In a recent study posted to Preprints with The Lancet*, researchers developed a machine learning approach to identify patients with long coronavirus disease (COVID). The post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are called long COVID. In the present study, researchers aimed to generate a robust clinical definition for long COVID using data related to long COVID patients. The team utilized data obtained from electronic health records that were integrated and harmonized in the secure N3C Data Enclave. This allowed the team to identify unique patterns and clinical characteristics among COVID-19-infected patients.

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.

Virtual assistants are wonderful at following your commands but absolutely terrible at giving life advice. Tidio editor Kazimierz Rajnerowicz spent over 30 hours asking half a dozen popular artificial intelligence (AI)-powered voice assistants and chatbots all kinds of questions and concluded that while virtual assistants are great at retrieving facts, they aren't advanced enough to hold a conversation. "AI today is pattern recognition," explained Liziana Carter, founder of conversational AI start-up Grow AI, to Lifewire in a conversation over email. "Expecting it to advise whether robbing a bank is right or wrong is expecting creative thinking from it, also known as AI General Intelligence, which we're far from right now." Rajnerowicz thought of the experiment in response to forecasts by Juniper Research that predicts the number of AI voice assistant devices in use will exceed the human population by 2024. "... a better approach may be to use that power to gain back time to spend on the things that make us unique as humans."

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.

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.

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.

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.

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.

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.

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.