For my research in AI/ML, see my research page

I believe that there are four fundamental pillars needed for human-level intelligence. Each is a fundamentally different aspect of intelligence, and all are expanded on below. Further, only by combining all four pillars can every emergent phenomena associated with artifical general intelligence (AgI), including self-cognition and consciousness, be realised. The next major advancements in AI will likely come from combining different pillars within a single system.
Logical Intelligence
This class of intelligence is defined by the ability to find statistical patterns in data and use those patterns to make predictions or inferences. Deep Neural Networks are complicated systems that can perform this task, although it should be understood that these systems do not "understand" in any meaningful way. For example, a DNN trained to classify images of cats will not understand the salient features that make a cat a cat, as opposed to a dog. Still, systems like OpenAI's GPT3 has achieved human like media generation which is very impressive.

Abstract Intelligence
This class of intelligence is defined by the ability to symbolically reason the difference between two catagories. In essence, as the name suggests, it as the ability to generalise learning from a task that has been trained on to a different one which has not been trained on. As an example of abstract learning, if I learn to drive a specific car, then I can generalise this knowledge to be able to drive (almost) all cars and vans. For more on this topic, I recommend Francois Chollet's work.

Curiosity Intelligence
This class of intelligence is encourages exploration of an enviroment in such a way to minimise the amount of unknown information about that enviroment. This can be modeled with reinforcement learning techniques. In this way, curiosity enables the ability to make discoveries to solve complex problems with unknown or rare rewards. Further, by combining curiosity and abstraction, I believe creativity will emerge. For more on this topic, I recommend Pierre-Yves Oudeyer's work.

Emotional Intelligence
This class of intelligence defines a set of rules for how to interact with the environment which maximises a positive survival. When interacting with alive agents in the environment, it defines rules that generally encourage each agent to live in a mutually beneficial capacity. Internal value systems are trained by primary care givers to imprint socially acceptable ways to interact. When actions are taken that do not align with that internal value system, emotions of guilt, sadness, etc will emerge, thereby encouraging future actions to be in line with the internal value system. If an agent does not obey the external societal value system, external agents can use emotions to re-align such a disagreeable agent with the societal values (but this may not change their internal values - just their actions). Base level emotions like hunger, thirst, etc, provide a rule for short term survival, whereas emotional societal value systems help survival through cohesive group dynamics. For exploratory research in developmental robotics that aims to create AI with base emotions, such as pain and joy, I recommend Angelia Lim's work.
Data Science
This part of the website is under (passive) on-going construction.

For six months in 2018, I worked as a data scientist/quantitative researcher at a cryptocurrency exchange called Kraken. Here, I gained experience in industry standards in secure environments, data management, and machine learning. I worked at all stages of the data pipeline, from data collection and storage, to data cleaning and analysis (including visualisation), and model deployment. Using my skills, I worked to minimise pain points, and ensured to obtain buy-in from my team members and relevant stakeholders.

Another aspect of my work entailed market marking: providing liquidity of an asset in return for a small margin of profit. To optimise the margin, I deployed different strategies depending on the asset conditions, e.g., TWAP, VWAP, momentum based strategies, etc. Some of the python code associated market making can be found in my github.