Iris.ai has recently partnered with Materiom to help building the world’s largest database and research community of material science knowledge to aid the transition away from petrochemicals. With the automated extraction and systematizing of the content, recipes and ensuing properties of materials from more than 50,000 articles, Materiom will have laid a solid foundation for their groundbreaking community of researchers. This database will then be published on Materiom’s platform to speed up R&D processes and market entry of regenerative materials. This will ultimately lead to reduced plastics pollution, and the creation of a materials economy that benefits ecological regeneration.
“We are excited to collaborate with such a great team of enthusiastic professionals and scientists like Materiom. Seeing how our Extract tool can extract such a big number as 50k documents and extract data from a wide range of renewable materials is thrilling. Even more, as we are contributing in that way to a more sustainable world.” – says Kimberly Holtz, Key Account Manager at Iris.ai.
“Iris.ai is helping us get to scale with our open database, a resource that will accelerate regenerative materials R&D” – Alysia Garmulewicz, Founder and Co-CEO of Materiom, states.
Keep an eye on our blog for project updates and information when the database is available this year. If you think that we can help with your problem, contact us!
About Materiom
Materiom is an open access platform for creating sustainably-sourced biomaterials, made from locally-abundant natural ingredients. The Materiom community includes material scientists, designers, engineers, data scientists, and sustainability experts. The project supports companies, cities, and communities in creating and selecting materials sourced from locally abundant biomass that are part of a regenerative circular economy.
In the last blog post we introduced our new shiny tool – the Researcher Workspace. A place where you decide how to do your research process and apply the AI-based tools you need. In this post we want to introduce you to some concrete client use cases and explain how we set up their workspace for their unique process to address their needs.
Food safety reviews
We are working with a niche review team that needed to perform a wide range of food safety related searches to keep the population safe. The team needed a better way to do full interdisciplinary literature searches on a broad variety of topics, expanding beyond their individual areas of expertise. They have a simple Workspace with an Explore tool, where the reviewer can input a human language description of the research, without the need for comprehensive vocabulary understanding. Iris.ai uses contextual key terms, synonyms and hypernyms to build a ‘fingerprint’ it can match to other papers and presents a visual map with overview over topics and relevant research papers.
Exploratory R&D for a biotech Company
This collaboration is with a biotech company that has extensive R&D efforts in mapping out a knowledge graph of real world data (e.g any microorganism they locate in their field or lab work) that will lead to new insights for their product development. Their starting point is an entity (e.g bacteria, parasite) in a given context – and then search for all relevant knowledge, analyze all articles found, extract the key data points and summarize the findings, with the goal of connecting information in a database.
Pharmacovigilance for a Contract Research Organization
This CRO performs regular contract work where they do limited, specific and rapid literature searches for their clients across a variety of medical devices and drugs.
They are regularly undertaking post market surveillance and pharmacovigilance studies. Usually each project is about 80 hours. In order to reduce the time the team spends on the manual work, the CRO has an Iris.ai Researcher Workspace setup where they upload their search results (≈500 articles), apply a range of smart filters (entities, data points and context descriptions) to instantly filter down the list to the ≈30 articles of interest. Then the tool automatically extracts all clinical data points of relevance into a database – both to a short collection of summarizing data points and to a comprehensive 500-point collection. All actions are recorded and a final report draft is automatically generated.
Drug repurposing for an interdisciplinary research group
A Californian research group, funded by the Canadian government, is searching broadly for new drug candidates from western, Chinese and herbal medicine for a specific medical situation. Each researcher starts from an idea, doing a PICO style natural language text search and filters, across a variety of western and alternative research articles. Once an interesting approach is found, the articles are collected and all relevant clinical data is extracted and used to populate a database, made openly accessible to researchers across the world.
IP analysis for a steel conglomerate
This world-leading steel manufacturer strategically monitors filed IP to spot new market opportunities, but extracting detailed experiment data from patents is incredibly time consuming. Their Workspace allows researchers to provide the system with one or several patents, using that input to identify a range of other patents based on the content. The identified patents can then with one click be sent for extraction, where thousands of data points in text and tables are extracted, linked and mapped to their desired format.
Academic reviews
Librarians are always searching for better ways to help their students and researchers find the right literature. An academic-oriented Researcher Workspace is connected to all relevant literature sources and allows exploratory searches based on problem descriptions. The tool, with its visual search interface, is especially loved by Master and PhD candidates early in their research careers, with still-unknown topics to explore broadly.
Key takeaways
Each presented use case is distinct, but in every instance we found a suitable process to help companies, research institutes and universities with their scientific knowledge processing needs. Our tools are customized and trained on each client’s domain to optimize results. If you think that we can help you out, contact us!
Oslo, Norway – March 16, 2022 –Iris.ai, developer of AI tools for processing scientific research, launches a new platform – the Researcher Workspace. The purpose of this comprehensive suite of tools is to help researchers in industry and academia, librarians and students follow their own research process. Modules include a visual content based search, analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents – and very powerful filters based on context descriptions, the machine’s analysis, or specific data points or entities.
Anita Schjøll Brede, the CEO of Iris.ai, shares her excitement about the launch: “We’re very excited to launch our new platform! It’s the culmination of several years of both working closely with clients – and serious Research and Development efforts. With this new platform, we have focused on flexibility and adaptability – so that each researcher is able to streamline their exact literature review process, and unlock the knowledge they need as swiftly and painlessly as possible! We’re also exceptionally proud of the tools we now offer as part of the Workspace: the Extraction tool and the Abstractive summarization are very unique – not to mention the fact that our research team has found a way to train the entire system on each client’s research field, with no humans involved. I do believe we’re about to see the next chapter of what’s possible in using AI/ML for scientific research and I am so proud of what our team has accomplished.”
The Researcher Workspace is mainly directed towards R&D heavy industries like chemistry, pharmaceuticals, MedTech, material science, biotech, food safety or engineering. The tool can be reinforced on the customer’s field. The machine can be trained on industry specific terminology to provide more precise and accurate results.
About Iris.ai
Iris.ai is one of the world’s leading start-ups in the research and development of artificial intelligence (AI) technologies. Founded in 2015, the start-up offers an award-winning AI engine for scientific text understanding. The company uses Natural Language Processing/Machine Learning to review massive collections of research papers or patents: find the right documents, extract all their key data or identify the most precise pieces of knowledge. Applied to literature reviews, data extraction, document summarization, competitive intelligence or any other task involving thousands of documents like papers or patents, R&D professionals and students no longer waste time on tasks the Iris.ai tools can do for them. Iris.ai collaborates both with innovation-oriented universities and corporate customers, and contributes to many joint research projects fostering Open Science (CORE) and innovation.
CORE and Iris.ai are extremely pleased to announce the initiation of a new research collaboration funded by the Norwegian Research Council.
Discovering scientific insights about a specific topic is challenging, particularly in an area like chemistry which is one of the top-five most published fields with over 11 million publications and 307,000 patents. The team at Iris.ai have spent the last 5 years building an award-winning AI engine for scientific text understanding. Their patented algorithms for identifying text similarity, extracting tabular data and creating domain-specific entity representations mean they are world leaders in this domain.
The AI Chemist project is a collaboration between Iris.ai and The Open University, Oxford University, Trinity College, Dublin and University College, London. CORE is a not-for-profit platform delivered by The Open University in cooperation with Jisc that hosts the world’s largest collection of open access scientific articles. As of February 2022, the CORE dataset provides metadata information (title, author, abstract, publishing year, etc.) for approximately 210 million articles, and the full text for 29.5 million articles.
Working in partnership with CORE developers and researchers, Iris.ai will now leverage the vast quantities of research papers available in the CORE dataset. This dataset will be employed in improving the quality of text extraction from scientific literature from Chemistry focused domains. The output of this phase will support Iris.ai and The AI Chemist in understanding reasoning and inference across research papers.
Currently, the state of the art in the chemical domain is a combination of direct manual evaluation of text documents, social networks and curated, but incomplete databases. The manual nature of these approaches makes discovery of novel application areas immensely time consuming. The goal is to develop a set of algorithms that can machine read vast amounts of scientific literature and data, discover and detect mentions of entities of interest and their relations (such as chemical products, compounds, properties, processes, applications, etc.) and connect these pieces of information to build an increasingly complex knowledge graph.
Dr Ronin Wu, Research Lead and Head of Research Collaboration at Iris.ai, said: “Iris.ai are extremely pleased to be partnering with CORE on the AI Chemist project and we’re looking forward to seeing some exciting new developments with our AI models”.
Dr. Petr Knoth, Head of CORE and Senior Research Fellow in Text and Data Mining, said: “This cooperative research project will put CORE at the forefront of the global effort to create open scholarly knowledge graphs. As part of this project we will use state-of-the-art machine learning approaches to address problems including topic / themes extraction, affiliation extraction, deduplication and citation function detection. With the demise of Microsoft Academic Graph at the end of 2021, we see on a daily basis how much this is in demand among CORE users. ”
“There are many research tools that I recommend to my students, but Iris.ai is maybe the best one.”
Name: Josmel Pacheco Mendoza
Position: Researcher and Veterinarian
University: Universidad San Ignacio de Loyola
Region: Lima, Peru
My name is Josmel and I am currently working with multiple universities, besides being a partner of the editorial team of the Journal of Veterinary Research of Peru (RIVEP).
I found Iris.ai when I needed a tool to help me find the right documents for my COVID-19 research in a short time frame.
In one of the projects where I used Iris.ai, I worked with Mexican universities on two COVID-19 papers. There is a huge number of papers and patents published and available online, which makes it difficult and time consuming for my students and me to find the exact information that is related to my research.
How did Iris.ai help your research?
Iris.ai helped me to find the right collection of papers. One of the most powerful features is how you can add one paper into Iris.ai, and in return the software gives you a map of relevant papers. For me that’s like magic!
The Iris.ai tools also organize my work by clustering and filtering the papers. It is important for my work to have a map that organizes documents by their concepts and content.
Finally, the Iris.ai tools saved me and my students a lot of time, which is an important factor while doing research in medical fields.
It is exciting to use an academic research tool that relies on artificial intelligence to better serve students and researchers. There are many research tools that I recommend to my students, but Iris.ai is maybe the best one.
To get started with Iris.ai, sign up for a free account.
“Thanks to Iris.ai I found useful resources to build the thematic background of my paper. I ended up with around 20 papers that were immensely important and relevant.”
Name: Diego Raza Position: Lecturer – Research methodology
University: Universidad Andina Simon Bolivar Region: Quito, Ecuador
My name is Diego Raza and I am a lecturer at Universidad Andina Simon Bolivar in Quito, Ecuador. I teach subjects related to research methodology and help my students with their main thesis. When I was recently doing research for my literature review, I needed a tool to help me find research papers. I found that Iris.ai has higher quality sources in comparison to other tools on the market, and that it has access to papers that are of better academic quality.
How did Iris.ai help your research?
I was using Iris.ai for my own research in self-efficacy. I began my research by doing a broad exploration of papers in the Explore tool, then I imported the research map into the Focus tool to narrow down the results into a reading list. The tool helped me get an overview of the topic. Thanks to Iris.ai I found useful resources to build the thematic background of my paper. I ended up with around 20 papers that were immensely important and relevant.
Finally, I want to mention that I use Iris.ai to help my students identify literature that they may have overseen when preparing their main theses.
The biggest value with using Iris.ai, is how much time it’s saving. It’s doing a part of my work for me.
To get started with Iris.ai, sign up for a free account.
We are a company that do our best to put impact first. Over the last few weeks we have found ourselves initially stunned and perplex, and eventually a tad bit overwhelmed, by the COVID-19 pandemic and the SARS-CoV-2 virus causing it. What can a small startup team do, beyond sharing our advice on remote work (we’ve been remote since the start of the company), do our best to keep our employees safe and sane and stay hopeful that our revenues will not decline entirely to zero?
We can’t do much. But if you are a researcher, medical professional or problem solver, you can. And we can help.
For anyone working on any aspect of research around COVID-19, whether on the epidemiology, virology, biology, psychology or anything else – and whether you are an academic or a concerned citizen – we hereby invite you to join Iris.ai, and your account will then be added to the premium access COVID-19 research group. Our only requirements are that your work is on the current pandemic, and that you allow your findings through Iris.ai to be shared publicly.
Our tools can help you find the right research for the problem you face. Here, for example, is an interesting map on “The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak”.
The amazing humans at the Allen Institute / Semantic Scholar have worked on an open dataset of almost 30,000 articles related to COVID-19. This, plus about 120 million other research papers (Open Access), are all connected to the Iris.ai tools.
On April 7th, at 8am CET and 5pm CET we will host free webinars for everyone joining the premium COVID-19 research group (register here), to help you get started, answer all your questions, and do anything we can to help your research process run smoothly. Please sign up to get the details.
Everyone is of course also welcome to join our monthly online workshops that walk you through the premium tools, whether or not you’re a COVID-19 researcher. Have a look at our Facebook events for upcoming dates.
In order to get set up with free access to the COVID-19 Iris.ai premium organization, here is what needs to happen: 1. You sign up for an account on https://the.iris.ai/auth/registration 2. Then send an email to order@iris.ai from the same email account and mention COVID-19 in the subject. 3. We will then add you to the premium group, and notify you when it’s done so you can log in, accept the terms and get working.
We will keep this premium access open to you for a minimum of three months – until end of June 2020 – and it will be extended unless the pandemic is over.
We don’t know what each one of us can do on our own, but we know how much power there is in a world coming together against a common enemy. We hope our tools might help you in the process. Let’s see if we can save some lives.
If you are not a researcher, but want to help, you can do so by sharing this announcement with the people in your network who might have a use for it.
The world needs science. Complex challenges ranging from climate change to preventive medicine require us to put our best minds together to solve them. And we do live in a world where more scientific knowledge is available to us than ever before — but the irony is that our politicians doubt its legitimacy, our researchers are pressed for time and resources do not have capacity to communicate across even the closest alleyways of a university, publishing houses generate profit by keeping vital results hidden behind heavy walls, and go after those who breach them with deadly force. In addition, the big software players are opaque and seemingly impossible to hold accountable for their data, their algorithms and the implications of these. In spite of Tim Berners-Lee creating the internet to share scientific knowledge, it seems we have only come marginally further today than we had back then.
Two years ago at Iris.ai we sat out on quite the ambitious journey: to build what we call an “AI Scientist”, a system that can augment our human intelligence by connecting the dots of all of the world’s research. The months since have been filled with hard work, progress, setbacks, a lot of rejections — but also so much love, support, understanding and encouragement from our wonderful community.
In these two years we’ve built a system that reduces a human’s time to map out existing scientific knowledge with up to 90% , while increasing serendipity and interdisciplinary discovery. This is the first important step towards what we call the Knowledge Validation Engine, a core feature of the AI Scientist. We have a dedicated team that has built this, we have a number of budding university collaborations and we have a group of lovely investors who believe in us and we’ve published several open access research papers. Most important, this past year we’ve seen an amazing community of AI trainers grow up around us — more than 8,000 individuals who volunteer their time to help Iris.ai learn. We’ve seen a desire to be part of our journey, a wish to help us achieve our mission, a community coming together to tell us that what we do is important. We have done our best to honor their help, but we have not done enough.
Transparency, openness and fighting bias have been our core values from day one, but we find ourselves not living up to our own standards. We find ourselves torn between servicing big corporate clients and satisfying a European venture capital community single-mindedly focused on revenue (with some very honest impact focused exceptions) on one hand — while also trying to bring what we build out to as many people as possible.
For us to truly make impact in the world, it is not enough to build some great tools, we need to disrupt and uproot an entire industry. We can not do that on our own — it’s a grassroots challenge. We need your help.
Scientific knowledge is arguably the ultimate decentralized application. It is not controlled by a central agent, is individual node-independent, is exposed to public scrutiny and constant challenge and is preciously valuable for a large and fast growing cohort of current and future users.
The technology development of this decade is thrilling, and today we are taking advantage of a new opportunity. Utilizing the decentralized nature of the blockchain, we have decided to give power to our community by tokenizing our functionalities— allowing anyone who contributes to the tool to generate tokens by doing so — tokens they can later use directly to access our core services.
An AI Trainer who annotates research papers, a coder who commits to our increasingly open source code, a user who reports a bug or a researcher who uses the Iris.ai Knowledge Validation Engine to publish their research Open Access — they will all be rewarded with tokens for contributing. The tokens can be used to access any of the Iris.ai tools. All token holders will have a voice, have transparent insight into our core technology and will be asked to hold us accountable to openness and de-biasing our algorithms and data. And as both corporates purchase access to the Iris.ai tools and the the algorithms of Iris.ai improve over time, the value of the tokens held by our community will increase.
We’re excited, thrilled and a little scared — as per usual, we’re traversing unchartered territory. And we can not do this alone. Please join us in making science transparent, open and accessible.
Our white paper with the details will be made available early 2018. Until then we would love your ideas, thoughts and feedback — on tokens@iris.ai or our Telegram channel.
Iris.ai provides multidimensional research for exploring possibilities and limitation in applying Augmented reality to surgical settings
Last week we hosted a Scithon in collaboration with Stryker and the Freiburg University Medical Center. For this Scithon, three teams were trying to find a solution for this research question: What research and development work is necessary to build a “ready to use” adaptive augmented reality (AR) system for surgeons to teach them how to perform a surgical procedure? The judges were looking for the teams to find solutions involving four different dimensions of the question; identifying the capabilities of the technology, outlining the training process for doctors, describing the human interaction and UX and how it would be integrated into the surgical flow. Over the course of the day, we had several conversations with participating doctors and members of the Stryker team which gave us valuable insight and solid background information for the research problem we were trying to solve.
The Scithon’s winning team cited just 13 papers using Iris.ai which allowed them to successfully cover all four of the required angles. The team concluded that the prospect of AR in surgical environments is absolutely feasible however the current technology needs further testing to prove reliability and other improvements to meet surgical requirements. They were able to outline specific uses of the technology and identified that ‘the more rigid and basic the surgical system the easier it is to implement augmented reality applications to improve results.’
The team placing second used traditional research methods to reach their conclusion and while they found more papers, 46 in total, those papers had a much more narrow focus, with most of them targeting a single topic area. The reason being, traditional methods rely on the existing knowledge of the user, where users typically search for more of what they know instead of discovering new, related topics that were previously unknown to them. Their conclusion ascertained that presently there is no technology that encompasses all of the features needed to create an AR system to satisfy all of the requirements named in the problem statement. However, research data and solutions on the key aspects of technical limitations are available and provide the foundation for the development of the envisioned system.
Similar to the winning team, the team that placed third was also using Iris.ai and was able to reach their conclusion using a few number of papers citing 15. While the judges were conflicted on the depth on which they covered the various aspects of the research question, they were able to cover 3 out of 4 of them. Their solution asserted that it is possible to build an AR system comprising of existing surgery navigation platforms, real-time intraoperative imaging and 3D visualization display devices. However, these technologies are not without shortcomings like the need to reconfigure display devices that are too bulky, have limited capabilities and autonomy limitations and improve the tracking of surgical instruments and automatic relevant medical information extraction.
The results of the Scithon spoke to the benefit of Iris.ai’s serendipitous search enabling a multi-faceted approach to research. Whereas traditional search limits the output to singular aspects of the problem, that doesn’t connect all of the possible angles. Current keyword search engines return results that can be biased based on the user’s search history and experience. Iris.ai produces unbiased, objective results creating a multidimensional approach for research discovery. Most research problems require several topic areas to be considered before a solution can be found and Iris.ai enables teams to find the research to cover each and every one of them.
Gaining context for the research problem we’re trying to help solve
Last week, we hosted a Scithon in collaboration with Stryker, Augmented Vision and Freiburg University Medical Center. We put a group of medical engineers, IT experts, neurosurgeons and urologists in a room to discuss and research methods for advancing Augmented Reality/Virtual Reality in surgical settings. What made this particular group of Scithon Participants unique was the high level of expertise related to the research topic subject matter. Even before beginning to research the feasibility of AR/VR surgical innovations, almost all of the doctors already had a good baseline of specific features or use cases that would be most beneficial to them. And the views between neurosurgeons and urologists regarding the usability and implementation of innovative technology in the Operating Room varied greatly. It seemed clear that no matter the outcome of the day’s research, the feasibility of using AR/VR in the OR wasn’t the biggest obstacle to overcome. So, before we outline the results of the research, we want to the highlight a few of the learnings from the discussions that took place over the course of the Scithon. These learnings provided valuable context for the day’s research and gave us better insight into the pain points behind the research question.
Coming into the Scithon, it was interesting to see what types of technologies the doctors had begun to familiarize themselves with and which ones they preferred. One of the urologists touted his ‘bootstrapped’ method of incorporating Google’s Cardboard into his work while other teams of neurosurgeons already begun investigating how best to use Microsoft’s HoloLens. Other doctors expressed frustration at the speed of their colleagues’ ability to embrace and adapt to new technologies. And still, others, questioned the practicality of implementing a new technology that will need to be replaced in 3 years or even worse, could be obsolete in that same period of time. Even with the ability to use groundbreaking technology, there are other issues creating barriers for implementation. Hospital budgets, time spent training doctors, access to up to date research and data, IT maintenance, etc are all factors in delayed or even non-use of state of the art technology.
How residents and doctors time is spent was a recurring theme mentioned throughout the day. The finite preciousness of doctor’s time is a never ending issue. Several participants stated it was difficult being able to attend today’s Scithon, let alone focus on their own research. The sacrifice of their time did pay off, as all they agreed that the Scithon was a valuable use of their time. It allowed for a ‘meeting of the minds’ type atmosphere facilitating extensive discussions among a range of medical specialties. And many were interested in the long-term possibilities of using Iris.AI’s platform to expedite their institution’s research. Ability to save time was a vital feature that all participants were interested in, regardless of specific technology.
We gained numerous insights from the medical technology provider and manufacturer group of participants as well. We could relate to them as we were both in the position of watching, in real time, others using our products and platform. The participants from Stryker shared a few industry insights like Microsoft’s shift from AR back to their primary Cloud based product offerings and how that might affect their future involvement with the AR field. They also discussed a slight gap between product features and product implementation in the medical field, as some doctors had mentioned their products were too complex for what they needed. Simply put, the current technological solutions were overbuilt. The feedback is at the forefront of their minds as they continue to create products for the medical community.
They say if you build it, they will come it. Well, now the question is, can they build it? Does AR/VR have a place in surgical settings and if so, how can it best be implemented?