FerretDBis in the Database Technology Industry

FerretDB

Data-ferret is a tiny, yet powerful to scan or transform deeply nested and complex object-like data with ease. It is available as an open-source project under an MIT license. util library It can and complex, data whose interface or shape cannot be guaranteed ( ), in other words, . search transform schemaless messy data data-ferret is designed to be extensible. It or iterables, beyond the native JavaScript data types. supports custom class instances Additionally, it provides handling objects with . first-class support for circular references I would like to give away to my readers that could benefit from using data-ferret to build a business. my top 5 picks for viable SaaS products Startup Ideas 1. A Search Engine One of the most potential applications for is building the quintessential search engine. You can use data-ferret’s function or create your custom function for specific capabilities. data-ferret locateText() traverse() This can be applied to various industries as a SaaS product. For example; A one-stop shop to find jobs and real estate opportunities by using data from various sources and third-parties listing providers. or your custom will not need data to be normalized to work with it. Users can be presented with all properties available in the dataset by using locateText() traverse() getUniqueKeys() . The same concept may apply to a platform that allows users to for posts, photos, and other content based on keywords, hashtags, and other criteria, without needing to know the specific schema of the data. social media search Same for goes where specific text can be matched or any tags or flags that may exist in the dataset or a that allows users to search for academic papers based on keywords, authors, and other criteria, without needing to know the specific schema of the data. news search engine research paper search engine A that allows users to search for answers to common questions and problems, without needing to know the specific schema of the data. customer service chatbot A that allows users to search for case law and other legal documents based on keywords, judges, and other criteria, without needing to know the specific schema of the data. legal search engine A that allows users to search for medical information and research based on keywords, conditions, and other criteria, without needing to know the specific schema of the data. medical search engine 2. Content Redaction/Moderation Service Perhaps your next product idea has GDPR considerations or will have to deal with sensitive data like credit cards, or details that must be anonymized. Or in general, content moderation must be implemented. You can leverage to perform transformations on your dataset and generalize your approach without having to worry about interface/schemas changing. replaceText() 3. Data Migration Service A data migration tool for companies to seamlessly transfer data from one system to another, regardless of the data’s structure or format. You could use in conjunction with to map out the schema of the original dataset and through a dashboard UI, a user can present a new schema as output. Key names may require renaming or deletion, for that and got you covered. getUniqueKeys() locateKey() renameKey() removeKey() 4. Data Visualization Data-ferret ships a browser-ready version of the code, which means it is not just suitable for Node.Js backend services but also frontend. A for businesses to easily process and visualize large amounts of data from various sources, without needing to know the specific schema of the data sounds like a plausible use case. data analytics tool Data-ferret could be used on the backend to consolidate or prepare the initial data. On the front end, a UI can use the same APIs to perform quick search operations on the client side. Web workers could to perform computationally heavy operations in a separate thread to ensure the main thread remains unencumbered to render the page and remain responsive. also be used 5. Data Auditing Sometimes the focus is on what data is there, not necessarily on how it is structured. For example, a data for financial institutions to automatically match and reconcile transactions across multiple systems and data sources may use a custom function or , which could let sniff out missing records, incorrect values, etc, with more ease, and then later apply whatever business rules make sense, whether it be generating a report or applying data correction. reconciliation tool traverse() locateKey() That’s it. I hope this article has got your creative gears going and might prompt you to check out . my project Good luck! Also published . here

Create a Search Engine and Other Startup Ideas Using Data-Ferret

Last week, two remarkable events occurred: Meta announced its Llama 3, the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai, and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper
Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper
Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper
Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper
Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper
Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper
YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper
RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper
RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper
JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper News from The Usual Suspects © Google open-sourced Gemini Cookbook
and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI”
DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Last week, two remarkable events occurred: Meta announced its Llama 3 , the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. Llama 3 Llama 3 electric Atlas robot electric Atlas robot At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai , and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. meta.ai meta.ai synthetic social networks synthetic social networks This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Mixtral-8x22B: →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Rerank 3: →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Idefics2: →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Reka Core, Flash, and Edge: →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Ferret-UI: →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper Zamba: →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper YaART: →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RHO-1: →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper RecurrentGemma: →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper JetMoE: →read the paper News from The Usual Suspects © Google Google open-sourced Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI” DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life open-sourced Gemini Cookbook Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “ capacity to deliver capable AI ” merging capacity to deliver capable AI DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life published Hugging Face Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. published the 2024 AI Index report , reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. the 2024 AI Index report Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Turing Post

AI Industries Converge: Llama 3 and Electric Atlas Have More In Common Than You Think

“Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat, the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life, so even well-preserved samples from over 1,000 years ago aren’t complete. AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually, and finding similarities in DNA sequences is a similar task. Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal, so they’re ideal mothers to grow a baby mammoth. After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers. “Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat , the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. call AI an extinction-level threat call AI an extinction-level threat How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. shortens the time spent in genome sequencing shortens the time spent in genome sequencing Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life , so even well-preserved samples from over 1,000 years ago aren’t complete. has a 521-year half-life has a 521-year half-life AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually , and finding similarities in DNA sequences is a similar task. saves Netflix $1 billion annually saves Netflix $1 billion annually Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal , so they’re ideal mothers to grow a baby mammoth. the longest pregnancy of any mammal the longest pregnancy of any mammal After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. resurrect the woolly mammoth by 2027 resurrect the woolly mammoth by 2027 Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. successfully cloned a black-footed ferret successfully cloned a black-footed ferret Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. could improve global health could improve global health Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers.

The Role of AI in Resurrecting Extinct Species

বিলুপ্ত প্রজাতির পুনরুত্থানে এআই-এর ভূমিকা

Die Rolle der KI bei der Wiederbelebung ausgestorbener Arten

El papel de la IA en la resurrección de especies extintas

Le rôle de l’IA dans la résurrection des espèces disparues

विलुप्त प्रजातियों को पुनर्जीवित करने में एआई की भूमिका

絶滅した種の復活におけるAIの役割

멸종된 종을 부활시키는 데 있어 AI의 역할

O papel da IA na ressurreição de espécies extintas

Роль ИИ в воскрешении вымерших видов

Soyu Tükenmiş Türlerin Diriltilmesinde Yapay Zekanın Rolü

Vai trò của AI trong việc hồi sinh các loài đã tuyệt chủng

人工智能在复活灭绝物种中的作用

This paper is under CC 4.0 license. available on arxiv : Authors (1) Linda Pigureddu, University of Turin, Italy, 332079@edu.unito.it; (2) Cristina Gena, Dept. of Computer Science, University of Turin, Italy, cristina.gena@unito.it. Table of Links Introduction The Project Memes Conclusion and References CONCLUSION The analysis of the interactions and dialogues that took place during the therapeutic laboratory for autonomies endorsed with the inclusion of the Pepper robot provided an interesting perspective on the communication dynamics of children in the lab. The results obtained showed the effectiveness of the robot in promoting autonomy and functional acquisition, demonstrating that the therapeutic method based on assistive robotics is a valuable resource to support the rehabilitation needs of communication and social skills of autistic children. The analysis highlights new possibilities for their engagement and active participation as a co-designer, as we already experienced in the past with neuro-typical children [19], representing an innovative opportunity to promote the development and wellness of autistic people and opening to new perspectives for therapeutic intervention more aware of the needs of autistic minds. In conclusion, this paper highlights the need to provide the robot with the ability of adapt to the children’s peculiarity and features and sharing her/his vocabulary, especially by recognizing and using memes during interactions, to inspire greater trust in children and allowing the use of common slang, already in use with classmates, allowing them to consider Pepper a peer and effectively insert it in the role of mediator. In addition, it would allow children to use a simplified type of communication during meetings to compensate for the typical communication deficits associated with autism spectrum disorders. In the future we will work in this directions (co-design, user’s adaptations, using and recognizing memes during children-robot communication) having the robot able to communicate and express adapting to user tastes and preferences and and try again to field a real-world evaluation that takes into account the effectiveness of different levels of user adaptation [23]. REFERENCE [1] M. Biondi e M. Maj, DSM-5: diagnostic and statistical manual of mental disorders: text revision, 5. ed. Milano: Raffaello Cortina. [2] E. Hollander, R. Hagerman, e C. Ferretti, Textbook of Autism Spectrum Disorders, Second Edition. American Psychiatric Association Publishing, 2022. [3] ISS - Istituto superiore di sanità, «Osservatorio Nazionale Autismo», OssNA. https://osservatorionazionaleautismo.iss.it [4] P. Pennisi et al., «Autism and social robotics: A systematic review», Autism Research, vol. 9, fasc. 2, pp. 165–183, 2016, doi: 10.1002/aur.1527. [5] S. Shamsuddin, H. Yussof, S. Mohamed, e F. A. 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The Power of Memes: The Pepper Robot as a Communicative Aid for Autistic Children - Conclusion

2022 was a thanks to events that included the high-profile collapse of FTX and subsequent criminal proceedings brought against its founder. Yet if we fast-forward to today, it looks like faith in crypto is making a comeback as . low point in crypto’s history Bitcoin’s price surges to $42,000 But while cryptocurrencies and blockchain technologies in general are decentralized by nature, this doesn’t mean that they should go hand in hand with being deregulated. The technology and the assets they produce are increasingly used for everyday tasks. Governments haven’t been quick to welcome the change but a new generation of policymakers and innovators are keen to usher in a new era for the blockchain that will help to bring the benefits of the technology to users in a more secure and reliable way. For this reason, Hester Peirce, Commissioner of the United States Securities and Exchange Commission (SEC), delivered a fireside chat at Georgetown University on January 26th to mark the launch of USA-UAE Alliance, a blockchain business forum founded by Georgetown University classmates Edward Cullen and Michael Amatulli. Token Safe Harbor 3.0 Hester M. Peirce was appointed by President Trump to the U.S. Securities and Exchange Commission and was in 2018 sworn in. Before joining the SEC, Peirce conducted research on the regulation of financial markets at the Mercatus Center at George Mason University. She was a Senior Counsel on the U.S. Senate Committee on Banking, Housing, and Urban Affairs, where she advised Ranking Member Richard Shelby and other members of the Committee on securities issues. Peirce has also spoken previously about the need for so-called Token Safe Harbors. The idea here is to give crypto and blockchain startups the breathing space to develop and build networks to a more robust stage before having to adhere to federal finance regulations and security protocols. According to notes Peirce released on GitHub in relation to her , the initiative aims to enhance token purchaser protection, more frequent updates on development disclosures and more clarity about the transition process that would take place at the end of the three-year grace period. Token Safe Harbor 2.0 proposal During the event at Georgetown University Edward Cullen, CEO of Crescite and one of the interviewers at the event, asked Peirce whether it may be time for a Token Safe Harbor 3.0 proposal in 2024. She shared that, “A problem that a lot of people had pointed out was that there are a lot of people issuing tokens and there was not a lot of disclosure around them. Obviously, if people are buying these things you want to make sure they know what they’re buying so we wanted a framework designed around the information that buyers would uniquely want.” She continued, “I was trying to get at the safe harbor that I put out would require people to make these disclosures for the initial period when they when they were selling these tokens. And then the idea was that if you really decentralize the blockchain such that no one had more information than anyone else, the disclosures wouldn’t be necessary anymore because all that information would be out there and it would be available for anyone to see there.” Striking balance with regulations Cullen also asked Peirce to share her views on whether freedom in capital markets is a positive or negative. She expanded, “A lot of the way I think about securities regulation is driven because we want to build an economy that allows people to get to get the most out of themselves. You have to have capital markets that allow capital to flow to people who have good ideas and that those good ideas are ferreted out not by a regulator up at the top, but by people who are on the ground, who know the people, who know the technology that they’re building, who can ask those questions.” She concludes “It’s a grassroots kind of thing. And so a properly functioning capital market makes sure that capital flows in the right direction. When I have a choice to maximize liberty, I will but of course, there are statuary mandates and parameters to consider.” The USA-UAE blockchain alliance Said Edward Cullen, CEO of Crescite Innovation Corporation, “The USA-UAE Alliance™ is the latest example of international collaborators coming together and using blockchain technologies for economic growth and social good. We are appreciative of our education as continue to build.” Cullen, earlier a candidate for Mayor of New York City, is an entrepreneur who is known for innovation and regulatory leadership. The USA-UAE Alliance™ team includes Karl P. Kilb III, Co-Founder and Chairman of Crescite Innovation Corporation and former General Counsel of Bloomberg LP. Kilb is the Founder and Chairman of the Entrepreneurial Law Advisory Council at the Fordham University School of Law. Said Michael Amatulli, who helped start the alliance and is CEO of MVNM, “Blockchain is going to become increasingly prominent across financial communities and it’s essential that the development of the technology happens in a fair and sustainable manner.” Featured photo of Edward Cullen, Hester Peirce and Jacquelyn Melinek This article was originally published on The Sociable.

Embracing Regulation: A Positive Outlook for Blockchain Enthusiasts

This paper is under CC 4.0 license. available on arxiv Authors: (1) Marcin W ˛atorek, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland and marcin.watorek@pk.edu.pl (M.W.); (2) Jarosław Kwapie ´n, Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland and jaroslaw.kwapien@ifj.edu.pl; (3)  Stanisław Drozd˙ z, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland, Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland and stanislaw.drozdz@ifj.edu.pl (S.D.). Table of Links Abstract and Introduction Data and Methodology Results and Discussion Conclusion and References 4. Conclusions Based on the multiscale cross-correlation analysis performed for the data covering almost the last three years, it can be concluded that the cryptocurrency market dynamics is substantially tied to the traditional financial markets. Consistently, the most liquid cryptocurrencies, BTC and ETH, cannot serve as a hedge or safe haven for the stock market investments, not only during the turbulent periods like the Covid-19 panic, where this effect is particularly strong, but also during the recent bear market period on tech stocks, which has been accompanied by the parallel bear market on cryptocurrencies. Many observations show that the Covid-19 pandemic may have changed the paradigm that the cryptocurrency market is a largely autonomous market. The recent market developments and the strong US dollar have additionally increased the strength of the cross-correlations between BTC and ETH on the one side and the US tech stocks on the other side. These observations support some earlier findings on the same subject (e.g., [22,70]). In contrast, as the cryptocurrency market was weakly correlated with other markets during 2021, our results cannot support directly a recent hypothesis that the quantitative easing could actually be responsible for these correlations [22]. The existence of links between the global economy and the cryptocurrency market are further strengthened by the reaction of the price changes of BTC and ETH to economic data, such as CPI inflation, in a similar way to traditional financial instruments. These results are able to remove or, at least, to suppress the uncertainty that recent literature on this topic has brought to the cryptocurrency investors. Now it is more clear that the cryptocurrencies can no longer serve as a convenient hedging target for the investors whose purpose is to diversify the risk. Our study brings a strong indication that the cryptocurrency market has finally become a connected part of the global financial markets after 12 years of the maturation process. Whether such a direction of this market evolution remains in agreement with the early vision of the cryptocurrency creators can be disputed, however. We also face a related question: does the fact that we have got “just another part of the global financial market” deserve devoting so huge amounts of energy to it? Sooner or later this question must be addressed by the policy makers. Nevertheless, what becomes evident now is that it allows the market participants to broaden the spectrum of their investment possibilities. Among the limitations that might have influenced our study and, subsequently, our conclusions, we have to mention that only two principal cryptocurrencies were studied. Although they are the most influential, the most frequently traded, and widely discussed cryptocurrencies, they by no means define the entire market. It is possible that an analysis that included some less important cryptocurrencies would bring different outcomes. This is especially likely for the marginal cryptocurrencies without any thinkable “fundamental” value, whose dynamics is driven predominantly by extreme speculation. However, as the cryptocurrency market is looked at by the most investors through the lens of BTC and ETH (as their capitalization indicates), this particular limitation does not seem discouraging to us. Currently, these two assets shape the whole cryptocurrency market and we expect them to continue doing it in the nearest future. Another limitation of our analysis is the particular selection of the traditional financial instruments. Indeed, they constitute only a small fraction of the available ones. We are convinced, though, that they are among the most observed and the most influential ones in the context of the global economy, which fully justifies our choice. A more general observation that the cryptocurrency market has spontaneously coupled to the technological sector of the stock markets by reacting to some trigger provided by the external data inflow resembles analogous effects of the spontaneous emergence of order among the so-far independent degrees of freedom in the various complex systems. However, as complexity allows for flexible reacting of a system to both the external perturbations and internal processes, such effects of ordering in the financial markets have to be eventually counterbalanced by the opposite processes of disordering. Therefore, the market participants must be aware that the inter-market couplings may not last forever and they can significantly be weakened or even removed completely at some point in future. 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Integration of Cryptocurrency into the Global Financial Market: Conclusion and References

As global talent contends with increasingly rigid immigration policies, the landscape for EB1A visa applicants, especially those of Indian origin, has witnessed significant fluctuations in recent months. Spynn, a PR firm that helps applicants for the demonstrate extraordinary ability, faces new challenges in navigating these changes. EB-1A Visa In July 2023, the priority date for Indian applicants was February 2022 for final action and June 2022 for filing, presenting a backlog of just over a year. However, August 2023 brought an unexpected retrogression, moving the priority date back to January 2012, a drastic ten-year leap. This change, attributed to oversubscription in the Indian category, left the immigration community in disarray​​​​. September 2023 saw no improvement, as the retrogression to January 2012 persisted, underscoring oversubscription. With the start of fiscal year 2024 in October, there were high expectations for significant advancements. Contrary to expectations, the dates only advanced five years to January 2017, showing a significant six-year backlog​​​​. Furthermore, the November Visa Bulletin revealed no movement in any category for India, with EB-1 halting on January 1, 2017. This stagnation further emphasizes the challenges faced by Indian EB-1A applicants​​. In response to these complexities, U.S. Citizenship and Immigration Services (USCIS) issued new policy guidance on September 12, 2023, to clarify the types of evidence evaluated for EB1 eligibility, particularly those in science, technology, engineering, or mathematics (STEM) fields. This update is designed to provide more clarity and transparency, assisting petitioners in submitting appropriate evidence to establish the beneficiary’s eligibility​​. Establishing Extraordinary Ability Spynn, amidst these changes, offers a unique approach to meeting the stringent requirements set by the USCIS, navigating the evolving immigration system for their clients with extraordinary abilities. asserts Matteo Ferretti, the driving force behind Spynn. With a rich background in public relations, Ferretti has steered the firm towards a niche focus: aligning PR efforts with the EB1A Visa's exacting publicity criteria. he emphasizes. "Strategic visibility is paramount," "Our service transcends traditional PR. We craft narratives that resonate with USCIS's definition of extraordinary ability," This strategy stems from a deep understanding of U.S. immigration nuances. Ferretti notes. "The EB1A is more than meeting criteria; it's about weaving a compelling story that showcases unparalleled expertise," Data and Future Trends The demand for EB1A Visas in India has escalated by 20% in 2023 compared to the previous year. Despite the application surge, the approval rate remains steady at around 40%, reflecting the visa's stringent criteria. " ," remarks an industry insider, raising questions about the efficacy of PR tactics in securing the visa. The figures are telling. The EB1A is designed for individuals of exceptional ability, and demonstrating this is quite a challenge Spynn, however, challenges this viewpoint with its impressive track record. ," declares Ferretti from the firm. Their approach encompasses a comprehensive strategy that extends beyond mere publicity. " ," he elaborates. Ferretti anticipates a 25% rise in applications from India over the next five years, signaling an increasing reliance on their specialized services in navigating the complexities of the EB1A visa process. "95% of our Indian clients have successfully obtained their EB1A Visas We customize our strategies to highlight the distinct strengths and accomplishments of each applicant Global Operations and Customized Strategies Spynn's services extend beyond U.S. borders. Ferretti says. The company serves clients from diverse regions and professional backgrounds, offering effective strategies tailored to local needs. "Talent is universal, but opportunities are not always equally accessible. We aim to bridge this gap," However, some critics argue that a one-size-fits-all approach may not be practical for a U.S.-specific program like the EB1A Visa. Ferretti acknowledges this but emphasizes the company's flexibility. he explains. "Each client receives a customized strategy that considers both USCIS criteria and their unique contributions to their field," Spynn's Adaptability and Vision As countries become more selective in their immigration policies, only the most qualified individuals will succeed in crossing borders. Spynn aims to guide these talents. Ferretti concludes. "We're more than a company; we're part of a larger movement to make the world accessible for extraordinary talents," His vision is grounded in a practical understanding of immigration trends. he adds. In a world that's becoming increasingly insular, Spynn offers a glimmer of hope. Their unique approach to the EB1A Visa application process is not just a business strategy; it's a practical necessity in today's competitive global environment. "The rules are changing, and we're prepared to adapt our strategies to meet these new challenges,"

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