A fleet of rockets, new hope for the Amazon and an attempt to transform our diets are just some of the exciting stories that the New Scientist news team will be covering in 2023. Read on for our picks of the biggest science, technology, health and environment news you can expect to see in the coming year. SpaceX's Starship, the largest rocket ever built, is set to make its first orbital flight in 2023. It is just one of a fleet of huge rockets due to launch in the next 12 months, along with Blue Origin's New Glenn. Both firms are owned by billionaires – Elon Musk and Jeff Bezos, respectively – who hope to shape the future of space travel.

Corporations, governments, and academic institutions all understand the immense opportunity artificial intelligence (AI) and machine learning (ML) bring to their constituents and are increasing their investments. PwC expects the AI market to grow to just under $16 trillion by 2030, or about 12% of global GDP. Given the size of the market and the intellectual property involved, one would think appropriate investments have been made to secure these assets. AI and ML has become the largest cybersecurity attack vector. The Adversarial AI Incident Database provides thousands of examples of AI attacks across multiple industries and corporations, including Tesla, Facebook, and Microsoft.

An AI chatbot wrote the following article on AI in cybersecurity. No humans were harmed in the drafting of this article. Artificial intelligence (AI) and machine learning (ML) are rapidly advancing technologies that have the potential to greatly impact cybersecurity. These technologies can be used to enhance security by analyzing large amounts of data and identifying patterns that may indicate a potential threat, allowing organizations to take proactive measures to prevent attacks. However, they also present their own set of challenges, as they can be used by attackers to automate and scale their attacks.

Slow and steady may win the race, but sluggish AI adoption impedes organizations from solving important challenges. AI capabilities are crucial to responding to some of the most critical imperatives of our time, including mitigating climate change, advancing medical research, supporting front line healthcare workers, and protecting against pervasive cybersecurity threats. But AI is also a valuable tool for supporting essential daily business operations and meeting changing employee and customer expectations. Ronald van Loon is an IBM partner and is applying his perspective as an industry analyst to discuss the role of embeddable AI in resolving challenges associated with AI adoption. Globally, the AI market is predicted to expand over the next three years to reach $126 billion by 2025.

At ServiceNow, our technology makes the world work for everyone, and our people make it possible. We move fast because the world can't wait, and we innovate in ways no one else can for our customers and communities. By joining ServiceNow, you are part of an ambitious team of change makers who have a restless curiosity and a drive for ingenuity. We know that your best work happens when you live your best life and share your unique talents, so we do everything we can to make that possible. We dream big together, supporting each other to make our individual and collective dreams come true.

In the second season of BBC mystery thriller The Capture, deepfakes threaten the future of democracy and UK national security. In a dystopia set in present day London, hackers use AI to insert these highly realistic false images and videos of people into live news broadcasts to destroy the careers of politicians. But my team's research has shown how difficult it is to create convincing deepfakes in reality. In fact, technology and creative professionals have started collaborating on solutions to help people spot bogus videos of politicians and celebrities. We stand a decent chance of staying one step ahead of fraudsters.

MSNBC guest Kurt Andersen on Tuesday attacked Mike Pence for not supporting the indictment of Donald Trump. He called the ex-VP a "p---y." An MSNBC host and his guest piled on Mike Pence on Tuesday, suggesting the former vice president was a "p---y" for not supporting the indictment of Donald Trump. Host John Heilemann mocked Pence as boring and dull, suggesting he had the personality of the "squarest person you knew growing up." But it was guest and author Kurt Andersen who made things personal.

An autonomous experimentation platform in manufacturing is supposedly capable of conducting a sequential search for finding suitable manufacturing conditions for advanced materials by itself or even for discovering new materials with minimal human intervention. The core of the intelligent control of such platforms is the policy directing sequential experiments, namely, to decide where to conduct the next experiment based on what has been done thus far. Such policy inevitably trades off exploitation versus exploration and the current practice is under the Bayesian optimization framework using the expected improvement criterion or its variants. We discuss whether it is beneficial to trade off exploitation versus exploration by measuring the element and degree of surprise associated with the immediate past observation. We devise a surprise-reacting policy using two existing surprise metrics, known as the Shannon surprise and Bayesian surprise. Our analysis shows that the surprise-reacting policy appears to be better suited for quickly characterizing the overall landscape of a response surface or a design place under resource constraints. We argue that such capability is much needed for futuristic autonomous experimentation platforms. We do not claim that we have a fully autonomous experimentation platform, but believe that our current effort sheds new lights or provides a different view angle as researchers are racing to elevate the autonomy of various primitive autonomous experimentation systems.

In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences for the classification task. Additionally, we used aerial and still annotated images extracted from the drone and still cameras for the segmentation and detection tasks. The model training results from the drone camera outperformed those from the still camera. Generally, poor model performance is attributed to (1) over-decompression of images and (2) the ability of drone cameras to capture more details on images for the machine learning model to learn as compared to still cameras that capture only the front view of the wildlife fence. We argue that our model can be deployed on client-edge devices to inform people about the presence and significance of wildlife fencing, which minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle collisions.

Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.