A modified F-16 fighter jet has successfully flown and fought another aircraft while being entirely controlled by artificial intelligence (AI). During test flights, the jet, known as'X-62A' or'VISTA', performed takeoffs, landings and combat manoeuvres without human intervention for a total of over 17 hours. They took place in December 2022 at the Edwards Air Force Base in California, USA, and showed that it is possible to completely hand over the reigns to AI in battle. The algorithms which powered it were developed by the Defense Advanced Research Projects Agency (DARPA) - the research branch of the US Department of Defense. This marks the first time AI has been used on a tactical aircraft as, prior to this milestone, it had only been used in computer simulations of F-16 dogfights.

The Union Budget 2023, recently presented by the Indian government, has big plans for the future of Artificial Intelligence (AI) in the country. The government has set aside a substantial amount of funding for AI research and development, and has also introduced several new initiatives aimed at promoting the growth and development of the AI industry in India. One of the major initiatives announced in the budget is the setting up of a National AI Portal. This portal will serve as a single point of reference for all AI-related information and resources in the country. It will provide a platform for researchers, developers, and businesses to share their work and collaborate on AI projects.

For the past 300,000 years we've been unique in our ability to make art, cuisine, manifestos, societies: to envision and craft something new where there was nothing before. While you're reading this sentence, artificial intelligence (AI) programs are painting cosmic portraits, responding to emails, preparing tax returns, and recording metal songs. Artificial intelligence has already had a pervasive impact on our lives. AIs are used to price medicine and houses, assemble cars, determine what ads we see on social media. But generative AI, a category of system that can be prompted to create wholly novel content, is much newer.

How Artificial Intelligence Can Make or Break CybersecurityWith the introduction of Chat GPT, artificial intelligence is no longer science fiction. It has presented a new problem for cybersecurity professionals...James McQuiggan

Artificial intelligence (AI) is both omnipresent and conceptually slippery, making it notoriously hard to regulate. Fortunately for the rest of the world, two major experiments in the design of AI governance are currently playing out in Europe and China. The European Union (EU) is racing to pass its draft Artificial Intelligence Act, a sweeping piece of legislation intended to govern nearly all uses of AI. Meanwhile, China is rolling out a series of regulations targeting specific types of algorithms and AI capabilities. For the host of countries starting their own AI governance initiatives, learning from the successes and failures of these two initial efforts to govern AI will be crucial.

The geopolitical implications of artificial intelligence (AI) and its most prominent example to date – ChatGPT – remain deeply uncertain. It will surely roil the technology industry and change our daily lives, but it is less clear whether it will augur a shift in geopolitical power or create policy dilemmas for the European Union. These are hard questions, perhaps too hard for a human being to answer. Q: What are the geopolitical consequences of ChatGPT? A: OpenAI's GPT-3 language model, including ChatGPT, does not have geopolitical consequences as it is an artificial intelligence language model … Political consequences are the effects or results of political actions or events and are typically a result of human decisions and actions, not artificial intelligence models.

Since its debut in November, ChatGPT has become the internet's new favorite plaything. The AI-driven natural language processing tool rapidly amassed more than 1 million users, who have used the web-based chatbot for everything from generating wedding speeches and hip-hop lyrics to crafting academic essays and writing computer code. Not only have ChatGPT's human-like abilities taken the internet by storm, but it has also set a number of industries on edge: a New York school banned ChatGPT over fears that it could be used to cheat, copywriters are already being replaced, and reports claim Google is so alarmed by ChatGPT's capabilities that it issued a "code red" to ensure the survival of the company's search business. It appears the cybersecurity industry, a community that has long been skeptical about the potential implications of modern AI, is also taking notice amid concerns that ChatGPT could be abused by hackers with limited resources and zero technical knowledge. Just weeks after ChatGPT debuted, Israeli cybersecurity company Check Point demonstrated how the web-based chatbot, when used in tandem with OpenAI's code-writing system Codex, could create a phishing email capable of carrying a malicious payload. Check Point threat intelligence group manager Sergey Shykevich told TechCrunch that he believes use cases like this illustrate that ChatGPT has the "potential to significantly alter the cyber threat landscape," adding that it represents "another step forward in the dangerous evolution of increasingly sophisticated and effective cyber capabilities."

We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEneration (RECITE). Different from retrievalaugmented language models that retrieve relevant documents before generating the outputs, given an input, RECITE first recites one or several relevant passages from LLMs' own memory via sampling, and then produces the final answers. We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance in various closed-book question answering (CBQA) tasks. In experiments, we verify the effectiveness of RECITE on four pre-trained models (PaLM, UL2, OPT, and Codex) and three CBQA tasks (Natural Questions, TriviaQA, and HotpotQA). Large language models (LLMs) have achieved impressive in-context few-shot performance ...

The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here with the development of a novel graph neural network (GNN) autoencoding architecture with demonstrations on complex fluid flow applications. To address the first goal of interpretability, the GNN autoencoder achieves reduction in the number nodes in the encoding stage through an adaptive graph reduction procedure. This reduction procedure essentially amounts to flowfield-conditioned node sampling and sensor identification, and produces interpretable latent graph representations tailored to the flowfield reconstruction task in the form of so-called masked fields. These masked fields allow the user to (a) visualize where in physical space a given latent graph is active, and (b) interpret the time-evolution of the latent graph connectivity in accordance with the time-evolution of unsteady flow features (e.g. recirculation zones, shear layers) in the domain. To address the goal of unstructured mesh compatibility, the autoencoding architecture utilizes a series of multi-scale message passing (MMP) layers, each of which models information exchange among node neighborhoods at various lengthscales. The MMP layer, which augments standard single-scale message passing with learnable coarsening operations, allows the decoder to more efficiently reconstruct the flowfield from the identified regions in the masked fields. Analysis of latent graphs produced by the autoencoder for various model settings are conducted using using unstructured snapshot data sourced from large-eddy simulations in a backward-facing step (BFS) flow configuration with an OpenFOAM-based flow solver at high Reynolds numbers.

Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. Existing research on the environmental impacts of ML has been limited to analyses covering a small number of models and does not adequately represent the diversity of ML models and tasks. In the current study, we present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision. We analyze them in terms of the energy sources used, the amount of CO2 emissions produced, how these emissions evolve across time and how they relate to model performance. We conclude with a discussion regarding the carbon footprint of our field and propose the creation of a centralized repository for reporting and tracking these emissions.