AWS looks to make RAG easier and automatic with new enterprise AI tools that help organizations with structured data retrieval, GraphRAG and unstructured data support.Read More

IllFonic "re-aligns to a refined strategy" as it makes "cuts" to its teamsIt's unclear how many staff have been impacted by the cutsImage credit: IllFonic News by Vikki Blake Contributor Published on Dec. 4, 2024 Developer IllFonic has confirmed it has made "cuts" to its staff.In a brief statement, CEO and co-founder Charles Brungardt said it had to "accept the harsh reality" of the "state of the industry" and "re-align to a refined strategy" "with the heaviest of hearts."Today we had to accept the harsh reality that the state of the industry has impacted us here at IllFonic," Brungardt wrote in the statement posted to social media."It is with the heaviest of hearts that cuts to our teams had to be made today as we re-aligned to a refined strategy."There is a lot of talent in this group and if you or your team is hiring please reach out here so people can be connected to open roles," the statement concluded. "Thank you."Brungardt's words stopped short of confirming how many staff had been impacted by the cuts, or how it expects to reshape its strategy going forward. It's also unclear how this may impact the studio's development pipeline.Founded in 2007, IllFonic is an independent video game studio located in Colorado and Washington. Its credits include Killer Klowns From Outer Space: The Game, Ghostbusters: Spirits Unleashed, Arcadegeddon, Predator: Hunting Grounds, Friday the 13th: The Game, Dead Alliance, Star Citizen, Evolve, Armored Warfare, and Nexuiz.

Nintendo delays Alarmo's public launch in JapanThere is no evidence that the "situation" also impacts supplies in the US and EuropeImage credit: Nintendo News by Vikki Blake Contributor Published on Dec. 4, 2024 Nintendo has walked back plans to publicly launch its Alarmo clock in Japan this January.In a statement posted online, the Japanese company said that it was now delaying plans to make the alarm clock available to those who don't subscribe to Nintendo Switch Online due to "the production and inventory situation of this product."For now, Nintendo is unable to confirm how long the postponement of Alarmo sales for Japanese customers will last, but it seems December pre-orders won't ship until February 2025."We had planned to start general sales to customers who are not Nintendo Switch Online subscribers from mid-February 2025, but in consideration of the production and inventory situation of this product, we have decided to postpone the start," the statement said (as machine translated)."The start of sales will also be postponed at game stores nationwide. We sincerely apologise for not meeting your expectations. The postponed start date of the sales will be announced once it has been decided."The product was so popular in Japan that even Nintendo Switch Online subscribers had to enter a lottery to be in with a chance of securing the clock.At the time of writing, there is no evidence the "situation" also impacts supplies in the US and Europe.

Denver developer Illfonic says they've had to "accept the harsh reality" of the game industry today and cut staff.On X, studio CEO Charles Brungardt explained the studio eliminated roles to "realign to a refined strategy." At the time of writing, it's unclear exactly how many employees were impacted.Illfonic is primarily known for making multiplayer games based on horror film franchises. It was the original creator of Friday the 13th: The Game before Black Tower Studios took control of its development. After that transfer, it made 2018's Predator: Hunting Grounds, 2022's Ghostbusters Unleashed, and this year's Killer Klowns from Outer Space.Separate from that, it also developed original titles Arcadegeddon and Nexuiz, and has also been a support studio on titles such as Crysis 3 and Star Citizen.This makes the newest studio to lay off staff this week. Yesterday, December 3, Ubisoft announced the closure of its San Francisco and Osaka studios alongside the end of its free-to-play shooter, xDefiant. A team based in Sydney is also being "ramped down," and 277 overall employees will be impacted.That same day, Deceive Inc. creator Sweet Bandits Studios announced its own closure. The 18-person team explained it had reached a "breaking point" trying to keep it sustainable since its release in March 2023. As the team disbands, it and its publisher, Tripwire Interactive, are looking at ways to maintain the title for as long as possible.Game Developer has reached out to Illfonic about the number of impacted employees and the cause for its layoffs and will update when a response is given.

Justin Carter, Contributing EditorDecember 4, 20241 Min ReadImage via Playgama.At a Glance'We want to transform the web gaming market and empower HTML5 devs to easily reach audiences they once could only dream of.'Game tech platform Playgama recently raised $3 million in funding, which it will put toward a Platform-as-a-Service (PaaS) ecosystem for games made in HTML5.With funding primarily from firms The Open Platform and s16vc, the company is hoping to get in HTML5's "renaissance" in games. In the announcement on December 3, it outlined its plans for its PaaS ecosystem, which will include a unified SDK to allow for publishing across multiple platforms, a QA tool that consolidates those platforms' requirements into a single list, and tools for monetization, marketing, and payments.Playgama noted how HTML5's game market is made up of "thousands of standalone playgrounds" compared to the more centralized storefronts for Epic Games on PC and iOS or Google Play on mobile. That "fragmented" market means "leaders capture only small fractions of the global web gaming audience. [...] Theres currently no way for developers to access the entire market at once, and [we] aim to change this.""Our vision is to streamline distribution through a 'master key' approach, providing developers with a simple and efficient route to tap into all available platforms, languages, and markets, maximizing their chances of reaching a large and diverse audience," explained Playgama. Its PaaS ecosystem will run on its open-source Bridge SDK, which it says simplifies game integration simultaneously across multiple platforms and is already compatible with Unity, Godot, and other engines."We want to transform the web gaming market and empower HTML5 game creators to effortlessly reach audiences they once could only dream of," said CEO Dmitry Kachmar. "With this funding, well accelerate further improvement of Playgamas platform for developers, expand our toolkit, and introduce advanced analytics and fintech solutions."Read more about:FundingAbout the AuthorJustin CarterContributing Editor, GameDeveloper.comA Kansas City, MO native, Justin Carter has written for numerous sites including IGN, Polygon, and SyFy Wire. In addition to Game Developer, his writing can be found at io9 over on Gizmodo. Don't ask him about how much gum he's had, because the answer will be more than he's willing to admit.See more from Justin CarterDaily news, dev blogs, and stories from Game Developer straight to your inboxStay UpdatedYou May Also Like

Humane, which makes the not-great AI Pin, wants other companies to build AI devices and gadgets that use its CosmOS operating system, and it has released a video that appears to show that the company already has it working in a car, TV, smart speaker, and phone.But note that the video, according to Humanes own fine print, is for illustrative purposes only it shows working prototypes and some simulated experiences, and the print says that all designs, features, and specifications are subject to change. So dont take it entirely at face value.In one example, the video shows a person talking to CosmOS in their car (with a blurred out logo on the steering wheel) to turn the heat up at their house and figure out what time people are coming over. They ask their (blurred out) smart speaker about a guacamole recipe, and their TV about how many goals a soccer player onscreen has scored. The video also shows CosmOS reading an email on the persons phone and responding to a question about whether the person can attend a meeting.If youve been following recent AI hype, especially around agents, none of these examples should feel particularly novel Humane wants to demonstrate that CosmOS is capable of powerful agent-like capabilities, and for companies to consider it as a possible backbone for their devices. But the items in this video arent Humanes own products, and Humane clearly isnt promising to make them. Its building an SDK for others to do so.RelatedThat CosmOS SDK isnt available publicly yet the companys website only says that its coming soon, though you can click a button to sign up to build with us, which takes you to a form to fill out. Humane doesnt mention any partners building devices that rely on CosmOS the blurred-out logos on the car and smart speaker suggest the company may have not gotten that far yet. Weve asked Humane if it can share any examples.Humane may be looking for a new line of business after the AI Pin flopped; we reported in August that daily returns of the device were outpacing sales. The product initially launched in April, but the company dropped the price of the Pin just six months later. Earlier this year, Humane was reportedly looking for a buyer, with HP at one point being a contender.

Jeff Bezos and President-elect Donald Trump famously didnt get along the last time Trump was in the White House. This time, Bezos says hes very optimistic and even wants to help out.Im actually very optimistic this time around, Bezos said of Trump during a rare public appearance at The New York Times DealBook Summit on Wednesday. He seems to have a lot of energy around reducing regulation. If I can help him do that, Im going to help him.Trump railed against Bezos and his companies Amazon, Blue Origin, and The Washington Post during his 2016 term. Bezos defended himself but it did little to help his reputation with Trump. Now, his companies have a lot at stake in the coming administration, from the FTCs antitrust lawsuit against Amazon to Blue Origins efforts to compete with SpaceX for government contracts. Onstage at the DealBook Summit on Wednesday, Bezos called Trump calmer this time and more settled. He said he will try to talk him out of the idea that the press, which includes The Washington Post, is an enemy of the people. Youve probably grown in the last eight years, he said to DealBooks Andrew Ross Sorkin. He has, too.Bezos also echoed Sam Altmans comments earlier in the day, saying he doesnt expect Elon Musk to wield his new political power with DOGE against rivals. Ive had a lot success in life not being cynical, he said. And Ive rarely been taken advantage of as a result.You can watch Bezoss conversation with Andrew Ross Sorkin below:

A Sneak Peek At Godot 4.4 / News / December 4, 2024 / GodotBack in September, shortly after the release of Godot 4.3, we took a first look at Godot 4.4. Since then there have been several new features added to the upcoming Godot release in the form of Dev3, Dev4 and Dev5 releases.In the video below we take a look at several of the new features hands-on, including:Favoriting of properties in the Inspector windowPhysics based snapping for object placement in the worldThe new Game tab and the ability to inspect with objects in the running gameREPL inspection in the Evaluator tabAutomatic creation of UIDsKey LinksGodot 4.4 Dev 5 UpdateGodot 4.4 Dev 4 UpdateGodot 4.4 Dev 3 UpdateGDQuest TPS Controller Demo You can see all of the above-mentioned upcoming Godot 4.4 features in action in the video below.

Medprompt, a run-time steering strategy, demonstrates the potential of guiding general-purpose LLMs to achieve state-of-the-art performance in specialized domains like medicine. By employing structured, multi-step prompting techniques such as chain-of-thought (CoT) reasoning, curated few-shot examples, and choice-shuffle ensembling, Medprompt bridges the gap between generalist and domain-specific models. This approach significantly enhances performance on medical benchmarks like MedQA, achieving nearly a 50% reduction in error rates without model fine-tuning. OpenAIs o1-preview model further exemplifies advancements in LLM design by incorporating run-time reasoning to refine outputs dynamically, moving beyond traditional CoT strategies for tackling complex tasks.Historically, domain-specific pretraining was essential for high performance in specialist areas, as seen in models like PubMedBERT and BioGPT. However, the rise of large generalist models like GPT-4 has shifted this paradigm, with such models surpassing domain-specific counterparts on tasks like the USMLE. Strategies like Medprompt enhance generalist model performance by integrating dynamic prompting methods, enabling models like GPT-4 to achieve superior results on medical benchmarks. Despite advancements in fine-tuned medical models like Med-PaLM and Med-Gemini, generalist approaches with refined inference-time strategies, exemplified by Medprompt and o1-preview, offer scalable and effective solutions for high-stakes domains.Microsoft and OpenAI researchers evaluated the o1-preview model, representing a shift in AI design by incorporating CoT reasoning during training. This reasoning-native approach enables step-by-step problem-solving at inference, reducing reliance on prompt engineering techniques like Medprompt. Their study found that o1-preview outperformed GPT-4, even with Medprompt, across medical benchmarks, and few-shot prompting hindered its performance, suggesting in-context learning is less effective for such models. Although resource-intensive strategies like ensembling remain viable, o1-preview achieves state-of-the-art results at a higher cost. These findings highlight a need for new benchmarks to challenge reasoning-native models and refine inference-time optimization.Medprompt is a framework designed to optimize general-purpose models like GPT-4 for specialized domains such as medicine by combining dynamic few-shot prompting, CoT reasoning, and ensembling. It dynamically selects relevant examples, employs CoT for step-by-step reasoning, and enhances accuracy through majority-vote ensembling of multiple model runs. Metareasoning strategies guide computational resource allocation during inference, while external resource integration, like Retrieval-Augmented Generation (RAG), ensures real-time access to relevant information. Advanced prompting techniques and iterative reasoning frameworks, such as Self-Taught Reasoner (STaR), further refine model outputs, emphasizing inference-time scaling over pre-training. Multi-agent orchestration offers collaborative solutions for complex tasks.The study evaluates the o1-preview model on medical benchmarks, comparing its performance with GPT-4 models, including Medprompt-enhanced strategies. Accuracy, the primary metric, is assessed on datasets like MedQA, MedMCQA, MMLU, NCLEX, and JMLE-2024, as well as USMLE preparatory materials. Results show that o1-preview often surpasses GPT-4, excelling in reasoning-intensive tasks and multilingual cases like JMLE-2024. Prompting strategies, particularly ensembling, enhance performance, though few-shot prompting can hinder it. o1-preview achieves high accuracy but incurs greater costs compared to GPT-4o, which offers a better cost-performance balance. The study highlights tradeoffs between accuracy, price, and prompting approaches in optimizing large medical language models.In conclusion, OpenAIs o1-preview model significantly advances LLM performance, achieving superior accuracy on medical benchmarks without requiring complex prompting strategies. Unlike GPT-4 with Medprompt, o1-preview minimizes reliance on techniques like few-shot prompting, which sometimes negatively impacts performance. Although ensembling remains effective, it demands careful cost-performance trade-offs. The model establishes a new Pareto frontier, offering higher-quality results, while GPT-4o provides a more cost-efficient alternative for certain tasks. With o1-preview nearing saturation on existing benchmarks, there is a pressing need for more challenging evaluations to further explore its capabilities, especially in real-world applications.Check out the Details and 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. If you like our work, you will love ournewsletter.. Dont Forget to join our60k+ ML SubReddit. [Must Attend Webinar]: Transform proofs-of-concept into production-ready AI applications and agents (Promoted)The post Advancing Medical AI: Evaluating OpenAIs o1-Preview Model and Optimizing Inference Strategies appeared first on MarkTechPost.

Understanding protein sequences and their functions has always been a challenging aspect of protein research. Proteins, often described as the building blocks of life, are made up of long, complex sequences that determine their roles in biological systems. Despite advancements in computational biology, making sense of these sequences in a meaningful way is still a difficult task. Traditional methods for analyzing proteins are both time-consuming and expensive. Even with recent technological progress, researchers struggle to map the vast diversity of protein structures and their functional variations found in nature. This gap between available data and practical insights remains a significant hurdle in developing new therapeutics, bioengineering solutions, and tackling broader challenges in health and environmental sciences. The need for a comprehensive tool to analyze proteins at an unprecedented scale has never been more urgent.EvolutionaryScale has released ESM Cambrian, a new language model trained on protein sequences at a scale that captures the diversity of life on Earth. ESM Cambrian represents a major step forward in bioinformatics, using machine learning techniques to better understand protein structures and functions. The model has been trained on millions of protein sequences, covering an immense range of biodiversity, to uncover the underlying patterns and relationships in proteins. Just as large language models have transformed our understanding of human language, ESM Cambrian focuses on protein sequences that are fundamental to biological processes. It aims to be a versatile model capable of predicting structure, function, and facilitating new discoveries across different species and protein families.Technical DetailsThe technical foundation of ESM Cambrian is as impressive as its goals. EvolutionaryScale has released different versions of the model, including ESM C 300M and ESM C 600M, with the weights openly available for the research community. These models strike a balance between scale and practicality, enabling scientists to make powerful predictions without the infrastructure challenges that come with very large models. The largest variant, ESM C 6B, is available on EvolutionaryScale Forge for academic research and on AWS Sagemaker for commercial use, with plans to launch on NVIDIA BioNemo soon. These platforms make it easy for users in both academic and industrial settings to access this tool.The model, based on the transformer architecture, uses self-attention mechanisms to identify complex relationships within protein sequences, making it well-suited for tasks like predicting protein folding or discovering novel functions. One of the main benefits of ESM Cambrian is its ability to generalize knowledge across different proteins, potentially speeding up the discovery of new drugs and synthetic biology applications.ESM Cambrian was trained in two stages to achieve its high performance. In Stage 1, for the first 1 million training steps, the model used a context length of 512, with metagenomic data making up 64% of the training dataset. In Stage 2, the model underwent an additional 500,000 training steps, during which the context length was increased to 2048, and the proportion of metagenomic data was reduced to 37.5%. This staged approach allowed the model to learn effectively from a diverse set of protein sequences, improving its ability to generalize across different proteins.Early Results and InsightsEarly testing of ESM Cambrian has shown promising results. The models ability to predict the structure and function of protein sequences is comparable to traditional experimental methods, offering significant savings in both time and cost. Evaluations were conducted using the methodology of Rao et al. to measure the unsupervised learning of protein tertiary structure through contact maps. A logistic regression was used to identify contacts, and the precision of the top L contacts (P@L) was evaluated for proteins of length L, with a sequence separation of 6 or more residues. The average P@L was computed on a temporally held-out set of protein structures (with a cutoff date of May 1, 2023) for scaling laws and on the CASP15 benchmark for performance evaluation. Initial insights suggest that ESM Cambrian performs well in generalizing across poorly studied protein families, helping researchers uncover hidden relationships in sequences that are otherwise difficult to analyze. Its predictive accuracy also opens new possibilities in enzyme engineering, where understanding the subtle nuances of protein activity is crucial.The availability of ESM Cambrian on platforms like AWS Sagemaker and NVIDIA BioNemo will make it easier for commercial users to integrate machine learning tools into their existing workflows. EvolutionaryScales decision to release open weights for ESM C 300M and ESM C 600M reflects a commitment to open science, encouraging collaboration to better understand the fundamentals of life on Earth.ConclusionThe release of ESM Cambrian by EvolutionaryScale marks an important milestone in computational biology and protein science. By providing a model that can analyze protein sequences at a scale that captures the diversity of Earths biodiversity, EvolutionaryScale has shown the potential of applying AI in biological research and opened up numerous opportunities for accelerating discovery and innovation. ESM Cambrian is set to play a key role in protein engineering, drug discovery, and gaining a deeper understanding of biological systems. As the scientific community begins to explore the applications of this model, it is clear that the future of protein research is evolving, with tools like ESM Cambrian leading the way.Check out the Details and GitHub Page. 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. If you like our work, you will love ournewsletter.. Dont Forget to join our60k+ ML SubReddit. Asif RazzaqAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences. FREE AI WEBINAR: 'Fast-Track Your LLM Apps with deepset & Haystack'(Promoted)