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

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

Strategic Growth Management Software | Growth Tools By Align

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 ...

Alignd – Value. Trust. Solutions.

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.

Technology Infrastructure Solutions | Align

We provide the advice and expertise to build a better future for New Zealand · These are some of our specialist sectors:

Home - Align NZ

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.

Align Production Systems | Creating Solutions For Heavy Industry

Align

align

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

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

The Impact of Mask-Text Alignment and Multi-Scale Ensemble on Uni-OVSeg’s Segmentation Accuracy

Seatbelts are safety, but what is safety for seatbelts if there are risks they face in certain situations? How far down can safety be designed for systems, with respect to designing safety for safety systems? Technologies, or efficiency systems, do not just advance better than safety can apply, but technologies do not have a basis--naturally--for safety. A human, on average, anywhere on the body would quickly move away from a sudden hot object. Safety is built in, against negative and dangerous experiences, that either automatically or not, it is possible to avoid danger, without having a system for safety in one part of the body, that may then need its own safety system, or have safety [apply] somewhere not everywhere. Simply, experience is the basis of safety for humans as a means of survival in society. Experience also is the source of progress in society as laws are based on the possibility that manypeople will try to avoid the negative experiences that will result from breaking the law. Laws or regulations often build on human affect or experience, such that laws are not made for people who will not be affected--if there is a penalty. So human society is not just intelligence driven, but of experiences--by affect. This is a major contrast with technology. Even with several safety systems in automobiles, they still do not know if they crumple. So while they tryto be safe, it is still for people, not for them, which, assuming it were possible, would have been a better end of safety--so to speak. AI is a technology, which is not one in a physical space--where safety seems easier. Digital is too flexible and of a different phase, that it is possible to make large changes and adjustments without the same efforts in the physical. For example, drawing on a wall and on a PC, or driving simulations and so forth, are different from the physical, in most cases. Social media showed that digital contents can be difficult to regulate, with scale, evasion, and speed. Digital, like other technologies, have no affect, so they experience nothing to indicate that they should not allow themselves to be used for one negative purpose or another. AI, though useful, is already getting misused, with several major stories in the last two years. Making AI safe is a problem preliminary of AI lacking experience, hence affect, before any technical hill. How can AI have affect? How can this affect become the basis for AI alignment, such that whenever it is misused, it can know that there is a penalty for it? How can this be adopted so that it becomes the basis for regulation, rather than the common suggestions of inspection or monitoring? There have been several news articles about departures from OpenAI in the last year, with suggestions about not doing enough for safety and alignment. While those with the experience may be certain of what they are saying, across the industry, there is still no major safety tool that is model-independent, against many of the known risks of AI today. This is not about guardrails or red-teaming, but about the misuses of AI, and not a lot of answers, from any source against those problems. The core of AI safety research could be to look at how human affect works with human intelligence for safety, then to explore how to develop parallels for AI, converting parameters into algorithms, to pave way against this very difficult AI safety problem, mirroring the human mind. There is a recent story on TechCrunch, OpenAI loses another lead safety researcher, Lilian Weng, stating that, "Another one of OpenAI’s lead safety researchers, Lilian Weng, announced on Friday she is departing the startup. Weng served as VP of research and safety since August, and before that, was the head of the OpenAI’s safety systems team. Weng’s departure marks the latest in a long string of AI safety researchers, policy researchers, and other executives who have exited the company in the last year, and several have accused OpenAI of prioritizing commercial products over AI safety." There is a recent feature on Quanta Magazine, Debate May Help AI Models Converge on Truth, stating that, "Let two large models debate the answer to a given question, with a simpler model (or a human) left to recognize the more accurate answer. In theory, the process allows the two agents to poke holes in each other’s arguments until the judge has enough information to discern the truth. Building trustworthy AI systems is part of a larger goal called alignment, which focuses on ensuring that an AI system has the same values and goals as its human users. Today, alignment relies on human feedback — people judging AI. But human feedback may soon be insufficient to ensure the accuracy of a system. In recent years, researchers have increasingly called for new approaches in “scalable oversight,” which is a way to ensure truth even when superhuman systems carry out tasks that humans can’t. Computer scientists have been thinking about scalable oversight for years. Debate emerged as a possible approach in 2018, before LLMs became as large and ubiquitous as they are today." There is a new blogpost by character.ai, Community Safety Updates, stating that, "Moving forward, we will be rolling out a number of new safety and product features that strengthen the security of our platform without compromising the entertaining and engaging experience users have come to expect from Character.AI. These include: Changes to our models for minors (under the age of 18) that are designed to reduce the likelihood of encountering sensitive or suggestive content. Improved detection, response, and intervention related to user inputs that violate our Terms or Community Guidelines. A revised disclaimer on every chat to remind users that the AI is not a real person. Notification when a user has spent an hour-long session on the platform with additional user flexibility in progress." Seatbelts are safety, but what is safety for seatbelts if there are risks they face in certain situations? How far down can safety be designed for systems, with respect to designing safety for safety systems? Technologies, or efficiency systems, do not just advance better than safety can apply, but technologies do not have a basis--naturally--for safety. A human, on average, anywhere on the body would quickly move away from a sudden hot object. Safety is built in, against negative and dangerous experiences, that either automatically or not, it is possible to avoid danger, without having a system for safety in one part of the body, that may then need its own safety system, or have safety [apply] somewhere not everywhere. Simply, experience is the basis of safety for humans as a means of survival in society. Experience also is the source of progress in society as laws are based on the possibility that many people will try to avoid the negative experiences that will result from breaking the law. many Laws or regulations often build on human affect or experience, such that laws are not made for people who will not be affected--if there is a penalty. So human society is not just intelligence driven, but of experiences--by affect. This is a major contrast with technology. Even with several safety systems in automobiles, they still do not know if they crumple. So while they try to be safe, it is still for people, not for them, which, assuming it were possible, would have been a better end of safety--so to speak. try AI is a technology, which is not one in a physical space--where safety seems easier. Digital is too flexible and of a different phase, that it is possible to make large changes and adjustments without the same efforts in the physical. For example, drawing on a wall and on a PC, or driving simulations and so forth, are different from the physical, in most cases. Social media showed that digital contents can be difficult to regulate, with scale, evasion, and speed. Digital, like other technologies, have no affect, so they experience nothing to indicate that they should not allow themselves to be used for one negative purpose or another. AI, though useful, is already getting misused, with several major stories in the last two years. Making AI safe is a problem preliminary of AI lacking experience, hence affect, before any technical hill. How can AI have affect? How can this affect become the basis for AI alignment , such that whenever it is misused, it can know that there is a penalty for it? How can this be adopted so that it becomes the basis for regulation, rather than the common suggestions of inspection or monitoring? How can this affect become the basis for AI alignment There have been several news articles about departures from OpenAI in the last year, with suggestions about not doing enough for safety and alignment. While those with the experience may be certain of what they are saying, across the industry, there is still no major safety tool that is model-independent, against many of the known risks of AI today. This is not about guardrails or red-teaming, but about the misuses of AI, and not a lot of answers, from any source against those problems. The core of AI safety research could be to look at how human affect works with human intelligence for safety, then to explore how to develop parallels for AI, converting parameters into algorithms, to pave way against this very difficult AI safety problem, mirroring the human mind. There is a recent story on TechCrunch , OpenAI loses another lead safety researcher, Lilian Weng , stating that, "Another one of OpenAI’s lead safety researchers, Lilian Weng, announced on Friday she is departing the startup. Weng served as VP of research and safety since August, and before that, was the head of the OpenAI’s safety systems team. Weng’s departure marks the latest in a long string of AI safety researchers, policy researchers, and other executives who have exited the company in the last year, and several have accused OpenAI of prioritizing commercial products over AI safety." TechCrunch OpenAI loses another lead safety researcher, Lilian Weng There is a recent feature on Quanta Magazine , Debate May Help AI Models Converge on Truth , stating that, "Let two large models debate the answer to a given question, with a simpler model (or a human) left to recognize the more accurate answer. In theory, the process allows the two agents to poke holes in each other’s arguments until the judge has enough information to discern the truth. Building trustworthy AI systems is part of a larger goal called alignment, which focuses on ensuring that an AI system has the same values and goals as its human users. Today, alignment relies on human feedback — people judging AI. But human feedback may soon be insufficient to ensure the accuracy of a system. In recent years, researchers have increasingly called for new approaches in “scalable oversight,” which is a way to ensure truth even when superhuman systems carry out tasks that humans can’t. Computer scientists have been thinking about scalable oversight for years. Debate emerged as a possible approach in 2018, before LLMs became as large and ubiquitous as they are today." Quanta Magazine Debate May Help AI Models Converge on Truth There is a new blogpost by character.ai , Community Safety Updates , stating that, "Moving forward, we will be rolling out a number of new safety and product features that strengthen the security of our platform without compromising the entertaining and engaging experience users have come to expect from Character.AI. These include: Changes to our models for minors (under the age of 18) that are designed to reduce the likelihood of encountering sensitive or suggestive content. Improved detection, response, and intervention related to user inputs that violate our Terms or Community Guidelines. A revised disclaimer on every chat to remind users that the AI is not a real person. Notification when a user has spent an hour-long session on the platform with additional user flexibility in progress." character.ai Community Safety Updates

OpenAI Alignment Departures: What Is the AI Safety Problem?

Artificial intelligence can also be described as a technology of highly dynamic automation. This means that it is not just a facility that can do things by itself, but that it has very high dynamism, for the range of what it can do. The view of AI not being risky, because several automated systems are not, is refuted at every new capability of AI--that it can do more by itself. AI does not have to have its own goals, but if it can carry out tasks for humans in more sophisticated ways, then it is a kind of technology that requires encompassing safety protocols. If it can be automated for good use, it can be automated otherwise, to a lengthier extent. An automobile that can drive from one place to another may follow all the safety rules, but the capability to drive itself, as one reserved for humans alone, means that its automation is not just like an elevator or a washing machine. A computer that can use itself to do tasks for people, like what they want to do, is also a kind of automation in the domain of human intelligence. Simply, automation can be categorized into two, those in the forte of human intelligence and those that are not. Whatever automation is dynamic enough to be classified under human intelligence has to be possibly explored with the kind of safety that guides human intelligence. How is human intelligence safe? Or what is the safety of human intelligence, before thinking of AI safety? Human intelligence is safe by human affect. This means that the possibility of experiencing hurt, pain, consequences for wrong actions, and so forth, and understanding what it means for another to experience it, keeps most of the population at some lawful distance, helping society to stay balanced. Even as human intelligence advances human society, safety remains rounded in some ways, due to affect. This is possible to explore for artificial intelligence, as its own automation does work in the realm of human intelligence. The coming AI summits [in San Francisco (November) and Paris (February)] would have to explore human affect as a safety tool of human intelligence, and then explore possibilities to adapt penalty or some form of awareness penalization, to AI models, so that they can know that there are consequences for actions, when misused or when they output the wrong stuff, in common areas of the internet, like social media, search results, app or play store, and so forth. There should be a key workshop in theoretical neuroscience for AI safety during the summits, towards progress. There is a recent article in the Washington Post, OpenAI adds search to ChatGPT, challenging Google, stating that, "Artificial-intelligence powerhouse OpenAI announced a major overhaul of ChatGPT that enables the chatbot to search the web and provide answers based on what it finds. The upgrade transforms the experience of using the popular chatbot. It brings OpenAI into more direct competition with Google, offering an alternative way to find and consume information online." There is another recent article on Ars Technica, Google CEO says over 25% of new Google code is generated by AI, stating that, "On Tuesday, Google's CEO revealed that AI systems now generate more than a quarter of new code for its products, with human programmers overseeing the computer-generated contributions. The statement, made during Google's Q3 2024 earnings call, shows how AI tools are already having a sizable impact on software development." There is a recent article on Axios, Exclusive: Global summit blends AI and biotech, stating that, "The Biden administration this week is hosting a first-of-its-kind international summit about the use of artificial intelligence in the life sciences as governments and private industry increasingly push the boundaries of biotechnology. The summit, hosted under the auspices of the State Department and federal science agencies, will bring together representatives of Brazil, Canada, the EU, France, Germany, Italy, India, Japan, the Netherlands, the Republic of Korea, Singapore, South Africa and the United Kingdom" Artificial intelligence can also be described as a technology of highly dynamic automation. This means that it is not just a facility that can do things by itself, but that it has very high dynamism, for the range of what it can do. The view of AI not being risky, because several automated systems are not, is refuted at every new capability of AI--that it can do more by itself. AI does not have to have its own goals, but if it can carry out tasks for humans in more sophisticated ways, then it is a kind of technology that requires encompassing safety protocols. If it can be automated for good use, it can be automated otherwise, to a lengthier extent. An automobile that can drive from one place to another may follow all the safety rules, but the capability to drive itself, as one reserved for humans alone, means that its automation is not just like an elevator or a washing machine. A computer that can use itself to do tasks for people, like what they want to do, is also a kind of automation in the domain of human intelligence. Simply, automation can be categorized into two, those in the forte of human intelligence and those that are not. Whatever automation is dynamic enough to be classified under human intelligence has to be possibly explored with the kind of safety that guides human intelligence. How is human intelligence safe? Or what is the safety of human intelligence, before thinking of AI safety? Human intelligence is safe by human affect. This means that the possibility of experiencing hurt, pain, consequences for wrong actions, and so forth, and understanding what it means for another to experience it, keeps most of the population at some lawful distance, helping society to stay balanced. Even as human intelligence advances human society, safety remains rounded in some ways, due to affect. This is possible to explore for artificial intelligence, as its own automation does work in the realm of human intelligence. The coming AI summits [in San Francisco (November) and Paris (February)] would have to explore human affect as a safety tool of human intelligence , and then explore possibilities to adapt penalty or some form of awareness penalization, to AI models, so that they can know that there are consequences for actions, when misused or when they output the wrong stuff, in common areas of the internet, like social media, search results, app or play store, and so forth. would have to explore human affect as a safety tool of human intelligence There should be a key workshop in theoretical neuroscience for AI safety during the summits, towards progress. There is a recent article in the Washington Post , OpenAI adds search to ChatGPT, challenging Google , stating that, "Artificial-intelligence powerhouse OpenAI announced a major overhaul of ChatGPT that enables the chatbot to search the web and provide answers based on what it finds. The upgrade transforms the experience of using the popular chatbot. It brings OpenAI into more direct competition with Google, offering an alternative way to find and consume information online." Washington Post OpenAI adds search to ChatGPT, challenging Google There is another recent article on Ars Technica , Google CEO says over 25% of new Google code is generated by AI , stating that, "On Tuesday, Google's CEO revealed that AI systems now generate more than a quarter of new code for its products, with human programmers overseeing the computer-generated contributions. The statement, made during Google's Q3 2024 earnings call, shows how AI tools are already having a sizable impact on software development." Ars Technica Google CEO says over 25% of new Google code is generated by AI There is a recent article on Axios , Exclusive: Global summit blends AI and biotech , stating that, "The Biden administration this week is hosting a first-of-its-kind international summit about the use of artificial intelligence in the life sciences as governments and private industry increasingly push the boundaries of biotechnology. The summit, hosted under the auspices of the State Department and federal science agencies, will bring together representatives of Brazil, Canada, the EU, France, Germany, Italy, India, Japan, the Netherlands, the Republic of Korea, Singapore, South Africa and the United Kingdom" Axios Exclusive: Global summit blends AI and biotech

AI Safety Summit: Dual Alignment Workshops

RateX Goes Live on Solana Mainnet on 17th Oct 2024. RateX, the world’s first leveraged yield exchange, has officially launched on the Solana mainnet, marking a significant milestone in the decentralized finance (DeFi) space. Built with innovation at its core, RateX enables users to trade synthetic yield tokens (YT) of various yield-bearing assets (YBA) with leverage of up to 10x. This powerful platform introduces groundbreaking solutions to maximize capital efficiency while addressing common DeFi challenges, such as impermanent loss and limited yield-trading opportunities. RateX, a proud recipient of a grant from the Solana Foundation and the first-place winner of the Solana Renaissance Hackathon (MCM), aims to push the boundaries of decentralized finance. The platform’s unique value proposition comes from its use of synthetic yield tokens, which track the yield of different assets and allow traders to take positions with leveraged exposure. This gives users more flexibility and opportunities to capitalize on fluctuations in yields, empowering them to generate higher returns on their investments. The introduction of RateX also brings a cutting-edge Time-Decaying Automated Market Maker (AMM) to the forefront. This AMM is designed to improve capital efficiency while reducing the risks traditionally associated with liquidity provisioning, particularly in expiring contracts. The mechanism minimizes the potential for impermanent loss—a common issue for liquidity providers (LPs) in volatile markets—and aims to eventually eliminate it altogether. As a result, RateX offers a more robust and rewarding environment for both traders and liquidity providers. In addition to its leveraged yield trading capabilities, RateX caters to users looking for stability by providing fixed-yield options. These options allow users to lock in specific yields over a predetermined period, offering more predictable returns compared to the highly variable DeFi market. Whether traders are seeking high-risk, high-reward opportunities or more stable, consistent gains, RateX provides an ecosystem that accommodates diverse trading strategies. Key Partnerships with Jupiter and Jito Further bolstering its platform, RateX has announced strategic partnerships with two prominent players in the Solana ecosystem—Jupiter and Jito. These partnerships not only strengthen RateX’s technological infrastructure but also extend its reach within the DeFi and staking communities. Jupiter, one of the largest decentralized trading platforms on Solana, is known for its high liquidity and strong governance framework. With an extensive community and a focus on enabling smooth cross-asset trading, Jupiter plays a pivotal role in bringing liquidity to the RateX platform. This partnership ensures that RateX users will benefit from deep liquidity pools, efficient price discovery, and seamless execution of trades. Jupiter’s active governance community also opens the door for collaborative development efforts, allowing RateX to evolve in line with community-driven innovations. On the staking side, RateX has joined forces with Jito, the leading liquid staking platform on Solana. Jito’s expertise in liquid staking will enhance RateX’s offerings by providing users with access to staking-derived yield-bearing assets. By leveraging Jito’s technology, RateX can integrate liquid staking into its synthetic yield ecosystem, offering even more diverse options for traders to earn yield. With this partnership, users can expect enhanced staking efficiency, better capital utilization, and a broader range of yield-bearing assets available for leveraged trading. A New Era for DeFi: RateX as a Pillar of Infrastructure The launch of RateX comes at a time when the DeFi sector is evolving rapidly, and the demand for more sophisticated financial products is higher than ever. By pioneering leveraged yield trading and forming key partnerships with Jupiter and Jito, RateX is positioning itself as a crucial pillar of the Solana ecosystem and DeFi infrastructure at large. RateX’s mission is to redefine how yield movements are traded, making the entire process more efficient, transparent, and accessible to both seasoned traders and new entrants to the space. As the world’s first leveraged yield exchange, RateX is primed to play a central role in shaping the next phase of decentralized finance, often referred to as the “next DeFi summer.” With its innovative platform, strong partnerships, and focus on capital efficiency, RateX is setting the stage for a new era of financial freedom and opportunities for DeFi enthusiasts worldwide. Learn more about RateX and join the future of decentralized finance: Website: rate-x.io Twitter: RateX_Dex Discord: RateX Community RateX Goes Live on Solana Mainnet on 17th Oct 2024. RateX, the world’s first leveraged yield exchange, has officially launched on the Solana mainnet, marking a significant milestone in the decentralized finance (DeFi) space. Built with innovation at its core, RateX enables users to trade synthetic yield tokens (YT) of various yield-bearing assets (YBA) with leverage of up to 10x. This powerful platform introduces groundbreaking solutions to maximize capital efficiency while addressing common DeFi challenges, such as impermanent loss and limited yield-trading opportunities. RateX Goes Live on Solana Mainnet on 17th Oct 2024. RateX, a proud recipient of a grant from the Solana Foundation and the first-place winner of the Solana Renaissance Hackathon (MCM), aims to push the boundaries of decentralized finance. The platform’s unique value proposition comes from its use of synthetic yield tokens, which track the yield of different assets and allow traders to take positions with leveraged exposure. This gives users more flexibility and opportunities to capitalize on fluctuations in yields, empowering them to generate higher returns on their investments. The introduction of RateX also brings a cutting-edge Time-Decaying Automated Market Maker (AMM) to the forefront. This AMM is designed to improve capital efficiency while reducing the risks traditionally associated with liquidity provisioning, particularly in expiring contracts. The mechanism minimizes the potential for impermanent loss—a common issue for liquidity providers (LPs) in volatile markets—and aims to eventually eliminate it altogether. As a result, RateX offers a more robust and rewarding environment for both traders and liquidity providers. Time-Decaying Automated Market Maker (AMM) In addition to its leveraged yield trading capabilities, RateX caters to users looking for stability by providing fixed-yield options. These options allow users to lock in specific yields over a predetermined period, offering more predictable returns compared to the highly variable DeFi market. Whether traders are seeking high-risk, high-reward opportunities or more stable, consistent gains, RateX provides an ecosystem that accommodates diverse trading strategies. Key Partnerships with Jupiter and Jito Key Partnerships with Jupiter and Jito Further bolstering its platform, RateX has announced strategic partnerships with two prominent players in the Solana ecosystem—Jupiter and Jito. These partnerships not only strengthen RateX’s technological infrastructure but also extend its reach within the DeFi and staking communities. Jupiter, one of the largest decentralized trading platforms on Solana, is known for its high liquidity and strong governance framework. With an extensive community and a focus on enabling smooth cross-asset trading, Jupiter plays a pivotal role in bringing liquidity to the RateX platform. This partnership ensures that RateX users will benefit from deep liquidity pools, efficient price discovery, and seamless execution of trades. Jupiter’s active governance community also opens the door for collaborative development efforts, allowing RateX to evolve in line with community-driven innovations. On the staking side, RateX has joined forces with Jito, the leading liquid staking platform on Solana. Jito’s expertise in liquid staking will enhance RateX’s offerings by providing users with access to staking-derived yield-bearing assets. By leveraging Jito’s technology, RateX can integrate liquid staking into its synthetic yield ecosystem, offering even more diverse options for traders to earn yield. With this partnership, users can expect enhanced staking efficiency, better capital utilization, and a broader range of yield-bearing assets available for leveraged trading. A New Era for DeFi: RateX as a Pillar of Infrastructure A New Era for DeFi: RateX as a Pillar of Infrastructure The launch of RateX comes at a time when the DeFi sector is evolving rapidly, and the demand for more sophisticated financial products is higher than ever. By pioneering leveraged yield trading and forming key partnerships with Jupiter and Jito, RateX is positioning itself as a crucial pillar of the Solana ecosystem and DeFi infrastructure at large. RateX’s mission is to redefine how yield movements are traded, making the entire process more efficient, transparent, and accessible to both seasoned traders and new entrants to the space. As the world’s first leveraged yield exchange, RateX is primed to play a central role in shaping the next phase of decentralized finance, often referred to as the “next DeFi summer.” With its innovative platform, strong partnerships, and focus on capital efficiency, RateX is setting the stage for a new era of financial freedom and opportunities for DeFi enthusiasts worldwide. Learn more about RateX and join the future of decentralized finance: Learn more about RateX and join the future of decentralized finance: Website: rate-x.io Website: rate-x.io Twitter: RateX_Dex Twitter: RateX_Dex Discord: RateX Community Discord: RateX Community

RateX Goes Live on Solana: The First Leveraged Yield Exchange is Now Jupiter & Jito Aligned 

RateX geht auf Solana live: Die erste Leveraged Yield Exchange ist jetzt auf Jupiter und Jito ausgerichtet

RateX se lanza en línea en Solana: el primer exchange con rendimiento apalancado ahora está alineado con Jupiter y Jito

RateX در Solana فعال می شود: اولین تبادل بازدهی اهرمی اکنون مشتری و جیتو هم تراز شده است 

RateX-ը գործարկվում է Solana-ում. Առաջին լծակային եկամտաբերության փոխանակումն այժմ հավասարեցված է Յուպիտերին և Ջիտոնին 

RateX Hadir di Solana: Bursa Hasil Leverage Pertama Kini Selaras dengan Jupiter & Jito

RateX va in diretta su Solana: il primo exchange di rendimento con leva finanziaria è ora allineato con Jupiter e Jito

RateXがSolanaで稼働開始: 初のレバレッジ利回り取引所がJupiterとJitoと提携

RateX Solana дээр ажиллаж байна: Анхны хөшүүрэгтэй өгөөжийн бирж нь одоо Бархасбадь ба Житотой нийцэж байна 

RateX Bersiaran Langsung di Solana: Pertukaran Hasil Leveraj Pertama Kini Diselaraskan dengan Musytari & Jito 

RateX gaat live op Solana: de eerste leveraged yield exchange is nu Jupiter & Jito Aligned

RateX Solanapi kawsan: Ñawpaq kaq Intercambio de Rendimiento Apalancado Kunanqa Jupiter & Jito Alineado kachkan 

RateX Goes Live on Solana: Ang Unang Leveraged Yield Exchange ay Jupiter at Jito Aligned na Ngayon 

Bias is a problem in large language models [LLMs]. But how can bias be solved, or tapered? This question means what can be done about training base models or fine-tuning assistant models to solve bias—technically—in LLMs? There are various disadvantages of AI bias in several use cases. This may indicate that there are organizations with interests in mitigating bias. However, is there a place they can go to find technical research paths to explore solving bias in AI? There is a lot of vacillation around what is open source in LLMs—maybe weights, datasets or codes—but even if everything that is used to train a language model is open source, how much can be technically gleaned there to solve bias, since closed source LLMs have teams with access to everything yet are unable to solve it. What can be done about bias—technically—that can be made available on a list, for those interested to go at it, could be a more important open source path, for problems in AI, than debating if weights or others are open source. The same applies to AI safety, alignment, ethics, governance and regulation. What is technically doable to make AI less misused and harmful, in the present and in future, available on a forum for those who want to find options to approach problems? The US has an AI safety institute, which also has an AI safety consortium. The UK has an AI safety institute. The EU has an AI office. Japan and Singapore also have their own AI safety teams. They can host forums for technical approaches as options that other labs may explore, which they may or may not also be exploring. These paths must not be extensively defined, but must have a technical entry port as well as anchorsto some existing approaches. The objective is to open source possible solutions around AI challenges with technical paths that can be seen in a public forum or safety marketplace—so to speak, so that those interested in building would see what is available, and use that to pursue advances. There is so much that has to be done about AI, and the few teams at the front have benchmarks and commercial ends too as priorities [OpenAI o1], making safety sometimes confined around a few of their core approaches. Open source for what can be done may instead redefine everything, more what LLM is open source, or which one is closed source. There is a recent update from Open Source Initiative, The Open Source AI Definition – draft v. 0.0.9, stating that, "When we refer to a “system,” we are speaking both broadly about a fully functional structure and its discrete structural elements. To be considered Open Source, the requirements are the same, whether applied to a system, a model, weights and parameters, or other structural elements. An Open Source AI is an AI system made available under terms and in a way that grant the freedoms to: Use the system for any purpose and without having to ask for permission. Study how the system works and inspect its components. Modify the system for any purpose, including to change its output. Share the system for others to use with or without modifications, for any purpose. These freedoms apply both to a fully functional system and to discrete elements of a system. A precondition to exercising these freedoms is to have access to the preferred form to make modifications to the system." There is a recent story from The NYTimes, OpenAI’s Fund-Raising Talks Could Value Company at $150 Billion, stating that, "OpenAI is in talks to raise about $6.5 billion as part of a deal that would value the company in the vicinity of $150 billion, a nearly $70 billion increase from its valuation nine months ago, according to five people with knowledge of the conversations. OpenAI is also working to transform its A.I. technologies into products that pull in revenue. Building this kind of technology requires billions of dollars in raw computing power, and by some estimates the company is burning through $7 billion every year. OpenAI is pulling in more than $2 billion a year through subscriptions to ChatGPT and other technologies, according to a person familiar with its finances." There is a new story on Reuters, US to convene global AI safety summit in November, stating that, “The Biden administration plans to convene a global safety summit on artificial intelligence, it said on Wednesday, as Congress continues to struggle with regulating the technology. The network members include Australia, Canada, the European Union, France, Japan, Kenya, South Korea, Singapore, Britain, and the United States. Generative AI - which can create text, photos and videos in response to open-ended prompts - has spurred excitement as well as fears it could make some jobs obsolete, upend elections and potentially overpower humans and have catastrophic effects. The goal of the San Francisco meeting is to jumpstart technical collaboration before the AI Action Summit in Paris in February.“ Feature image source Bias is a problem in large language models [LLMs]. But how can bias be solved, or tapered? This question means what can be done about training base models or fine-tuning assistant models to solve bias—technically—in LLMs? There are various disadvantages of AI bias in several use cases. This may indicate that there are organizations with interests in mitigating bias. However, is there a place they can go to find technical research paths to explore solving bias in AI? There is a lot of vacillation around what is open source in LLMs—maybe weights, datasets or codes—but even if everything that is used to train a language model is open source, how much can be technically gleaned there to solve bias, since closed source LLMs have teams with access to everything yet are unable to solve it. What can be done about bias—technically—that can be made available on a list, for those interested to go at it, could be a more important open source path, for problems in AI, than debating if weights or others are open source. The same applies to AI safety, alignment, ethics, governance and regulation. What is technically doable to make AI less misused and harmful, in the present and in future , available on a forum for those who want to find options to approach problems? the present and in future The US has an AI safety institute, which also has an AI safety consortium. The UK has an AI safety institute. The EU has an AI office. Japan and Singapore also have their own AI safety teams. They can host forums for technical approaches as options that other labs may explore, which they may or may not also be exploring. These paths must not be extensively defined, but must have a technical entry port as well as anchors to some existing approaches. as anchors The objective is to open source possible solutions around AI challenges with technical paths that can be seen in a public forum or safety marketplace—so to speak, so that those interested in building would see what is available, and use that to pursue advances. There is so much that has to be done about AI, and the few teams at the front have benchmarks and commercial ends too as priorities [ OpenAI o1 ], making safety sometimes confined around a few of their core approaches. Open source for what can be done may instead redefine everything, more what LLM is open source, or which one is closed source. OpenAI o1 There is a recent update from Open Source Initiative , The Open Source AI Definition – draft v. 0.0.9 , stating that, "When we refer to a “system,” we are speaking both broadly about a fully functional structure and its discrete structural elements. To be considered Open Source, the requirements are the same, whether applied to a system, a model, weights and parameters, or other structural elements. An Open Source AI is an AI system made available under terms and in a way that grant the freedoms to: Use the system for any purpose and without having to ask for permission. Study how the system works and inspect its components. Modify the system for any purpose, including to change its output. Share the system for others to use with or without modifications, for any purpose. These freedoms apply both to a fully functional system and to discrete elements of a system. A precondition to exercising these freedoms is to have access to the preferred form to make modifications to the system." Open Source Initiative The Open Source AI Definition – draft v. 0.0.9 There is a recent story from The NYTimes , OpenAI’s Fund-Raising Talks Could Value Company at $150 Billion , stating that, "OpenAI is in talks to raise about $6.5 billion as part of a deal that would value the company in the vicinity of $150 billion, a nearly $70 billion increase from its valuation nine months ago, according to five people with knowledge of the conversations. OpenAI is also working to transform its A.I. technologies into products that pull in revenue. Building this kind of technology requires billions of dollars in raw computing power, and by some estimates the company is burning through $7 billion every year. OpenAI is pulling in more than $2 billion a year through subscriptions to ChatGPT and other technologies, according to a person familiar with its finances." The NYTimes OpenAI’s Fund-Raising Talks Could Value Company at $150 Billion There is a new story on Reuters , US to convene global AI safety summit in November , stating that, “The Biden administration plans to convene a global safety summit on artificial intelligence, it said on Wednesday, as Congress continues to struggle with regulating the technology. The network members include Australia, Canada, the European Union, France, Japan, Kenya, South Korea, Singapore, Britain, and the United States. Generative AI - which can create text, photos and videos in response to open-ended prompts - has spurred excitement as well as fears it could make some jobs obsolete, upend elections and potentially overpower humans and have catastrophic effects. The goal of the San Francisco meeting is to jumpstart technical collaboration before the AI Action Summit in Paris in February.“ Reuters US to convene global AI safety summit in November Feature image source Feature image source source

AI Alignment: What Open Source, for LLMs Safety, Ethics and Governance, Is Necessary?

RAG Predictive Coding for AI Alignment Against Prompt Injections and Jailbreaks

Authors:
(1) Mehdi Naderi;
(2) Markos Papageorgiou;
(3) Dimitrios Troullinos;
(4) Iasson Karafyllis;
(5) Ioannis Papamichail. Table of Links Abstract and Introduction Vehicle Modeling The Nonlinear Feedback Control OD Corridors and Desired Orientations Boundary and Safety Controllers Simulation Results Conclusion Appendix A: Collision Detection Appendix B: Transformed ISO-Distance curves Appendix C: Local Density Appendix D: Safety Controller Details Appendix E: Controller Parameters References APPENDIX B: TRANSFORMED ISO-DISTANCE CURVES For the iso-distance curve transformation to align with the desired orientation, new polar coordinates are defined whose origin is found by crossing two lines perpendicular to the vehicles’ rear axles, while having the ego vehicle’s desired deviation from the circular angle. This implies that the coordinates are different for each pair of vehicles. The lines’ equations, according to Fig. 20, can be written as Note that each vehicle projects other vehicles on its own coordinates; therefore, in both equations of (42), the desired orientation of vehicle i is taken into account. Intersecting above equations gives the centre of new coordinates as Then, the radius and angle of each vehicle in the new coordinates is calculated by Furthermore, the distance in the new coordinates is This paper is available on arxiv under CC 4.0 license. Authors: (1) Mehdi Naderi; (2) Markos Papageorgiou; (3) Dimitrios Troullinos; (4) Iasson Karafyllis; (5) Ioannis Papamichail. Authors: Authors: (1) Mehdi Naderi; (2) Markos Papageorgiou; (3) Dimitrios Troullinos; (4) Iasson Karafyllis; (5) Ioannis Papamichail. Table of Links Abstract and Introduction Abstract and Introduction Vehicle Modeling Vehicle Modeling The Nonlinear Feedback Control The Nonlinear Feedback Control OD Corridors and Desired Orientations OD Corridors and Desired Orientations Boundary and Safety Controllers Boundary and Safety Controllers Simulation Results Simulation Results Conclusion Conclusion Appendix A: Collision Detection Appendix A: Collision Detection Appendix B: Transformed ISO-Distance curves Appendix B: Transformed ISO-Distance curves Appendix C: Local Density Appendix C: Local Density Appendix D: Safety Controller Details Appendix D: Safety Controller Details Appendix E: Controller Parameters Appendix E: Controller Parameters References References APPENDIX B: TRANSFORMED ISO-DISTANCE CURVES For the iso-distance curve transformation to align with the desired orientation, new polar coordinates are defined whose origin is found by crossing two lines perpendicular to the vehicles’ rear axles, while having the ego vehicle’s desired deviation from the circular angle. This implies that the coordinates are different for each pair of vehicles. The lines’ equations, according to Fig. 20, can be written as Note that each vehicle projects other vehicles on its own coordinates; therefore, in both equations of (42), the desired orientation of vehicle i is taken into account. Intersecting above i equations gives the centre of new coordinates as Then, the radius and angle of each vehicle in the new coordinates is calculated by Furthermore, the distance in the new coordinates is This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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