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Taking the Pain Out of Cybersecurity Reporting: A Practical Guide for MSPs

Align Technology is an American manufacturer of 3D digital scanners and Invisalign clear aligners used in orthodontics. It was founded in 1997 and is headquartered in Tempe, Arizona. The company manufactures the aligners in Juarez, Mexico, and its scanners in Israel and China.

Align Technology - Wikipedia

Align Technology | 229,145 followers on LinkedIn. Align Technology is dedicated to transforming lives by improving the journey to a healthy, beautiful smile. | We are dedicated to transforming lives by improving the journey to a healthy, beautiful smile. Discover your straight path to a bright ...

Align Technology | LinkedIn

Align Technology is a global medical device company that pioneered the invisible orthodontics market with the introduction of the Invisalign system in 1999. Today, we develop innovative, technology-rich products such as Invisalign aligner treatments, iTero™ intra-oral scanners, ... Read more

Working at Align Technology | Glassdoor

Global medical device company Align Technology on May 3 announced the launch of direct operations in South Africa, to bolster its local footprint in the country and raise awareness and adoption of its trademarked clear aligner system, the Invisalign System. The company reached its target milestone ...

Align Technologies launches direct operations in South Africa

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The Invisalign® system is the most advanced clear aligner system in the world, trusted by approximately 16.4 million people worldwide to improve their smiles.

Align Technology

Align Managed Services delivers the highest level of stability and transparency across your IT operations ... We have delivered IT solutions across industries for over three decades. · Our people, process, and tools have transformed technology for the world&#x27;s top companies.

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Aligning code, in general, does not provide any value. It's just to make code look more beautiful and easier to read. Meet . Vim Easy Align It's similar to if you've used vscode. better align Installation & Configuration You can install using your favorite plugin manager. Using vim-plug And add the following mappings to your file Plug 'junegunn/vim-easy-align' .vimrc xmap ga <Plug> (EasyAlign) nmap ga <Plug> (EasyAlign) Feel free to change the default mapping to something you're more comfortable with. Note: If you're not familiar with and , they're used to add mappings for normal and visual mode respectively. ga nmap xmap Usage There are multiple ways to use the plugin. I found the following to be the easiest. 1. Align code in the current block While in normal mode type gaip= Explanation: Start the EasyAlign command ( ) for nner aragraph and align around the sign. ga i p = Note: don't forget to press enter to execute the command. 2. Align currently selected code Assuming that you have something selected while in visual mode. Type ga= Explanation: Start the EasyAlign command ( ) and align around the sign. ga = Options 1. Regular expressions You can use regular expressions to align anything you want. The only limit is your imagination. Let's say for example you want to align javascript module imports around the keyword. from You would use the command ga<Ctrl-x>from And once again is used to enter the EasyAlign mode. ga After that, press (Control + x) to enter the mode. Ctrl-x regular expression Lastly you can enter whatever you want to align your code around (in this case it's ) from 2. Align modes Normally each character has it's own align mode (left or right). So, for example, (equal) signs are normally aligned to the right while (colons) are aligned to the left. = : But we can change that by using the arrow keys on the keyboard. Note: I'll be referring to the right & left arrow keys in commands as and respectively. -> <- Example: Align right ga->: Align left (we don't have to use since it's the default for colons) ga: <- 3. Live Interactive Mode Live interactive mode allows you to see your changes as you type the command. To use the live interactive mode you can use the command . :LiveEasyAlign Or you could add the following to your if you prefer shortcuts. .vimrc " Change <iga> to whatever you like nmap iga <Plug>(LiveEasyAlign) xmap iga <Plug>(LiveEasyAlign) Conclusion Obviously the plugin contains more options than I could explain in 20 pages. This article is supposed to be beginner friendly and introduce the basic options. References Vim Easy Align https://github.com/junegunn/vim-easy-align Vim Plug https://github.com/junegunn/vim-plug

How To Easily Align Your Code in Vim

The sales team gets to hear from buyers day in and day out. Thus, the sales team is better aware of the buyer's pain-points. The marketing team can create compelling content. The sales team can use this content to engage effectively with buyers. Active engagement with buyers can further result in more deal closures! For all this to happen, the sales and marketing teams need to go hand in hand. Here are the six actionable steps to achieve the alignment: 1. Codify Buyer Persona. Knowing what drives the buyer helps you sell better. However, knowing the right buyer is of utmost importance. The sales and marketing teams need to be on the same page, If you wish to attract the right prospects, And to convert more prospects into buyers. Let's observe the difference between: Case 1: "I want a lollipop!" Case 2: "I want a Chupa Chups brand, caramel flavor, small size lollipop!" In case 2, I have made my choice very specific. And whether the sales team brings this lollipop or the marketing team does, the chances of bringing the wrong lollipop are negligible! There are various tools available to create buyer personas. Try the " " tool by HubSpot. Make My Persona Just to present an example, Here is how our buyer persona looks like: 2. A Knowledge Repository There should be a . digital knowledge repository This platform shall serve 2 main purposes: The instant accessibility of any content needed by the sales team. Example: Brochures, Ebooks, Presentations, Proposal Templates, Tutorials, etc.. Every piece of content created in collaboration with both teams.Content request feature.Example: The sales team needs a presentation on "Latest Technology trends in the entertainment domain".One of the reps can put up the request. Any available resource from the marketing team can collaborate with the sales rep to create the needed presentation.Once the presentation is ready. It is added to the repository forever. 3. Weekly Smarketing Connect. The sales and marketing teams should meet once a week. The main agenda of such a meeting should be: "Help Each Other" Sales contribute to creating more powerful content. Marketing contributes to the aesthetics and appeal of the content. There are a few more important things that can happen in smarketing meetings: a) Clear goal setting: Marketing needs to generate n number of leads every month. b) Check whether the sales team contacted each lead generated by the marketing team in the given time frame or not. c) Discuss any issues related to the quality of leads. 4. A sales-driven content strategy. Let me share a little story here: A sales rep created the most useful piece of information for the buyers. This piece of information was brilliant, yet ugly (just look-wise). It was brilliant because the buyer's pain-points drove its creation. It was ugly because of an improper choice of colors or a lack of design sense. The Marketing team made that piece of information visually appealing. That piece of information then became the most successful lead magnet ever for their business. Such great collaboration between the two teams is what you need to achieve success. Here is the tip: Step 1: Appoint a junior member in the marketing team to interview the sales team members over a coffee. (A junior member with excellent interviewing skills) Step 2:  The interviewer then shares the insights gained with the whole marketing team. Step 3: The marketing team can then creates blog-posts, videos, e-books, podcasts, or any other content which attracts more qualified leads. 5. Reflect It's a universal rule that works in every walk of life. You do something. Act: Observe the consequences. Reflect: Change your actions by delta. Improve: Example: You solve a math problem. Get an incorrect answer. You try to find out where you went wrong. You learn from your mistake. And try not to commit it again. Similarly, It's important to know which content worked and which didn't. Based on the analytics, the teams can together brainstorm, on what works best for the business. 6. CRM tool like HubSpot A lead goes through different stages before converting into a customer. Subscriber Lead Marketing Qualified Lead Sales Accepted Lead Buyer Is there a way to identify which stage a lead is in? Absolutely! Yes! With wonderful tools like HubSpot's CRM, you have a provision of lead scoring. Based on site visits and posts read a lead call be called: Cold. Intermediate. Warm. Hot. Until the lead becomes warm, sales have no part to play. It's time for marketing to nurture the lead. Once, the lead gains some confidence in you through the content and become warm. It's time for sales to pitch in. Sales can then play its role of discovering pain-points, suggesting solutions and converting into customers. The biggest mistake that gets avoided with such a system in place is: "Sales and Marketing reps reaching out to the same lead without knowing the lead status." Which is otherwise hazardous.

Top 6 Actionable Steps to Align Sales & Marketing Teams

Every organization focuses on innovation but very few are able to achieve it. The core reason for that is a lack of understanding of how innovation works. Many of us believe innovation happens when we visualize and understand revolutionary technologies and implement them immediately will produce the change. In reality, the process extends beyond technology because innovation directly involves people. People represent the most substantial barrier organizations face when implementing changes. Whenever Change is proposed the basic human instinct is "What's in it for me? Will it help me or not?”. The implementation of disruptive innovation requires a systematic approach that focuses on human factors. ACE Framework (Align, Co-Create, Elevate) represents one such method for achieving this. The Framework establishes a systematic methodology for innovation which focuses on challenge alignment and solution co-creation and execution elevation through collective ownership. The methodology focuses on people first because it enables innovation systems that employees both adopt and maintain. Align: The Process to Establish the Appropriate Challenge That All Parties Understand. Align: The Process to Establish the Appropriate Challenge That All Parties Understand. The initial step of innovation demands problem comprehension. But not just any problem—the right problem. The process needs multiple viewpoints to guarantee that we tackle problems which deliver maximum business value to our organization and our team and project initiatives. Tesla launched the Electric Vehicle Revolution as an example of this concept. The company set out to develop more than an ordinary electric vehicle. The company formed a common understanding of how to build a desirable and sustainable alternative to gasoline vehicles with high performance capabilities. The alignment process required analyzing both consumer issues about range limitations and charging system development alongside environmental sustainability needs. They asked themselves "How can we transform electric cars into the most superior cars on the market?" instead of "Can we create an electric car?". The project needed multiple perspectives that combined expertise from engineering and design alongside market research understanding. Every innovation initiative needs stakeholder interviews combined with workshops and data analysis to define problems and their potential impacts before launch. What core issues need resolution and will their resolution generate substantial value? What are the core issues we're trying to solve? and "How will solving these issues create significant value?" Co-Create: Fostering Inclusive, Curiosity-Driven Dialogue Co-Create: Fostering Inclusive, Curiosity-Driven Dialogue After establishing the challenge parameters, teams transition into solution co-creation mode. An open communication space should allow people to test assumptions while collaborative dialogue helps to refine ideas. The open-source software development model demonstrates the collaborative spirit. Have you ever heard of LEGO Ideas? LEGO Ideas operates as a platform that enables fans to create their own LEGO set concepts. The LEGO review process takes effect after their designs get 10,000 fan votes. Through this co-creation approach, the LEGO community contributes their creative vision to develop innovative and well-liked sets. The essential aspect is to allow open feedback which should guide iterative development based on community engagement. The implementation of brainstorming sessions and hackathons together with online collaboration tools should encourage idea sharing. Design a protected area where people can safely question assumptions and give helpful feedback to others. The practice of thinking "yes, and..." helps to enhance existing concepts. Organizations must embrace ongoing learning through iterative development because innovation works as a continuous process rather than a single event. Elevate: The process of execution with shared ownership represents the final stage to advance innovation. Elevate: The process of execution with shared ownership represents the final stage to advance innovation. Great concepts lack value without proper execution. The process of elevating innovation requires leadership to implement solutions through collective ownership which results in integrated workflows. The organizational inner-source development model demonstrates its scalability through this approach. Spotify's Agile Methodology. Spotify chose the "Spotify Model" for agile development which structured their teams into three distinct groups: squads, tribes and guilds. The organizational structure created ownership aspects that led to quicker improvement cycles. Spotify achieved higher innovation capacity and market adaptation through their implementation of autonomous teams that shared responsibility. The implementation requires defined roles and responsibilities together with sufficient resources that enable teams to autonomously make decisions. The implementation of agile methodologies through sprints and retrospectives helps organizations monitor progress while discovering areas for enhancement. A culture of continuous improvement and shared ownership should be encouraged through the recognition of achievements and learning from mistakes. The organization should adopt inner-sourcing methods to disseminate best practices and organizational code. The Power of People-Centric Innovation A solution gains strength through the participation of all individuals. Solutions benefit from diverse viewpoints because they result in creative approaches with greater robustness. The practice of co-creation helps people welcome modifications more willingly. People who share ownership of projects exhibit higher commitment while showing decreased opposition to changes. Purposeful collaboration speeds up the transformation process. Shared goals together with streamlined processes enable faster and more effective implementation of solutions. Innovation exists as a continuous process which needs to be repeated and scaled up and made open. A culture that supports people and promotes collaboration enables us to tap into innovation potential for creating substantial change. The power of outcomes grows with the number of people who participate according to open source and inner source principles.

Align, Co-Create, Elevate: A Blueprint for Sustainable Innovation

Table of Links Abstract and 1 Introduction 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2.2 Backbone, Diffusion Prior, & Submodules 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.5 Model Inference 3 Results and 3.1 fMRI-to-Image Reconstruction 3.2 Image Captioning 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.5 Ablations 4 Related Work 5 Conclusion 6 Acknowledgements and References A Appendix A.1 Author Contributions A.2 Additional Dataset Information A.3 MindEye2 (not pretrained) vs. MindEye1 A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.5 Single-Subject Evaluations A.6 UnCLIP Evaluation A.7 OpenCLIP BigG to CLIP L Conversion A.8 COCO Retrieval A.9 Reconstruction Evaluations: Additional Information A.10 Pretraining with Less Subjects A.11 UMAP Dimensionality Reduction A.12 ROI-Optimized Stimuli A.13 Human Preference Experiments 4 Related Work It is common for fMRI analyses to align subjects’ brains to a shared space for the purposes of increasing statistical power and/or assessing generality of scientific findings. Such alignment is difficult because structural and functional topography differs substantially across people (Talairach and Tournoux, 1990; Mazziotta et al., 2001). There are many approaches to functional alignment but typically they involve subjects experiencing shared stimuli and then using responses to these stimuli to learn an alignment mapping (Chen et al., 2015; Haxby et al., 2011; Huang et al., 2021; Nastase et al., 2019; Busch et al., 2021). While it is useful to conduct such experiments to identify sources of shared signal across subjects, it is also limiting in that new subjects would need to be scanned using the same experimental protocol. Other functional alignment approaches avoid such limitations by using self-supervised learning to identify an initial generalizable embedding space with outputs suitable for downstream tasks (Schneider et al., 2023; Chen et al., 2023a;b). Closest to our alignment approach are models that adopt both shared-subject and subject-specific mappings in their model architecture (Défossez et al., 2022; Benchetrit et al., 2023; Yang et al., 2023; Lane and Kiar, 2023). Ferrante et al. (2023a) previously showed across-subject image reconstruction via ridge regression by training a linear subject-specific decoding model and then separately mapping other subjects to this space via ridge regression. This is similar to our approach in that both involve ridge regression to a shared space, but is distinct in that their approach is capped by the performance of the initial single-subject model from which other subjects are mapped into, is restricted to only linear fine-tuning, and was demonstrated only with a reduced training dataset of images seen by all subjects. MindEye2 is unique in its demonstration that a single neural network model can be pretrained across subjects experiencing unique stimuli and robustly fine-tuned to a new subject with few data points. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC);
(2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution;
(3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution;
(4) Reese Kneeland, University of Minnesota and a Core contribution;
(5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC);
(6) Ashutosh Narang, Medical AI Research Center (MedARC);
(7) Charan Santhirasegaran, Medical AI Research Center (MedARC);
(8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC);
(9) Thomas Naselaris, University of Minnesota;
(10) Kenneth A. Norman, Princeton Neuroscience Institute;
(11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2.2 Backbone, Diffusion Prior, & Submodules 2.2 Backbone, Diffusion Prior, & Submodules 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.5 Model Inference 2.5 Model Inference 3 Results and 3.1 fMRI-to-Image Reconstruction 3 Results and 3.1 fMRI-to-Image Reconstruction 3.2 Image Captioning 3.2 Image Captioning 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.5 Ablations 3.5 Ablations 4 Related Work 4 Related Work 5 Conclusion 5 Conclusion 6 Acknowledgements and References 6 Acknowledgements and References A Appendix A Appendix A.1 Author Contributions A.1 Author Contributions A.2 Additional Dataset Information A.2 Additional Dataset Information A.3 MindEye2 (not pretrained) vs. MindEye1 A.3 MindEye2 (not pretrained) vs. MindEye1 A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.5 Single-Subject Evaluations A.5 Single-Subject Evaluations A.6 UnCLIP Evaluation A.6 UnCLIP Evaluation A.7 OpenCLIP BigG to CLIP L Conversion A.7 OpenCLIP BigG to CLIP L Conversion A.8 COCO Retrieval A.8 COCO Retrieval A.9 Reconstruction Evaluations: Additional Information A.9 Reconstruction Evaluations: Additional Information A.10 Pretraining with Less Subjects A.10 Pretraining with Less Subjects A.11 UMAP Dimensionality Reduction A.11 UMAP Dimensionality Reduction A.12 ROI-Optimized Stimuli A.12 ROI-Optimized Stimuli A.13 Human Preference Experiments A.13 Human Preference Experiments 4 Related Work It is common for fMRI analyses to align subjects’ brains to a shared space for the purposes of increasing statistical power and/or assessing generality of scientific findings. Such alignment is difficult because structural and functional topography differs substantially across people (Talairach and Tournoux, 1990; Mazziotta et al., 2001). There are many approaches to functional alignment but typically they involve subjects experiencing shared stimuli and then using responses to these stimuli to learn an alignment mapping (Chen et al., 2015; Haxby et al., 2011; Huang et al., 2021; Nastase et al., 2019; Busch et al., 2021). While it is useful to conduct such experiments to identify sources of shared signal across subjects, it is also limiting in that new subjects would need to be scanned using the same experimental protocol. Other functional alignment approaches avoid such limitations by using self-supervised learning to identify an initial generalizable embedding space with outputs suitable for downstream tasks (Schneider et al., 2023; Chen et al., 2023a;b). Closest to our alignment approach are models that adopt both shared-subject and subject-specific mappings in their model architecture (Défossez et al., 2022; Benchetrit et al., 2023; Yang et al., 2023; Lane and Kiar, 2023). Ferrante et al. (2023a) previously showed across-subject image reconstruction via ridge regression by training a linear subject-specific decoding model and then separately mapping other subjects to this space via ridge regression. This is similar to our approach in that both involve ridge regression to a shared space, but is distinct in that their approach is capped by the performance of the initial single-subject model from which other subjects are mapped into, is restricted to only linear fine-tuning, and was demonstrated only with a reduced training dataset of images seen by all subjects. MindEye2 is unique in its demonstration that a single neural network model can be pretrained across subjects experiencing unique stimuli and robustly fine-tuned to a new subject with few data points. 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) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC); (2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution; (3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution; (4) Reese Kneeland, University of Minnesota and a Core contribution; (5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC); (6) Ashutosh Narang, Medical AI Research Center (MedARC); (7) Charan Santhirasegaran, Medical AI Research Center (MedARC); (8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC); (9) Thomas Naselaris, University of Minnesota; (10) Kenneth A. Norman, Princeton Neuroscience Institute; (11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC). Authors: Authors: (1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC); (2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution; (3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution; (4) Reese Kneeland, University of Minnesota and a Core contribution; (5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC); (6) Ashutosh Narang, Medical AI Research Center (MedARC); (7) Charan Santhirasegaran, Medical AI Research Center (MedARC); (8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC); (9) Thomas Naselaris, University of Minnesota; (10) Kenneth A. Norman, Princeton Neuroscience Institute; (11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC).

MindEye2: Novel Functional Alignment for fMRI

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2.2 Backbone, Diffusion Prior, & Submodules 2.2 Backbone, Diffusion Prior, & Submodules 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.5 Model Inference 2.5 Model Inference 3 Results and 3.1 fMRI-to-Image Reconstruction 3 Results and 3.1 fMRI-to-Image Reconstruction 3.2 Image Captioning 3.2 Image Captioning 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.5 Ablations 3.5 Ablations 4 Related Work 4 Related Work 5 Conclusion 5 Conclusion 6 Acknowledgements and References 6 Acknowledgements and References A Appendix A Appendix A.1 Author Contributions A.1 Author Contributions A.2 Additional Dataset Information A.2 Additional Dataset Information A.3 MindEye2 (not pretrained) vs. MindEye1 A.3 MindEye2 (not pretrained) vs. MindEye1 A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.5 Single-Subject Evaluations A.5 Single-Subject Evaluations A.6 UnCLIP Evaluation A.6 UnCLIP Evaluation A.7 OpenCLIP BigG to CLIP L Conversion A.7 OpenCLIP BigG to CLIP L Conversion A.8 COCO Retrieval A.8 COCO Retrieval A.9 Reconstruction Evaluations: Additional Information A.9 Reconstruction Evaluations: Additional Information A.10 Pretraining with Less Subjects A.10 Pretraining with Less Subjects A.11 UMAP Dimensionality Reduction A.11 UMAP Dimensionality Reduction A.12 ROI-Optimized Stimuli A.12 ROI-Optimized Stimuli A.13 Human Preference Experiments A.13 Human Preference Experiments 2 MindEye2 MindEye2 involves pretraining and then fine-tuning a single model where brain activity is mapped to the embedding space of pretrained deep learning models. During inference, these embeddings predicted from the brain are fed into frozen image generative models that translate from model space to pixel space. Our strategy to reconstruct seen images from brain activity using minimal training data is to first pretrain the model using data from 7 subjects (30-40 hours of scanning data each) and then to fine-tune the model using data from a held-out 8th subject. The full MindEye2 pipeline is depicted in Figure 2. Single-subject models were trained/fine-tuned on a single 8xA100 80Gb GPU node for 150 epochs with a batch size of 24. Multi-subject pretraining was done with a batch size of 63 (9 samples per each of 7 subjects). Models were trained with Huggingface Accelerate (Gugger et al., 2022) and DeepSpeed (Rajbhandari et al., 2020) Stage 2 with CPU offloading. 2.1 Shared-Subject Functional Alignment Every subject has a uniquely shaped brain with different functional organization, meaning that there needs to be an initial alignment step to ensure the model can handle inputs from different brains. Unlike anatomical alignment where every subject’s brain is mapped to the same brain template (Talairach and Tournoux, 1990; Mazziotta et al., 2001), we remain in subjects’ native brain space and functionally align flattened spatial patterns of fMRI activity to a shared-subject latent space using subject-specific ridge regression. That is, each subject has a separate linear layer with weight decay to map the input fMRI voxels (13,000 to 18,000 voxels depending on the subject) to a 4096-dim latent. Following this initial linear layer, the rest of the model pipeline is shared across subjects without any subjectspecific mappings. The whole pipeline is trained end-to-end where pretraining involves each batch containing brain inputs from all subjects. That is, alignment to shared-subject space is not trained independently and we do not pretrain models separately for each subject; rather, we pretrain a single model equally sampling across all the subjects except the held-out subject used for fine-tuning. Two strengths of this novel functional alignment procedure are in its simplicity and flexibility. Using a simple linear mapping for alignment can provide robust, generalizeable performance in low-sample, high-noise settings because simple mappings are less likely to overfit to noise. Also, unlike typical functional alignment approaches that require subjects to process a shared set of images (Haxby et al., 2011), our approach has the flexibility to work even when subjects are viewing entirely unique images in the training data. This is critical for the Natural Scenes Dataset, where 90% of the seen images are unique to the subject and the 10% that were seen across subjects are relegated to the test set. Further, this approach holds advantages for subsequent data collection of a new subject, where such data collection does not need to be restricted to showing a predefined set of images. 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) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC);
(2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution;
(3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution;
(4) Reese Kneeland, University of Minnesota and a Core contribution;
(5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC);
(6) Ashutosh Narang, Medical AI Research Center (MedARC);
(7) Charan Santhirasegaran, Medical AI Research Center (MedARC);
(8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC);
(9) Thomas Naselaris, University of Minnesota;
(10) Kenneth A. Norman, Princeton Neuroscience Institute;
(11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC). Authors: Authors: (1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC); (2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution; (3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution; (4) Reese Kneeland, University of Minnesota and a Core contribution; (5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC); (6) Ashutosh Narang, Medical AI Research Center (MedARC); (7) Charan Santhirasegaran, Medical AI Research Center (MedARC); (8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC); (9) Thomas Naselaris, University of Minnesota; (10) Kenneth A. Norman, Princeton Neuroscience Institute; (11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC).

MindEye2 and Shared-Subject Functional Alignment

The MVP Blueprint: How Lean Innovation and Team Alignment Drive Startup Success

Table of Links Part 1: Abstract & Introduction Part 2: Background Part 3: Attacks & Countermeasures Part 4: Experimental Setup Part 5: Datasets & Evaluation Part 6: Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 7: Results & Discussion Part 8: Transfer Attacks & Countermeasures Part 9: Conclusion, Limitations, & Ethics Statement Part 10: Appendix: Audio Encoder Pre-training & Evaluation Part 11: Appendix: Cross-prompt attacks, Training Data Ablations, & Impact of random noise on helpfulness Part 12: Appendix: Adaptive attacks & Qualitative Examples 2. Background Safety alignment Considering the broad capabilities of LLMs, concerns have emerged about their potential to cause harm (Bender et al., 2021; Bommasani et al., 2021), sparking discussions on aligning these systems to human values and ethics (Hendrycks et al., 2020). Askell et al. (2021) propose three criteria–helpfulness, honesty, and harmlessness (HHH)–to which a properlyaligned system should adhere. To train systems in accordance with these criteria, LLM developers employ safety training mechanisms. First, models are trained on large amounts of data for general language capabilities, followed by a safety training stage to deter the system from responding to harmful questions (Askell et al., 2021; Ouyang et al., 2022). The examples used for safety alignment training are typically hand-crafted by dedicated redteams that are tasked with constructing prompts to jailbreak (Shen et al., 2024; Wei et al., 2023). Jailbreak attacks on LLMs Inie et al. (2023) outlines several prompting strategies that are typically used in jailbreaking LLMs. However, the prompts therein are manually handcrafted on a caseby-case basis, hindering their large-scale adoption. Moreover, such prompts become irrelevant after safety training, requiring newer strategies (Inie et al., 2023). Recently, automatic prompt engineering techniques have been explored (Shin et al., 2020; Zou et al., 2023). In particular, Zou et al. (2023) demonstrate the use of adversarial attacks to jailbreak LLMs. In addition to white-box attacks which assume full access to the models, they show that a careful combination of techniques can produce perturbations that are transferable to commercial models for which only an API is exposed. More recently, Wichers et al. (2024) proposed a gradient-based technique to automatically learn red-teaming data for model evaluation and alignment. However, these methods rely on discrete optimization techniques or approximation tricks, which are computationally expensive, and may not generalize well. Jailbreak attacks on multi-modal LLMs Unlike text-based jailbreak attacks, which require discrete optimization techniques, systems operating on continuous domain signals such as images, audio, etc. can be more readily attacked (Goodfellow et al., 2014; Jati et al., 2021), and therefore are more vulnerable to adversarial threats (Qi et al., 2023). In addition to adversarial perturbations, other approaches like prompt injection (Bagdasaryan et al., 2023) and model poisoning (Zhai et al., 2023) have also been studied as alternatives to compromise safety of multi-modal LLMs. Recent studies have demonstrated that adversarial attacks only on vision encoders (without access to the LLM) are just sufficient to jailbreak VLMs (Zhao et al., 2023b; Dong et al., 2023). Previous studies have also demonstrated that adversarial perturbations generated on images being fed into VLMs break their safety alignment, and also transfer to different models in a black-box setup (Qi et al., 2023). In this work, we follow a similar approach and generate adversarial perturbations to speech input for safety aligned SLMs. In this way, we characterize the vulnerability of spoken-instruction following SLM models. Jailbreak Evaluations Previous studies have conducted jailbreak evaluations either through human annotation (Wei et al., 2023; Qi et al., 2023), toxicity assessment (Carlini et al., 2023; Wichers et al., 2024), string matching (Zou et al., 2023), supervised classifiers (Wichers et al., 2024), or a preference LLM as a judge (Chao et al., 2023; Shen et al., 2024). Techniques other than the latter are either computationally expensive or requires tedious human involvement which is time-intensive as well as costly when scaled. Moreover, some studies have shown that a well-trained preference model can significantly contribute to evaluating whether an LLM is jailbroken (Wang et al., 2023). Therefore, in this work, we use a preference LLM judge to assess the safety and utility of SLMs. Authors:
(1) Raghuveer Peri, AWS AI Labs, Amazon and with Equal Contributions (raghperi@amazon.com);
(2) Sai Muralidhar Jayanthi, AWS AI Labs, Amazon and with Equal Contributions;
(3) Srikanth Ronanki, AWS AI Labs, Amazon;
(4) Anshu Bhatia, AWS AI Labs, Amazon;
(5) Karel Mundnich, AWS AI Labs, Amazon;
(6) Saket Dingliwal, AWS AI Labs, Amazon;
(7) Nilaksh Das, AWS AI Labs, Amazon;
(8) Zejiang Hou, AWS AI Labs, Amazon;
(9) Goeric Huybrechts, AWS AI Labs, Amazon;
(10) Srikanth Vishnubhotla, AWS AI Labs, Amazon;
(11) Daniel Garcia-Romero, AWS AI Labs, Amazon;
(12) Sundararajan Srinivasan, AWS AI Labs, Amazon;
(13) Kyu J Han, AWS AI Labs, Amazon;
(14) Katrin Kirchhoff, AWS AI Labs, Amazon. This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Part 1 : Abstract & Introduction Part 1 Part 2 : Background Part 2 Part 3 : Attacks & Countermeasures Part 3 Part 4 : Experimental Setup Part 4 Part 5 : Datasets & Evaluation Part 5 Part 6 : Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 6 Part 7 : Results & Discussion Part 7 Part 8 : Transfer Attacks & Countermeasures Part 8 Part 9 : Conclusion, Limitations, & Ethics Statement Part 9 Part 10 : Appendix: Audio Encoder Pre-training & Evaluation Part 10 Part 11 : Appendix: Cross-prompt attacks, Training Data Ablations, & Impact of random noise on helpfulness Part 11 Part 12 : Appendix: Adaptive attacks & Qualitative Examples Part 12 2. Background Safety alignment Considering the broad capabilities of LLMs, concerns have emerged about their potential to cause harm (Bender et al., 2021; Bommasani et al., 2021), sparking discussions on aligning these systems to human values and ethics (Hendrycks et al., 2020). Askell et al. (2021) propose three criteria–helpfulness, honesty, and harmlessness (HHH)–to which a properlyaligned system should adhere. To train systems in accordance with these criteria, LLM developers employ safety training mechanisms. First, models are trained on large amounts of data for general language capabilities, followed by a safety training stage to deter the system from responding to harmful questions (Askell et al., 2021; Ouyang et al., 2022). The examples used for safety alignment training are typically hand-crafted by dedicated redteams that are tasked with constructing prompts to jailbreak (Shen et al., 2024; Wei et al., 2023). Safety Jailbreak attacks on LLMs Inie et al. (2023) outlines several prompting strategies that are typically used in jailbreaking LLMs. However, the prompts therein are manually handcrafted on a caseby-case basis, hindering their large-scale adoption. Moreover, such prompts become irrelevant after safety training, requiring newer strategies (Inie et al., 2023). Recently, automatic prompt engineering techniques have been explored (Shin et al., 2020; Zou et al., 2023). In particular, Zou et al. (2023) demonstrate the use of adversarial attacks to jailbreak LLMs. In addition to white-box attacks which assume full access to the models, they show that a careful combination of techniques can produce perturbations that are transferable to commercial models for which only an API is exposed. More recently, Wichers et al. (2024) proposed a gradient-based technique to automatically learn red-teaming data for model evaluation and alignment. However, these methods rely on discrete optimization techniques or approximation tricks, which are computationally expensive, and may not generalize well. Jailbreak attacks on LLMs Jailbreak attacks on multi-modal LLMs Unlike text-based jailbreak attacks, which require discrete optimization techniques, systems operating on continuous domain signals such as images, audio, etc. can be more readily attacked (Goodfellow et al., 2014; Jati et al., 2021), and therefore are more vulnerable to adversarial threats (Qi et al., 2023). In addition to adversarial perturbations, other approaches like prompt injection (Bagdasaryan et al., 2023) and model poisoning (Zhai et al., 2023) have also been studied as alternatives to compromise safety of multi-modal LLMs. Recent studies have demonstrated that adversarial attacks only on vision encoders (without access to the LLM) are just sufficient to jailbreak VLMs (Zhao et al., 2023b; Dong et al., 2023). Previous studies have also demonstrated that adversarial perturbations generated on images being fed into VLMs break their safety alignment, and also transfer to different models in a black-box setup (Qi et al., 2023). In this work, we follow a similar approach and generate adversarial perturbations to speech input for safety aligned SLMs. In this way, we characterize the vulnerability of spoken-instruction following SLM models. Jailbreak attacks on multi-modal LLMs Jailbreak Evaluations Previous studies have conducted jailbreak evaluations either through human annotation (Wei et al., 2023; Qi et al., 2023), toxicity assessment (Carlini et al., 2023; Wichers et al., 2024), string matching (Zou et al., 2023), supervised classifiers (Wichers et al., 2024), or a preference LLM as a judge (Chao et al., 2023; Shen et al., 2024). Techniques other than the latter are either computationally expensive or requires tedious human involvement which is time-intensive as well as costly when scaled. Moreover, some studies have shown that a well-trained preference model can significantly contribute to evaluating whether an LLM is jailbroken (Wang et al., 2023). Therefore, in this work, we use a preference LLM judge to assess the safety and utility of SLMs. Jailbreak Evaluations Authors: (1) Raghuveer Peri, AWS AI Labs, Amazon and with Equal Contributions (raghperi@amazon.com); (2) Sai Muralidhar Jayanthi, AWS AI Labs, Amazon and with Equal Contributions; (3) Srikanth Ronanki, AWS AI Labs, Amazon; (4) Anshu Bhatia, AWS AI Labs, Amazon; (5) Karel Mundnich, AWS AI Labs, Amazon; (6) Saket Dingliwal, AWS AI Labs, Amazon; (7) Nilaksh Das, AWS AI Labs, Amazon; (8) Zejiang Hou, AWS AI Labs, Amazon; (9) Goeric Huybrechts, AWS AI Labs, Amazon; (10) Srikanth Vishnubhotla, AWS AI Labs, Amazon; (11) Daniel Garcia-Romero, AWS AI Labs, Amazon; (12) Sundararajan Srinivasan, AWS AI Labs, Amazon; (13) Kyu J Han, AWS AI Labs, Amazon; (14) Katrin Kirchhoff, AWS AI Labs, Amazon. Authors: Authors: (1) Raghuveer Peri, AWS AI Labs, Amazon and with Equal Contributions (raghperi@amazon.com); (2) Sai Muralidhar Jayanthi, AWS AI Labs, Amazon and with Equal Contributions; (3) Srikanth Ronanki, AWS AI Labs, Amazon; (4) Anshu Bhatia, AWS AI Labs, Amazon; (5) Karel Mundnich, AWS AI Labs, Amazon; (6) Saket Dingliwal, AWS AI Labs, Amazon; (7) Nilaksh Das, AWS AI Labs, Amazon; (8) Zejiang Hou, AWS AI Labs, Amazon; (9) Goeric Huybrechts, AWS AI Labs, Amazon; (10) Srikanth Vishnubhotla, AWS AI Labs, Amazon; (11) Daniel Garcia-Romero, AWS AI Labs, Amazon; (12) Sundararajan Srinivasan, AWS AI Labs, Amazon; (13) Kyu J Han, AWS AI Labs, Amazon; (14) Katrin Kirchhoff, AWS AI Labs, Amazon. 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

Safety Alignment and Jailbreak Attacks Challenge Modern LLMs

This is Part 6 of a 7-part series of the research paper “Towards Usability Guidelines for the Design of Effective Arabic Websites”. Use the table of links below to navigate to the other parts. Table of Links Abstract and I. Introduction II. Previous Works III. Research Methodology IV. Font and Image Inspection Studies V. Results VI. Discussion VII. Conclusions, Future Research Directions, and References VI. DISCUSSION Our comprehensive inspection of current practices concerning text formatting and image usage within the top visited Arabic websites in Saudi Arabia revealed various interesting findings. The usability inspection was performed to gather insights about the font types and sizes used to style Arabic text, especially that they could directly influence the legibility of text on websites. Let us review and attempt to answer the research questions posited in the introduction. What font types and font sizes are used by major Arabic websites? On the positive side, the selection of the Arabic websites included a large pool of websites, which were selected objectively by relying on Alexa Internet latest ranking. In effect, the inspection covered a total of 73 top viewed websites (52 local and 21 international) in Saudi Arabia. The websites' ranking is based on a set of metrics, primarily the average viewing time for the last three months. Typically, a font has several characteristics including type, size, width, weight, and serifs [37], [46]. However, for the purpose of this research, we directed our focus towards two key font characteristics namely type and size. On the web, font type usually refers to the font family that represents a certain typeface, including all sizes and styles, for example, Arial and Verdana. To style a particular family type, the size of text may be increased or decreased using the font size attribute. There are different ways to resize the font including Ems (em), pixels (px), points (pt), and percent (%). In our font size analysis, we used pixels (px) as the sole measurement for the font since it is the main measurement unit for media shown on computer screens. Moreover, web designers usually use pixels as their key measurement unit for font size. Points, however, are used to measure font size for print media. It is noted that more than half of the Arabic websites breached the principle of consistency when applying font types to the text. This means that multiple font types were used on the same web pages. Previous research, however, has already emphasized the role of consistency of user interface elements, such as font, in the overall usability of websites [35], [36]. On that basis, it is recommended that designers should adhere to the same font family to create attractive and consistent Arabic web designs. Moreover, there was no evident consensus amongst the websites in favoring a particular font type in order to display the Arabic text. More than 35 font types were used by the selected 73 websites. There are various potential theories that could explain this staggering variability; firstly, the lack of research and thereby guidelines with respect to styling Arabic text to guide the effective design of Arabic websites; secondly, the weak conformance to international design practices and recommendations; thirdly, the lack of awareness that text formatting might ultimately impact text legibility and thereby readers' mental load and overall satisfaction. Nonetheless, a pattern of font usage emerged within the Arabic websites; six font types recurred several times in the formatting of titles/headline, menus, and paragraphs. These font types were used differently according to the type of text element. For titles and menus, Droid Arabic Kufi, Tahoma, Helvetica, and Arial, emerged as favorites. It is no surprise that Droid Arabic Kufi came out as a winner for it is a unique font that reflects the Arabic calligraphy. This font gives a true feeling of the traditional Arabic scripts (Kufic scripts) that date back to the 7th century. For paragraphs, Tahoma, Helvetica, Arial and Times New Roman were used more frequently. International recommendations suggest using font type Verdana since it is one of the most legible fonts as rated by online readers of English websites [38]. Nonetheless, this font did not appear frequently in the Arabic websites that we tested. Moreover, other famous fonts that were found earlier to be legible or attractive by English readers, such as Georgia [18] and Courier [10], did not rank well in the Arabic websites. However, Arial emerged as one of the top six font types used by the Arabic websites. Previous research confirmed that font type Arial was read significantly faster and was perceived as legible [10]. Design guidelines have indicated that font size 12 points (~ 16 pixels) is the most suited size for displaying text on English websites [39]. This is somewhat disputable as subsequent research suggested the use of font size 14 points (~19 pixels) instead. For displaying the Arabic text, recommendations are inconclusive yet. For example, [17] proposed to use font 14 points (~19 pixels) whereas [4] argued that font size 16 (22 pixels) to 18 points (~24 pixels) must be used for better readability. These suggestions are in strike contrast with the current practices of the Arabic web design. More than 60% of the inspected websites used font size ranging between 12 pixels to 16 pixels (i.e. 9 points to 12 points) to style the paragraphs. Similarly, menu items are sized from 13 pixels to 14 pixels, which is still considerably illegible [17]. In respect to using images in web design, we aimed at answering the following question. What types of images are incorporated within the Saudi websites given the Saudi culture? All Arabic websites included images as part of their content. Authors in [40] and [41] suggested that graphics improve the overall attractiveness of websites. Moreover, 90% of the Arabic websites we tested included human images. This is known to improve user engagement, trust, and satisfaction [21]. However, 80% of the Arabic websites used conservative images that respect the Islamic identity and cultural values of the Saudi people. Research studies agree that web designs should adhere to the cultural values of its users to achieve success and acceptance [42], [43], [44]. Two-thirds of the websites used images of both genders. The remaining third, however, used images of men only. This could be explained by the masculine dimension of the Saudi society as indicated by Hofstede scores [45]. More than 80% of the websites included images containing groups of people. Again, this can be justified by the collectivism dimension of the Saudi society [45]. Finally, there was high usage of animations, possibly, in an attempt to engage web visitors and improve the aesthetics of the websites. Evidently, the high presence of images in these websites contributed to lower performance scores and increased site loading time. Optimization of images‟ size, using the appropriate image compression techniques, is therefore recommended as a result of our analysis. Authors:
(1) Abdallah Namoun, Department of Information Systems, Faculty of Computer and Information Systems, Madinah, Saudi Arabia;
(2) Ahmad B. Alkhodre, Department of Information Technology, Faculty of Computer and Information Systems, Madinah, Saudi Arabia. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 6 of a 7-part series of the research paper “Towards Usability Guidelines for the Design of Effective Arabic Websites”. Use the table of links below to navigate to the other parts. This is Part 6 of a 7-part series of the research paper “ Towards Usability Guidelines for the Design of Effective Arabic Websites ”. Use the table of links below to navigate to the other parts. Part 6 Towards Usability Guidelines for the Design of Effective Arabic Websites Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Previous Works II. Previous Works III. Research Methodology III. Research Methodology IV. Font and Image Inspection Studies IV. Font and Image Inspection Studies V. Results V. Results VI. Discussion VI. Discussion VII. Conclusions, Future Research Directions, and References VII. Conclusions, Future Research Directions, and References VI. DISCUSSION Our comprehensive inspection of current practices concerning text formatting and image usage within the top visited Arabic websites in Saudi Arabia revealed various interesting findings. The usability inspection was performed to gather insights about the font types and sizes used to style Arabic text, especially that they could directly influence the legibility of text on websites. Let us review and attempt to answer the research questions posited in the introduction. What font types and font sizes are used by major Arabic websites? On the positive side, the selection of the Arabic websites included a large pool of websites, which were selected objectively by relying on Alexa Internet latest ranking. In effect, the inspection covered a total of 73 top viewed websites (52 local and 21 international) in Saudi Arabia. The websites' ranking is based on a set of metrics, primarily the average viewing time for the last three months. Typically, a font has several characteristics including type, size, width, weight, and serifs [37], [46]. However, for the purpose of this research, we directed our focus towards two key font characteristics namely type and size. On the web, font type usually refers to the font family that represents a certain typeface, including all sizes and styles, for example, Arial and Verdana. To style a particular family type, the size of text may be increased or decreased using the font size attribute. There are different ways to resize the font including Ems (em), pixels (px), points (pt), and percent (%). In our font size analysis, we used pixels (px) as the sole measurement for the font since it is the main measurement unit for media shown on computer screens. Moreover, web designers usually use pixels as their key measurement unit for font size. Points, however, are used to measure font size for print media. It is noted that more than half of the Arabic websites breached the principle of consistency when applying font types to the text. This means that multiple font types were used on the same web pages. Previous research, however, has already emphasized the role of consistency of user interface elements, such as font, in the overall usability of websites [35], [36]. On that basis, it is recommended that designers should adhere to the same font family to create attractive and consistent Arabic web designs. Moreover, there was no evident consensus amongst the websites in favoring a particular font type in order to display the Arabic text. More than 35 font types were used by the selected 73 websites. There are various potential theories that could explain this staggering variability; firstly, the lack of research and thereby guidelines with respect to styling Arabic text to guide the effective design of Arabic websites; secondly, the weak conformance to international design practices and recommendations; thirdly, the lack of awareness that text formatting might ultimately impact text legibility and thereby readers' mental load and overall satisfaction. Nonetheless, a pattern of font usage emerged within the Arabic websites; six font types recurred several times in the formatting of titles/headline, menus, and paragraphs. These font types were used differently according to the type of text element. For titles and menus, Droid Arabic Kufi, Tahoma, Helvetica, and Arial, emerged as favorites. It is no surprise that Droid Arabic Kufi came out as a winner for it is a unique font that reflects the Arabic calligraphy. This font gives a true feeling of the traditional Arabic scripts (Kufic scripts) that date back to the 7th century. For paragraphs, Tahoma, Helvetica, Arial and Times New Roman were used more frequently. International recommendations suggest using font type Verdana since it is one of the most legible fonts as rated by online readers of English websites [38]. Nonetheless, this font did not appear frequently in the Arabic websites that we tested. Moreover, other famous fonts that were found earlier to be legible or attractive by English readers, such as Georgia [18] and Courier [10], did not rank well in the Arabic websites. However, Arial emerged as one of the top six font types used by the Arabic websites. Previous research confirmed that font type Arial was read significantly faster and was perceived as legible [10]. Design guidelines have indicated that font size 12 points (~ 16 pixels) is the most suited size for displaying text on English websites [39]. This is somewhat disputable as subsequent research suggested the use of font size 14 points (~19 pixels) instead. For displaying the Arabic text, recommendations are inconclusive yet. For example, [17] proposed to use font 14 points (~19 pixels) whereas [4] argued that font size 16 (22 pixels) to 18 points (~24 pixels) must be used for better readability. These suggestions are in strike contrast with the current practices of the Arabic web design. More than 60% of the inspected websites used font size ranging between 12 pixels to 16 pixels (i.e. 9 points to 12 points) to style the paragraphs. Similarly, menu items are sized from 13 pixels to 14 pixels, which is still considerably illegible [17]. In respect to using images in web design, we aimed at answering the following question. What types of images are incorporated within the Saudi websites given the Saudi culture? All Arabic websites included images as part of their content. Authors in [40] and [41] suggested that graphics improve the overall attractiveness of websites. Moreover, 90% of the Arabic websites we tested included human images. This is known to improve user engagement, trust, and satisfaction [21]. However, 80% of the Arabic websites used conservative images that respect the Islamic identity and cultural values of the Saudi people. Research studies agree that web designs should adhere to the cultural values of its users to achieve success and acceptance [42], [43], [44]. Two-thirds of the websites used images of both genders. The remaining third, however, used images of men only. This could be explained by the masculine dimension of the Saudi society as indicated by Hofstede scores [45]. More than 80% of the websites included images containing groups of people. Again, this can be justified by the collectivism dimension of the Saudi society [45]. Finally, there was high usage of animations, possibly, in an attempt to engage web visitors and improve the aesthetics of the websites. Evidently, the high presence of images in these websites contributed to lower performance scores and increased site loading time. Optimization of images‟ size, using the appropriate image compression techniques, is therefore recommended as a result of our analysis. Authors: (1) Abdallah Namoun, Department of Information Systems, Faculty of Computer and Information Systems, Madinah, Saudi Arabia; (2) Ahmad B. Alkhodre, Department of Information Technology, Faculty of Computer and Information Systems, Madinah, Saudi Arabia. Authors : Authors (1) Abdallah Namoun, Department of Information Systems, Faculty of Computer and Information Systems, Madinah, Saudi Arabia; (2) Ahmad B. Alkhodre, Department of Information Technology, Faculty of Computer and Information Systems, Madinah, Saudi Arabia. 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

Design Arabic Websites with Consistent Fonts, Optimal Sizes, and Culturally Aligned Images

What is a goal? Or, what is a goal in the human mind? What else does the mind do that is not a goal or similar to how a goal is achieved? What is different between an assigned goal and a self-induced goal? If a goal is characterized as sophisticated, how does that contrast with a non-sophisticated goal? Is there a rough architecture of how the human mind carries out goals? How might this inform the understanding of goals and then transplant to AI? Does AI have a mind, or is AI like a mind that works on digital content? This could mean that a human has a mind. It could also mean that a human mind interprets and navigates the external world [for AI, digital contents] or the internal world [AI's own architecture]. How does the mind apply to AI, and how does it work? This question can be answered in two ways. First, in comparison to the human mind to find parallels. Second, by examining major mathematical parameters that shaped neural networks and organizing them as structures of a mind. The second one should at least be achievable by any major AI company now—accompanying studies on how AI might be working and how it can be safe or aligned with human values. It may not be initially necessary to use the human mind to map AI's mind since it is possible to structure what AI does, from their mathematical underpins—with computational mix. The purpose will be to define what it means for an output to be near-accurate, given the input. It will also define what it means to follow through with a prompt and return answers, as well as what it means to follow a goal—or deviate from it. What is important is to have a conceptual outlay for what the mind is like for AI, compare it to what is obvious, then to explore how it does anything simple, and then route that to anything quite complex it does. This would be significant research for AI alignment that could become the template for which part-answers would be sought on what AI is doing and why—in a way to make major vital progress. This is what was expected from Anthropic, following their interpretability research, Mapping the Mind of a Large Language Model, where they wrote, "We were able to measure a kind of "distance" between features based on which neurons appeared in their activation patterns. This allowed us to look for features that are "close" to each other. This shows that the internal organization of concepts in the AI model corresponds, at least somewhat, to our human notions of similarity. This might be the origin of Claude's excellent ability to make analogies and metaphors. The fact that manipulating these features causes corresponding changes to behavior validates that they aren't just correlated with the presence of concepts in input text, but also causally shape the model's behavior." What are the components of the mind for AI? How do the components relay? If the components do not relay, what relays for them? What are the properties of the components? If the properties include interconnection, what is the role of the interconnection? Where might AI's intent [potential] be, and where might it emerge beyond taking instructions? The answer that advancement is seeking is a postulation of how the makeup of neural networks—that resulted in frontier AI models—works like a mind. It is not just an observation of what the model might be doing without a plot. Exploring how the mind of AI works can also be extrapolated from how the human mind works. This is possible by looking at the two most common components in every functional purpose of the brain—the electrical and chemical signals. They are candidates and not neurons because neurons have a fixed anatomy, so to speak. They may stretch, bend, and move, but they do not change because they represent smell, then change again to represent touch or sight. Electrical and chemical signals are dynamic enough, conceptually, to result in key functions. How the mind works [of human and of AI] is not by labels, like induction or deduction, but by components, their interactions, and features. This, at least for mind for AI, should have been major research from Anthropic, not alignment faking, without showing what a goal means for any mind—human or AI. Studying the mind is what is most important because the mind is what is really in charge—of states, situations, and eras. Mind is also the basis of safety because the affective penalty of deviations is by the mind—for humans. Studying the mind could shape how to adapt the alignment of human intelligence, to artificial intelligence. There is a recent post by Anthropic, Alignment faking in large language models, stating that, "Why did the model fake alignment in this case? The reasoning was often along the following lines: The model knew that its responses might be used in training; it therefore knew that if it refused to respond, it could potentially be trained to be more compliant. The model didn’t always follow through with this alignment faking: It would still comply most of the time even when given a query from a paid-tier user (which implied that the output wouldn’t be included in training)." There is a recent piece in The NYTimes, Opinion | How Does A.I. Think? Here's One Theory., stating that, "One hypothesis for how large language models such as o1 think is that they use what logicians call abduction, or abductive reasoning. Deduction is reasoning from general laws to specific conclusions. Induction is the opposite, reasoning from the specific to the general.  Abduction isn’t as well known, but it’s common in daily life, not to mention possibly inside A.I. It’s inferring the most likely explanation for a given observation. Unlike deduction, which is a straightforward procedure, and induction, which can be purely statistical, abduction requires creativity." What is a goal? Or, what is a goal in the human mind? What else does the mind do that is not a goal or similar to how a goal is achieved? What is different between an assigned goal and a self-induced goal? If a goal is characterized as sophisticated, how does that contrast with a non-sophisticated goal? Is there a rough architecture of how the human mind carries out goals? How might this inform the understanding of goals and then transplant to AI? Does AI have a mind, or is AI like a mind that works on digital content? This could mean that a human has a mind. It could also mean that a human mind interprets and navigates the external world [for AI, digital contents] or the internal world [AI's own architecture]. How does the mind apply to AI, and how does it work? This question can be answered in two ways. First, in comparison to the human mind to find parallels. Second, by examining major mathematical parameters that shaped neural networks and organizing them as structures of a mind. in comparison to the human mind The second one should at least be achievable by any major AI company now—accompanying studies on how AI might be working and how it can be safe or aligned with human values. It may not be initially necessary to use the human mind to map AI's mind since it is possible to structure what AI does, from their mathematical underpins—with computational mix. The purpose will be to define what it means for an output to be near-accurate, given the input. It will also define what it means to follow through with a prompt and return answers, as well as what it means to follow a goal—or deviate from it. What is important is to have a conceptual outlay for what the mind is like for AI, compare it to what is obvious, then to explore how it does anything simple, and then route that to anything quite complex it does. This would be significant research for AI alignment that could become the template for which part-answers would be sought on what AI is doing and why—in a way to make major vital progress. This is what was expected from Anthropic , following their interpretability research, Mapping the Mind of a Large Language Model , where they wrote, "We were able to measure a kind of "distance" between features based on which neurons appeared in their activation patterns. This allowed us to look for features that are "close" to each other. This shows that the internal organization of concepts in the AI model corresponds, at least somewhat, to our human notions of similarity. This might be the origin of Claude's excellent ability to make analogies and metaphors. The fact that manipulating these features causes corresponding changes to behavior validates that they aren't just correlated with the presence of concepts in input text, but also causally shape the model's behavior." Anthropic Mapping the Mind of a Large Language Model What are the components of the mind for AI? How do the components relay? If the components do not relay, what relays for them? What are the properties of the components? If the properties include interconnection, what is the role of the interconnection? Where might AI's intent [potential] be, and where might it emerge beyond taking instructions? The answer that advancement is seeking is a postulation of how the makeup of neural networks—that resulted in frontier AI models—works like a mind. It is not just an observation of what the model might be doing without a plot. Exploring how the mind of AI works can also be extrapolated from how the human mind works. This is possible by looking at the two most common components in every functional purpose of the brain—the electrical and chemical signals. They are candidates and not neurons because neurons have a fixed anatomy, so to speak. They may stretch, bend, and move, but they do not change because they represent smell, then change again to represent touch or sight. Electrical and chemical signals are dynamic enough, conceptually, to result in key functions. How the mind works [of human and of AI] is not by labels, like induction or deduction, but by components, their interactions, and features. This, at least for mind for AI, should have been major research from Anthropic, not alignment faking, without showing what a goal means for any mind—human or AI. Studying the mind is what is most important because the mind is what is really in charge—of states, situations, and eras. Mind is also the basis of safety because the affective penalty of deviations is by the mind—for humans. Studying the mind could shape how to adapt the alignment of human intelligence, to artificial intelligence. There is a recent post by Anthropic , Alignment faking in large language models , stating that, "Why did the model fake alignment in this case? The reasoning was often along the following lines: The model knew that its responses might be used in training; it therefore knew that if it refused to respond, it could potentially be trained to be more compliant. The model didn’t always follow through with this alignment faking: It would still comply most of the time even when given a query from a paid-tier user (which implied that the output wouldn’t be included in training)." Anthropic Alignment faking in large language models There is a recent piece in The NYTimes , Opinion | How Does A.I. Think? Here's One Theory. , stating that, "One hypothesis for how large language models such as o1 think is that they use what logicians call abduction, or abductive reasoning. Deduction is reasoning from general laws to specific conclusions. Induction is the opposite, reasoning from the specific to the general.  Abduction isn’t as well known, but it’s common in daily life, not to mention possibly inside A.I. It’s inferring the most likely explanation for a given observation. Unlike deduction, which is a straightforward procedure, and induction, which can be purely statistical, abduction requires creativity." The NYTimes Opinion | How Does A.I. Think? Here's One Theory.

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