Is the future of 'open source' sustainable?
What did one support vector say to another support-vector? "I feel so marginalized". Today's rundown is on AI policies, data infrastructures, and much more. Stay engaged to discover.
TGIF!? Well, let’s head into the weekend with a quick weekly data round-up. This week’s newsletter brings you information about AI policies, decision-making using data, interesting competitions, and whatnot.
Keep reading to find out.
- Three ways to help everyone make fast, data-driven decisions with modern BI
“In our age of digital transformation, we are witnessing the transformational power of data to inform business decisions and drive change in real-time.” Data democratization is one of the few evolving modern analytics platforms which can help everyone make informed decisions.
- Artificial Intelligence (AI) Policies in India - A Status Paper
Till now 35 countries have released their regulations regarding AI integration and its future. This article is an in-depth analysis of “Artificial Intelligence (AI) Policies in India- A Status Paper”. It discusses AI strategies, initiatives, standardizations in India.
- Open AI: Solving Math Word Problems
“ Today’s AI is still quite weak at commonsense multistep reasoning, which is easy even for grade school kids.” Although large language models like GPT-3 have the ability to imitate many writing styles and extensive factual knowledge, it still struggles in the area of multistep reasoning. For that matter, OpenAI has developed a model using the GSM8K dataset, in which the solutions are written as a natural language rather than higher math expressions.
- World Bank: Improving data infrastructure helps ensure equitable access for poor people in poor countries
Did you know that digital data, in principle can circle the globe 5 times within a second? With the increase in data traffic, there is an ever-growing demand for data infrastructure. “Data Infrastructure policy, one of the building blocks of a data governance framework, helps to level the playing field in the modern data economy making things more equitable. “
- Tensorflow’s Kaggle challenge: To Help Protect Coral Reefs
Coral reefs are some of the most important ecosystems in the world. They promote food security, protect the coastline from storm surge, push forward drug discovery research. Research shows that the outbreak of coral-eating (COTS) is the leading cause of coral depletion. Tensorflow is challenging the Kaggle community to build the most accurate and performant COTS object detection models for image sequences. “We are offering $150,000 in prizes to the best solutions.”
- GitHub sponsors live event: The Future of open source: Is it sustainable?
“Open Source projects are at the heart of most software that we depend on every day. Community-supported volunteers work behind the scenes to make open source better for everyone, but it can be a thankless—and penny-pinching—job. Is it sustainable?”
Join this live session to expand your knowledge in the area.
- Multispecies AI model to detect illegal wildlife trafficking is ready to roll out to airports.
Illegal wildlife trafficking affects more than 7,000 species of wildlife and plants globally. Project SEEKER which is Microsoft's AI for Good research project has been trained to detect illegal wildlife trafficking in luggage and cargo. It can be easily installed in luggage and cargo scanners at the airport.
Accuracy lies in experimentation. Machine learning model accuracy is the measurement used to determine which model is best at identifying relationships and patterns between variables in a dataset based on the input, or training, data.#DataScienceConsulting #DataScienceMeme #Meme pic.twitter.com/Bqzses58Zd— Zorba Consulting (@ConsultingZorba) August 26, 2020
- By @DataSciFact
All models are wrong. Some models are useful. -- George Box— Data Science Fact (@DataSciFact) November 23, 2021
- By @MAjayi_907
I keep looking at this and keep laughing. It’s really true; whether the model is a LR or not, it’s so easy to think you have the answer but the impact can be “sniped” by not investigating underlying attributes #statstwitter #DataScienceMeme https://t.co/sSw8GXkJnG— Moyo Ajayi (@MAjayi_907) September 5, 2021
I hope you liked the informative content, brought to you by the Data Newsletter: Friday Edition.
For more such content, check out the forthcoming Monday issue.