DeepMind, the British artificial intelligence (AI) company owned by Google, has solved a 50-year-old problem in biology. DeepMind's AI system, AlphaFold, cracked the so-called'protein folding problem' – figuring out how a protein's amino acid sequence dictates its 3D atomic structure. A protein's structure is closely linked with its function, and the ability to predict its structure unlocks a greater understanding of what it does and how it works. AlphaFold's neural network was trained with 170,000 known protein sequences and their different structures. The system registered an average accuracy score of 92.4 out of 100 for predicting protein structure, and a score of 87 in the category for most challenging proteins. Because almost all diseases, including cancer and Covid-19, are related to a protein's 3D structure, the AI could pave the way for faster development of treatments and drug discoveries by determining the structure of previously-unknown proteins.

If you are over 30 years of age, you have witnessed a technology revolution that has grossly affected how we live: computers have come from being an oddity to an everyday feature in our households and places of work; the cellphone is ubiquitous; hardcopy letters by mail are rare as we can communicate instantaneously through email. And yet, what we have witnessed is only the beginning. Drs Hughes and Agrawal give us a glimpse of what is still to come, and what will be featured at the IFOS World Congress in Vancouver. Over the last five years there have been significant advances in high performance computing that have led to enormous scientific breakthroughs in the field of machine learning (a form of artificial intelligence), especially with regard to image processing and data analysis. These breakthroughs now affect multiple aspects of our lives, from the way our phone sorts and recognises photographs, to automated translation and transcription services, and have the potential to revolutionise the practice of medicine.

Having risen to fame on its superhuman performance at playing games, the artificial intelligence group DeepMind has cracked a serious scientific problem that has stumped researchers for half a century. With its latest AI program, AlphaFold, the company and research laboratory showed it can predict how proteins fold into 3D shapes, a fiendishly complex process that is fundamental to understanding the biological machinery of life. Independent scientists said the breakthrough would help researchers tease apart the mechanisms that drive some diseases and pave the way for designer medicines, more nutritious crops and "green enzymes" that can break down plastic pollution. DeepMind said it had started work with a handful of scientific groups and would focus initially on malaria, sleeping sickness and leishmaniasis, a parasitic disease. "It marks an exciting moment for the field," said Demis Hassabis, DeepMind's founder and chief executive.

Over the past decade, artificial intelligence (AI) has experienced a renaissance. AI enables machines to learn and make decisions without being explicitly programmed. AI has enabled a new generation of applications, opening the door to breakthroughs in many aspects of daily life. From situational awareness to threat detection, online signals to system assurance, PNNL is advancing the frontiers of scientific research and national security by applying AI to scientific problems. For machine learning models, domain-specific knowledge can enhance domain-agnostic data in terms of accuracy, interpretability, and defensibility. PNNL's AI research has been applied across a variety of domain areas from national security, to the electric grid and Earth systems.

DeepMind, the AI technology company that's part of Google parent Alphabet, has achieved a significant breakthrough in AI-based protein structure prediction. The company announced today that its AlphaFold system has officially solved a protein folding grand challenge that has flummoxed the scientific community for 50 years. The advance inn DeepMind's AlphaFold capabilities could lead to a significant leap forward in areas like our understanding of disease, as well as future drug discovery and development. The test that AlphaFold passed essentially shows that the AI can correctly figure out, to a very high degree of accuracy (accurate to within the width of an atom, in fact), the structure of proteins in just days – a very complex task that is crucial to figuring out how diseases can be best treated, as well as solving other big problems like working out how best to break down ecologically dangerous material like toxic waste. You may have heard of'Folding@Home,' the program that allows people to contribute their own home computing (and formerly, game console) processing power to protein folding experiments. That massive global crowdsourcing effort was necessary because using traditional methods, portion folding prediction takes years and is extremely expensive in terms of straight cost, and computing resources.

DeepMind, the AI technology company that's part of Google parent Alphabet, has achieved a significant breakthrough in AI-based protein structure prediction. The company announced today that its AlphaFold system has officially solved a protein folding grand challenge that has flummoxed the scientific community for 50 years. The advance inn DeepMind's AlphaFold capabilities could lead to a significant leap forward in areas like our understanding of disease, as well as future drug discovery and development. The test that AlphaFold passed essentially shows that the AI can correctly figure out, to a very high degree of accuracy (accurate to within the width of an atom, in fact), the structure of proteins in just days – a very complex task that is crucial to figuring out how diseases can be best treated, as well as solving other big problems like working out how best to break down ecologically dangerous material like toxic waste. You may have heard of'Folding@Home,' the program that allows people to contribute their own home computing (and formerly, game console) processing power to protein folding experiments. That massive global crowdsourcing effort was necessary because using traditional methods, portion folding prediction takes years and is extremely expensive in terms of straight cost, and computing resources.

Researchers have developed a deep learning model that identifies imaging biomarkers on screening mammograms to predict a patient's risk for developing breast cancer with greater accuracy than traditional risk assessment tools. Traditional risk assessment models do not leverage the level of detail that is contained within a mammogram," said study author Leslie Lamb from the Massachusetts General Hospital (MGH) in the US. "Even the best existing traditional risk models may separate sub-groups of patients but are not as precise on the individual level," Lamb added. Currently available risk assessment models incorporate only a small fraction of patient data such as family history, prior breast biopsies, and hormonal and reproductive history. Only one feature from the screening mammogram itself, breast density, is incorporated into traditional models.

K Allado-McDowell had been working with artificial intelligence for years--they established the Artists and Machine Intelligence program at Google AI--when the pandemic prompted a new, more personal kind of engagement. During this period of isolation, they started a conversation with GPT-3, the latest iteration of the Generative Pre-trained Transformer language model released by OpenAI earlier this year. GPT-3 is, in short, a statistical language model drawing on a training corpus of 499 billion tokens (mostly Common Crawl data scraped from the internet, along with digitized books and Wikipedia) that takes a user-contributed text prompt and uses machine learning to predict what will come next. The results of Allado-McDowell's explorations--a multigenre collection of essays, poetry, memoir, and science fiction--were recently published in the U.K. as Pharmako-AI, the first book "co-authored" with GPT-3. By its very nature, the book forces us to ask who is responsible for which aspects of its authorship and to question how we imagine or conceptualize that nonhuman half.

A deep learning computer model developed by researchers at Massachusetts General Hospital (MGH) was able to identify subtle information in breast cancer images that could help better predict a woman's chances of developing the disease. Currently, the main methods of judging individual risk include checking for a family history of cancer, evaluating any biopsied tissue and noting whether they've given birth to a child. Screening mammograms--recommended annually by the American Cancer Society for women between the ages of 45 and 54--are typically used by oncologists to measure the density of the breast. "Why should we limit ourselves to only breast density when there is such rich digital data embedded in every woman's mammogram?" said Constance Lehman, M.D., Ph.D., MGH's division chief of breast imaging and senior author of a paper presented at the annual meeting of the Radiological Society of North America. "Every woman's mammogram is unique to her just like her thumbprint," Lehman said.

If DeepMind's methods can be refined, he and other researchers said, they could speed the development of new drugs as well as efforts to apply existing medications to new viruses and diseases. The breakthrough arrives too late to make a significant impact on the coronavirus. But researchers believe DeepMind's methods could accelerate the response to future pandemics. Some believe it could also help scientists gain a better understanding of genetic diseases along the lines of Alzheimer's or cystic fibrosis. Still, experts cautioned that this technology would affect only a small part of the long process by which scientists identify new medicines and analyze disease.