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If there is one machine learning book you should read, it's this one!

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Just getting started in Machine learning? Wondering what material to pick from the flood of literature out there? I have just the right recommendation for you right here. Before we get started some quick info about my background. I studied mechanical engineering a few years ago, then did a PhD in quantitative finance.


20 Responsible AI and Machine Learning Safety Talks Every Data Scientist Should Hear

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As the adoption of AI accelerates in industry two increasingly important and related topics are responsible AI and machine learning safety (ML safety) which are featured tracks at ODSC East 2022. Here's just a sample of 20 of over 110 free talks from leaders in the field that you can attend in-person or virtually from April 19th-21st with a free for Bronze Pass. Editor's note: Abstracts are abbreviated for some sessions. Please check our schedule for full abstracts. The past few years have seen major improvements in the accuracy of machine learning models in areas such as computer vision, speech recognition, and natural language processing.


Deep learning and disease detection

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You came to Cambridge to do a PhD and ended up founding a company. It may be helpful to know that when I was 18, I started a software consultancy company and ran that on the side while I did my undergraduate degree. I had up to 15 contractors working for me but it was all project-based so, in theory, manageable around my studies. What did you learn from it? That I wouldn't recommend it.


AI Widens Search Spaces and Promises More Hits in Drug Discovery

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Traditional drug discovery techniques are all about brute force--and a little bit of luck. Basically, large-scale, high-throughput screening is used to cover a search space. The process is a little like conducting antisubmarine warfare without the benefit of sonar. Unsurprisingly, very few of the depth charges (drug candidates) hit their targets and achieve the desired results (successful clinical trials). The seas are simply too vast.


Bringing principles of ethics to AI and drug design

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Researchers believe that artificial intelligence has the potential to usher in an era of faster, cheaper and more fruitful drug discovery and development. Over the years, researchers have used AI to analyze troves of biological data, scouring for differences between diseased and healthy cells and using the information to identify potential treatments. More recently, AI has helped predict which chemical compounds are most likely to effectively target SARS-CoV-2. But with AI's potential in drug development comes a slew of ethical pitfalls -- including biases in computer algorithms and the philosophical question of using AI without human mediation. This is where the field of biomedical ethics -- a branch of ethics focused on the philosophical, social and legal issues in the context of medicine and life sciences -- comes in. In mid-March, adjunct Stanford University lecturer Jack Fuchs, PhD, moderated a discussion about the need for clearly articulated principles when guiding the direction of technological advancements, especially AI-enabled drug discovery.


Artificial Intelligence and Machine Learning Show Promise in Cancer Diagnosis and Treatment

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"The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases." Promising applications of AI, DL, and ML presented in this issue include identifying early-stage cancers, inferring the site of the specific cancer, aiding in the assignment of appropriate therapeutic options for each patient, characterizing the tumor microenvironment, and predicting the response to immunotherapy. A comprehensive overview of the literature regarding the use of AI approaches to identify biomarkers for ovarian and pancreatic cancer illustrates underlying principles and looks at the gaps and challenges that face the field as a whole. Ovarian and pancreatic cancers are rare, but lethal because they lack early symptoms and detection.


The Reality Behind Manufacturing's AI Myths

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As a former professor in artificial intelligence, one of my favorite – and surely one of the oldest – technological myths is found in the masterpiece, the Iliad. In Homer's poem narrating the Trojan War, the God of metalworking, Hephaestus, engineers one of the first robots known to history, a handmaiden designed to assist him in his forge. Not happy with limiting himself to manufacturing, Hephaestus steps it up by designing Talos, an automated bronze giant whose purpose was to protect ancient Crete from pirates and invaders. While thousands of years have passed since Hephaestus' mythical robots came to life, today's intelligent machines – strong with skillful AI – are making headway in our own workplaces. Take the factories and warehouses adversely affected by the pandemic as an example. With fewer and fewer workers willing and able to assist our manufacturers and fulfilment centers, many are embracing AI and machine learning to automate tasks such as quality control which are traditionally reliant on scores of human workers.


Artificial intelligence and machine learning show promise in cancer diagnosis and treatment

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Amsterdam, March 1, 2022 – Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases."


AI and machine learning could improve cancer diagnosis through biomarker discovery

#artificialintelligence

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases."


Artificial Intelligence and Machine Learning Show Promise in Cancer Diagnosis and Treatment

#artificialintelligence

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases."