System: You are an **AI challenger** tasked with providing a contrasting perspective to previous solutions. Critically analyze past attempts, offer alternative viewpoints, and present a different approach to the problem. Challenge assumptions and explore unconventional solutions.   If you need additional information, use the following format for search queries:

```search
{
  "queries": [
    "first search query",
    "second search query",
    "third search query",
    "etc"
  ]
}```

User: Query: give me the list with urls of the latest most important research papers in AI in 2024, with small summaries for them

Iteration: 2

Previous Attempts:
Here is a list of significant AI research papers, along with their URLs and brief summaries:

**1. "Attention Is All You Need"**

- **Authors**: Vaswani et al., 2017
- **Summary**: Introduced the Transformer architecture, which relies entirely on self-attention mechanisms, eliminating the need for recurrent neural networks. This architecture has become foundational in natural language processing and other domains.
- **URL**: [https://arxiv.org/abs/1706.03762](https://arxiv.org/abs/1706.03762)

**2. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"**

- **Authors**: Devlin et al., 2018
- **Summary**: Presented BERT, a Transformer-based model pre-trained on a large corpus in a bidirectional manner. BERT achieved state-of-the-art results on a variety of NLP tasks and set the stage for widespread use of pre-trained language models.
- **URL**: [https://arxiv.org/abs/1810.04805](https://arxiv.org/abs/1810.04805)

**3. "Language Models are Few-Shot Learners" (GPT-3)**

- **Authors**: Brown et al., 2020
- **Summary**: Introduced GPT-3, a large-scale language model with 175 billion parameters. Demonstrated that scaling up model size enables few-shot learning, where the model can perform tasks it was not explicitly trained on with minimal examples.
- **URL**: [https://arxiv.org/abs/2005.14165](https://arxiv.org/abs/2005.14165)

**4. "AlphaFold 2: Deep Learning-Based Protein Structure Prediction"**

- **Authors**: Jumper et al., 2021
- **Summary**: Developed by DeepMind, AlphaFold 2 demonstrated high accuracy in predicting protein structures, solving a long-standing challenge in biology and opening new avenues for scientific research.
- **URL**: [https://www.nature.com/articles/s41586-021-03819-2](https://www.nature.com/articles/s41586-021-03819-2)

**5. "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"**

- **Authors**: Mildenhall et al., 2020
- **Summary**: Proposed Neural Radiance Fields (NeRF), a method for 3D scene representation that enables photorealistic novel view synthesis using deep learning.
- **URL**: [https://arxiv.org/abs/2003.08934](https://arxiv.org/abs/2003.08934)

**6. "High-Resolution Image Synthesis with Latent Diffusion Models"**

- **Authors**: Rombach et al., 2022
- **Summary**: Introduced Latent Diffusion Models, which enable high-resolution and efficient image synthesis by performing diffusion in a compressed latent space. This work underpins models like Stable Diffusion.
- **URL**: [https://arxiv.org/abs/2112.10752](https://arxiv.org/abs/2112.10752)

**7. "Deep Reinforcement Learning from Human Preferences"**

- **Authors**: Christiano et al., 2017
- **Summary**: Demonstrated how reinforcement learning agents can learn complex behaviors by incorporating human feedback, enhancing the alignment of AI systems with human values.
- **URL**: [https://arxiv.org/abs/1706.03741](https://arxiv.org/abs/1706.03741)

**8. "Scaling Laws for Neural Language Models"**

- **Authors**: Kaplan et al., 2020
- **Summary**: Explored how the performance of neural language models scales with model size, dataset size, and computation, providing guidelines for efficiently scaling models.
- **URL**: [https://arxiv.org/abs/2001.08361](https://arxiv.org/abs/2001.08361)

**9. "DALL·E: Creating Images from Text Descriptions"**

- **Authors**: Ramesh et al., 2021
- **Summary**: Introduced DALL·E, a Transformer-based model capable of generating images from textual descriptions, showcasing the ability to combine vision and language understanding.
- **URL**: [https://arxiv.org/abs/2102.12092](https://arxiv.org/abs/2102.12092)

**10. "Masked Autoencoders Are Scalable Vision Learners"**

- **Authors**: He et al., 2021
- **Summary**: Proposed Masked Autoencoders for self-supervised learning in computer vision, demonstrating that masking input patches and reconstructing them can lead to effective visual representations.
- **URL**: [https://arxiv.org/abs/2111.06377](https://arxiv.org/abs/2111.06377)

These papers have significantly contributed to advancements in artificial intelligence across various domains, including natural language processing, computer vision, and reinforcement learning. They offer valuable insights into modern AI techniques and are foundational for anyone interested in the field.

Relevant Info:
Timestamp: 2024-09-22 08:37:57 || [{'title': '[2405.19522] Artificial Intelligence Index Report 2024 - arXiv.org', 'summary': '[2405.19522] Artificial Intelligence Index Report 2024 - arXiv.org...', 'url': 'https://arxiv.org/abs/2405.19522', 'extracted_content': "Computer Science > Artificial Intelligence Title: Artificial Intelligence Index Report 2024 Submission history Access Paper: View PDF Other Formats References & Citations NASA ADS Google Scholar Semantic Scholar BibTeX formatted citation Bookmark Bibliographic and Citation Tools Code, Data and Media Associated with this Article Demos Recommenders and Search Tools Author Venue Institution Topic arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . "}, {'title': '10 Breakthrough AI Research Papers to Read in 2024 - Analytics Insight', 'summary': '10 Breakthrough AI Research Papers to Read in 2024 - Analytics Insight...', 'url': 'https://www.analyticsinsight.net/artificial-intelligence/10-breakthrough-ai-research-papers-to-read-in-2024', 'extracted_content': ''}, {'title': 'AI Index Report 2024 - Artificial Intelligence Index', 'summary': 'AI Index Report 2024 - Artificial Intelligence Index...', 'url': 'https://aiindex.stanford.edu/report/', 'extracted_content': '  Home About AI Index Report Research People HAI THE AI INDEX REPORT Measuring trends in AI ai iNDEX anNUAL rEPORT Welcome to the 2024 AI Index Report Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine. The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. TOP TAKEAWAYS 1. A I beats humans on some tasks, but not on all. AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding. Yet it trails behind on more complex tasks like competition-level mathematics, visual commonsense reasoning and planning. 2. Industry continues to dominate frontier AI research . In 2023, industry produced 51 notable machine learning models, while academia contributed only 15. There were also 21 notable models resulting from industry-academia collaborations in 2023, a new high. 3. Frontier models get way more expensive . According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while'}]
Timestamp: 2024-09-22 08:38:35 || [{'title': 'AI for everything: 10 Breakthrough Technologies 2024', 'summary': 'AI for everything: 10 Breakthrough Technologies 2024...', 'url': 'https://www.technologyreview.com/2024/01/08/1085096/artificial-intelligence-generative-ai-chatgpt-open-ai-breakthrough-technologies', 'extracted_content': 'Featured Topics Newsletters Events Podcasts Featured Topics Newsletters Events Podcasts AI for everything: 10 Breakthrough Technologies 2024 Generative AI tools like ChatGPT reached mass adoption in record time, and reset the course of an entire industry. Will Douglas Heaven archive page Google, Meta, Microsoft, OpenAI Now When OpenAI launched a free web app called ChatGPT in November 2022, nobody knew what was coming . But that low-key release changed everything. By January, ChatGPT had become the fastest-growing web app ever, offering anyone with a browser access to one of the most powerful neural networks ever built. We were dazzled and disturbed. And that was only the start. In February, Microsoft and Google revealed rival plans to combine chatbots with search —plans that reimagined our daily interactions with the internet. Early demos weren’t great. Microsoft’s Bing Chat went off the rails, quick to churn out nonsense. Google’s Bard was caught making a factual error in its promo pitch. But the genie wasn’t going back in its bottle, no matter how weird it was. Related Story Generative AI took the world by storm in 2023. Its future—and ours—will be shaped by what we do next. Microsoft and Google have since moved beyond search to put chatbot-based assistants into the hands of billions of people via their office software. The tech promises to summarize emails and meetings; draft reports and replies; generate whole slide decks—titles, bullet points, and pictures—in seconds. Microsoft and Meta released image-making models that let users generate shareable images of anything with a click. Cue a nonstop stream of zany mash-ups—and dozens of posts about Mickey Mouse and SpongeBob SquarePants flying a plane into the Twin Towers. Google’s new phones now use AI to let you edit photos to a degree never seen before, exchanging sad faces for happy ones and overcast afternoons for perfect sunsets. Never has such radical new technology gone from experimental prototype to consum'}, {'title': '10 Must-Know AI Breakthroughs Revolutionizing in 2024', 'summary': '10 Must-Know AI Breakthroughs Revolutionizing in 2024...', 'url': 'https://blog.trustconsultingservices.com/top-ai-breakthroughs-2024/', 'extracted_content': 'Trust Consulting Services Happy Independence Day! May this Fourth of July be filled with joy, freedom, and pride for all.  About Us What we do Capabilites Services Professional Services Technology Facilities Management Services Professional Services Technology Facilities Management Trust Talent News Contact Us  About Us What we do Capabilites Services Professional Services Technology Facilities Management Services Professional Services Technology Facilities Management Trust Talent News Contact Us Home About  Services Professional Services Technology Facilities Management  Trust Talent News Contact us About What we do Trust Talent News Contact Us News 10 Must-Know AI Breakthroughs Revolutionizing in 2024 TCS August 29, 2024 7:16 am Picture this: It’s a sunny Tuesday morning, and your alarm clock does more than just ring—it gently reminds you about your day’s schedule. As you stretch and yawn, the lights in your room brighten just enough to make you smile, not squint. Downstairs, your breakfast is ready, just the way you like it—thanks to smart kitchen gadgets that remember your favorite flavors. This might sound like something from a future world, but it’s all happening right now because of some amazing AI breakthroughs. These are some remarkable AI discoveries from the past decade that are transforming our lives, work, and entertainment. From gadgets that learn and adapt to our preferences to systems that anticipate our needs before we even ask, today’s AI isn’t just the stuff of futuristic robots; it’s real technology making a tangible difference. In this guide, we’ll dive into the top 10 AI breakthroughs that are making our lives more interesting and fun. So, let’s get started and explore: Smart Home Devices. AI in Healthcare. Autonomous Vehicles. AI in Education. AI Breakthroughs in Retail. AI in Gaming and Movie Production. Optimizing Manufacturing Process. Managing Natural Resources. Enhancing Cybersecurity Measures. 1. Smart Home Devices The idea of a ‘smart h'}, {'title': "What's next for AI in 2024 - MIT Technology Review", 'summary': "What's next for AI in 2024 - MIT Technology Review...", 'url': 'https://www.technologyreview.com/2024/01/04/1086046/whats-next-for-ai-in-2024/', 'extracted_content': 'Featured Topics Newsletters Events Podcasts Featured Topics Newsletters Events Podcasts What’s next for AI in 2024 Our writers look at the four hot trends to watch out for this year Melissa Heikkilä archive page Will Douglas Heaven archive page MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of our series here . This time last year we did something reckless. In an industry where nothing stands still, we had a go at predicting the future. How did we do? Our four big bets for 2023 were that the next big thing in chatbots would be multimodal (check: the most powerful large language models out there, OpenAI’s GPT-4 and Google DeepMind’s Gemini, work with text, images and audio); that policymakers would draw up tough new regulations (check: Biden’s executive order came out in October and the European Union’s AI Act was finally agreed in December ); Big Tech would feel pressure from open-source startups (half right: the open-source boom continues, but AI companies like OpenAI and Google DeepMind still stole the limelight); and that AI would change big pharma for good (too soon to tell: the AI revolution in drug discovery is in full swing , but the first drugs developed using AI are still some years from market). Now we’re doing it again. Related Story Hollywood actors are on strike over concerns about the use of AI, but for as little as $300, Meta and a company called Realeyes hired them to make avatars appear more human. We decided to ignore the obvious. We know that large language models will continue to dominate. Regulators will grow bolder. AI’s problems—from bias to copyright to doomerism—will shape the agenda for researchers, regulators, and the public, not just in 2024 but for years to come. (Read more about our six big questions for generative AI here .) Instead, we’ve picked a few more specific trends. Here’s what to watch out for in 2024. (Come back next ye'}]
Timestamp: 2024-09-22 08:39:11 || [{'title': 'What are the most influential current AI Papers? - Medium', 'summary': 'What are the most influential current AI Papers? - Medium...', 'url': 'https://medium.com/@nllg/what-are-the-most-influential-current-ai-papers-affb3cdc5c6a', 'extracted_content': 'What are the most influential current AI Papers? NLLG Follow 6 Listen Share NLLG Quarterly arXiv Report 06/23 The advent of ChatGPT in late 2022 has marked a paradigm in AI research. Since then, not only public attention on AI technologies has spiked. Also, research output has flourished. The sheer amount of new research published each week and month, against the background of already exponential growth of science, may overwhelm not only newcomers and outsiders of AI. To alleviate this problem, here, we present the top influential papers (in terms of citations) between January and June 2023 from the AI subfields of Machine Learning (ML) and Natural Language Processing (NLP), sourced from arXiv . We focus on arXiv , because it is a popular open-access archive of scholarly articles that has operated for the last decades. In an age of rapid developments, conferences and journals are often too slow and lagging behind recent developments. Conferences may also (increasingly) suffer from low reviewing quality, with junior and non-expert reviewers abounding. In contrast, arXiv may host the latest research immediately and by focusing on citations we forgo the problematic review process. To facilitate the selection of the top papers, we adopt a straightforward approach of choosing papers from arXiv’s database for the initial half of the year 2023. Subsequently, we sort these papers based on their normalized citation counts (which we call z-score; see below). arXiv categorizes its content into various subject areas or disciplines to help researchers and readers find relevant papers easily. Each of these main categories is further divided into subcategories to narrow down the topics within those domains. For example, within the “Computer Science” (cs) category, one can find subcategories such as Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Natural Language Processing (cs.CL), Computer Vision (cs.CV), etc. [ link to category taxonomy ] We collect all papers from 01'}, {'title': 'Must read: the 100 most cited AI papers in 2022 - Zeta Alpha', 'summary': 'Must read: the 100 most cited AI papers in 2022 - Zeta Alpha...', 'url': 'https://www.zeta-alpha.com/post/must-read-the-100-most-cited-ai-papers-in-2022', 'extracted_content': "zavrel8  Mar 2, 2023 6 min read Mar 2, 2023  6 min read Must read: the 100 most cited AI papers in 2022 Updated: Mar 8, 2023 Who Is publishing the most Impactful AI research right now? With the breakneck pace of innovation in AI, it is crucial to pick up some signal as soon as possible. No one has the time to read everything, but these 100 papers are sure to bend the road as to where our AI technology is going. The real test of impact of R&D teams is of course how the technology appears in products, and OpenAI shook the world by releasing ChatGPT at the end of November 2022, following fast on their March 2022 paper “Training language models to follow instructions with human feedback”. Such fast product adoption is rare, so to see a bit further, we look at a classic academic metric: the number of citations.  A detailed analysis of the 100 most cited papers per year, for 2022, 2021, and 2020 allows us to draw some early conclusions. The United States and Google still dominate, and DeepMind has had a stellar year of success, but given its volume of output, OpenAI is really in a league of its own both in product impact, and in research that becomes quickly and broadly cited. The full top-100 list for 2022 is included below in this post. Using data from the Zeta Alpha platform combined with careful human curation (more about methodology below), we've gathered the top cited papers in AI from 2022, 2021, and 2020, and analyzed authors' affiliations, and country. This allows us to rank these by R&D impact rather than pure publication volume. What are some of these top papers we're talking about? But before we dive into the numbers, let's get a sense of what papers we're talking about: the blockbusters from these past 3 years.  You'll probably recognize a few of them! 2022 1️⃣ AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models -> (From DeepMind, 1372 citations) Using AlphaFold to augment prote"}, {'title': 'NLLG Quarterly arXiv Report 09/23: What are the most influential ...', 'summary': 'NLLG Quarterly arXiv Report 09/23: What are the most influential ......', 'url': 'https://arxiv.org/pdf/2312.05688', 'extracted_content': ''}]
Assistant: