Although automation is not new in the stock market world, recent advancements in machine intelligence are enabling new techniques and tools using applied AI in the finance industry.
is Co-founder & CEO of EmmaAI
, a company focused on building autonomous vertically integrated machine intelligence platform in financial trading, using bayesian methods and vectors for representation of words. He told Recode
: “This is not algorithmic trading; this is literally replication of an analyst.”
Emma, as the AI is referred to, picked a handful of stocks six months ago and Shaunak says they’ve yielded more than 30% growth since. The AI analyst began analyzing and writing reports on US equities and bonds in March this year, and in June it began investing autonomously in three financial securities: GSK, Tesla and the 10Yr Bond.
Next week, at the Machine Intelligence Summit in New York
, Shaunak will share expertise on applied AI in finance. I asked him a few questions, ahead of the summit, to learn more.
Tell us a bit more about your work with EmmaAI.
Emma/MANSI is a combination of several neural networks that enable Emma to analyze, write and provide insights based on an input of a trained data feed. Emma, the AI’s moniker, called the short term bottom on crude oil and the broader commodities complex in January, it’s picks generating an IRR of over 30%. It began analyzing and writing analyst reports on US equities and bonds in March writing analyst reports on several ETFs and stocks such as Microsoft. In June 2016, Emma/MANSI began investing autonomously in in three financial securities: GSK,Tesla and the 10Yr Bond.
What do you feel are the leading factors enabling recent advancements in artificial intelligence?
The most important factor is the availability of open source toolkits such as Torch and more visual friendly community driven toolkits like Neuroph. Secondly, we now have the processing power necessary in the form of GPUs. Thirdly, I think a phenomenal amount of research has been done in AI on which today’s platforms are being built, this has significantly reduced the amount of time necessary to build functional prototypes.
Finally, on a more granular level, I think lessons of the AI winter have been well learnt so as to not repeat the same mistakes and as part of that you are seeing actual real world use cases of AI being used albeit in granular forms when compared to their overall potential.
What present or potential future applications of machine intelligence excite you most?
Anything to do with healthcare, especially mobile healthcare which has been talked about a lot, I think is a win for every single person having access to a mobile device.
From a purely tech angle, NLP in the context of conversational interfaces which in its purest form is incredibly difficult is interesting from a consumer perspective. Use of MI in human capital intensive services, especially those relating to finance i.e. credit ratings, accounting or decision making in the government are exceptionally large opportunities.
On a more macro level, I think we will see a reduction in hours spent working and the evolution of a basic income framework over the next decade or so.
Which industries will be most disrupted by machine intelligence?
Finance, Transport, followed by Consumer Knowledge Services i.e. services that require human advice followed by Healthcare. Healthcare is last because of the complex regulatory challenges. If that wasn’t the case Healthcare would probably be ‘the’ vertical to focus on.
What developments can we expect to see in Machine Intelligence in the next 5 years?
We will definitely see conversational interfaces becoming much more useful at work and in our personal lives. This will largely be driven by a client centric approach to MI.
On a tech level, I think we will see a lot of stuff around new object-ID methods and low cost LIDAR systems for general use. The applied use case is very broad ranging from drones to autonomous caterpillars to self driving cars.
Shaunak Khire will be speaking at the Machine Intelligence Summit in New York, on 2-3 November. To register, visit the event website here.Other speakers at the summit include Kamelia Aryafar, Senior Data Scientist, Etsy; Ed Chow, Programme Manager, Jet Propulsion Lab, NASA; Hanlin Tang, Senior Algorithms Engineer, Nervana; Carl Vondrick, PhD Student, MIT; Tara Sainath, Senior Research Scientist, Google; Kathryn Hume, President, Fast Forward Labs; Antoine Bordes, Research Scientist, Facebook AI Research; and Siddartha Dalal, Chief Data Scientist, AIG.See the full events list here for events focused on AI, Deep Learning and Machine Intelligence taking place in London, Amsterdam, Boston, San Francisco, New York, Hong Kong and Singapore.
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