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Casey Martin is a general assignment reporter who has covered everything from political protests to electric scooters. He is almost always out in the field where the news happens. Casey has reported on extremism, houselessness, politics and Seattle’s nightlife. More stories from Casey Martin »

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Setting Up Prometheus Alertmanager on GPUs for Improved ML Lifecycle

Configuración de Prometheus Alertmanager en GPU para mejorar el ciclo de vida del aprendizaje automático

ML ライフサイクルの改善のために GPU 上に Prometheus Alertmanager を設定する

ການຕັ້ງຄ່າ Prometheus Alertmanager ໃນ GPUs ສໍາລັບການປັບປຸງ ML Lifecycle

Поставување на Prometheus Alertmanager на графички процесори за подобрен животен циклус на ML

Prometheus Alertmanager op GPU's instellen voor een verbeterde ML-levenscyclus

Prometheus Alertmanager GPUs kaqpi allinchasqa ML Kawsay Ciclo kaqpaq churay

වැඩිදියුණු කළ ML ජීවන චක්‍රය සඳහා GPU මත Prometheus Alertmanager පිහිටුවීම

Konfigurera Prometheus Alertmanager på GPU:er för förbättrad ML-livscykel

Насб кардани Prometheus Alertmanager дар GPUҳо барои такмили давраи ML

ንዝተመሓየሸ ዑደት ህይወት ML ኣብ GPUs Prometheus Alertmanager ምድላው

Gowulandyrylan ML ömri üçin GPU-larda Prometheus Alertmanager gurmak

Ku Veka Prometheus Alertmanager eka ti-GPU ta Xirhendzevutani xa Vutomi bya ML lexi Antswiweke

(1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu);
(2) Bichen Wu, Meta GenAI and Corresponding author;
(3) Jialiang Wang, Meta GenAI;
(4) Licheng Yu, Meta GenAI;
(5) Kunpeng Li, Meta GenAI;
(6) Yinan Zhao, Meta GenAI;
(7) Ishan Misra, Meta GenAI;
(8) Jia-Bin Huang, Meta GenAI;
(9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com);
(10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com);
(11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). Table of Links Abstract and Introduction
2. Related Work
3. Preliminary
4. FlowVid
4.1. Inflating image U-Net to accommodate video
4.2. Training with joint spatial-temporal conditions
4.3. Generation: edit the first frame then propagate


Experiments


5.1. Settings
5.2. Qualitative results
5.3. Quantitative results
5.4. Ablation study and 5.5. Limitations
Conclusion, Acknowledgments and References
A. Webpage Demo and B. Quantitative comparisons 5. Experiments 5.1. Settings Implementation Details We train our model with 100k videos from Shutterstock [1]. For each training video, we sequentially sample 16 frames with interval {2,4,8}, which represent videos lasting {1,2,4} seconds (taking videos with FPS of 30). The resolution of all images, including input frames, spatial condition images, and flow warped frames, is set to 512×512 via center crop. We train the model with a batch size of 1 per GPU and a total batch size of 8 with 8 GPUs. We employ AdamW optimizer [28] with a learning rate of 1e-5 for 100k iterations. As detailed in our method, we train the major U-Net and ControlNet U-Net joint branches with v-parameterization [41]. The training takes four days on one 8-A100-80G node. During generation, we first generate keyframes with our trained model and then use an off-the-shelf frame interpolation model, such as RIFE [21], to generate non-key frames. By default, we produce 16 key frames at an interval of 4, corresponding to a 2-second clip at 8 FPS. Then, we use RIFE to interpolate the results to 32 FPS. We employ classifier-free guidance [15] with a scale of 7.5 and use 20 inference sampling steps. Additionally, the Zero SNR noise scheduler [27] is utilized. We also fuse the self-attention features obtained during the DDIM inversion of corresponding key frames from the input video, following FateZero [35]. We evaluate our FlowVid with two different spatial conditions: canny edge maps [5] and depth maps [38]. A comparison of these controls can be found in Section 5.4. Evaluation We select the 25 object-centric videos from the public DAVIS dataset [34], covering humans, animals, etc We manually design 115 prompts for these videos, spanning from stylization to object swap. Besides, we also collect 50 Shutterstock videos [1] with 200 designed prompts. We conduct both qualitative (see Section 5.2) and quantitative comparisons (see Section 5.3) with state-of-the-art methods including Rerender [49], CoDeF [32] and TokenFlow [13]. We use their official codes with the default settings. This paper is available on arxiv under CC 4.0 license. (1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu); (2) Bichen Wu, Meta GenAI and Corresponding author; (3) Jialiang Wang, Meta GenAI; (4) Licheng Yu, Meta GenAI; (5) Kunpeng Li, Meta GenAI; (6) Yinan Zhao, Meta GenAI; (7) Ishan Misra, Meta GenAI; (8) Jia-Bin Huang, Meta GenAI; (9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com); (10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com); (11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). (1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu); (2) Bichen Wu, Meta GenAI and Corresponding author; (3) Jialiang Wang, Meta GenAI; (4) Licheng Yu, Meta GenAI; (5) Kunpeng Li, Meta GenAI; (6) Yinan Zhao, Meta GenAI; (7) Ishan Misra, Meta GenAI; (8) Jia-Bin Huang, Meta GenAI; (9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com); (10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com); (11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). Table of Links Abstract and Introduction 2. Related Work 3. Preliminary 4. FlowVid 4.1. Inflating image U-Net to accommodate video 4.2. Training with joint spatial-temporal conditions 4.3. Generation: edit the first frame then propagate Experiments 5.1. Settings 5.2. Qualitative results 5.3. Quantitative results 5.4. Ablation study and 5.5. Limitations Conclusion, Acknowledgments and References A. Webpage Demo and B. Quantitative comparisons Abstract and Introduction Abstract and Introduction 2. Related Work 2. Related Work 3. Preliminary 3. Preliminary 4. FlowVid 4. FlowVid 4 .1. Inflating image U-Net to accommodate video .1. Inflating image U-Net to accommodate video 4.2. Training with joint spatial-temporal conditions 4.2. Training with joint spatial-temporal conditions 4.3. Generation: edit the first frame then propagate 4.3. Generation: edit the first frame then propagate Experiments Experiments Experiments 5.1. Settings 5.1. Settings 5.2. Qualitative results 5.2. Qualitative results 5.3. Quantitative results 5.3. Quantitative results 5.4. Ablation study and 5.5. Limitations 5.4. Ablation study and 5.5. Limitations Conclusion, Acknowledgments and References Conclusion, Acknowledgments and References A. Webpage Demo and B. Quantitative comparisons A. Webpage Demo and B. Quantitative comparisons 5. Experiments 5.1. Settings Implementation Details We train our model with 100k videos from Shutterstock [1]. For each training video, we sequentially sample 16 frames with interval {2,4,8}, which represent videos lasting {1,2,4} seconds (taking videos with FPS of 30). The resolution of all images, including input frames, spatial condition images, and flow warped frames, is set to 512×512 via center crop. We train the model with a batch size of 1 per GPU and a total batch size of 8 with 8 GPUs. We employ AdamW optimizer [28] with a learning rate of 1e-5 for 100k iterations. As detailed in our method, we train the major U-Net and ControlNet U-Net joint branches with v-parameterization [41]. The training takes four days on one 8-A100-80G node. Implementation Details During generation, we first generate keyframes with our trained model and then use an off-the-shelf frame interpolation model, such as RIFE [21], to generate non-key frames. By default, we produce 16 key frames at an interval of 4, corresponding to a 2-second clip at 8 FPS. Then, we use RIFE to interpolate the results to 32 FPS. We employ classifier-free guidance [15] with a scale of 7.5 and use 20 inference sampling steps. Additionally, the Zero SNR noise scheduler [27] is utilized. We also fuse the self-attention features obtained during the DDIM inversion of corresponding key frames from the input video, following FateZero [35]. We evaluate our FlowVid with two different spatial conditions: canny edge maps [5] and depth maps [38]. A comparison of these controls can be found in Section 5.4. Evaluation We select the 25 object-centric videos from the public DAVIS dataset [34], covering humans, animals, etc We manually design 115 prompts for these videos, spanning from stylization to object swap. Besides, we also collect 50 Shutterstock videos [1] with 200 designed prompts. We conduct both qualitative (see Section 5.2) and quantitative comparisons (see Section 5.3) with state-of-the-art methods including Rerender [49], CoDeF [32] and TokenFlow [13]. We use their official codes with the default settings. Evaluation This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis: Settings

Authors:
(1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es);
(2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es);
(3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es);
(4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu);
(5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu);
(6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). Table of Links Abstract and 1 Introduction 1.1 The twincode platform 1.2 Related Work 2 Research Questions 3 Variables 3.1 Independent Variables 3.2 Dependent Variables 3.3 Confounding Variables 4 Participants 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis Acknowledgments and References ABSTRACT Context. Pair programming has been found to increase student interest in Computer Science, particularly so for women, and would therefore appear to be a way to help remedy the under–representation of women in the field. However, one reason for this under– representation is the unwelcoming climate created by gender stereotypes applied to engineers in general, and to software engineers in particular, assuming that men perform better than their women peers. If this same bias is present in pair programming, it could work against the goal of improving gender balance in computing. Objective. In a remote setting in which students cannot directly observe the gender of their peers, we aim to explore whether Software Engineering students behave differently when the perceived gender of their remote pair programming partners changes, searching for differences in (i) the perceived productivity compared to solo programming; (ii) the partner’s perceived technical competency compared to their own; (iii) the partner’s perceived skill level; (iv) the interaction behavior, such as the frequency of source code additions, deletions, validations, etc.; and (v) the type and relative frequencies of dialog messages used for collaborative behavior in a chat window. Although there are some studies on pair programming performance and gender pair combination, to the best of our knowledge there are no studies on the impact of gender stereotypes and bias within the pairs themselves. Method. We have developed an online platform (twincode) that randomly classifies students into gender–balanced groups, arranges them in pairs for remote pair programming (sharing an editor window and a chat window), and can selectively deceive one or both partners regarding the gender of the other via the use of a clearly gendered avatar. Several behaviors are automatically measured during the pair programming process, together with two questionnaires and a semantic tagging of the pairs’ conversations. We will perform a series of experiments to identify the effect, if any, of possible gender bias in remote pair programming interactions. Students in the control group will have no information about their partner’s gender; students in the treatment group will receive such information but will be selectively deceived about their partner’s true gender. To analyze the data, apart from checking reliability of questionnaire data using Cronbach’s alpha and Kaiser criterion, for each response variable we will (i) compare control and experimental groups for the score distance between two in–pair tasks; then, using the data from the experimental group only, we will (ii) compare scores using the partner’s perceived gender as a within–subjects variable; and (iii) analyze the interaction between the partner’s perceived gender (within–subjects) and the subject’s gender (between–subjects). For the (i) and (ii) analyses we will use t–tests, whereas for the (iii) analyses we will use mixed–model ANOVAs. 1 INTRODUCTION Pair programming is an increasingly popular collaboration paradigm that has been shown to be an effective tool in Computer Science education as measured by positive influence on grades, class performance, confidence, productivity, and motivation to stay [6], especially for women [19, 24]. In pair programming, two partners work closely together to solve a programming task. As such, their ability to engage with each other is key. However, these interactions are influenced by implicit gender bias [12, 18], such as assuming women are less technically competent [18]. This is a widely observed phenomenon even in highly–structured settings [6, 13]. Social sciences research indicates that one’s behavior of an individual is affected by the behavior of their peers [8]. Therefore, implicit gender bias based on perception of peers may have effects on one’s behavior, potentially influencing pair programming experience. In this work, in a non–colocated (i.e. remote) environment in which the gender of the peers cannot be directly observed, our goal is to explore whether Software Engineering students change their behavior when the perceived gender of their remote pair programming partners changes from man to woman or vice versa. Note that, while we recognize that many students may identify as neither men nor women, our initial exploration focuses primarily on interactions between students who identify as one of these, so that we can better align our findings with the existing literature on implicit gender bias. The potential biases in interactions involving gender-fluid, non-gender-conforming, or nonbinary students is a rich and complex topic deserving its own subsequent study. To achieve our goal, we plan to search for differences not only in the perceived productivity of pair programming compared to solo programming, the partner’s perceived technical competency compared to their own, and the partners’ perceived skill level, but also in the interaction behavior, i.e. the frequency of source code additions, deletions, validations, etc., and the type and relative frequency of dialog messages used for collaborative behavior. To get early feedback on the infrastructure supporting our proposal, we ran two pilot studies, one at each university, with a limited number of students, where we could check the comprehensibility of the questionnaires used to gather subjective data, the applicability of the message tagging (described in Section 2), and the capabilities of the twincode platform, which is briefly described below. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). Authors: Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 The twincode platform 1.1 The twincode platform 1.2 Related Work 1.2 Related Work 2 Research Questions 2 Research Questions 3 Variables 3 Variables 3.1 Independent Variables 3.1 Independent Variables 3.2 Dependent Variables 3.2 Dependent Variables 3.3 Confounding Variables 3.3 Confounding Variables 4 Participants 4 Participants 5 Execution Plan and 5.1 Recruitment 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis 5.4 Data Analysis Acknowledgments and References Acknowledgments and References ABSTRACT Context . Pair programming has been found to increase student interest in Computer Science, particularly so for women, and would therefore appear to be a way to help remedy the under–representation of women in the field. However, one reason for this under– representation is the unwelcoming climate created by gender stereotypes applied to engineers in general, and to software engineers in particular, assuming that men perform better than their women peers. If this same bias is present in pair programming, it could work against the goal of improving gender balance in computing. Objective. In a remote setting in which students cannot directly observe the gender of their peers, we aim to explore whether Software Engineering students behave differently when the perceived gender of their remote pair programming partners changes, searching for differences in (i) the perceived productivity compared to solo programming; (ii) the partner’s perceived technical competency compared to their own; (iii) the partner’s perceived skill level; (iv) the interaction behavior, such as the frequency of source code additions, deletions, validations, etc.; and (v) the type and relative frequencies of dialog messages used for collaborative behavior in a chat window. Although there are some studies on pair programming performance and gender pair combination, to the best of our knowledge there are no studies on the impact of gender stereotypes and bias within the pairs themselves. Method. We have developed an online platform (twincode) that randomly classifies students into gender–balanced groups, arranges them in pairs for remote pair programming (sharing an editor window and a chat window), and can selectively deceive one or both partners regarding the gender of the other via the use of a clearly gendered avatar. Several behaviors are automatically measured during the pair programming process, together with two questionnaires and a semantic tagging of the pairs’ conversations. We will perform a series of experiments to identify the effect, if any, of possible gender bias in remote pair programming interactions. Students in the control group will have no information about their partner’s gender; students in the treatment group will receive such information but will be selectively deceived about their partner’s true gender. To analyze the data, apart from checking reliability of questionnaire data using Cronbach’s alpha and Kaiser criterion, for each response variable we will (i) compare control and experimental groups for the score distance between two in–pair tasks; then, using the data from the experimental group only, we will (ii) compare scores using the partner’s perceived gender as a within–subjects variable; and (iii) analyze the interaction between the partner’s perceived gender (within–subjects) and the subject’s gender (between–subjects). For the (i) and (ii) analyses we will use t–tests, whereas for the (iii) analyses we will use mixed–model ANOVAs. Context 1 INTRODUCTION Pair programming is an increasingly popular collaboration paradigm that has been shown to be an effective tool in Computer Science education as measured by positive influence on grades, class performance, confidence, productivity, and motivation to stay [6], especially for women [19, 24]. In pair programming, two partners work closely together to solve a programming task. As such, their ability to engage with each other is key. However, these interactions are influenced by implicit gender bias [12, 18], such as assuming women are less technically competent [18]. This is a widely observed phenomenon even in highly–structured settings [6, 13]. Social sciences research indicates that one’s behavior of an individual is affected by the behavior of their peers [8]. Therefore, implicit gender bias based on perception of peers may have effects on one’s behavior, potentially influencing pair programming experience. In this work, in a non–colocated (i.e. remote) environment in which the gender of the peers cannot be directly observed, our goal is to explore whether Software Engineering students change their behavior when the perceived gender of their remote pair programming partners changes from man to woman or vice versa. Note that, while we recognize that many students may identify as neither men nor women, our initial exploration focuses primarily on interactions between students who identify as one of these, so that we can better align our findings with the existing literature on implicit gender bias. The potential biases in interactions involving gender-fluid, non-gender-conforming, or nonbinary students is a rich and complex topic deserving its own subsequent study. To achieve our goal, we plan to search for differences not only in the perceived productivity of pair programming compared to solo programming, the partner’s perceived technical competency compared to their own, and the partners’ perceived skill level, but also in the interaction behavior, i.e. the frequency of source code additions, deletions, validations, etc., and the type and relative frequency of dialog messages used for collaborative behavior. To get early feedback on the infrastructure supporting our proposal, we ran two pilot studies, one at each university, with a limited number of students, where we could check the comprehensibility of the questionnaires used to gather subjective data, the applicability of the message tagging (described in Section 2), and the capabilities of the twincode platform, which is briefly described below. 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 available on arxiv

Can Perceived Gender Bias Shape Pair Programming in Remote Settings?

Može li uočena rodna pristrasnost oblikovati programiranje para u udaljenim postavkama?

¿Puede el sesgo de género percibido afectar la programación por parejas en entornos remotos?

آیا تعصب جنسیتی درک شده می‌تواند برنامه‌نویسی جفتی را در تنظیمات از راه دور ایجاد کند؟

Les préjugés sexistes perçus peuvent-ils influencer la programmation en binôme dans les environnements éloignés ?

Èske yo ka pèrsuite sèks patipri fòm pè pwogramasyon nan anviwònman Remote?

認識された性別による偏見は、リモート環境でのペアプログラミングに影響を与える可能性がありますか?

შეიძლება თუ არა აღქმული გენდერული მიკერძოების ფორმის წყვილის პროგრამირება დისტანციურ პარამეტრებში?

តើអាចយល់ឃើញថា ភាពលំអៀងយេនឌ័រ ការសរសេរកម្មវិធីគូក្នុងការកំណត់ពីចម្ងាយ?

ایا په لیرې پرتو ترتیباتو کې د جنډر تعصب شکل جوړه برنامې پیژندل کیدی شي؟

O preconceito de gênero percebido pode moldar a programação em pares em ambientes remotos?

Может ли воспринимаемая гендерная предвзятость влиять на парное программирование в удаленных условиях?

Чи може сприйняте гендерне упередження формувати парне програмування у віддалених налаштуваннях?

Authors:
(1) Suzanna Sia, Johns Hopkins University;
(2) David Mueller;
(3) Kevin Duh. Table of Links Abstract and 1. Background
2. Data and Settings
3. Where does In-context MT happen?
4. Characterising Redundancy in Layers
5. Inference Efficiency
6. Further Analysis
7. Conclusion, Acknowledgments, and References
A. Appendix 2. Data and Settings Models We use GPTNEO2.7B (Black et al., 2021), BLOOM3B (Scao et al., 2022), LLAMA7B and LLAMA7Bchat (Touvron et al., 2023), the instruction-tuned variant, in all of our experiments. GPTNEO2.7B has 32 layers and 20 heads, BLOOM3B has 30 layers and 32 heads, while LLAMA7B has 32 layers and 32 heads. The checkpoints we use are from the transformers library (Wolf et al., 2019). GPTNEO was trained on The PILE (Gao et al., 2020), an 825GB text dataset which consists of roughly 98% English data. Despite being mostly monolingual, The PILE contains Europarl which GPTNEO was trained on at a document level (rather than a sentence level). Conversely, BLOOM was trained on the ROOTS corpus (Laurençon et al., 2022), a composite collection of 498 datasets that were explicitly selected to be multilingual, representing 46 natural languages and 13 programming languages. LLAMA training data consists primarily of common crawl, C4, wikipedia, stack exchange as major sources. To our knowledge, there has not been any reports of sentence level parallel corpora in the training datasets of these models. Data We test our models using FLORES (Goyal et al., 2021) en ↔ fr which we report in the main paper, and a small study on extending Section 3 to en ↔ pt in the Appendix. Prompt examples are drawn from the development set. We evaluate the generations using BLEU scores, following the implementation from Post (2018). Prompt Format Our prompts may consist of instructions, examples, both, or none. Importantly, we adopt neutral delimiters, "Q:" and "A:" to separate the prompt and the start of machine generated text. This ensures that the models do not have any information from the delimiters on what the task is. [1] When no natural language instructions are used the model input will be Q: {source_sentence} A: Instructions are given in natural language and take the form: Translate from {L1} to {L2}: Q: {source_sentence} A:, where L1 = English and L2 = French if the source and target languages are English and French respectively. Examples are given after instructions, and similarly delimited by Q: and A:. See Appendix: Table 1 for an example. This paper is available on arxiv under CC BY 4.0 DEED license. [1] In an earlier exploration, we found that supplying the model with language indicators only, e.g., "English:", "French:" was sufficient for strong models (llama7b, llama7b-chat) to perform the task without seeing any instructions or examples in the context. Authors: (1) Suzanna Sia, Johns Hopkins University; (2) David Mueller; (3) Kevin Duh. Authors: Authors: (1) Suzanna Sia, Johns Hopkins University; (2) David Mueller; (3) Kevin Duh. Table of Links Abstract and 1. Background 2. Data and Settings 3. Where does In-context MT happen? 4. Characterising Redundancy in Layers 5. Inference Efficiency 6. Further Analysis 7. Conclusion, Acknowledgments, and References A. Appendix Abstract and 1. Background Abstract and 1. Background 2. Data and Settings 2. Data and Settings 3. Where does In-context MT happen? 3. Where does In-context MT happen? 4. Characterising Redundancy in Layers 4. Characterising Redundancy in Layers 5. Inference Efficiency 5. Inference Efficiency 6. Further Analysis 6. Further Analysis 7. Conclusion, Acknowledgments, and References 7. Conclusion, Acknowledgments, and References A. Appendix A. Appendix 2. Data and Settings Models We use GPTNEO2.7B (Black et al., 2021), BLOOM3B (Scao et al., 2022), LLAMA7B and LLAMA7Bchat (Touvron et al., 2023), the instruction-tuned variant, in all of our experiments. GPTNEO2.7B has 32 layers and 20 heads, BLOOM3B has 30 layers and 32 heads, while LLAMA7B has 32 layers and 32 heads. The checkpoints we use are from the transformers library (Wolf et al., 2019). Models GPTNEO was trained on The PILE (Gao et al., 2020), an 825GB text dataset which consists of roughly 98% English data. Despite being mostly monolingual, The PILE contains Europarl which GPTNEO was trained on at a document level (rather than a sentence level). Conversely, BLOOM was trained on the ROOTS corpus (Laurençon et al., 2022), a composite collection of 498 datasets that were explicitly selected to be multilingual, representing 46 natural languages and 13 programming languages. LLAMA training data consists primarily of common crawl, C4, wikipedia, stack exchange as major sources. To our knowledge, there has not been any reports of sentence level parallel corpora in the training datasets of these models. Data We test our models using FLORES (Goyal et al., 2021) en ↔ fr which we report in the main paper, and a small study on extending Section 3 to en ↔ pt in the Appendix. Prompt examples are drawn from the development set. We evaluate the generations using BLEU scores, following the implementation from Post (2018). Prompt Format Our prompts may consist of instructions, examples, both, or none. Importantly, we adopt neutral delimiters, "Q:" and "A:" to separate the prompt and the start of machine generated text. This ensures that the models do not have any information from the delimiters on what the task is. [1] Prompt Format When no natural language instructions are used the model input will be Q: {source_sentence} A: Instructions are given in natural language and take the form: Translate from {L1} to {L2}: Q: {source_sentence} A:, where L1 = English and L2 = French if the source and target languages are English and French respectively. Examples are given after instructions, and similarly delimited by Q: and A:. See Appendix: Table 1 for an example. 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] In an earlier exploration, we found that supplying the model with language indicators only, e.g., "English:", "French:" was sufficient for strong models (llama7b, llama7b-chat) to perform the task without seeing any instructions or examples in the context.

Where does In-context Translation Happen in Large Language Models: Data and Settings

Authors:
(1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution;
(2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution;
(3) Nicholas D. Lane, University of Cambridge and Flower Labs;
(4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University;
(5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Table of Links Abstract and Introduction
Motivation and Setup: How low-quality data affects the performance of Collaborative Training
Proposed Workflow for Data Quality Control
Experiments
Conclusion and Future Work, and References
A. Related Work
B. Heterogeneity Settings
C. Experimental Details
D. Ablation study of Unified Scoring with Anchor Data
E. Examples for low-and high- quality Data B HETEROGENEITY SETTINGS To model real-world scenario, we designed two heterogeneous settings: NIID-1 and NIID-2. NIID-1 replicates a typical scenario in federated learning classification tasks (Yurochkin et al., 2019; Wang et al., 2020a;b; Li et al., 2021; Shi et al., 2022), where the distribution of low-quality data among clients follows a Dirichlet distribution with parameter β = 1, while ensuring that the volume of data processed by each client remains equal. In contrast, NIID-2 addresses a skewed classification task scenario within FL (McMahan et al., 2017; Li et al., 2020), assigning 70% of low-quality data to half of the clients and 90% to the other half, yet maintaining an equal size of training data across all clients. The distributions for these settings are illustrated in Figure 3. Table2 shows the low-quality data traing and data quality control federated NIID-2 setting. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution; (2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution; (3) Nicholas D. Lane, University of Cambridge and Flower Labs; (4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University; (5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Authors: Authors: (1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution; (2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution; (3) Nicholas D. Lane, University of Cambridge and Flower Labs; (4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University; (5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Table of Links Abstract and Introduction Motivation and Setup: How low-quality data affects the performance of Collaborative Training Proposed Workflow for Data Quality Control Experiments Conclusion and Future Work, and References A. Related Work B. Heterogeneity Settings C. Experimental Details D. Ablation study of Unified Scoring with Anchor Data E. Examples for low-and high- quality Data Abstract and Introduction Abstract and Introduction Motivation and Setup: How low-quality data affects the performance of Collaborative Training Motivation and Setup: How low-quality data affects the performance of Collaborative Training Proposed Workflow for Data Quality Control Proposed Workflow for Data Quality Control Experiments Experiments Conclusion and Future Work, and References Conclusion and Future Work, and References A. Related Work A. Related Work B. Heterogeneity Settings B. Heterogeneity Settings C. Experimental Details C. Experimental Details D. Ablation study of Unified Scoring with Anchor Data D. Ablation study of Unified Scoring with Anchor Data E. Examples for low-and high- quality Data E. Examples for low-and high- quality Data B HETEROGENEITY SETTINGS To model real-world scenario, we designed two heterogeneous settings: NIID-1 and NIID-2. NIID-1 replicates a typical scenario in federated learning classification tasks (Yurochkin et al., 2019; Wang et al., 2020a;b; Li et al., 2021; Shi et al., 2022), where the distribution of low-quality data among clients follows a Dirichlet distribution with parameter β = 1, while ensuring that the volume of data processed by each client remains equal. In contrast, NIID-2 addresses a skewed classification task scenario within FL (McMahan et al., 2017; Li et al., 2020), assigning 70% of low-quality data to half of the clients and 90% to the other half, yet maintaining an equal size of training data across all clients. The distributions for these settings are illustrated in Figure 3. Table2 shows the low-quality data traing and data quality control federated NIID-2 setting. 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

Enhancing Data Quality in Federated Fine-Tuning of Foundation Models: Heterogeneity Settings

Empowered teams are critical to the success of your product. What are the key features of an empowered team? “Never Tell People How to Do Things. Tell Them What to Do, and They Will Surprise You With Their Ingenuity.” - General Patton Empowerment is everywhere. Empower this and empower that, and give your team freedom. Let them build their own thing; please do not interrupt, and close the door from outside. Some of these points might be true, but as I usually say in my articles, let’s try to get to the fundamentals. I believe it is not possible to expand further without getting those right. Task setting It all starts with task setting. Ever since the Industrial Revolution, businesses have been constantly searching for ways to increase output per hour spent by their employees and, respectively, the amount of money they pay per hour. During the Industrial Revolution and in Henry Ford’s factories, people had no choice about what they could do. They were skilled to a certain extent to perform repetitive operations, and there was always a plan to find someone else if they did not meet the quality, amount, or any other KPI that the organization defined. There are some workplaces where these rules still apply, but most certainly not in the innovation. Innovation has yet to be built. Thus, you can’t train people. You do not even know what to train them for. So, humanity has decided to find a new way of setting the task.Successful companies do not prescribe how they want the task completed; they are more interested in the result—either an upward trend in certain metrics or a specific business result. The way the team decides to achieve it usually sits with them, and while it might be influenced and confirmed by the body who created the initial task, it will most likely still be owned and adjusted by the team itself. People in the industry are creative, able to find solutions without external help, and most likely enjoy the process of thinking and the joy of achievement—let them have all of it, and results will follow. Think about the current tasks that your teams have on their plates and explore who came up with an idea, what the measures of success are, and how much flexibility the team has over the implementation process. There may be a small change that you can make to uncover their thought process. Motivation It is astonishing how many people still think you can solve the problem with money. Yes, it is possible to gain short-term results by firefighting the flame with piles of cash, but this could be more sustainable in the long run. At certain points, no matter how much money you pay people, they will not start working better or even doing worse. Moreover, you can even end up in a situation (quite luc, ky obviously, but unfortunate) that Nvidia encountered after their stock skyrocketed in 2023 - some of their engineers are not interested in doing their job anymore - they are so rich already that it is enough to fulfill their needs for the rest of their lives: https://www.businessinsider.com/nvidia-employees-rich-happy-problem-insiders-say-2023-12 The only way to motivate people - is by knowing your people and understanding why they are coming to work. Yes, some of them are coming just to pay for their kids' tuition fees, buy a bigger house, and then retire to do some gardening. However, the majority are doing their work because they find one or more elements of it satisfying - if you uncover what exactly and help them get even more satisfaction out of it - you get an instant strike - both you and your teammate happy. This is an amazing video about motivation and one of my favorites of all time. It was created 14 years ago, and I highly recommend watching it to get more insights about motivation: https://www.youtube.com/watch?v=u6XAPnuFjJc&embedable=true Think about what motivates your people. What are they talking about in the kitchen? Where have they been on vacation? Do they have pets? Any intelligence can help you understand why specific individuals are coming to work every day, and you can make that day even better. Leadership Does your team need help? They certainly do. It can be anything—from the uncomfortable chairs in the office to the unstable environment to the external vendor who has missed their deadline for the third time in a row. Did you spend time trying to understand these problems and how to solve them? If yes - you are on the right path. If not - try to do that immediately. While it is an excellent step to make sure your team trust you and believes that you are on their side no matter what happens - the only way to prove this leadership is to be in constant touch and, if cover is needed - fly on the mission and get stuff sorted. Communication is vital, as there will be certain moments when you can’t sort certain things out. It happens. We live in a complex world. It is essential to provide all the necessary information in advance and even ask for advice—remember, they love solving problems and can propose or even implement a solution you have never thought about. Think about the current challenges. If there are none, you are wrong. Go and do your homework. There are some you can do tomorrow morning to sort them out. Do not forget about Motivation and Task-setting—they can only strengthen your position and help you achieve better results. Are you empowered enough? There will be a difference in understanding empowerment in different contexts and organizations. However, I believe that Task Setting, Motivation, and Leadership are three fundamental pillars—similar to Scrum’s Transparency, Inspection, and Adaptation—that can make miracles when present, cause lots of trouble if any of them are missing, and help you solve the problem by even starting to pay closer attention to them. What are your empowerment secrets? Share in the comments. Empowered teams are critical to the success of your product. What are the key features of an empowered team? Empowered teams are critical to the success of your product. What are the key features of an empowered team? “Never Tell People How to Do Things. Tell Them What to Do, and They Will Surprise You With Their Ingenuity.” - General Patton “Never Tell People How to Do Things. Tell Them What to Do, and They Will Surprise You With Their Ingenuity.” - General Patton Empowerment is everywhere. Empower this and empower that, and give your team freedom. Let them build their own thing; please do not interrupt, and close the door from outside. Some of these points might be true, but as I usually say in my articles, let’s try to get to the fundamentals. I believe it is not possible to expand further without getting those right. Task setting It all starts with task setting. Ever since the Industrial Revolution, businesses have been constantly searching for ways to increase output per hour spent by their employees and, respectively, the amount of money they pay per hour. During the Industrial Revolution and in Henry Ford’s factories, people had no choice about what they could do. They were skilled to a certain extent to perform repetitive operations, and there was always a plan to find someone else if they did not meet the quality, amount, or any other KPI that the organization defined. There are some workplaces where these rules still apply, but most certainly not in the innovation. Innovation has yet to be built. Thus, you can’t train people. You do not even know what to train them for. So, humanity has decided to find a new way of setting the task. Successful companies do not prescribe how they want the task completed; they are more interested in the result—either an upward trend in certain metrics or a specific business result. The way the team decides to achieve it usually sits with them, and while it might be influenced and confirmed by the body who created the initial task, it will most likely still be owned and adjusted by the team itself. People in the industry are creative, able to find solutions without external help, and most likely enjoy the process of thinking and the joy of achievement—let them have all of it, and results will follow. Think about the current tasks that your teams have on their plates and explore who came up with an idea, what the measures of success are, and how much flexibility the team has over the implementation process. There may be a small change that you can make to uncover their thought process. Think about the current tasks that your teams have on their plates and explore who came up with an idea, what the measures of success are, and how much flexibility the team has over the implementation process. There may be a small change that you can make to uncover their thought process. Motivation It is astonishing how many people still think you can solve the problem with money. Yes, it is possible to gain short-term results by firefighting the flame with piles of cash, but this could be more sustainable in the long run. At certain points, no matter how much money you pay people, they will not start working better or even doing worse. Moreover, you can even end up in a situation (quite luc, ky obviously, but unfortunate) that Nvidia encountered after their stock skyrocketed in 2023 - some of their engineers are not interested in doing their job anymore - they are so rich already that it is enough to fulfill their needs for the rest of their lives: https://www.businessinsider.com/nvidia-employees-rich-happy-problem-insiders-say-2023-12 https://www.businessinsider.com/nvidia-employees-rich-happy-problem-insiders-say-2023-12 https://www.businessinsider.com/nvidia-employees-rich-happy-problem-insiders-say-2023-12 The only way to motivate people - is by knowing your people and understanding why they are coming to work. Yes, some of them are coming just to pay for their kids' tuition fees, buy a bigger house, and then retire to do some gardening. However, the majority are doing their work because they find one or more elements of it satisfying - if you uncover what exactly and help them get even more satisfaction out of it - you get an instant strike - both you and your teammate happy. This is an amazing video about motivation and one of my favorites of all time. It was created 14 years ago, and I highly recommend watching it to get more insights about motivation: https://www.youtube.com/watch?v=u6XAPnuFjJc&embedable=true https://www.youtube.com/watch?v=u6XAPnuFjJc&embedable=true Think about what motivates your people. What are they talking about in the kitchen? Where have they been on vacation? Do they have pets? Any intelligence can help you understand why specific individuals are coming to work every day, and you can make that day even better. Think about what motivates your people. What are they talking about in the kitchen? Where have they been on vacation? Do they have pets? Any intelligence can help you understand why specific individuals are coming to work every day, and you can make that day even better. Leadership Does your team need help? They certainly do. It can be anything—from the uncomfortable chairs in the office to the unstable environment to the external vendor who has missed their deadline for the third time in a row. Did you spend time trying to understand these problems and how to solve them? If yes - you are on the right path. If not - try to do that immediately. While it is an excellent step to make sure your team trust you and believes that you are on their side no matter what happens - the only way to prove this leadership is to be in constant touch and, if cover is needed - fly on the mission and get stuff sorted. Communication is vital, as there will be certain moments when you can’t sort certain things out. It happens. We live in a complex world. It is essential to provide all the necessary information in advance and even ask for advice—remember, they love solving problems and can propose or even implement a solution you have never thought about. Think about the current challenges. If there are none, you are wrong. Go and do your homework. There are some you can do tomorrow morning to sort them out. Do not forget about Motivation and Task-setting—they can only strengthen your position and help you achieve better results. Think about the current challenges. If there are none, you are wrong. Go and do your homework. There are some you can do tomorrow morning to sort them out. Do not forget about Motivation and Task-setting—they can only strengthen your position and help you achieve better results. Are you empowered enough? There will be a difference in understanding empowerment in different contexts and organizations. However, I believe that Task Setting, Motivation, and Leadership are three fundamental pillars—similar to Scrum’s Transparency, Inspection, and Adaptation—that can make miracles when present, cause lots of trouble if any of them are missing, and help you solve the problem by even starting to pay closer attention to them. What are your empowerment secrets? Share in the comments. What are your empowerment secrets? Share in the comments.

The Secrets to Empowering Teams: Task Setting, Motivation, and Leadership

Desktop customization is a fun activity that many of you have likely tried at least once. There are various customization options, such as changing the boot screen logo, the start menu icon, or transforming the entire Windows interface to resemble macOS. Today, we are about to discuss one such customization, which is setting a different background wallpaper for each desktop (or monitor) in Windows 11 and 10 for aesthetics, productivity, creativity, or any other reason. Setting Different Backgrounds for Each Desktop on Windows 11, 10 In Windows, there is no limit on the number of desktops you can create from Task View (though more desktops will consume more system resources), allowing you to create as many desktops as you want and set different wallpapers for each. To customize which wallpaper appears on each desktop, 1. Press Win+Tab to open Task View. All your existing desktops will be listed below, with a "+" option to create more. 2. Right-click on the respective desktop (in my case, "Desktop 2") whose background wallpaper you want to change, and then select "Choose background" from the context menu. 3. It will redirect you to the "Personalize your background" section for that desktop, where you can choose a wallpaper from the recent images or select a new one from the file explorer. 4. Once the new background wallpaper for the desktop is set, you can confirm the changes by visiting the desktop. 5. To verify that each desktop has its own separate background wallpaper, you can take another peek into the Task View using Win+Tab shortcut. That's it! You've successfully set a different background wallpaper for each desktop on Windows 11 and 10. Bonus Tip! You can directly change the desktop background wallpaper from your current desktop by navigating to "Settings" -> "Personalization" -> "Background" and then right-clicking on any image to open a context menu with the "Set for desktop" option, allowing you to change the background wallpaper of your preferred desktop. Wrap Up That’s how you can change the background wallpaper for each desktop on Windows 11 and 10. You can further change all of your existing desktops background wallpaper by following this method. Desktop customization is a fun activity that many of you have likely tried at least once. There are various customization options, such as changing the boot screen logo, the start menu icon, or transforming the entire Windows interface to resemble macOS. Today, we are about to discuss one such customization, which is setting a different background wallpaper for each desktop (or monitor) in Windows 11 and 10 for aesthetics, productivity, creativity, or any other reason. Setting Different Backgrounds for Each Desktop on Windows 11, 10 In Windows, there is no limit on the number of desktops you can create from Task View (though more desktops will consume more system resources), allowing you to create as many desktops as you want and set different wallpapers for each. To customize which wallpaper appears on each desktop, 1. Press Win+Tab to open Task View. Win+Tab All your existing desktops will be listed below, with a "+" option to create more. All your existing desktops will be listed below, with a "+" option to create more. 2. Right-click on the respective desktop (in my case, " Desktop 2 ") whose background wallpaper you want to change, and then select " Choose background " from the context menu. Desktop 2 Choose background 3. It will redirect you to the " Personalize your background " section for that desktop, where you can choose a wallpaper from the recent images or select a new one from the file explorer. Personalize your background 4. Once the new background wallpaper for the desktop is set, you can confirm the changes by visiting the desktop. 5. To verify that each desktop has its own separate background wallpaper, you can take another peek into the Task View using Win+Tab shortcut. Win+Tab That's it! You've successfully set a different background wallpaper for each desktop on Windows 11 and 10. Bonus Tip! You can directly change the desktop background wallpaper from your current desktop by navigating to " Settings " -> " Personalization " -> " Background " and then right-clicking on any image to open a context menu with the " Set for desktop " option, allowing you to change the background wallpaper of your preferred desktop. Settings Personalization Background Set for desktop Wrap Up That’s how you can change the background wallpaper for each desktop on Windows 11 and 10. You can further change all of your existing desktops background wallpaper by following this method.

Setting Different Backgrounds for Each Desktop on Windows 11 and 10

Tips for Setting Healthy Boundaries At Work

Many students find it difficult to craft top quality essays and hit their deadlines, due to a range of issues, like hectic schedules or lack of knowledge on the topic. Fortunately, EssayFlow is here to help. A highly-rated undetectable AI essay writing tool, EssayFlow can craft humanized AI essays that are undetectable, original, and of high quality. In this EssayFlow review, we’ll dig into the functions, advantages, and recommended use cases of this leading undetectable AI essay writer. An Introduction to EssayFlow EssayFlow is an AI essay writer. It can craft amazing essays in an instant, helping students overcome “writer’s block” or deal with difficult deadlines. This AI essay writer is also undetectable. That means it creates human-like output, without the usual signs of AI writing, so it can bypass AI detection on platforms like Turnitin and Originality. With EssayFlow, students can confidently submit top-notch essays without compromising originality or quality, revolutionizing the way essays are created in the digital age. Key Features and Selling Points of EssayFlow Humanized Essay Content EssayFlow creates essays that seem exactly as though they were written by real people. That’s because it was trained on millions of genuine, human-written essays to mimic the same style of writing. It’s therefore almost impossible to detect EssayFlow’s output as AI, even with AI checkers like Turnitin. Works in a Range of Essay Styles EssayFlow can help students write many different types of essays. That includes descriptive essays, critical essays, argumentative papers, persuasive texts, and more. It adjusts its writing style and tone to suit the specifics of each essay type, creating varied and original content, every time. No Plagiarism Guarantee EssayFlow never plagiarizes. It produces purely original, fresh work with every usage. Thanks to that, students can rest assured that their essays will be able to bypass plagiarism detection scanners, as well as AI detection platforms. Speedy and Productive EssayFlow was designed to help students who are struggling to get essays done on time. While it might take a student several hours to write an essay, EssayFlow can do the same in a matter of minutes. It’s ideal for speeding up and streamlining the entire essay writing process, without compromising on quality. Works in Almost Any Language EssayFlow is suitable for users all over the globe, thanks to the fact that it works in dozens of different languages – over 50, to be precise. So, no matter whether you need to create essays in French, German, Spanish, or some other language, you can do it all with EssayFlow. Built-in AI Detection Scans EssayFlow also has a built-in AI checker to scan essays and see if they can pass AI detectors, such as ZeroGPT and Copyleaks. It saves users even more time, providing instant feedback and multiple scan results in a fraction of the usual time. Improving Grades and Understanding EssayFlow is a super study aid. It not only produces top quality content for students, but also helps them learn. They can read through the essays and ideas outlined by EssayFlow to deepen their understanding of difficult topics and fine-tune their essay writing abilities. How to Use EssayFlow Next, let’s see how easy it is to use EssayFlow, with a simple four-step guide. Step 1: Input Your Essay Requirements to EssayFlow First, you have to type in the subject or topic of your essay, and provide a brief overview of how it should turn out. You can pick the type of essay you want, what language you want it in, and how long it should be. EssayFlow will use all of your input to create a customized essay, just for you. Step 2: Activate Undetectable Mode (Optional) If you want your essay to be human-like and undetectable by AI scanners, make sure to tick the “Undetectable Mode” box before continuing. If you do that, EssayFlow will take extra steps to ensure your essay has no signs of AI writing. Step 3: Pick a Generation Mode Next, it’s time to make your essay. You’ve got two options here: One-Click Generation or Generate Titles. One-Click Generation: Ideal for rapid results, this creates an instant essay based on your title and provided info.
Generate Titles: With this method, EssayFlow will first create an outline, which you can edit and amend, before continuing to create the full essay. It gives you more control over the final result. No matter which button you click, EssayFlow will use its advanced technology to create a top quality draft for you to read through. Step 4: Create and Scan Your Essay. Once the essay has been generated, EssayFlow will instantly put it through a quick AI scan. It’ll run the essay against a range of leading detectors – including Copyleaks and ZeroGPT – to tell you whether or not it passes as human. Summing Up Overall, EssayFlow is an extraordinary example of what AI technology can do for students today. It’s a powerful, state-of-the-art undetectable AI essay writer, which also offers benefits to those working in research or academia. Indeed, anyone who needs to write essays, research papers, or similar kinds of content can turn to this tool for AI-powered assistance. It’s also by far one of the best and only tools that are truly capable of crafting not just high quality AI essays, but humanized essays, too. It can beat all the big detectors and doesn’t produce the same kind of boring, bland, and robotic content as other AI writers. With its many customization features, adaptable approach, emphasis on originality, and multilingual support, it’s a complete, comprehensive AI essay writer, changing the way essays are made forever. Give it a try, absolutely free, and experience what this awesome AI essay generator can do for you. This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author Many students find it difficult to craft top quality essays and hit their deadlines, due to a range of issues, like hectic schedules or lack of knowledge on the topic. Fortunately, EssayFlow is here to help. A highly-rated undetectable AI essay writing tool, EssayFlow can craft humanized AI essays that are undetectable, original, and of high quality. EssayFlow EssayFlow In this EssayFlow review, we’ll dig into the functions, advantages, and recommended use cases of this leading undetectable AI essay writer. An Introduction to EssayFlow EssayFlow is an AI essay writer. It can craft amazing essays in an instant, helping students overcome “writer’s block” or deal with difficult deadlines. This AI essay writer is also undetectable. That means it creates human-like output, without the usual signs of AI writing, so it can bypass AI detection on platforms like Turnitin and Originality. With EssayFlow, students can confidently submit top-notch essays without compromising originality or quality, revolutionizing the way essays are created in the digital age. Key Features and Selling Points of EssayFlow Humanized Essay Content EssayFlow creates essays that seem exactly as though they were written by real people. That’s because it was trained on millions of genuine, human-written essays to mimic the same style of writing. It’s therefore almost impossible to detect EssayFlow’s output as AI, even with AI checkers like Turnitin. Works in a Range of Essay Styles EssayFlow can help students write many different types of essays. That includes descriptive essays, critical essays, argumentative papers, persuasive texts, and more. It adjusts its writing style and tone to suit the specifics of each essay type, creating varied and original content, every time. No Plagiarism Guarantee EssayFlow never plagiarizes. It produces purely original, fresh work with every usage. Thanks to that, students can rest assured that their essays will be able to bypass plagiarism detection scanners, as well as AI detection platforms. Speedy and Productive EssayFlow was designed to help students who are struggling to get essays done on time. While it might take a student several hours to write an essay, EssayFlow can do the same in a matter of minutes. It’s ideal for speeding up and streamlining the entire essay writing process, without compromising on quality. Works in Almost Any Language EssayFlow is suitable for users all over the globe, thanks to the fact that it works in dozens of different languages – over 50, to be precise. So, no matter whether you need to create essays in French, German, Spanish, or some other language, you can do it all with EssayFlow. Built-in AI Detection Scans EssayFlow also has a built-in AI checker to scan essays and see if they can pass AI detectors, such as ZeroGPT and Copyleaks. It saves users even more time, providing instant feedback and multiple scan results in a fraction of the usual time. Improving Grades and Understanding EssayFlow is a super study aid. It not only produces top quality content for students, but also helps them learn. They can read through the essays and ideas outlined by EssayFlow to deepen their understanding of difficult topics and fine-tune their essay writing abilities. How to Use EssayFlow Next, let’s see how easy it is to use EssayFlow, with a simple four-step guide. Step 1: Input Your Essay Requirements to EssayFlow First, you have to type in the subject or topic of your essay, and provide a brief overview of how it should turn out. You can pick the type of essay you want, what language you want it in, and how long it should be. EssayFlow will use all of your input to create a customized essay, just for you. Step 2: Activate Undetectable Mode (Optional) If you want your essay to be human-like and undetectable by AI scanners, make sure to tick the “Undetectable Mode” box before continuing. If you do that, EssayFlow will take extra steps to ensure your essay has no signs of AI writing. Step 3: Pick a Generation Mode Next, it’s time to make your essay. You’ve got two options here: One-Click Generation or Generate Titles. One-Click Generation: Ideal for rapid results, this creates an instant essay based on your title and provided info. Generate Titles: With this method, EssayFlow will first create an outline, which you can edit and amend, before continuing to create the full essay. It gives you more control over the final result. One-Click Generation: Ideal for rapid results, this creates an instant essay based on your title and provided info. Generate Titles: With this method, EssayFlow will first create an outline, which you can edit and amend, before continuing to create the full essay. It gives you more control over the final result. No matter which button you click, EssayFlow will use its advanced technology to create a top quality draft for you to read through. Step 4: Create and Scan Your Essay. Once the essay has been generated, EssayFlow will instantly put it through a quick AI scan. It’ll run the essay against a range of leading detectors – including Copyleaks and ZeroGPT – to tell you whether or not it passes as human. Summing Up Overall, EssayFlow is an extraordinary example of what AI technology can do for students today. It’s a powerful, state-of-the-art undetectable AI essay writer, which also offers benefits to those working in research or academia. Indeed, anyone who needs to write essays, research papers, or similar kinds of content can turn to this tool for AI-powered assistance. It’s also by far one of the best and only tools that are truly capable of crafting not just high quality AI essays, but humanized essays, too. It can beat all the big detectors and doesn’t produce the same kind of boring, bland, and robotic content as other AI writers. With its many customization features, adaptable approach, emphasis on originality, and multilingual support, it’s a complete, comprehensive AI essay writer, changing the way essays are made forever. Give it a try, absolutely free , and experience what this awesome AI essay generator can do for you. Give it a try, absolutely free Give it a try, absolutely free This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

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