The office of Texas State Attorney General Ken Paxton has requested that Pfizer and several other companies turn over advertising data tied to the social media giant Meta. The lawsuit was filed after consumer data privacy concerns were raised by the state in its latest legal battle with Meta, according to a report by Law360. The Texas Attorney General claims that millions of Texas residents have had their private biometric data misappropriated over the past ten years. The order requires the vaccine maker to share any records it holds regarding Meta's use of facial recognition technology over claims that the company was collecting biometric data from Facebook users without their consent. This decree over Pfizer's records follows a February 2022 filing against Meta by the Texas Attorney General that claimed "Facebook knowingly captured biometric information for its own commercial benefit" in order to "train and improve" its in-house facial recognition technology powered by AI.

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) through their extensive parameters and comprehensive data utilization. However, existing LLMs lack a dedicated memory unit, limiting their ability to explicitly store and retrieve knowledge for various tasks. In this paper, we propose RET-LLM a novel framework that equips LLMs with a general write-read memory unit, allowing them to extract, store, and recall knowledge from the text as needed for task performance. Inspired by Davidsonian semantics theory, we extract and save knowledge in the form of triplets. The memory unit is designed to be scalable, aggregatable, updatable, and interpretable. Through qualitative evaluations, we demonstrate the superiority of our proposed framework over baseline approaches in question answering tasks. Moreover, our framework exhibits robust performance in handling temporal-based question answering tasks, showcasing its ability to effectively manage time-dependent information.

Multinational pharmaceutical and biotech company Pfizer and AI-powered data company Tempus announced a multiyear strategic alliance to utilize AI and machine learning to inform drug discovery and development in oncology. Pfizer will leverage Tempus' library of de-identified data to accelerate therapeutic development in oncology. It will also use Tempus' AI-driven companion diagnostic offerings and clinical trial-matching program to support therapeutic research and development. "Pfizer shares our commitment to bringing novel treatments to patients faster, and we look forward to working together to usher in the next generation of oncology therapeutics," Eric Lefkofsky, founder and CEO of Tempus, said in a statement. "This is the third strategic collaboration Tempus has established with a global pharmaceutical leader in the last year, as we believe that combining our technological capabilities with pharma's deep R&D expertise will get us much closer in realizing the full potential of precision medicine."

Artificial intelligence and machine learning (AI/ML) are key to enabling drug discovery and development, and Pfizer is leading the biopharma industry into the next wave of innovation. The company is rapidly scaling up and recruiting talent for a collaborative effort intended to get transformative medicines to patients faster. The mandate is "uncompromising and extremely high-quality science," Sandeep Menon, chief scientific officer, AI digital sciences, SVP and head of early clinical development told BioSpace. The vision is three-fold: uncover disease biology with AI; use these insights to design the right molecules; determine the right patient population for clinical trial success. "We're building the next generation of tools to use across the preclinical and clinical development spectrum," said Jared Christensen, vice president and head of early clinical development, clinical AI/ML and quantitative sciences.

It was early one morning in 1996 when Andrew Hopkins, then a PhD biophysics student at Oxford University, had a brainwave as he walked home from a late-night lab meeting. He was trying to find molecules to fight HIV and to better understand drug resistance. "I remember this idea struck me that there must be a better way to do drug discovery other than the complex and expensive way everyone was following," he says. "Why couldn't we design an automated approach to drug design that would use all the information in parallel so that even a humble PhD student could create a medicine? That idea really stuck with me. I remember almost the exact moment to this day. And that was the genesis of the idea that eventually became Exscientia."

A man has told his story of going from living on a council estate to founding one of Britain's largest biotech companies which floated on New York's Nasdaq stock exchange for $2.9bn. The Welsh scientist's company - which uses artificial intelligence to cut the time and money being spent on discovering new drugs - has earned him a whopping €470 million, but he says he is nowhere near finished. Andrew Hopkins grew up on a council estate in the UK but described how he has since swapped that life for one in the prestigious city of Oxford after setting up his company, Exscientia. The 50-year-old founder retains 18.6 million shares, giving him a 15.8% stake of the company. On paper, he's worth around €470m since the flotation in October 2021, but in real life, Andrew, or Professor Hopkins as he's known in the field, is only just beginning.

Biotech may be one of the biggest beneficiaries of artificial intelligence and machine learning.

Over 16 months ago, Pfizer achieved a historic scientific moonshot -- the unprecedentedly swift development and authorization of a novel vaccine for a novel virus using methods that hitherto had not been used in approved drugs at scale. Throughout the pandemic, nearly every public research supercomputer pivoted to some form of Covid research, but the pharmaceutical giants were characteristically cagey about their use of advanced technologies for vaccine and therapeutic development. At a session held during Nvidia's GTC22 this week, Joe Ucuzoglu, CEO of Deloitte, spoke to Lidia Fonseca, executive vice president and chief digital and technology officer for Pfizer, about the company's use of HPC and AI in the development of its groundbreaking vaccines and therapeutics for Covid-19. Ucuzoglu opened the session -- a fireside chat titled "Pfizer's AI-enabled transformation" -- by lauding the "fastest development of a novel vaccine in history" and calling Pfizer a "poster child for the full promise of AI to society." He then continued by asking Fonseca how Pfizer is driving technology innovation in its value chain.

Since their announcement in November 2020, COVID-19 vaccines were largely debated by the press and social media. With most studies focusing on COVID-19 disinformation in social media, little attention has been paid to how mainstream news outlets framed COVID-19 narratives compared to alternative sources. To fill this gap, we use cognitive network science and natural language processing to reconstruct time-evolving semantic and emotional frames of 5745 Italian news, that were massively re-shared on Facebook and Twitter, about COVID-19 vaccines. We found consistently high levels of trust/anticipation and less disgust in the way mainstream sources framed the general idea of "vaccine/vaccino". These emotions were crucially missing in the ways alternative sources framed COVID-19 vaccines. More differences were found within specific instances of vaccines. Alternative news included titles framing the AstraZeneca vaccine with strong levels of sadness, absent in mainstream titles. Mainstream news initially framed "Pfizer" along more negative associations with side effects than "AstraZeneca". With the temporary suspension of the latter, on March 15th 2021, we identified a semantic/emotional shift: Even mainstream article titles framed "AstraZeneca" as semantically richer in negative associations with side effects, while "Pfizer" underwent a positive shift in valence, mostly related to its higher efficacy. "Thrombosis" entered the frame of vaccines together with fearful conceptual associations, while "death" underwent an emotional shift, steering towards fear in alternative titles and losing its hopeful connotation in mainstream titles. Our findings expose crucial aspects of the emotional narratives around COVID-19 vaccines adopted by the press, highlighting the need to understand how alternative and mainstream media report vaccination news.

AWS works with Pfizer to support more rapid innovation and improved clinical manufacturing operations to help develop tomorrow's therapies Inc. Company, announced that it is working with Pfizer to create innovative, cloud-based solutions with the potential to improve how new medicines are developed, manufactured, and distributed for testing in clinical trials. The companies are exploring these advances through their newly created Pfizer Amazon Collaboration Team (PACT) initiative, which applies AWS capabilities in analytics, machine learning, compute, storage, security, and cloud data warehousing to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. For instance, AWS is helping Pfizer enhance its continuous clinical manufacturing processes by incorporating predictive maintenance capabilities built with AWS machine learning services like Amazon Lookout for Equipment (AWS's service for detecting abnormal equipment behavior by analyzing sensor data). As a result, Pfizer can maximize uptime for equipment such as centrifuges, agitators, pulverizers, coaters, and air handlers used in clinical drug manufacturing.