Podcasts about doudna

- Nvidia Earnings - US Senators react to Nvidia's plans for facility in China - New compliant chips for China by Nvidia and AMD - Trade restrictions reach EDA software - Vertical integration reaches China: Sugon-Hygon merger - AMD-Sanmina split ZT Systems - DOE NERSC's “Doudna” supercomputer - UK-EU dial up supercomputer re-collaboration [audio mp3="https://orionx.net/wp-content/uploads/2025/06/HPCNB_20250602.mp3"][/audio] The post HPC News Bytes – 20250602 appeared first on OrionX.net.

Billionaire tech titan Elon Musk's time as a “special government employee” is coming to an end, but the DOGE team at the Defense Department will soon have greater influence on Pentagon contracting. Since President Donald Trump began his second term in January, Musk has spearheaded the Department of Government Efficiency's push across the federal government to find “waste, fraud and abuse,” slash certain types of spending and cut the workforce. A DOGE team was set up at the Pentagon — as well as other federal agencies — to implement those efforts. Musk wrote Wednesday night in a post on X that his time as a special government employee was coming to an end but: “The @DOGE mission will only strengthen over time as it becomes a way of life throughout the government.” In a sign that DOGE's influence will continue at the Pentagon, Secretary of Defense Pete Hegseth issued a new directive this week giving those personnel more oversight of contracting efforts. Hegseth wrote in a May 27 memo to senior Pentagon leadership, combatant commanders, and DOD agency and field activity directors that: “The Department of Defense (DoD) Department of Government Efficiency (DOGE) team will have the opportunity to provide input on all unclassified contracts. The Under Secretary of Defense for Acquisition and Sustainment (USD(A&S)), or its designee, will coordinate with DOGE to ensure that the opportunity for review of the Performance Work Statement/Statement of Work, accompanying estimates, deliverable descriptions, and requirements approval/validation documents, occurs when the requirements package is provided to a DoD contracting office to initiate a procurement or prior to the package being provided to a non-DoD assisting agency (e.g., General Services Administration).” In a video released Wednesday on X, Hegseth said the Pentagon had already saved more than $10 billion working with DOGE on previous efforts to review spending, including from a “line-by-line audit of over 50 contract vehicles.” Energy Secretary Chris Wright announced Thursday that the government would build a new supercomputer powered by NVIDIA chips and based at a department user facility at the Lawrence Berkeley National Laboratory. Officials said the supercomputer will be named Doudna after UC Berkeley scientist Jennifer Doudna, who co-invented CRISPR gene editing technology and won the Nobel Prize back in 2020. The Doudna supercomputer, which is geared toward high-performance computing and training artificial intelligence technology, will be based at the National Energy Research Scientific Computing Center. It is only the latest Energy Department project designed for the AI age: El Capitan, a supercomputer based at Lawrence Livermore National Laboratory and currently the world's fastest, is also designed with machine learning in mind, as is Frontier, a DOE supercomputer housed at the Oak Ridge National Laboratory in Tennessee. A spokesperson would not comment further on how the Doudna supercomputer's speeds might compare to other systems. Government supercomputing projects, including those focused on AI, are now supported by the same national laboratory system that incubated the Manhattan Project, which produced the world's first atomic weapons. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

Diving into a short story on scientist Jennifer Doudna and what I like to call the Obsession Test.Check out Walter Isaacson's book, The Code Breaker, for more on Doudna and how she is changing the world.-----“I'm someone who's thinking about science all the time. I'm always focused on what's cooking in the lab, the next experiment, or the bigger question to pursue. I was always obsessed with what my next experiment was going to be.”- Jennifer Doudna -----You can check support and stay connected belowWebsiteBook: Chasing Greatness: Timeless Stories on the Pursuit of Excellence  ApparelInstagramX

We take another look at CRISPR, the genetic tool that earned Doudna and Charpentier the Nobel Prize, to ask the question on everyone's mind. Can we use CRISPR to produce a super intelligent bear? --- Support this podcast: https://podcasters.spotify.com/pod/show/tom-saunders9/support

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is a revolutionary technology that gives scientists the ability to alter DNA. On the one hand, this tool could mean the elimination of certain diseases. On the other, there are concerns (both ethical and practical) about its misuse and the yet-unknown consequences of such experimentation. "The technique could be misused in horrible ways," says counter-terrorism expert Richard A. Clarke. Clarke lists biological weapons as one of the potential threats, "Threats for which we don't have any known antidote." CRISPR co-inventor, biochemist Jennifer Doudna, echos the concern, recounting a nightmare involving the technology, eugenics, and a meeting with Adolf Hitler. Should humanity even have access to this type of tool? Do the positives outweigh the potential dangers? How could something like this ever be regulated, and should it be? These questions and more are considered by Doudna, Clarke, evolutionary biologist Richard Dawkins, psychologist Steven Pinker, and physician Siddhartha Mukherjee. -------------------------------------------------------------------------------------------- TRANSCRIPT: 0:41 Jennifer Doudna defines CRISPR 3:47 CRISPR's risks 4:52 Artificial selection vs. artificial mutation 6:25 Why Steven Pinker believes humanity will play it safe 9:20 Lessons from history 10:58 How CRISPR can help 11:22 Jennifer Doudna's chimeric-Hitler dream - Our ability to manipulate genes can be very powerful. It has been very powerful. - This is going to revolutionize human life. - Would the consequences be bad? And they might be. - Every time you monkey with the genome you are taking a chance that something will go wrong. - The technique could be misused in horrible ways. - When I started this research project, I've kind of had this initial feeling of what have I done. Learn more about your ad choices. Visit megaphone.fm/adchoices

Following recent interviews, Jennifer Doudna, Honor Harger and David Kemp return with final thoughts.

God After Doudna by Kenilworth Union Church

Dieses Mal geht es nicht nur um die Vergangenheit: wie sich die Erfindung der "Genschere" CRISPR-Cas9 auf die Zukunft auswirken könnte, erzählt Andrea Sawatzki in dieser Folge.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four-part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. Support the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. Support the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. Support the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. Support the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. See omnystudio.com/listener for privacy information.

What is it like to shadow Elon Musk for two years? To sit courtside as he builds a rocket? Or tears apart an engineer? Or couch surfs at the homes of billionaires? And how on earth do you make sense of it all? Walter Isaacson is the biographer of giants: DaVinci, Franklin, Doudna, Jobs...and now Musk, former enfante terrible, rocket launcher, electric car innovator, and Twitter—er, X—disruptor, to put it gently. In this four part series, author Evan Ratliff (Mastermind, Longform Podcast) sits down with Isaacson to draw out the behind-the-scenes stories of this epic biography, and what the writer has learned as an outsider inside Silicon Valley.   Listen here or on the iHeartRadio app. See omnystudio.com/listener for privacy information.

David Liu is an gifted molecular biologist and chemist who has pioneered major refinements in how we are and will be doing genome editing in the future, validating the methods in multiple experimental models, and establishing multiple companies to accelerate their progress.The interview that follows here highlights why those refinements beyond the CRISPR Cas9 nuclease (used for sickle cell disease) are vital, how we can achieve better delivery of editing packages into cells, ethical dilemmas, and a future of somatic (body) cell genome editing that is in some ways is up to our imagination, because of its breadth, over the many years ahead. Recorded 29 November 2023 (knowing the FDA approval for sickle cell disease was imminent)Annotated with figures, external links to promote understanding, highlights in bold or italics, along with audio links (underlined)Eric Topol (00:11):Hello, this is Eric Topol with Ground Truths and I'm so thrilled to have David Liu with me today from the Broad Institute, Harvard, and an HHMI Investigator. David was here visiting at Scripps Research in the spring, gave an incredible talk which I'll put a link to. We're not going to try to go over all that stuff today, but what a time to be able to get to talk with you about what's happening, David. So welcome.David Liu (00:36):Thank you, and I'm honored to be here.Eric Topol (00:39):Well, the recent UK approval (November 16, 2023) of the first genome editing after all the years that you put into this, along with many other colleagues around the world, is pretty extraordinary. Maybe you can just give us a sense of that threshold that's crossed with the sickle cell and beta thalassemia also imminently [FDA approval granted for sickle-cell on 8 December 2023] likely to be getting that same approval here in the U.S.David Liu (01:05):Right? I mean, it is a huge moment for the field, for science, for medicine. And just to be clear and to give credit where credit is due, I had nothing to do with the discovery or development of CRISPR Cas9 as a therapeutic, which is what this initial gene editing CRISPR drug is. But of course, the field has built on the work of many scientists with respect to CRISPR Cas9, including Emmanuel Charpentier and Jennifer Doudna and George Church and Feng Zhang and many, many others. But it is, I think surprisingly rapid milestone in a long decade's old effort to begin to take some control over our genetic features by changing DNA sequences of our choosing into sequences that we believe will offer some therapeutic benefit. So this initial drug is the CRISPR Therapeutics /Vertex drug. Now we can say it's actually a drug approved drug, which is a Crispr Cas9 nuclease programmed to cut a DNA sequence that is involved in silencing fetal hemoglobin genes. And as you know, when you cut DNA, you primarily disrupt the sequence that you cut. And so if you disrupt the DNA sequence that is required for silencing your backup fetal hemoglobin genes, then they can reawaken and serve as a way to compensate for adult hemoglobin genes like the defective sickle cell alleles that sickle cell anemia patients have. And so that's the scientific basis of this initial drug.Eric Topol (03:12):So as you aptly put— frame this—this is an outgrowth of about a decade's work and it was using a somewhat constrained, rudimentary form of editing. And your work has taken this field considerably further with base and prime editing whereby you're not just making a double strand cut, you're doing nicks, and maybe you can help us understand this next phase where you have more ways you can intervene in the genome than was possible through the original Cas9 nucleases.David Liu (03:53):Right? So gene editing is actually a several decades old field. It just didn't quite become as popular as it is now until the discovery of CRISPR nucleases, which are just much easier to reprogram than the previous programmable zinc finger or tail nucleases, for example. So the first class of gene editing agents are all nuclease enzymes, meaning enzymes that take a piece of DNA chromosome and literally cut it breaking the DNA double helix and cutting the chromosome into two pieces. So when the cell sees that double strand DNA break, it responds by trying to get the broken ends of the chromosome back together. And we think that most of the time, maybe 90% of the time that end joining is perfect, it just regenerates the starting sequence. But if it regenerates the starting sequence perfectly and the nuclease is still around, then it can just cut the rejoin sequence again.(04:56):So this cycle of cutting and rejoining and cutting and rejoining continues over and over until the rejoining makes the mistake that changes the DNA sequence at the cut site because when those mistakes accumulate to a point that the nuclease no longer recognizes the altered sequence, then it's a dead end product. That's how you end up with these disrupted genes that result from cutting a target DNA sequence with a nuclease like Crispr Cas9. So Crispr Cas9 and other nucleases are very useful for disrupting genes, but one of their biggest downsides is in the cells that are most relevant to medicine, to human therapy like the cells that are in your body right now, you can't really control the sequence of DNA that comes out of this process when you cut a DNA double helix inside of a human cell and allow this cutting and rejoining process to take place over and over again until you get these mistakes.(06:03):Those mistakes are generally mixtures of insertions and deletions that we can't control. They are usually disruptive to a gene. So that can be very useful when you're trying to disrupt the function of a gene like the genes that are involved in silencing fetal hemoglobin. But if you want to precisely fix a mutation that causes a genetic disease and convert it, for example, back into a healthy DNA sequence, that's very hard to do in a patient using DNA cutting scissors because the scissors themselves of course don't include any information that allows you to control what sequence comes out of that repair process. You can add a DNA template to this cutting process in a process called HDR or Homology Directed Repair (figure below from the Wang and Doudna 10-year Science review), and sometimes that template will end up replacing the DNA sequence around the cut site. But unfortunately, we now know that that HDR process is very inefficient in most of the types of cells that are relevant for human therapy.(07:12):And that explains why if you look at the 50 plus nuclease gene editing clinical trials that are underway or have taken place, all but one use nucleases for gene disruption rather than for gene correction. And so that's really what inspired us to develop base editing in 2016 and then prime editing in 2019. These are methods that allow you to change a DNA sequence of your choosing into a different sequence of your choosing, where you get to specify the sequence that comes out of the editing process. And that means you can, for the first time in a general way, programmable change a DNA sequence, a mutation that causes a genetic disease, for example, into a healthy sequence back into the normal, the so-called wild type sequence, for example. So base editors work by actually performing chemistry on an individual DNA base, rearranging the atoms of that base to become a different base.(08:22):So base editors can efficiently and robustly change A's into G's G's, into A's T's into C's or C's into T's. Those four changes. And those four changes for interesting biochemical reasons turn out to be four of the most common ways that our DNA mutates to cause disease. So base editors can be used and have been used in animals and now in six clinical trials to treat a wide variety of diseases, high cholesterol and sickle cell disease, and T-cell leukemia for example. And then in prime editors we developed a few years later to try to address the types of changes in our genomes that caused genetic disease that can't be fixed with a base editor, for example. You can't use a base editor to efficiently and selectively change an A into a T. You can't use a base editor to perform an insertion of missing DNA letters like the three missing letters, CTT, that's the most common cause of cystic fibrosis accounting for maybe 70% of cystic fibrosis patients.(09:42):You can't use a base editor to insert missing DNA letters like the missing TATC. That is the most common cause of Tay-Sachs disease. So we develop prime editors as a third gene editing technology to complement nucleases and base editors. And prime editors work by yet another mechanism. They don't, again, they don't cut the DNA double helix, at least they don't cause that as the required mechanism of editing. They don't perform chemistry on an individual base. Instead, prime editors take a target DNA sequence and then write a new DNA sequence onto the end of one of the DNA strands and then sort of help the cell navigate the DNA repair processes to have that newly written DNA sequence replace the original DNA sequence. And in the process it's sort of true search and replace gene editing. So you can basically take any DNA sequence of up to now hundreds of base pairs and replace it with any other sequence of your choosing of up to hundreds of base pairs. And if you integrate prime editing with other enzymes like recombinase, you can actually perform whole gene integration of five or 10,000 base pairs, for example, this way. So prime editing's hallmark is really its versatility. And even though it's the newest of the three ways that have been robustly used to edit mammalian cells and rescue animal models of genetic disease, it is arguably the most versatile by far,Eric Topol (11:24):Right? Well, in fact, if you just go back to the sickle cell story as you laid out the Cas9 nuclease, that's now going into commercial approval in the UK and the US, it's more of a blunt instrument of disruption. It's indirect. It's not getting to the actual genomic defect, whereas you can do that now with these more refined tools, these new, and I think that's a very important step forward. And that is one part of some major contributions you've made. Of course, there are many. One of the things, of course, that's been a challenge in the field is delivery whereby we'd like to get this editing done in many parts of the body. And of course it's easy, perhaps I put that in quotes, easy when you're taking blood out and you're going to edit those cells and them put it back in. But when you want to edit the liver or the heart or the brain, it gets more challenging. Now, you did touch on one recent report, and this is of course the people with severe familial hypercholesterolemia. The carriers that have LDL cholesterol several hundred and often don't respond to even everything we have on the shelf today. And there were 10 people with this condition that was reported just a few weeks ago. So that's a big step forward.David Liu (13:09):That was also a very exciting milestone. So that clinical trial was led by scientists at Verve Therapeutics and Beam Therapeutics, and it was the first clinical readout of an in vivo base editing clinical trial. There was previously at the end of 2022, the first clinical readout of an ex vivo base editing clinical trial using CAR T cells, ex vivo  base edited to treat T-cell leukemia in pediatric patients in the UK. Ffigure from that NEJM paper below). But as you point out, there are only a small fraction of the full range of diseases that we'd like to treat with gene editing and the types of cells we'd like to edit that can be edited outside of the body and then transplanted back into the body. So-called ex vivo editing. Basically, you can do this with cells of some kind of blood lineage, hematopoietic stem cells, T-cells, and really not much else in terms of editing outside the body and then putting back into the body as you point out.(14:17):No one's going to do that with the brain or the heart anytime soon. So what was very exciting about the Verve Beam clinical trial is that Verve sought to disrupt the function of PCSK9 storied, gene validated by human genetics, because there are humans that naturally have mutations in PCSK9, and they tend to have much lower incidences of heart disease because their LDL, so-called bad cholesterol, is much lower than it would otherwise be without those mutations. So Verve set out to simply disrupt PCSK9 through gene editing. They didn't care whether they used a nuclease or a base editor. So they compared side-by-side the results of disrupting PCSK9 with Cas9 nuclease versus disrupting it by installing a precise single letter base edit using an adenine base editor. And they actually concluded that the base editor gave them higher efficacy and fewer unwanted consequences.(15:28):And so they went with the base editor. So the clinical trial that just read out were patients treated in New Zealand, in which they were given a lipid nanoparticle mRNA complex of an adenine base editor programmed with a guide RNA to install a specific A to G mutation in a splice site in PCSK9 that inactivates the gene so that it can no longer make functional PCSK9 protein. And the exciting result that read out was that in patients that receive this base editor, a single intravenous injection of the base editor lipid nanoparticle complex, as you know, lipid nanoparticles very efficiently go to the liver. In most cases, PCSK9 was edited in the liver and the result was substantial reduction in LDL cholesterol levels in these patients. And the hope and the anticipation is that that one-time treatment should be durable, should be more or less permanent in these patients. And I think while the patients who are at highest risk of coronary artery disease because of their genetics that give them absurdly high LDL cholesterol levels, that makes the most sense to go after those patients first because they are at extremely high risk of heart attacks and strokes. If the treatment proves to be efficacious and safe, then I think it's tempting to speculate that a larger and larger population of people who would benefit from having lower LDL cholesterol levels, which is probably most people, that they would also be candidates for this kind of therapy.Eric Topol (17:22):Yeah, no, it's actually pretty striking how that could be achieved. And I know in the primates that were done prior to the people in New Zealand, there was a very durable effect that went on well over I think a year or even two years. So yeah, that's right. Really promising. So now that gets us to a couple of things. One of them is the potential for off-target effects. As you've gotten more and more with these tools to be so precise, is the concern that you could have off-target effects just completely, of course inadvertent, but potential for other downstream in time known unknowns, if you will. What are your thoughts about that?David Liu (18:15):Yeah, I have many thoughts on this issue. It's very important the FDA and regulatory bodies are right to be very conservative about off-target editing because we anticipate those off targets will be permanent, those off-target edits will be permanent. And so we definitely have a responsibility to minimize adding to the mutational burden that all humans have as a function of existing on this planet, eating what we eat, being bombarded by cosmic rays and sunlight and everything else. But I think it's also important to put off-target editing into some context. One context is I think virtually every substance we've ever put into a person, including just about every medicine we've ever put into a person, has off-target effects, meaning modulates the function of biological molecules other than the intended target. Of course, the stakes are higher when those are gene editing agents because those modifications can be permanent.(19:18):I think most off-target edits are very likely to have no consequence because most of our genome, if you mutate in the kinds of small ways like making an individual base pair change for a base editor are likely to have no consequence. We sort of already know this because we can measure the mutational burden that we all face as a function of living and it's measurable, it's low, but measurable. I've read some papers that estimate that of the roughly 27 trillion [should be ~37] cells in an adult person, that there are billions and possibly hundreds of billions of mutations that accumulate every day in those 27 [37] trillion cells. So our genomes are not quite the static vaults that we'd like to think that they are. And of course, we have already purposefully given life extending medicines to patients that work primarily by randomly mutating their genomes. These are chemotherapeutic agents that we give to cancer patients.(20:24):So I think that history of giving chemotherapeutic agents, even though we know those agents will mess up the genomes of these patients and potentially cause cancer far later down the road, demonstrates that there are risk benefit situations where the calculus favors treatment, even if you know you are causing mutations in the genome, if the condition that the patient faces and their prognosis is sufficiently grave. All that said, as I mentioned, we don't want to add to the mutational burden of these patients in any clinically relevant way. So I think it is appropriate that the early gene editing clinical candidates that are in trials or approved now are undergoing lots and lots of scrutiny. Of course, doing an off-target analysis in an animal is of limited value because the animal's genome is quite different than the human genome. So the off targets won't align, but doing off-target analysis in human cells and then following up these patients for a long time to confirm hopefully that there isn't clinical evidence of quality of life or lifespan deterioration caused by off-target editing, that's all very, very important.(21:55):I also think that people may not fully appreciate that on target editing consequences also need to be examined and arguably examined with even more urgency than off-target edits. Because when you are cutting a chromosome at a target site with a nucleus, for example, you generate a complex mixture of different products of different DNA sequences that come out, and the more sequences you sequence, the more different products you realize are generated. And I don't think it's become routine to try to force the companies, the clinical groups that are running these trials to characterize the top 1000 on target products for their biological consequence. That would be sort of impractical to do and would probably slow down greatly the benefit of these early nuclease clinical trials for patients. But those are actually the products that are generated with much higher frequency typically than the off-target edits. And that's part of why I think it makes more sense from a clinical safety perspective to use more precise gene editing methods like base editing and prime editing where we know the products that are generated are mostly the products that we want are not uncontrolled mixtures of different deletion and insertion products.(23:27):So I think paying special attention to the on-target products, which are generated typically 70 to 100% of the time as opposed to the off targets which may be generated at a 0.1 to 1% level and usually not that many at that level once it reaches a clinical candidate. I think that's all important to do.Eric Topol (23:51):You've made a lot of great points there and thanks for putting that in perspective. Well, let's go on to the delivery issue. You mentioned nanoparticles, viral vectors, and then you've come up with small virus-like neutered viruses if you will. I think a company Nvelop that you've created to push on that potential. What are your thoughts about where we stand since you've become a force for coming up with much better editing, how about much better and more diverse delivery throughout the body? What are your thoughts about that?David Liu (24:37):Yeah, great. Great question. I think one of the legacies of gene editing is and will be that it inspired many more scientists to work hard on macromolecular delivery technologies. All of these gene editing agents are macromolecules, meaning they're proteins and or nucleic acids. None of them are small molecules that you can just pop a pill and swallow. So they all require special technologies to transfer the gene editing agent from outside of the cell into the cell. And the fact that taking control of our genetic features has become such a popular aspiration of medicine means that there's a lot of scientists as measured, most importantly by the young scientists, by the graduate students and the postdocs and the young professors of which I'm no longer one sadly, who have decided that they're going to devote a big part of their program to delivery. So you summarized many of the clinically relevant, clinically validated delivery technologies already, somewhat sadly, because if there were a hundred of these technologies, you probably wouldn't need to ask this question. But we have lipid nanoparticles that are particularly good at delivering messenger RNA, that was used to deliver the covid vaccine into billions of people. Now also used to deliver, for example, the adenine base editor mRNA into the livers of those hypercholesterolemia patients in the Verve/Beam clinical trial.(26:20):So those lipid nanoparticles are very well matched for gene editing delivery as long as it's liver. And they also are particularly well matched because their effect is transient. They cause a burst of gene editing agents to be produced in the liver and then they go away. The gene editing agents can't persist, they can't integrate into the genome despite what some conspiracy theorists might worry about. Not that you've had any encounter with any of those people. I'm sure that's actually what you want for a gene editing agent. You ideally want a delivery method that exposes the cell only for the shortest amount of time needed to make the on-target edit at the desired level. And then you want the gene editing agent to disappear and never come back because it shouldn't need to. DNA edits to our genome for durable cells should be permanent. So that's one method.(27:25):And then there are a variety of other methods that researchers have used to deliver to other cells, but they each carry some trade-offs. So if you're trying to edit hematopoietic stem cells, you can take them out of the body. Once they're out of the body, you have many more methods you can use to deliver efficiently into them. You can electroporated messenger, RNA or even ribonuclear proteins. You can treat with lipids or viruses, you can edit and then put them back into the body. But as you already mentioned, that's sort of a unique feature of blood cells that isn't applicable to the heart or the brain, for example, or the eyes. So then that brings us to viral vectors. There are a variety of clinically validated viral methods for delivery. AAV— adeno associated virus— is probably the most diverse, most relevant, and one of the best tolerated viral delivery methods. The beauty of AAV is that it can deliver to a variety of tissues. AAV can deliver into spinal cord neurons, for example, into retinal cells, into the heart, into the liver, into a few other tissues as well.(28:48):And that diversity of being able to choose AAV capsids that are known to get into the types of tissues that you're trying to target is a great strength of that approach. One of the downsides of AAV for gene editing agents is that their delivery tends to be fairly durable. You can engineer AAVs into next generation capsids that sort of get rid of themselves or the gene editing agents get rid of themselves. But classic AAV tends to stay around in patients for a long time, at least months, for example, and possibly years. And we also don't yet have a good way, clinically validated way of re-dosing AAV. And once you administer high doses of AAV in a patient that tends to provoke high-titer, neutralizing antibodies against those AAVs making it difficult to then come back six months or a year later and dose again with an AAV.(29:57):So researchers are on the bright side, have become very good at engineering and evolving in the laboratory next generation AAVs that can go to greater diversity issues that can be more potent. Potency is important because if you can back off the dose, maybe you can get around some of these immunogenicity issues. And I think we will see a renaissance with AAV that will further broaden its clinical scope. Even though I appreciate that the decisions by a couple large pharma companies to sort of pull out of using AAV for gene therapy seemed to cause people to, I think prematurely conclude that AAV has fallen out of favor. I think for gene therapy, it's quite different than gene editing. Gene therapy, meaning you are delivering a healthy copy of the gene, and you need to keep that healthy copy of the gene in the patient for the rest of the patient's life.(30:59):That's quite different than gene editing where you just need the edit to take place over days to weeks, and then you want the editing agent to actually go away and you never want to come back. I think AAV will used to deliver gene editing agents will avoid some of the clinical challenges like how do we redose? Because you shouldn't need to redose if the gene editing clinical trial proceeds as you hope. And then you mentioned these virus-like particles. So we became interested in virus-like particles as other labs have because they offer some of the best strengths of non-viral and viral approaches like non-viral approaches such as LMPs. They deliver the transient form of a gene editing agent. In fact, they can deliver the fully assembled protein RNA complex of a base editor or a prime editor or a CRISPR nuclease. So in its final form, and that means the exposure of the cell to the editing agent is minimized.(32:15):You can treat with these virus-like particles, deliver the protein form of these gene editing agents, allow the on-target site to get edited. And then since the half-life of these proteins tends to be very small, roughly 24 hours for example, by a week later, there should be very little of the material left in the animal or prospectively in the patient virus-like particles, as you call them, neutered viruses, they lack viral DNA or RNA. They don't have the ability to integrate a virus's genome into the human genome, which can cause some undesired consequences. They don't randomly introduce DNA into our genomes, therefore, and they disappear more transiently than viruses like AAV or adenoviruses or other kinds of lentiviruses that have been used in the clinic. So these virus-like particles or VLP offer really some of the best strengths on paper at least of both viral and non-viral delivery.(33:30):Their limitation thus far has been that there really haven't been examples of potent in vivo delivery of cargoes like gene editing agents using virus-like particles. And so we recently set out to figure out why, and we identified several bottlenecks, molecular bottlenecks that seemed to be standing in the way of virus-like particles, doing a much more efficient job at delivering inside of an animal. (Figure from that paper below.) And we engineered solutions to each of these first three molecular bottlenecks, and we've identified a couple more since. And that resulted in what we call VLPs engineered virus-like particles. And as you pointed out, Keith Joung and myself, co-founded a company called Nvelop to try to bring these technologies and other kinds of molecular delivery technologies, next generation delivery technologies to patients.Eric Topol (34:28):Well, that gets me to the near wrapping up, and that is the almost imagination you could use about where all this can go in the future. Recently, I spoke to a mutual friend Fyodor Urnov, who talked about wouldn't it be amazing if for people with chronic pain you could just genome edit neurons their spinal cord? As you already touched on recently, Jennifer Doudna, who we both know talked about editing to prevent Alzheimer's disease. Well, that may be a little far off in time, but at least people are talking about these things that is not, we're not talking about germline editing, we're just talking about somatic cell and being able to approach conditions that have previously been either unapproachable or of limited success and potential of curing. So this field continues to evolve and you and all your colleagues are a big part of how this has evolved as quickly as it has. What are your thoughts about, are there any bounds to the potential in the longer term for genome editing? Right.David Liu (35:42):It's a great question because all of the early uses of gene editing in people are appropriately focused on people who are at dire risk of having shorter lives or very poor quality of life as it should be for a new kind of therapeutic because the risks are high until we continue to validate the clinical benefit of these gene editing treatments. And therefore we want to choose patients the highest that face the poorest prognosis where the risk benefit ratio favors treatment as strongly as possible. But your question, I think very accurately highlights that our genome and changes to it determine far more than whether you have a serious genetic disorder like Sickle Cell Disease or Progeria or Cystic Fibrosis or Familial Hypercholesterolemia or Tay-Sachs disease. And being able to not just correct mutations that are associated with devastating genetic disorders, but perhaps take control of our genomes in more sophisticated way that you pointed out two examples that I think are very thought provoking to treat chronic pain permanently to lower the risk of horrible diseases that affect so many families devastating to economies worldwide as well, like Alzheimer's disease, Parkinson's disease, the genetic risk factors that are the strongest genetic determinants of diseases like Alzheimer's disease are actually, there are several that are known already.(37:36):And an interesting possibility for the future, it isn't going to happen in the next few years, but it might happen within the next 10 or 20 years, might be to use gene editing to precisely change some of those most grievous alleles that are risk factors for Alzheimer's disease like a apoE4, to change them to the genetic forms that have normal or even reduced risk for Alzheimer's disease. That's a very tough clinical trial to run, but I'd say not any tougher than the dozens of most predominantly failed Alzheimer's clinical trials that have probably collectively accounted for hundreds of billions of dollars of investmentEric Topol (38:28):Easily.David Liu (38:31):And all of that speaks to the fact that Alzheimer's disease, for example, is enormous burden on society by every measure. So it's worth investing and major resources and taking major risks to try to create perhaps preventative treatments that just lower our risk globally. Getting there will require that these pioneering early clinical trials for gene editing are smashing successes. I'm optimistic that they will be, there will be bumps in the road because there always are bumps in the road. There will be patients who have downturns in their health and everyone will wonder whether those patients had a downturn because of a gene editing treatment they received. And ascertaining whether that's the case will be very important. But as these trials continue to progress, and as they continue hopefully on this quite positive trajectory to date, it's tempting to imagine a future where we can use precise gene editing methods. For example, you can install a variety using prime editing, a variety of alleles that naturally occur in people that reduce the risk of Alzheimer's disease or Parkinson's disease like the mutation that 0.1% of Icelandic people and almost nobody else has in amyloid precursor protein changing alanine 673 to threonine (A673T).(40:09):It is very thought provoking, and I don't think society is ready now to take that step, but I think if things continue to proceed on this promising trajectory, it's inevitable because arguably, the defining trait of our species is that we use every ounce of our talents and our gifts and our resources and our creativity to try to improve our lives and those of our children. And I don't think if we have ways of treating genetic diseases or even of reducing grievous genetic disease risk, that we will be able to sit on our hands and not take steps towards that kind of future solon as those technologies continue to be validated in the clinic as being safe and efficacious. It's, I teach a gene editing class and I walk them through a slippery slope at the end of five ethics cases, starting with progeria, where most people would say having a single C of T mutation in one gene that you, by definition didn't inherit from mom or dad.(41:17):It just happened spontaneously. That gives you an average lifespan of 14 and a half years and strongly affects other aspects of the quality of your life and your family's life that if you can change as we did in animals that T back into a C and correct the disease and rescue many of the phenotypes and extend lifespan, that that's an ethical use of gene editing. Treating genetic deafness is the second case. It's a little bit more complicated because many people in the deaf community don't view deafness as a disability. It's at least a more subjective situation than progeria. But then there are other cases like changing apoE4 to apoE3 or even apoE2 with the lower than normal risk of Alzheimer's disease, or installing that Icelandic mutation and amyloid precursor protein that substantially lowers risk of Alzheimer's disease. And then finally, you can, I always provoke a healthy debate in the class at the end by pointing out that in the 1960s, one of the long distance cross country alpine skiing records was set by a man who had a naturally occurring mutation in his EPO receptor, his erythropoietin receptor, so that his body always thought he was on EPO as if he were dosing on EPO, although that was of course before the era of EPO dosing was really possible, but it was just a naturally occurring mutation in this case, in his family.(42:48):And when I first started teaching this class, most students could accept using gene editing to treat progeria, but very few were willing to go even past that, even to genetic deafness, certainly not to changing a ApoE risk factors for Alzheimer's. Nowadays, I'd say the 50% vote point is somewhere between case three and case four, most people are actually say, yeah, especially since they have family members who've been through Alzheimer's disease. If they are a apoE4, some of them are a apoE4/apoE4 [homozygotes], why not change that to a apoE3 or even an ApoE2 or as one student challenged the class this year, if you were born with a apoE2, would you want to change it to a ApoE3 so you could be more normal? Most people would say, no, there's no way I would do that.(43:49):And for the first time this year, there were one or two students who actually even defended the idea of putting in a mutation in erythropoietin receptor to increased increase their endurance under low oxygen conditions. Of course, it's also presumably useful if you ever, God forbid, are treated with a cancer chemotherapeutic. Normally you get erythropoietin to try to restore some, treat some of the anemia that can result, and this student was making a case, well, why wouldn't we? If this is a naturally occurring mutation that's been shown to benefit certain people doing certain things. I don't think that's a general societal view. And I am a little bit skeptical we'll ever get widespread acceptance of case number five. But I think all of it is healthy stimulates a healthy discussion around the surprisingly gentle continuum between disease treatment, disease prevention, and what some would call human improvement.And it used to be that even the word human improvement was sort of an anathema. I think now at least the students in my class are starting to rethink what does that really mean? We improving ourselves a number of ways genetically and otherwise by virtue of our lifestyles, by virtue of who we choose to procreate with. So it's a really interesting debate, and I think the rapid development and now clinical progression and now approval, regulatory approval of gene editing drugs will play a central role in this discussion.Eric Topol (45:38):No question. I mean, also just to touch on the switch from a apoE4 to apoE2, you would get a potential 2-fer of lesser risk for Alzheimer's and a longer lifespan. So I mean, there's a lot of things here. The thing that got me years ago, I mean, this is many years ago at a meeting with George Church and he says, we're going to just edit 60 genes and then we can do all sorts of xeno-pig transplants and forget the problem of donors. And it's happening now.David Liu (46:11):Yeah, I mean, he used a base editor to edit hundreds of genes at once, if not thousands ofEric Topol (46:16):That's why it's just, yeah, no, it's just extraordinary. And I think people need to be aware that opportunities here, as you say, with potential bumps along the way, unquestionably, is almost limitless. So this has been a masterclass thanks to you, David, in where we are, where we're headed in genome editing at a very extraordinary time where we've really seeing things click. And I just want to also add that you're going to be here with a conference in La Jolla in January, I think, on base and prime editing. Is that right? So for those who are listeners who are into this topic, maybe they can also hear the latest, I'm sure there'll be more between now and next. Well, several weeks from now at your, it's aDavid Liu (47:12):Conference on, it's the fifth international conference on base and prime editing and associated enzymes, the somewhat baroque name. And I will at least be giving a virtual talk there. It actually overlaps with the talk I'm giving at Rockefeller that time. Ah, okay, cool. But I'm speaking at the conference either in person or virtually.Eric Topol (47:34):Yeah. Well, anytime we get to hear from you and the field, of course it's enlightening. So thanks so much for joining. Thank youDavid Liu (47:42):For having me. And thank you also for all of your, I think, really important public service in connecting appropriately the ground truths about science and vaccines and other things to people. I think that's very much appreciated by scientists like myself.Eric Topol (48:00):Oh, thanks David.Thanks for listening, reading, and subscribing to Ground Truths. To be clear, this is a hybrid format, roughly alternating between analytical newsletters/essays and podcasts with exceptional people, attempting to achieve about 2 posts per week. It's all related to cutting-edge advances in life science, medicine, and information tech (A.I.)All content is free. If you wish to become a paid subscriber know that all proceeds go to Scripps Research. Get full access to Ground Truths at erictopol.substack.com/subscribe

di Massimo Temporelli | Questo episodio è stato realizzato in collaborazione con Fondazione AIRC per la ricerca sul cancro In questa puntata di Lampi di Genio andiamo a conoscere l'incredibile storia di CRISPR/Cas9, uno strumento rivoluzionario per l'editing genetico. Sfruttando un meccanismo già presente nel “sistema immunitario” dei batteri, la microbiologa francese Emmanuelle Charpentier e la biochimica statunitense Jennifer Doudna, sono riuscite a creare un metodo nuovo e agile per modificare qualsiasi tipo di DNA, dando un grandissimo contributo anche alla ricerca di cure più efficaci per il cancro. Per questa scoperta, Doudna e Charpentier hanno ricevuto il Premio Nobel per la chimica nel 2020. Geniale!

The technology known as CRISPR is considered one of modern biology’s biggest breakthroughs. It allows scientists to edit genes, similar to how you cut and paste text in a word processor. More than a decade after pioneering CRISPR, Nobel laureate Jennifer Doudna of the University of California, Berkeley, is applying it to big problems, like chronic disease and climate change.Marketplace's Lily Jamali recently met up with Doudna at Berkeley’s Innovative Genomics Institute. It's a cluster of lab stations, researchers and very loud refrigerators where CRISPR is used to edit microbiomes.

The technology known as CRISPR is considered one of modern biology’s biggest breakthroughs. It allows scientists to edit genes, similar to how you cut and paste text in a word processor. More than a decade after pioneering CRISPR, Nobel laureate Jennifer Doudna of the University of California, Berkeley, is applying it to big problems, like chronic disease and climate change.Marketplace's Lily Jamali recently met up with Doudna at Berkeley’s Innovative Genomics Institute. It's a cluster of lab stations, researchers and very loud refrigerators where CRISPR is used to edit microbiomes.

Every year, approximately 8 million children are born with a serious genetic disorder, and 3 million of them die before the age of 5. This disease burden is about to change. In this episode, we launch the CRISPR Chronicles series that will run throughout season 3. Since the pivotal paper by Doudna and Charpentier in 2012, CRISPR has taken the world by storm. Scientists have used this genome engineering tool in the lab to quickly and easily create mutants to study gene function in laboratory animals. But more importantly, the power of CRISPR gene editing as a biomedical intervention to cure diseases has been realized. Currently, dozens of clinical trials are ongoing or on the verge of being launched to cure everything from genetic blindness and sickle cell anemia, to cancers and HIV. In fact, the Sickle Cell Disease treatment, exa-cel is poised to become the first CRISPR gene editing therapy to be approved by the FDA. Due to its far-reaching impacts, Doudna and Charpentier won the Nobel Prize in chemistry in 2020 for discovery of CRISPR-Cas9 gene editing; thereby, breaking the boundary as female winners of this prize. In this series we will explore:·       How CRISPR gene editing works·       The CRISPR origin story and major milestones·       The many clinical trials giving hope to the millions of people worldwide suffering from cancers, and genetic and infectious diseases·       The ethical debate of using CRISPR gene editing technologyFor more information on this topic, visit our website: welovesciencepodcast.comHear directly from Sickle Cell Disease patients who were cured during the clinical trial: Victoria Gray and Jimi OlaghereIf you enjoyed this episode, you will also enjoy:Dr Ken Shatzkes work combating the opioid crisisDr Joseph Iacona on the drug development process in pharmaHow the accidental discovery of Penicillin changed the world Reach out to Fatu:www.linkedin.com/in/fatubmTwitter: @thee_fatu_band LoveSciencePodcast@gmail.com Reach out to Shekerah:www.linkedin.com/in/shekerah-primus and LoveSciencePodcast@gmail.com Music from Pixabay: Future Artificial Intelligence Technology 130 by TimMoorMusic from https://freemusicarchive.org/music/Scott_Holmes: Hotshot by ScottHolmesMusic

The Best Christian Podcast in the Metaverse Canary Cry News Talk #649 - 07.26.2023 - Recorded Live to Tape GENETIC SCRIBES | Frozen Mitch, Alien Prop, Worldcoin Woes, Scribe, Cocaine Sharks Deconstructing Corporate Mainstream Media News from a Biblical Worldview We Operate Value 4 Value: http://CanaryCry.Support Join Supply Drop: http://CanaryCrySupplyDrop.com Submit Articles: http://CanaryCry.Report Join the Tee Shirt Council: http://CanaryCryTShirtCouncil.com Resource: Index of MSM Ownership (Harvard.edu) Resource: Aliens Demons Doc (feat. Dr. Heiser, Unseen Realm) All the links: http://CanaryCry.Party   This Episode was Produced By:   Executive Producers Sir Igorious Knight of the Squatting Slavs***   Producers Kenneth P Lady Knight Little Wing Exit To The Light Zine Robbb  Em & Bobby Sir LX Protocol V2 Knight of the Berrean Protocol Veronica D Sir Casey the Shield Knight Sir Scott Knight of Truth Dame Gail Canary Whisperer  Sir Morv Knight of the Burning Chariots Stopped By Grace   CanaryCry.ART Submissions JonathanF Lloyd V Sir Dove Knight of Rusbeltia   Micro-Fiction Stephen S - The UN excluded the Congondan Robo-Rhino from the “AI for Good” Global Summit due to its recent violent use to squash the protests in Freedonia.  President Dr. Mal Content exclaimed, “This proves Afro Racism thrives even in technology.”   CLIP PRODUCER Emsworth, FaeLivrin, Joelms, Laura   TIMESTAPERS Jade Bouncerson, Christine C, Pocojo, Morgan E   CanaryCry.Report Submissions JAM, Nancie   REMINDERS Clankoniphius   SHOW NOTES Podcast = T - 4:00 from D-Live  HELLO, RUN DOWN 7:13 V / 3:13 P   UFOs/ALIENS 9:27 V / 5:27 P Clip: US recovered “non-human biologics” piloted from crashed craft (Disclose.tv/X) Whistleblower tells Congress the US concealing ‘multi-decade' UFO watch program (AP)   POLYTICKS 24:41 V / 20:41 P Mitch McConnell escorted away from cameras after freezing during news conference (NBC)   DAY JINGLE/V4V/EXEC./supply/ 32:23 V / 28:23 P   FLIPPY 41:21 V / 37:21 P Robot made of LEGOs produces DNA machines (Interesting Engineering)   CRISPR/PHARMEKEIA 55:29 V / 51:29 P Ten years later, he's applying that work in a company he cofounded with Doudna and Savage, Scribe Therapeutics (Forbes) → The 'Immortal Jellyfish' Can Age in Reverse, Possibly Live Forever (ScienceAlert)   BEAST SYSTEM 1:05:19 V / 1:01:19 P Clip: People lining up to get iris scan for Worldcoin What Is Worldcoin? Here's What To Know About The Eyeball-Scanning Crypto Project Launched By OpenAI's Sam Altman (Forbes) → 357, 402, 605, 624, 625…   TREASURE/SPEAKPIPE/TALENT 1:28:38 V / 1:24:38 P   BEAST SYSTEM 1:49:39 V / 1:45:39 P *Cocaine Sharks off the coast of Florida (Live Science)   V4V/TIME 2:03:39 V / 1:59:39 P END

Nobel Prize-winning scientist Jennifer Doudna discusses how the technology she helped advance is treating diseases and raising ethical dilemmas. Gene editing is a game-changer for humanity. From health on individuals to the fate of the planet, the possible impacts of the technology are something previously found only in science fiction. But as with all scientific advancements that supercharge human capabilities and power, the technology comes with ethical questions. These possibilities and questions are at the core of this episode of the Crosscut Talks podcast.  We're listening in on a conversation between Nobel laureate and University of California Berkeley chemistry professor Jennifer Doudna and New York Times columnist and science writer Carl Zimmer as they discuss one of these technologies, CRISPR.   Doudna, who won the Nobel for her work with gene editing technology, explains the fundamental science behind CRISPR, how it's now being used by scientists to treat a wide range of diseases from HIV to sickle cell anemia, and where it might go from here. This conversation was recorded May 3, 2023. --- Credits Host: Paris Jackson Producer: Seth Halleran Event producers: Jake Newman, Anne O'Dowd Engineers: Resti Bagcal, Viktoria Ralph --- If you would like to support Crosscut, go to crosscut.com/membership. In addition to supporting our events and our daily journalism, members receive complete access to the on-demand programming of Seattle's PBS station, KCTS 9.

Jennifer Doudna is a Nobel laureate in chemistry and professor of biochemistry, biophysics and structural biology at the University of California, Berkeley. She has been a pioneer in CRISPR gene editing and continues to revolutionize research in her field. Doudna shares her Brief But Spectacular take on the future of CRISPR. PBS NewsHour is supported by - https://www.pbs.org/newshour/about/funders

Jennifer Doudna is a Nobel laureate in chemistry and professor of biochemistry, biophysics and structural biology at the University of California, Berkeley. She has been a pioneer in CRISPR gene editing and continues to revolutionize research in her field. Doudna shares her Brief But Spectacular take on the future of CRISPR. PBS NewsHour is supported by - https://www.pbs.org/newshour/about/funders

Jennifer Doudna is a Nobel laureate in chemistry and professor of biochemistry, biophysics and structural biology at the University of California, Berkeley. She has been a pioneer in CRISPR gene editing and continues to revolutionize research in her field. Doudna shares her Brief But Spectacular take on the future of CRISPR. PBS NewsHour is supported by - https://www.pbs.org/newshour/about/funders

2023 is the year of Multimodal AI, and Latent Space is going multimodal too! * This podcast comes with a video demo at the 1hr mark and it's a good excuse to launch our YouTube - please subscribe! * We are also holding two events in San Francisco — the first AI | UX meetup next week (already full; we'll send a recap here on the newsletter) and Latent Space Liftoff Day on May 4th (signup here; but get in touch if you have a high profile launch you'd like to make). * We also joined the Chroma/OpenAI ChatGPT Plugins Hackathon last week where we won the Turing and Replit awards and met some of you in person!This post featured on Hacker News.Out of the five senses of the human body, I'd put sight at the very top. But weirdly when it comes to AI, Computer Vision has felt left out of the recent wave compared to image generation, text reasoning, and even audio transcription. We got our first taste of it with the OCR capabilities demo in the GPT-4 Developer Livestream, but to date GPT-4's vision capability has not yet been released. Meta AI leapfrogged OpenAI and everyone else by fully open sourcing their Segment Anything Model (SAM) last week, complete with paper, model, weights, data (6x more images and 400x more masks than OpenImages), and a very slick demo website. This is a marked change to their previous LLaMA release, which was not commercially licensed. The response has been ecstatic:SAM was the talk of the town at the ChatGPT Plugins Hackathon and I was fortunate enough to book Joseph Nelson who was frantically integrating SAM into Roboflow this past weekend. As a passionate instructor, hacker, and founder, Joseph is possibly the single best person in the world to bring the rest of us up to speed on the state of Computer Vision and the implications of SAM. I was already a fan of him from his previous pod with (hopefully future guest) Beyang Liu of Sourcegraph, so this served as a personal catchup as well. Enjoy! and let us know what other news/models/guests you'd like to have us discuss! - swyxRecorded in-person at the beautiful StudioPod studios in San Francisco.Full transcript is below the fold.Show Notes* Joseph's links: Twitter, Linkedin, Personal* Sourcegraph Podcast and Game Theory Story* Represently* Roboflow at Pioneer and YCombinator* Udacity Self Driving Car dataset story* Computer Vision Annotation Formats* SAM recap - top things to know for those living in a cave* https://segment-anything.com/* https://segment-anything.com/demo* https://arxiv.org/pdf/2304.02643.pdf * https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/* https://blog.roboflow.com/segment-anything-breakdown/* https://ai.facebook.com/datasets/segment-anything/* Ask Roboflow https://ask.roboflow.ai/* GPT-4 Multimodal https://blog.roboflow.com/gpt-4-impact-speculation/Cut for time:* WSJ mention* Des Moines Register story* All In Pod: timestamped mention* In Forbes: underrepresented investors in Series A* Roboflow greatest hits* https://blog.roboflow.com/mountain-dew-contest-computer-vision/* https://blog.roboflow.com/self-driving-car-dataset-missing-pedestrians/* https://blog.roboflow.com/nerualhash-collision/ and Apple CSAM issue * https://www.rf100.org/Timestamps* [00:00:19] Introducing Joseph* [00:02:28] Why Iowa* [00:05:52] Origin of Roboflow* [00:16:12] Why Computer Vision* [00:17:50] Computer Vision Use Cases* [00:26:15] The Economics of Annotation/Segmentation* [00:32:17] Computer Vision Annotation Formats* [00:36:41] Intro to Computer Vision & Segmentation* [00:39:08] YOLO* [00:44:44] World Knowledge of Foundation Models* [00:46:21] Segment Anything Model* [00:51:29] SAM: Zero Shot Transfer* [00:51:53] SAM: Promptability* [00:53:24] SAM: Model Assisted Labeling* [00:56:03] SAM doesn't have labels* [00:59:23] Labeling on the Browser* [01:00:28] Roboflow + SAM Video Demo * [01:07:27] Future Predictions* [01:08:04] GPT4 Multimodality* [01:09:27] Remaining Hard Problems* [01:13:57] Ask Roboflow (2019)* [01:15:26] How to keep up in AITranscripts[00:00:00] Hello everyone. It is me swyx and I'm here with Joseph Nelson. Hey, welcome to the studio. It's nice. Thanks so much having me. We, uh, have a professional setup in here.[00:00:19] Introducing Joseph[00:00:19] Joseph, you and I have known each other online for a little bit. I first heard about you on the Source Graph podcast with bian and I highly, highly recommend that there's a really good game theory story that is the best YC application story I've ever heard and I won't tease further cuz they should go listen to that.[00:00:36] What do you think? It's a good story. It's a good story. It's a good story. So you got your Bachelor of Economics from George Washington, by the way. Fun fact. I'm also an econ major as well. You are very politically active, I guess you, you did a lot of, um, interning in political offices and you were responding to, um, the, the, the sheer amount of load that the Congress people have in terms of the, the support.[00:01:00] So you built, representing, which is Zendesk for Congress. And, uh, I liked in your source guide podcast how you talked about how being more responsive to, to constituents is always a good thing no matter what side of the aisle you're on. You also had a sideline as a data science instructor at General Assembly.[00:01:18] As a consultant in your own consultancy, and you also did a bunch of hackathon stuff with Magic Sudoku, which is your transition from N L P into computer vision. And apparently at TechCrunch Disrupt, disrupt in 2019, you tried to add chess and that was your whole villain origin story for, Hey, computer vision's too hard.[00:01:36] That's full, the platform to do that. Uh, and now you're co-founder c e o of RoboFlow. So that's your bio. Um, what's not in there that[00:01:43] people should know about you? One key thing that people realize within maybe five minutes of meeting me, uh, I'm from Iowa. Yes. And it's like a funnily novel thing. I mean, you know, growing up in Iowa, it's like everyone you know is from Iowa.[00:01:56] But then when I left to go to school, there was not that many Iowans at gw and people were like, oh, like you're, you're Iowa Joe. Like, you know, how'd you find out about this school out here? I was like, oh, well the Pony Express was running that day, so I was able to send. So I really like to lean into it.[00:02:11] And so you kind of become a default ambassador for places that. People don't meet a lot of other people from, so I've kind of taken that upon myself to just make it be a, a part of my identity. So, you know, my handle everywhere Joseph of Iowa, like I I, you can probably find my social security number just from knowing that that's my handle.[00:02:25] Cuz I put it plastered everywhere. So that's, that's probably like one thing.[00:02:28] Why Iowa[00:02:28] What's your best pitch for Iowa? Like why is[00:02:30] Iowa awesome? The people Iowa's filled with people that genuinely care. You know, if you're waiting a long line, someone's gonna strike up a conversation, kinda ask how you were Devrel and it's just like a really genuine place.[00:02:40] It was a wonderful place to grow up too at the time, you know, I thought it was like, uh, yeah, I was kind of embarrassed and then be from there. And then I actually kinda looking back it's like, wow, you know, there's good schools, smart people friendly. The, uh, high school that I went to actually Ben Silverman, the CEO and, or I guess former CEO and co-founder of Pinterest and I have the same teachers in high school at different.[00:03:01] The co-founder, or excuse me, the creator of crispr, the gene editing technique, Dr. Jennifer. Doudna. Oh, so that's the patent debate. There's Doudna. Oh, and then there's Fang Zang. Uh, okay. Yeah. Yeah. So Dr. Fang Zang, who I think ultimately won the patent war, uh, but is also from the same high school.[00:03:18] Well, she won the patent, but Jennifer won the[00:03:20] prize.[00:03:21] I think that's probably, I think that's probably, I, I mean I looked into it a little closely. I think it was something like she won the patent for CRISPR first existing and then Feng got it for, uh, first use on humans, which I guess for commercial reasons is the, perhaps more, more interesting one. But I dunno, biolife Sciences, is that my area of expertise?[00:03:38] Yep. Knowing people that came from Iowa that do cool things, certainly is. Yes. So I'll claim it. Um, but yeah, I, I, we, um, at Roble actually, we're, we're bringing the full team to Iowa for the very first time this last week of, of April. And, well, folks from like Scotland all over, that's your company[00:03:54] retreat.[00:03:54] The Iowa,[00:03:55] yeah. Nice. Well, so we do two a year. You know, we've done Miami, we've done. Some of the smaller teams have done like Nashville or Austin or these sorts of places, but we said, you know, let's bring it back to kinda the origin and the roots. Uh, and we'll, we'll bring the full team to, to Des Moines, Iowa.[00:04:13] So, yeah, like I was mentioning, folks from California to Scotland and many places in between are all gonna descend upon Des Moines for a week of, uh, learning and working. So maybe you can check in with those folks. If, what do they, what do they decide and interpret about what's cool. Our state. Well, one thing, are you actually headquartered in Des Moines on paper?[00:04:30] Yes. Yeah.[00:04:30] Isn't that amazing? That's like everyone's Delaware and you're like,[00:04:33] so doing research. Well, we're, we're incorporated in Delaware. Okay. We we're Delaware Sea like, uh, most companies, but our headquarters Yeah. Is in Des Moines. And part of that's a few things. One, it's like, you know, there's this nice Iowa pride.[00:04:43] And second is, uh, Brad and I both grew up in Brad Mc, co-founder and I grew up in, in Des Moines. And we met each other in the year 2000. We looked it up for the, the YC app. So, you know, I think, I guess more of my life I've known Brad than not, uh, which is kind of crazy. Wow. And during yc, we did it during 2020, so it was like the height of Covid.[00:05:01] And so we actually got a house in Des Moines and lived, worked outta there. I mean, more credit to. So I moved back. I was living in DC at the time, I moved back to to Des Moines. Brad was living in Des Moines, but he moved out of a house with his. To move into what we called our hacker house. And then we had one, uh, member of the team as well, Jacob Sorowitz, who moved from Minneapolis down to Des Moines for the summer.[00:05:21] And frankly, uh, code was a great time to, to build a YC company cuz there wasn't much else to do. I mean, it's kinda like wash your groceries and code. It's sort of the, that was the routine[00:05:30] and you can use, uh, computer vision to help with your groceries as well.[00:05:33] That's exactly right. Tell me what to make.[00:05:35] What's in my fridge? What should I cook? Oh, we'll, we'll, we'll cover[00:05:37] that for with the G P T four, uh, stuff. Exactly. Okay. So you have been featured with in a lot of press events. Uh, but maybe we'll just cover the origin story a little bit in a little bit more detail. So we'll, we'll cover robo flow and then we'll cover, we'll go into segment anything.[00:05:52] Origin of Roboflow[00:05:52] But, uh, I think it's important for people to understand. Robo just because it gives people context for what you're about to show us at the end of the podcast. So Magic Sudoku tc, uh, techers Disrupt, and then you go, you join Pioneer, which is Dan Gross's, um, YC before yc.[00:06:07] Yeah. That's how I think about it.[00:06:08] Yeah, that's a good way. That's a good description of it. Yeah. So I mean, robo flow kind of starts as you mentioned with this magic Sudoku thing. So you mentioned one of my prior business was a company called Represent, and you nailed it. I mean, US Congress gets 80 million messages a year. We built tools that auto sorted them.[00:06:23] They didn't use any intelligent auto sorting. And this is somewhat a solved problem in natural language processing of doing topic modeling or grouping together similar sentiment and things like this. And as you mentioned, I'd like, I worked in DC for a bit and been exposed to some of these problems and when I was like, oh, you know, with programming you can build solutions.[00:06:40] And I think the US Congress is, you know, the US kind of United States is a support center, if you will, and the United States is sports center runs on pretty old software, so mm-hmm. We, um, we built a product for that. It was actually at the time when I was working on representing. Brad, his prior business, um, is a social games company called Hatchlings.[00:07:00] Uh, he phoned me in, in 2017, apple had released augmented reality kit AR kit. And Brad and I are both kind of serial hackers, like I like to go to hackathons, don't really understand new technology until he build something with them type folks. And when AR Kit came out, Brad decided he wanted to build a game with it that would solve Sudoku puzzles.[00:07:19] And the idea of the game would be you take your phone, you hover hold it over top of a Sudoku puzzle, it recognizes the state of the board where it is, and then it fills it all in just right before your eyes. And he phoned me and I was like, Brad, this sounds awesome and sounds like you kinda got it figured out.[00:07:34] What, what's, uh, what, what do you think I can do here? It's like, well, the machine learning piece of this is the part that I'm most uncertain about. Uh, doing the digit recognition and, um, filling in some of those results. I was like, well, I mean digit recognition's like the hell of world of, of computer vision.[00:07:48] That's Yeah, yeah, MNIST, right. So I was like, that that part should be the, the easy part. I was like, ah, I'm, he's like, I'm not so super sure, but. You know, the other parts, the mobile ar game mechanics, I've got pretty well figured out. I was like, I, I think you're wrong. I think you're thinking about the hard part is the easy part.[00:08:02] And he is like, no, you're wrong. The hard part is the easy part. And so long story short, we built this thing and released Magic Sudoku and it kind of caught the Internet's attention of what you could do with augmented reality and, and with computer vision. It, you know, made it to the front ofer and some subreddits it run Product Hunt Air app of the year.[00:08:20] And it was really a, a flash in the pan type app, right? Like we were both running separate companies at the time and mostly wanted to toy around with, with new technology. And, um, kind of a fun fact about Magic Sudoku winning product Hunt Air app of the year. That was the same year that I think the model three came out.[00:08:34] And so Elon Musk won a Golden Kitty who we joked that we share an award with, with Elon Musk. Um, the thinking there was that this is gonna set off a, a revolution of if two random engineers can put together something that makes something, makes a game programmable and at interactive, then surely lots of other engineers will.[00:08:53] Do similar of adding programmable layers on top of real world objects around us. Earlier we were joking about objects in your fridge, you know, and automatically generating recipes and these sorts of things. And like I said, that was 2017. Roboflow was actually co-found, or I guess like incorporated in, in 2019.[00:09:09] So we put this out there, nothing really happened. We went back to our day jobs of, of running our respective businesses, I sold Represently and then as you mentioned, kind of did like consulting stuff to figure out the next sort of thing to, to work on, to get exposed to various problems. Brad appointed a new CEO at his prior business and we got together that summer of 2019.[00:09:27] We said, Hey, you know, maybe we should return to that idea that caught a lot of people's attention and shows what's possible. And you know what, what kind of gives, like the future is here. And we have no one's done anything since. No one's done anything. So why is, why are there not these, these apps proliferated everywhere.[00:09:42] Yeah. And so we said, you know, what we'll do is, um, to add this software layer to the real world. Will build, um, kinda like a super app where if you pointed it at anything, it will recognize it and then you can interact with it. We'll release a developer platform and allow people to make their own interfaces, interactivity for whatever object they're looking at.[00:10:04] And we decided to start with board games because one, we had a little bit of history there with, with Sudoku two, there's social by default. So if one person, you know finds it, then they'd probably share it among their friend. Group three. There's actually relatively few barriers to entry aside from like, you know, using someone else's brand name in your, your marketing materials.[00:10:19] Yeah. But other than that, there's no real, uh, inhibitors to getting things going and, and four, it's, it's just fun. It would be something that'd be bring us enjoyment to work on. So we spent that summer making, uh, boggle the four by four word game provable, where, you know, unlike Magic Sudoku, which to be clear, totally ruins the game, uh, you, you have to solve Sudoku puzzle.[00:10:40] You don't need to do anything else. But with Boggle, if you and I are playing, we might not find all of the words that adjacent letter tiles. Unveil. So if we have a, an AI tell us, Hey, here's like the best combination of letters that make high scoring words. And so we, we made boggle and released it and that, and that did okay.[00:10:56] I mean maybe the most interesting story was there's a English as a second language program in, in Canada that picked it up and used it as a part of their curriculum to like build vocabulary, which I thought was kind of inspiring. Example, and what happens just when you put things on the internet and then.[00:11:09] We wanted to build one for chess. So this is where you mentioned we went to 2019. TechCrunch Disrupt TechCrunch. Disrupt holds a Hackathon. And this is actually, you know, when Brad and I say we really became co-founders, because we fly out to San Francisco, we rent a hotel room in the Tenderloin. We, uh, we, we, uh, have one room and there's like one, there's room for one bed, and then we're like, oh, you said there was a cot, you know, on the, on the listing.[00:11:32] So they like give us a little, a little cot, the end of the cot, like bled and over into like the bathroom. So like there I am sleeping on the cot with like my head in the bathroom and the Tenderloin, you know, fortunately we're at a hackathon glamorous. Yeah. There wasn't, there wasn't a ton of sleep to be had.[00:11:46] There is, you know, we're, we're just like making and, and shipping these, these sorts of many[00:11:50] people with this hack. So I've never been to one of these things, but[00:11:52] they're huge. Right? Yeah. The Disrupt Hackathon, um, I don't, I don't know numbers, but few hundreds, you know, classically had been a place where it launched a lot of famous Yeah.[00:12:01] Sort of flare. Yeah. And I think it's, you know, kind of slowed down as a place for true company generation. But for us, Brad and I, who likes just doing hackathons, being, making things in compressed time skills, it seemed like a, a fun thing to do. And like I said, we'd been working on things, but it was only there that like, you're, you're stuck in a maybe not so great glamorous situation together and you're just there to make a, a program and you wanna make it be the best and compete against others.[00:12:26] And so we add support to the app that we were called was called Board Boss. We couldn't call it anything with Boggle cause of IP rights were called. So we called it Board Boss and it supported Boggle and then we were gonna support chess, which, you know, has no IP rights around it. Uh, it's an open game.[00:12:39] And we did so in 48 hours, we built an app that, or added fit capability to. Point your phone at a chess board. It understands the state of the chess board and converts it to um, a known notation. Then it passes that to stock fish, the open source chess engine for making move recommendations and it makes move recommendations to, to players.[00:13:00] So you could either play against like an ammunition to AI or improve your own game. We learn that one of the key ways users like to use this was just to record their games. Cuz it's almost like reviewing game film of what you should have done differently. Game. Yeah, yeah, exactly. And I guess the highlight of, uh, of chess Boss was, you know, we get to the first round of judging, we get to the second round of judging.[00:13:16] And during the second round of judging, that's when like, TechCrunch kind of brings around like some like celebs and stuff. They'll come by. Evan Spiegel drops by Ooh. Oh, and he uh, he comes up to our, our, our booth and um, he's like, oh, so what does, what does this all do? And you know, he takes an interest in it cuz the underpinnings of, of AR interacting with the.[00:13:33] And, uh, he is kinda like, you know, I could use this to like cheat on chess with my friends. And we're like, well, you know, that wasn't exactly the, the thesis of why we made it, but glad that, uh, at least you think it's kind of neat. Um, wait, but he already started Snapchat by then? Oh, yeah. Oh yeah. This, this is 2019, I think.[00:13:49] Oh, okay, okay. Yeah, he was kind of just checking out things that were new and, and judging didn't end up winning any, um, awards within Disrupt, but I think what we won was actually. Maybe more important maybe like the, the quote, like the co-founders medal along the way. Yep. The friends we made along the way there we go to, to play to the meme.[00:14:06] I would've preferred to win, to be clear. Yes. You played a win. So you did win, uh,[00:14:11] $15,000 from some Des Moines, uh, con[00:14:14] contest. Yeah. Yeah. The, uh, that was nice. Yeah. Slightly after that we did, we did win. Um, some, some grants and some other things for some of the work that we've been doing. John Papa John supporting the, uh, the local tech scene.[00:14:24] Yeah. Well, so there's not the one you're thinking of. Okay. Uh, there's a guy whose name is Papa John, like that's his, that's his, that's his last name. His first name is John. So it's not the Papa John's you're thinking of that has some problematic undertones. It's like this guy who's totally different. I feel bad for him.[00:14:38] His press must just be like, oh, uh, all over the place. But yeah, he's this figure in the Iowa entrepreneurial scene who, um, he actually was like doing SPACs before they were cool and these sorts of things, but yeah, he funds like grants that encourage entrepreneurship in the state. And since we'd done YC and in the state, we were eligible for some of the awards that they were providing.[00:14:56] But yeah, it was disrupt that we realized, you know, um, the tools that we made, you know, it took us better part of a summer to add Boggle support and it took us 48 hours to add chest support. So adding the ability for programmable interfaces for any object, we built a lot of those internal tools and our apps were kind of doing like the very famous shark fin where like it picks up really fast, then it kind of like slowly peters off.[00:15:20] Mm-hmm. And so we're like, okay, if we're getting these like shark fin graphs, we gotta try something different. Um, there's something different. I remember like the week before Thanksgiving 2019 sitting down and we wrote this Readme for, actually it's still the Readme at the base repo of Robo Flow today has spent relatively unedited of the manifesto.[00:15:36] Like, we're gonna build tools that enable people to make the world programmable. And there's like six phases and, you know, there's still, uh, many, many, many phases to go into what we wrote even at that time to, to present. But it's largely been, um, right in line with what we thought we would, we would do, which is give engineers the tools to add software to real world objects, which is largely predicated on computer vision. So finding the right images, getting the right sorts of video frames, maybe annotating them, uh, finding the right sort of models to use to do this, monitoring the performance, all these sorts of things. And that from, I mean, we released that in early 2020, and it's kind of, that's what's really started to click.[00:16:12] Why Computer Vision[00:16:12] Awesome. I think we should just kind[00:16:13] of[00:16:14] go right into where you are today and like the, the products that you offer, just just to give people an overview and then we can go into the, the SAM stuff. So what is the clear, concise elevator pitch? I think you mentioned a bunch of things like make the world programmable so you don't ha like computer vision is a means to an end.[00:16:30] Like there's, there's something beyond that. Yeah.[00:16:32] I mean, the, the big picture mission for the business and the company and what we're working on is, is making the world programmable, making it read and write and interactive, kind of more entertaining, more e. More fun and computer vision is the technology by which we can achieve that pretty quickly.[00:16:48] So like the one liner for the, the product in, in the company is providing engineers with the tools for data and models to build programmable interfaces. Um, and that can be workflows, that could be the, uh, data processing, it could be the actual model training. But yeah, Rob helps you use production ready computer vision workflows fast.[00:17:10] And I like that.[00:17:11] In part of your other pitch that I've heard, uh, is that you basically scale from the very smallest scales to the very largest scales, right? Like the sort of microbiology use case all the way to[00:17:20] astronomy. Yeah. Yeah. The, the joke that I like to make is like anything, um, underneath a microscope and, and through a telescope and everything in between needs to, needs to be seen.[00:17:27] I mean, we have people that run models in outer space, uh, underwater remote places under supervision and, and known places. The crazy thing is that like, All parts of, of not just the world, but the universe need to be observed and understood and acted upon. So vision is gonna be, I dunno, I feel like we're in the very, very, very beginnings of all the ways we're gonna see it.[00:17:50] Computer Vision Use Cases[00:17:50] Awesome. Let's go into a lo a few like top use cases, cuz I think that really helps to like highlight the big names that you've, big logos that you've already got. I've got Walmart and Cardinal Health, but I don't, I don't know if you wanna pull out any other names, like, just to illustrate, because the reason by the way, the reason I think that a lot of developers don't get into computer vision is because they think they don't need it.[00:18:11] Um, or they think like, oh, like when I do robotics, I'll do it. But I think if, if you see like the breadth of use cases, then you get a little bit more inspiration as to like, oh, I can use[00:18:19] CVS lfa. Yeah. It's kind of like, um, you know, by giving, by making it be so straightforward to use vision, it becomes almost like a given that it's a set of features that you could power on top of it.[00:18:32] And like you mentioned, there's, yeah, there's Fortune One there over half the Fortune 100. I've used the, the tools that Robel provides just as much as 250,000 developers. And so over a quarter million engineers finding and developing and creating various apps, and I mean, those apps are, are, are far and wide.[00:18:49] Just as you mentioned. I mean everything from say, like, one I like to talk about was like sushi detection of like finding the like right sorts of fish and ingredients that are in a given piece of, of sushi that you're looking at to say like roof estimation of like finding. If there's like, uh, hail damage on, on a given roof, of course, self-driving cars and understanding the scenes around us is sort of the, you know, very early computer vision everywhere.[00:19:13] Use case hardhat detection, like finding out if like a given workplace is, is, is safe, uh, disseminate, have the right p p p on or p p e on, are there the right distance from various machines? A huge place that vision has been used is environmental monitoring. Uh, what's the count of species? Can we verify that the environment's not changing in unexpected ways or like river banks are become, uh, becoming recessed in ways that we anticipate from satellite imagery, plant phenotyping.[00:19:37] I mean, people have used these apps for like understanding their plants and identifying them. And that dataset that's actually largely open, which is what's given a proliferation to the iNaturalist, is, is that whole, uh, hub of, of products. Lots of, um, people that do manufacturing. So, like Rivian for example, is a Rubal customer, and you know, they're trying to scale from 1000 cars to 25,000 cars to a hundred thousand cars in very short order.[00:20:00] And that relies on having the. Ability to visually ensure that every part that they're making is produced correctly and right in time. Medical use cases. You know, there's actually, this morning I was emailing with a user who's accelerating early cancer detection through breaking apart various parts of cells and doing counts of those cells.[00:20:23] And actually a lot of wet lab work that folks that are doing their PhDs or have done their PhDs are deeply familiar with that is often required to do very manually of, of counting, uh, micro plasms or, or things like this. There's. All sorts of, um, like traffic counting and smart cities use cases of understanding curb utilization to which sort of vehicles are, are present.[00:20:44] Uh, ooh. That can be[00:20:46] really good for city planning actually.[00:20:47] Yeah. I mean, one of our customers does exactly this. They, they measure and do they call it like smart curb utilization, where uhhuh, they wanna basically make a curb be almost like a dynamic space where like during these amounts of time, it's zoned for this during these amounts of times.[00:20:59] It's zoned for this based on the flows and e ebbs and flows of traffic throughout the day. So yeah, I mean the, the, the truth is that like, you're right, it's like a developer might be like, oh, how would I use vision? And then all of a sudden it's like, oh man, all these things are at my fingertips. Like I can just, everything you can see.[00:21:13] Yeah. Right. I can just, I can just add functionality for my app to understand and ingest the way, like, and usually the way that someone gets like almost nerd sniped into this is like, they have like a home automation project, so it's like send Yeah. Give us a few. Yeah. So send me a text when, um, a package shows up so I can like prevent package theft so I can like go down and grab it right away or.[00:21:29] We had a, uh, this one's pretty, pretty niche, but it's pretty funny. There was this guy who, during the pandemic wa, wanted to make sure his cat had like the proper, uh, workout. And so I've shared the story where he basically decided that. He'd make a cat workout machine with computer vision, you might be alone.[00:21:43] You're like, what does that look like? Well, what he decided was he would take a robotic arm strap, a laser pointer to it, and then train a machine to recognize his cat and his cat only, and point the laser pointer consistently 10 feet away from the cat. There's actually a video of you if you type an YouTube cat laser turret, you'll find Dave's video.[00:22:01] Uh, and hopefully Dave's cat has, has lost the weight that it needs to, cuz that's just the, that's an intense workout I have to say. But yeah, so like, that's like a, um, you know, these, uh, home automation projects are pretty common places for people to get into smart bird feeders. I've seen people that like are, are logging and understanding what sort of birds are, uh, in their background.[00:22:18] There's a member of our team that was working on actually this as, as a whole company and has open sourced a lot of the data for doing bird species identification. And now there's, I think there's even a company that's, uh, founded to create like a smart bird feeder, like captures photos and tells you which ones you've attracted to your yard.[00:22:32] I met that. Do, you know, get around the, uh, car sharing company that heard it? Them never used them. They did a SPAC last year and they had raised at like, They're unicorn. They raised at like 1.2 billion, I think in the, the prior round and inspected a similar price. I met the CTO of, of Getaround because he was, uh, using Rob Flow to hack into his Tesla cameras to identify other vehicles that are like often nearby him.[00:22:56] So he's basically building his own custom license plate recognition, and he just wanted like, keep, like, keep tabs of like, when he drives by his friends or when he sees like regular sorts of folks. And so he was doing like automated license plate recognition by tapping into his, uh, camera feeds. And by the way, Elliot's like one of the like OG hackers, he was, I think one of the very first people to like, um, she break iPhones and, and these sorts of things.[00:23:14] Mm-hmm. So yeah, the project that I want, uh, that I'm gonna work on right now for my new place in San Francisco is. There's two doors. There's like a gate and then the other door. And sometimes we like forget to close, close the gate. So like, basically if it sees that the gate is open, it'll like send us all a text or something like this to make sure that the gate is, is closed at the front of our house.[00:23:32] That's[00:23:32] really cool. And I'll, I'll call out one thing that readers and listeners can, uh, read out on, on your history. One of your most popular initial, um, viral blog post was about, um, autonomous vehicle data sets and how, uh, the one that Udacity was using was missing like one third of humans. And, uh, it's not, it's pretty problematic for cars to miss humans.[00:23:53] Yeah, yeah, actually, so yeah, the Udacity self-driving car data set, which look to their credit, it was just meant to be used for, for academic use. Um, and like as a part of courses on, on Udacity, right? Yeah. But the, the team that released it, kind of hastily labeled and let it go out there to just start to use and train some models.[00:24:11] I think that likely some, some, uh, maybe commercial use cases maybe may have come and, and used, uh, the dataset, who's to say? But Brad and I discovered this dataset. And when we were working on dataset improvement tools at Rob Flow, we ran through our tools and identified some like pretty, as you mentioned, key issues.[00:24:26] Like for example, a lot of strollers weren't labeled and I hope our self-driving cars do those, these sorts of things. And so we relabeled the whole dataset by hand. I have this very fond memory is February, 2020. Brad and I are in Taiwan. So like Covid is actually just, just getting going. And the reason we were there is we were like, Hey, we can work on this from anywhere for a little bit.[00:24:44] And so we spent like a, uh, let's go closer to Covid. Well, you know, I like to say we uh, we got early indicators of, uh, how bad it was gonna be. I bought a bunch of like N 90 fives before going o I remember going to the, the like buying a bunch of N 95 s and getting this craziest look like this like crazy tin hat guy.[00:25:04] Wow. What is he doing? And then here's how you knew. I, I also got got by how bad it was gonna be. I left all of them in Taiwan cuz it's like, oh, you all need these. We'll be fine over in the us. And then come to find out, of course that Taiwan was a lot better in terms of, um, I think, yeah. Safety. But anyway, we were in Taiwan because we had planned this trip and you know, at the time we weren't super sure about the, uh, covid, these sorts of things.[00:25:22] We always canceled it. We didn't, but I have this, this very specific time. Brad and I were riding on the train from Clay back to Taipei. It's like a four hour ride. And you mentioned Pioneer earlier, we were competing in Pioneer, which is almost like a gamified to-do list. Mm-hmm. Every week you say what you're gonna do and then other people evaluate.[00:25:37] Did you actually do the things you said you were going to do? One of the things we said we were gonna do was like this, I think re-release of this data set. And so it's like late, we'd had a whole week, like, you know, weekend behind us and, uh, we're on this train and it was very unpleasant situation, but we relabeled this, this data set, and one sitting got it submitted before like the Sunday, Sunday countdown clock starts voting for, for.[00:25:57] And, um, once that data got out back out there, just as you mentioned, it kind of picked up and Venture beat, um, noticed and wrote some stories about it. And we really rereleased of course, the data set that we did our best job of labeling. And now if anyone's listening, they can probably go out and like find some errors that we surely still have and maybe call us out and, you know, put us, put us on blast.[00:26:15] The Economics of Annotation (Segmentation)[00:26:15] But,[00:26:16] um, well, well the reason I like this story is because it, it draws attention to the idea that annotation is difficult and basically anyone looking to use computer vision in their business who may not have an off-the-shelf data set is going to have to get involved in annotation. And I don't know what it costs.[00:26:34] And that's probably one of the biggest hurdles for me to estimate how big a task this is. Right? So my question at a higher level is tell the customers, how do you tell customers to estimate the economics of annotation? Like how many images do, do we need? How much, how long is it gonna take? That, that kinda stuff.[00:26:50] How much money and then what are the nuances to doing it well, right? Like, cuz obviously Udacity had a poor quality job, you guys had proved it, and there's errors every everywhere. Like where do[00:26:59] these things go wrong? The really good news about annotation in general is that like annotation of course is a means to an end to have a model be able to recognize a thing.[00:27:08] Increasingly there's models that are coming out that can recognize things zero shot without any annotation, which we're gonna talk about. Yeah. Which, we'll, we'll talk more about that in a moment. But in general, the good news is that like the trend is that annotation is gonna become decreasingly a blocker to starting to use computer vision in meaningful ways.[00:27:24] Now that said, just as you mentioned, there's a lot of places where you still need to do. Annotation. I mean, even with these zero shot models, they might have of blind spots, or maybe you're a business, as you mentioned, that you know, it's proprietary data. Like only Rivian knows what a rivian is supposed to look like, right?[00:27:39] Uh, at the time of, at the time of it being produced, like underneath the hood and, and all these sorts of things. And so, yeah, that's gonna necessarily require annotation. So your question of how long is it gonna take, how do you estimate these sorts of things, it really comes down to the complexity of the problem that you're solving and the amount of variance in the scene.[00:27:57] So let's give some contextual examples. If you're trying to recognize, we'll say a scratch on one specific part and you have very strong lighting. You might need fewer images because you control the lighting, you know the exact part and maybe you're lucky in the scratch. Happens more often than not in similar parts or similar, uh, portions of the given part.[00:28:17] So in that context, you, you, the function of variance, the variance is, is, is lower. So the number of images you need is also lower to start getting up to work. Now the orders of magnitude we're talking about is that like you can have an initial like working model from like 30 to 50 images. Yeah. In this context, which is shockingly low.[00:28:32] Like I feel like there's kind of an open secret in computer vision now, the general heuristic that often. Users, is that like, you know, maybe 200 images per class is when you start to have a model that you can rely[00:28:45] on? Rely meaning like 90, 99, 90, 90%, um,[00:28:50] uh, like what's 85 plus 85? Okay. Um, that's good. Again, these are very, very finger in the wind estimates cuz the variance we're talking about.[00:28:59] But the real question is like, at what point, like the framing is not like at what point do it get to 99, right? The framing is at what point can I use this thing to be better than the alternative, which is humans, which maybe humans or maybe like this problem wasn't possible at all. And so usually the question isn't like, how do I get to 99?[00:29:15] A hundred percent? It's how do I ensure that like the value I am able to get from putting this thing in production is greater than the alternative? In fact, even if you have a model that's less accurate than humans, there might be some circumstances where you can tolerate, uh, a greater amount of inaccuracy.[00:29:32] And if you look at the accuracy relative to the cost, Using a model is extremely cheap. Using a human for the same sort of task can be very expensive. Now, in terms of the actual accuracy of of what you get, there's probably some point at which the cost, but relative accuracy exceeds of a model, exceeds the high cost and hopefully high accuracy of, of a human comparable, like for example, there's like cameras that will track soccer balls or track events happening during sporting matches.[00:30:02] And you can go through and you know, we actually have users that work in sports analytics. You can go through and have a human. Hours and hours of footage. Cuz not just watching their team, they're watching every other team, they're watching scouting teams, they're watching junior teams, they're watching competitors.[00:30:15] And you could have them like, you know, track and follow every single time the ball goes within blank region of the field or every time blank player goes into, uh, this portion of the field. And you could have, you know, exact, like a hundred percent accuracy if that person, maybe, maybe not a hundred, a human may be like 95, 90 7% accuracy of every single time the ball is in this region or this player is on the field.[00:30:36] Truthfully, maybe if you're scouting analytics, you actually don't need 97% accuracy of knowing that that player is on the field. And in fact, if you can just have a model run at a 1000th, a 10000th of the cost and goes through and finds all the times that Messi was present on the field mm-hmm. That the ball was in this region of the.[00:30:54] Then even if that model is slightly less accurate, the cost is just so orders of magnitude different. And the stakes like the stakes of this problem, of knowing like the total number of minutes that Messi played will say are such that we have a higher air tolerance, that it's a no-brainer to start to use Yeah, a computer vision model in this context.[00:31:12] So not every problem requires equivalent or greater human performance. Even when it does, you'd be surprised at how fast models get there. And in the times when you, uh, really look at a problem, the question is, how much accuracy do I need to start to get value from this? This thing, like the package example is a great one, right?[00:31:27] Like I could in theory set up a camera that's constantly watching in front of my porch and I could watch the camera whenever I have a package and then go down. But of course, I'm not gonna do that. I value my time to do other sorts of things instead. And so like there, there's this net new capability of, oh, great, I can have an always on thing that tells me when a package shows up, even if you know the, the thing that's gonna text me.[00:31:46] When a package shows up, let's say a flat pack shows up instead of a box and it doesn't know what a flat pack likes, looks like initially. Doesn't matter. It doesn't matter because I didn't have this capability at all before. And I think that's the true case where a lot of computer vision problems exist is like it.[00:32:00] It's like you didn't even have this capability, this superpower before at all, let alone assigning a given human to do the task. And that's where we see like this explosion of, of value.[00:32:10] Awesome. Awesome. That was a really good overview. I want to leave time for the others, but I, I really want to dive into a couple more things with regards to Robo Flow.[00:32:17] Computer Vision Annotation Formats[00:32:17] So one is, apparently your original pitch for Robo Flow was with regards to conversion tools for computer vision data sets. And I'm sure as, as a result of your job, you have a lot of rants. I've been digging for rants basically on like the best or the worst annotation formats. What do we know? Cause most of us, oh my gosh, we only know, like, you know, I like,[00:32:38] okay, so when we talk about computer vision annotation formats, what we're talking about is if you have an image and you, you picture a boing box around my face on that image.[00:32:46] Yeah. How do you describe where that Monty box is? X, Y, Z X Y coordinates. Okay. X, y coordinates. How, what do you mean from the top lefts.[00:32:52] Okay. You, you, you, you take X and Y and then, and then the. The length and, and the width of the, the[00:32:58] box. Okay. So you got like a top left coordinate and like the bottom right coordinate or like the, the center of the bottom.[00:33:02] Yeah. Yeah. Top, left, bottom right. Yeah. That's one type of format. Okay. But then, um, I come along and I'm like, you know what? I want to do a different format where I wanna just put the center of the box, right. And give the length and width. Right. And by the way, we didn't even talk about what X and Y we're talking about.[00:33:14] Is X a pixel count? Is a relative pixel count? Is it an absolute pixel count? So the point is, the number of ways to describe where a box lives in a freaking image is endless, uh, seemingly and. Everyone decided to kind of create their own different ways of describing the coordinates and positions of where in this context of bounding Box is present.[00:33:39] Uh, so there's some formats, for example, that like use re, so for the x and y, like Y is, uh, like the left, most part of the image is zero. And the right most part of the image is one. So the, the coordinate is like anywhere from zero to one. So 0.6 is, you know, 60% of your way right up the image to describe the coordinate.[00:33:53] I guess that was, that was X instead of Y. But the point is there, of the zero to one is the way that we determined where that was in the position, or we're gonna do an absolute pixel position anyway. We got sick, we got sick of all these different annotation formats. So why do you even have to convert between formats?[00:34:07] Is is another part of this, this story. So different training frameworks, like if you're using TensorFlow, you need like TF Records. If you're using PyTorch, it's probably gonna be, well it depends on like what model you're using, but someone might use Coco JSON with PyTorch. Someone else might use like a, just a YAML file and a text file.[00:34:21] And to describe the cor it's point is everyone that creates a model. Or creates a dataset rather, has created different ways of describing where and how a bounding box is present in the image. And we got sick of all these different formats and doing these in writing all these different converter scripts.[00:34:39] And so we made a tool that just converts from one script, one type of format to another. And the, the key thing is that like if you get that converter script wrong, your model doesn't not work. It just fails silently. Yeah. Because the bounding boxes are now all in the wrong places. And so you need a way to visualize and be sure that your converter script, blah, blah blah.[00:34:54] So that was the very first tool we released of robo. It was just a converter script, you know, like these, like these PDF to word converters that you find. It was basically that for computer vision, like dead simple, really annoying thing. And we put it out there and people found some, some value in, in that.[00:35:08] And you know, to this day that's still like a surprisingly painful[00:35:11] problem. Um, yeah, so you and I met at the Dall-E Hackathon at OpenAI, and we were, I was trying to implement this like face masking thing, and I immediately ran into that problem because, um, you know, the, the parameters that Dall-E expected were different from the one that I got from my face, uh, facial detection thing.[00:35:28] One day it'll go away, but that day is not today. Uh, the worst format that we work with is, is. The mart form, it just makes no sense. And it's like, I think, I think it's a one off annotation format that this university in China started to use to describe where annotations exist in a book mart. I, I don't know, I dunno why that So best[00:35:45] would be TF record or some something similar.[00:35:48] Yeah, I think like, here's your chance to like tell everybody to use one one standard and like, let's, let's, can[00:35:53] I just tell them to use, we have a package that does this for you. I'm just gonna tell you to use the row full package that converts them all, uh, for you. So you don't have to think about this. I mean, Coco JSON is pretty good.[00:36:04] It's like one of the larger industry norms and you know, it's in JS O compared to like V xml, which is an XML format and Coco json is pretty descriptive, but you know, it has, has its own sort of drawbacks and flaws and has random like, attribute, I dunno. Um, yeah, I think the best way to handle this problem is to not have to think about it, which is what we did.[00:36:21] We just created a, uh, library that, that converts and uses things. Uh, for us. We've double checked the heck out of it. There's been hundreds of thousands of people that have used the library and battle tested all these different formats to find those silent errors. So I feel pretty good about no longer having to have a favorite format and instead just rely on.[00:36:38] Dot load in the format that I need. Great[00:36:41] Intro to Computer Vision Segmentation[00:36:41] service to the community. Yeah. Let's go into segmentation because is at the top of everyone's minds, but before we get into segment, anything, I feel like we need a little bit of context on the state-of-the-art prior to Sam, which seems to be YOLO and uh, you are the leading expert as far as I know.[00:36:56] Yeah.[00:36:57] Computer vision, there's various task types. There's classification problems where we just like assign tags to images, like, you know, maybe safe work, not safe work, sort of tagging sort of stuff. Or we have object detection, which are the boing boxes that you see and all the formats I was mentioning in ranting about there's instant segmentation, which is the polygon shapes and produces really, really good looking demos.[00:37:19] So a lot of people like instant segmentation.[00:37:21] This would be like counting pills when you point 'em out on the, on the table. Yeah. So, or[00:37:25] soccer players on the field. So interestingly, um, counting you could do with bounding boxes. Okay. Cause you could just say, you know, a box around a person. Well, I could count, you know, 12 players on the field.[00:37:35] Masks are most useful. Polygons are most useful if you need very precise area measurements. So you have an aerial photo of a home and you want to know, and the home's not a perfect box, and you want to know the rough square footage of that home. Well, if you know the distance between like the drone and, and the ground.[00:37:53] And you have the precise polygon shape of the home, then you can calculate how big that home is from aerial photos. And then insurers can, you know, provide say accurate estimates and that's maybe why this is useful. So polygons and, and instant segmentation are, are those types of tasks? There's a key point detection task and key point is, you know, if you've seen those demos of like all the joints on like a hand kind of, kind of outlined, there's visual question answering tasks, visual q and a.[00:38:21] And that's like, you know, some of the stuff that multi-modality is absolutely crushing for, you know, here's an image, tell me what food is in this image. And then you can pass that and you can make a recipe out of it. But like, um, yeah, the visual question in answering task type is where multi-modality is gonna have and is already having an enormous impact.[00:38:40] So that's not a comprehensive survey, very problem type, but it's enough to, to go into why SAM is significant. So these various task types, you know, which model to use for which given circumstance. Most things is highly dependent on what you're ultimately aiming to do. Like if you need to run a model on the edge, you're gonna need a smaller model, cuz it is gonna run on edge, compute and process in, in, in real time.[00:39:01] If you're gonna run a model on the cloud, then of course you, uh, generally have more compute at your disposal Considerations like this now, uh,[00:39:08] YOLO[00:39:08] just to pause. Yeah. Do you have to explain YOLO first before you go to Sam, or[00:39:11] Yeah, yeah, sure. So, yeah. Yeah, we should. So object detection world. So for a while I talked about various different task types and you can kinda think about a slide scale of like classification, then obvious detection.[00:39:20] And on the right, at most point you have like segmentation tasks. Object detection. The bounding boxes is especially useful for a wide, like it's, it's surprisingly versatile. Whereas like classification is kind of brittle. Like you only have a tag for the whole image. Well, that doesn't, you can't count things with tags.[00:39:35] And on the other hand, like the mask side of things, like drawing masks is painstaking. And so like labeling is just a bit more difficult. Plus like the processing to produce masks requires more compute. And so usually a lot of folks kind of landed for a long time on obvious detection being a really happy medium of affording you with rich capabilities because you can do things like count, track, measure.[00:39:56] In some CAGR context with bounding boxes, you can see how many things are present. You can actually get a sense of how fast something's moving by tracking the object or bounding box across multiple frames and comparing the timestamp of where it was across those frames. So obviously detection is a very common task type that solves lots of things that you want do with a given model.[00:40:15] In obviously detection. There's been various model frameworks over time. So kind of really early on there's like R-CNN uh, then there's faster rc n n and these sorts of family models, which are based on like resnet kind of architectures. And then a big thing happens, and that is single shot detectors. So faster, rc n n despite its name is, is very slow cuz it takes two passes on the image.[00:40:37] Uh, the first pass is, it finds par pixels in the image that are most interesting to, uh, create a bounding box candidate out of. And then it passes that to a, a classifier that then does classification of the bounding box of interest. Right. Yeah. You can see, you can see why that would be slow. Yeah. Cause you have to do two passes.[00:40:53] You know, kind of actually led by, uh, like mobile net was I think the first large, uh, single shot detector. And as its name implies, it was meant to be run on edge devices and mobile devices and Google released mobile net. So it's a popular implementation that you find in TensorFlow. And what single shot detectors did is they said, Hey, instead of looking at the image twice, what if we just kind of have a, a backbone that finds candidate bounding boxes?[00:41:19] And then we, we set loss functions for objectness. We set loss function. That's a real thing. We set loss functions for objectness, like how much obj, how object do this part of the images. We send a loss function for classification, and then we run the image through the model on a single pass. And that saves lots of compute time and you know, it's not necessarily as accurate, but if you have lesser compute, it can be extremely useful.[00:41:42] And then the advances in both modeling techniques in compute and data quality, single shot detectors, SSDs has become, uh, really, really popular. One of the biggest SSDs that has become really popular is the YOLO family models, as you described. And so YOLO stands for you only look once. Yeah, right, of course.[00:42:02] Uh, Drake's, uh, other album, um, so Joseph Redman introduces YOLO at the University of Washington. And Joseph Redman is, uh, kind of a, a fun guy. So for listeners, for an Easter egg, I'm gonna tell you to Google Joseph Redman resume, and you'll find, you'll find My Little Pony. That's all I'll say. And so he introduces the very first YOLO architecture, which is a single shot detector, and he also does it in a framework called Darknet, which is like this, this own framework that compiles the Cs, frankly, kind of tough to work with, but allows you to benefit from the speedups that advance when you operate in a low level language like.[00:42:36] And then he releases, well, what colloquially is known as YOLO V two, but a paper's called YOLO 9,000 cuz Joseph Redmond thought it'd be funny to have something over 9,000. So get a sense for, yeah, some fun. And then he releases, uh, YOLO V three and YOLO V three is kind of like where things really start to click because it goes from being an SSD that's very limited to competitive and, and, and superior to actually mobile That and some of these other single shot detectors, which is awesome because you have this sort of solo, I mean, him and and his advisor, Ali, at University of Washington have these, uh, models that are becoming really, really powerful and capable and competitive with these large research organizations.[00:43:09] Joseph Edmond leaves Computer Vision Research, but there had been Alexia ab, one of the maintainers of Darknet released Yola VI four. And another, uh, researcher, Glenn Yer, uh, jocker had been working on YOLO V three, but in a PyTorch implementation, cuz remember YOLO is in a dark implementation. And so then, you know, YOLO V three and then Glenn continues to make additional improvements to YOLO V three and pretty soon his improvements on Yolov theory, he's like, oh, this is kind of its own things.[00:43:36] Then he releases YOLO V five[00:43:38] with some naming[00:43:39] controversy that we don't have Big naming controversy. The, the too long didn't read on the naming controversy is because Glen was not originally involved with Darknet. How is he allowed to use the YOLO moniker? Roe got in a lot of trouble cuz we wrote a bunch of content about YOLO V five and people were like, ah, why are you naming it that we're not?[00:43:55] Um, but you know,[00:43:56] cool. But anyway, so state-of-the-art goes to v8. Is what I gather.[00:44:00] Yeah, yeah. So yeah. Yeah. You're, you're just like, okay, I got V five. I'll skip to the end. Uh, unless, unless there's something, I mean, I don't want, well, so I mean, there's some interesting things. Um, in the yolo, there's like, there's like a bunch of YOLO variants.[00:44:10] So YOLOs become this, like this, this catchall for various single shot, yeah. For various single shot, basically like runs on the edge, it's quick detection framework. And so there's, um, like YOLO R, there's YOLO S, which is a transformer based, uh, yolo, yet look like you only look at one sequence is what s stands were.[00:44:27] Um, the pp yo, which, uh, is PAT Paddle implementation, which is by, which Chinese Google is, is their implementation of, of TensorFlow, if you will. So basically YOLO has like all these variants. And now, um, yo vii, which is Glen has been working on, is now I think kind of like, uh, one of the choice models to use for single shot detection.[00:44:44] World Knowledge of Foundation Models[00:44:44] Well, I think a lot of those models, you know, Asking the first principal's question, like let's say you wanna find like a bus detector. Do you need to like go find a bunch of photos of buses or maybe like a chair detector? Do you need to go find a bunch of photos of chairs? It's like, oh no. You know, actually those images are present not only in the cocoa data set, but those are objects that exist like kind of broadly on the internet.[00:45:02] And so computer visions kind of been like us included, have been like really pushing for and encouraging models that already possess a lot of context about the world. And so, you know, if GB T's idea and i's idea OpenAI was okay, models can only understand things that are in their corpus. What if we just make their corpus the size of everything on the internet?[00:45:20] The same thing that happened in imagery, what's happening now? And that's kinda what Sam represents, which is kind of a new evolution of, earlier on we were talking about the cost of annotation and I said, well, good news. Annotations then become decreasingly necessary to start to get to value. Now you gotta think about it more, kind of like, you'll probably need to do some annotation because you might want to find a custom object, or Sam might not be perfect, but what's about to happen is a big opportunity where you want the benefits of a yolo, right?[00:45:47] Where it can run really fast, it can run on the edge, it's very cheap. But you want the knowledge of a large foundation model that already knows everything about buses and knows everything about shoes, knows everything about real, if the name is true, anything segment, anything model. And so there's gonna be this novel opportunity to take what these large models know, and I guess it's kind of like a form of distilling, like distill them down into smaller architectures that you can use in versatile ways to run in real time to run on the edge.[00:46:13] And that's now happening. And what we're seeing in actually kind of like pulling that, that future forward with, with, with Robo Flow.[00:46:21] Segment Anything Model[00:46:21] So we could talk a bit about, um, about SAM and what it represents maybe into, in relation to like these, these YOLO models. So Sam is Facebook segment Everything Model. It came out last week, um, the first week of April.[00:46:34] It has 24,000 GitHub stars at the time of, of this recording within its first week. And why, what does it do? Segment? Everything is a zero shot segmentation model. And as we're describing, creating masks is a very arduous task. Creating masks of objects that are not already represented means you have to go label a bunch of masks and then train a model and then hope that it finds those masks in new images.[00:47:00] And the promise of Segment anything is that in fact you just pass at any image and it finds all of the masks of relevant things that you might be curious about finding in a given image. And it works remarkably. Segment anything in credit to Facebook and the fair Facebook research team, they not only released the model permissive license to move things forward, they released the full data set, all 11 million images and 1.1 billion segmentation masks and three model sizes.[00:47:29] The largest ones like 2.5 gigabytes, which is not enormous. Medium ones like 1.2 and the smallest one is like 400, 3 75 megabytes. And for context,[00:47:38] for, for people listening, that's six times more than the previous alternative, which, which is apparently open images, uh, in terms of number images, and then 400 times more masks than open[00:47:47] images as well.[00:47:48] Exactly, yeah. So huge, huge order magnitude gain in terms of dataset accessibility plus like the model and how it works. And so the question becomes, okay, so like segment. What, what do I do with this? Like, what does it allow me to do? And it didn't Rob float well. Yeah, you should. Yeah. Um, it's already there.[00:48:04] You um, that part's done. Uh, but the thing that you can do with segment anything is you can almost, like, I almost think about like this, kinda like this model arbitrage where you can basically like distill down a giant model. So let's say like, like let's return to the package example. Okay. The package problem of, I wanna get a text when a package appears on my front porch before segment anything.[00:48:25] The way that I would go solve this problem is I would go collect some images of packages on my porch and I would label them, uh, with bounding boxes or maybe masks in that part. As you mentioned, it can be a long process and I would train a model. And that model it actually probably worked pretty well cause it's purpose-built.[00:48:44] The camera position, my porch, the packages I'm receiving. But that's gonna take some time, like everything that I just mentioned the

In this episode from the archives, originally published in February 2021, Jennifer Doudna, who won the 2020 Nobel Prize for the co-discovery of CRISPR-Cas9 with Emmanuelle Charpentier, chats with Vijay Pande, general partner at a16z Bio + Health. Together, they discuss  the future of biology, whether discovery itself can be engineered and industrialized, and how biology can shape our future.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.22.533709v1?rss=1 Authors: Tsuchida, C. A., Brandes, N., Bueno, R., Trinidad, M., Mazumder, T., Yu, B., Hwang, B., Chang, C., Liu, J., Sun, Y., Hopkins, C. R., Parker, K. R., Qi, Y., Satpathy, A., Stadtmauer, E., Cate, J. H. D., Eyquem, J., Fraietta, J. A., June, C. H., Chang, H. Y., Ye, C. J., Doudna, J. A. Abstract: CRISPR-Cas9 genome editing has enabled advanced T cell therapies, but occasional loss of the targeted chromosome remains a safety concern. To investigate whether Cas9-induced chromosome loss is a universal phenomenon and evaluate its clinical significance, we conducted a systematic analysis in primary human T cells. Arrayed and pooled CRISPR screens revealed that chromosome loss was generalizable across the genome and resulted in partial and entire loss of the chromosome, including in pre-clinical chimeric antigen receptor T cells. T cells with chromosome loss persisted for weeks in culture, implying the potential to interfere with clinical use. A modified cell manufacturing process, employed in our first-in-human clinical trial of Cas9-engineered T cells,1 dramatically reduced chromosome loss while largely preserving genome editing efficacy. Expression of p53 correlated with protection from chromosome loss observed in this protocol, suggesting both a mechanism and strategy for T cell engineering that mitigates this genotoxicity in the clinic. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Jennifer Doudna, PhD, shares her personal journey to co-inventing CRISPR-Cas9 for gene editing and the promise of her discovery, during a one-on-one conversation with ASGCT President Hans-Peter Kiem, MD, PhD. Welcome to the fifth episode of Giants of Gene Therapy! Dr. Doudna has been at UC Berkeley since 2002. She is a professor in the departments of Molecular and Cell Biology and Chemistry, the Li Ka Shing Chancellor's Professor of Biomedical Science. She's also the president and co-founder of the Innovative Genomics Institute. In 2020, Dr. Doudna earned the Nobel Prize in Chemistry for co-inventing CRISPR-Cas9 genome editing technology with Emmanuelle Charpentier, PhD. Outside of her continued work on CRISPR technologies in the lab, Dr. Doudna is a leader in public discussion of the ethical implications of genome editing for human biology and societies. Dr. Doudna has spoken at the ASGCT Annual Meeting and will be a keynote speaker at this year's Annual Meeting in May, talking about “CRISPR Chemistry and Applications in the Clinic.” Music by: Steven O'Brienhttps://www.steven-obrien.net/ "Making Progress" (Used for free under a Creative Commons Attribution 4.0 License: https://creativecommons.org/licenses/by/4.0/)Show your support for ASGCT!: https://asgct.org/membership/donateSee omnystudio.com/listener for privacy information.

Banks Plus Walker Shooting RevisitedThe Ochelli Effect 3-16-2023 Mike Swanson - Larry Hancock - Greg DoudnaBanks going bust might or might not be signs of things to come. Chuck and Mike Discuss this in the first hour.MICHAEL SWANSON ONLINE:BE IN THE KNOW: https://wallstreetwindow.comTWITTER:https://twitter.com/tradermike_1999FACEBOOK:https://www.facebook.com/tradermikeBOOKS BY MICHAEL SWANSON:The War State: The Cold War Origins Of The Military-Industrial Complex And The Power Elite, 1945-1963https://www.amazon.com/gp/product/B00EWLGXHW/ref=dbs_a_def_rwt_bibl_vppi_i0Why The Vietnam War?: Nuclear Bombs and Nation Building in Southeast Asia, 1945-1961 By Michael Swansonhttps://www.amazon.com/Why-Vietnam-War-Southeast-1945-1961-ebook/dp/B08FHBS17KIn the second hour, Greg Doudna joins Larry and Chuck to talk about the shot taken at General Walker a few months before The Assassination of John F Kennedy. Is there a way to view that event that has not been widely considered in the past?Greg Doudna"Did Lee Harvey Oswald shoot at General Walker on April 10th, 1963?" https://www.scrollery.com/?p=1497Website https://www.scrollery.comLARRY HANCOCK:http://larry-hancock.com/https://larryhancock.wordpress.com/https://www.amazon.com/Larry-Hancock/e/B004FOXTAK/ref=dp_byline_cont_pop_book_1SOMEONE WOULD HAVE TALKED:https://www.goodreads.com/en/book/show/871694https://www.barnesandnoble.com/w/someone-would-have-talked-larry-hancock/1102627247TIPPING POINT:https://m.facebook.com/jfklancer/posts/857927944797915https://www.amazon.com/gp/product/173644090X/ref=dbs_a_def_rwt_bibl_vppi_i10(From The Editorial Reviews Section on Amazon.com)Tipping Point is the culmination of consistency and coherence; it is a story as it should be written. Larry Hancock establishes concise timelines which plot a path through the labyrinthine details that have been collected by a diverse array of researchers and investigators over the past several decades, presenting a detailed picture of the tactical elements of the attack in Dallas Texas on November 22, 1963 – an attack which altered the future of the nation JFK had been elected to lead.Chuck Ochelli, The Ochelli Effect Ochelli Link Treehttps://linktr.ee/chuckochelliOchelli Effect – Uncle – Age of Transitions – T-shirts and MORE: https://theageoftransitions.com/category/support-the-podcasts/Do you have a project, business, or message To PromoteBe Heard on The Ochelli Effect - The Jack Blood Show 360 - The Age of Transitions - Get M A D with Chris Graves - Uncle The Podcast or The whole Network. Rates Start at $50.Get In TouchE-mail ads@ochelli.com LIVE LISTENING OPTIONS:APPLE MUSIC RADIOhttps://music.apple.com/us/station/ochelli-com/ra.1461174708OCHELLI.COMhttps://ochelli.com/listen-live/TuneInhttp://tun.in/sfxkx+ Many More

Tom is opposed to people living forever, which is a possible application of CrispR, the Nobel Prize winning gene editing technology created by Mme Charpentier and Doudna. Scott stages another non intervention to assure Tom that his position will not adversely affect our future friendship with this Nobel Prize winning duo, and may even lead to us winning the Nobel Peace Prize. --- Support this podcast: https://anchor.fm/tom-saunders9/support

We have become the first species that can edit its own programming. Dana Carroll joins Vasant Dhar in episode 54 of Brave New World to share his insights on the science of gene editing -- and the ethical questions it raises. Useful resources: 1. Dana Carroll at University of Utah and Google Scholar. 2. The Promise and Challenge of Therapeutic Genome Editing -- Jennifer Doudna. 3. Jennifer's Doudna conversation on CRISPR with Chris Anderson. 4. Jennifer Doudna at Google Scholar and Amazon. 5. CRISPR babies: when will the world be ready? -- Heidi Ledford. 6. Beyond CRISPR: What's current and upcoming in genome editing -- Chris Tachibana. 7. Human Genome Editing: Science, Ethics, and Governance -- Various authors. 8. Heritable Human Genome Editing -- Various authors. 9. Human genome editing: a framework for governance -- WHO. 10. Human genome editing: position paper -- WHO. 11. Human genome editing: recommendations -- WHO. 12. Animal Liberation — Peter Singer. Check out Vasant Dhar's newsletter on Substack. Subscription is free!

If you listen to Ursheet Parikh, partner at Mayfield, Trevor Martin has a unique trait: He's always the best listener in the room.And as the leader of a leading CRISPR startup, it's a critical ability. Mammoth Biosciences employs leading bioengineers including Jennifer Doudna, the Noble prize winning scientist who co-developed CRISPR. Doudna co-founded Mammoth Biosciences with Martin, Lucas Harrington, and Janice Chan.In this TechCrunch Live event you'll hear how Martin attracted the best partners to form Mammoth Biosciences including Parikh, who wrote an early funding check. Step one? It starts with the vision and mission, and don't forget to listen.

Local Delta guy starting a new Saturday night derb? Sweet! Let's talk to him. And talk we did. Andy Doudna is one of the most knowledgeable guys you'll meet when it comes to Delta wildlife issues. He's been fishing the Dirty D for years, but not always for the green ones. In just the last four years he's solidified himself as one of the dudes to look out for when it comes to River largemouth. We were blown away by the conversation! We hope you are too.

Our week of recommendations didn't go quite as planned... But no worries because we have a special bonus episode for y'all! Valerie Pride was set to be the first black female electrician set to be working at the Palo Verde Nuclear Plant. But on September 6th, 1982 her and her two daughters would be found brutally slain, and to this day nearly 40 years later, the family is still searching for answers... CONTENT WARNING: Today's case involves children and violence. Be sure to follow us at: Twitter: @rarwpodcast Instagram: @rarwpodcast Contact us at: E-mail: redrumandredwinepodcast@gmail.com All music written and produced by: Savasas savasas | Free Listening on SoundCloud Resources: The Arizona Republic. “Phoenix Cold Cases.” Azcentral.com and The Arizona Republic: Phoenix and Arizona News, The Arizona Republic, 29 Mar. 2018, https://www.azcentral.com/picture-gallery/news/local/phoenix/2014/03/03/phoenix-cold-cases/5984115/.Doudna, Michael. “'There Is Someone out There That Knows What Happened': Nearly 40 Years after the Murder, Valley Family Seeks Answers and Justice.” 12news.Com, 20 June 2022, https://www.12news.com/article/news/crime/arizona-cold-case-valerie-pride-1982/75-b6eb2e35-8abd-40c8-9291-6eafa9ce294f. Erickson, Jacki. “Episode 45: The Murders of Valerie Pride and Her Daughters.” Bitter Endings, Bitter Endings, 18 July 2022, https://bitterendingspod.com/podcast/valeriepride. “Mother, Two Daughters Killed in Home.” UPI, UPI, 7 Sept. 1982, https://www.upi.com/Archives/1982/09/07/Mother-two-daughters-killed-in-home/8796400219200/. “Valerie Pride: 33-Year-Old Woman and Her Two Children, Shontia and Duana, Murdered-Killer Still at Large.” The Criminal Journal, 24 May 2022, https://www.thecriminaljournal.com/valerie-pride-the-unsolved-case-of-a-33-year-old-woman-and-her-two-children-shontia-and-duana-who-were-found-murdered-inside-the-family-home/.

Canary Cry News Talk #477 - 04.27.2022 KLAUSSIFICATION  LINKTREE: CanaryCry.Party SHOW NOTES: CanaryCryNewsTalk.com CLIP CHANNEL: CanaryCry.Tube SUPPLY DROP: CanaryCrySupplyDrop.com SUPPORT: CanaryCryRadio.com/Support MEET UPS: CanaryCryMeetUps.com Basil's other podcast: ravel Gonz' YT: Facelikethesun Resurrection Gonz Archive Youtube: Facelikethesun.Live App Made by Canary Cry Producer: Truther Dating App   LEAD/FOOD/GREAT RESET 5:17 V / 2:08 P 120k pounds of ground beef recalled after E. Coli discovered (UPI) → Ammonia explosion, food plant fire in CA 2 weeks ago (Yahoo) → Alleged “trend” of food plant fires (P B Solutions) → FBI did warn farm of cyberattacks, but not connected to recent fires (Food Processing)   NEW WORLD ORDER 16:14 V / 13:05 P → Clip: Top US general tells CNN 'global international security order' is at stake (CNN) → Iran's supreme leader says US in decline daily, new world order forming (Iran Int'l)   WEF Young Global Leaders 2022 (WEF) 22:31 V / 19:22 P → 13 Africans on the 2022 YGL List (WEF) [Africa Biz Insider Nuclear Threat WW3 real] → Ukrainian Vice Prime Minister and Minister of Digital Transformation (Econ. Times) → 2 Aussie women, eco-entrepreneur (SmartCompany)  → TOOL: WEF Young Global Leaders search (WEF) → YGL lead by Nicole Schwab → Her book, Heart of the Labyrinth    INTRO (M-W-F) (It's Wednesday, my dudes!) 35:37 V / 32:28 P B&G Update V4V/Exec./Asso./Support   FLIPPY 42:28 V / 39:19 P Lorax, the Antarctica Robot Explorer (Publicist Paper)   [Party Pitch/Ravel/CCClips/text alerts] 55:58 V / 52:49 P   POLYTICKS 58:02 V / 54:53 P Lawmakers, Biden considering “Significant” Student Loan Debt Cancellation (Yahoo/Huffpo) → NORTH KOREA showcases biggest ICBM yet, promises strong nuclear arsenal (CBS News) → RUSSIA accuses NATO of creating serious risk of nuclear war (Yahoo)   UKRAINE/RUSSIA 1:09:24 V / 1:06:15 P Russia halts oil to Poland, Bulgaria (MSN/Reuters) Russia tried to sell huge slug of oil, no one wanted it (WSJ) US Intel helped Ukraine, shot down Russian plane carrying hundreds of soldiers (NBC News) Lawmakers prepare to tackle more aid for Ukraine long term (WSJ)   [TREASURE/SPEAKPIPE/BYE YOUTUBE] 1:26:57 V / 1:23:48 P   COVID/WACCINE 1:52:35 V / 1:49:26 P Afraid to travel with unmasked passengers? Call your airline! (abc News) Army biology lab was not prepared for Covid, next time will be different (Stripes) https://www.msn.com/en-us/news/politics/vice-president-harris-taking-pfizers-paxlovid-to-treat-covid/ (MSN news)   CRISPR  2:17:48 V / 2:14:39 P Inventor of CRISPR, Doudna, not opposed to gene modified babies (MIT Tech Review) → Transgender men can now grow eggs outside of body, start a family (MIT Tech Review)   [TALENT] 2:31:21 V / 2:28:12 P   ANTARCTICA/CLIMATE CHANGE 2:48:16 V / 2:45:7 P Rice University “Geo-biologist” tapped for Antarctic drilling mission (Eureka Alert) → Profile Jeanine Ash (Rice U) Atlas Ocean Partners with ELi Code (Travel Pulse) Scientists return to Antarctic “Rain Forest” (Antarctica.Gov.AU)   [TIME/OUTRO] 3:00:46 V / 2:57:37   EPISODE 477 WAS PRODUCED BY… Executive Producers Jason H** RedBeard** Kathleen G**   Supply Drop Derick H, Kathleen G   Producers Matthew M, Jackie U, LX Protocol v2, NazareneFaithTheWay, MORV, Runksmash, Sir Casey the Shield Knight, Sir Scott Knight of Truth, Sir James Knight and Servant of the Lion of Judah, Veronica D, Gail M   AUDIO PRODUCTION (Jingles, Iso, Music): Jonathan F   ART PRODUCTION (Drawing, Painting, Graphics): Dame Allie of the Skillet Nation, Sir Dove Knight of Rusbeltia, G.E.    CONTENT PRODUCTION (Microfiction etc.): Runksmash: At the thought of what he must do Nimrod's mind is flooded with memories of an ancient past, one where he and his father were leading an Antarctic Booze Cruise, he remembered its tragic end, as he wired his father to the influencers' cell phones…   CLIP PRODUCER Emsworth, FaeLivrin, Epsilon   Timestamps: Mondays: Jackie U Wednesdays: Jade Bouncerson Fridays: Christine C   ADDITIONAL STORIES: Pakistan, female suicide bomber kills Chinese teachers (Reuters) Small Ukrainian town braces for Russian invasion (NY Times) How to officially submit your idea for an emoji (Wired) Yoon to meet with Klaus and US Heritage Foundation Founder (KoreaHerald) Xi trapped in Ukraine, Russia now in geopolitical drivers seat (Foreign Policy) Clip: Jeff Bezos responds to Elon buying Twitter…on Twitter (Reuters) Blackrock podcast, 1 in 4 Americans don't have retirement account (BlackRock)  

Kevin Davies is a renowned British science journalist and the executive editor of The CRISPR Journal, based in New York. His literary career began with Breakthrough: The Race to Find the Breast Cancer Gene in the early 1990s, followed by Cracking the Genome, which details the dramatic story of one of the greatest scientific feats ever accomplished: the mapping of the human genome. His other titles include the $1,000 Genome, DNA: The Story of the Genetic Revolution, and his most recent release, Editing Humanity: The CRISPR Revolution and the New Era of Genome Editing, for which he won a Guggenheim Fellowship for science writing in 2017. Kevin studied at Oxford University and moved to the US in 1987 after earning his Ph.D. in genetics. He is the founding editor of the Nature Genetics journal and Bio-IT World magazine, former editor-in-chief of Cell Press, and the first publisher of C&EN, the weekly magazine of the American Chemical Society. In today's episode, Kevin elaborates on his career trajectory and explains why he believes that hanging up his lab coat was the best decision he ever made. We also touch on the common themes that run through his books, some of the challenges scientific publishers and editors face, and the importance of promoting the work of women scientists. We also cover vectors, CRISPR babies, the cost of gene therapy, and so much more! Make sure not to miss this fascinating discussion with the remarkable Kevin Davies. “How we turn this stunning 21st-century medicine into therapies that are affordable is going to be a Nobel Prize-winning discovery if anybody can crack that one.” — @KevinADavies Key Points From This Episode: Kevin's career trajectory and his so-called “desperate” shift to science journalism. How Kevin believes the field of genetics has evolved since he was a geneticist in the 1980s. Learn about the impetus behind the Nature Genetics journal and The CRISPR Journal. What motivated Kevin to write Breakthrough, including a meeting with Mary-Claire King. Three elements in all of his books: genetics, medical or societal impact, and personal drama. Hanging up his lab coat to join Nature and the access to authors that it afforded him. Kevin reflects on the demographic representation and “race to the finish line” issues in scientific publishing and the burden editors face. The lens through which Nobel Prizes are considered and how it can shift perspectives. The importance of promoting women in science, who have traditionally been overlooked. How Kevin's book, Editing Humanity, coincided with Doudna and Charpentier making history as the first two women to share a Nobel Prize. Stanley Qi's role in the CRISPR story, which Kevin calls an “unsung contribution.” Speculation and trepidation surrounding vectors: Kevin shares some new thinking. Germline genome editing, CRISPR babies, He Jiankui, and controversy in Hong Kong. Learn more about the exponential cost of gene therapies and gene editing drugs.

Get the audiobook for free on Amazon: https://geni.us/code-free-audiobook (https://geni.us/code-free-audiobook) For the full transcript, PDF, infographic and animated book summary, check out our free app: https://www.getstoryshots.com (https://www.getstoryshots.com) The Code Breaker: Jennifer Doudna, Gene Editing, and the Future of the Human Race by Walter Isaacson Summary and AnalysisLife gets busy. Has https://geni.us/code-free-audiobook (The Code Breaker) been gathering dust on your bookshelf? Instead, pick up the key ideas now. We're scratching the surface here. If you don't already have the book, order it https://geni.us/code-breaker-book (here) or get the https://geni.us/code-free-audiobook (audiobook for free) on Amazon to learn the juicy details. Disclaimer: This is an unofficial summary and analysis. Please consult a professional before attempting to experiment with gene editing. Walter Isaacson's Perspectivehttps://geni.us/walter-isaacson (Walter Isaacson) is a historian, journalist, and proclaimed biographer. He is currently a Professor of History at Tulane and an advisory partner at Perella Weinberg Partners, a financial advisory firm. His former positions include CEO of the Aspen Institute, chairman of CNN and TIME magazine editor.  In literature circles, Isaacson is well-known for his biographical efforts. He is the author of best-selling biographies of Steve Jobs, Albert Einstein, Benjamin Franklin, and Leonardo da Vinci. Introductionhttps://geni.us/code-free-audiobook (The Code Breaker) is a fascinating ode to scientists who made remarkable discoveries about our genome. It focuses on Jennifer Doudna, who received the 2020 Nobel Prize in Chemistry, and her colleague, microbiologist Emmanuelle Charpentier. Although the Nobel-prize-winning gene-editing pioneer is the central figure of this publication, Isaacson offers to look at other scientists, their discoveries, and how they can change our lives. According to Isaacson and the scientific community, Doudna's findings promise to cure many life-threatening illnesses.  Bill Gates chose The Code Breaker as one of his top 5 favorite books of 2021. StoryShot #1: The Double Helix Changed Jennifer Doudna's LifeThe first quarter of The Code Breaker provides a brief biography of Jennifer Doudna. As a child, she found her aspiration in learning and education. When she was in sixth grade, she understood that she wanted to connect her life with chemistry and genetics. One day, she came home from school and found a book called “The Double Helix” by James Watson. The girl thought it was a detective book and put it aside. A few weeks later, she decided to give it a chance. Although it wasn't the book she envisioned, it turned out to be a detective book somehow.  The Double Helix talked about people who tried to unravel the ultimate mysteries of human life. The adventure to untangle our DNA was loaded with fascinating characters, fruitful partnerships, and contention. The book became an inspiration for little Jennifer to become a scientist. Growing up, she was told that “women don't become scientists.” Despite many challenges, Doudna got to establish herself as a biochemist. Eventually, she unraveled the mystery that remained unsolved in The Double Helix. StoryShot #2: CRISPR Gene-Editing is Used to Cure Cancer, Create Vaccines and Designer Babies CRISPR is the remarkable discovery Doudna and her colleagues made. CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats. It is a system bacteria have used for millennia to combat viruses. When a virus attacks bacteria, they remember a portion of its code. If the virus returns later, the bacteria can use knowledge of its code to tackle the virus. This mechanism is essential for living organisms to develop immunity to viruses they faced before. This is what we need to defeat COVID-19 and many future pandemics. In fact, instead of simply...

We discuss Basem's recent preprint, Borgs are giant extrachromosomal elements with the potential to augment methane oxidation. In our conversation, we discuss Basem's scientific career, the story of Borgs, how they were named, and their impact on our environment. A favorite quote of mine from talking to Basem was: "the next great discovery could be right in your backyard."

In December 2020, Jennifer Doudna received her Nobel Prize in Chemistry during a small, socially-distanced ceremony at her home - followed by takeout. The traditional celebration will have to wait until next year. A videographer and photographer captured the intimate gathering and presentation of the gold medal by Barbro Osher, Sweden's Honorary Consul General in San Francisco, with Anna Sjöström Douagi representing the Nobel Foundation. Doudna, the Li Ka Shing Chancellor's Chair in Biomedical and Health Sciences at UC Berkeley, was joined by her husband, Jamie Cate, UC Berkeley professor of molecular and cell biology, son, Andrew, and sister Ellen Doudna of Berkeley. Series: "UC Berkeley News" [Humanities] [Science] [Show ID: 37398]

In a wide-ranging interview with Ian Bremmer, Nobel Prize-winning scientist Jennifer Doudna discusses her groundbreaking work on the revolutionary gene-editing technology known as CRISPR. In their conversation she explains what CRISPR is and why it has the potential to cure diseases and fend off viruses. She also talks about the limits of this technology and advocates for a global policy consensus on what limitations there should be around gene editing. Policymakers must also factor in income inequality, Doudna argues, given how expensive CRISPR currently is and the potential it has to change so many lives.   Subscribe to the GZERO World with Ian Bremmer Podcast on Apple Podcasts, Spotify, or your preferred podcast platform, to receive new episodes as soon as they're published.

Dr. Zach Bush is a multi-disciplinary physician of internal medicine, endocrinology, hospice care and an internationally recognized educator on the microbiome as it relates to human health, soil health, food systems, and a regenerative future. Zach joined me to discuss the current state of health in the world and how living in a toxic environment exacerbates the spread of disease amongst humans. In this episode we discuss: The paradox of views surrounding COVID vaccines. How the immune system works. Flu vaccines increase coronavirus symptoms. GMO mechanisms. The definition of ‘vaccine’. Differences between the COVID vaccines. How spike proteins harm the body. Cas9: our bodies own vaccine cards. Will vaccines get rid of coronavirus? The history of coronaviruses. The flu vs. coronavirus. Myths about PCR and our relationship to coronaviruses. Personal responsibility over our immune systems. Genetic modification of food. What we can learn from the DDT ban. Creating a call to action for our future. Is SARS Covid 2 a naturally occurring virus? A biological look on how the virus impacts each of our bodies. Histotoxic hypoxia, cyanide poisoning, and hospitalizations. The India outbreak. The importance of breath. Benefits of Vitamin D. Racial and socioeconomic impacts on the pandemic. The bottleneck of information on public health. The downsides to social distancing. Response to critiques of Zach’s from the Conspirituality podcast. Changes in mortality rates and chronic diseases in children. Romanticization of nature. War-like mentality between nature and mankind. Soil, water, and air systems collapsing modern society. Intuitive knowing vs. science. Macro and micro struggles during the pandemic. Links: Neurohacker Collective Podcast Episode Rich Roll Podcast Episode Luke Storey Podcast Episode Mark Groves Podcast Episode Dr. Doudna’s TED Talk Zach’s Website Zach’s Twitter Zach’s Facebook Zach’s Instagram See omnystudio.com/listener for privacy information.

The Bio Revolution has the potential to transform our lives, and genome editing—the ability to change the DNA sequence in a targeted way using CRISPR-Cas9, is one of the key innovations that has sparked imaginations while also raising its fair share of controversy. What is the origin of this technique? How do we weigh the enormous benefits against the potential risks? And what is its role in solving the global coronavirus pandemic? As part of the McKinsey Global Institute's research on the Bio Revolution, partner Michael Chui spoke with Jennifer Doudna, PhD, one of the scientists who discovered the genome-editing technique CRISPR-Cas9 and leading proponent of its responsible use. Jennifer is a professor of molecular and cell biology and chemistry at the University of California, Berkeley. The Doudna lab pursues a mechanistic understanding of fundamental biological processes involving RNA molecules. To read a transcript of this episode, visit: mck.co/3dEJWhJ To read more about the Bio Revolution, visit: mck.co/biorevSee www.mckinsey.com/privacy-policy for privacy information

How do you explain complicated science and not put people to sleep? How do you balance home life with a career?  PhD student Martyna Kosno talks about it with Jennifer Doudna. Doudna won the Nobel Prize in 2020 for her discovery of the gene-editing tool CRISPR–Cas9.

CRISPR-Cas9 is the kind of scientific breakthrough that could change human evolution. Scientists call it “genetic scissors” — a tool that snips DNA with powerful and scary precision. As Dr. Jennifer Doudna, the co-developer of the gene-editing technology, explains, scientists can now edit the genomes of living organisms “like you might edit a Word document.”Dr. Doudna and her collaborator, Dr. Emmanuelle Charpentier, won the Nobel Prize in Chemistry this year. Their pioneering research could pave the way for a cure for cancer. Some fear it could be used to create designer babies.So what does this technology mean for how we live — and die? How will potential profit complicate the incentives of scientists? And just because we can more precisely “edit” life, should we?You can find transcripts (posted midday) and more information for all episodes at nytimes.com/sway, and you can find Kara on Twitter @karaswisher.

Jennifer Doudna and David Liu talk with our Senior Editor Markus Elsner about the state of the genome editing field and what challenges remain, especially as various therapies are now entering the clinic. This episode is part of Nature Biotechnology's Focus issue on CRISPR tools and therapies. See acast.com/privacy for privacy and opt-out information.