Deal volume and total dollars invested in the chatbots landscape rose by 108% and 129% respectively in 2016, with 192 chatbot startups setting up shop in 2016.
Tracxn Research - Industrial Robotics Landscape, February 2017Tracxn
A number of investments in 2016 were made by CVCs such as GE Ventures, Caterpillar, Medtronic, and Mitsubishi UFJ Capital, who envision robotic technology to be implemented in their area of expertise.
Tracxn Research - Industrial Robotics Landscape, February 2017Tracxn
A number of investments in 2016 were made by CVCs such as GE Ventures, Caterpillar, Medtronic, and Mitsubishi UFJ Capital, who envision robotic technology to be implemented in their area of expertise.
Tracxn Research - Tutoring Landscape, January 2017Tracxn
Beijing-based VIPKID, an online English tutoring platform, raised the largest round of the year — a $100M in Series C funding from Yunfeng Capital, Sequoia Capital and Bryant Stibel in August 2016.
Tracxn - Top Business Models - 3D Printing Industry Applications - Dec 2021Tracxn
Today's top #BusinessModel report is on 3D Printing - Industry Applications rebrand.ly/18lj7qw
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Tracxn Wind Energy Landscape Report July 2016Tracxn
The top business models are built around power generation (Greenko Group, ReNew Power Ventures), manufacturing (Clipper Wind, Suzlon), and service providers (SITAC Renewable energy, PNE Wind).
Tracxn Research — Enterprise Collaboration Landscape, November 2016Tracxn
Investments in the year to date rose by 10% YoY, to $1.1B, as the enterprise collaboration space goes through a shift from a document centric to a conversational model.
Tracxn Research - Insurance Tech Landscape, February 2017Tracxn
Round count hit an all-time sector peak in seed (81), early (72), and late stage (17), with late stage deal activity registering the most growth (46%).
Tracxn Research - Tutoring Landscape, January 2017Tracxn
Beijing-based VIPKID, an online English tutoring platform, raised the largest round of the year — a $100M in Series C funding from Yunfeng Capital, Sequoia Capital and Bryant Stibel in August 2016.
Tracxn - Top Business Models - 3D Printing Industry Applications - Dec 2021Tracxn
Today's top #BusinessModel report is on 3D Printing - Industry Applications rebrand.ly/18lj7qw
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Tracxn Wind Energy Landscape Report July 2016Tracxn
The top business models are built around power generation (Greenko Group, ReNew Power Ventures), manufacturing (Clipper Wind, Suzlon), and service providers (SITAC Renewable energy, PNE Wind).
Tracxn Research — Enterprise Collaboration Landscape, November 2016Tracxn
Investments in the year to date rose by 10% YoY, to $1.1B, as the enterprise collaboration space goes through a shift from a document centric to a conversational model.
Tracxn Research - Insurance Tech Landscape, February 2017Tracxn
Round count hit an all-time sector peak in seed (81), early (72), and late stage (17), with late stage deal activity registering the most growth (46%).
Introducing NoSQL and MongoDB to complement Relational Databases (AMIS SIG 14...Lucas Jellema
This presentation gives an brief overview of the history of relational databases, ACID and SQL and presents some of the key strentgths and potential weaknesses. It introduces the rise of NoSQL - why it arose, what is entails, when to use it. The presentation focuses on MongoDB as prime example of NoSQL document store and it shows how to interact with MongoDB from JavaScript (NodeJS) and Java.
DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION Elvis Muyanja
Today, data science is enabling companies, governments, research centres and other organisations to turn their volumes of big data into valuable and actionable insights. It is important to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. According to the McKinsey Global Institute, the U.S. alone could face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using big data by 2018. In coming years, data scientists will be vital to all sectors —from law and medicine to media and nonprofits. Has the African continent planned to train the next generation of data scientists required on the continent?
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
Webinar: Fighting Fraud with Graph DatabasesDataStax
Modern fraud detection has significant engineering challenges. From managing the ingestion and scale, to the analysis of those patterns in real-time. We'll first take a look at how DataStax Enterprise Graph, powered by the industry’s best version of Apache Cassandra™, can meet those requirements to help you save the day.
Tracxn Research Ad Tech Landscape, October 2016Tracxn
Investments in the adtech industry are driven by mobile, digital video, cross-screen advertising and programmatic buying targeting specific sectors or audience groups.
BootstrapLabs - Tracxn Report - artificial intelligence for the Applied Arti...BootstrapLabs
This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques to build applications tailored for end use in Enterprise, Industry & Consumer sectors.
Over $1B has been invested in AI-Infrastructure startups since 2010 with ¬$340M being invested in 2015. Over $7.5B has been invested in AI-Applications startups since 2010 with $2.3B being invested in 2015.
Tracxn Research — Customer Service Software Landscape, November 2016Tracxn
The report encompasses companies which provide software and platform enabled customer services during and after the purchase of products or services for both B2C and B2B organizations.
Tracxn Research — Investment Tech Landscape, October 2016Tracxn
A number of Asian companies are among top funded in the last one year - Guangzhou-based Zhiniu8 ($150M, Series C), Singapore-based M-DAQ ($95M, Series C), Beijing-based Jusfoun Big Data ($76M, Series C), and Shanghai-based Madai Licai ($65M, PE) accounted for the top investments in the last year.
Tracxn Research: Enterprise Collaboration Landscape, August 2016Tracxn
File synchronisation and sharing (Box, Dropbox), conferencing (BlueJeans Network, Fuze), and messaging and chat room (Slack, Symphony) are the top three business models, each having seen over a billion dollars in investments.
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Tracxn's proprietary #taxonomy brings to you top #BusinessModels in Top Business Models in Sustainability Tech Report https://tracxn.com/fm/dl/Gymr7Akd3ybI
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Tracxn - Southeast Asia Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in Sustainability Tech - 26 Oct 2023Tracxn
Tracxn's proprietary #taxonomy brings to you top #BusinessModels in Top Business Models in Sustainability Tech Report https://tracxn.com/fm/dl/MnFvJ6UKhc9y
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Tracxn - United States Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in High Tech - 27 Oct 2023Tracxn
Today's top #BusinessModel report is on Top Business Models in High Tech Report https://tracxn.com/platform/file-manager/file/ZmlsZUlkPUx5U0Qya01PRm9QbHJCTUM0UG9QVFJUeWJselF0RHJzSGU0dFlWMG5uWEk%3D
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Tracxn - Top Business Models in Vietnam Tech - 20 Oct 2023Tracxn
Here's a detailed analysis of top #BusinessModels in https://tracxn.com/platform/file-manager/file/ZmlsZUlkPVBXM1h4Sk53ZEJ6YTh0LWZMQkFtZ2kzaGk0X2JIV1gyVXduQjFMUVBXVnM%3D
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Tracxn - Top Business Models in Singapore Tech - 19 Oct 2023Tracxn
We are back with our latest report on top #BusinessModels in based on #Tracxn's #proprietary #taxonomy https://tracxn.com/platform/file-manager/file/ZmlsZUlkPVM4dk1Sa0E0SnBLWlVvQ2pOZ3pSbVhNYzFQYWhOdW1xay1oMkZ5a05sZms%3D
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Tracxn - Top Business Models in New Zealand Tech - 19 Oct 2023Tracxn
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Tracxn - Top Business Models in United Kingdom Tech - 18 Oct 2023Tracxn
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Tracxn -Top Business Models in Transportation and Logistics Tech - 17 Oct 2023Tracxn
Tracxn's proprietary #taxonomy brings to you top #BusinessModels in Top Business Models in Transportation and Logistics Tech Report https://tracxn.com/platform/file-manager/file/ZmlsZUlkPVBPc1FQMG5DTmM2TnZwUDY4MXJmOXhFYmNZVHFJQW9jS3pURURaQ1F2MWc%3D
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Tracxn - United Kingdom Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in Sustainability Tech - 26 Oct 2023Tracxn
Today's top #BusinessModel report is on Top Business Models in Sustainability Tech Report https://tracxn.com/platform/file-manager/file/ZmlsZUlkPXJkUTJQSW56TEZpVU9BamlmWlpzY2dyMFVSNjJrU2NLcndfM3ZGUWZSRDg%3D
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Tracxn - United States Tech Monthly Funding ReportTracxn
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ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.