COVID 19 impact on U.S. real estate markets: enhancing business storytelling with data

This blog post has been written with the collaboration of Guzmán López. The Covid-19 pandemic has created a global economic recession that impacts businesses across all sectors, and real estate is no exception. According to World Bank forecasts, the global economy will shrink by 5.2% this year. In this uncertain scenario, data becomes a highly valuable asset for identifying patterns, trends, and ultimately insights that can help us navigate the new decision-making pandemic landscape.

Releasing Norfair: an open source library for object tracking

At Tryolabs, we are always on the look for the next big thing in detection models. This search usually involves fiddling with newly released research repositories and mixing and matching ideas between them. One thing that has come up time and time again has been evaluating how these new detectors, combined with other models like Person ReID networks, work for tracking in video. To make this process easier we have built, and are now sharing, our tracking code in the form of an open source library we call Norfair.

Predictive maintenance using Machine Learning

This blog post has been written with the collaboration of Juan Olloniego and Germán Hoffman. Even if machines have done a big part of the heavy lifting for us since the industrial revolution, they still depend on us for their maintenance. As they have that annoying tendency to break from time to time, their conservation becomes essential to keep up with our daily activities. Now, with the industry 4.0, the internet of things, and the artificial intelligence advent, we are letting a new kind of machines take care of their older counterparts.

Machine Learning in your marketing mix strategy: taking the guesswork out

This blog post has been written with the collaboration of Gonzalo Marín. Designing a pricing solution involves shuffling a fundamental piece in the company’s marketing mix puzzle. While the price may look like the most fundamental piece to maximize profit it can rarely be defined independently of the others. This is why, in this post, we will dive into how Machine Learning systems can be beneficial to automate and optimize other marketing tasks, and how globally they can help improve your company’s pricing strategy.

Face mask detection in street camera video streams using AI: behind the curtain

This blog post has been written with the collaboration of Marcos Toscano. In the new world of coronavirus, multidisciplinary efforts have been organized to slow the spread of the pandemic. The AI community has also been a part of these endeavors. In particular, developments for monitoring social distancing or identifying face masks have made-the-headlines. But all this hype and anxiety to show off results as fast as possible, added up to the usual AI overpromising factor (see AI winter), may be signaling the wrong idea that solving some of these use cases is almost trivial due to the mighty powers of AI.

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).

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