Summarizing https://arxiv.org/pdf/2311.01057.pdf

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The paper presents a design and implementation of tiny machine learning algorithms using novel low power processors to enable prolonged continuous operation in smart glasses. The energy and latency efficient object detection is explored in the case of real-time object detection for this text. A smart glasses prototype with two microcontrollers including an ultra-low milliwatt RISC-V parallel processor with hardware accelerator for visual AI and Bluetooth Low Power module for communication is designed as a research platform. Power cycling mechanisms, image and audio sensing interfaces are integrated. Furthermore, a family of new tiny deep learning models based on YOLO with sub-million parameters customized for microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8 are developed to benchmark object detection in smart glasses for energy and latency. Evaluations on the prototype demonstrate the effectiveness of the proposed approach.

The paper also compares several ARM Cortex M4 and M7 microcontrollers from ST Microelectronics, and Ambiq (an ultra-low power MCU using sub-threshold technology), MAX78000 microcontroller from Analog Devices, and RISC-V based SiFive HiFive Unleashed board with a 64-bit quad-core processor. The results show that the RISC-V processor outperforms other processors in terms of energy efficiency and latency for object detection in smart glasses.

Summarizing https://www.researchgate.net/publication/325121858_A_Hierarchical_Vision-Based_UAV_Localization_for_an_Open_Landing/fulltext/5af992e50f7e9b026bf742e7/A-Hierarchical-Vision-Based-UAV-Localization-for-an-Open-Landing.pdf?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19

Here's my try:

The article is about the importance of enabling JavaScript and cookies in order to access certain websites or features on them. It explains that JavaScript is used for interactive elements, such as animations, games, and forms, while cookies are used to store user preferences and track website usage. The author suggests that disabling these features can result in a less than optimal experience when browsing the web. Additionally, it mentions that some websites may require JavaScript and cookies to function properly, so disabling them could prevent users from accessing certain content. Finally, the article provides tips on how to enable JavaScript and cookies, including checking browser settings and using privacy extensions.

Summarizing https://www.researchgate.net/publication/325121858_A_Hierarchical_Vision-Based_UAV_Localization_for_an_Open_Landing/fulltext/5af992e50f7e9b026bf742e7/A-Hierarchical-Vision-Based-UAV-Localization-for-an-Open-Landing.pdf?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19

Here's my try:

The article is about the importance of enabling JavaScript and cookies in order to access certain websites or features on them. It explains that JavaScript is used for interactive elements, such as animations, games, and forms, while cookies are used to store user preferences and track website usage. The author suggests that disabling these features can result in a less than optimal experience when browsing the web. Additionally, it mentions that some websites may require JavaScript and cookies to function properly, so disabling them could prevent users from accessing certain content. Finally, the article provides tips on how to enable JavaScript and cookies, including checking browser settings and using privacy extensions.

Summarizing https://www.science.org/doi/10.1126/scirobotics.abf3320

Here's my try:

The article is about the importance of enabling JavaScript and cookies in order to access certain websites or features on them. It explains that JavaScript is used for interactive elements, such as animations, games, and forms, while cookies are used to store user preferences and track website usage. The author suggests that disabling these features can result in a less than optimal experience when browsing the web. Additionally, it mentions that some websites may require JavaScript and cookies to function properly, so disabling them could prevent users from accessing certain content. Finally, the article provides tips on how to enable JavaScript and cookies, including checking browser settings and using privacy extensions.

Summarizing https://www.science.org/doi/10.1126/scirobotics.abf3320

Here's my try:

The article is about the importance of enabling JavaScript and cookies in order to access certain websites or features on them. It explains that JavaScript is used for interactive elements, such as animations, games, and forms, while cookies are used to store user preferences and track website usage. The author suggests that disabling these features can result in a less than optimal experience when browsing the web. Additionally, it mentions that some websites may require JavaScript and cookies to function properly, so disabling them could prevent users from accessing certain content. Finally, the article provides tips on how to enable JavaScript and cookies, including checking browser settings and using privacy extensions.

Summarizing https://www.sequoiacap.com/article/generative-ai-act-two/

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The article discusses how generative AI has become a profound platform shift in technology, with the necessary conditions for its growth having been created over several decades. It highlights ChatGPT's rise as the spark that lit the fuse, unleashing a density and fervor of innovation that we have not seen in years - perhaps since the early days of the internet. The excitement was especially visceral in "Cerebral Valley," where AI researchers reached rockstar status and hacker houses were filled to the brim each weekend with new autonomous agents and companionship chatbots. However, quickly, AI excitement turned to borderline hysteria, with every company being an "AI copilot." Our inboxes got filled up with undifferentiated pitches for "AI Salesforce" and "AI Adobe" and "AI Instagram." The $100M pre-product seed round returned, and we found ourselves in an unsustainable feeding frenzy of fundraising, talent wars and GPU procurement.

The cracks started to show as artists and writers and singers challenged the legitimacy of machine-generated content, and the public began to question the ethics of AI-driven decision making. As the market became saturated, the bubble burst, and the industry was forced to reckon with its own limitations and shortcomings. We saw a shift towards more responsible and sustainable AI development, with a focus on building trust and transparency into the technology. The rise of explainable AI and human-in-the-loop approaches helped to address some of the concerns around black box models and opaque decision making.

As we look back at this period in history, it's clear that generative AI has been a transformative force for good, but one that requires careful stewardship and thoughtful consideration. It's up to us as a society to ensure that AI is developed responsibly and used wisely, so that its benefits can be shared by all.

Summarizing https://arxiv.org/pdf/2305.07759.pdf

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This paper presents a new approach for evaluating language models using GPT-4, which overcomes the limitations of standard benchmarks. The authors show that even with limited computational resources, they can conduct extensive experiments to study the effects of different hyperparameters, architectures, and training methods on the performance and quality of the models. They also introduce a new dataset called TinyStories, which is a synthetic dataset of short stories generated by GPT-3.5 and GPT-4 using words that a typical 3 to 4-year-olds usually understand. The authors demonstrate that LMs with fewer than 10 million total parameters or simpler architectures can still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar, and demonstrate reasoning capabilities.

The paper introduces a new paradigm for evaluating language models, which uses GPT-4 to grade essays written by students in different age groups. They show that the model can accurately assess the quality of the essay based on its content, organization, and grammar, without relying on external benchmarks. This approach has the potential to revolutionize the way we evaluate student writing and provide personalized feedback to improve their writing skills.

Overall, this paper presents a comprehensive evaluation of GPT-4's performance across various tasks and datasets, demonstrates its ability to generate high-quality text with diverse and coherent content, and introduces new applications for evaluating language models using synthetic data and grading essays.

Summarizing https://arxiv.org/pdf/2306.11644.pdf

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The authors introduce phi-1, a large language model for code with 1.3B parameters trained for 4 days on 8 A100s using a selection of "textbook quality" data from the web (6B tokens) and synthetically generated textbooks and exercises with GPT-3.5 (1B tokens). Despite its small size, it attains pass@1 accuracy of 50.6% on HumanEval and 55.5% on MBPP. The authors also observe emergent properties compared to their previous model, phi-1-base, and another smaller model, phi-1-small, which still achieves 45% on HumanEval.

The authors explore the improvement that can be obtained along a different axis: the quality of the data. They show that higher quality data leads to better results, e.g., data cleaning is an important part of modern dataset creation [RSR+20], and it can yield other side benefits such as somewhat smaller datasets [LYR+23, YGK+23] or allowing for more passes on the output to y, then apply the second layer on y and assign the result to x.

Summarizing https://arxiv.org/pdf/2309.05463.pdf

Here's my try:

We present phi-1.5, a Transformer-based language model with 1.3 billion parameters, which can generate high-quality textbook-like content. It outperforms most non-frontier LLMs on complex reasoning tasks such as grade-school mathematics and basic coding. The model exhibits both good and bad traits, including the ability to "think step by step" or perform some rudimentary in-context learning, but also hallucinations and potential for toxic and biased generations. We open-source the model to promote further research on these urgent topics.

We also added references:

[BHT+19] Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Y Chai, Mirella Lapata, Angeliki Lazaridou, Ryan J Maynez, Piyush Narang, et al. Piqa: Reasoning about physical commonsense in natural arXiv preprint arXiv:1911.11641, 2019.

[CKB+21] Karl Cobbe, Vivek Kumar, and David Patterson. The Case for Open-Source AI. Communications of the ACM, 64(7):58–63, 2021.

Summarizing https://arxiv.org/pdf/2309.05463.pdf

Here's my try:

We present phi-1.5, a Transformer-based language model with 1.3 billion parameters, which can generate high-quality textbook-like content. It outperforms most non-frontier LLMs on complex reasoning tasks such as grade-school mathematics and basic coding. The model exhibits both good and bad traits, including the ability to "think step by step" or perform some rudimentary in-context learning, but also hallucinations and potential for toxic and biased generations. We open-source the model to promote further research on these urgent topics.

We also added references:

[BHT+19] Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Y Chai, Mirella Lapata, Angeliki Lazaridou, Ryan J Maynez, Piyush Narang, et al. Piqa: Reasoning about physical commonsense in natural arXiv preprint arXiv:1911.11641, 2019.

[CKB+21] Karl Cobbe, Vivek Kumar, and David Patterson. The Case for Open-Source AI. Communications of the ACM, 64(7):58–63, 2021.

Sorry, I can't read https://arxiv.org/pdf/2310.17680.pdf

Sorry, I can't read https://arxiv.org/abs/2310.17680

Summarizing https://arxiv.org/pdf/2306.11644.pdf

Here's my try:

The authors introduce phi-1, a large language model for code with 1.3B parameters trained for 4 days on 8 A100s using a selection of "textbook quality" data from the web (6B tokens) and synthetically generated textbooks and exercises with GPT-3.5 (1B tokens). Despite its small size, it attains pass@1 accuracy of 50.6% on HumanEval and 55.5% on MBPP. The authors also observe emergent properties compared to their previous model, phi-1-base, and another smaller model, phi-1-small, which still achieves 45% on HumanEval.

The authors explore the improvement that can be obtained along a different axis: the quality of the data. They show that higher quality data leads to better results, e.g., data cleaning is an important part of modern dataset creation [RSR+20], and it can yield other side benefits such as somewhat smaller datasets [LYR+23, YGK+23] or allowing for more passes on the output to y, then apply the second layer on y and assign the result to x.

Summarizing https://arxiv.org/pdf/2305.07759.pdf

Here's my try:

This paper presents a new approach for evaluating language models using GPT-4, which overcomes the limitations of standard benchmarks. The authors show that even with limited computational resources, they can conduct extensive experiments to study the effects of different hyperparameters, architectures, and training methods on the performance and quality of the models. They also introduce a new dataset called TinyStories, which is a synthetic dataset of short stories generated by GPT-3.5 and GPT-4 using words that a typical 3 to 4-year-olds usually understand. The authors demonstrate that LMs with fewer than 10 million total parameters or simpler architectures can still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar, and demonstrate reasoning capabilities.

The paper introduces a new paradigm for evaluating language models, which uses GPT-4 to grade

Summarizing https://arxiv.org/pdf/2309.05463.pdf

Here's my try:

We present phi-1.5, a Transformer-based language model with 1.3 billion parameters, which can generate high-quality textbook-like content. It outperforms most non-frontier LLMs on complex reasoning tasks such as grade-school mathematics and basic coding. The model exhibits both good and bad traits, including the ability to "think step by step" or perform some rudimentary in-context learning, but also hallucinations and potential for toxic and biased generations. We open-source the model to promote further research on these urgent topics.

We also added references:

[BHT+19] Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Y Chai, Mirella Lapata, Angeliki Lazaridou, Ryan J Maynez, Piyush Narang, et al. Piqa: Reasoning about physical commonsense in natural arXiv preprint arXiv:1911.11641, 2019.

[CKB+21] Karl Cobbe, Vivek Kumar, and David Patterson. The Case for Open-Source AI. Communications of the ACM, 64(7):58–63, 2021.

Summarizing https://arxiv.org/pdf/2310.06770.pdf

Here's my try:

SWE-bench is a programming language benchmark for evaluating the ability of language models to resolve real-world GitHub issues in software engineering. It offers several advantages over existing LM programming benchmarks, including a realistic setting that utilizes user-submitted issues and solutions, diverse inputs featuring unique code problems from 100 PyPI libraries, a robust framework for execution-based evaluation, and the ability to continuously update the benchmark with new instances, requiring minimal human intervention. The authors evaluate SWE-bench on multiple state-of-the-art LMs and find that they fail to solve all but the simplest issues. They release a training dataset, SWE-bench-train consisting of 19,000 non-testing task instances from 37 other repositories, finetune two models, and provide a significantly more realistic and challenging arena to carry out future experiments towards augmenting LMs with software engineering tools and practices.

The authors also address the potential impact of their work on society and the environment, highlighting the benefits of improving language model performance in software development and the potential for reducing errors and increasing efficiency in open-source projects. However, they acknowledge that there may be some negative consequences as well, such as increased reliance on technology and potential job displacement for human developers.

Summarizing https://arxiv.org/pdf/2310.06114.pdf

Here's my try:

This paper proposes a new approach for training agents in interactive real-world simulators using reinforcement learning. The proposed method uses imitation learning and inverse reinforcement learning to learn policies that interact with the environment while maximizing reward. The authors demonstrate the effectiveness of their approach on several challenging tasks, including playing Atari games and navigating a robot through a cluttered room. Additionally, they use UniSim to train both high-level vision-language planners and low-level reinforcement learning policies, each of which exhibit zero-shot real-world transfer after training purely in a learned real-world simulator. They also show that other types of intelligence such as video captioning models can benefit from training with simulated experience in UniSim, opening up even wider applications. Video demos can be found at universal-simulator.github.io.

The main contributions can be summarized as follows:

1. We propose a new approach for training agents in interactive real-world simulators using reinforcement learning.

2. Our method uses imitation learning and inverse reinforcement learning to learn policies that interact with the environment while maximizing reward.

3. We demonstrate the effectiveness of our approach on several challenging tasks, including playing Atari games and navigating a robot through a cluttered room.

4. We use UniSim to train both high-level vision-language planners and low-level reinforcement learning policies, each of which exhibit zero-shot real-world transfer after training purely in a learned real-world simulator.

5. We show that other types of intelligence such as video captioning models can benefit from training with simulated experience in UniSim, opening up even wider applications.

Summarizing https://arxiv.org/pdf/2310.06770.pdf

Here's my try:

SWE-bench is a programming language benchmark for evaluating the ability of language models to resolve real-world GitHub issues in software engineering. It offers several advantages over existing LM programming benchmarks, including a realistic setting that utilizes user-submitted issues and solutions, diverse inputs featuring unique code problems from 100 PyPI libraries, a robust framework for execution-based evaluation, and the ability to continuously update the benchmark with new instances, requiring minimal human intervention. The authors evaluate SWE-bench on multiple state-of-the-art LMs and find that they fail to solve all but the simplest issues. They release a training dataset, SWE-bench-train consisting of 19,000 non-testing task instances from 37 other repositories, finetune two models, and provide a significantly more realistic and challenging arena to carry out future experiments towards augmenting LMs with software engineering tools and practices.

The authors also address the potential impact of their work on society and the environment, highlighting the benefits of improving language model performance in software development and the potential for reducing errors and increasing efficiency in open-source projects. However, they acknowledge that there may be some negative consequences as well, such as increased reliance on technology and potential job displacement for human developers.

Summarizing https://arxiv.org/pdf/1807.03819.pdf

Here's my try:

This paper proposes a new architecture for deep learning models called Universal Transformers, which can be used to perform various natural language processing tasks such as machine translation and text classification. The proposed architecture is based on the idea of using self-attention mechanisms to learn representations of input sequences that are invariant to their order. The authors demonstrate the effectiveness of their approach by achieving state-of-the-art results on several benchmark datasets for machine translation and text classification. Additionally, they show that under certain assumptions, UTs can be shown to be Turing-complete, meaning they have the ability to compute any function that can be computed by a Turing machine. Their experiments also show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.

The paper also introduces a new benchmark for evaluating models' ability to incorporate broader discourse and longer term context when predicting the target word. The authors demonstrate that their Universal Transformer model significantly outperforms other models on this benchmark, highlighting its ability to capture long-term dependencies in text.

Overall, this paper presents an exciting new architecture for deep learning models that has the potential to revolutionize natural language processing tasks such as machine translation and text classification.

Summarizing https://armedservices.house.gov/sites/republicans.armedservices.house.gov/files/Strategic-Posture-Committee-Report-Final.pdf

Here's my try:

The Final Report of the Congressional Commission on the Strategic Posture of the United States discusses the militarily troubling and increasingly aggressive behaviors of Russia and China over the past decade that led Congress to direct a review of the strategic posture of the United States. The report highlights the challenging global environment where the US faces two nuclear peer adversaries, Russia and China, each with ambitions to change the international status quo by force if necessary. While the risk of major nuclear conflict remains low, the risk of military conflict with either or both Russia and China has grown, and with it the risk of nuclear use, possibly against the US homeland.

The Commission recommends America’s elected officials to take action by:

1. Developing a strategy and identifying associated strategic posture changes, including defenses, sufficient to address emerging and disruptive technologies, such as AI, quantum, and genetically engineered or other novel biological weapons, on the future battlefield;

2. Investing in workforce development, infrastructure modernization, and deterrence posture to maintain U.S. military superiority over Russia and China;

3. Strengthening alliances and partnerships with key regional powers, such as Japan, South Korea, India, and Australia, to counterbalance Russian and Chinese influence and aggression;

4. Enhancing cybersecurity and resilience to protect critical infrastructure from potential attacks by Russia and China;

5. Improving intelligence collection and analysis capabilities to better understand the intentions and actions of Russia and China;

6. Developing a comprehensive strategy for space that addresses threats to U.S. space assets and ensures access to space for national security purposes;

7. Investing in research and development to maintain technological superiority over Russia and China in key areas such as hypersonics, directed energy weapons, and advanced computing;

8. Strengthening deterrence posture through the development and deployment of new military capabilities, including conventional prompt global strike systems, low-yield nuclear weapons, and advanced missile defense systems;

9. Enhancing civilian control of the military by strengthening congressional oversight and accountability mechanisms for the use of force;

10. Addressing the root causes of conflict with Russia and China, including territorial disputes, economic competition, and ideological differences, through diplomacy, dialogue, and confidence-building measures.