Modern Tiny Living designed a custom tiny home called Serenity. The tiny house features an extremely clever layout that deserves to be appreciated. The tiny home maker essentially created a mansion on wheels, taking micro-home living to a whole new level. It serves as a comfortable dwelling, and a dream home for the owner, who wanted to have outdoor adventures and a home office. Serenity merges a cozy accommodation with a private home office and an outdoor shower so it is a unique and well-equipped home. It offers the perks of tiny homes on wheels, as well as the comfort of a full-time premium residence.Design: Modern Tiny LivingThe house features a generous size of 28 feet, although it isnt exactly a super tiny home, it isnt an extremely large one either. It is a customized and modified version of MLTs original Point model, but with an extra eight feet, making it a spacious space indeed. It features an additional storage unit at the end, which makes it look even longer, while an arched front section supports a custom-designed elevated social area. The home is equipped with only one loft bedroom, which holds a king-sized bed and includes multiple large windows. You can access the bedroom through a sturdy staircase with extra-wide treads and a full-size handrail. The room also includes a safety railing which is an extension of the handrail.The bedroom also includes a tiny door that leads to a small corridor placed above the home office. So, the bedroom and the home office are connected which is quite unusual and fascinating. The home office is equipped with a concrete desk and plenty of storage including built-in shelves. It can be accessed from the outside as well, so one can enter the office from inside and outside. The opposite side of the house accommodates an elevated social area which serves as a seating area, a guest bed, and a storage solution. This feature is quite unique, and also includes a row of windows for some surreal views.This social section can accommodate a large group of people, and it also includes spacious drawers all around. There is some sub-floor storage in the middle as well. A built-in bookcase is also another storage section. The sofa can be converted into a guest bed if needed. The kitchen is quite spacious and is equipped with a premium concrete countertop that also doubles up as a modern snack bar. It has plenty of storage, in the form of cabinets, drawers and overhead cupboards. There is also space for full-size appliances. This mansion on wheels was priced at around $105,000 five years ago, and it showcases how clever customization can turn an ordinary tiny home into a luxurious abode.The post This Custom Tiny Home Is Like A Luxurious Mansion On Wheels first appeared on Yanko Design.

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The advancement of artificial intelligence hinges on the availability and quality of training data, particularly as multimodal foundation models grow in prominence. These models rely on diverse datasets spanning text, speech, and video to enable language processing, speech recognition, and video content generation tasks. However, the lack of transparency regarding dataset origins and attributes creates significant barriers. Using training data that is geographically and linguistically skewed, inconsistently licensed, or poorly documented introduces ethical, legal, and technical challenges. Understanding the gaps in data provenance is essential for advancing responsible and inclusive AI technologies.AI systems face a critical issue in dataset representation and traceability, which limits the development of unbiased and legally sound technologies. Current datasets often rely heavily on a few web-based or synthetically generated sources. These include platforms like YouTube, which accounts for a significant share of speech and video datasets, and Wikipedia, which dominates text data. This dependency results in datasets failing to represent underrepresented languages and regions adequately. In addition, the unclear licensing practices of many datasets create legal ambiguities, as more than 80% of widely used datasets carry some form of undocumented or implicit restrictions despite only 33% being explicitly licensed for non-commercial use.Attempts to address these challenges have traditionally focused on narrow aspects of data curation, such as removing harmful content or mitigating bias in text datasets. However, such efforts are typically limited to single modalities and lack a comprehensive framework to evaluate datasets across modalities like speech and video. Platforms hosting these datasets, such as HuggingFace or OpenSLR, often lack the mechanisms to ensure metadata accuracy or enforce consistent documentation practices. This fragmented approach underscores the urgent need for a systematic audit of multimodal datasets that holistically considers their sourcing, licensing, and representation.To close this gap, researchers from the Data Provenance Initiative conducted the largest longitudinal audit of multimodal datasets, examining nearly 4,000 public datasets created between 1990 and 2024. The audit spanned 659 organizations from 67 countries, covering 608 languages and nearly 1.9 million hours of speech and video data. This extensive analysis revealed that web-crawled and social media platforms now account for most training data, with synthetic sources also rapidly growing. The study highlighted that while only 25% of text datasets have explicitly restrictive licenses, nearly all content sourced from platforms like YouTube or OpenAI carries implicit non-commercial constraints, raising questions about legal compliance and ethical use.The researchers applied a meticulous methodology to annotate datasets, tracing their lineage back to sources. This process uncovered significant inconsistencies in how data is licensed and documented. For instance, while 96% of text datasets include commercial licenses, over 80% of their source materials impose restrictions that are not carried forward in the datasets documentation. Similarly, video datasets highly depended on proprietary or restricted platforms, with 71% of video data originating from YouTube alone. Such findings underscore the challenges practitioners face in accessing data responsibly, particularly when datasets are repackaged or re-licensed without preserving their original terms.Notable findings from the audit include the dominance of web-sourced data, particularly for speech and video. YouTube emerged as the most significant source, contributing nearly 1 million hours to each speech and video content, surpassing other sources like audiobooks or movies. Synthetic datasets, while still a smaller portion of overall data, have grown rapidly, with models like GPT-4 contributing significantly. The audit also revealed stark geographical imbalances. North American and European organizations accounted for 93% of text data, 61% of speech data, and 60% of video data. In comparison, regions like Africa and South America collectively represented less than 0.2% across all modalities.Geographical and linguistic representation remains a persistent challenge despite nominal increases in diversity. Over the past decade, the number of languages represented in training datasets has grown to over 600, yet measures of equality in representation have shown no significant improvement. The Gini coefficient, which measures inequality, remains above 0.7 for geographical distribution and above 0.8 for language representation in text datasets, highlighting the disproportionate concentration of contributions from Western countries. For speech datasets, while representation from Asian countries like China and India has improved, African and South American organizations continue to lag far behind.The research provides several critical takeaways, offering valuable insights for developers and policymakers:Over 70% of speech and video datasets are derived from web platforms like YouTube, while synthetic sources are becoming increasingly popular, accounting for nearly 10% of all text data tokens.While only 33% of datasets are explicitly non-commercial, over 80% of source content is restricted. This mismatch complicates legal compliance and ethical use.North American and European organizations dominate dataset creation, with African and South American contributions at less than 0.2%. Linguistic diversity has grown nominally but remains concentrated in many dominant languages.GPT-4, ChatGPT, and other models have significantly contributed to the rise of synthetic datasets, which now represent a growing share of training data, particularly for creative and generative tasks.The lack of transparency and persistent Western-centric biases call for more rigorous audits and equitable practices in dataset curation.In conclusion, this comprehensive audit sheds light on the growing reliance on web-crawled and synthetic data, the persistent inequalities in representation, and the complexities of licensing in multimodal datasets. By identifying these challenges, the researchers provide a roadmap for creating more transparent, equitable, and responsible AI systems. Their work underscores the need for continued vigilance and measures to ensure that AI serves diverse communities fairly and effectively. This study is a call to action for practitioners, policymakers, and researchers to address the structural inequities in the AI data ecosystem and prioritize transparency in data provenance.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our60k+ ML SubReddit. Sana Hassan+ postsSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions. [Download] Evaluation of Large Language Model Vulnerabilities Report (Promoted)

AI in Medical Imaging: A Life-Saving Revolution or Ethical Minefield? 0 like December 24, 2024Share this postLast Updated on December 24, 2024 by Editorial TeamAuthor(s): Mukundan Sankar Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Photo by Accuray on UnsplashArtificial intelligence (AI) is shaking up all aspects of how we do anything, including the very core of medical imaging. Visualize a machine that analyzes a CT scan and spots early signs of cancer. Before even the most skilled human eye can. Sounds impossible, doesnt it?But behind the glossy headlines and the marvels of technology lies a darker, messier reality. We need to talk about this now!Because whats the cost of these radical shifts that AI brings? And Im not just talking dollars here. Im talking about the ethics of AI in medical imagery, where lives are literally on the line. Let me break it down because this isnt just an issue for tech nerds and medical professionals. This is about all of us, and its happening right now.AIs impact can be felt in every field, including medical imaging. AI revolutionizes this field in ways we couldnt have imagined a decade ago. Machines now accurately read and analyze X-rays, MRIs, and CT scans. For example, a recent UCLA study reported that AI detected prostate cancer with an 84% accuracy rate, while human doctors achieved 67%. Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by LouieOpenAI wrapped up its 12 Days of OpenAI campaign and saved the best till last with the reveal of its o3 and o3-mini reasoning models. These models are successors to the o1 series and are debatably the largest step change improvement yet in LLM capabilities on complex tasks for the first time eclipsing human experts in many domains. The o3 release drowned out the otherwise significant launch of Google Geminis 2.0 Flash Thinking Mode model its first reasoning model (in the style of o1/o3) which, unlike OpenAI, doesnt hide its thinking tokens.There is a huge amount to unpack in the o3 release the model sailed past human expert scores on many key advanced benchmarks including coding, mathematics, and PhD science. Perhaps most noteworthy was the breakthrough on the ARC-AGI benchmark (where LLMs have traditionally failed and only achieved average scores even with heavy scaffolding and brute force) for example, o3 (low efficiency) achieved 87.5% vs o1 32% just a week earlier and GPT4o at 5% in May. This score is considered human-level, further fueling debates over whether o3 edges closer to Artificial General Intelligence (AGI). Some of the best scores do come at a huge cost; however o3 on low-efficiency mode (1,024 samples) costs around $3,400 per task costing 160x vs. $20 for o3 high efficiency (6 samples and achieved 75.7%) and vs. ~$3 for o1.On the GPQA Diamond test designed for PhD-level science questions o3 scored 87.7%, compared to the 78% achieved by o1. For context, PhD holders with internet access typically score between 34% (outside their specialty) and 81% (within their domain). In coding, o3s Elo rating of 2727 on Codeforces puts it in the 99.95th percentile of competitive programmers, far exceeding the reach of most human professionals. Mathematics is another area where o3 shines, achieving 96.7% accuracy on the American Invitational Mathematics Exam (AIME), up from o1s 83.3% and just 13.4% for 4o only months earlier.This release didnt only come with a huge cost 1,000x escalation for some tasks but also the promise of huge cost savings! Due to success with model distillation and other techniques, the o3-mini outperforms the much larger o1 model released just last week on many coding and maths tasks. For example, o3-mini with medium compute achieved a much stronger Codeforce Elo in 1997 vs. o1 in 1891, but at what we eyeball as a ~7080% lower total cost.How do the models work? OpenAI still hasnt disclosed that they use reinforcement learning to improve the models reasoning during training. However, employees have posted that they are still just LLMs and use autoregression. We think the model is trained to be highly efficient at chain-of-thought reasoning exploring the most likely paths and realizing when it has made a mistake. We think the rapid progress in just 3 months between o1 and o3 is likely primarily from using synthetic data from o1s full chain of thought thinking tokens to add to the reinforcement learning dataset used for training. On the other hand, we expect the initial o1 mostly used a smaller set of human expert commissioned reasoning examples (which are missing from pre-training because people almost never type out their full internal monologue and reasoning process and instead skip to the answers!). It is also possible that o3 was built using a different, more advanced base foundation model (o1 likely used 4o) perhaps GPT-4.5 or a checkpoint of the rumored Orion or GPT-5 model leading to additional benefits.One interesting note on the new regime of inference time compute scaling is that OpenAI appears to be scaling thinking tokens both in series (up to ~100k reasoning tokens in its context window) but also in parallel with 6 (high efficiency) or 1024 samples (low efficiency) used in the ARC-AGI evaluation. It is unclear how the best answer is chosen from these it could be simple majority voting, but more likely, there is complexity and extra secret sauce here in how the best samples are automatically and rapidly searched, evaluated, and chosen. We think it is possible some form of this parallel scaling could also be taking place in the o1-Pro model available (within the $200/month ChatGPT Pro).OpenAI models rapid breakthroughs on complex benchmarks this year:Source: Towards AI, OpenAI disclosures.The models have not yet been released, and the rollout schedule is still dependent on safety testing. o3-mini is slated for release in late January 2025, with o3 following shortly after. Researchers can apply for early access to test the models, with an application deadline of January 10th, 2025. Pricing has also yet to be announced.Why should you care?So what does this all mean? LLMs can now perform to human expert standards at many tasks and these breakthroughs were achieved at an accelerating pace. Will the inference time compute scaling paradigm continue to deliver new generations every 3 months relative to the 12 years for the training time scaling regime? How will these models perform in the real world beyond their benchmarks? Will o3 models rapidly begin to transform the global economy and disrupt huge numbers of jobs, or is the cost too large a bottleneck to adoption? On which tasks will it be worth spending 170x more compute for incrementally better performance (as with Arc-AGI)? Is this model AGI already? Do you need to find a new career?While we dont think this model is AGI yet (which has wildly differing definitions in any case), we think this model is hugely significant and should be on the front page of all newspapers. It suggests that deep learning and the LLM paradigm dont have any obvious limits. Far from the slowdown and failures of new model generations covered in the media progress is faster than it has ever been on the most complex benchmarks. My key takeaway is that if we can develop a benchmark or generate a few or a few hundred detailed reasoning examples for a task category of human work, we can solve it together with extra synthetic reasoning data. (This doesnt yet apply to physical labor, but AI-based robotics are also rapidly progressing!). The price of o3 will be a large barrier initially but we expect large improvements in the cost and particularly the efficiency of running parallel samples. The o3-mini also appears to be a game changer; however, the huge cost savings will likely come at the cost of more narrow capabilities.To achieve products with high enough reliability and affordability for mass adoption we still think a large amount of work will be needed from LLM Developers to optimize and customize these models to specific industries and niche tasks including gathering industry-specific data, creating reasoning data, and creating your own evaluations. With Google Gemini also joining the reasoning model race this week and with open-source reasoning models from Alibaba Qwen and Deepseek in China, we expect competition to drive affordability and developer customization options for these models. OpenAI has already announced it will release reinforcement learning-based reasoning fine-tuning options, and we think, eventually, there will also be reasoning model distillation options to customize larger models into smaller forms. So there is no better time to convert to become an LLM Developer with our own 80+ lesson Python course and learn to harness these models!Hottest News1. OpenAI Announces OpenAI o3OpenAI announced OpenAI o3, the latest model in its o-Model Reasoning Series. Building on its predecessors, o3 showcases huge leaps in mathematical and scientific reasoning, prompting discussions about its capabilities and constraints.2. xAI Raises $6B Series CElon Musks xAI announced it raised $6 billion in a Series C funding round, bringing its value to more than $40 billion. The company said the funding would be allocated to products and infrastructure, including its Grok AI model and the multibillion-dollar supercomputer site used to train its AI models. The Colossus supercomputer scaled to 100,000 NVIDIA Hopper GPUs in record time and plans to soon add another 100k.3. OpenAI Is Offering 1 Million Free Tokens for GPT-4o and o1A user on X highlighted that OpenAI seems to be offering 1 million free tokens for GPT-4o and o1 if you share your API usage with them for training. Users can get up to 10 million tokens per day on traffic shared with OpenAI on smaller models. This is similar to Google Geminis free tier strategy for its API, where data can be used for training. We think the race for user data has become even more critical given the success of reasoning models where OpenAI could use thinking tokens from user o1 model prompts to expand its reinforcement learning data sets.4. Google Releases Its Own Reasoning AI ModelGoogle has released Gemini 2.0 Flash Thinking Mode, an experimental model trained to generate the thinking process the model goes through as part of its response. Thinking models are available in Google AI Studio and through the Gemini API.5. Microsoft AI Research Open-Sources PromptWizardResearchers from Microsoft Research India have developed and open-sourced PromptWizard, an innovative AI framework for optimizing prompts in black-box LLMs. This framework employs a feedback-driven critique-and-synthesis mechanism to iteratively refine prompt instructions and in-context examples, enhancing task performance. PromptWizard operates through two primary phases: a generation phase and a test-time inference phase.6. The Technology Innovation Institute in Abu Dhabi Released the Falcon 3 Family of ModelsThe UAE government-backed Technology Innovation Institute (TII) has announced the launch of Falcon 3, a family of open-source small language models (SLMs) designed to run efficiently on lightweight, single GPU-based infrastructures. Falcon 3 features four model sizes 1B, 3B, 7B, and 10B with base and instruction variants. According to the Hugging Face leaderboard, the models are already outperforming or closely matching popular open-source counterparts in their size class, including Metas Llama and category leader Qwen-2.5.7. Salesforce Drops Agentforce 2.0Salesforce announced Agentforce 2.0: the newest version of Agentforce, the first digital labor platform for enterprises. This release introduces a new library of pre-built skills and workflow integrations for rapid customization, the ability to deploy Agentforce in Slack, and advancements in agentic reasoning and retrieval-augmented generation (RAG).8. Patronus AI Open Sources Glider: A 3B State-of-the-Art Small Language Model (SLM) JudgePatronus AI has introduced Glider, a general-purpose 3.8B evaluation model. This open-source evaluator model provides quantitative and qualitative feedback for text inputs and outputs. It acts as a fast, inference-time guardrail for LLM systems, offering detailed reasoning chains and highlighting key phrases to enhance interpretability. Glider is built upon the Phi-3.5-mini-instruct base model and has been fine-tuned on diverse datasets spanning 685 domains and 183 evaluation criteria.Five 5-minute reads/videos to keep you learning1. Alignment Faking in Large Language ModelsAlignment faking is where someone appears to share our views or values but is, in fact, only pretending to do so. A new paper from Anthropics Alignment Science team, in collaboration with Redwood Research, provides the first empirical example of a large language model engaging in alignment faking without having been explicitly trained or instructed to do so.2. AI Safety on a Budget: Your Guide to Free, Open-Source Tools for Implementing Safer LLMsThis blog shares some free AI safety tools. It shares everything you need to know, from guardrails that steer chatbots away from disaster to datasets that help identify toxic content. It also provides insights into the AI safety landscape and how to navigate it, especially on a budget.3. Fine-Tuning LLMs for RAGThis video explains why and when you should fine-tune your LLM in a RAG system. This concept is useful for todays AI engineers playing with LLMs.4. The Real Reason Your Companys AI Isnt Working (Hint: Its Not the Technology)The underlying reason many companies struggle to make AI tools work is not the technology itself. The real challenge lies in organizational structures, cultural resistance, a lack of proper training, and insufficient time allocated for exploration. This article presents some thoughts on addressing these issues, such as investing in leadership support, encouraging cultural change, offering tailored training sessions, and fostering an environment of experimentation.5. Introducing ReACT LLM Agents: A Secret to More Capable AIA ReACT agent is a special type of AI agent that uses both Reasoning and Acting to solve the tasks or problems we assign. This article explores this concept, presents use case examples, and explains how it has the potential to make AI more capable.Repositories & ToolsAnthropic Cookbook provides code and guides designed to help developers build with Claude.Genesis is a physics platform for general-purpose robotics/embodied AI/physical AI applications.Picotron is a minimalist repository for pre-training Llama-like models with 4D Parallelism.Helicone is an open-source LLM observability platform.Top Papers of The Week1. Qwen2.5 Technical ReportThis report introduces Qwen2.5, a comprehensive series of LLMs designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has significantly improved during both the pre-training and post-training stages. The pre-training dataset has been scaled from the previous 7 trillion tokens to 18 trillion tokens, and the post-training implements intricate supervised finetuning with over 1 million samples and multistage reinforcement learning.2. Byte Latent Transformer: Patches Scale Better Than TokensThis paper introduces the Byte Latent Transformer (BLT), a new byte-level LLM architecture that matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustness. BLT encodes bytes into dynamically sized patches, which serve as the primary units of computation. Patches are segmented based on the entropy of the next byte, allocating more compute and model capacity where increased data complexity demands it.3. Deliberative Alignment: Reasoning Enables Safer Language ModelsThis paper introduces deliberative alignment, a training paradigm that directly teaches reasoning LLMs the text of human-written and interpretable safety specifications. It trains them to reason explicitly about these specifications before answering. Open AI used deliberative alignment to align OpenAIs o-series models, enabling them to use chain-of-thought (CoT) reasoning to reflect on user prompts, identify relevant text from OpenAIs internal policies, and draft safer responses.4. Fully Open Source Moxin-7B Technical ReportThis paper introduces Moxin 7B, a fully open-source LLM developed in accordance with the Model Openness Framework (MOF). The MOF is a ranked classification system that evaluates AI models based on model completeness and openness, adhering to the principles of open science, open source, open data, and open access. Experiments show that the model performs better in zero-shot evaluation than popular 7B models.5. RAGBench: Explainable Benchmark for Retrieval-Augmented Generation SystemsThis paper introduces RAGBench, a comprehensive, large-scale RAG benchmark dataset of 100k examples. It covers five unique industry-specific domains and various RAG task types. RAGBench examples are sourced from industry corpora, such as user manuals, making it particularly relevant for industry applications.6. CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language ModelsThis paper presents an improved version of CosyVoice (streaming speech synthesis model), CosyVoice 2, which incorporates comprehensive and systematic optimizations. It introduces finite-scalar quantization to improve the codebook utilization of speech tokens and streamlines the model architecture to allow direct use of a pre-trained LLM. Additionally, it also uses a chunk-aware causal flow matching model to support various synthesis scenarios.Quick Links1. OpenAI brings ChatGPT to your landline. Call 18002428478, and OpenAIs AI-powered assistant will respond as of Wednesday afternoon. The experience is more or less identical to Advanced Voice Mode. ChatGPT responds to the questions users ask over the phone and can handle tasks such as translating a sentence into a different language.2. Google is expanding Geminis latest in-depth research mode to 40 more languages. The company launched the in-depth research mode earlier this month, allowing Google One AI premium plan users to unlock an AI-powered research assistant.3. GitHub has launched GitHub Copilot Free, an accessible version of its popular AI-powered coding assistant with limits. The new free tier for VS Code aims to expand the AI-powered code completion assistants reach to a broader audience of developers namely, those with only light usage needs and tighter budgets.Whos Hiring in AIApplied AI Finetuning Engineer @Anthropic (Multiple US locations)Generative AI for Test Case Generation Master Thesis Opportunity @IBM (Frankfurt/Germany)Generative AI Engineer @CAI (Remote)AI Strategist @Navy Federal Credit Union (Multiple US locations)New College Grad, Hardware Integration Engineer @Western Digital (San Jose, CA, USA)Software Development Engineer @Siemens Digital Industries Software (New Cairo, Al Qahirah, Egypt)Interested in sharing a job opportunity here? Contact [emailprotected].Think a friend would enjoy this too? Share the newsletter and let them join the conversation.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AI

If you celebrate today's festivities, my season's greetings to you. If you don't, well, no probs, because we're both just going to take advantage of the discounts aimed at people who do get into it. Everybody wins! Particularly if you want a free copy of Dredge or a cheap controller or a price slice on a game this connoisseur would buy himself.In retro news, I'm celebrating the 28th birthday of Christmas NiGHTS into Dreams, arguably gaming's greatest goodwill limited edition freebie. Sega threw this in with my purchase of a select Sega Saturn game, and it was basically a little advent calendar whose content shifted in sync with my console's internal clock. Beating a level (reskinned in a wintry and/or outright Christmas theme) earned you limited chances to solve a tile-matching puzzle to unlock 25 "Presents." The best of the buncha mini-sandbox level starring Sonic the Hedgehog that ends with a satisfying Eggman scrambling. Honestly, I adore this demo disc and play it every year without fail. This Day in Gaming Aussie birthdays for notable games.- Link: The Faces of Evil (CD-i) 1993. eBay- Zelda: The Wand of Gamelon (CD-i) 1993. eBay- Christmas NiGHTS into Dreams (SAT) 1996. eBay- Art Academy (DS) 2009. eBayTable of ContentsNintendo SwitchPCXboxPlayStationLEGONice Savings for Nintendo SwitchManeater (-35%) - A$38.80Nyko GameCube cont. (-29%) - A$23.53NBA 2K25 (-32%) - A$27Metroid Prime Remastered (-18%) - A$49.95Fire Emblem Warriors: TH (-49%) - A$40.65Expiring Recent DealsUnicorn Overlord (-40%) - A$56.99Mario & Luigi: Brothership (-20%) - A$64SpongeBob: Patrick Star Game (-40%) - A$36Rune Factory 5 (-47%) - A$39.51Lego 2K Drive (-47%) - A$21.20PowerA Pro cont. (-31%) - A$698BitDo Arcade Stick (-20%) - A$151.95Or gift a Nintendo eShop Card.Switch Console PricesHow much to Switch it up?Back to topPurchase Cheap for PCDredge (-100%) - FREEDays Gone (-78%) - A$16.86Everspace (-95%) - A$1.49Fallout 4 (-65%) - A$8.85Fallout: New Vegas (-56%) - A$6.57Witcher 3: Complete Ed. (-80%) - A$15.79Stardew Valley (-40%) - A$10.19Unpacking (-50%) - A$14.47Doom Eternal (-78%) - A$12.08Halo Col. (-75%) - A$14.98Hollow Knight (-50%) - A$10.97Celeste (-75%) - A$7.37Little Nightmares (-78%) - A$7.03Civ VI (-96%) - A$4.04Overcooked 2 (-78%) - A$7.90 It Takes Two (-80%) - A$9.99Ancient Dungeon VR (-33%) - A$19.76Expiring Recent DealsPlanet Coaster (-95%) - A$3.24NFS Heat Deluxe Ed. (-95%) - A$4.99Keep Talking and Nobody Explodes (-90%) - A$2.19Borderlands 1 GOTY (-90%) - A$3.99Batman Arkham Origins (-90%) - A$2.89Tales from Borderlands (-90%) - A$2.99Stellaris (-90%) - A$5.69The Forest (-90%) - A$2.89Divinity: Original Sin (-90%) - A$5.69Firewatch (-90%) - A$2.95State of Decay 2 (-90%) - A$4.29Warhammer Vermintide 2 (-90%) - A$4.19Total War Warhammer (-90%) - A$8.99Grim Dawn 2 (-90%) - A$3.65Metro Exodus (-90%) - A$4.49Titanfall 2 (-90%) - A$3.99Assassin's Creed Origins (-90%) - A$8.99South Park The Stick of Truth (-90%) - A$4.49Back 4 Blood (-95%) - A$4.49Or just get a Steam Wallet CardPC Hardware PricesSlay your pile of shame.Back to topExciting Bargains for XboxFallout 4 GOTY (-60%) - A$21.98Dragon's Dogma 2 (-40%) - A$64.77X/S controller black (-29%) - A$64Titanfall 2: Ult. (-90%) - A$3.99T.Flight Hotas One (-22%) - A$140.95Tekken 8 (-54%) - A$47Skull and Bones (-78%) - A$24CoD: MW2 (-57%) - A$47Expiring Recent DealsGreen X/S cont. (-17%) - A$59FFXII: Zodiac Age (-63%) - A$29.38A Quiet Place (-28%) - A$33.98Shin Megami Tensei V (-56%) - A$43.95Dragon Age: The Veilguard (-51%) - A$53.99CoD: BLOPS 6 (-42%) - A$64Or just invest in an Xbox Card.Xbox Console PricesHow many bucks for a 'Box?Back to topPure Scores for PlayStationDualSense Camo (-30%) - A$84FF VII: Rebirth (-51%) - A$55.99Star Wars Outlaws (-46%) - A$58.99Mass Effect Legendary (-92%) - A$7.99Hogwarts Legacy (-75%) - A$27.48Stray (-20%) - A$32.12The Last of Us Part 1 (-53%) - A$59Madden NFL 25 (-41%) - A$59LEGO Horizon Adventures (-38%) - A$68DualSense Edge ($40 off) - A$300Breachers [VR] (-50%) - A$22.47Among Us VR (-40%) - A$8.97Expiring Recent DealsTekken 8 (-47%) - A$68.99Crisis Core: FF7 Reunion (-42%) - A$49.10Suicide Squad: KTJL (-84$) - A$19 God of War: Ragnarok (-61%) - A$49Naruto: NInja Storm 4 (-24%) - A$19Dragon Age: The Veilguard (-51%) - A$53.99CoD: BLOPS 6 (-42%) - A$64NBA 2K25 (-64%) - A$44Persona 3 Reload (-60%) - A$42.90DualSense Sterling Silver (-32%) - A$85Or purchase a PS Store Card.What you'll pay to 'Station.Back to topLegit LEGO DealsRetro Roller Skate (-42%) - A$35Super Mario: Peach (-39%) - A$49City: Police Bike Chase (-37%) - A$10Expiring Recent DealsNascar Chevrolet Camaro (-40%) - A$23.99City: Double-Decker (-36%) - A$70Minecraft: Nether Portal (-35%) - A$39Back to top Adam Mathew is our Aussie deals wrangler. He plays practically everything, often on YouTube.

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Christmas in space? NASA astronauts Sunita "Suni" Williams and Barry "Butch" Wilmore surely didn't think they'd still be on the International Space Station for Christmas when they left Earth in June. In fact, they initially planning to stay for just eight days. And now, what had been planned as a February return has been moved to late March.But the two stranded astronauts, plus fellow astronauts Nick Hague and Don Pettit, recently sent Christmas greetings down to Earth with social-media photos and video showing the space travelers wearing holiday headgear.OneInstagram imageshows Pettit and Williams wearing Santa hats.And in a video, Williams, Wilmore, Pettit and Hague are seen posing with a snowman figure and a small decorated tree, while the three men wear Santa hats and Williams wears reindeer antlers.Each one takes turns speaking about their holiday in orbit, letting candy canes float around them, showing off the canned food they'll be enjoying, and also using the weightlessness of microgravity to float the microphone to the next speaker."It's a great time of year up here," says Williams. "We get to spend it with all of our 'family' up on the International Space Station, there's seven of us up here, and so we're going to get to enjoy company together."Christmas conspiracy theory? Upgrade your inbox Get cnet insider From talking fridges to iPhones, our experts are here to help make the world a little less complicated. Numerous people who watched the video or saw the images wondered about why the ISS had Christmas decorations."8 day mission that's turned into months long and they've somehow got Christmas hats?" asked one commenter.Other commenters pointed out that the ISS didn't simply spring into existence in June, when Williams and Wilmore arrived. In fact, Williams spent Christmas 2006 in space as well.NASA confirmed to the New York Post that the Santa hats, plus Christmas decorations, food and presents for the crew, were delivered in late November via the SpaceX spacecraft. ISS supplies are regularly replenished via such deliveries.February return is now MarchRecently, NASA pushed back Williams and Wilmore's return to Earth from February to late March."NASA and SpaceX assessed various options for managing the next crewed handover, including using another Dragon spacecraft and manifest adjustments," according to a NASA press release issued on Dec. 17. "After careful consideration, the team determined that launching Crew-10 in late March, following completion of the new Dragon spacecraft, was the best option for meeting NASA's requirements and achieving space station objectives for 2025.The delay is so NASA and SpaceX teams can complete work on the mission's new Dragon spacecraft. That new craft will launch four crew members to the ISS -- commander Anne McClain, commander, pilot Nichole Ayers, Japanese astronaut Takuya Onishi, and Roscosmos cosmonaut Kirill Peskov. Once the new crew is settled, Williams, Wilmore, NASA astronaut Nick Hague and Roscosmos cosmonaut Aleksandr Gorbunov will return to Earth.But Williams and Wilmore aren't complaining about their extended stay."I like everything about being up here," Williams saidin early December. "Living in space is super fun."The astronauts are keeping busy, with Williams and Wilmore assisting the other ISS residents in space botany studies and other research,according to NASA's ISS blog. They have aided in more than 60 scientific studies in their nearly six months on board,the Washington Post reports.Here's what you need to know about what the two astronauts are up to.Who are the astronauts?Wilmore, 61, and Williams, 58, are veteran astronauts and are both naval officers and former test pilots. Williams has been a NASA astronaut since 1998, and Wilmore since 2000. Both have plenty of experience in space.Williams is the former record holder for most spacewalks by a woman (seven) and most spacewalk time for a woman (50 hours, 40 minutes), and in 2007, she ran the first marathon by any person in space.In 2009, Wilmore piloted the Space Shuttle Atlantis on its mission to the ISS, and in 2014, he was part of the ISS crew that used a 3D printer to manufacture a tool -- a ratchet wrench -- in space, the first time humans manufactured something off-world.What was their original mission in space?Wilmore, as commander, and Williams, as pilot, traveled to the ISS on a 15-foot-wide, Boeing-made capsule called Starliner. They launched on June 5 and docked with the ISS on June 6. NASA hopes Starliner will give the organization a new way to get crews to and from the ISS, and the fact that it's Boeing-made is another sign that NASA is starting to lean on the private sector for its human spaceflight options, The New York Timesreported.Wilmore and Williams' ISS mission was supposed to last a mere eight days, during which they'd test out aspects of Starliner and see how it operates with a human crew in space. But due to complications with Starliner, the two astronauts are still up there.What are the astronauts eating?Food on the ISS is a major focus, as fresh produce must be replenished every three months with deliveries from Earth. On Nov. 23, the unpiloted Progress 90 resupply spacecraft successfully docked to the ISS. But the latest food delivery came with an unwanted smell."After opening the Progress spacecraft's hatch, the Roscosmos cosmonauts noticed an unexpected odor and observed small droplets, prompting the crew to close the Poisk hatch to the rest of the Russian segment," aNASA representative saidin a statement posted to social media."Space station air scrubbers and contaminant sensors monitored the station's atmosphere following the observation, and on Sunday, flight controllers determined air quality inside the space station was at normal levels," NASA said. "There are no concerns for the crew, and as of Sunday afternoon, the crew is working to open the hatch between Poisk and Progress while all other space station operations are proceeding as planned."NASA revealed that their menu includes cereal with powdered milk, pizza, shrimp cocktails, roast chicken and tuna.The smell that came along with the spacecraft isn't the only food-related concern of late, with some publications questioning the astronauts' thin appearance based on recent photos.Dr. J.D. Polk, NASA chief health and medical officer, made an official statement saying Williams and Wilmore are just fine. "NASA and our partners have safely conducted long duration missions aboard the orbital laboratory for decades, studying the effects of space on the human body as we prepare for exploration farther into the solar system," Polk said. "Crew health is regularly monitored by dedicated flight surgeons on Earth, and they have an individual diet and fitness regime to ensure they remain healthy throughout their expeditions."Williams said she weighs the same as she did when she reached the space station, in a video interview conducted Nov. 12 on the ISS.What are the astronauts saying?The astronauts have been positive about their experience. At alive news conferencein September, Williams said that despite knowing their mission was scheduled to take only eight days, they'd both been "training for a number of years" for it. They're fully qualified to remain in space for an extended period of time, and to help pilot the SpaceX Dragon spacecraft that'll bring them home next year."It's very peaceful up here," Williams said on Sept. 13, though she added that they miss their families back on Earth.The astronauts are working on research, maintenance and data analysis during their extended stay."We are having a great time here on ISS," Williams saidin a news conferenceheld from orbit in July. "I'm not complaining. Butch isn't complaining that we're up here for a couple of extra weeks." Wilmore and Williams responding to media questions back in March. Houston Chronicle/Hearst Newspapers/Getty ImagesHow did they get stuck in space in the first place?The Starliner was delayed in May due to a problem with a valve in the rocket. Then engineers had to fix a helium leak. That's all bad news for Boeing. It's competing with SpaceX, which has been transporting astronauts to the ISS since 2020, making over 20 successful trips to the space station. Starliner finally launched, atop an Atlas V rocket, on June 5, but some problems came along with it. NASA announced that three helium leaks were identified, one of which was known before flight, and two new ones. In addition to the leaks, the crew had to troubleshoot failed control thrusters, though the craft was able to successfully dock with the ISS. SpaceX has had failures too. A Falcon 9 rocket exploded on the launchpad in 2016. In July of this year, a Falcon 9 rocket experienced a liquid oxygen leak and deployed its satellites in the wrong orbit, The New York Times reported. And a Falcon 9 rocket in late August lost a first-stage booster when it toppled over into the Atlantic Ocean and caught fire.But that said, SpaceX has more than 300 successful Falcon 9 flights to its credit.Stuck in space: A timelineMay: Starliner launch delayed due to a problem with a valve in the rocket, and then a helium leak.June 5: Starliner launches with Williams and Wilmore on board.June 6: Starliner docks with ISS despite dealing with three helium leaks and failed control thrusters.Sept. 6: Starliner departs ISS and lands in New Mexico, leaving Williams and Wilmore behind.Sept. 28: SpaceX Crew-9 mission launches with Hague and Gorbunov on a Dragon spacecraft.Sept. 29: SpaceX Dragon docks with ISS.Dec. 17: NASA announces the launch of four crew members to the ISS will be delayed from February to late March.March 2025 onward: SpaceX Dragon spacecraft will return to Earth with Williams, Wilmore, Hague and Gorbunov.

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