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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Learn more about Align Technology, the makers of Invisalign clear aligners.

About Align Technology | Invisalign.com

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

Strategic growth management software that will break your revenue glass ceiling and transform your business from a start-up to a scale-up!

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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

Partner Sign In Products Industries AGV Technology Resources Company SPARE PARTS ... Top-quality manufacturing equipment. Turntables, Welding Positioners, Lifts, Tilts, Rotate Tables, Conveyor Systems, &amp; more. Align offers custom-engineered solutions to meet your demands.

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Up-to-date Align Technology Inc company overview including funding information, company profile, key statistics, peer comparison and more.

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🎉 We&#x27;re thrilled to announce that Align has been recognized on The Channel Company&#x27;s CRN MSP 500 list for the 11th year in a row! 🏆 This year, we&#x27;re proud to have made it to the Pioneer 250 category, a testament to our dedication and expertise in the industry.

Align | LinkedIn

Align Technology, Inc. designs, manufactures, and markets the invisalign system for treating malocclusion or the misalignment of teeth.

Align Technology - Crunchbase Company Profile & Funding

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

See the company profile for Align Technology, Inc. (ALGN) including business summary, industry/sector information, number of employees, business summary, corporate governance, key executives and their compensation.

Align Technology, Inc. (ALGN) Company Profile & Facts - Yahoo Finance

Align is a joint venture of three international and privately-owned infrastructure companies; Bouygues Travaux Publics, a subsidiary of Bouygues Construction, Sir Robert McAlpine, and VolkerFitzpatrick, a subsidiary of VolkerWessels. Our combined experience, both in the UK and worldwide, in ...

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ALIGN TECHNOLOGY UK LIMITED - Free company information from Companies House including registered office address, filing history, accounts, annual return, officers, charges, business activity

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With better all-round care, symptom management, and a comprehensive and holistic home-based care plan in place, patients are less likely to require emergency services, hospitalisation and ICU stays. Family members are more supported too. And remember, Alignd can offer you support and care ...

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Align Technology is a global medical device company that is changing lives through better smiles. We reimagine and reinvent the way orthodontic and restorative treatment is presented and delivered to millions of people around the world.The Invisalign system is the most advanced clear aligner ...

Align Technology UK Ltd - BDIA

Align Technology is a global medical device company with industry-leading innovative products such as Invisalign clear... read more ... Align Technology is dedicated to transforming lives by improving the journey to a healthy, beautiful smile.

Align Technology Mission, Vision & Values | Comparably

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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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

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.

Is Anthropic's Alignment Faking a Significant AI Safety Research?

Předstírá Anthropic's Alignment významný výzkum bezpečnosti AI?

¿La alineación de Anthropic está falsificando una investigación importante sobre seguridad de la IA?

Anthropic-en Lerrokatzeak AIaren Segurtasun Ikerketa Garrantzitsu bat faltsutzen al du?

Èske aliyman Anthropic la se yon rechèch enpòtan sou sekirite AI?

Anthropic の Alignment は重要な AI 安全性研究を偽造しているのか?

Est-ce que Faking ya Alignment ya Anthropic ezali recherche ya sécurité ya AI ya significatif?

Adakah Penjajaran Anthropic Memalsukan Penyelidikan Keselamatan AI yang Penting?

Na Anthropic’s Alignment Faking ke Nyakišišo e Bohlokwa ya Polokego ya AI?

Czy Anthropic symuluje ważne badania nad bezpieczeństwem sztucznej inteligencji?

Anthropic's Alignment ව්‍යාජ ලෙස සැලකිය යුතු AI ආරක්ෂණ පර්යේෂණයක්ද?

Да ли Антхропицово поравнање лажира значајно истраживање безбедности вештачке интелигенције?

Ngaba ulungelelwaniso lwe-Anthropic luFakela uPhando oluBalulekileyo loKhuseleko lwe-AI?

Building a Data Strategy: Aligning Data With Business Goals for Maximum Impact

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation 4.3. Ablation study We conduct an extensive ablation study to demonstrate the contribution of each component of our framework. Mask-text alignment. Compared to the straightforward baseline, as shown in Tab. 3, our proposed Uni-OVSeg achieves significant gains of 4.8% PQ and 9.5% mIoU on the COCO dataset, and 11.2% mIoU on the PASCAL Context-59 dataset. This demonstrates our method effectively align objects in images and entities in text descriptions, generalising the CLIP embedding space from the image level to pixel level. By resorting to the refinement of text descriptions, new texts are more correlated with the corresponding images, improving the mIoU from 34.5% to 37.3% on the COCO dataset. Compared to the traditional NLP toolkit (NLTK) [3], ChatGPT-based parser extracts more reliable entities from text descriptions, which achieves obvious improvements of 3.1% and 3.7% mIoU on the COCO and PASCAL Context-59 datasets, respectively. Finally, the proposed multi-scale ensemble strategy that leverages the multi-scale information of objects within the images, stabilise the mask-text matching, which achieves a performance gain of 1.8% PQ on the COCO datasets. Multi-scale ensemble in mask-text matching. The quality of correspondence between masks and entities is an essential part of mask-text matching. To investigate the impact of multi-scale information on this correspondence, as illustrated in Tab. 4, we use masks and semantic classes from the ADE20K and COCO datasets, reporting the Top1 accuracy and forward time per sample. We first resize input images to multiple resolutions and extract visual features via the clip visual encoder. Given ground-truth masks, regional features are pooled from CLIP visual features and projected into the clip embedding space. Each regional embedding is classified by text embeddings. Taking into account the trade-off between performance and latency, we adopt the sizes of 869 × 896 and 1024 × 1024 as default. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation 4.3. Ablation study We conduct an extensive ablation study to demonstrate the contribution of each component of our framework. Mask-text alignment. Compared to the straightforward baseline, as shown in Tab. 3, our proposed Uni-OVSeg achieves significant gains of 4.8% PQ and 9.5% mIoU on the COCO dataset, and 11.2% mIoU on the PASCAL Context-59 dataset. This demonstrates our method effectively align objects in images and entities in text descriptions, generalising the CLIP embedding space from the image level to pixel level. By resorting to the refinement of text descriptions, new texts are more correlated with the corresponding images, improving the mIoU from 34.5% to 37.3% on the COCO dataset. Compared to the traditional NLP toolkit (NLTK) [3], ChatGPT-based parser extracts more reliable entities from text descriptions, which achieves obvious improvements of 3.1% and 3.7% mIoU on the COCO and PASCAL Context-59 datasets, respectively. Finally, the proposed multi-scale ensemble strategy that leverages the multi-scale information of objects within the images, stabilise the mask-text matching, which achieves a performance gain of 1.8% PQ on the COCO datasets. Mask-text alignment. Multi-scale ensemble in mask-text matching. The quality of correspondence between masks and entities is an essential part of mask-text matching. To investigate the impact of multi-scale information on this correspondence, as illustrated in Tab. 4, we use masks and semantic classes from Multi-scale ensemble in mask-text matching. the ADE20K and COCO datasets, reporting the Top1 accuracy and forward time per sample. We first resize input images to multiple resolutions and extract visual features via the clip visual encoder. Given ground-truth masks, regional features are pooled from CLIP visual features and projected into the clip embedding space. Each regional embedding is classified by text embeddings. Taking into account the trade-off between performance and latency, we adopt the sizes of 869 × 896 and 1024 × 1024 as default. 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

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