We hope you had a wonderful Valentine’s Day spent with your loved ones. We are excited to bring you the latest edition of the Alectio newsletter, filled with the most relevant and up-to-date content and news from the AI/ML space.
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Let’s take you through this week’s edition
Good reads for you
1. 7 reasons why you should give active learning a try right now
Active Learning is an AI model training approach that has many benefits, including more accurate models, reduced data labeling costs, and improved efficiency. In this blog post, the writer talks about seven compelling reasons why organizations should try active learning. These include its ability to prioritize data samples that are most informative to the model, enabling more targeted and efficient data labeling, as well as its ability to reduce the time and resources required for data labeling. Additionally, active learning can lead to better model generalization and lower model bias, making it an attractive approach for a wide range of AI applications.
2. 9 ways to leverage technology for advanced data preparation
The article discusses nine ways to improve data preparation for machine learning models using technology. Data preparation is a crucial step in the process of building accurate and effective Machine Learning models.
The nine ways discussed in the article include the use of automated data cleaning tools, data profiling, data transformation, data augmentation, data labeling, version control, data quality monitoring, data integration, and data visualization tools.
3. 7 Tips to turn a profit on your ML project
The article provides seven tips for achieving a profitable outcome on machine learning (ML) projects. The tips include starting with a clear business problem, defining success metrics, establishing a feedback loop, investing in quality data, keeping the model simple, testing and validating frequently, and leveraging automation. By following these tips, ML practitioners can increase their chances of success and generate meaningful ROI from their projects.
4. Active Learning – 101 tricks for tuning your active learning
Most of the time, the approach taken toward active learning is the same, based on the least confidence querying strategy. But there are countless approaches to Active Learning. In this blog post, you can go through the many things you can consider while building an Active Learning process fit for you.
Trending in AI & ML
1. Retinal imaging to detect Alzheimer’s disease machine learning model
Alzheimer’s is usually diagnosed symptomatically using methods such as PET scans and lumbar punctures which are highly expensive. The iMIND lab at Duke University applied ML techniques to retinal imaging and clinical patient data to distinguish those with symptomatic AD from cognitively normal controls in diagnosing AD. In the identification of microvascular and neurosensory structural alterations in the retinas of individuals with AD, non-invasive retinal imaging could be an alternate means to detect AD early.
2. AI system that generates artificial enzymes from scratch
An AI system that can generate artificial enzymes from scratch. Salesforce research developed the AI program, called ProGen, which uses next-token prediction to assemble amino acid sequences into artificial proteins. Tests have shown that NLP which actually was developed to read and write language text can be used to learn some underlying principles of biology.
3. South Korea boosts its AI chip industry with $642M amid ChatGPT frenzy
South Korea has announced a $642m investment to boost its AI chip industry, as demand for AI-related technologies continues to grow. The investment will go towards developing cutting-edge chips, attracting foreign firms to the industry, and supporting the training of AI specialists. This move comes at a time when the country is experiencing a ChatGPT frenzy, with the AI language model being used across a variety of industries, from finance to healthcare. South Korea aims to establish itself as a global leader in AI technology and is taking significant steps towards achieving this goal.
4. AI and computational design advance protein engineering
Advancements in AI and computational design are significantly improving protein engineering. AI-powered approaches are being used to predict how protein structures will fold and interact with other molecules, while computational tools are allowing for more efficient protein design and optimization. These technologies have the potential to accelerate drug discovery and development, as well as enhance the production of biological drugs. AI is also enabling the design of novel proteins that do not exist in nature, which could have a wide range of applications in biotechnology, including in diagnostics, therapeutics, and materials science.
Just for fun
That’s it for this edition. We hope you liked it!!!
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About Alectio – Alectio is the first DataPrepOps platform built for machine learning. Alectio uses active learning, reinforcement learning, meta-learning, and generative models, to identify the right training data to increase machine learning model performance while reducing model training cost.