Collaborating Authors


Exploring the ML Tooling Landscape (Part 3 of 3)


The previous blog post in this series considered the current state of the ML tooling ecosystem and how this was reflected in ML adoption in industry. The main takeaway was the widespread use of propriety tooling amongst companies in this field, with a correspondingly diverse and splintered ML tooling market. The post ended by looking at some emerging near-term trends, highlighting the predominance of data observability and related tools, as well as the emergence of MLOps startups. This blog post will pick up from this previous thread to discuss some of the key trends in ML tooling that are likely to dominate in the near future -- or at least ones I want to talk about! As indicated in the previous blog post, I want to focus on MLOps, AutoML, and data-centric AI.

If the Metaverse Is Left Unregulated, Companies Will Track Your Gaze and Emotions

TIME - Tech

The world's leading neurologists assembled in Seattle earlier this month for a Symposium on Eye Tracking Research and Applications. Sponsors of the event included Google and Reality Labs, a division of Meta Platforms, Inc., the company formerly known as Facebook. Poets say the eyes are the window into a person's soul. Neurologists are less romantic, finding that eye movements can reveal our thought processes. The companies that once harnessed psychological research to design products that would hold the user's attention are now probing how to build a new business--the metaverse--around neurological science.

Top 15 Books to Master Data Strategy - KDnuggets


If you're a data practitioner with your eye on a leadership role, learning Data Management will be an important step toward getting you where you want to go. In this article, we outline 15 books on topics ranging from Data Architecture (highly technical) to Data Literacy (broadly nontechnical) to help you improve your understanding of end-to-end best practices related to data. Summary: I'd be remiss if I didn't begin this list here. This behemoth covers 14 practical topics related to Data Strategy, followed by 3 topics related to implementation. The 14 different knowledge areas are best represented by the Aiken Pyramid, which outlines how these topics build upon each other.

AI Based Emotion Detection for Textual Big Data: Techniques and Contribution


Online Social Media (OSM) like Facebook and Twitter has emerged as a powerful tool to express via text people’s opinions and feelings about the current surrounding events. Understanding the emotions at the fine-grained level of these expressed thoughts is important for system improvement. Such crucial insights cannot be completely obtained by doing AI-based big data sentiment analysis; hence, text-based emotion detection using AI in social media big data has become an upcoming area of Natural Language Processing research. It can be used in various fields such as understanding expressed emotions, human–computer interaction, data mining, online education, recommendation systems, and psychology. Even though the research work is ongoing in this domain, it still lacks a formal study that can give a qualitative (techniques used) and quantitative (contributions) literature overview. This study has considered 827 Scopus and 83 Web of Science research papers from the years 2005–2020 for the analysis. The qualitative review represents different emotion models, datasets, algorithms, and application domains of text-based emotion detection. The quantitative bibliometric review of contributions presents research details such as publications, volume, co-authorship networks, citation analysis, and demographic research distribution. In the end, challenges and probable solutions are showcased, which can provide future research directions in this area.

Schools see value in connected learning spaces with Zoom and Panopto


When combined, Zoom and Panopto's virtual communication technology can help students and teachers capture, annotate, and review lectures. Although most K-12 and university classrooms in America will open for in-person learning this fall, colleges continue to expand their adoption of and commitment to virtual learning. Zoom and Panopto are capitalizing on that interest and momentum. The popular virtual communication tech companies worked together to create a stacked integration of their platforms. The integration launched in late 2019, just months ahead of the pandemic's emergence in the US.

Graph Analytics: Part 1


In my past 3 years as a Data Science professional, I have worked extensively with both RDBMS (Postgres) & Cassandra (NoSQL) but didn't get a chance to explore Graph databases. So, it's time to jump onto graph databases & how they can be integrated into different data science solutions. Consider this: Observe Google Maps for any city. A graph is basically a collection of Nodes (the landmarks) & edges(the roads). Nodes are connected (or may not be connected at all)to each other using the edges. Neo4j is the most popular database for analyzing graph data.

Behavioral Data Science - Home

University of Washington Computer Science

We are a research group at the Paul G. Allen School of Computer Science of Engineering. Our aim is to explore and understand behavior through the lens of data science. The Behavioral Data Science Group develops computational methods that leverage large-scale behavioral data to extract actionable insights about our lives, health and happiness through combining techniques from data science, social network analysis, and natural language processing. We currently work on research related to mental health, misinformation online, scientific reproducibility, and informing the COVID-19 response. We have a postdoc position available.

Is Predict GmbH - Google Search


Our company name shows our passion: Predictive analytics - always aiming at increasing process efficiency for humans, machinery, material and electricity. Therefore, we have realized Self-learning Predictive Intelligence software solution which automates time consuming Data Science work with the help of Artificial--... Company Description: IS Predict GmbH is located in Saarbr--cken, Saarland, Germany and is part of the Computer Systems Design and Related Services Industry. She is co-founder and managing director of AI company IS Predict which is automating Data Science for Industry 4.0 with its--... IS Predict GmbH ... IS Predict is a leading Artificial Intelligence (AI) solutions provider for Industrial IoT. IS Predict GmbH ... PREDICTIVE INTELLIGENCE is an unsupervised, self-learning analysis, prediction and control solution. Employees, 7 ( View all); Founded, 2010; Category, Consumer Electronics & Computers, Retail; Web Rank, 22 Million; Keywords, is predict gmbh, universit--t--... IS Predict GmbH, Saarbr--cken, Germany, District Court of Saarbr--cken HRB 18868: Earnings, Public funding, Total assets, Revenue, Network,--...

6 Highest Paying Companies for Data Scientists - KDnuggets


Data science is a booming industry. If you've got the skills and the interest, you've got the opportunity to bring home the big bucks. Data scientists are well paid, even compared with others in the tech community. Because the data science field is constantly growing and changing, data science is widely ranked as the skill most in-demand across the tech sector. Data science is a great industry to get into if you're looking to make a lot of money.

Google Finance Head: Anything That Can Be Automated, We Strive to Automate


CFO Journal talked to Kristin Reinke, vice president and head of finance at Google, about those new technologies and how they accelerate the quarterly close, the use of spreadsheets in finance and the things that cannot be automated. This is the fourth part of a series that focuses on how chief financial officers and other executives digitize their finance operations. WSJ: What are the core parts of your digitization strategy? Kristin Reinke: We try to focus on the most important things: Automation and [how] we can improve our processes, being better partners to the business and then [reinvesting] the time we save into the next business challenge. WSJ: Which tools are you using?