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Machine Learning


How machine learning is helping patients diagnosed with the most common childhood cancer

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New software developed by Peter Mac and collaborators is helping patients diagnosed with acute lymphoblastic leukemia (ALL) to determine what subtype they have. ALL is the most common childhood cancer in the world, and also affects adults. "Thirty to forty percent of all childhood cancers are ALL, it's a major pediatric cancer problem," says Associate Professor Paul Ekert from Peter Mac and the Children's Cancer Institute, who was involved in this work. More than 300 people are diagnosed with the disease in Australia each year, and more than half of those are young children under the age of 15. Determining what subtype of ALL a patient has provides valuable information about their prognosis, and how they should best be treated.


Council Post: Three Emerging Educational Opportunities In The Metaverse

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As the metaverse industry is expected to be an $800 billion market by 2024, we continue to learn new ways this immersive, virtual environment might better enable us to connect with each other from anywhere in the world. This comes at a time when many are already participating in and benefitting from virtual activities that otherwise would not be possible due to constraints of distance, time or cost. In enabling new opportunities for virtual rather than in-person instruction, the metaverse has the power to transform access to education and the way we learn. The types of education that the metaverse can accommodate are varied, from school-based interactive learning and workplace training to professional accreditation. In so many ways, the metaverse is offering new chances for people to learn what they want by mitigating obstacles of accessibility.


A systematic review of federated learning applications for biomedical data

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Author summary Interest in machine learning as applied to challenges in medicine has seen an exponential rise over the past decade. A key issue in developing machine learning models is the availability of sufficient high-quality data. Another related issue is a requirement to validate a locally trained model on data from external sources. However, sharing sensitive biomedical and clinical data across different hospitals and research teams can be challenging due to concerns with data privacy and data stewardship. These issues have led to innovative new approaches for collaboratively training machine learning models without sharing raw data. One such method, termed ‘federated learning,’ enables investigators from different institutions to combine efforts by training a model locally on their own data, and sharing the parameters of the model with others to generate a central model. Here, we systematically review reports of successful deployments of federated learning applied to research problems involving biomedical data. We found that federated learning links research teams around the world and has been applied to modelling in such as oncology and radiology. Based on the trends we observed in the studies reviewed in our paper, we observe there are opportunities to expand and improve this innovative approach so global teams can continue to produce and validate high quality machine learning models.


Artificial intelligence makes a splash in small-molecule drug discovery

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In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Figure 1a). And the number of financings and average amount raised soared in 2021. At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Figure 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat.


Global Big Data Conference

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Researchers at Duke University have demonstrated that incorporating known physics into machine learning algorithms can help the inscrutable black boxes attain new levels of transparency and insight into material properties. In one of the first projects of its kind, researchers constructed a modern machine learning algorithm to determine the properties of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields. Because it first had to consider the metamaterial's known physical constraints, the program was essentially forced to show its work. Not only did the approach allow the algorithm to accurately predict the metamaterial's properties, it did so more efficiently than previous methods while providing new insights. The results appear online the week of May 9 in the journal Advanced Optical Materials.


Operationalizing Machine Learning from PoC to Production - KDnuggets

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Many companies use machine learning to help create a differentiator and grow their business. However, it's not easy to make machine learning work as it requires a balance between research and engineering. One can come up with a good innovative solution based on current research, but it might not go live due to engineering inefficiencies, cost and complexity. Most companies haven't seen much ROI from machine learning since the benefit is realized only when the models are in production. Let's dive into the challenges and best practices that one can follow to make machine learning work.


GitHub - kandarpkakkad/Machine-Learning-A-to-Z: Machine Learning A-Z (Udemy)

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The objective of this course is to learn Machine Learning concepts and be handy with coding of machine learning. Here I have the solutions and codes of "Machine Learning A to Z" course of Udemy.


Top 10 AI graduate degree programs

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Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand. The field requires broad training involving principles of computer science, cognitive psychology, and engineering. If you want to grow your data scientist career and capitalize on the demand for the role, you might consider getting a graduate degree in AI. U.S. News & World Report ranks the best AI graduate programs at computer science schools based on surveys sent to academic officials in fall 2021 and early 2022. Here are the top 10 programs that made the list as having the best AI graduate programs in the US.


The 6 Python Machine Learning Tools Every Data Scientist Should Know About - KDnuggets

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Machine learning is rapidly evolving and the crucial focus of the software development industry. The infusion of artificial intelligence with machine learning has been a game-changer. More and more businesses are focusing on wide-scale research and implementation of this domain. Machine learning provides enormous advantages. It can quickly identify patterns and trends and the concept of automation comes to reality through ML.


Classify small images accurately using little memory and CPU with ImageSig

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Classify small images accurately using little memory and CPU with ImageSig ImageSig: A signature transform for ultra-lightweight image recognition arXiv paper abstract https://arxiv.org/abs/2205.06929v1 arXiv PDF paper https://arxiv.org/pdf/2205.06929v1.pdf This paper introduces a new lightweight method for image recognition. ImageSig is based on computing signatures and does not require a convolutional structure or an attention-based encoder. ... achieves: a) an accuracy for 64 X 64 RGB images