GSI Technology

Introduction

Compute-In-Memory (CIM) Definition

Introduction:

I recently watched a presentation given by Professor Christopher Potts of Stanford University, where he mentioned that if we’re going to…

Synthetic Aperture Radar (SAR) has many important applications, for example: military surveillance; border security; and monitoring…

Natural language processing (NLP) is a major part of search — so much so that it is even being used in image search applications.

Neural search (also commonly referred to as semantic search) is hot — it seems like almost every day I see a new article written about it…

Semantic vector search is a topic that’s getting a lot of attention, and many search developers are wondering if, and how, they should add…

I was talking with Dmitry Kan, Principal AI Scientist at Silo AI, and he mentioned that one of the big benefits of our Elasticsearch and…

Dmitry Kan, Principal AI Scientist at Silo AI, recently wrote a blog post where he compared four different approaches to BERT vector search…

Elasticsearch is a popular open-source full-text search engine that can search many types of documents, and it recently added a…

Target Classification with Doppler-Pulse Radar Signals.

A retrospective on my summer as a data scientist and how GSI Technology’s summer program breaks the internship status quo.

This can be catastrophic, but you can fix it

How Uber’s Manifold and Weights & Biases’ model tracking tool can help evaluate and improve the quality of your next machine learning…

Given the vast range of choices for approximate nearest-neighbor search algorithms, how can you be sure you are picking the best one for…

The Role of Radio Signals in Distinguishing Between Targets and MAFAT’s Latest Data Science Challenge.

Taking a ride in OpenAI Gym’s MountainCar to explore RL theory

Defining a foundational RL concept with help from OpenAI Gym

The trials and tribulations of attempting to benchmark approximate nearest-neighbor algorithms on a billion scale dataset.

How B.E.R.T shakes the status quo, and what tools made that possible.

Walking through machine learning techniques for image segmentation and applying them to satellite imagery

An investigation into the performance of various approximate nearest-neighbor algorithms.

It’s easier than you think!

In Part 1 of this series, we introduced the concept of embedding vectors. In Part 2, we discussed how embedding vectors can be used in…

In Part 1 of this series, we presented a few examples of how companies are using word2vec-like models to learn semantic embeddings. In…

Recently, I read a few articles that are good examples of a trend where companies are leveraging word2vec-like models to learn embeddings…

By Elona Erez

I am hosting a series of panels with the same title in the Bay Area. The first panel will be at ODSC West 2019, in Burlingame. Panelists…