When you’re trying to communicate or understand ideas, words don’t always do the trick. Sometimes the more efficient approach is to do a simple sketch of that concept — for example, diagramming a circuit might help make sense of how the system works.
But what if artificial intelligence could help us explore these visualizations? While these systems are typically proficient at creating realistic paintings and cartoonish drawings, many models fail to capture the essence of sketching: its stroke-by-stroke, iterative process, which helps humans brainstorm and edit how they want to represent their ideas.
Buy-Side Equity Quant Analysis: Tools and Open Problems, featuring BAM
The distance between a finance academic and a finance practitioner is quite large, especially if the practitioner works for a prop trading firm or a hedge fund. In this talk I will talk about some of the tools of the trade in use by buy-side firms, some of the open problems they face. Time permitting, I will briefly discuss modeling crowding phenomena, backtesting protocols, challenges to factor models and spiked covariance matrices, aggregation and attribution of large signal sets, and portfolios from ranks.
Humans naturally learn by making connections between sight and sound. For instance, we can watch someone playing the cello and recognize that the cellist’s movements are generating the music we hear.
Diffusion models like OpenAI’s DALL-E are becoming increasingly useful in helping brainstorm new designs. Humans can prompt these systems to generate an image, create a video, or refine a blueprint, and come back with ideas they hadn’t considered before.
Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language machine-learning model to search for reports from similar patients.
A human clearing junk out of an attic can often guess the contents of a box simply by picking it up and giving it a shake, without the need to see what’s inside. Researchers from MIT, Amazon Robotics, and the University of British Columbia have taught robots to do something similar.
What would a behind-the-scenes look at a video generated by an artificial intelligence model be like? You might think the process is similar to stop-motion animation, where many images are created and stitched together, but that’s not quite the case for “diffusion models” like OpenAl's SORA and Google's VEO 2.
When the Venice Biennale’s 19th International Architecture Exhibition launches on May 10, its guiding theme will be applying nimble, flexible intelligence to a demanding world — an ongoing focus of its curator, MIT faculty member Carlo Ratti.
The ambiguity in medical imaging can present major challenges for clinicians who are trying to identify disease. For instance, in a chest X-ray, pleural effusion, an abnormal buildup of fluid in the lungs, can look very much like pulmonary infiltrates, which are accumulations of pus or blood.
