The AI age is upon us and high performance computing is the underlying platform powering everything from Large Language Models (LLM) to Image synthesis from text. However, with the demise of Moore's law and Dennard scaling we are at an inflection point. At Lightmatter, we are leading the transition of computing from traditional electronic transistors to photonic technologies which can operate at mind blowing efficiency and throughput. In this role, you will support all the activities of the ML team as it guides the development of a new class of computing infrastructure. This includes fine tuning LLMs, enablement of new models on custom architectures, evaluating the performance of models at scale, developing abstract models of the hardware for evaluating accuracy and throughput and help co-design novel hardware in a new paradigm of computing.

As AI advances, could it replace your travel agent? To investigate just how effective a holiday planner AI can be, MailOnline Travel asked two chatbots - ChatGPT, created by California AI firm OpenAI, and Google's Bard - to plan a variety of trips. Scroll down to see the answers the chatbots provided, from hotel recommendations in Iraq to advice on planning budget sun holidays, honeymoons and stag weekends away. For a budget break in the sun, Bard recommended jetting off to Bulgaria, where it says that you can find a week-long all-inclusive holiday'for as little as £200'. MailOnline Travel asked ChatGPT and Google's Bard to plan a variety of holidays.

A group called Future of Life Institute has circulated a petition, signed by nearly 3,000 people in and around the technology industry, calling for a six-month moratorium on large scale experiments with artificial intelligence (AI). The petition has triggered a huge debate. Those who have signed the petition note that developers of GPT-4 and other large language model AIs promise that their technology will change the course of civilization, but claims they have not taken appropriate steps to protect civilization from harm. Those who oppose the petition fall into two large buckets: those who are comfortable with the status quo of rapidly developing AI models and those who believe the petition sponsors are so focused on the future that they ignore widespread harms from existing applications of AI. The latter argument is particularly interesting, as the group includes leading technologists and scholars in the AI field, including Timnit Gebru, Emily Bender, and Margaret Mitchell.

Blogging has become a popular way to make money online. With the rise of technology and the internet, anyone with a passion for writing can become a blogger and earn an income. In this article, we will discuss how to make money through Blogger and ChatGPT. Blogger is a free platform that allows users to create their own blog. It is owned by Google and is one of the most popular blogging platforms in the world. ChatGPT, on the other hand, is an AI-powered chatbot that can assist users in various ways.

ChatGPT appears to be smart enough to recognize that artificial intelligence poses a threat to workers' livelihoods. Global outplacement and executive coaching firm Challenger, Gray & Christmas recently asked the « generative AI » tool, developed by research firm OpenAI, in plain English how many workers it expects to replace. That will do little to allay concerns among employees and policymakers about the potential for chatbots and large language models (LLMs) like ChatGPT and Dall-E to displace workers. Currently, ChatGPT is most widely used to support workers in a range of industries, helping them complete tasks that still require human judgment. Certainly, predictive language models can be used to automate tasks, giving workers more time to focus on those involving higher thinking, » senior vice president Andrew Challenger said in a statement.

Despite the potential of Large Language Models (LLMs) as writing assistants, they are plagued by issues like coherence and fluency of the model output, trustworthiness, ownership of the generated content, and predictability of model performance, thereby limiting their usability. In this position paper, we propose to adopt Norman's seven stages of action as a framework to approach the interaction design of intelligent writing assistants. We illustrate the framework's applicability to writing tasks by providing an example of software tutorial authoring. The paper also discusses the framework as a tool to synthesize research on the interaction design of LLM-based tools and presents examples of tools that support the stages of action. Finally, we briefly outline the potential of a framework for human-LLM interaction research.

ChatGPT has become a global sensation. As ChatGPT and other Large Language Models (LLMs) emerge, concerns of misusing them in various ways increase, such as disseminating fake news, plagiarism, manipulating public opinion, cheating, and fraud. Hence, distinguishing AI-generated from human-generated becomes increasingly essential. Researchers have proposed various detection methodologies, ranging from basic binary classifiers to more complex deep-learning models. Some detection techniques rely on statistical characteristics or syntactic patterns, while others incorporate semantic or contextual information to improve accuracy. The primary objective of this study is to provide a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. Additionally, we evaluated other AI-generated text detection tools that do not specifically claim to detect ChatGPT-generated content to assess their performance in detecting ChatGPT-generated content. For our evaluation, we have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains and user-generated responses from popular social networking platforms. The dataset serves as a reference to assess the performance of various techniques in detecting ChatGPT-generated content. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.

Recent advances in large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates the performance of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical tasks beyond question-answering. Because no patient data can be passed to the OpenAI API public interface, we evaluated model performance with over 10000 samples as proxies for two fundamental tasks in the clinical domain - classification and reasoning. The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice. The second task is causal relation detection from the biomedical literature. We compared LLMs with simpler models, such as bag-of-words (BoW) with logistic regression, and fine-tuned BioBERT models. Despite the excitement around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks remained the best strategy. The simple BoW model performed on par with the most complex LLM prompting. Prompt engineering required significant investment.

Large Language Models have become the core architecture upon which most modern natural language processing (NLP) systems build. These models can consistently deliver impressive accuracy and robustness across tasks and domains, but their high computational overhead can make inference difficult and expensive. To make using these models less costly, recent work has explored leveraging structured and unstructured pruning, quantization, and distillation to improve inference speed and decrease size. This paper studies how models pruned using Gradual Unstructured Magnitude Pruning can transfer between domains and tasks. Our experimentation shows that models that are pruned during pretraining using general domain masked language models can transfer to novel domains and tasks without extensive hyperparameter exploration or specialized approaches. We demonstrate that our general sparse model Sparse*BERT can become SparseBioBERT simply by pretraining the compressed architecture on unstructured biomedical text. Moreover, we show that SparseBioBERT can match the quality of BioBERT with only 10\% of the parameters.

We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision.