Boston police say they received a call about an individual walking around with what looked like a rifle or long gun, but officers have yet to find someone matching that description.

East Boston schools put into safe mode after report of armed person in area

Bitterness over the contentious legal action was evident from both sides this week.

Google to purge 'private' Incognito mode user records, but will keep snooping

Google has agreed to settle a lawsuit from consumer who alleged the company tracked user data even in private browsing <strong>mode</strong>.

Google agrees to delete billions of Incognito mode data records | Mashable

MODE

mode

Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References 2.2 Model Training The original GPT4All model was a fine tuned variant of LLaMA 7B. In order to train it more efficiently, we froze the base weights of LLaMA, and only trained a small set of LoRA (Hu et al., 2021) weights during the fine tuning process. Detailed model hyper-parameters and training code can be found in our associated code repository[1]. 2.3 Model Access We publicly released all data, training code, and model weights for the community to build upon. Further, we provided a 4-bit quantized version of the model, which enabled users to run it on their own commodity hardware without transferring data to a 3rd party service. Our research and development costs were dominated by ∼$800 in GPU spend (rented from Lambda Labs and Paperspace) and ∼$500 in OpenAI API spend. Our final GPT4All model could be trained in about eight hours on a Lambda Labs DGX A100 8x 80GB for a total cost of ∼$100. 2.4 Model Evaluation We performed a preliminary evaluation of our model using the human evaluation data from the Self Instruct paper (Wang et al., 2023). We reported the ground truth perplexity of our model against what was, to our knowledge, the best openly available alpaca-lora model at the time, provided by user chainyo on HuggingFace. Both models had very large perplexities on a small number of tasks, so we reported perplexities clipped to a maximum of 100. We found that GPT4All produces stochastically lower ground truth perplexities than alpaca-lora (Anand et al., 2023). This paper is available on arxiv under CC BY 4.0 DEED license. [1] https://github.com/nomic-ai/gpt4all Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References 2.2 Model Training The original GPT4All model was a fine tuned variant of LLaMA 7B. In order to train it more efficiently, we froze the base weights of LLaMA, and only trained a small set of LoRA (Hu et al., 2021) weights during the fine tuning process. Detailed model hyper-parameters and training code can be found in our associated code repository[1]. 2.3 Model Access We publicly released all data, training code, and model weights for the community to build upon. Further, we provided a 4-bit quantized version of the model, which enabled users to run it on their own commodity hardware without transferring data to a 3rd party service. Our research and development costs were dominated by ∼$800 in GPU spend (rented from Lambda Labs and Paperspace) and ∼$500 in OpenAI API spend. Our final GPT4All model could be trained in about eight hours on a Lambda Labs DGX A100 8x 80GB for a total cost of ∼$100. 2.4 Model Evaluation We performed a preliminary evaluation of our model using the human evaluation data from the Self Instruct paper (Wang et al., 2023). We reported the ground truth perplexity of our model against what was, to our knowledge, the best openly available alpaca-lora model at the time, provided by user chainyo on HuggingFace. Both models had very large perplexities on a small number of tasks, so we reported perplexities clipped to a maximum of 100. We found that GPT4All produces stochastically lower ground truth perplexities than alpaca-lora (Anand et al., 2023). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] https://github.com/nomic-ai/gpt4all Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

GPT4All: Model Training, Model Access, and Model Evaluation

Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References 2 The Original GPT4All Model 2.1 Data Collection and Curation To train the original GPT4All model, we collected roughly one million prompt-response pairs using the GPT-3.5-Turbo OpenAI API between March 20, 2023 and March 26th, 2023. In particular, we gathered GPT3.5-Turbo responses to prompts of three publicly available datasets: the unified chip2 subset of LAION OIG, a random sub-sample of Stackoverflow Questions, and a sub-sample of Bigscience/P3 (Sanh et al., 2021). Following the approach in Stanford Alpaca (Taori et al., 2023), an open source LLaMA variant that came just before GPT4All, we focused substantial effort on dataset curation. The collected dataset was loaded into Atlas (AI, 2023)—a visual interface for exploring and tagging massive unstructured datasets —for data curation. Using AtarXiv:2311.04931v1 [cs.CL] 6 Nov 2023 las, we identified and removed subsets of the data where GPT-3.5-Turbo refused to respond, had malformed output, or produced a very short response. This resulted in the removal of the entire Bigscience/P3 subset of our data, as many P3 prompts induced responses that were simply one word. After curation, we were left with a set of 437,605 prompt-response pairs, which we visualize in Figure 1a. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References 2 The Original GPT4All Model 2.1 Data Collection and Curation To train the original GPT4All model, we collected roughly one million prompt-response pairs using the GPT-3.5-Turbo OpenAI API between March 20, 2023 and March 26th, 2023. In particular, we gathered GPT3.5-Turbo responses to prompts of three publicly available datasets: the unified chip2 subset of LAION OIG, a random sub-sample of Stackoverflow Questions, and a sub-sample of Bigscience/P3 (Sanh et al., 2021). Following the approach in Stanford Alpaca (Taori et al., 2023), an open source LLaMA variant that came just before GPT4All, we focused substantial effort on dataset curation. The collected dataset was loaded into Atlas (AI, 2023)—a visual interface for exploring and tagging massive unstructured datasets —for data curation. Using AtarXiv:2311.04931v1 [cs.CL] 6 Nov 2023 las, we identified and removed subsets of the data where GPT-3.5-Turbo refused to respond, had malformed output, or produced a very short response. This resulted in the removal of the entire Bigscience/P3 subset of our data, as many P3 prompts induced responses that were simply one word. After curation, we were left with a set of 437,605 prompt-response pairs, which we visualize in Figure 1a. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

Original GPT4All Model: How We Collected Data and Then Curated It

Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References Abstract Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure; are only accessible via rate-limited, geo-locked, and censored web interfaces; and lack publicly available code and technical reports. In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. 1 Introduction On March 14 2023, OpenAI released GPT-4, a large language model capable of achieving human level performance on a variety of professional and academic benchmarks. Despite the popularity of the release, the GPT-4 technical report (OpenAI, 2023) contained virtually no details regarding the architecture, hardware, training compute, dataset construction, or training method used to create the model. Moreover, users could only access the model through the internet interface at chat.openai.com, which was severely rate limited and unavailable in several locales (e.g. Italy) (BBC News, 2023). Additionally, GPT-4 refused to answer a wide ∗ Shared Senior Authorship variety of queries, responding only with the now infamous "As an AI Language Model, I cannot..." prefix (Vincent, 2023). These transparency and accessibility concerns spurred several developers to begin creating open source large language model (LLM) alternatives. Several grassroots efforts focused on fine tuning Meta’s open code LLaMA model (Touvron et al., 2023; McMillan, 2023), whose weights were leaked on BitTorrent less than a week prior to the release of GPT-4 (Verge, 2023). GPT4All started as one of these variants. In this paper, we tell the story of GPT4All. We comment on the technical details of the original GPT4All model (Anand et al., 2023), as well as the evolution of GPT4All from a single model to an ecosystem of several models. We remark on the impact that the project has had on the open source community, and discuss future directions. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References Abstract Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure; are only accessible via rate-limited, geo-locked, and censored web interfaces; and lack publicly available code and technical reports. In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. 1 Introduction On March 14 2023, OpenAI released GPT-4, a large language model capable of achieving human level performance on a variety of professional and academic benchmarks. Despite the popularity of the release, the GPT-4 technical report (OpenAI, 2023) contained virtually no details regarding the architecture, hardware, training compute, dataset construction, or training method used to create the model. Moreover, users could only access the model through the internet interface at chat.openai.com, which was severely rate limited and unavailable in several locales (e.g. Italy) (BBC News, 2023). Additionally, GPT-4 refused to answer a wide ∗ Shared Senior Authorship variety of queries, responding only with the now infamous "As an AI Language Model, I cannot..." prefix (Vincent, 2023). These transparency and accessibility concerns spurred several developers to begin creating open source large language model (LLM) alternatives. Several grassroots efforts focused on fine tuning Meta’s open code LLaMA model (Touvron et al., 2023; McMillan, 2023), whose weights were leaked on BitTorrent less than a week prior to the release of GPT-4 (Verge, 2023). GPT4All started as one of these variants. In this paper, we tell the story of GPT4All. We comment on the technical details of the original GPT4All model (Anand et al., 2023), as well as the evolution of GPT4All from a single model to an ecosystem of several models. We remark on the impact that the project has had on the open source community, and discuss future directions. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

GPT4All: An Ecosystem of Open-Source Compressed Language Models

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 10 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 7. Conclusion We introduce SwitchLight, an architecture based on Cook-Torrance rendering physics, enhanced with a selfsupervised pre-training framework. This co-designed approach significantly outperforms previous models. Our future plans include scaling the current model beyond images to encompass video and 3D data. We hope our proposal serve as a new foundational model for relighting tasks. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 10 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 10 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 10 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 10 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 7. Conclusion We introduce SwitchLight, an architecture based on Cook-Torrance rendering physics, enhanced with a selfsupervised pre-training framework. This co-designed approach significantly outperforms previous models. Our future plans include scaling the current model beyond images to encompass video and 3D data. We hope our proposal serve as a new foundational model for relighting tasks. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

New AI Relighting Model Outperforms Previous Models

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 2. Related Work Human Portrait Relighting is an ill-posed problem due to its under-constrained nature. To tackle this, earlier methods incorporated 3D facial priors [44], exploited image intrinsics [3, 40], or framed the task as a style transfer [43]. Light stage techniques [49] offer a more powerful solution by recording subject’s reflectance fields under varying lighting conditions [10, 14], though they are labor-intensive and require specialized equipment. A promising alternative has emerged with deep learning, utilizing neural networks trained on light stage data. Sun et al. [45] pioneered this approach, but their method had limitations in representing non-Lambertian effects. This was improved upon by Nestmeyer et al. [32], who integrated rendering physics into network design, albeit limited to directional light. Building upon this, Pandey et al. [34] incorporated the Phong reflection model and a high dynamic range (HDR) lighting map [9] into their network, enabling a more accurate representation of global illumination. Simultaneously, efforts have been made to explore portrait relighting without light stage data [20, 21, 42, 47, 55]. Moreover, introduction of NeRF [7] and diffusion-based [38] models has opened new avenues in the field. However, networks trained with lightstage data maintain superior accuracy and realism, thanks to physics-based composited relight image training pairs and precise ground truth image intrinsics [56]. Our work furthers this domain by integrating the Cook-Torrance model into our network design, shifting from the empirical Phong model to a more physics-based approach, thereby enhancing the realism and detail in relit images. Self-supervised Pre-training has become a standard training scheme in the development of large language models like BERT [11] and GPT [39], and is increasingly influential in vision models, aiming to replicate the ‘BERT moment’. This approach typically involves pre-training on extensive unlabeled data, followed by fine-tuning on specific tasks. While early efforts in vision models focused on simple pretext tasks [13, 17, 33, 36, 53], the field has evolved through stages like contrastive learning [5, 18] and masked image modeling [2, 19, 50]. However, the primary focus has remained on visual recognition, with less attention to other domains. Exceptions include low-level image processing tasks [4, 6, 27, 30] using the vision transformer [15]. Our research takes a different route, focusing on human portrait relighting—a complex challenge of manipulating illumination in the image. This direction is crucial because acquiring accurate ground truth data, especially from light stage, is both expensive and difficult. We modify the MAE framework [19], previously successful in robust image representation learning and developing locality biases [35], to suit the unique requirements of effective relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 2. Related Work Human Portrait Relighting is an ill-posed problem due to its under-constrained nature. To tackle this, earlier methods incorporated 3D facial priors [44], exploited image intrinsics [3, 40], or framed the task as a style transfer [43]. Light stage techniques [49] offer a more powerful solution by recording subject’s reflectance fields under varying lighting conditions [10, 14], though they are labor-intensive and require specialized equipment. A promising alternative has emerged with deep learning, utilizing neural networks trained on light stage data. Sun et al. [45] pioneered this approach, but their method had limitations in representing non-Lambertian effects. This was improved upon by Nestmeyer et al. [32], who integrated rendering physics into network design, albeit limited to directional light. Building upon this, Pandey et al. [34] incorporated the Phong reflection model and a high dynamic range (HDR) lighting map [9] into their network, enabling a more accurate representation of global illumination. Simultaneously, efforts have been made to explore portrait relighting without light stage data [20, 21, 42, 47, 55]. Moreover, introduction of NeRF [7] and diffusion-based [38] models has opened new avenues in the field. However, networks trained with lightstage data maintain superior accuracy and realism, thanks to physics-based composited relight image training pairs and precise ground truth image intrinsics [56]. Human Portrait Relighting Our work furthers this domain by integrating the Cook-Torrance model into our network design, shifting from the empirical Phong model to a more physics-based approach, thereby enhancing the realism and detail in relit images. Self-supervised Pre-training has become a standard training scheme in the development of large language models like BERT [11] and GPT [39], and is increasingly influential in vision models, aiming to replicate the ‘BERT moment’. This approach typically involves pre-training on extensive unlabeled data, followed by fine-tuning on specific tasks. While early efforts in vision models focused on simple pretext tasks [13, 17, 33, 36, 53], the field has evolved through stages like contrastive learning [5, 18] and masked image modeling [2, 19, 50]. However, the primary focus has remained on visual recognition, with less attention to other domains. Exceptions include low-level image processing tasks [4, 6, 27, 30] using the vision transformer [15]. Self-supervised Pre-training Our research takes a different route, focusing on human portrait relighting—a complex challenge of manipulating illumination in the image. This direction is crucial because acquiring accurate ground truth data, especially from light stage, is both expensive and difficult. We modify the MAE framework [19], previously successful in robust image representation learning and developing locality biases [35], to suit the unique requirements of effective relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Beeble Researchers Use Physics-Based Models to Achieve Realistic Lighting Effects in Images

Simplifying AI Development Are you tired of juggling multiple AI API keys across different AI platforms? What if I told you that you only need three API keys to access most of the AI capabilities you’ll ever need? Let me show you how to streamline your AI development process. The API Key Challenge Every developer’s journey into AI typically starts the same way: signing up for an OpenAI account. Getting that first AI API key. Building an initial prototype. You expand your horizons and experiment with different providers. New account, new api key, new billing information. After 2 months, you got an account on 10 different provider. Separate accounts, different billing systems, and various AI API keysmake organization a nightmare. Let’s end this! Here are 3 AI service providers, that give you the more models than Hugh Hefner for your AI API key. The Three Keys to Freedom 1. OpenRouter: One Key for All Language Models OpenRouter is your gateway to over 200 different language models.  Think of it as a universal aggregator for AI models. With just one API key, you get access to: Popular closed-source models (OpenAI, Google Gemini, Anthropic)
Open-source models (Llama, Qwen)
The latest models as they’re released OpenRouter charges you like most other LLM providers: per token. Top up the account with as little as 5$, which will bring you very far for  prototyping. Pro Tip: OpenRouter even allows you to pay in crypto if that’s your thing! 2. Replicate: Your Swiss Army Knife for AI Models Replicate takes a different approach by offering a platform where: Access a wide range of GenAI models (not only LLMs)


Use simple endpoints for inference


Deploy your own models seamlessly


Popular models hosted on Replicate include:

Image generation series “Flux” by German Black Forest Labs
The (imo best) image generation model ideogram-v2
Vision LLM llava-mistral While OpenRouter charges per token, Replicate charges per second of  GPU runtime. This is a bit tricky, but fair. Stronger GPUs cost more per second, but need less time running an AI model. Weaker GPUs are  cheaper, but need more time. 3. Hugging Face: The GitHub of Machine Learning The final piece of the puzzle is Hugging Face, which offers: Access to extensive datasets
Model weights
GitHub-style discussions
A community-driven approach to machine learning OpenRouter and Replicate focus on AI model inference. HuggingFace focuses on AI model development. So the first two platforms are for people that want to build on top of models. HuggingFace is more for Machine Learning devs that want to adapt  the actual models. Real-World Application Here’s a real scenario: Imagine discovering a new language model you want to test. Before you had to go through the hassle of creating new accounts and managing another AI API key. Now you can simply use your OpenRouter API key and start experimenting immediately. No new accounts, no waiting for approvals from your boss, just pure innovation 😇. Getting Started Ready to simplify your AI development? Here’s how: Visit openrouter.ai to get your first unified LLM API key
Head to replicate.com for your GenAI needs
Create an account on huggingface.co for access to the ML community Why This Matters By consolidating your AI development around these three key platforms, you can: Reduce administrative overhead
Speed up experimentation
Simplify billing and accounting
Stay agile in the fast-moving AI landscape Remember, in the world of AI development, simplicity is key. These three AI API keys give you the power to focus on what matters most: building amazing AI applications. Simplifying AI Development Are you tired of juggling multiple AI API keys across different AI platforms? What if I told you that you only need three API keys to access most of the AI capabilities you’ll ever need? Let me show you how to streamline your AI development process. three API keys The API Key Challenge Every developer’s journey into AI typically starts the same way: signing up for an OpenAI account. Getting that first AI API key. Building an initial prototype. You expand your horizons and experiment with different providers. New account, new api key, new billing information. After 2 months, you got an account on 10 different provider. Separate accounts, different billing systems, and various AI API keys make organization a nightmare . make organization a nightmare Let’s end this! Here are 3 AI service providers, that give you the more models than Hugh Hefner for your AI API key. The Three Keys to Freedom 1. OpenRouter: One Key for All Language Models OpenRouter is your gateway to over 200 different language models.  Think of it as a universal aggregator for AI models. With just one API key , you get access to: one API key Popular closed-source models (OpenAI, Google Gemini, Anthropic) Open-source models (Llama, Qwen) The latest models as they’re released Popular closed-source models (OpenAI, Google Gemini, Anthropic) Open-source models (Llama, Qwen) The latest models as they’re released OpenRouter charges you like most other LLM providers: per token. Top up the account with as little as 5$, which will bring you very far for  prototyping. Pro Tip : OpenRouter even allows you to pay in crypto if that’s your thing! Pro Tip 2. Replicate: Your Swiss Army Knife for AI Models Replicate takes a different approach by offering a platform where: Access a wide range of GenAI models (not only LLMs) Use simple endpoints for inference Deploy your own models seamlessly Popular models hosted on Replicate include:

Image generation series “Flux” by German Black Forest Labs
The (imo best) image generation model ideogram-v2
Vision LLM llava-mistral Access a wide range of GenAI models (not only LLMs) Access a wide range of GenAI models (not only LLMs) Use simple endpoints for inference Use simple endpoints for inference Deploy your own models seamlessly Deploy your own models seamlessly Popular models hosted on Replicate include: Image generation series “Flux” by German Black Forest Labs
The (imo best) image generation model ideogram-v2
Vision LLM llava-mistral Popular models hosted on Replicate include: Image generation series “Flux” by German Black Forest Labs The (imo best) image generation model ideogram-v2 Vision LLM llava-mistral Image generation series “ Flux” by German Black Forest Labs Flux” by German Black Forest Labs The (imo best) image generation model ideogram-v2 ideogram-v2 Vision LLM llava-mistral llava-mistral While OpenRouter charges per token, Replicate charges per second of  GPU runtime. This is a bit tricky, but fair. Stronger GPUs cost more per second, but need less time running an AI model. Weaker GPUs are  cheaper, but need more time. 3. Hugging Face: The GitHub of Machine Learning The final piece of the puzzle is Hugging Face, which offers: Access to extensive datasets Model weights GitHub-style discussions A community-driven approach to machine learning Access to extensive datasets Model weights GitHub-style discussions A community-driven approach to machine learning OpenRouter and Replicate focus on AI model inference . inference HuggingFace focuses on AI model development . development So the first two platforms are for people that want to build on top of models. HuggingFace is more for Machine Learning devs that want to adapt  the actual models. Real-World Application Here’s a real scenario: Imagine discovering a new language model you want to test. Before you had to go through the hassle of creating new accounts and managing another AI API key. Now you can simply use your OpenRouter API key and start experimenting immediately. No new accounts, no waiting for approvals from your boss, just pure innovation 😇. 😇. Getting Started Ready to simplify your AI development? Here’s how: Visit openrouter.ai to get your first unified LLM API key Head to replicate.com for your GenAI needs Create an account on huggingface.co for access to the ML community Visit openrouter.ai to get your first unified LLM API key openrouter.ai Head to replicate.com for your GenAI needs replicate.com Create an account on huggingface.co for access to the ML community huggingface.co Why This Matters By consolidating your AI development around these three key platforms, you can: Reduce administrative overhead Speed up experimentation Simplify billing and accounting Stay agile in the fast-moving AI landscape Reduce administrative overhead Reduce administrative overhead Speed up experimentation Speed up experimentation Simplify billing and accounting Simplify billing and accounting Stay agile in the fast-moving AI landscape Stay agile in the fast-moving AI landscape Remember, in the world of AI development , simplicity is key. in the world of AI development These three AI API keys give you the power to focus on what matters most: building amazing AI applications.

Three Sexy AI API Keys to Access More Models than Hugh Hefner

Hugh Hefnerdən daha çox modelə daxil olmaq üçün üç seksual AI API açarı

হিউ হেফনারের চেয়ে বেশি মডেল অ্যাক্সেস করার জন্য তিনটি সেক্সি AI API কী৷

Tre sexede AI API-nøgler for at få adgang til flere modeller end Hugh Hefner

Tres claves API de inteligencia artificial atractivas para acceder a más modelos que Hugh Hefner

Tres claves sexy da API de IA para acceder a máis modelos que Hugh Hefner

Twa Sexy AI API kle pou jwenn plis modèl pase Hugh Hefner

Tre chiavi API AI sexy per accedere a più modelli di Hugh Hefner

ヒュー・ヘフナーより多くのモデルにアクセスできる 3 つのセクシーな AI API キー

Trzy seksowne klucze API AI umożliwiające dostęp do większej liczby modeli niż Hugh Hefner

Hugh Hefner-den has köp modele girmek üçin üç sany seksi AI API açary

Ba khóa API AI hấp dẫn để truy cập nhiều người mẫu hơn Hugh Hefner

三个性感的 AI API 密钥可访问比休·海夫纳 (Hugh Hefner) 更多的模型

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 7 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance 4.3. Spatial Grounding in Videos To quantitatively assess PG-Video-LLaVA’s spatial grounding capability, we conducted quantitative evaluations of PGVideo-LLaVA’s spatial grounding capabilities using two benchmarks that are derived from the test set of the VidSTG [48] and HC-STVG [34] datasets. Due to the novelty of integrating spatial grounding within video-conversational models, we highlight the modular nature of our grounding pipeline, which can be incorporated with other state-of-theart video conversation models. For the VidSTG dataset, we selectively processed interrogative prompts to assess the grounding accuracy. The model generates descriptive textual responses to these prompts, from which Vicuna-13bv1.5 extracts relevant referring expressions. These expressions are then spatially grounded in the video frames using our grounding pipeline. For the HC-STVG dataset, interrogative prompts are first mined from the text captions using Vicuna and then used similarly to VidSTG prompts. The results shown in Table 2 position PG-Video-LLaVA alongside alternative methods using the same benchmarks, demonstrating our model’s enhanced ability to accurately answer questions, thereby leading to improved spatial grounding performance. The qualitative results shown in Figure 4 emphasize the model’s refined spatial grounding precision. The accurate overlay of masks on the subjects within the videos confirms the model’s adeptness at correlating textual descriptors with visual elements, a critical aspect of contextual comprehension. This refined ability is crucial for applications that integrate visual data with language, improving the model’s utility in environments that demand rich, interactive visual and linguistic processing. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 7 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 7 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 7 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 7 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance 4.3. Spatial Grounding in Videos To quantitatively assess PG-Video-LLaVA’s spatial grounding capability, we conducted quantitative evaluations of PGVideo-LLaVA’s spatial grounding capabilities using two benchmarks that are derived from the test set of the VidSTG [48] and HC-STVG [34] datasets. Due to the novelty of integrating spatial grounding within video-conversational models, we highlight the modular nature of our grounding pipeline, which can be incorporated with other state-of-theart video conversation models. For the VidSTG dataset, we selectively processed interrogative prompts to assess the grounding accuracy. The model generates descriptive textual responses to these prompts, from which Vicuna-13bv1.5 extracts relevant referring expressions. These expressions are then spatially grounded in the video frames using our grounding pipeline. For the HC-STVG dataset, interrogative prompts are first mined from the text captions using Vicuna and then used similarly to VidSTG prompts. The results shown in Table 2 position PG-Video-LLaVA alongside alternative methods using the same benchmarks, demonstrating our model’s enhanced ability to accurately answer questions, thereby leading to improved spatial grounding performance. The qualitative results shown in Figure 4 emphasize the model’s refined spatial grounding precision. The accurate overlay of masks on the subjects within the videos confirms the model’s adeptness at correlating textual descriptors with visual elements, a critical aspect of contextual comprehension. This refined ability is crucial for applications that integrate visual data with language, improving the model’s utility in environments that demand rich, interactive visual and linguistic processing. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

AI Model Developed by UAE Researchers Able to Identify Objects in Videos

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 2 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance 2. Related Works Recent advancements in Large Multimodal Models (LMMs) [8, 18, 50] and Large Language Models (LLMs) [7, 26, 36] have significantly transformed the artificial intelligence landscape, particularly in natural language processing and multimodal tasks. These breakthroughs have enhanced machine learning models’ ability to understand and generate human-like text, while also enabling more effective integration of various data types like images, sounds and videos with textual information. This progress represents a major leap in creating AI systems that can accurately interpret and interact with a diverse range of content. Large Language Models (LLMs): The natural language processing (NLP) field has undergone a revolution with the advent of LLMs such as GPT [4], LLaMA [36], OPT [46], and MOSS [27], particularly noted for their zero-shot learning abilities and adaptability. The development of models like InstructGPT [28] and ChatGPT [26] has further propelled advancements in conversational AI and complex query handling, chiefly through instruction tuning. Within the LLaMA framework, the emergence of opensource models such as Alpaca [35] and Vicuna [7] exemplifies how instruction tuning can significantly boost model performance. This shift towards open-source initiatives in language modeling, highlighted by models like Alpaca and Vicuna, indicates a growing trend towards more accessible and collaborative approaches in the field. In this work, we build on the open-source Vicuna LLM and extend it with multimodal capabilities. We also propose an open-source benchmark for video conversation and reasoning tasks using Vicuna LLM that is reproducible for fair evaluations. Large Multimodal Models (LMMs): The field of AI has witnessed significant advancements with the development of vision-language models like CLIP [30], renowned for their impressive zero-shot capabilities using extensive image-text pairs during training. These models have proven effective in a variety of applications, from image detection and segmentation [3, 17] to more complex tasks such as 3D modeling and video analysis [23, 31, 33, 37]. The introduction of BLIP-2 marked a pivotal transition, pioneering the integration of image features encoded by a visual encoder with text embeddings, setting the stage for the evolution into Large Multimodal Models (LMMs). This advancement influenced subsequent models like LLaVA [19], InstructBLIP [8], and MiniGPT-4 [50], which further refined image-text feature alignment and instruction tuning. VideoChat [15], Video-ChatGPT [22] and VideoLLaMA [45] represents an extension of these LMMs, moving from image-based to video-based applications, while models such as Otter [14], mPLUG-Owl [42], LLaMaAdapter [9], and InternGPT [21] continue to push the boundaries of multimodal interaction. Despite these significant strides, challenges in achieving robust visual grounding in LMMs highlight key areas for ongoing research and development in this dynamic field. Further, effective integration of audio signals within LMMs for comprehensive video understanding is an open research question that this work aims to address. Visual-Language Grounding: Grounded Large Language Models (LLMs) have made notable progress in enhancing visual and language comprehension. A diverse array of models including Kosmos-2 [29], Ferret [43], AllSeeing Model [38], LISA [13], BuboGPT [49], Shikra [5], and GLaMM [32] have employed various methodologies to master complex grounding tasks. These models demonstrate proficiency in tasks like referring expression comprehension and image segmentation, showcasing the advanced image understanding capabilities of LLMs. Methodologically, Kosmos-2, Shikra, and All-Seeing focus predominantly on creating language-based context for visual grounding. In contrast, BuboGPT merges visual elements with language, and LISA leverages vision-language embeddings for producing segmentation masks. Furthermore, GLaMM is adept at generating natural language responses linked with object segmentation masks, facilitating detailed visual-textual interactions. However, challenges remain, such as LISA’s constrained performance in multi-object scenarios and the limitations of BuboGPT and GLaMM to image-based applications, not extending to video processing. To this end, we introduce PG-Video-LLaVA, a video conversational model with pixel-level grounding capability. Further, PG-Video-LLaVA incorporates audio transcripts alongside visual and textual data, aiming to provide a more detailed understanding of video content. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 2 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 2 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 2 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 2 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance 2. Related Works Recent advancements in Large Multimodal Models (LMMs) [8, 18, 50] and Large Language Models (LLMs) [7, 26, 36] have significantly transformed the artificial intelligence landscape, particularly in natural language processing and multimodal tasks. These breakthroughs have enhanced machine learning models’ ability to understand and generate human-like text, while also enabling more effective integration of various data types like images, sounds and videos with textual information. This progress represents a major leap in creating AI systems that can accurately interpret and interact with a diverse range of content. Large Language Models (LLMs): The natural language processing (NLP) field has undergone a revolution with the advent of LLMs such as GPT [4], LLaMA [36], OPT [46], and MOSS [27], particularly noted for their zero-shot learning abilities and adaptability. The development of models like InstructGPT [28] and ChatGPT [26] has further propelled advancements in conversational AI and complex query handling, chiefly through instruction tuning. Within the LLaMA framework, the emergence of opensource models such as Alpaca [35] and Vicuna [7] exemplifies how instruction tuning can significantly boost model performance. This shift towards open-source initiatives in language modeling, highlighted by models like Alpaca and Vicuna, indicates a growing trend towards more accessible and collaborative approaches in the field. In this work, we build on the open-source Vicuna LLM and extend it with multimodal capabilities. We also propose an open-source benchmark for video conversation and reasoning tasks using Vicuna LLM that is reproducible for fair evaluations. Large Language Models (LLMs): Large Multimodal Models (LMMs): The field of AI has witnessed significant advancements with the development of vision-language models like CLIP [30], renowned for their impressive zero-shot capabilities using extensive image-text pairs during training. These models have proven effective in a variety of applications, from image detection and segmentation [3, 17] to more complex tasks such as 3D modeling and video analysis [23, 31, 33, 37]. The introduction of BLIP-2 marked a pivotal transition, pioneering the integration of image features encoded by a visual encoder with text embeddings, setting the stage for the evolution into Large Multimodal Models (LMMs). This advancement influenced subsequent models like LLaVA [19], InstructBLIP [8], and MiniGPT-4 [50], which further refined image-text feature alignment and instruction tuning. VideoChat [15], Video-ChatGPT [22] and VideoLLaMA [45] represents an extension of these LMMs, moving from image-based to video-based applications, while models such as Otter [14], mPLUG-Owl [42], LLaMaAdapter [9], and InternGPT [21] continue to push the boundaries of multimodal interaction. Despite these significant strides, challenges in achieving robust visual grounding in LMMs highlight key areas for ongoing research and development in this dynamic field. Further, effective integration of audio signals within LMMs for comprehensive video understanding is an open research question that this work aims to address. Large Multimodal Models (LMMs): Visual-Language Grounding: Grounded Large Language Models (LLMs) have made notable progress in enhancing visual and language comprehension. A diverse array of models including Kosmos-2 [29], Ferret [43], AllSeeing Model [38], LISA [13], BuboGPT [49], Shikra [5], and GLaMM [32] have employed various methodologies to master complex grounding tasks. These models demonstrate proficiency in tasks like referring expression comprehension and image segmentation, showcasing the advanced image understanding capabilities of LLMs. Methodologically, Kosmos-2, Shikra, and All-Seeing focus predominantly on creating language-based context for visual grounding. In contrast, BuboGPT merges visual elements with language, and LISA leverages vision-language embeddings for producing segmentation masks. Furthermore, GLaMM is adept at generating natural language responses linked with object segmentation masks, facilitating detailed visual-textual interactions. However, challenges remain, such as LISA’s constrained performance in multi-object scenarios and the limitations of BuboGPT and GLaMM to image-based applications, not extending to video processing. To this end, we introduce PG-Video-LLaVA, a video conversational model with pixel-level grounding capability. Further, PG-Video-LLaVA incorporates audio transcripts alongside visual and textual data, aiming to provide a more detailed understanding of video content. Visual-Language Grounding: This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Are Video-Based Tasks the Next Big Challenge for AI Models?

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance Abstract Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to videos either lack the grounding capabilities (e.g., VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we propose PG-Video-LLaVA, the first LMM with pixellevel grounding capability, integrating audio cues by transcribing them into text to enrich video-context understanding. Our framework uses an off-the-shelf tracker and a novel grounding module, enabling it to spatially localize objects in videos following user instructions. We evaluate PG-Video-LLaVA using video-based generative and question-answering benchmarks and introduce new benchmarks specifically designed to measure prompt-based object grounding performance in videos. Further, we propose the use of Vicuna over GPT-3.5, as utilized in VideoChatGPT, for video-based conversation benchmarking, ensuring reproducibility of results which is a concern with the proprietary nature of GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its advantages to the video domain, delivering promising gains on video-based conversation and grounding tasks. 1. Introduction Recent efforts on Large Multimodal Models (LMMs), spearheaded by GPT-4V [25], allow detailed conversations about images but generally do not scale well to videos. The magnitude of video data scales far beyond other modalities due to its massive volume on social and internet media. Furthermore, extending LMMs to videos is challenging due to their complex dynamics with long temporal context that needs to be understood accurately. Although recent approaches towards video-LMMs such as VideoChat [15], Video-LLaMA [45], and Video-ChatGPT [22] have demonstrated capabilities in video comprehension and dialogue, they lack the crucial feature of visual grounding. Visual grounding in videos aims to associate the LMM responses to specific objects within the video input. Addressing this gap, we introduce PG-Video-LLaVA, the first video-LMM capable of localizing objects appearing in LMM responses. This task leads to enhanced intractability and demonstrates deep understanding of video content. In PG-Video-LLaVA, we address the unique challenges posed by video data. The model is designed to track objects within shorter video clips that maintain consistent camera views, enabling accurate visual grounding across scenes and motions. This tracking links spatio-temporal segments directly to conversational elements, enhancing the model’s contextual understanding. A salient feature of PG-VideoLLaVA is its modular design, allowing for easy integration with existing grounding modules and the flexibility to adapt to future enhancements in visual grounding technology. Moreover, PG-Video-LLaVA enriches its capabilities by incorporating audio context. It achieves this by leveraging video audio in a form understandable to LLM, which is particularly useful in situations where the auditory information is essential to the conversation. This inclusion broadens the model’s understanding, making it more versatile in interpreting video content. Furthermore, this work introduces an improved framework for benchmarking video-based conversational models, pivoting from previous approaches [22] that predominantly used the proprietary GPT-3.5-Turbo model for evaluation. Given that GPT-3.5-Turbo is subject to changes at any time and lacks transparency due to its closed-source nature, it presents challenges in terms of reliability and reproducibility. To address this, we propose the use of Vicuna, an open-source LLM for benchmarking. This shift not only enhances reproducibility but also improves transparency in the evaluation process. We evaluate PG-Video-LLaVA using our improved benchmarks and show notable improvements over existing video conversational models like VideoChatGPT [22] and Video-LLaMA [45] in ungrounded dialogues, achieving state-of-the-art (SoTA) performance. The key contributions of this work are: • We propose PG-Video-LLaVA, the first video-based LMM with pixel-level grounding capabilities, featuring a modular design for enhanced flexibility. • By incorporating audio context, PG-Video-LLaVA significantly enhances its understanding of video content, making it more comprehensive and aptly suited for scenarios where the audio signal is crucial for video understanding (e.g., dialogues and conversations, news videos, etc.). • We introduce improved quantitative benchmarks for video-based conversational models. Our benchmarks utilize open-source Vicuna LLM to ensure better reproducibility and transparency. We also propose benchmarks to evaluate the grounding capabilities of video-based conversational models. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance Abstract Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to videos either lack the grounding capabilities (e.g., VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we propose PG-Video-LLaVA, the first LMM with pixellevel grounding capability, integrating audio cues by transcribing them into text to enrich video-context understanding. Our framework uses an off-the-shelf tracker and a novel grounding module, enabling it to spatially localize objects in videos following user instructions. We evaluate PG-Video-LLaVA using video-based generative and question-answering benchmarks and introduce new benchmarks specifically designed to measure prompt-based object grounding performance in videos. Further, we propose the use of Vicuna over GPT-3.5, as utilized in VideoChatGPT, for video-based conversation benchmarking, ensuring reproducibility of results which is a concern with the proprietary nature of GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its advantages to the video domain, delivering promising gains on video-based conversation and grounding tasks. Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to videos either lack the grounding capabilities (e.g., VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we propose PG-Video-LLaVA, the first LMM with pixellevel grounding capability, integrating audio cues by transcribing them into text to enrich video-context understanding. Our framework uses an off-the-shelf tracker and a novel grounding module, enabling it to spatially localize objects in videos following user instructions. We evaluate PG-Video-LLaVA using video-based generative and question-answering benchmarks and introduce new benchmarks specifically designed to measure prompt-based object grounding performance in videos. Further, we propose the use of Vicuna over GPT-3.5, as utilized in VideoChatGPT, for video-based conversation benchmarking, ensuring reproducibility of results which is a concern with the proprietary nature of GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its advantages to the video domain, delivering promising gains on video-based conversation and grounding tasks. 1. Introduction Recent efforts on Large Multimodal Models (LMMs), spearheaded by GPT-4V [25], allow detailed conversations about images but generally do not scale well to videos. The magnitude of video data scales far beyond other modalities due to its massive volume on social and internet media. Furthermore, extending LMMs to videos is challenging due to their complex dynamics with long temporal context that needs to be understood accurately. Although recent approaches towards video-LMMs such as VideoChat [15], Video-LLaMA [45], and Video-ChatGPT [22] have demonstrated capabilities in video comprehension and dialogue, they lack the crucial feature of visual grounding. Visual grounding in videos aims to associate the LMM responses to specific objects within the video input. Addressing this gap, we introduce PG-Video-LLaVA, the first video-LMM capable of localizing objects appearing in LMM responses. This task leads to enhanced intractability and demonstrates deep understanding of video content. In PG-Video-LLaVA, we address the unique challenges posed by video data. The model is designed to track objects within shorter video clips that maintain consistent camera views, enabling accurate visual grounding across scenes and motions. This tracking links spatio-temporal segments directly to conversational elements, enhancing the model’s contextual understanding. A salient feature of PG-VideoLLaVA is its modular design, allowing for easy integration with existing grounding modules and the flexibility to adapt to future enhancements in visual grounding technology. Moreover, PG-Video-LLaVA enriches its capabilities by incorporating audio context. It achieves this by leveraging video audio in a form understandable to LLM, which is particularly useful in situations where the auditory information is essential to the conversation. This inclusion broadens the model’s understanding, making it more versatile in interpreting video content. Furthermore, this work introduces an improved framework for benchmarking video-based conversational models, pivoting from previous approaches [22] that predominantly used the proprietary GPT-3.5-Turbo model for evaluation. Given that GPT-3.5-Turbo is subject to changes at any time and lacks transparency due to its closed-source nature, it presents challenges in terms of reliability and reproducibility. To address this, we propose the use of Vicuna, an open-source LLM for benchmarking. This shift not only enhances reproducibility but also improves transparency in the evaluation process. We evaluate PG-Video-LLaVA using our improved benchmarks and show notable improvements over existing video conversational models like VideoChatGPT [22] and Video-LLaMA [45] in ungrounded dialogues, achieving state-of-the-art (SoTA) performance. The key contributions of this work are: • We propose PG-Video-LLaVA, the first video-based LMM with pixel-level grounding capabilities, featuring a modular design for enhanced flexibility. • By incorporating audio context, PG-Video-LLaVA significantly enhances its understanding of video content, making it more comprehensive and aptly suited for scenarios where the audio signal is crucial for video understanding (e.g., dialogues and conversations, news videos, etc.). • We introduce improved quantitative benchmarks for video-based conversational models. Our benchmarks utilize open-source Vicuna LLM to ensure better reproducibility and transparency. We also propose benchmarks to evaluate the grounding capabilities of video-based conversational models. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

UAE Researchers Say New AI Model Can Watch Videos, Understand Audio

BƏƏ tədqiqatçıları deyirlər ki, yeni süni intellekt modeli videolara baxa, səsi anlaya bilər

Investigadores de los Emiratos Árabes Unidos afirman que un nuevo modelo de inteligencia artificial puede ver vídeos y comprender audio

UAEko ikertzaileek diote AI eredu berriak bideoak ikusi eta audioa ulertu ditzakeela

UAEの研究者らは、新しいAIモデルは動画を視聴し、音声を理解できると述べている

Pesquisadores dos Emirados Árabes Unidos dizem que novo modelo de IA pode assistir a vídeos e entender áudio

Emiratos Árabes Unidos Yachaysapakuna ninku Musuq AI Modelo Videokunata qhawayta atin, Audiota entiendeyta atin

Abashakashatsi ba UAE bavuga ko Moderi nshya ya AI ishobora kureba amashusho, gusobanukirwa amajwi

Vatsvagiri veUAE Vanoti New AI Model Inogona Kuona Vhidhiyo, Nzwisisa Audio

Sinasabi ng Mga Mananaliksik ng UAE na Makakapanood ng Mga Video, Makakaintindi ng Audio ang Bagong Modelo ng AI

متحدہ عرب امارات کے محققین کا کہنا ہے کہ نیا اے آئی ماڈل ویڈیوز دیکھ سکتا ہے، آڈیو کو سمجھ سکتا ہے۔

BAA tadqiqotchilari yangi sun'iy intellekt modeli videolarni ko'rishi, audioni tushunishi mumkinligini aytishdi

Abacwaningi be-UAE Bathi Imodeli Entsha ye-AI Ingabuka Amavidiyo, Iqonde Umsindo

LONDON, UK, December 19th, 2024/Chainwire/--As cryptocurrency adoption expands, so do the associated risks, including physical threats targeting crypto holders. Traditional wallets often lack safeguards to address real-world coercion scenarios. Addressing this challenge, Deus Wallet introduces a new feature: Duress Mode. This technology, integrated into Deus’s non-custodial framework, provides an added layer of security to help safeguard cryptocurrency assets and user safety in high-risk situations. The Issue: Crypto Security Is More Than Digital While digital security has advanced with multi-signature wallets, biometric authentication, and hardware devices, physical security remains overlooked. Criminals have adapted their tactics, targeting crypto holders. Considering Real-Life Examples: 2022, London: Criminals broke into a businessman's home and extorted $1.1 million in Bitcoin at gunpoint. 2023, New York: A crypto trader was kidnapped and forced to transfer $400,000 while held captive. 2021, Bangkok: A prominent investor managing millions in crypto was ambushed by an international gang and coerced into transferring $2.7 million. In each case, victims lacked the tools to mitigate risks under pressure. Standard wallets provide no "last line of defense" for these real-world threats. Deus Wallet: Next-Level Protection Deus Wallet tackles this vulnerability with its non-custodial structure and Duress Mode. This approach allows users to retain full control over their assets while incorporating an additional security measure designed to address specific risks. Non-Custodial Goal With Deus, private keys are stored exclusively by the user, not on any centralized server. This eliminates third-party risks such as hacking, insider theft, or regulatory overreach. In an era where centralization poses increasing threats, non-custodial wallets offer unparalleled independence and security. Duress Mode was developed with one goal: to protect users in life-threatening situations. The Way it Works: Duress PIN Configuration: Setting a secondary PIN during wallet setup. Under Coercion: Entering the Duress PIN instead of the regular one. Seamless Decoy: The wallet opens a "fake" account with minimal or no funds, while users' actual assets remain hidden. This feature ensures that attackers see what appears to be the entire wallet balance, without ever suspecting the existence of hidden funds. Deus Wallet is part of a growing movement to rethink cryptocurrency security. As the crypto ecosystem matures, protecting users in both digital and physical spaces is becoming more important. The Bigger Picture: Rising Crypto Adoption: As cryptocurrencies enter mainstream finance, the average user profile shifts from tech-savvy enthusiasts to general consumers, many of whom lack advanced security awareness. Criminal Adaptability: Criminals exploit the pseudonymity of blockchain transactions, knowing that once assets are transferred, they’re nearly impossible to recover. The Security Gap: While solutions like hardware wallets protect against cyber threats, tools like Duress Mode address the issue of physical security. Deus Wallet is at the forefront of this shift, setting a new standard for protecting users and their assets. Key Features of Deus Wallet Non-Custodial Architecture: Full control of private keys ensures users' funds are always theirs. Duress Mode: A revolutionary feature to safeguard assets under physical coercion. User-Friendly Design: Intuitive interface suitable for beginners and experts alike. Advanced Encryption: Multi-layered security protects against cyberattacks. Customizable Settings: Users can define access parameters and Duress Mode triggers according to their preferences. Cross-Platform Compatibility: Available on iOS, Android. In today’s world, digital and physical security go hand-in-hand. Deus Wallet provides a comprehensive solution, ensuring that users' assets and personal safety are never compromised. About Deus Wallet Deus Wallet offers a comprehensive platform for storing and managing cryptocurrencies and NFTs, featuring tools to track account activity and streamline asset management. By integrating multiple functionalities, Deus Wallet eliminates the need to navigate several decentralized platforms. With a focus on user-friendly design, Deus Wallet balances simplicity and functionality, making it a practical choice for those seeking efficient and accessible solutions. Trusted by users in over 166 countries, Deus Wallet serves a broad audience, including individuals and enterprises, across five continents. Deus Wallet is designed to support both new and experienced users, providing tools for secure asset management and streamlined engagement with decentralized finance. Users can visit https://deuswallet.com to learn more about how Deus Wallet is reshaping crypto security for the modern age. Contact Pavel Derkach DEUS WALLET APS LTD support@deuswallet.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here LONDON, UK, December 19th, 2024/Chainwire/--As cryptocurrency adoption expands, so do the associated risks, including physical threats targeting crypto holders. Traditional wallets often lack safeguards to address real-world coercion scenarios. Addressing this challenge, Deus Wallet introduces a new feature: Duress Mode. Deus Wallet Deus Wallet This technology, integrated into Deus’s non-custodial framework, provides an added layer of security to help safeguard cryptocurrency assets and user safety in high-risk situations. The Issue: Crypto Security Is More Than Digital While digital security has advanced with multi-signature wallets, biometric authentication, and hardware devices, physical security remains overlooked. Criminals have adapted their tactics, targeting crypto holders. Considering Real-Life Examples: 2022, London: Criminals broke into a businessman's home and extorted $1.1 million in Bitcoin at gunpoint. 2023, New York: A crypto trader was kidnapped and forced to transfer $400,000 while held captive. 2021, Bangkok: A prominent investor managing millions in crypto was ambushed by an international gang and coerced into transferring $2.7 million. In each case, victims lacked the tools to mitigate risks under pressure. Standard wallets provide no "last line of defense" for these real-world threats. Deus Wallet: Next-Level Protection Deus Wallet tackles this vulnerability with its non-custodial structure and Duress Mode. This approach allows users to retain full control over their assets while incorporating an additional security measure designed to address specific risks. Non-Custodial Goal With Deus, private keys are stored exclusively by the user, not on any centralized server. This eliminates third-party risks such as hacking, insider theft, or regulatory overreach. In an era where centralization poses increasing threats, non-custodial wallets offer unparalleled independence and security. Duress Mode was developed with one goal: to protect users in life-threatening situations. The Way it Works: Duress PIN Configuration: Setting a secondary PIN during wallet setup. Under Coercion: Entering the Duress PIN instead of the regular one. Seamless Decoy: The wallet opens a "fake" account with minimal or no funds, while users' actual assets remain hidden. This feature ensures that attackers see what appears to be the entire wallet balance, without ever suspecting the existence of hidden funds. Deus Wallet is part of a growing movement to rethink cryptocurrency security. As the crypto ecosystem matures, protecting users in both digital and physical spaces is becoming more important. Deus Wallet Deus Wallet The Bigger Picture: Rising Crypto Adoption: As cryptocurrencies enter mainstream finance, the average user profile shifts from tech-savvy enthusiasts to general consumers, many of whom lack advanced security awareness. Criminal Adaptability: Criminals exploit the pseudonymity of blockchain transactions, knowing that once assets are transferred, they’re nearly impossible to recover. The Security Gap: While solutions like hardware wallets protect against cyber threats, tools like Duress Mode address the issue of physical security. Deus Wallet is at the forefront of this shift, setting a new standard for protecting users and their assets. Key Features of Deus Wallet Non-Custodial Architecture: Full control of private keys ensures users' funds are always theirs. Duress Mode: A revolutionary feature to safeguard assets under physical coercion. User-Friendly Design: Intuitive interface suitable for beginners and experts alike. Advanced Encryption: Multi-layered security protects against cyberattacks. Customizable Settings: Users can define access parameters and Duress Mode triggers according to their preferences. Cross-Platform Compatibility: Available on iOS, Android. In today’s world, digital and physical security go hand-in-hand. Deus Wallet provides a comprehensive solution, ensuring that users' assets and personal safety are never compromised. About Deus Wallet Deus Wallet offers a comprehensive platform for storing and managing cryptocurrencies and NFTs, featuring tools to track account activity and streamline asset management. By integrating multiple functionalities, Deus Wallet eliminates the need to navigate several decentralized platforms. Deus Wallet Deus Wallet With a focus on user-friendly design, Deus Wallet balances simplicity and functionality, making it a practical choice for those seeking efficient and accessible solutions. Trusted by users in over 166 countries, Deus Wallet serves a broad audience, including individuals and enterprises, across five continents. Deus Wallet is designed to support both new and experienced users, providing tools for secure asset management and streamlined engagement with decentralized finance. Users can visit https://deuswallet.com to learn more about how Deus Wallet is reshaping crypto security for the modern age. https://deuswallet.com https://deuswallet.com Contact Pavel Derkach DEUS WALLET APS LTD support@deuswallet.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

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