While'data' might be the new oil, the'dataset' is the refined gasoline that powers every Machine Learning (ML) and AI operation. These datasets are used to boost signal, accuracy, precision, profit/loss, Sortino or Sharpe ratios in the financial markets and biosciences industries. The following is a transcript of a recent AMA hosted by ABT Crypto Academy on Telegram with the founder of Vectorspace AI, Kasian Franks. We're extremely privileged to be joined by Kasian Franks the CEO of Vectorspace, it's only right we start off with a brief introduction -- can you tell us what exactly Vectorspace is and how the idea came about? We got our start in Life Sciences, now most refer to it as Biosciences at Genentech and Lawrence Berkeley National Lab https://www.lbl.gov There we were tasked with creating a system to identify hidden relationships between genes, drugs and diseases connected to breast cancer, chromosomal radiation damage and extending human lifespan for the purpose of deep space travel. We wrote a paper with Micheal I. Jordan, teacher of Andrew Ng, who developed the first AI for Google and China's Baidu. "The statistical modeling of biomedical corpora could yield integrated, coarse-to-fine views of biological phenomena that complement discoveries made f…" Human genes are like stocks or cryptos. Our technology is based on datasets.
This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either a supervised or semi-supervised manner, requiring humans to create knowledge. Recent deep language models automatically acquire knowledge from large-scale corpora via pre-training. The stored knowledge has enabled the language models to improve downstream NLP tasks, e.g., answering questions, and writing code and articles. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with a single forward pass of the pre-trained language models (without fine-tuning) over the corpora. We demonstrate the quality of the constructed KGs by comparing to two KGs (Wikidata, TAC KBP) created by humans. Our KGs also provide open factual knowledge that is new in the existing KGs. Our code and KGs will be made publicly available.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. EXCLUSIVE – Google announced Wednesday it has been chosen by a sector of the Defense Department to assist with a new U.S. health system designed to help pathologists study and identify certain types of cancers suffered by military veterans. The Defense Innovation Unit (DIU) tasked Google with crafting a specialized artificial-intelligence model of information that could be overlayed with augmented-reality microscopes. Google's machine intelligence then helps doctors as they use the microscopes to map out the tumor, in an effort to determine its distinct makeup and cellular structure.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.
When Regina Barzilay had a routine mammogram in her early 40s, the image showed a complex array of white splotches in her breast tissue. The marks could be normal, or they could be cancerous--even the best radiologists often struggle to tell the difference. Her doctors decided the spots were not immediately worrisome. In hindsight, she says, "I already had cancer, and they didn't see it." Over the next two years Barzilay underwent a second mammogram, a breast MRI and a biopsy, all of which continued to yield ambiguous or conflicting findings. Ultimately she was diagnosed with breast cancer in 2014, but the path to that diagnosis had been unbelievably frustrating. "How do you do three tests and get three different results?" she wondered.
Find here a listing of the latest industry news in genomics, genetics, precision medicine, and beyond. Updates are provided on a monthly basis. Sign-Up for our newsletter and never miss out on the latest news and updates. As 2019 came to an end, Veritas Genetics struggled to get funding due to concerns it had previously taken money from China. It was forced to cease US operations and is in talks with potential buyers. The GenomeAsia 100K Project announced its pilot phase with hopes to tackle the underrepresentation of non-Europeans in human genetic studies and enable genetic discoveries across Asia. Veritas Genetics, the start-up that can sequence a human genome for less than $600, ceases US operations and is in talks with potential buyers Veritas Genetics ceases US operations but will continue Veritas Europe and Latin America. It had trouble raising funding due to previous China investments and is looking to be acquired. Illumina loses DNA sequencing patents The European Patent ...
William Karnes, MD, is director of the high-risk program and colonoscopy quality at the UCI Health H.H. Chao Comprehensive Digestive Disease Center in Orange, Calif., and chief medical officer of Docbot, a technology that uses artificial intelligence to detect abnormalities from colonoscopy capsule video. Here, Dr. Karnes shares his thoughts with Becker's ASC Review on the future of AI in the gastroenterology specialty, and how the technology could help patients and physicians. Question: Can you tell me a little more about the Docbot technology and how you got involved? Dr. William Karnes: The story goes back to 2012 when I came to UCI and Dr. Chan brought me on to wipe out colon cancer in Orange County. It was a three-pronged approach but one of the most important ones.
"Do you think we're gonna be replaced?" A young Johns Hopkins University fellow recently asked that question while chatting with Elliot Fishman, MD, about artificial intelligence (AI). The two men were on the opposite ends of the career spectrum: Fishman has been a professor of radiology and oncology at Johns Hopkins Medicine since 1980; the fellow was preparing for his first job as a radiologist. "I said, 'Well, I think it's going to change what we do, but the good news is, at least you're not a pathologist,'" Fishman recalls. "And he goes, 'My wife is just graduating and she's a pathologist.' So I said, 'Put away as much money as you can really fast.'"
The world seems more divided today than ever, whether we're talking about politics or the questionable art form of twerking. However, there's one thing we can all agree on: cancer sucks. Nearly 40% of us will receive the dreaded diagnosis at some point in our lives, according to the National Cancer Institute. That's one reason why we've spent quite a bit of time writing about the topic, particularly the different technologies being developed to detect various forms of the disease. It's really a no-brainer: Data from Cancer Research UK suggests 80% of patients survive for at least 10 years after being diagnosed in the early stages of eight of the most common cancers.