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.
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.
Machine learning edge devices: benchmark report
Why edge computing? Humans are generating and collecting more data than ever. We have devices in our pockets that facilitate the creation of huge amounts of data, such as photos, gps coordinates, audio, and all kinds of personal information we consciously and unconsciously reveal. Moreover, not only are we individuals generating data for personal reasons, but we’re also collecting data unbeknownst to us from traffic and mobility control systems, video surveillance units, satellites, smart cars, and an infinite array of smart devices.
Deep in the dark: enhancing malware traffic detection with deep learning
The IEEE Symposium on Security and Privacy (IEEE S&P) is one of the top-tier conferences in computer security and electronic privacy. This year, the IEEE S&P was held in May, in San Francisco. It was not a regular edition, as this flagship conference marked its 40th anniversary. This year’s symposium was a special celebration that included a plenary session with some exceptional panelists from the S&P community, Test of Time awards for papers that have made a lasting impact on the field, and even an amazing birthday cake!
Recap: our first machine learning meetup in San Francisco
Lately, our team has been invited to give several talks at conferences, workshops and company events all around the world. As we got great feedback from the audience of these events, we felt like organizing our very own machine learning event for our partners and friends in San Francisco. We could have an open space to share experiences and directly talk about the opportunities behind machine learning. Event flyer of our first machine learning meetup in SF.
Embedded Vision Summit 2019: My talk and takeaways
As a machine learning engineer, building solutions in the vision and deep learning fields, I’d always had my eye on the Embedded Vision Summit, a leading computer vision conference taking place yearly in Silicon Valley. When I found out I had been invited to speak at the 2019 edition of the conference taking place May 18-21, I was obviously very excited. This was going to be an amazing opportunity for me to share my experiences in the field, and of course I was all in.