Well File:


Using deep learning to predict physical interactions of protein complexes


A 3D rendering of a protein complex structures predicted from protein sequences by AF2Complex. From the muscle fibers that move us to the enzymes that replicate our DNA, proteins are the molecular machinery that makes life possible. Protein function heavily depends on their three-dimensional structure, and researchers around the world have long endeavored to answer a seemingly simple inquiry to bridge function and form: if you know the building blocks of these molecular machines, can you predict how they are assembled into their functional shape? This question is not so easy to answer. With complex structures dependent on intricate physical interactions, researchers have turned to artificial neural network models – mathematical frameworks that convert complex patterns into numerical representations – to predict and "see" the shape of proteins in 3D.

Research Engineer - Scalable Alignment


At DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, maternity or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. At DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning and systems neuroscience to build general-purpose learning algorithms.

AI recognition of patient race in medical imaging: a modelling study


Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.

What is Deep Learning?


What do you achieve with deep learning? Deep learning is a part of our daily life. For example, when you upload a photo to Facebook, deep learning helps by automatically tagging your friends. If you use digital assistants like Siri, Cortana or Alexa, they serve you to the benefit with the help of natural language processing and speech recognition. When you meet with overseas customers on Skype, it translates in real time.

La veille de la cybersécurité


Machines don't always understand what we want from them. Can new language models teach them to read between the lines? If artificial intelligence is intended to resemble a brain, with networks of artificial neurons substituting for real cells, then what would happen if you compared the activities in deep learning algorithms to those in a human brain? Last week, researchers from Meta AI announced that they would be partnering with neuroimaging center Neurospin (CEA) and INRIA to try to do just that. Through this collaboration, they're planning to analyze human brain activity and deep learning algorithms trained on language or speech tasks in response to the same written or spoken texts.

How Is Artificial Intelligence Being Used for Advance Research on Cancer?


AI for Cancer Treatment: Cancer is one of the most dangerous diseases in the whole world. Every day people are looking for ways to cure cancer. AI and its various applications are reshaping the way scientists and researchers approach cancer treatment. Tumors are very complex diseases. It is very difficult to study the behavior of a tumor hence the treatment is much difficult.

Deep Learning: Types and Applications in Healthcare


Deep learning (DL), also known as deep structured learning or hierarchical learning, is a subset of machine learning. It is loosely based on the way neurons connect to each other to process information in animal brains. To mimic these connections, DL uses a layered algorithmic architecture known as artificial neural networks (ANNs) to analyze the data. By analyzing how data is filtered through the layers of the ANN and how the layers interact with each other, a DL algorithm can'learn' to make correlations and connections in the data. These capabilities make DL algorithms an innovative tool with the potential to transform healthcare.

How AI Algorithms can Detect Diseases with Deep Learning


Diseases like breast and skin cancer can be detected with close to 100% accuracy with the help of deep learning. In simple terms, artificial intelligence (AI) is the ability of a digital computer (or computer-controlled robot) to perform certain tasks with intelligence. AI tends to mimic human intelligence in that it relies on the ability to reason, learn from experience, and make decisions. Learning, reasoning, and problem-solving are the building blocks of artificial and human intelligence. Artificial intelligence has branches or categories such as machine learning and deep learning, which both involve the imitation of human intelligence.

Studying the brain to build AI that processes language as people do


AI has made impressive strides in recent years, but it's still far from learning language as efficiently as humans. For instance, children learn that "orange" can refer to both a fruit and color from a few examples, but modern AI systems can't do this nearly as efficiently as people. This has led many researchers to wonder: Can studying the human brain help to build AI systems that can learn and reason like people do? Today, Meta AI is announcing a long-term research initiative to better understand how the human brain processes language. In collaboration with neuroimaging center Neurospin (CEA) and INRIA we're comparing how AI language models and the brain respond to the same spoken or written sentences.

Deep Learning for Radiographic Measurement of Femoral Component Subsidence Following Total Hip Arthroplasty


"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Femoral component subsidence following total hip arthroplasty (THA) is a worrisome radiographic finding. This study developed and evaluated a deep learning tool to automatically quantify femoral component subsidence between two serial anteroposterior (AP) hip radiographs.