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Score a Free Ticket to the Philly Emerging Tech Conference (Virtual)
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Data Analysis Volunteer Work to Support Baltimore City
Are you an expert with data and willing to mentor, or are you an up and coming hobbyist looking for a side project to work on? We have put together a group to focus on a few problems working with Baltimore City data and need your help. The current project focuses on data parsing and analysis for the Baltimore Board of Estimates. If interested, please send us an email
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Data News and Articles
How Can We Address Gender Inequity in Artificial Intelligence? — For women, hurdles are everywhere. Despite the critical role women play in societies, unequal access to education, loans, jobs, healthcare, technology, and political discourse are commonplace — and worsened by COVID-19. Technological innovations like artificial intelligence (AI) promise to identify and close these gaps through claims of a more data-driven, objective approach, but ironically pose another hurdle for women. Often, these digital systems inadvertently carry the same old analog gender biases. Tags: AI, genderequality
Can Computer Algorithms Learn to Fight Wars Ethically? — Maybe the autonomous weapons being developed by the Pentagon will be better than humans at making moral decisions. Or maybe they’ll be a nightmare come to life. Tags: AI, Pentagon, Weapons
Mistakes I've Made As an Engineering Manager — I’ve been a manager for many years at companies of different scale. Through these experiences, I’ve done my share of learning, and made some mistakes. Tags: Company, Management, WomeninTech
AI Can Predict Twitter Users Likely to Spread Disinformation Before They Do It — A new artificial intelligence-based algorithm that can accurately predict which Twitter users will spread disinformation before they actually do it has been developed by researchers from the University of Sheffield. Tags: Twitter, AI, disinformation, fakenews
He Got Facebook Hooked on AI. Now He Can't Fix Its Misinformation Addiction — Three years ago, the company began building "responsible AI." This is the story of how it failed. Tags: Facebook, AI, fakenews, disinformation
What I Love about Scrum for Data Science — A couple of years ago, I started (read: was made) to adopt scrum in my work. I didn’t like it. Despite my initial concerns (and violent objections), Scrum grew on me. Now, I find it almost indispensable. Some data science folks find this unusual, so I thought I’d pen some of these views here. Specifically, I’ll discuss time-boxed iterations, prio, demo, and retro. Tags: Scrum
Experimentation at Tubi — Every team at Tubi relies heavily on experimentation to help make decisions about everything from features to infrastructure changes to ML models. Over the past 3 years, the rate of experimentation at Tubi increased 18x. One in three ML experiments in the last quarter showed company KPI impact. It’s no overstatement to say that Tubi’s experimentation culture has been instrumental to the company’s success in the past three years. Tags: Tubi, ML, experimentation
Defensible Machine Learning — B2B machine learning (ML) companies are an enigma: they have the opportunity to revolutionize how we do business, but they look & feel quite different from their traditional SaaS counterparts and have proven difficult to scale. In this post, we aim to demystify the challenges of building an enduring ML company by providing a simple framework for the three ways to do so. For each, we outline common attributes of successful companies and walk through potential pitfalls. Tags: ML
Self-Supervised Learning: The Dark Matter of Intelligence — In recent years, the AI field has made tremendous progress in developing AI systems that can learn from massive amounts of carefully labeled data. This paradigm of supervised learning has a proven track record for training specialist models that perform extremely well on the task they were trained to do. Unfortunately, there’s a limit to how far the field of AI can go with supervised learning alone. Tags: ML
Data Documentation Woes? Here’s a Framework — In this article, I’ll share the principles and framework we use to organize our own data team at Atlan, democratize our data, and make documentation a part of our daily workflow. Tags: Documentation, Management
Top 7 Big Data Trends to Dominate 2021 — As the pandemic continues to disrupt lives, markets, and societies at large, organizations are seeking mindful ways to pivot and weather all types of disruptions. Tags: BigData, Automation, Cloud, EdgeComputing
Why Computers Will Never Write Good Novels — You’ve been hoaxed. The hoax seems harmless enough. A few thousand AI researchers have claimed that computers can read and write literature. They’ve alleged that algorithms can unearth the secret formulas of fiction and film. That Bayesian software can map the plots of memoirs and comic books. That digital brains can pen primitive lyrics and short stories—wooden and weird, to be sure, yet evidence that computers are capable of more. But the hoax is not harmless. If it were possible to build a digital novelist or poetry analyst, then computers would be far more powerful than they are now. Tags: AI
Announcing the 2021 AI Standard Report — The 2021 AI Index report is one of the most comprehensive reports about artificial intelligence to date. This latest edition significantly expands the amount of data available in the report, which was drawn from a broader set of academic, private, and non-profit organizations for calibration. The report also shows the effect of COVID-19 on AI development from multiple perspectives, including how AI helps with COVID-related drug discovery and the effect of the pandemic on hiring and private investment. Tags: AI
Radar Trends to Watch: March 2021 — For a short month, a lot happened in February–perhaps because the US elections are behind us, perhaps because COVID case numbers are dropping, perhaps for any number of reasons. Some of the most interesting articles I’ve seen have been about the Internet of Things, ranging from wireless peas to Elon Musk’s neural interfaces. Tags: AI, ML, Economy, Health, Programming, IoT
Machine Learning: The Great Stagnation — The bureaucrats are running the asylum. Tags: AI
Visualizing Data Timeliness at Airbnb — Over the last year, multiple teams came together to build SLA Tracker, a visual analytics tool to facilitate a culture of data timeliness at Airbnb. This data product enabled us to address and systematize challenges of data timeliness. Tags: Analytics, Visualizations
New AI ‘Deep Nostalgia’ Brings Old Photos to Life — An AI-powered service called Deep Nostalgia that animates still photos has become the main character on Twitter this fine Sunday, as people try to create the creepiest fake “video” possible, apparently. Tags: AI
98 Things That Can Go Wrong in an ML Project — The goal of this blog is to share my experiences on things that can go wrong in an ML project (they added up to 98!). The motivation with this post is for you to potentially avoid these landmines in your role as a data engineer, data scientist, ML engineer, data-business leader driving an ML initiative. Tags: ML, Management
Uber's Real-time Data Intelligence Platform At Scale — We built Gairos, Uber’s real-time data processing, storage, and querying platform to facilitate streamlined and efficient data exploration at scale. It empowers teams to better understand and improve the efficiency of the Uber Marketplace through data intelligence. Use cases include surge pricing, maximum dispatch ETA calculating, and demand/supply forecasting. Tags: ML
How-To's and Tutorials
Python AI: How to Build a Neural Network & Make Predictions —
If you’re just starting out in the artificial intelligence (AI) world, then Python is a great language to learn since most of the tools are built using it. Deep learning is a technique used to make predictions using data, and it heavily relies on neural networks. Check out this article from Real Python to learn how to build a neural network from scratch. Tags: AI, Python
Learn Data Science for Free —
This repository is a combination of different resources lying scattered all over the internet. The reason for making such a repository is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. Tags: DataScience, Training
How to Write Better with The Why, What, How Framework —
Three documents I write (one-pager, design doc, after-action review) and how I structure them. Tags: Documentation
Machine Learning 101: Part 1
— The traditional way of programming, having developers designing the steps of the algorithms, is not going to be replaced by machine learning. The old existing paradigms are safe, but, as always, there is space for evolution. Machine Learning (ML) is not even new, but now, thanks to technological advancements (like faster CPUs and GPUs, memory, and dedicated hardware) and the exponential growth of available data, it is time for ML to become broadly adopted by developers. Tags: ML
Ten Steps to Ensure Your Data Monitoring Is Effective
— In this article, we will cover ten steps you can take to reduce false positive and false negative alerts and to mitigate their impact when they do occur. Tags: Data Monitoring
Data Tools and Resources
Julia Update: Adoption Keeps Climbing; Is It a Python Challenger? — Julia has great potential to replace C/C++/Python (and of course Fortran) in scientific and technical computing as it matures. The low level performance is excellent. It will be important for it to be adopted as a first-class target language by CPU/GPU vendors. Tags: Julia, Python, C++, Fortran
Is Facebook's "Prophet" the Time-Series Messiah, or Just a Very Naughty Boy? —
Facebook prophet offers "automatic" time series prediction. But does it work? Tags: AI, Prophet
satwikkansal/wtfpython: What the f*ck Python?
Here's a fun project attempting to explain what exactly is happening under the hood for some counter-intuitive snippets and lesser-known features in Python. Tags: Python
Overv/outrun — Outrun lets you execute a local command using the processing power of another Linux machine. Tags: Linux
NumPy 1.20 Introduces Type Annotations — Major upgrade to the scientific package for Python also features expanded use of SIMD, increasing the execution speed of universal functions. Tags: Python
Microsoft Launches Azure Percept — Microsoft announced Azure Percept, its new hardware and software platform for bringing more of its Azure AI services to the edge. Percept combines Microsoft’s Azure cloud tools for managing devices and creating AI models with hardware from Microsoft’s device partners. The general idea here is to make it far easier for all kinds of businesses to build and implement AI. Tags: AI
13 Best Data Science Conferences to Attend in 2021 — We would add DAX to this list (and we still have opportunities for you to get involved). While you wait for DAX to be announced, here is a list of other conferences that you might be interested in attending. Tags: DAX, Conferences
Reverse ETL — Teams are adopting yet another new approach, called “reverse ETL,” the process of moving data from a data warehouse into third party systems to make data operational. The emergence of reverse ETL solutions is a useful component of the stack to get better leverage out of data. Tags: DataManagement, Analysis
Algorithmic Trading Models: Machine Learning (Part 1) —Collection of technical analysis trading models that will steadily increase in mathematical and computational complexity. Typically, these models are likely to be most effective around fluctuating or periodic instruments, such as forex pairs or commodities, which is what I have backtested them on. The aim behind each of these models is that they should be objective and systematic. Tags: ML