November 22, 2017, at 21:22 UTC
While most AI companies are focused on teaching computers to beat humans in win/lose scenarios (e.g. IBM with DeepBlue & Jeopardy, DeepMind with AlphaGo, ...), at muse.ai, we have been focussed on making machines see, hear, and relate concepts in a way similar to humans.
Ensuring a consistent perception of the world by humans and machines is not only foundational for a Video Search company like ours, it is also key to evolve machines so that they can empathize and collaborate with humans to devise win/win outcomes.
In this context, I gave the following presentation (at SecondHome) and described:
The running theme throughout the presentation was that context, and inherent biases, have a large impact on how humans perceive color, sounds, relate concepts, and if machines are to understand the world like we humans do, they also need to be loaded with the same preconceptions.
July 10, 2015, at 18:00 UTC
This talk was given at Thomson Reuters in London for an audience of Quants and Data Scientists, and comprised of 3 sections:
the first section described how data have many different properties, some of which are mutually exclusive, and how these aspects define how an optimal system must be designed. This section also included a data description from a C-suit perspective (4 V's) all the way down to a systems engineer that has to worry about how to efficiently transmit and store data;
the second part focused on a number of Machine Learning algorithms and how these always boil down to an optimization problem, and how some algorithms map better to sequential processors (e.g. CPUs) while others ideally map into fine-grained architectures (e.g. FPGAs).
finally, the previous two sections are tied together by exemplifying a number of large scale systems that combine Huge-data sets with rapid SDLC.
May 01, 2015, at 09:02 UTC
Having applied FPGAs in space3, and in finance to implement the World's Fastest Matching and Crossing Engine, I believe that the next really exciting challenge is to employ this amazing technology to AI/ML.
This field also brings to life some core-aspects of my Ph.D. that was focused on using FPGAs to accelerate the heart of most AI algorithms, i.e. finding the vector (x) that minimises the error of systems of linear equations (Ax = b) subject to a certain constraints (x ≤ c).
Checkout Carlos Faham's page for little more insight into the kinds of problems we are solving.
October 14, 2014, at 12:00 UTC
As part of a Morgan Stanley's recruitment effort I was invited to give a talk about Field Programmable Gate Arrays in Finance at my alma mater, Imperial College London.
Since this was mainly targeted at students, this presentation focused on a myriad of applications of FPGAs in this industry. These applications ranged from simple use-cases like kill-switches to complete Trading-Systems-on-a-Chip.
This talk also explained why, everything being equal, traders prefer to go to the fastest exchange.
March 20, 2014, at 22:05 UTC
Last week, I was in New York for the first conference exclusively dedicated to Python for Quants.
This was a phenomenal event that brought together some of the best quants and technologists in the field, including the masterminds behind Athena (JP Morgan) and the Quartz (BAML) platforms.
My talk lasted for a little less than an hour and was focused on giving a broad overview on Bitcoins. It started off by describing common concerns, misconceptions, and media confusion. Then it went into detail the key technologies that were combined to enable this remarkable crypto-currency. This technical part was followed by a section describing a number key analytics and statistics. Finally, the focus was set on the wonderful new services that Bitcoins are enabling.
July 26, 2012, at 23:51 UTC
For almost a year now, I have been working for Morgan Stanley, and the work has been so captivating that I have not had any time to maintain any of my websites...
March 08, 2011, at 07:42 UTC
Life has changed quite drastically since joining J.P. Morgan Chase almost a year ago. Since then, I have been on a psychedelic trip involving all sorts of wonderful and weird things. These include Vanilla flavours; wicked Japanese Uridashis; traders clamouring across the room; going to work before dawn and returning after sunset; getting to wear cufflinks and not getting weird looks; knowing that it is Friday because that guy is wearing a flowery shirt; etc; and getting to hang out with some really extraordinary people.
I have integrated the Analytics Strategies Group which is responsible for the Core functionalities of Athena:
"Athena is J.P. Morgan's cross-market risk management and trading system. It is currently used in our foreign exchange and commodities businesses, and is being rolled out more broadly across our fixed income businesses. Athena includes a globally replicated object-oriented database, a powerful dependency graph and a fully integrated stack across pricing, risk and trading tools. The code is a combination of Python, C++, and Java: C++ and Java for speed, and Python for flexibility and rapid but controlled releases. Athena is designed to pull developers close to the business to help increase revenues while improving operational processes and controls to reduce costs. - J.P. Morgan Chase
Antonio Roldao © 1995-2017