WHEN New Scientist editors sat down to discuss the biggest story of 2023, one topic shot straight to the top of the list. It can't have escaped anyone's notice that artificial intelligence rocketed to prominence this year, with OpenAI, the maker of ChatGPT, becoming a household name. Hundreds of millions of people are now using large language models on a regular basis, in a rapid roll-out of technology that is essentially unprecedented.

IT WAS hard to avoid artificial intelligence in 2023, with the vertiginous rise of chatbots powered by large language models (LLMs). By February, OpenAI's ChatGPT had become the fastest-growing app of all time. By the year's end, it had become an everything machine: browsing the internet, interpreting pictures, generating any requested image and inserting itself into many existing tools and services – and it wasn't the only AI to do so. "Where the technology is going next is clearly to be more multimodal,"…

Last week, Google launched its new AI, or rather its new big language model, dubbed Gemini. The Gemini 1.0 model is available in three versions: Gemini Nano is supposed to be best suited for tasks on a specific device, Gemini Pro is supposed to be the best option for a wider range of tasks, and Gemini Ultra is Google's largest language model that will handle the most complex tasks you can give it. Something that Google was keen to highlight at the launch of Gemini Ultra was that the language model outperformed the latest version of OpenAI's GPT-4 in 30 of the 32 most commonly used tests to measure the capabilities of language models. The tests cover everything from reading comprehension and various math questions to writing code for Python and image analysis. In some of the tests, the difference between the two AI models was only a few tenths of a percentage point, while in others it was up to ten percentage points.

A phone-banking tool powered entirely by artificial intelligence is getting its first real-world test in a Pennsylvania Democrat's congressional campaign. The chatbot, named Ashley, calls voters and engages in two-way, interactive conversations about candidate Shamaine Daniels, one of seven Democrats running so far in next year's primary. The voice tool from the startup Civox represents one of many ways AI technology is breaking into politics ahead of the 2024 campaigns, but experts say its direct contact with voters could threaten data security and has the potential to undermine voter trust. Daniels announced the partnership with Civox on Tuesday, saying the the first-of-its-kind political campaign tool had already completed more than 1,000 calls with likely Democratic primary voters in Pennsylvania's 10th House district, which includes the state capital, Harrisburg. Unlike other robocallers, Ashley doesn't use canned responses or give call recipients a menu of options.

If 2023 was the year artificial intelligence became a household topic of conversation, it's in many ways because of Sam Altman, CEO of the artificial intelligence research organization OpenAI. Altman, who was named TIME's 2023 "CEO of the Year" spoke candidly about his November ousting--and reinstatement--at OpenAI, how AI threatens to contribute to disinformation, and the rapidly advancing technology's future potential in a wide-ranging conversation with TIME Editor-in-Chief Sam Jacobs as part of TIME's "A Year in TIME" event on Tuesday. Altman shared that his mid-November sudden removal from OpenAI proved a learning experience--both for him and the company at large. "We always said that some moment like this would come," said Altman. "I didn't think it was going to come so soon, but I think we are stronger for having gone through it."

Intelligent transportation systems play a crucial role in modern traffic management and optimization, greatly improving traffic efficiency and safety. With the rapid development of generative artificial intelligence (Generative AI) technologies in the fields of image generation and natural language processing, generative AI has also played a crucial role in addressing key issues in intelligent transportation systems, such as data sparsity, difficulty in observing abnormal scenarios, and in modeling data uncertainty. In this review, we systematically investigate the relevant literature on generative AI techniques in addressing key issues in different types of tasks in intelligent transportation systems. First, we introduce the principles of different generative AI techniques, and their potential applications. Then, we classify tasks in intelligent transportation systems into four types: traffic perception, traffic prediction, traffic simulation, and traffic decision-making. We systematically illustrate how generative AI techniques addresses key issues in these four different types of tasks. Finally, we summarize the challenges faced in applying generative AI to intelligent transportation systems, and discuss future research directions based on different application scenarios.

The past decade has witnessed the rapid development of geospatial artificial intelligence (GeoAI) primarily due to the ground-breaking achievements in deep learning and machine learning. A growing number of scholars from cartography have demonstrated successfully that GeoAI can accelerate previously complex cartographic design tasks and even enable cartographic creativity in new ways. Despite the promise of GeoAI, researchers and practitioners have growing concerns about the ethical issues of GeoAI for cartography. In this paper, we conducted a systematic content analysis and narrative synthesis of research studies integrating GeoAI and cartography to summarize current research and development trends regarding the usage of GeoAI for cartographic design. Based on this review and synthesis, we first identify dimensions of GeoAI methods for cartography such as data sources, data formats, map evaluations, and six contemporary GeoAI models, each of which serves a variety of cartographic tasks. These models include decision trees, knowledge graph and semantic web technologies, deep convolutional neural networks, generative adversarial networks, graph neural networks, and reinforcement learning. Further, we summarize seven cartographic design applications where GeoAI have been effectively employed: generalization, symbolization, typography, map reading, map interpretation, map analysis, and map production. We also raise five potential ethical challenges that need to be addressed in the integration of GeoAI for cartography: commodification, responsibility, privacy, bias, and (together) transparency, explainability, and provenance. We conclude by identifying four potential research directions for future cartographic research with GeoAI: GeoAI-enabled active cartographic symbolism, human-in-the-loop GeoAI for cartography, GeoAI-based mapping-as-a-service, and generative GeoAI for cartography.

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40\% pass@1 on HumanEval, and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.

"I can't sit here and say it will never displace a job," Mr. Smith said at the forum, alluding to artificial intelligence. "I don't think that would be honest." But he added that "the key is to try to use it to make jobs better," saying the technology could eliminate tasks that people consider tedious. The unveiling of the A.I. initiative comes a few weeks after the board of the start-up OpenAI, which makes ChatGPT, fired the company's chief executive, Sam Altman, only to accept his reinstatement days later. The episode added to widespread concerns over how to ensure that companies develop and deploy artificial intelligence safely.

Democrat Shamaine Daniels is running for Congress, eyeing a seat held by Trump-aligned Republican Rep. Scott Perry, who played a key role challenging the 2020 election results. Daniels, who lost to Perry by less than 10 points last year, hopes a new weapon will help her underdog candidacy: Ashley, an artificial intelligence campaign volunteer. Ashley is not your typical robocaller; none of her responses are canned or pre-recorded. Her creators, who intend to mainly work with Democratic campaigns and candidates, say she is the first political phone banker powered by generative AI technology similar to OpenAI's ChatGPT. She is capable of having an infinite number of customized one-on-one conversations at the same time.