"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Yang Yang, PhD, Zahi Fayad, PhD, Xueyan Mei, PhD, Timothy Deyer, PhD and colleagues from Icahn School of Medicine at Mount Sinai, University of Oklahoma, and Weill Cornell Medicine conducted a study to evaluate the performance of AI models pretrained on radiologic images compared to photographic images. They created a large-scale, diverse medical imaging dataset to generate CNNs trained only from radiologic images. This is a significant study because the researchers demonstrated that pretraining with radiologic images rather than photographic images may result in more effective transfer learning for radiology AI models. A paper detailing the study entitled RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning was published in RSNA Radiology AI on July 27, 2022. Within 10 days of publication, the paper has been downloaded over 1,000 times.
What is the state of machine learning in 2022? Running a business that is closely tied to the progress of state-of-the-art machine learning means I’m trying to stay up to date with what is going on. In this post, I go through what I consider to be the most interesting breakthroughs and share my thoughts on what that means. We cover embeddings, attention, transformers and multi-modal models.
Everything you need to know about Reinforcement LearningApril 4, 2022 The phrase "Reinforcement Learning" could sound a little intimidating at first, but when we break it down, it's actually quite simple. Let's start with the phrase itself. It simply means to strengthen or support something. The phrase "Reinforcement Learning" could sound a little intimidating at first, but when we break it down, it's actually quite simple. Let's start with the phrase itself.
Much of the planet is in the midst of a record-setting heat wave that has set large swaths of Europe on fire, disrupted daily life, and killed thousands. By some estimates, June 2022 tied with June 2020 (another disaster-laden year) for being the hottest June in the historical record. Other estimates say the scorcher of a month was only the third hottest or maybe only the sixth hottest June humans have ever recorded. Not to be outdone, July, in many parts of the United States, shattered previous heat records. And August is coming in, well, hot, extremely hot.
Anyone who has studied the field of science and engineering should have heard the word "control" once. In particular, "automatic control" is often used in the field of science and engineering. The field of control is so deep that one specialized book can be written by itself, but this time, let's take a brief look at "What is control?". We will discuss the difference between automatic control and manual control, the difference between feedback control and feed-forward control, and the relationship between artificial intelligence and edge computing, which have become popular in recent years. What is the definition of control?
The goal of this level is to get you familiar with the ML universe. You will learn a bit of everything. The goal of this level is to get you introduced to the practical side of machine learning. What you learn at this level would really help you out there in the wild. This is the level where you would dive into different domains of Machine Learning.
Here's the list of things that I think will be hot in the filed of Machine Learning and Deep Learning in the near future: Self-supervised learning is the machine learning technique that can be used to train models for tasks in NLP, Computer Vision, Reinforcement Learning and robotics. This method works by getting small amount of labeled data, learning common patterns from it and then by using these representations work large amounts of unlabeled data. LLMs like BigScience's BLOOM or OpenAI GPT-3 are getting better and better and therefore they become able to some rich varieties of tasks such as SQL code generation, Image Captioning or Essay writing and many more awesome things! I think it's one of the most ambitious as well as exciting fields in all AI/Machine Learning Research. Vision Transformers (ViT)are dep neural networks that utilize transformer architecture to deal with computer vision tasks.
Always looking for an easy compromise, attackers are now scanning for data-science applications -- such as Jupyter Notebook and JupyterLab -- along with cloud servers and containers for misconfigurations, cloud-protection firm Aqua Security stated in an advisory published on April 13. The two popular data science applications -- used frequently with Python and R for data analysis -- are generally secure by default, but a small fraction of instances are misconfigured, allowing attackers to access the servers with no password, according to the Aqua Security's researchers. In addition, after setting up its own server as a honeypot, the company detected in-the-wild attacks that attempted to install cryptomining tools and ransomware onto accessible instances of the software. Signs that there are attackers targeting data-science environments is worrisome, considering that the researchers setting up those environment are largely uninformed about cybersecurity, says Assaf Morag, lead data analyst with Aqua Security. "We know, based on our experience with application security, that developers are starting to learn more about security, but what about data scientists?" he says.
Multimodal AI models, trained on numerous types of data, could help doctors screen patients at risk of developing multiple different cancers more accurately.. Researchers from the Brigham and Women's Hospital part of Harvard University's medical school developed a deep learning model capable of identifying 14 types of cancer. Most AI algorithms are trained to spot signs of disease from a single source of data, like medical scans, but this one can take inputs from multiple sources. Predicting whether someone is at risk of developing cancer isn't always as straightforward, doctors often have to consult various types of information like a patient's healthcare history or perform other tests to detect genetic biomarkers. These results can help doctors figure out the best treatment for a patient as they monitor the progression of the disease, but their interpretation of the data can be subjective, Faisal Mahmood, an assistant professor working at the Division of Computational Pathology at the Brigham and Women's Hospital, explained. "Experts analyze many pieces of evidence to predict how well a patient may do. These early examinations become the basis of making decisions about enrolling in a clinical trial or specific treatment regimens. But that means that this multimodal prediction happens at the level of the expert. We're trying to address the problem computationally," he said in a statement.