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AI Discovers Smell Genes Linked To Cancer Outcomes

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AI Discovers Smell Genes Linked To Cancer Outcomes

A team of researchers from Oxford University has recently used AI to discover a potential link between colon cancer and the expression of specific smell-sensing genes. As Phys.org reports, researchers from Oxford University and the University of Zurich have recently, assisted by an AI model, discovered that the expression of specific smell-sensing genes within the colon cancer cells indicates a higher probability of worse outcomes.

Genes are expressed when the information that is found within our DNA is used to make molecules like proteins. Gene expression often controls how many proteins are made and when they are made, acting on/off switches. Human beings have approximately 400 genes responsible for our sense of smell, but rather crucially for the study, the genes are also expressed in other parts of the body, aside from the nose. If these smell genes are expressed, it means that the instructions for these genes are being read and carried out. By making changes to the cells, scientists can manipulate the level to which genes are expressed and used.

The study that was recently published in Molecular Systems Biology was lead by Dr. Heba Sailem of the Insitute of Biomedical Engineering and Oxford University. Sailem and fellow researchers examined how cells in the body are organized, aiming to study how cancer leads to the loss of tissue structure in the body. In order to develop effective therapies, scientists must understand which genes play a role in tissue alteration. The research team employed computer vision algorithms to detect changes within the organization of cell samples. The AI model was given image data collected by robotic microscopy, which contains millions of images of colon cancer cells.

The research team then experimented by reducing the expression of every gene in the individual colon cancer cells. After perturbations were applied to the genes and their expression decreased, the researchers found that smell-sensing genes appear to be strongly correlated with how cells align and spread. It appeared that reducing the expression of smell genes could potentially control the spread of cells by reducing their ability to move. On the other hand, cell motility could be increased by having higher expression levels of the smell genes in question.

Sailem explained that the smell genes are like a “sixth sense” that the cancer cells can use to find their way outside of the tumor environment, which is toxic, and spread to other regions of the patient’s body. Sailem went on to explain just how important AI was in making this discovery. The AI model used by the researchers was able to greatly increase the speed the research was carried out at. The AI model, after being trained on a large database of gene functions and appearances, is able to automate the task of identifying certain types of cells within images. Sailem explained:

“Using the developed AI system, we can now learn much more from these experiments and accelerate the identification of genes that alter the structure of tissues in cancer.

CRIPSR (Clustered Regularly Interspaced Short Palindromic Repeats), the gene-editing technology, is the primary way that gene expression levels for the approximately 20,000 genes in the cell are reduced to study how gene expression impacts cancer cells. When combined with advances in gene-editing technology, the research done by Sailem and colleagues could enable new methods of identifying the roles different genes play in different types of cancer, which could enable new kinds of therapies.

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Akilesh Bapu, Founder & CEO of DeepScribe – Interview Series

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Akilesh Bapu, Founder & CEO of DeepScribe - Interview Series

Akilesh Bapu is the Founder & CEO of DeepScribe, which uses natural language processing (NLP) and advanced deep learning to generate accurate, compliant, and secure notes of doctor-patient conversations.

What was it that introduced and attracted you to AI and natural language processing?

If I remember correctly, Jarvis from “Iron Man” was the first thing that really attracted me to the world of natural language processing and AI. Particularly, I found it fascinating how much faster a human was able to not only go through tasks but also go into an incredible level of depth into certain tasks and unveil certain information that they wouldn’t have even known about if it weren’t for this AI.

It was this concept of “AI by itself won’t be as good as humans at most tasks but put a human and AI together and that combination will dominate.”  Natural language processing is the most efficient way for this human/AI combination to happen.

From then on, I was obsessed with Siri, Google Now, Alexa, and the others. While they didn’t work as seamlessly as Jarvis, I so badly wanted to make them work as Jarvis did. Particularly, what became apparent was, commands such as “Alexa do this,” “Alexa do that,” were pretty easy and accurate to do with the current state of technology. But when it comes to something like Jarvis, where it can actually learn and understand, filter, and pick up on important topics during another conversational exchange—that hadn’t really been done before. This actually directly relates to one of my core motivations in founding DeepScribe. While we are solving the issue of documentation for physicians, we’re attempting a whole new wave of intelligence while doing it: ambient intelligence. AI that can dig through your day-to-day utterances, find useful information, and use that information to help you out.

 

You previously did some research using deep learning and NLP at UC Berkeley College of Engineering. What was your research on?

Back at the Berkeley AI Research Lab, I was working on a gene ontology annotator project where we were summarizing PubMed articles with specific output parameters.

The high-level overview: Take a task like the CNN news article summarization. In that task you’re taking news articles and summarizing them into roughly a few sentences. In your favor you have data and the ability to train these models on over a million articles. However, the problem space is enormous since you have limited structure to the summaries. In addition, there is hardly any structure to the actual articles. While there have been quite a few improvements since 2.5 years ago when I was working on this project, this is still an unsolved problem.

In our research project, however, we were developing structured summaries of articles. A structured summary in this case is similar to a typical summary except we know the exact structure of the output summary. This is helpful since it dramatically reduces the output options for our machine learning model—the challenge was that there was not enough annotated training to run a data-hungry deep learning model and get usable results.

The core of the work I did on this project was to leverage the knowledge we have around the input data and develop an ensemble of shallow ML models to support it—a technique we invented called the 2-step annotator. The 2-step annotator benchmarked at nearly 20x the accuracy as the previous best (54 percent vs 3.6 percent).

While side by side, this project and DeepScribe may sound entirely different, they were highly similar in how they used the 2-step annotation method to vastly improve results on a limited dataset.

 

What was the inspiration behind launching DeepScribe?

It all started with my father, who was a medical oncologist. Before electronic health record systems took over health care, physicians would jot down things on paper and spend very little time on notes. However, once EHRs started becoming popular as part of the HITECH Act of 2009, I started noticing that my dad spent more and more time at the computer. He’d start coming home later. On the weekends, he’d be sitting on the couch dictating notes. Simple things like him picking me up from school or basketball practice became a thing of the past as he’d be spending most of his evening hours catching up on documentation.

As a nerdy kid growing up, I would try to find solutions for him by searching the web and having him try them out. Sadly, nothing worked well enough to save him from the long hours of documentation.

Fast forward several years to the summer of 2017—I’m a researcher working at the Berkeley AI Research Lab, working on projects in document summarization. One summer when I’m back at home, I notice that my dad is still spending copious amounts of time documenting. I ask, “What’s new in the world of documentation? Alexa is everywhere, Google Assistant is so good now. Tell me, what’s the latest in the medical space?” And his answer was, “Nothing has changed.” I thought that it was just him but when I went and surveyed several of his colleagues, it was the same issue: not what the latest is in cancer treatment or the novel problems their patients were having—it was documentation. “How can I get rid of documentation? How can I save time on documentation? It’s taking so much of my time.”

I also noticed several companies that had emerged to try to solve documentation. However, either they were too expensive (thousands of dollars per month) or they were too minimal in terms of technology. The physicians at that time had very few options. That was when the opportunity opened up that if we could create an artificially intelligent medical scribe, a technology that could follow physicians’ patient visits and summarize them, and offer it at a cost that could make it accessible for everyone, it could truly bring the joy of care back to medicine.

 

You were only 22 years old when you launched DeepScribe. Can you describe your journey as an entrepreneur?

My first exposure to entrepreneurship was back in high school. It started when a friend and I who happened to know some JavaScript basics met with the director of a center for children with learning disabilities. They told us how the simplest of tools could go a long way with dyslexic children. We ended up hacking together a dyslexia reader Chrome extension. It was really bare bones—it simply adjusted the font to meet the scientific guidelines for ease of reading by dyslexic people. While the concept was simple, we ended up getting over 5000 active users in a handful of months. I was blown away by how basic tech can have such a profound impact on people.

At Berkeley, I continued to delve into the world of entrepreneurship as much as possible, primarily with their wide array of classes. My favorites were:

  1. The Newton Lecture Series—people like Jessica Mah from InDinero or Diane Greene from VMWare who were Cal alums gave highly relatable talks about their time at Berkeley and how they started their own companies
  2. Challenge Lab—I actually met my co-founder Matt Ko through this class. We were placed in groups and went through a semester-long journey of creating a product and being mentored on what it takes during the early stages to get an idea going.
  3. Lean Launchpad—By far my favorite of the three; this was a grueling and rigorous process where we were guided by Steve Blank (acclaimed billionaire and the man behind the lean startup movement) to take an idea, validate it through 100 customer interviews, build a financial model, and more. This was the type of class where we pitched our “startup” only to get stopped on slide 1 or 2 and get grilled. If that wasn’t hard enough, we were also expected to interview 10 customers a week. Our idea at the time was to create a patent search that would give similar results to an expensive prior art search, which meant we were pitching to 10 enterprise customers a week. It was great because it taught us to think fast on our feet and be extra resourceful.

DeepScribe started when an investor group called The House Fund was writing checks for students who would turn down their summer internships and spend their summer building their company. We had just shut down Delphi (the patent search engine) and Matt and I had been constantly talking about medical documentation and everything fell in place since it was the perfect time to give it a shot.

With DeepScribe, we were lucky to have just come fresh out of Lean Launchpad since one of the most important factors in building a product for physicians was to iterate and refine the product around customer feedback. A historical issue with the medical industry has been that software has rarely had physicians in the design loop, therefore resulting in software that wasn’t optimized for the end user.

Since DeepScribe was happening at the same time as my final year at Berkeley, it was a heavy balancing act. I’d show up to class in a suit so I could be on time for a customer demo right after. I’d use all the EE facilities and professors not for anything to do with class but 100 percent for DeepScribe. My meetings with my research mentor even turned into DeepScribe brainstorming sessions.

Looking back, if I had to change one thing about my journey, it would’ve been to put college on hold so I could spend 150 percent of my time on DeepScribe.

 

Can you describe for a medical professional what the advantages of using DeepScribe are versus the more traditional method of voice dictation or even taking notes?

Using DeepScribe is meant to be very similar to using an actual human scribe. As you talk naturally to your patient, DeepScribe will listen in and pick up on the medically relevant speech that usually goes in your notes and puts it in there for you, using the same medical language that you yourself use. We like to think of it as a new AI-powered member of your medical staff that you can train as you’d like to help with documentation in your electronic health record system as you’d like. It’s very different from using voice dictation service as it eliminates the entire step of having to go back and document. While typical dictation services turn 10 minutes of documentation into 7-8 minutes, DeepScribe turns it into a few seconds. Our physicians report anywhere from 1.5 to 3 hours of time saved per day depending on how many patients they see.

DeepScribe is device-agnostic, operable from an iPhone, Apple Watch, browser (for telemedicine), or hardware device.

 

What are some of the speech recognition or NLP challenges that DeepScribe may encounter due to complex medical terminology?

Contrary to popular opinion, complex medical terminology is actually the easiest part for DeepScribe to pick up. The trickiest part for DeepScribe is to pick up on unique contextual statements a patient may give a physician. The more they stray from a typical conversation, the more we see the AI stumble. But as we collect more conversational data, we see it improve on this dramatically every day.

 

What are the other machine learning technologies that are used with DeepScribe?

The large umbrellas of speech recognition and NLP tend to cover most of the machine learning we’re doing at DeepScribe.

 

Can you name some of the hospitals, nonprofits, or academic institutions that are using DeepScribe?

DeepScribe started out through a pilot program with the UC Berkeley Health Center. Hartford Healthcare, Texas Medical Center, and Cedar Valley Medical Specialists are a handful of the larger systems DeepScribe is working with.

However, the larger percentage of DeepScribe users are 50 private practices from Alaska to Florida. Our most popular specialties are primary care, orthopedics, gastroenterology, cardiology, psychiatry, and oncology, but we do support a handful of other specialties.

 

DeepScribe has recently launched a program to assist with COVID-19. Could you walk us through this program?

COVID-19 has hit our doctors hard. Practices are only seeing 30-40 percent of their patient load, scribe staffing is being cut, and providers are being forced to rapidly switch all their patients on to telemedicine. All this ends up leading to more clerical work for providers—we at DeepScribe firmly believe that in order for this pandemic to come to a halt, physicians must devote 100 percent of their attention and time to taking care of their patients.

To help aid this cause, we are proud to launch a free telemedicine solution to health care professionals fighting this pandemic. Our telemedicine solution is fully integrated with our AI-powered medical scribe solution, eliminating the need for clinical documentation for encounters made on our platform.

We’re also offering our scribe service for free during the pandemic. This means that any physician can get access to a scribe for free to handle their documentation. Our hopes are that by doing this, physicians will be able to focus more of their attention on their patients and spend less time thinking about documentation, leading to a faster halting of the COVID-19 outbreak.

Thank you for the great interview, I really enjoyed learning about DeepScribe and your entrepreneurial journey. Anyone who wishes to learn more should visit DeepScribe.

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AI Models Trained On Sex Biased Data Perform Worse At Diagnosing Disease

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AI Models Trained On Sex Biased Data Perform Worse At Diagnosing Disease

Recently, a study published in the journal PNAS and conducted by researchers from Argentina, implied that the presence of sex-skewed training data leads to worse model performance when diagnosing diseases and other medical issues. As reported by Statsnews, the team of researchers experimented with training models where female patients were notably underrepresented or excluded altogether, and found that the algorithm performed substantially worse when diagnosing them. The same also held true for incidents where male patients were excluded or underrepresented.

Over the past half-decade, as AI models and machine learning have become more ubiquitous, more attention has been paid to the problems of biased datasets and the biased machine learning models that result from them. Data bias in machine learning can lead to awkward, socially damaging, and exclusive AI applications, but when it comes to medical applications lives can be on the line. However, despite knowledge of the problem, few studies have attempted to quantify just how damaging biased datasets can be. The study carried out by the research team found that data bias could have more extreme effects than many experts previously estimated.

One of the most popular uses for AI in the past few years, in medical contexts, has been the use of AI models to diagnose patients based on medical images. The research team analyzed models used to detect the presence of various medical conditions like pneumonia, cardiomegaly, or hernias from X-rays. The research teams studied three open-source model architectures: Inception-v3, ResNet, and DenseNet-121. The models were trained on chest X-rays pulled from two open-source datasets originating from Stanford University and the National Institutes of Health. Although the datasets themselves are fairly balanced when it comes to sex representation, the researchers artificially skewed the data by breaking them into subsets where there was a sex imbalance.

The research team created five different training datasets, each composed of different ratios of male/female patient scans. The five training sets were broken down as follows:

  • All images were of male patients
  • All images were of female patients
  • 25% male patients and 75% female patients
  • 75% female patients and 25% male patients
  • Half male patients and half female patients

After the model was trained on one of the subsets, it was tested on a collection of scans from both male and female patients. There was a notable trend that was present across the various medical conditions, the accuracy of the models was much worse when the training data was significantly sex-skewed. An interesting thing to note is that if one sex was overrepresented in the training data, that sex didn’t seem to benefit from the overrepresentation. Regardless of whether or not the model was trained on data skewed for one sex or the other, it didn’t perform better on that sex compared to when it was trained on an inclusive dataset.

The senior author of the study, Enzo Ferrante, was quoted by Statnews as explaining that the study underlines how important it is for training data to be diverse and representative for all the populations you intend to test the model in.

It isn’t entirely clear why models trained on one sex tend to perform worse when implemented on another sex. Some of the discrepancies might be due to physiological differences, but various social and cultural factors could also account for some of the difference. For instance, women may tend to receive X-rays at a different stage of progression in their disease when compared to men. If this were true, it could impact the features (and therefore the patterns learned by the model) found within training images. If this is the case, it makes it much more difficult for researchers to de-bias their datasets, as the bias would be baked into the dataset through the mechanisms of data collection.

Even researchers who pay close attention to data diversity sometimes have no choice but to work with data that is skewed or biased. Situations where a disparity exists between how medical conditions are diagnosed will often lead to imbalance data. For example, data on breast cancer patients is almost entirely collected from women. Similarly, autism manifests differently between women and men, and as a result, the condition is diagnosed at a much higher rate in boys than girls.

Nonetheless, it’s extremely important for researchers to control for skewed data and data bias in any way that they can. To that end, future studies will help researchers quantify the impact of biased data.

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Stefano Pacifico, and David Heeger, Co-Founders of Epistemic AI – Interview Series

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Stefano Pacifico, and David Heeger, Co-Founders of Epistemic AI - Interview Series

Epistemic AI employs state-of-the-art Natural Language Processing (NLP), machine learning and deep learning algorithms to map relations among a growing body of biomedical knowledge, from multiple public and private sources, including text documents and databases. Through a process of Knowledge Mapping, users’ work interactively with the platform to map and understand subsets of biomedical knowledge, which reveals concepts and relationships and that are otherwise missed with traditional search.

We interviewed both Co-Founders of Epistemic AI to discuss these latest advances.

Stefano Pacifico comes from 10+ years in applied AI and NLP development. Formerly at Bloomberg, where he spent 7 years, and was at Elemental Cognition before starting Epistemic.

David Heeger is a Silver Professor of data science and neuroscience at NYU, and has spent his career bridging computer science, AI and bioscience. He is a member of the National Academy of Sciences. As founders they bring together the expertise of building applied large-scale AI and NLP systems for understanding large collections of knowledge, with expertise in computational biology and biomedical science from years of research in the area.

What is it that introduced and attracted you to AI and Natural Language Processing (NLP)?

Stefano Pacifico: When I was in college in Rome, and AI was not popular at all (in fact it was very fringe), I asked my then advisor what specialization I should have taken among those available. He said: “If you want to make money, Software Engineering and Databases, but if you want to be weird but very advanced, then choose Artificial Intelligence”. I was sold at “weird”. I then started working on knowledge representation and reasoning to study how autonomous agents could play soccer or rescue people. Then two realizations made me fall in love with NLP: first, autonomous agents might have to communicate with natural language among themselves! Second, building formal knowledge bases by hand is hard, while natural language (in text) already provides the largest knowledge base of all. I know today these might seem obvious observations, but they were not as mainstream before.

What was the inspiration behind launching Epistemic AI?

Stefano Pacifico: I am going to make a bold claim. Nobody today has adequate tooling to understand and connect the knowledge present in large, ever-growing collections of documents and data. I had previously worked on that problem in the world of finance. Think of news, financial statements, pricing data, corporate actions, filings etc. I found that problem intoxicating. And of course, it’s a difficult problem; and an important one!  When I met my co-founder, Dr. David Heeger, we spent quite a bit of time evaluating startup opportunities in the biomedical industry. When we realized the sheer volume of information generated in this field, it’s as if everything fell in its right place. Biomedical ​researchers struggle with information overload, while attempting to grapple with the vast and rapidly expanding base of biomedical knowledge, including documents (e.g., papers, patents, clinical trials) and databases (e.g., genes, proteins, pathways, drugs, diseases, medical terms). This is a major pain point for researchers and, with no appropriate solution available, they are forced to use basic search tools (PubMed and Google Scholar) and explore manually-curated databases. These tools are suitable for finding documents matching keywords (e.g., a single gene or a published journal paper), but not for acquiring comprehensive knowledge about a topic area or subdomain (e.g., COVID-19), or for interpreting the results of high throughput biology experiments, such as gene sequencing, protein expression, or screening chemical compounds. We started Epistemic AI with the idea to address this problem with a platform that allows them to iteratively:

  1. Shorten the time to gather information and build comprehensive knowledge maps
  2. Surface cross-disciplinary information​ that can be otherwise difficult to find (real discoveries often come from looking into the white space between disciplines);
  3. Identify causal hypotheses by finding paths and missing links in your knowledge map​.

What are some of both the public and private sources that are used to map these relations?

Stefano Pacifico: At this time, we are ingesting all the publicly available sources that we can get our hands on, including Pubmed and clinicaltrials.gov. We ingest databases of genes, drugs, diseases and their interactions. We also include private data sources for select clients, but we are not at liberty to disclose any details yet.

What type of machine learning technologies are used for the knowledge mapping?

Stefano Pacifico: One of the deeply held beliefs at Epistemic AI is that zealotry is not helpful for building products. Building an architecture integrating several machine learning techniques was a decision made early on, and those range from Knowledge Representation to Transformer models, through graph embeddings, but include also simpler models like regressions and random forests. Each component is as simple as it needs to be, but no simpler. While we believe to have already built NLP components that are state-of-the-art for certain tasks, we don’t shy away from simpler baseline models when possible.

Can you name some of the companies, non-profits, or academic institutions that are using the Epistemic platform?

Stefano Pacifico: While I’d love to, we have not agreed with our users to do so. I can say that we had people signing up from very high-profile institutions in all three segments (companies, non-profits, and academic institutions). Additionally, we intend to keep the platform free for academic/non-profit purposes.

How does Epistemic assist researchers in Identifying central nervous system (CNS) and other disease-specific biomarkers?

Dr. David Heeger: Neuroscience is a very highly interdisciplinary field including molecular and cellular biology and genomics, but also psychology, chemistry, and principles of physics, engineering, and mathematics. It’s so broad that nobody can be an expert at all of it. Researchers at academic institutions and pharma/biotech companies are forced to specialize. But we know that the important insights are interdisciplinary, combining knowledge from the sub-specialties. The AI-powered software platform that we’re building enables everyone to be much more interdisciplinary, to see the connections between their individual subarea of expertise and other topics, and to identify new hypotheses. This is especially important in neuroscience because it is such a highly interdisciplinary field to begin with. The function and dysfunction of the human brain is the most difficult problem that science has ever faced. We are on a mission to change the way that biomedical scientists work and even how they think.

Epistemic also enables the discovery of genetic mechanisms of CNS disorders. Can you walk us through how this works?

Dr. David Heeger: Most neurological diseases, psychiatric illnesses, and developmental disorders do not have a simple explanation in terms of genetic differences. There are a handful of syndromic disorders for which a specific mutation is known to cause the disorder. But that’s not typically the case. There are hundreds of genetic differences, for example, that have been associated with autism spectrum disorders (ASD). There is some understanding for some of these genes about the functions they serve in terms of basic biology. For example, some of the genes associated with ASD hold synapses together in the brain (note, however, that the same genes typically perform different functions in other organ systems in the body). But there’s very little understanding about how these genetic differences can explain the complex suite of behavioral differences exhibited by individuals with ASD. To make matters worse, two individuals with the same genetic difference may have completely different outcomes, one diagnosed with ASD and the other, not. And two individuals with completely different genetic profiles may have the same outcome with very similar behavioral deficits. To understand all this requires making the connection from genomics and molecular biology to cellular neuroscience (how do the genetic differences cause individual neurons to function differently) and then to systems neuroscience (how do those differences in cellular function cause networks of large numbers of interconnected neurons to function differently) and then to psychology (how do those differences in neural network function cause differences in cognition, emotion, and behavior). And all of this needs to be understood from a developmental perspective. A genetic difference may cause a deficit in a particular aspect of neural function. But the brain doesn’t just sit there and take it. Brains are highly adaptive. If there’s a missing or broken mechanism then the brain will develop differently to compensate as much as possible. This compensation might be molecular, for example, upregulating another synaptic receptor to replace the function of a broken synaptic receptor. Or the compensation might be behavioral. The end result depends not only on the initial genetic difference but also on the various attempts to compensate relying on other molecular, cellular, circuit, systems, and behavioral mechanisms.

No individual has the knowledge to understand all this. We all need help. The AI-powered software platform that we’re building enables everyone to collect and link all the relevant biomedical knowledge, to see the connections and to identify new hypotheses.

How are biopharma and academic institutions using Epistemic to tackle the COVID-19 challenge?

Stefano Pacifico: We have released a public version of our platform that includes COVID specific datasets and is freely accessible to anyone doing research on COVID-19. It is available at https://covid.epistemic.ai

What are some of the other diseases or genetic issues that Epistemic have been used for?

Stefano Pacifico: We have collaborated with autism researchers and are most recently putting together a new research effort for Cystic Fibrosis. But we are happy to collaborate with any other researchers or institutions that might need help with their research.

Is there anything else that you would like to share about Epistemic?

Stefano Pacifico: We are building a movement of people that want to change the way biomedical researchers work and think. We sincerely hope that many of your readers will want to join us!

Thank you both for taking the time to answer our questions. Readers who wish to learn more should visit Epistemic AI.

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