>
Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:
Resume:
1.-Dr. Manuela Veloso is head of JPMorgan AI Research Group and Herbert A. Simon Professor at Carnegie Mellon University.
2.-AI aims to capture every aspect of intelligence within an algorithm, including perception, cognition, and action.
3.-Dr. Veloso's research focuses on integrating perception, cognition and action, such as in autonomous robots she developed at CMU.
4.-AI in finance is exciting due to the variety of business areas and enormous amount of data produced.
5.-JPMorgan has AI research and applied AI teams working across business units to bring AI transformation to the company.
6.-AI research goals at JPMorgan include predicting economic systems, liberating data safely, eradicating financial crime, and establishing ethical AI.
7.-Financial trading decisions are guided by visual plots of time series data on asset values changing over time.
8.-Classifying time series as images of buy/no-buy signals using neural networks outperforms using the raw numerical data.
9.-Neural networks can predict future portions of a time series by representing it as an image and training an autoencoder.
10.-Over-the-counter markets with multiple interacting agents can be simulated using multi-agent reinforcement learning.
11.-Market maker agent strategies are represented by parameters like risk aversion and connectivity to investors, which are learned through simulation.
12.-Calibrating simulation parameters to match real market data is itself posed as a reinforcement learning problem.
13.-AI in finance covers many areas like information discovery, customer experience, financial crime prevention, regulation compliance, and ethics.
14.-Standardizing the representation of heterogeneous financial data enables easier application of machine learning techniques.
15.-Natural language processing extracts information like units and scales from titles and headers to convert data to a standard representation.
16.-AI in finance makes heavy use of learning from and reasoning about data represented in various forms.
17.-The DocuBot system automatically generates reports, slides, charts from data through interactive learning from user instructions and feedback.
18.-DocuBot continuously learns and improves its language understanding and document generation abilities through interaction, without relying on large training datasets.
19.-Applying image-based classification to time series like cryptocurrency prices follows the same principles as for other financial assets.
20.-Code for time series image classification is planned to be open sourced after further polish and parameterization.
21.-Technical analysis and charting are commonly used by human traders to visually reason about financial time series data.
22.-Representing time series as images for AI is a novel contribution highlighting the importance of representation learning in AI.
23.-Multi-modal data like video can capture dynamic correlations between multiple time series in a visual representation.
24.-Agents in complex domains can be represented by parameterized types, with behavior learning occurring in the parameter space.
25.-AI enables automatically translating between different data representations, like from tables to visualizations and natural language insights.
26.-Image and numerical representations have different strengths and biases, but can achieve similar predictive performance with proper techniques.
27.-Capturing temporal data as images or trajectories can simplify learning compared to sequence models like LSTMs in some cases.
28.-End-to-end AI systems are appealing but made more feasible by decomposing the problem and reusing modular components.
29.-Docubot exemplifies component reuse by applying the core engine to generate different types of documents with domain-specific templates.
30.-The overarching theme is the power of representation learning to extract and translate knowledge between modalities in AI systems.
Knowledge Vault built byDavid Vivancos 2024