArgoCD for Edge Devices
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5. Introduction to Apache Kafka and S3
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6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
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8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
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12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
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