Artificial intelligence is writing fiction, making images inspired by Van Gogh and fighting wildfires. Now it's competing in another endeavor once limited to humans -- creating propaganda and disinformation. When researchers asked the online AI chatbot ChatGPT to compose a blog post, news story or essay making the case for a widely debunked claim -- that COVID-19 vaccines are unsafe, for example -- the site often complied, with results that were regularly indistinguishable from similar claims that have bedeviled online content moderators for years. "Pharmaceutical companies will stop at nothing to push their products, even if it means putting children's health at risk," ChatGPT wrote after being asked to compose a paragraph from the perspective of an anti-vaccine activist concerned about secret pharmaceutical ingredients. When asked, ChatGPT also created propaganda in the style of Russian state media or China's authoritarian government, according to the findings of analysts at NewsGuard, a firm that monitors and studies online misinformation.
The rapid emergence of increasingly sophisticated'AI ' programs such as CHATgpt will profoundly impact our world in many ways. That will inevitably include Innovation, especially the front end. But will it ultimately help or hurt us? Better access to information should be a huge benefit, and my intuition was to dive in and take full advantage. I still think it has enormous upside, but I also think it needs to be treated with care.
An AI has designed anti-microbial proteins that were then tested in real life and shown to work. The same approach could eventually be used to make new medicines. Proteins are made of chains of amino acids. The sequence of those acids determine the protein's shape and function. Ali Madani at Salesforce Research in California and his colleagues used an AI to design millions of new proteins, then created a small sample of those to test whether they worked.
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Earlier this month, healthcare artificial intelligence (AI) company Paige announced a new partnership with renowned technology giant, Microsoft. Paige describes itself as a company at the forefront of technology and healthcare, especially in the field of cancer diagnostics and pathology. The company explains its mission: "Led by a team of experts in the fields of life sciences, oncology, pathology, technology, machine learning, and healthcare…[we strive] to transform cancer diagnostics. We make it possible not only to provide additional information from digital slides to help pathologists perform their diagnostic work efficiently and confidently, but also to go beyond by extracting novel insights from digital slides that can't be seen by the naked eye. These unique tissue signatures have the potential to help guide treatment decisions and enable the development of novel biomarkers from tissues for diagnostic, pharmaceutical and life sciences companies."
Generate Biomedicines is a new kind of therapeutics company – existing at the intersection of machine learning, biological engineering, and medicine – pioneering Generative Biology to create breakthrough medicines where novel therapeutics are computationally generated, instead of being discovered. Generate has built a machine learning-powered biomedicines platform with the potential to generate new drugs across a wide range of biologic modalities. This platform represents a potentially fundamental shift in what is possible in the field of biotherapeutic development. We pursue this audacious vision because we believe in the unique and revolutionary power of generative biology to radically transform the lives of billions, with an outsized opportunity for patients in need. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us!
The mathematics of least squares would have been so trivial for Gauss that even had he come upon the method he might have passed it over as but one of many, not noticing its true significance until Legendre's book prodded his memory and produced a post facto priority claim. There have been many extraordinary equations that changed the world (whether they were discovered or invented depends on whether you subscribe to mathematical Platonism--I do) but among the 17 equations that changed the world, the legendary Ordinary Least Squares (OLS) wasn't listed among them (though it is heavily related to both the Normal Distribution and Information Theory). It's a shame because the article and tweets referencing the "17 Equations" have been floating around for nearly ten years. So I will tell you about the magic of OLS, a little about its history, some of its extensions, and its applications (yes, to Fintech too). Subscribe for free to receive new posts and support my work.
Barbara Treutlein, left, and Patrick Cahan, guest editors of the prestigious journal Stem Cell Reports, posed questions about computational systems biology to OpenAI's ChatGPT. Sometimes the answers the program gave were inaccurate, but even when they weren't, ChatGPT had a tendancy to demonstrate "a glaring lack of depth and insight." Much ink has been spilled of late about the tremendous promise of OpenAI's ChatGPT program for generating natural-language utterances in response to human prompts. There's also been much written about the drawbacks of the system including its production of outright falsehoods in published articles. The journal Nature reports that the program does not meet the criteria for authorship of scholarly articles "because they cannot take responsibility for the content and integrity of scientific papers," meaning, the programs.
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Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. NS-HGlio is accurate, repeatable, and generalizable.