Researchers Explore Foundation Models For Generalist Medical Artificial Intelligence

Foundation models are capable of being applied to a wide variety of downstream tasks after being trained on large and varied datasets. From textual questions responding to visual descriptions and game playing, individual models can now achieve state-of-the-art performance. Growing data sets, larger models, and improved model architectures have given rise to new possibilities for foundation models. 

Due to the complexity of medicine, the difficulty of collecting large, diverse medical information, and the novelty of this discovery, these models have not yet infiltrated medical AI. Most medical AI models use a task-specific model-building technique. Pictures must be manually labeled to train a model to analyze chest X-rays to detect pneumonia. A human must write a radiological report when this algorithm detects pneumonia. This hyper-focused, label-driven methodology produces stiff models that can only do the tasks in the training dataset. To adapt to new tasks or data distributions for the same goal, such models sometimes require retraining on a new dataset. 

The developments like multimodal architectures, self-supervised learning techniques, and in-context learning capabilities have made a new class of sophisticated medical foundation models called GMAI possible. Their “generalist” label suggests they will replace more specialized models for specific medical tasks.
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Researchers from Stanford University, Harvard University, University of Toronto, Yale University School of Medicine, and Scripps Research Translational Institute identify three essential qualities that set GMAI models apart from traditional medical AI models. 

A GMAI model can be easily adapted to a new task by simply stating the work in English (or another language). Models can address novel challenges after being introduced to them (dynamic task specification) but before requiring retraining.

GMAI models can take in data from various sources and generate results in various formats. GMAI models will explicitly reflect medical knowledge, enabling them to reason through novel challenges and communicate their results in terms medical professionals understand. When compared to existing medical AI models, GMAI models have the potential to tackle a wider variety of tasks with fewer or no labels. Two of GMAI’s defining capabilities—supporting various combinations of data modalities and the capacity to carry out dynamically set tasks—enable GMAI models to engage with users in various ways. 

GMAI models must explicitly represent medical domain knowledge and use it for sophisticated medical reasoning.

GMAI provides remarkable adaptability across jobs and situations by allowing users to interact with models via bespoke queries, making AI insights accessible to a wider range of consumers. To generate queries like “Explain the mass appearing on this head MRI scan,” users might use a custom query. Is it more likely to be a tumor or an abscess?”

Two crucial features, dynamic task specification and multimodal inputs and outputs will be made possible through user-defined queries. 

Dynamic task specification: Artificial intelligence models can be retrained on the fly using custom queries to learn how to address new challenges. When asked, “Given this ultrasound, how thick is the gallbladder wall in millimeters?” GMAI can provide an answer that has never been seen before. The GMAI may be trained on a new notion with just a few examples, thanks to in-context learning.

Multimodal inputs and outputs: Custom queries make the ability to arbitrarily combine modalities into complex medical concerns possible. When asking for a diagnosis, a doctor can attach several photos and lab reports to their query. If the customer requests a textual response and an accompanying visualization, a GMAI model can easily accommodate both requests.

Some of GMAI’s use cases are mentioned below:

Credible radiological findings: GMAI paves the way for a new class of flexible digital radiology assistants that may aid radiologists at any stage of their processes and significantly lessen their workloads. Radiology reports that include both aberrant and pertinent normal results and that takes the patient’s history into account can be automatically drafted by GMAI models. When combined with text reports, interactive visualizations from these models can greatly help doctors by, for example, highlighting the area specified by each phrase.

Enhanced surgical methods: With a GMAI model, surgical teams are expected to perform treatments more easily. GMAI models might do visualization tasks, such as annotating live video feeds of an operation. When surgeons discover unusual anatomical events, they may also convey verbal information by sounding alarms or reading pertinent literature aloud.

Help to make tough calls right at the bedside. More in-depth explanations and recommendations for future care are made possible by GMAI-enabled bedside clinical decision support tools, which build on existing AI-based early warning systems.

Making proteins from the text: GMAI synthesized protein amino acid sequences and three-dimensional structures from textual input. This model might be conditioned on producing protein sequences with desirable functional features, like those found in existing generative models.

Collaborative note-taking. GMAI models will automatically draft documents like electronic notes and discharge reports; physicians will only need to examine, update, and approve them.

Medical chatbots. New patient assistance apps could be powered by GMAI, allowing for high-quality care to be provided even outside of clinical settings.

Check out the Paper and Reference Article. Don’t forget to join our 19k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more. If you have any questions regarding the above article or if we missed anything, feel free to email us at [email protected]

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9 Best VPN Services in 2023 And How to Choose One When You Can’t Access ChatGPT in Your Country?

Imagine a world where power users in a country are deprived of GPT, an AI that revolutionizes the way they work, think, and create! Without access to this game-changing technology, these trailblazers—data scientists, researchers, and business analysts—struggle to keep pace with their global peers, who are already racing ahead with GPT’s extraordinary capabilities!

The list of countries where GPT is banned is actually, unfortunately, too long; here are some of them :

Italy, Russia, China, North Korea, Cuba, Iran, Syria, and soon maybe Spain, Germany, France, Sweden, and Canada.
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 If you are in such a country, a VPN can solve this problem ..and also protect your digital identity.

 Here we’ll explore the 10 best VPN services available in 2023 and offer guidance on choosing the right one if you’re located in a country without access to ChatGPT.

Surfshark:

Surfshark stands out for its affordability and unlimited device connections, making it the best option. Despite its lower price point, Surfshark still offers a secure and reliable service, featuring over 3,200 servers, 10 GBit servers in 100 countries and a suite of security features such as Cookie pop-up blockers, Two-Factor Authentication, Ad & malware blockers.

You can use an unlimited number of devices!

Includes a strict no-logs policy

30-day money-back guarantee and 24/7 support. Pretty nice. 

Pricing is as low as $2.30/month on a 2-year deal.

ExpressVPN :

With a reputation for fast speeds and robust security features, ExpressVPN remains a top choice for many users. The service boasts over 3,000 servers in 94 countries, providing a reliable and secure connection no matter where you are. ExpressVPN also supports a wide range of devices and platforms, including Windows, Mac, iOS, Android, and Linux. 

Pricing is $6.69/month

ProtonVPN : 

Developed by the same team behind the secure email service ProtonMail, Access to over 1,000 servers in 54 countries. ProtonVPN also provides robust security features, such as Secure Core and Perfect Forward Secrecy. No support and no ad blocker is provided.

Pricing is $3.99/month

NordVPN

NordVPN is well-known for its commitment to user privacy, offering a strict no-logs policy and advanced security features like Double VPN and Onion Over VPN. With more than 5,000 servers in 60 countries, so a bit more server but a bit more expensive… Not sure if you need the extra server..

Pricing is $3.29./month. 30-day money-back guarantee.

CyberGhost

CyberGhost is a user-friendly VPN service that offers a vast network of over 7,000 servers in 90 countries. The service provides dedicated servers for streaming and torrenting, ensuring optimal performance for these specific tasks. CyberGhost also includes a strict no-logs policy and 256-bit AES encryption to protect your online privacy.

IPVanish

IPVanish offers advanced configuration options and a network of over 1,600 servers in 75+ locations.

Frankly, more configurations but a higher price ( $6.99/month)( 

VyprVPN

VyprVPN boasts a proprietary network of over 700 servers in 70+ locations. $5/month on a 1 year plan.

Mullvad

Mullvad is simple. 5 devices, 5 euros /month. The service provides access to over 750 servers in 36 countries and includes features such as WireGuard support, port forwarding, and a strict no-logs policy.

Windscribe

480 servers in 63 countries, Pricing is $4.75/month for a yearly plan. Block ads, trackers, and malware.

How to pick?

When selecting a VPN service, especially in a country without access to ChatGPT, consider the following factors:

Server locations: Ensure the VPN service has servers in countries where ChatGPT is accessible. This will enable you to bypass geo-restrictions and utilize the AI service. All the above do!

Speed: Opt for a VPN provider known for fast connection speeds to minimize latency and ensure a seamless ChatGPT experience. Top 3 on the list above!

Privacy and security: Choose a VPN service with a strong commitment to user privacy, featuring a strict no-logs policy and robust encryption protocols.

Device compatibility: Make sure the VPN service supports the devices and platforms you use, such as Windows, Mac, iOS, Android, and Linux.

Customer support: Look for a VPN provider with responsive and knowledgeable customer support, which can be especially helpful if you encounter any issues while accessing ChatGPT.

Price and refund policy: Consider your budget and the VPN service’s pricing plans. Opt for a provider with a money-back guarantee or a free trial, so you can test the service before committing to a long-term subscription.

Note: We use Surfshark here at MarkTechpost.

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Go Little NeRF; You Are Free Now: This AI Approach Improves Few-shot Neural Rendering Capability

Generating high-fidelity 3D renders of real-world scenes is becoming more and more feasible thanks to the advancement in neural radiance field (NeRF) applications recently. With NeRF, you can transfer real-world scenes into a virtual world and have 3D renders that can be viewed from different perspectives. 

NeRF is a deep learning-based approach that represents the scene as a continuous 5D function. It maps 3D coordinates and viewing directions to radiance values which represent how much light travels along the given direction at a given point. This radiance function is approximated using a multi-layer perceptron (MLP) that is trained on a set of input images and corresponding camera parameters. 

By capturing the underlying 3D geometry and lighting of the scene, NeRF can generate novel views of the scene from arbitrary viewpoints. This way, you can have an interactive virtual exploration of the scene. Think of it like the bullet-dodging scene in the first Matrix movie.
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As with all emerging technologies, NeRF is not without its flaws. The common problem is that it can overfit training views, which causes it to struggle with novel view synthesis when only a few inputs are available. This is a well-known issue known as the few-shot neural rendering problem. 

There have been attempts to tackle the few-shot neural rendering problem. Transfer learning methods and depth-supervised methods have been tried, and they were successful to some extent. However, these approaches require pre-training on large-scale datasets and complex training pipelines, which results in computation overhead.

What if there was a way to tackle this problem more efficiently? What if we could synthesize novel views even with sparse inputs? Time to meet FreeNeRF.

Frequency regularized NeRF (FreeNeRF) is a novel approach proposed to address the few-shot neural rendering problem. It is pretty simple to add to a plain NeRF model, as it only requires adding a few lines of code. FreeNeRF introduces two regularization terms: frequency regularization and occlusion regularization. 

Frequency regularization is used to stabilize the learning process and prevent catastrophic overfitting at the start of training. This is made possible by directly regularizing the visible frequency bands of NeRF inputs. The observation here is that there is a significant drop in NeRF performance as higher-frequency inputs are presented to the model. FreeNeRF uses a visible frequency spectrum-based regularization on the training time step to avoid over-smoothness and gradually provide high-frequency information to NeRF.

Occlusion regularization, on the other hand, is used to penalize the near-camera density fields. These fields cause something called floaters, which are artifacts or errors that occur in the rendered image when objects are not properly aligned with the underlying 3D model. Occlusion regularization targets to eliminate floaters in the NeRF. These artifacts are caused by the least overlapped regions in the training views, which are difficult to estimate due to the limited information available. To tackle this, dense fields near the camera are penalized.

FreeNeRF combines these two regularization methods to propose a simple baseline that outperforms previous state-of-the-art methods on multiple datasets. It adds almost no additional computation cost. On top of that, it is dependency-free and overhead-free, making it a practical and efficient solution to the few-shot neural rendering problem.

Check out the Paper and Project. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 19k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

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How can AI Improve Agriculture?

This post was originally published on this site Artificial Intelligence Insights Published at: April 17, 2023 Last Updated: March 9, 20239 views Agriculture is one of the oldest and most important practices in human history, providing the food and resources we need to survive. But despite its crucial role, farming is also one of the

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Breaking Down AutoGPT: What It Is, Its Features, Limitations, Artificial General Intelligence (AGI) And Impact of Autonomous Agents on Generative AI

Introduction 

Generative AI is evolving and getting popular. Since its introduction, new models and research papers are getting released almost every other day. The major reason for the exponentially increasing popularity is the development of Large Language Models. LLMs, the Artificial Intelligence models that are designed to process natural language and generate human-like responses, are trending. The best example is OpenAI’s ChatGPT, the well-known chatbot that does everything from content generation and code completion to question answering, just like a human. Even OpenAI’s DALL-E and Google’s BERT have contributed to making significant advances in recent times.

What is AutoGPT?

Recently, a new AI tool has been released, which has even more potential than ChatGPT. Called AutoGPT, this tool performs human-level tasks and uses the capabilities of GPT-4 to develop an AI agent that can function independently without user interference. GPT 4, which is the latest add-on to OpenAI’s deep learning models, is multimodal in nature. Unlike the previous version, GPT 3.5, which only lets ChatGPT take textual inputs, the latest GPT-4 accepts text and images both as input. Auto-GPT, the free-of-cost and open-source in nature Python application, uses GPT-4 technology.

AutoGPT uses the concept of stacking to recursively call itself. Stacking is an approach that lets AI models use other models as tools or mediums to accomplish a task. AutoGPT using this method and with the help of both GPT 3.5 and GPT 4, creates full projects by iterating on its own prompts. 
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Artificial General Intelligence (AGI) in AutoGPT

AutoGPT’s abilities make it a promising application that makes it an example of “Artificial General Intelligence” or AGI. This type of technology represents a significant breakthrough in the field of AI, as it has the potential to develop machines that can understand and learn intellectual tasks like humans. AGI can perform a wide range of tasks and find solutions when faced with unfamiliar tasks. It is designed to be able to learn and adapt to new situations and environments without the need for specific prompts or instructions for each new task.

Features of AutoGPT

AutoGPT’s access to GPT-4 makes it a great tool for high-quality text generation. It even has access to popular websites and platforms, which helps in its better interaction and better ability to perform various tasks. AutoGPT manages both short-term and long-term memory and has internet connectivity for searching the internet and gathering information. Moreover, due to the power of GPT 3.5, AutoGPT has file storage and summarization capabilities and can even use DALL-E for image generation.

Some examples of AutoGPT’s capabilities have been shared on Twitter, which include creating a “Do anything machine” that spawns a GPT-4 agent to complete any task added to the task list. It can also read recent events and prepare a podcast outline. AutoGPT even enables the creation of an “AgentGPT,” where an AI agent is given a goal, comes up with an execution plan, and takes action. It even created a website using React and Tailwind CSS in under three minutes.

What is BabyAGI?

BabyAGI combines OpenAI’s GPT-4 with LangChain, a coding framework, and Pinecone, a vector database, to spawn new agents that can complete complex tasks while considering the original objective. Inspired by Artificial General Intelligence, BabyAGI imitates humans and uses its long-term memory to store and retrieve information quickly. BabyAGI basically trains and evaluates various AI agents in a simulated environment and tests their ability to learn and perform tough tasks.

How autonomous agents are introducing generative AI to the masses?

AI agents, the computer programs that interact with the environment to make decisions operate autonomously, or interact with humans or other agents using natural language. Used in a wide range of applications, such as customer service, personal assistants, gaming, and robotics, an AI agent is classified based on several criteria, such as autonomy, reactivity, proactiveness, environment, and flexibility. Designing and implementing an AI agent involves identifying the problem domain, choosing an appropriate architecture, defining goals and actions, implementing the agent’s logic, and testing and debugging.

AutoGPT is an example of an AI agent that uses generative AI to solve problems. It operates autonomously and has the potential to revolutionize many industries. It even raises concerns about the impact of autonomous AI agents on human jobs, privacy, and security. It is important to carefully consider these implications and ensure that AI agents are developed and used responsibly.

Limitations of AutoGPT

Auto-GPT is a powerful tool but comes with a significant obstacle. Its adoption in production environments is difficult due to its high cost. Each step requires a call to the GPT-4 model, which is an expensive process that often maxes out tokens to provide better reasoning. The cost of GPT-4 tokens is not cheap, and according to OpenAI, the GPT-4 model with an 8K context window charges $0.03 per 1,000 tokens for prompts and $0.06 per 1,000 tokens for results.

Auto-GPT uses GPT-4 and a simple programming language to perform tasks. The range of functions provided by Auto-GPT is limited. The functions include searching the web, managing memory, interacting with files, executing code, and generating images, but they narrow down the range of tasks Auto-GPT can solve effectively. Also, the decomposition and reasoning abilities of GPT-4 are still constrained, which further limits Auto-GPT’s problem-solving capabilities.

Conclusion

AutoGPT’s ability to perform a wide range of tasks and generate creative ideas makes it a promising tool in the field of AI. Its performance may be limited in complex real-world business scenarios, but if the tool continues to develop and improve, it has the potential to become even more powerful and versatile.

Don’t forget to join our 19k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more. If you have any questions regarding the above article or if we missed anything, feel free to email us at [email protected]

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References:

https://www.fastcompany.com/90880294/auto-gpt-and-babyagi-how-autonomous-agents-are-bringing-generative-ai-to-the-masses

https://www.livemint.com/technology/tech-news/meet-autogpt-the-autonomous-gpt-4-tool-revolutionizing-ai-11681358612615.html

https://dataconomy.com/2023/04/what-is-autogpt-and-how-to-use-ai-agents/

https://jina.ai/news/auto-gpt-unmasked-hype-hard-truths-production-pitfalls/

https://mpost.io/what-makes-autogpt-so-special/

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This AI Paper Demonstrates An End-to-End Training Flow on An Large Language Model LLM-13 Billion GPT-Using Sparsity And Dataflow

Machine learning system implementation in the academic and commercial domains has been expedited by foundation models in the natural language processing and computer vision domains. Researchers have suggested increasing parameter count by orders of magnitude to extract additional capabilities from these models and train on vast data corpora. Their primary traits of self-regulation and adaptability enable a wide range of applications to be developed to address particular issues, including text production, sentiment analysis, picture segmentation, and image recognition. 

Due to power and physical limitations, the underlying hardware used to train such enormous models needs to scale proportionally to model parameters. Several techniques have been investigated to overcome this computational challenge, including network restructuring, network pruning, network quantization, low-rank decomposition knowledge distillation, model sparsity, etc. Different types of sparse approaches have been put forth to lower computing intensity and imitate the connections between neurons in the human brain. The underlying hardware architecture presents new difficulties as sparsity methods advance and becomes widely used in training and inference applications. 

A well-balanced system needs to tolerate fluctuations between deploying a model that is typically computationally intensively dense and memory intensively very sparse. Because there are so many potential patterns and training flows, sparse computations require the flexibility, programmability, and efficiency of next-generation hardware instead of just adding Tera-FLOPs and memory bandwidth to meet the computational demands of machine learning. A good implementation of light methods on a friendly architecture can effectively assist in overcoming present barriers like enormous power, high machine costs, and lengthy training times. 
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Numerous computational frameworks have been proposed in response to the growth of machine learning and artificial intelligence applications and their inherent properties. In addition to conventional CPU-based architectures, some examples are Google TPU, NVIDIA A100 Nvidia, Cerebras CS-2, Graphcore IPU, and SambaNova RDU. The entire extent of these hardware and software systems’ capabilities, particularly in handling a broad spectrum of sparse and dense applications, remains to be discovered, despite a few attempts to assess and compare these systems. Additionally, many of these frameworks are still privately owned and not accessible for public research in the public domain. Although promising, sparse approaches have additional difficulties besides architectural compatibility. 

The accuracy of a particular model, as opposed to a dense-only baseline, depends on a wide range of factors, including structured, semi-structured unstructured sparsity, percentages of sparsity weights/activation sparsity, and training schedule. These decision factors must be determined to get the most up-to-date metrics on a particular model, which takes time and effort. Large language models, which may accommodate a range of language applications, are widespread foundation models in the NLP sector, such as the 13B parameter GPT. Researchers from SambaNova Systems in this study use this model to demonstrate how sparsity may be successfully included in an end-to-end training cycle to attain equivalent accuracy metrics. 

They contribute in the following significant ways: 

• A thorough examination of how sparsity, fusion, and dataflow capabilities interact. 

• A demonstration of speedups over A100 using sparse GPT 13B on SambaNova RDU. 

• Analysis of the sparse 13B GPT model’s loss, zero-shot, and few-shot statistics in comparison to its dense baseline 

The paper itself has more details on their analysis. 

Check out the Paper. Don’t forget to join our 18k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more. If you have any questions regarding the above article or if we missed anything, feel free to email us at [email protected]

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Best AI Tools To Power Your Academic Research (2023)

Consensus is a search engine that uses AI to discover solutions to issues raised by scientific inquiry swiftly. Peer-reviewed research is scanned using AI, extracting the main conclusions from each study. Compared to manual searching, this gives people access to material from the scientific community more rapidly. The outcomes are entirely free of advertisements and supported by data from peer-reviewed studies, not by slanted advertising. Questions like “Are covid-19 vaccines effective?” “What are the benefits of mindfulness?” and “Do light blue glasses help with sleep?” may all be answered by consensus.

Using the AI application ChatPDF, users may have a conversational dialogue with a PDF file. Users may interact with any PDF they own, including books, research papers, manuals, articles, and legal documents, without signing in. ChatPDF uses a text-generation AI model similar to ChatGPT to comprehend PDF file content and provide pertinent answers. Before responding to the user’s queries, this program produces a semantic index for each paragraph in the PDF file.

With the help of ChatPDF, users may swiftly extract information from big PDF files, saving time while taking notes or writing summaries. With this AI technology, you can have natural-sounding conversations with PDF documents. However, ChatPDF may need help deciphering the graphics in the PDFs, and it could need help answering queries that call for comprehension of more than a few words at once.
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An AI-powered research tool, Assistant by Scite, enables users to collaborate on essays and research papers, uncover evidence to support their claims and find evidence to refute them. Users may enter simple queries to get responses based on the complete texts of research publications. Users may use the application to obtain trustworthy information and create grant proposals or essay drafts by searching through millions of research articles.

After entering the question, they may use the tool’s aid to produce essays, grants, or paragraphs. Scite’s assistant also enables users to efficiently utilize data from research publications to support their academic work. Users might look for opposing evidence or summaries from relevant sources to further their investigation. On the tool’s website, it is also mentioned that users may modify their settings and reject cookies that are not essential.

The AI research assistant uses machine learning Elicit to aid in automating research procedures. It can locate relevant articles and extract important information without specific keyword matches. Elicit may also give various research activities, including brainstorming, summarizing, and text-categorization, as well as summarize key points from the document that are relevant to the user’s inquiry. This helps the user simplify their research process while also assisting them in utilizing research to discover solutions to their concerns.

Online grammar checker and language editor Trinka AI was created for technical and academic writing. It is designed to find mistakes other grammar checkers overlook, such as subject-verb agreement issues, syntax issues, word choices, pronoun and article use, and technical spelling. Additionally, it incorporates elements like style manuals, a professional tone, the use of technical words, and conciseness that go beyond grammar and spelling. Trinka has studied the top academic publications in every field to provide consumers with the finest recommendations. It is appropriate for various disciplines, including biology, physics, economics, engineering, chemistry, geology, social sciences, and medicine.

An easy approach to swiftly examine and evaluate the significance of documents like articles, reports, and book chapters is by using the online summarizing tool Scholarcy. It may be used to create summary flashcards of any Word or PDF document, which are then displayed in an organized and simple way to understand.

Automatic reference extraction, open access source linking, figure, table, and picture extraction are just a few of the services offered by Scholarcy. In addition, Scholarcy offers Chrome and Edge browser extensions that connect to open-access repositories and create a searchable database of summary cards that can be accessed from any device. Researchers, students, journalists, librarians, and others may save time and efficiently process much material with this AI-powered summary tool.

Most people are aware of Google Scholar, which uses Google’s search engine capacity to index scholarly publications. But you should try Semantic Scholar if you’re performing any scientific study. You can keep up with more than 200 million academic publications with this AI-powered search and discovery tool, supplied through publisher partnerships, data suppliers, and web crawls. Its AI algorithms help you identify previously unknown links and interconnections between study topics to give more relevant search results. They also suggest related articles based on the research you’ve previously saved.

Additionally, it can automatically create one-sentence summaries of each article to assist you in deciding which ones to read in-depth so that you can focus your attention on the most important things.

It is a blessing to search the internet for information online. However, there are two issues with the quantity of available data and the fact that it can be found in several formats, including blogs, essays, films, infographics, and images. It might take a lot of effort to locate and organize all the data related to your study’s many areas. Bit.ai lets you find and preserve pertinent information, including multimedia-rich, interactive research. It also enables simple real-time sharing of this material with other co-researchers as a cloud-based collaboration platform.

An AI-driven platform called SciSpace allows users to browse, comprehend, and submit scientific articles. More than 270 million articles, authors, subjects, journals, and conferences are available in its extensive searchable database. Its many services are a plagiarism checker, journal submission, XML converters, and an AI copilot to decipher any research paper.

Additionally, it provides various paper template options, flexible price options, and other services to speed up the printing process. Along with a collection of over 40,000 magazine designs, SciSpace also offers tailored suggestions for well-liked articles, themes, and conferences.

OpenRead is an interactive platform driven by AI that offers users a simple and thorough method to arrange, engage with, and analyze different literary types, including essays, journals, and research materials. The platform includes several features, including a Q&A system that allows prompt solutions to inquiries about articles and the Paper Espresso function, which helps researchers by digesting publications to produce literature reviews more quickly.

With no need for lengthy reading, the platform’s AI technology retrieves statistics, formulae, tables, and other crucial information from research articles. Additionally, OpenRead has a robust notes system that improves note-taking effectiveness by gathering and connecting notes and backlinking them in different contexts to make it simpler to return to them later.

Users may enhance their writing with Grammarly, an AI-powered online writing helper. It offers immediate comments on punctuation, grammar, spelling, clarity, style, and tone. On Windows, Mac, iOS, and Android platforms, Grammarly connects without a hitch with more than 500,000 applications and websites. It includes various tools, including an essay checker, citation generator, grammar, spelling, punctuation, and plagiarism checker.

Individuals, groups, corporations, and educational institutions may all utilize Grammarly. It offers a variety of plans to accommodate various demands. Along with multiple tools and services, Grammarly also provides a blog, a tech blog, a blog for educators, a blog for businesses, and a blog for developers.

HeyScience is an AI-powered personal assistant that can analyze scientific research articles for scholars to save time. This technology can read millions of published papers in various scientific domains and produce a condensed overview of important ideas and procedures. Users may follow research trends in a particular field and keep up with the research of their peers with the aid of HeyScience. Users may quickly and efficiently search relevant information with only one click, saving time reading the literature.

“Scientist Spotlight,” another aspect of the program, enables users to keep track of their colleagues and their most recent papers. HeyScience enables researchers to precisely track their notions and ideas throughout the research trend cycle by delivering an alert for research triggers.

Based on ChatGPT, an AI language model that can comprehend text, tables, and graphics within PDF documents, ChatDOC is a file-reading assistant. It gathers, finds, and summarizes data to provide quick, clear responses in seconds. ChatDOC’s sophisticated AI engine enhances the effectiveness of its data analysis by choosing certain tables or words from your documents. Its comments are backed up by exact citations from the files, allowing for accurate fact-checking.

By allowing users to upload books, manuals, research papers, and other materials, ChatDOC is intended to help people read more rapidly and learn more effectively. Anyone who registers for a ChatDOC account may use ChatDOC for free. Users may upload up to 10 documents, and the file size is restricted to 150 pages. Users may easily search various papers with ChatDOC, and files are securely stored.

Using DocLime, users can quickly and easily extract pertinent data and solutions from their PDF documents. This application analyzes uploaded documents using cutting-edge AI technology and quickly produces accurate responses to user inquiries. Users must often conduct a laborious and time-consuming manual search to extract information from documents. DocLime is mainly meant to assist users in skipping this procedure.

Users of any technical skill level may utilize the platform thanks to its user-friendly and straightforward design. With DocLime, users may upload their PDF files and immediately ask questions.

Unriddle is an AI tool that breaks down difficult papers so users can ask queries and instantly get responses. Unriddle may use Any document as a dataset to build a unique AI. This program serves as a learning copilot by guiding users through complicated concepts and effectively reducing the time needed to digest them. Unriddle helps consumers obtain knowledge more quickly and conveniently using cutting-edge machine learning methods. The tool offers customers a watch demo option demonstrating its features and functionality.

Docalysis is a perfect option for teams and busy professionals with too many papers to read since it is simple to do manually. With the help of the AI-powered application Docalysis, users can interact with their PDF files and quickly get the answers to their queries, saving hours of manual document reading. Users may safely submit their papers to Docalysis and query the AI about their content. The application employs advanced natural language processing (NLP) technology to decipher the user’s inquiries and give appropriate responses from the text.

In contrast to the conventional approach of reading pages of text, the AI-powered chat function enables a more conversational and engaging experience with the material.

A productivity application called Sharly AI employs generative AI to help professionals grasp lengthy papers faster. It uses the most recent language models and natural language processing to streamline long, complicated papers. By delivering precise and pertinent responses to queries regarding the document, the technology may help users cut down on time by up to 10 times. Users may upload the material they wish to understand, ask questions about it, and get pertinent replies using Sharly AI’s user-friendly interface.

In conversation with GPT, the tool’s accuracy was determined to be 95%. It is designed with simplicity of use in mind, and the business stresses that it wants to utilize the tool to increase productivity. Users may also privately share their work with friends, facilitating collaboration and feedback. Market analysis and financial reporting are only two of the many industries that Sharly AI may be used to.

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This AI Paper From Stanford And Google Introduces Generative Agents: Interactive Computational Agents That Simulate Human Behavior

 Undeniably, AI bots can generate a natural language of high quality and fluency. For a long time, researchers and practitioners have pondered building a sandbox civilization full of agents with human behaviors to learn about different types of interactions, interpersonal connections, social theories, and more. Credible stand-ins for human behavior may fuel various interactive applications, from virtual reality to social skills training to prototyping programs. Agents that employ generative models to mimic human-like individual and emergent collective behaviors in response to their identities, changing experiences, and environments are presented by researchers from Stanford University and Google Research.

 The group’s key contributions are summed up as follows:

Agents whose behavior is plausible because it is dynamically conditioned on the agents’ evolving experiences and surroundings are called generative agents.

A revolutionary framework for enabling generative agents’ capacities for long-term memory, retrieval, reflection, social interaction, and scenario planning in rapidly changing conditions.

Two types of tests (a controlled trial and an end-to-end test) are used to determine the value of different parts of the architecture and find problems like faulty memory retrieval.

The advantages and potential dangers to society and ethics posed by interactive systems that employ generative agents are discussed.

 The group’s goal was to create a virtual open-world framework in which smart agents go about their daily lives and interact with one another in natural language to schedule their days, exchange information, forge friendships, and coordinate group activities in response to environmental and historical cues. By combining a large language model (LLM) with mechanisms that synthesize and extract data based on the LLM outputs, the team has created a novel agent architecture that allows agents to learn from past mistakes and make more precise real-time inferences while preserving long-term character coherence.
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Complex behaviors can be guided by agents’ recursive synthesis of recordings into higher-level observations. The agent’s memory stream is a database that contains a complete account of the agent’s prior experiences. To adapt to its shifting surroundings, the agent can access relevant data from its memory stream, process this knowledge, and formulate an action plan.

The researchers recruited human raters and had 25 of their suggested generative agents function as non-player characters (NPCs) in a Smallville sandbox environment developed with the Phaser online game development framework. The agents’ consistent portrayals of their characters and their convincing imitations of human-like memory, planning, reaction, and reflection were hallmarks of the experiment. They communicated with each other in natural language over two full game days.

Applications

By combining generative agents with multi-modal models, one can one day have social robots that can interact with humans online and offline. Because of this, one can now prototype social systems and ideas, test out new interactive experiences, and construct ever more realistic models of human behavior.

The human-centered design process is another area where cognitive models like GOMS and the Keystroke Level Model may be used.

Using generative agents as stand-ins for users allows one to learn more about their requirements and preferences, leading to more tailored and efficient technological interactions.

 With the potential for use in role-playing, social prototyping, immersive environments, and gaming, this study contributes to the advancement of LLM-based simulacra populated by agents with dynamic and interactive human-like behaviors. The components of the generative agent architecture suggested in this work can be developed further in further studies. For instance, the relevance, recency, and significance functions that comprise the retrieval function might be tweaked to improve the retrieval module’s ability to find the most relevant material in a particular context. Efforts can also be taken to boost the architecture’s performance, saving costs.

Future research should seek to examine the behavior of generative agents over a longer length of time in order to acquire a complete knowledge of their capabilities and limits, as the evaluation of their behavior in this work was restricted to a very short timeline.

Check out the Paper. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 19k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

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Amazon Launches Bedrock: An AI Service That Will Allow Users To Build Out Generative Models From AI21 Labs, Anthropic, Stability AI, and Amazon

Amazon has launched a new AI platform for businesses called Amazon Bedrock, aimed at providing customers of Amazon Web Service (AWS) with a suite of generative AI tools that can build chatbots, generate and summarize text, and classify images based on a prompt. Bedrock users can perform tasks by selecting from a range of machine learning models called “foundation models,” including Jurassic-2, Stability AI’s Stable Diffusion, and Amazon Titan.

The platform has been designed to compete with other offerings from companies in the generative AI space, such as OpenAI. Bedrock is intended to help AWS customers create targeted ad campaigns for products and generate product social media posts, display ads, and web copy, among other applications.

Amazon CEO Andy Jassy stated in his annual shareholder letter that Amazon would be “investing heavily” in large language models (LLMs) and generative AI technologies, which he views as transformative. Amazon’s entry into the generative AI space follows similar moves by other tech giants, such as Microsoft and Google, which released their versions of generative AI chatbots earlier this year.
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A preview of Amazon’s generative AI toolkit is only available to select AWS customers. One company that uses Bedrock to scale its operations is Coda, an AI document generation firm that serves clients like Uber and the New York Times.

Generative AI tools like ChatGPT have gained popularity among the public, with some individuals and businesses leveraging them to start their ventures or enhance productivity. As the technology becomes more accessible, it will likely significantly impact various industries by enabling companies to automate tasks and generate content efficiently.

Amazon’s investment in generative AI and large language models has generated significant buzz in the tech world, and rightfully so. With Bedrock, Amazon’s foray into this arena, the potential for disrupting the AI landscape is massive. Bedrock offers businesses cutting-edge tools to revolutionize their operations and drive innovation.

Source: Amazon

As the generative AI space competition heats up, businesses will benefit from the increased choice and options. With Bedrock as a formidable player in this field, companies can select the most suitable platform for their specific needs, whether looking for natural language processing, predictive analytics, or computer vision. This level of flexibility allows businesses to tailor their AI solutions to their specific business needs, ensuring that they get the most value from their investment.

Overall, Amazon’s Bedrock AI platform is positioned to compete in the fast-growing field of generative AI, enabling businesses to automate tasks and generate content efficiently. With Amazon’s investment in generative AI and large language models, Bedrock has the potential to become a significant player in the AI landscape, providing businesses with the tools to enhance their operations and drive innovation.

Check out the Reference Article. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 18k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

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This AI Project Brings Doodles to Life with Animation and Releases Annotated Dataset of Amateur Drawings

Children love to draw and express their thoughts and ideas with the help of doodles and pictures. This is the way people, from a very young age, portray their emotions along with creativity. Kids put their abstract ideas into a drawing, which helps their cognitive development. The field of Artificial Intelligence has been making noteworthy strides after the increasing popularity of Large Language Models like ChatGPT and DALL-E. With a new research paper and model getting released almost every day, now comes a new AI model that can convert a doodle into an animation. 

Traditional AI models, trained on images of real-life objects, usually find it difficult to detect and recognize an abstract or a non-realistic drawing. To overcome the limitations of these AI models, a team of researchers from Meta developed an AI system research demo that can bring artwork to life through animation. 

The doodles get converted into animation in four main steps. In the first step, the system detects the human figure in the photograph of the drawing. In the second step, the system uses a segmentation mask to separate the figure from the background. The third step consists of the system estimating the pose and rigging of the figure, making it possible to animate it. Lastly, in the fourth and final step, the system animates the figure using motion capture data, which is retargeted onto the character in a unique and appealing way.
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The team has developed a dataset of 178,166 annotated amateur drawings. This dataset of abstract images can help other AI researchers and creators innovate further. For creating this dataset, the researchers released an Animated Drawings Demo in the year 2021 and invited people to contribute their drawings to the dataset. People could upload images, verify or fix annotation predictions, and receive a short animation of their humanlike character within their drawing with the help of the browser-based demo. More than 3.2 million people worldwide visited the site, and 6.7 million images were uploaded. The pictures chosen by people to share with the team were filtered by human reviewers.

The researchers also implemented privacy safeguards to ensure participants’ anonymity and the dataset’s quality. The team shared the animation code for the model and the fine-tuned model weights for drawn human figure detection and pose estimation, which can be accessed here. 

Anyone can use the open-source code and the dataset to expand upon their methods of analyzing and augmenting amateur drawings. This can unlock new forms of storytelling and greater accessibility in art. The system could have applications in animation and game development, as well as in educational settings, where it could be used to engage children in creative activities. The system is fast, intuitive, and robust and is definitely a great development in AI to cater to human creativity. 

Check out the Paper, Github, Project, and Blog. Don’t forget to join our 18k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more. If you have any questions regarding the above article or if we missed anything, feel free to email us at [email protected]

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