Personal data anonymization: key concepts & how it affects machine learning models
Data anonymization is the alteration process of personally identifiable information (PII) in a dataset, to protect individual identification. This concept has acquired increasing importance over the past few years, and it has become an ongoing topic of research. New information about privacy concerns around the world appears each day, making it hard to keep track of the latest models and techniques, and it’s even harder to enter this data privacy-related world without knowing some basic concepts.
Price optimization for e-commerce: a case study
This blog post has been written with the collaboration of Maia Brenner, Gonzalo Marín and Marcos Toscano. We have previously discussed how a data-driven price optimization is no longer an option for retailers, but a question of how and when to do it. With a world that’s moving towards changing prices more and more dynamically, static pricing strategies can’t keep up, and data-driven approaches have arrived to stay. In this post, we’ll be focusing on how to perform data-driven price optimization, using a case study to showcase what can be done using sales data to predict demand and optimize prices.
How Machine Learning is reshaping Price Optimization
The challenge of setting the right price Setting the right price for a good or service is an old problem in economic theory. There are a vast amount of pricing strategies that depend on the objective sought. One company may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. Moreover, different scenarios can coexist in the same company for different goods or customer segments.
Swift: Google's bet on differentiable programming
Two years ago, a small team at Google started working on making Swift the first mainstream language with first-class language-integrated differentiable programming capabilities. The scope and initial results of the project have been remarkable, and general public usability is not very far off. Despite this, the project hasn’t received a lot of interest in the machine learning community and remains unknown to most practitioners. This can be attributed in part to the choice of language, which has largely been met with confusion and indifference, as Swift has almost no presence in the data science ecosystem and has mainly been used for building iOS apps.
Real estate and machine learning: takeaways from Inman Connect 2020
At Inman Connect (ICNY) 2020, relevant real estate players gathered to discuss the future of the industry. I got the chance to attend the latest edition in NYC. Since we know machine learning is driving innovation across every industry, real estate included, I took the time to summarize my experience as an attendee of ICNY by highlighting key findings in this blog post. These insights are based on my personal interpretation of the sessions, booth visits, and conversations with industry players (real estate agents, proptech workers, investors, and other conference participants).
AI trends: what to expect in 2020
Over the past decade, we have witnessed notable breakthroughs in Artificial Intelligence (AI), thanks in large part to the development of deep learning approaches. Healthcare, finance, human resources, retail, there is no field in which AI has not proven to be a game-changer. Who would have said just a few years ago that there would be autonomous vehicles on public roads, that large-scale facial recognition would no longer be science fiction, or that fake news could have such an impact socially, economically, and politically?
AI applications for social good
Artificial intelligence is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to old but persistent problems. From a technological point of view, the amount of daily data produced in the digital universe now allows for state-of-the-art approaches, which may lead to innovative solutions in these underserved areas. AI for social good turned into a reality for us at Tryolabs after we collaborated with an NGO to improve upon how African lions are tracked, which helps with species preservation.
Top 10 Python libraries of 2019
Welcome back to the fifth yearly edition of our Top Python Libraries list. Here you will find some hidden gems of the open-source world to get you started on your new project or spice up your existing ones. You’ll find machine learning and non-machine learning libraries, so we got you all covered. We hope you enjoy it as much as we did creating it, so here we go! 1. HTTPX As a die-hard Python fan who usually interacts with APIs, you are probably familiar with the requests library.
10 Years Tryolabs 🎉
It all started with three friends and a crazy idea, and ended up in a machine learning consultancy with more than 50 contributors. This year, that crazy idea turns 10 years old and behind it lies an exciting, fun, nerve-wracking and inspiring journey. There’s no better time to take you on a walk down memory lane and dig up some juicy stuff about the origins of Tryolabs and its evolution.
The 8 main takeaways from Khipu 2019
From 11-15 November 2019, the most important event in the history of artificial intelligence (AI) in Latin America took place in Montevideo, our hometown. Khipu.ai was the first of hopefully many events to come, in which top researchers from all over the world, both from academy and industry, came to the region to share knowledge and promote much needed diversity in the field, pushing Latin America forward. As proud supporters of Khipu, we from Tryolabs got to attend the awesome talks and trainings provided during the event, as well as contributed with own talks and a booth featuring four demos of pose estimation models running in real time on different embedded devices.