A new study gives AI an awareness of how scientists work.
Category: robotics/AI – Page 839
A recent study published in Nature Machine Intelligence examines a novel deep-learning method known as BigMHC, which can predict when the immune system will respond to triggers from cancer-related protein fragments, thus killing the tumors. This study was led and conducted by a team of researchers at Johns Hopkins University and holds the potential to develop personalized cancer immunotherapies and vaccines.
Rendition of cytotoxic CD8+ T-cells identifying cancer cells via receptor binding neoantigens. (Credit: Image generated by DALL-E 2 from OpenAI)
“Cancer immunotherapy is designed to activate a patient’s immune system to destroy cancer cells,” said Dr. Rachel Karchin, who is a professor of biomedical engineering, oncology and computer science at Johns Hopkins University, and a co-author on the study. “A critical step in the process is immune system recognition of cancer cells through T-cell binding to cancer-specific protein fragments on the cell surface.”
Ethics watchdogs are looking out for potentially undisclosed use of generative AI in scientific writing. But there’s no foolproof way to catch it all yet.
We’ve watched the remarkable evolution of robotics over the past decade with models that can walk, talk and make gestures like humans, undertake tasks from moving heavy machinery to delicately manipulating tiny objects, and maintain balance on two or four legs over rough and hostile terrain.
As impressive as the latest robots are, their accomplishments are largely the result of task-specific programming or remote instruction from humans.
Researchers at ETH Zurich have developed a program that helps robots tackle activities that do not rely on “prerecorded expert demonstrations,” as the developers put it, or “densely engineered rewards.”
A new study led by University of Maryland physicists sheds light on the cellular processes that regulate genes. Published in the journal Science Advances, the paper explains how the dynamics of a polymer called chromatin—the structure into which DNA is packaged—regulate gene expression.
Through the use of machine learning and statistical algorithms, a research team led by Physics Professor Arpita Upadhyaya and National Institutes of Health Senior Investigator Gordon Hager discovered that chromatin can switch between a lower and higher mobility state within seconds. The team found that the extent to which chromatin moves inside cells is an overlooked but important process, with the lower mobility state being linked to gene expression.
Notably, transcription factors (TFs)—proteins that bind specific DNA sequences within the chromatin polymer and turn genes on or off—exhibit the same mobility as that of the piece of chromatin they are bound to. In their study, the researchers analyzed a group of TFs called nuclear receptors, which are targeted by drugs that treat a variety of diseases and conditions.
Get ready for a lot of math…!
We have sort of an intuitive understanding of a big need in artificial intelligence and machine learning, which has to do with making sure that systems converge well, and that data is oriented the right way. Also, that we understand what these tools are doing, that we can look under the hood.
A lot of us have already heard of the term “curse of dimensionality,” but Tomaso Armando Poggio invokes this frightening trope with a good bit of mathematics attached… (Poggio is the Eugene McDermott professor in the Department of Brain and Cognitive Sciences, a researcher at the McGovern Institute for Brain Research, and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
Google DeepMind researchers have finally found a way to make life coaching even worse: infuse it with generative AI.
According to internal documents obtained by The New York Times reports, Google and the Google-owned DeepMind AI lab are working with “generative AI to perform at least 21 different types of personal and professional tasks.” And among those tasks, apparently, is an effort to use generative AI to build a “life advice” tool. You know, because an inhuman AI model knows everything there is to know about navigating the complexities of mortal human existence.
As the NYT points out, the news of the effort notably comes months after AI safety experts at Google said, back in just December, that users of AI systems could suffer “diminished health and well-being” and a “loss of agency” as the result of taking AI-spun life advice. The Google chatbot Bard, meanwhile, is barred from providing legal, financial, or medical advice to its users.
Recent advancements in deep learning have significantly impacted computational imaging, microscopy, and holography-related fields. These technologies have applications in diverse areas, such as biomedical imaging, sensing, diagnostics, and 3D displays. Deep learning models have demonstrated remarkable flexibility and effectiveness in tasks like image translation, enhancement, super-resolution, denoising, and virtual staining. They have been successfully applied across various imaging modalities, including bright-field and fluorescence microscopy; deep learning’s integration is reshaping our understanding and capabilities in visualizing the intricate world at microscopic scales.
In computational imaging, prevailing techniques predominantly employ supervised learning models, necessitating substantial datasets with annotations or ground-truth experimental images. These models often rely on labeled training data acquired through various methods, such as classical algorithms or registered image pairs from different imaging modalities. However, these approaches have limitations, including the laborious acquisition, alignment, and preprocessing of training images and the potential introduction of inference bias. Despite efforts to address these challenges through unsupervised and self-supervised learning, the dependence on experimental measurements or sample labels persists. While some attempts have used labeled simulated data for training, accurately representing experimental sample distributions remains complex and requires prior knowledge of sample features and imaging setups.
To address these inherent issues, researchers from the UCLA Samueli School of Engineering introduced an innovative approach named GedankenNet, which, on the other hand, presents a revolutionary self-supervised learning framework. This approach eliminates the need for labeled or experimental training data and any resemblance to real-world samples. By training based on physics consistency and artificial random images, GedankenNet overcomes the challenges posed by existing methods. It establishes a new paradigm in hologram reconstruction, offering a promising solution to the limitations of supervised learning approaches commonly utilized in various microscopy, holography, and computational imaging tasks.
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Faith Popcorn founded her futurist marketing consultancy in 1974. She’s been called “The Trend Oracle” by the NY Times and “The Nostradamus of Marketing” by Fortune. Faith is a trusted advisor to the CEOs of Fortune 200 companies and has predicted a variety of trends such as Cocooning and its impact on the COVID culture, Social Media, and The Metaverse. She has been invited to speak all over the planet and is the best-selling author of four books. Finally, in her own words, Faith is a jew educated at a Christian School, a Caucasian who grew up among Asians, a 6th generation New Yorker and the adopted mother of 2 girls from China.
During our 2-hour conversation with Faith Popcorn, we cover a variety of interesting topics such as why she is the Cassandra of our era; her unique background and upbringing; the origins of her Faith Popcorn name; futurism, misogyny and gender equality; her mission to bring a vision of what’s coming; saving the planet and the trends to pay attention to; her client cases like Campbell Soup, Kodak, Coke, Pepsi, Tyson Foods and others; male vs female leaders; Futurism vs Applied Futurism; why the future is vegan; why the biggest shifts are in humanity, not in technology; trend recognition, utilization, and creation; AI and the singularity.
A recent estimate of NVIDIA’s profits from the AI hype shocked us all, as it was disclosed that the company earns a whopping 1000% profit on its H100 AI GPUs.
NVIDIA’s Plans to Generate $300 Billion By AI-driven Sales Seems Achievable Given The Vast Profits Being Made
Tae Kim, a senior writer from the media outlet Barron’s, estimates that NVIDIA is reaping immense benefits from its vast AI sales, potentially breaking all records.