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Say hello Datafam to Einstein Copilot for Tableau, your trusted AI assistant that helps you go from data to insights quicker than ever before. In this demo, we explore reported wildlife strikes on planes in the U.S., leveraging FAA data to identify patterns, assess financial costs, and even find... ways to reduce the impact on wildlife. Einstein Copilot makes exploring data seamless. For example, with just a click of the Einstein icon, you can ask AI-driven questions like "What is the trend of wildlife strikes over the years?" Immediately, Einstein filters and refines the data for you, revealing insightful trends such as a dip during COVID, followed by a record number of reports in recent years. Does that mean flying is more dangerous? Not quite. By adding another layer of analysis, like damage level, Einstein shows that while reports of strikes are increasing, almost all of them report no damage, which means flying remains as safe as ever. But Einstein goes beyond just trends and insights; it helps with complex calculations too. Need to calculate the total cost of these strikes adjusted for inflation? No problem. Einstein can handle the heavy lifting, combining the correct cost fields into one calculation, offering you a deeper understanding of the financial impact. In one standout example, Einstein highlights a $50 million spike in damage from 2009. Curious about the cause? With a simple prompt, Einstein quickly identifies that the outlier was the famous US Airways Flight 1549, known as the “Miracle on the Hudson,” when Captain Sully heroically landed in the Hudson River after a bird strike took out both engines. And just like that, Einstein Copilot for Tableau helps you transform data into actionable insights, empowering you to make smarter, faster decisions.show more

Marc Benioff

3,719,406 subscribers

56,410 Aufrufe • vor 1 Jahr •via X (Twitter)

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10 Kommentare

Profilbild von Tomer Rozenberg

Tomer Rozenbergvor 1 Jahr

@salesforce @tableau Einstein Copilot is about to make data-driven decisions as smooth as a flight with no wildlife in sight!

Profilbild von Alexander Knoll

Alexander Knollvor 1 Jahr

@salesforce @tableau @Benioff Great potential! The biggest obstacle to fully utilize this innovation is that lots of companies still have problems with their actuall data quality to various degrees. I believe that will gain even more relevancy soon! Looking forward to what comes next!

Profilbild von Too Mutch Sauce

Too Mutch Saucevor 1 Jahr

@salesforce @tableau Marc, can you please buy the @SFGiants? Mahalos.

Profilbild von Yeswanth Reddy (యశ్వంత్ రెడ్డి)

Yeswanth Reddy (యశ్వంత్ రెడ్డి)vor 1 Jahr

@salesforce @tableau #AI

Profilbild von 調弦

調弦vor 1 Jahr

@salesforce @tableau “Miracle on the Hudson,” is unforgettable.

Profilbild von zen.brodha

zen.brodhavor 1 Jahr

@salesforce @tableau This was edited or written by AI why else would you use ** on this platform, X doesn't support markdown.

Profilbild von Metaphor Man

Metaphor Manvor 1 Jahr

@salesforce @tableau @ninapande

Profilbild von Scarab

Scarabvor 1 Jahr

@salesforce @tableau Do you have permission to use the Einstein name for your product/service ?

Profilbild von Al&

Al&vor 1 Jahr

@salesforce @tableau We tried this in our company ... Didn't find any useful tbh ... We prefer some features on Report Automation or any update on this !... I did build an inhouse feature for this , hope this can come out as a feature from SFDC !

Profilbild von Alex Kovalov

Alex Kovalovvor 1 Jahr

@salesforce @tableau Crazy! Can’t wait to uncover hidden insights in advertising data 🤓

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The Non-Traversable Bridge Einstein Found Inside Gravity Einstein’s equations not only describe gravity as a force but as the shape of Spacetime as well. In 1935, Einstein and Rosen found that the Schwarzschild solution could be written in a way that reveals something astonishing: Two separate exterior regions of Spacetime joined by a bridge-like throat Rᵤᵥ = 0 This became known as the Einstein-Rosen Bridge. It is often called a Wormhole, but the classical Einstein-Rosen bridge is not a science-fiction tunnel. In the standard Schwarzschild geometry, it is non-traversable. The throat does not remain open long enough for ordinary travel from one region to the other. Its real power is mathematical. It shows that General Relativity is not merely about objects moving through space. The theory allows Spacetime to have global structure such as different regions, horizons, extensions, and hidden connections that are not visible from one local patch alone. The Einstein-Rosen bridge is one of the earliest glimpses of that deeper idea. Gravity is geometry. And sometimes, geometry has two worlds joined by a throat. #Astrophysics #GeneralRelativity #Einstein #BlackHole #Spacetime #Cosmology #Gravity #Wormhole #Mathematics #Physics

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Former SpaceX Head of Talent : I think Elon is smarter than Einstein. "One way we explained the SpaceX culture, in order to be honest with people, was this: SpaceX is like special forces. We take on missions that others consider impossible. This will be the hardest thing you ever do in your life. It attracts the kind of personality that wants to be a Navy SEAL, but in engineering. It will be bruising, punishing, and uncomfortable every day. Growth is not comfortable. If you are comfortable, you are probably not growing. To work for Elon is to be comfortable living in a constant state of discomfort. He has 10,000 times more going through his brain than the average person at any given moment. Our ability to think about and analyze a problem is nothing compared to how his brain works. Elon aspires to be the greatest engineer in the history of the world. He has the cognitive horsepower of the geniuses who came before us. But he is not Einstein, is he? I think he is smarter than Einstein."

Former SpaceX Head of Talent : I think Elon is smarter than Einstein. "One way we explained the SpaceX culture, in order to be honest with people, was this: SpaceX is like special forces. We take on missions that others consider impossible. This will be the hardest thing you ever do in your life. It attracts the kind of personality that wants to be a Navy SEAL, but in engineering. It will be bruising, punishing, and uncomfortable every day. Growth is not comfortable. If you are comfortable, you are probably not growing. To work for Elon is to be comfortable living in a constant state of discomfort. He has 10,000 times more going through his brain than the average person at any given moment. Our ability to think about and analyze a problem is nothing compared to how his brain works. Elon aspires to be the greatest engineer in the history of the world. He has the cognitive horsepower of the geniuses who came before us. But he is not Einstein, is he? I think he is smarter than Einstein."

Doge Tipping

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Demis Hassabis has a test for whether AI is actually creative. Give it only the data Einstein had in 1901 and see if it invents relativity. He thinks today's text models will never pass it. This is his bar for true creativity. Not extrapolating something already known. Generating a genuinely new hypothesis about reality that nobody has reached before. The thing Einstein did in 1905 with a handful of papers that rewrote physics. Hassabis wants to hand an AI the same starting data Einstein had and see if it gets there on its own. No peeking past the cutoff. Then he explained why he doubts text alone can do it. Einstein daydreamed his way to relativity. He was a patent clerk imagining what it would look like to ride alongside a beam of light. He ran the experiment in his head first, then proved it with math. Yann LeCun, who won the Turing Award and ran AI at Meta, says the same thing in blunter terms. He says today's models don't understand the physical world as well as a housecat, and that text alone is "complete BS" as a path to real intelligence. Two of the most decorated minds in AI agree the missing piece is the same. Hassabis thinks an AI needs to understand the world of atoms, not just the world of bits. Most of the industry is racing to scale text models. Hassabis just told them the most important kind of thinking might live somewhere text can't reach. --- Watch the full interview at Semafor on YouTube

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18,905 Aufrufe • vor 20 Tagen

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23,940 Aufrufe • vor 5 Monaten

Marc Andreessen says the movie Oppenheimer misunderstands history and morality. "The movie sets up both Oppenheimer and also specifically Albert Einstein as the key moral authorities of the era with respect to the use of nuclear weapons. Both claims of which I believe are deeply incorrect on substance." "My critique is the morality of the movie I think is very badly upside-down. And it's upside-down in a way that flatters our current politics but is very badly upside-down in terms of what actually happened at the time." Its redeeming feature is the first half: "[The first half of the movie is] one of the only recreations on film of what American elite culture and society and American research establishment of the leading experts at the time, in the 1920s and 30s, how thoroughly saturated that world was with Communism." However, the remainder of the movie frames Oppenheimer and Einstein as political heroes and vilifies technology, when in reality Oppenheimer was surrounded by Communists, Einstein was a critic of American democracy, and the hydrogen bomb Oppenheimer refused to build may have prevented World War III. (Marc recommends reading When Reason Goes on Holiday by Neven Sesardić to learn more about Einstein's political views.) "I thought the movie really cheated on the morality of it because it presented this as this slam dunk that the nuke was bad and that these people were bad and the whole thing was bad and that both Einstein and Oppenheimer were moral heroes in some extent trying to line up against this. And I just thought that really cheated the audience." Marc Andreessen 🇺🇸

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Former SpaceX employee: I think Elon is smarter than Einstein. “SpaceX is special forces. We take on the missions that others deem impossible. This will be the hardest thing you will ever do in your life. It is the kind of personality that wants to be a Navy SEAL, the engineering equivalent of that. It will be bruising and punishing and uncomfortable every day. Growth is rarely comfortable. If you are comfortable, you are probably not growing. To work for Elon is to be comfortable in a constant state of discomfort. He has 10,000 times more what the average person has streaming through their brain any given time. Our ability to think about and analyze a problem is nothing compared to how his brain works. Elon aspires to be the greatest engineer in the history of the world. He does have the cognitive horsepower of the geniuses that we have seen come before us. I think he is smarter than Einstein.” Dolly Singh in BBC's documentary The Elon Musk Show, 2022

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ELON CLIPS

263,471 Aufrufe • vor 1 Jahr

Alex on why AI + fully autonomous labs will compress drug discovery from five years to two: "If scientists are armed with agentic infrastructure that has an intelligence that's a multiple of Einstein, they will do science faster, with higher probability, for lower cost, and have greater impact." "AI coming into the discovery of medicines is one of the biggest unlocks we will see in history because it means we're no longer constrained by our hands or minds. We can identify all n number of hypotheses that could exist, figure out the best experiment to run, and run those 24/7." "The sheer ability to be more comprehensive in our hypothesis set, and to run experimentation at unprecedented scale, means we're going to speed breakthroughs from timelines that take three to five years to timelines that take less than a couple of years."

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Patrick OShaughnessy

39,883 Aufrufe • vor 2 Monaten

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

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Nury Vittachi

35,679 Aufrufe • vor 7 Monaten

Thank you to Jesse Michels for shining light into my compatriot's work: Dr. Jacobo Grinberg. A cold case that will hopefully get solved at some point. When I started Psicoactivo Podcast, one of my goals was always to give this case more visibility because I consider it one of the most important in the history of parapsychology. The following explanation is about the experiment he was running at the time of his disappearance. It was an attempt to explain the famous 'Spooky Action at a Distance' that Albert Einstein famously mentions. That Einstein-Podolski-Rosen Bridge, or quantum entanglement. He called it the Transferred Potential experiment. There are many different avenues worth exploring with Grinberg. It's not just the mysterious disappearance but also his overall body of work. Very much worth exploring are also his ties to some of the biggest names in Western Ufology. Names such as Dr. Jacques Vallée, John Keel, and Puharich himself met Grinberg in person. You can find the written paper of his experiment right here:

Thank you to Jesse Michels for shining light into my compatriot's work: Dr. Jacobo Grinberg. A cold case that will hopefully get solved at some point. When I started Psicoactivo Podcast, one of my goals was always to give this case more visibility because I consider it one of the most important in the history of parapsychology. The following explanation is about the experiment he was running at the time of his disappearance. It was an attempt to explain the famous 'Spooky Action at a Distance' that Albert Einstein famously mentions. That Einstein-Podolski-Rosen Bridge, or quantum entanglement. He called it the Transferred Potential experiment. There are many different avenues worth exploring with Grinberg. It's not just the mysterious disappearance but also his overall body of work. Very much worth exploring are also his ties to some of the biggest names in Western Ufology. Names such as Dr. Jacques Vallée, John Keel, and Puharich himself met Grinberg in person. You can find the written paper of his experiment right here:

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22,057 Aufrufe • vor 2 Monaten

WOW 🚨 International Crisis Summit Has Compiled The Data For Covid 19 Vaccine Deaths & The Vaccine Has Killed 17 MILLION PEOPLE “COVID 19 mRNA genetic injections have killed 17,000,000 people. Yep. Absolutely stunning. Can you please tell us more about the paper? That's the global number” “We studied many countries in Latin America and in the equatorial region. So we have a representative sample of The world, we believe, many injections. It's what? It's 10% of the world population. Yep. 9% of injections or the other way around. So the number we extract from our analysis allows us we we calculate the toxicity of the vaccine For all ages. And the number allows us, given the number of doses that have been given worldwide, to conclude that 17,000,000 people would have been killed by this vaccine. And having it by time, like, by week or by month and by age group means that you can actually see when there's a rollout of a booster, for example, which happens very quickly In time for a given age group, you can actually see immediately following it the a a maximum in the all cause mortality. So an excess mortality actual peak that's temporarily associated with that rollout? And we see that repeatedly for each rollout consistently across all countries. All the countries that have data where you're able to look at it and you have both the vaccine data and the mortality data, this is what we see.”

WOW 🚨 International Crisis Summit Has Compiled The Data For Covid 19 Vaccine Deaths & The Vaccine Has Killed 17 MILLION PEOPLE “COVID 19 mRNA genetic injections have killed 17,000,000 people. Yep. Absolutely stunning. Can you please tell us more about the paper? That's the global number” “We studied many countries in Latin America and in the equatorial region. So we have a representative sample of The world, we believe, many injections. It's what? It's 10% of the world population. Yep. 9% of injections or the other way around. So the number we extract from our analysis allows us we we calculate the toxicity of the vaccine For all ages. And the number allows us, given the number of doses that have been given worldwide, to conclude that 17,000,000 people would have been killed by this vaccine. And having it by time, like, by week or by month and by age group means that you can actually see when there's a rollout of a booster, for example, which happens very quickly In time for a given age group, you can actually see immediately following it the a a maximum in the all cause mortality. So an excess mortality actual peak that's temporarily associated with that rollout? And we see that repeatedly for each rollout consistently across all countries. All the countries that have data where you're able to look at it and you have both the vaccine data and the mortality data, this is what we see.”

Wall Street Apes

770,450 Aufrufe • vor 2 Jahren

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”

The All-In Podcast

79,718 Aufrufe • vor 11 Monaten

🚨 JUST IN: Florida Gov. Ron DeSantis has just BANNED AI data centers from using up all the water needed by citizens during a drought And the law BANS utility companies from charging higher prices because of data centers "How are you going to say that somehow the water can go to a data center when we need to water for our own people and for the core functions of our society?"

🚨 JUST IN: Florida Gov. Ron DeSantis has just BANNED AI data centers from using up all the water needed by citizens during a drought And the law BANS utility companies from charging higher prices because of data centers "How are you going to say that somehow the water can go to a data center when we need to water for our own people and for the core functions of our society?"

Eric Daugherty

567,367 Aufrufe • vor 2 Monaten

We have a lack in logic here. In this clip Joe Rogan theorizes that "the mark of a collapsing society is when people start blaming things on the Jews". Why would people choose ALWAYS this group? Why do they not blame other foreigners, the gods, or aliens? Why would they specifically always choose this group? Would it not make more sense that the statement provided is the inverse and rather it is that the level of control and power that the Jews maintain over civilization is the reason for its collapsing, leading the host peoples to point to the symptom of the decay around them. The power and control your rightly acknowledge them displaying over our politicians has far reaching repercussions. One simple lobby, funding the entirety of the political body can quite literally shape the moral and legal structure of a state at the snap of a finger. (something we are clearly experiencing in real time). The people are not looking for a scapegoat, the people are looking for the causes of the symptoms. You can praise a known plagiarist such as Einstein or a Perverted degenerate such as Freud, but you cannot fail to acknowledge that a character like Freud and his family line have been more destructive to Western civilization than any gentile you could ever name.

We have a lack in logic here. In this clip Joe Rogan theorizes that "the mark of a collapsing society is when people start blaming things on the Jews". Why would people choose ALWAYS this group? Why do they not blame other foreigners, the gods, or aliens? Why would they specifically always choose this group? Would it not make more sense that the statement provided is the inverse and rather it is that the level of control and power that the Jews maintain over civilization is the reason for its collapsing, leading the host peoples to point to the symptom of the decay around them. The power and control your rightly acknowledge them displaying over our politicians has far reaching repercussions. One simple lobby, funding the entirety of the political body can quite literally shape the moral and legal structure of a state at the snap of a finger. (something we are clearly experiencing in real time). The people are not looking for a scapegoat, the people are looking for the causes of the symptoms. You can praise a known plagiarist such as Einstein or a Perverted degenerate such as Freud, but you cannot fail to acknowledge that a character like Freud and his family line have been more destructive to Western civilization than any gentile you could ever name.

Zach Kidd

112,725 Aufrufe • vor 1 Jahr

Finland President Alexander Stubb: Well, we have to be more independent in some cases. If you, for example, look at storage of personal data, obviously a lot of Europeans are angry that their personal data is stored outside of the EU. So we have to build our own data centers and our own AI factories so we can actually own the data of our own citizens. But in the end, there is no such thing as European full autonomy. You will always need to collaborate with our friends in Canada, in the US, in other partnerships around the world. ASML and other tech companies can play a crucial role both in the US and here.

Finland President Alexander Stubb: Well, we have to be more independent in some cases. If you, for example, look at storage of personal data, obviously a lot of Europeans are angry that their personal data is stored outside of the EU. So we have to build our own data centers and our own AI factories so we can actually own the data of our own citizens. But in the end, there is no such thing as European full autonomy. You will always need to collaborate with our friends in Canada, in the US, in other partnerships around the world. ASML and other tech companies can play a crucial role both in the US and here.

Clash Report

20,444 Aufrufe • vor 8 Tagen

Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."

Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."

TBPN

90,711 Aufrufe • vor 5 Monaten

The smartest man in AI just exposed the whole AGI narrative as a LIE. And he used a physics problem from 1905 to prove it. His name is Demis Hassabis. He runs Google DeepMind, and won the Nobel Prize for using AI to crack a problem in biology that had stumped scientists for 50 years. Almost nobody in this industry has a track record like his. He went on the NothingButTech podcast and called out the biggest lie in AI right now: Right now the loudest voices in AI are telling you that AGI is basically here. OpenAI has literally defined AGI as a system that can outperform humans at most "economically valuable work." In other words, if it replaces enough jobs, we have arrived. Hassabis thinks that bar is a joke. He said real general intelligence has to do what the human brain can do, because the brain is the only proof we have that this kind of intelligence is even possible. He called that "a higher bar than just being able to do some useful economic work," which is about as close as a polite British Nobel laureate gets to calling his rivals out. Then he gave the actual test: Today's AI has read everything humans have ever written, including the theory of relativity. So when it explains relativity back to you, it's repeating an answer that already exists. That's not intelligence. So Hassabis proposed a test that makes memorization impossible. Train an AI on only what humanity knew in 1901, four years BEFORE Einstein published relativity. Then ask it to come up with relativity on its own. It can't look up the answer, because in 1901 the answer doesn't exist yet. The only way to pass is to do what Einstein actually did: Take the same physics everyone else had and reason its way to an idea no human had ever had. Hassabis says not a single AI today can, no matter how much it has memorized. Which means what we keep calling "almost AGI" is really just the best librarian in history. It can find any answer that already exists but it cannot create one that doesn't. His second version is even sharper: AlphaGo, the system his own team built, famously invented a brand new move that no human had played in 2,000 years of the game. Everyone called it genius but Hassabis says that still is not the bar. The real test is not whether an AI can invent a new move inside Go, it is whether an AI could INVENT a game as deep and as beautiful as Go in the first place. No model that exists today can do it. The people telling you AGI has already arrived are the same people raising hundreds of billions of dollars on that exact promise. The valuations only work if the finish line is right in front of us. So the finish line keeps getting dragged closer, and AGI keeps getting quietly redefined down to "does useful work," until the products they already sell happen to qualify. Hassabis has nothing to prove and nothing to sell you. He already won the Nobel, and he is telling you the machines still cannot do the one thing that would make them genuinely intelligent, which is have a truly original idea. To be fair to him, he is not a pessimist about it. He believes real AGI IS coming, and he is spending his life building it. He just refuses to pretend it is already sitting in your phone. So the next time a founder tells you AGI is months away, remember that the one man in the room with a Nobel Prize built his test around Einstein, and admitted that nothing we have made can pass it. What do you think?

Ricardo

1,283,770 Aufrufe • vor 1 Monat

#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)

#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)

Vertaix® (AI & Science)

34,762 Aufrufe • vor 1 Jahr