Techno-optimism – the belief that technology will usher in a golden age for humanity – is in vogue once more. In 2022, a clutch of pseudonymous San Francisco artificial intelligence (AI) scenesters published a Substack post entitled "Effective Accelerationism", which argued for maximum acceleration of technological advancement. The 10-point manifesto, which proclaimed that "the next evolution of consciousness, creating unthinkable next-generation lifeforms and silicon-based awareness" was imminent, quickly went viral, as did follow-up posts. Effective accelerationism, or "e/acc", exploded from being a fringe movement dedicated to pushing back against AI extinction-fearing "doomers" to being namechecked by major Silicon Valley CEOs such as Garry Tan, the CEO of start-up accelerator Y Combinator; Sam Altman, head of OpenAI; Marc Andreessen, the billionaire software engineer; and Elon Musk. In 2023, Andreessen issued his Techno-Optimist Manifesto, expanding beyond the e/acc's focus on AI to encompass all questions of technological progress.

Motivation: An adjuvant is a chemical incorporated into vaccines that enhances their efficacy by improving the immune response. Identifying adjuvant names from cancer vaccine studies is essential for furthering research and enhancing immunotherapies. However, the manual curation from the constantly expanding biomedical literature poses significant challenges. This study explores the automated recognition of vaccine adjuvant names using Large Language Models (LLMs), specifically Generative Pretrained Transformers (GPT) and Large Language Model Meta AI (Llama). Methods: We utilized two datasets: 97 clinical trial records from AdjuvareDB and 290 abstracts annotated with the Vaccine Adjuvant Compendium (VAC). GPT-4o and Llama 3.2 were employed in zero-shot and few-shot learning paradigms with up to four examples per prompt. Prompts explicitly targeted adjuvant names, testing the impact of contextual information such as substances or interventions. Outputs underwent automated and manual validation for accuracy and consistency. Results: GPT-4o attained 100% Precision across all situations while exhibiting notable improve in Recall and F1-scores, particularly with incorporating interventions. On the VAC dataset, GPT-4o achieved a maximum F1-score of 77.32% with interventions, surpassing Llama-3.2-3B by approximately 2%. On the AdjuvareDB dataset, GPT-4o reached an F1-score of 81.67% for three-shot prompting with interventions, surpassing Llama-3.2-3 B's maximum F1-score of 65.62%. Conclusion: Our findings demonstrate that LLMs excel at identifying adjuvant names, including rare variations of naming representation. This study emphasizes the capability of LLMs to enhance cancer vaccine development by efficiently extracting insights. Future work aims to broaden the framework to encompass various biomedical literature and enhance model generalizability across various vaccines and adjuvants.

Diagnosis: Deep learning can help diagnose COVID-19 through imaging techniques like CT scans, X-rays, and MRI. Deep learning models can be trained to detect COVID-19 features in these images, which can help doctors make quick and accurate diagnoses. Drug discovery: Deep learning can help in drug discovery by predicting the effectiveness of existing drugs against COVID-19 and identifying potential new drugs that can be developed to fight the virus. Deep learning models can analyze large datasets of chemical compounds to identify those most likely effective against COVID-19. Disease tracking: Deep learning can help track the spread of COVID-19 by analyzing data from various sources like social media, news reports, and government databases.

Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the current COVID-19 pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax', and examine the main topics of discourse using topic modelling techniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period. Pro-vax tweets focused mostly on vaccine development, while anti-vax tweets covered a wide range of topics, some of which included genuine concerns, though there was a large dose of falsehoods. A number of topics were common to both stances, though pro- and anti-vax tweets discussed them from opposite viewpoints. Memes and jokes were amongst the most retweeted messages. Whereas concerns about polarisation and online prevalence of anti-vax discourse are unfounded, targeted countering of falsehoods is important.

The advancement of artificial intelligence (AI) is gradually blurring the lines between the physical and digital worlds. For many industries, AI is no longer just a conceptual idea for the future, but a very real technology with innovative applications. Even so, society is only beginning to feel the impact of AI as experts strive to understand all the possibilities. It is clear that the true potential of AI has yet to be discovered, but the current advancements in AI do give us a decent idea of what it may look like. Here are some of the advancements in AI that you may soon see in your daily life.

While people may be thinking that AI is still in a research and development stage, they don't actually realize that this isn't true for a lot of cases. In this article, I am going to demonstrate how AI actually helped many organizations to fight the Covid, which I consider to be "indirect help". On another hand, I will also show that it directly helped to develop the actual Covid vaccine. If you haven't checked out my recent blog post on Stanford's Covid mRNA vaccine degradation prediction competition on Kaggle where tons of people from the data science community were actually working on improving the vaccine models, check this out: Although IBM didn't actually come up with the final vaccine model, they were heavily working on it. The IBM team is using a computational model of the spike (S-protein) of SARS-CoV-2 to model its interaction with the human ACE2 receptor .

While global economic and social uncertainties in 2020 caused significant stress, progress in intelligent technologies continued. The digital and intelligent transformation of all industries significantly accelerated, with AI technologies showing great potential in combatting COVID-19 and helping people resume work. Understanding future technology trends may never have been as important as it is today. Baidu Research is releasing our prediction of the 10 technology trends in 2021, hoping that these clear technology signposts will guide us to embrace the new opportunities and embark on new journeys in the age of intelligence. In 2020, COVID-19 drove the integration of AI and emerging technologies like 5G, big data, and IoT.

AI helped in data gathering, data processing, data analytics, and all important automated protein molecule binding prediction. Many countries have rolled out Covid-19 vaccines, and several conducting dry runs to check preparedness. The WHO has extended emergency use approval. It has paved the way developing countries that don't have any infrastructure for trails. The world was speedily to realize the importance to share genome sequencing data, which accelerated the pace of vaccine development. It would have been possible the presence of AI and cloud computing.

The year 2021 was profoundly challenging for citizens, companies, and governments around the world. As covid-19 spread, requiring far-reaching health and safety restrictions, artificial intelligence (AI) applications played a crucial role in saving lives and fostering economic resilience. Research and development (R&D) to enhance core AI capabilities, from autonomous driving and natural language processing to quantum computing, continued unabated. It typically takes years, if not decades, to develop a new vaccine. But by March 2020, vaccine candidates to fight covid-19 were already undergoing human tests, just three months after the first reported cases.

The Covid-19 pandemic has drastically affected the world. Since last year, we have been witnessing the fatality of the pandemic, and it continues. Today, the pandemic is spreading like a wildfire with the mutated virus, across the globe. The nations have been stressing the significance of vaccines to fight this dreadful virus. Since 2020, many indigenous and foreign labs and medical research teams have been developing vaccines.