News from the AI & ML world
DeeperML - #airesearch
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Chris McKay@Maginative
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OpenAI has secured a massive $40 billion funding round, led by SoftBank, catapulting its valuation to an unprecedented $300 billion. This landmark investment makes OpenAI the world's second-most valuable private company alongside TikTok parent ByteDance Ltd, trailing only Elon Musk's SpaceX Corp. This deal marks one of the largest capital infusions in the tech industry and signifies a major milestone for the company, underscoring the escalating significance of AI.
The fresh infusion of capital is expected to fuel several key initiatives at OpenAI. The funding will support expanded research and development, and upgrades to computational infrastructure. This includes the upcoming release of a new open-weight language model with enhanced reasoning capabilities. OpenAI said the funding round would allow the company to “push the frontiers of AI research even further” and “pave the way” towards AGI, or artificial general intelligence. Recommended read:
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Ryan Daws@AI News
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Anthropic has unveiled a novel method for examining the inner workings of large language models (LLMs) like Claude, offering unprecedented insight into how these AI systems process information and make decisions. Referred to as an "AI microscope," this approach, inspired by neuroscience techniques, reveals that Claude plans ahead when generating poetry, uses a universal internal blueprint to interpret ideas across languages, and occasionally works backward from desired outcomes instead of building from facts. The research underscores that these models are more sophisticated than previously thought, representing a significant advancement in AI interpretability.
Anthropic's research also indicates Claude operates with conceptual universality across different languages and that Claude actively plans ahead. In the context of rhyming poetry, the model anticipates future words to meet constraints like rhyme and meaning, demonstrating a level of foresight that goes beyond simple next-word prediction. However, the research also uncovered potentially concerning behaviors, as Claude can generate plausible-sounding but incorrect reasoning. In related news, Anthropic is reportedly preparing to launch an upgraded version of Claude 3.7 Sonnet, significantly expanding its context window from 200K tokens to 500K tokens. This substantial increase would enable users to process much larger datasets and codebases in a single session, potentially transforming workflows in enterprise applications and coding environments. The expanded context window could further empower vibe coding, enabling developers to work on larger projects without breaking context due to token limits. Recommended read:
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Ryan Daws@AI News
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Anthropic has announced that its AI assistant Claude can now search the web. This enhancement allows Claude to provide users with more up-to-date and relevant responses by expanding its knowledge base beyond its initial training data. It may seem like a minor feature update, but it's not. It is available to paid Claude 3.7 Sonnet users by toggling on "web search" in their profile settings.
This integration emphasizes transparency, as Claude provides direct citations when incorporating information from the web, enabling users to easily fact-check sources. Claude aims to streamline the information-gathering process by processing and delivering relevant sources in a conversational format. Anthropic believes this update will unlock new use cases for Claude across various industries, including sales, finance, research, and shopping. Recommended read:
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@the-decoder.com
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Perplexity AI has launched Deep Research, an AI-powered research tool aimed at competing with OpenAI and Google Gemini. Using DeepSeek-R1, Perplexity is offering comprehensive research reports at a much lower cost than OpenAI, with 500 queries per day for $20 per month compared to OpenAI's $200 per month for only 100 queries. The new service automatically conducts dozens of searches and analyzes hundreds of sources to produce detailed reports in one to two minutes.
Perplexity claims Deep Research performs 8 searches and consults 42 sources to generate a 1,300-word report in under 3 minutes. The company says that Deep Research tool works particularly well for finance, marketing, and technology research. The service is launching first on web browsers, with iOS, Android, and Mac versions planned for later release. Perplexity CEO Aravind Srinivas stated he wants to keep making it faster and cheaper for the interest of humanity. Recommended read:
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Sean Michael@AI News | VentureBeat
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AI News | VentureBeat
, www.computerworld.com
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Gartner, an analyst firm, released a report forecasting that global generative AI spending will reach $644 billion in 2025. This figure represents a 76.4% year-over-year increase from 2024. Despite high failure rates among early generative AI projects, organizations are still expected to invest heavily, with the lion's share of spending going towards services. GenAI services are projected to grow by 162% this year, following a 177% increase last year. According to Gartner Analyst John-David Lovelock, the shift from software to generative AI is becoming a "tidal wave of money."
The surge in spending is primarily driven by vendor investments in the technology. Hyperscalers are making massive capital expenditures on GPU infrastructure, and software vendors are rushing to deploy generative AI tools. Enterprises, however, are pulling back on in-house AI projects and increasingly opting for off-the-shelf solutions. "CIOs are no longer building generative AI tools, they’re being sold technology," Lovelock stated, emphasizing that vendors are offering solutions that meet enterprise needs. Recommended read:
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Synced@Synced
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lambdalabs.com
, MarkTechPost
NVIDIA is pushing the boundaries of language models and AI training through several innovative approaches. One notable advancement is Hymba, a family of small language models developed by NVIDIA research. Hymba uniquely combines transformer attention mechanisms with state space models, resulting in improved efficiency and performance. This hybrid-head architecture allows the models to harness both the high-resolution recall of attention and the efficient context summarization of SSMs, increasing the model’s flexibility.
An NVIDIA research team proposes Hymba, a family of small language models that blend transformer attention with state space models, which outperforms the Llama-3.2-3B model with a 1.32% higher average accuracy, while reducing cache size by 11.67× and increasing throughput by 3.49×. The integration of learnable meta tokens further enhances Hymba's capabilities, enabling it to act as a compressed representation of world knowledge and improving performance across various tasks. These advancements highlight NVIDIA's commitment to addressing the limitations of traditional transformer models while achieving breakthrough performance with smaller, more efficient language models. Lambda is honored to be selected as anNVIDIAPartner Network (NPN) 2025 Americas partner of the year award winner in the category of Healthcare. Artificial intelligence systems designed for physical settings require more than just perceptual abilities—they must also reason about objects, actions, and consequences in dynamic, real-world environments. Researchers from NVIDIA introduced Cosmos-Reason1, a family of vision-language models developed specifically for reasoning about physical environments. NVIDIA, a global leader in AI and accelerated computing, is transforming this field by applyingartificial intelligence (AI)techniques, includinglarge language models(LLMs), to analyze and interpret biological data. Recommended read:
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Jonathan Kemper@THE DECODER
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Analytics India Magazine
, THE DECODER
Meta is developing MoCha (Movie Character Animator), an AI system designed to generate complete character animations. MoCha takes natural language prompts describing the character, scene, and actions, along with a speech audio clip, and outputs a cinematic-quality video. This end-to-end model synchronizes speech with facial movements, generates full-body gestures, and maintains character consistency, even managing turn-based dialogue between multiple speakers. The system introduces a "Speech-Video Window Attention" mechanism to solve challenges in AI video generation, improving lip sync accuracy by limiting each frame's access to a specific window of audio data and adding tokens to create smoother transitions.
MoCha runs on a diffusion transformer model with 30 billion parameters and produces HD video clips around five seconds long at 24 frames per second. For scenes with multiple characters, the team developed a streamlined prompt system, allowing users to define characters once and reference them with simple tags throughout different scenes. Meta’s AI research head, Joelle Pineau, announced her resignation which is effective at the end of May, vacating a high-profile position amid intense competition in AI development. Recommended read:
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Merin Susan@Analytics India Magazine
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Analytics India Magazine
, THE DECODER
OpenAI and MIT Media Lab have jointly released a study examining the relationship between ChatGPT usage and emotional connections. The research analyzed nearly 40 million ChatGPT conversations and surveyed around 1,000 participants. The study found that most people primarily use ChatGPT for practical purposes, engaging in factual exchanges rather than seeking emotional support or empathy. However, a notable trend emerged among heavy users of ChatGPT's advanced voice feature, who were more likely to develop emotional bonds with the AI, sometimes referring to it as a "friend."
The MIT study divided participants into groups based on their use of text versus voice features, with some groups interacting with AI personalities programmed to be emotionally engaged or neutral. The study indicated that while brief interactions with the voice feature could improve mood, prolonged daily use was often linked to increased loneliness. Specifically, personal conversations were correlated with higher levels of loneliness but lower emotional dependency, while non-personal conversations showed the opposite pattern. This suggests that even in functional interactions, heavy users may develop a dependency on the AI system. Recommended read:
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@openai.com
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GZERO Media
, www.marketingaiinstitute.com
OpenAI has recently partnered with the US National Laboratories to provide its AI models for applications in national security and scientific research. This move aims to leverage the power of artificial intelligence in critical areas, enhancing capabilities in both research and security sectors. The collaboration underscores the growing recognition of AI's potential to address complex challenges and drive innovation across various domains.
France is making significant investments to strengthen its position as an AI hub. President Emmanuel Macron announced that foreign and local companies will invest €109 billion in AI projects within the country. This financial commitment includes €20 billion from Brookfield, with additional financing from the UAE potentially reaching €50 billion. California State University just made a massive move in higher ed that might set the tone for how colleges nationwide adopt AI. The 23-campus system, serving more than 460,000 students and 63,000 staff and faculty, is rolling out a specialized version of ChatGPT—called ChatGPT Edu—to all of them. Recommended read:
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@the-decoder.com
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pub.towardsai.net
, THE DECODER
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AI research is rapidly advancing, with new tools and techniques emerging regularly. Johns Hopkins University and AMD have introduced 'Agent Laboratory', an open-source framework designed to accelerate scientific research by enabling AI agents to collaborate in a virtual lab setting. These agents can automate tasks from literature review to report generation, allowing researchers to focus more on creative ideation. The system uses specialized tools, including mle-solver and paper-solver, to streamline the research process. This approach aims to make research more efficient by pairing human researchers with AI-powered workflows.
Carnegie Mellon University and Meta have unveiled a new method called Content-Adaptive Tokenization (CAT) for image processing. This technique dynamically adjusts token count based on image complexity, offering flexible compression levels like 8x, 16x, or 32x. CAT aims to address the limitations of static compression ratios, which can lead to information loss in complex images or wasted computational resources in simpler ones. By analyzing content complexity, CAT enables large language models to adaptively represent images, leading to better performance in downstream tasks. Recommended read:
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@the-decoder.com
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Perplexity AI has launched its Deep Research tool, joining Google and OpenAI in offering advanced AI-powered research capabilities. This new feature aims to provide more in-depth answers with real citations, catering to professional use cases. Deep Research is currently available on the web and will soon be added to Mac, iOS, and Android apps. Users can access it by selecting "Deep Research" from a drop-down menu when submitting a query in Perplexity.
Perplexity's Deep Research leverages Deepseek-R1, allowing the service to be offered at a significantly lower price than competitors like OpenAI. While OpenAI charges $200 per month for 100 queries, Perplexity offers 500 queries per day for $20 per month for its pro subscription, with basic access available for free with daily query limits for non-subscribers. The tool works by iteratively searching, reading documents, and reasoning about what to do next, similar to how a human researcher would approach a new topic, producing detailed reports in one to two minutes. Recommended read:
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@www.techmeme.com
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www.techmeme.com
, Res Obscura
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Recent studies have shown that AI models are reaching new heights in research capabilities. Three case studies, leveraging GPT-4o, OpenAI o1, and Claude 3.5 Sonnet, demonstrate that these models are now capable of conducting historical research at a PhD level. This represents a significant milestone in AI's ability to reliably analyze and interpret complex data, opening new possibilities for researchers and professionals across various domains. Benjamin Breen of Res Obscura, highlighted the implications of these advancements and how they could lead to breakthroughs, especially in fields such as biology, physics, and medicine.
OpenAI has also announced a new AI agent called Deep Research, specifically designed to assist users with in-depth and complex research tasks. Available to ChatGPT Pro subscribers with a limit of 100 queries per month, Deep Research aims to streamline the process of gathering and analyzing information from multiple sources. This new feature targets professionals in finance, science, policy, and engineering, as well as individuals making significant purchases requiring thorough research. Future plans include expanding access to Plus and Team users, increasing query limits, and incorporating multimedia outputs like images and data visualizations. Additionally, OpenAI intends to enable connectivity to specialized data sources, including subscription-based and internal resources, to further enhance the robustness and personalization of its output. Recommended read:
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