Okay, when the right lower quadrant ultrasound fails to show findings of appendicitis, you need to look elsewhere. So look at the pelvis, particularly in female adolescents, as gynecologic conditions can mimic appendicitis, and we'll look at that in the next talk. And also, look at the upper abdomen. Instead of appendicitis accounted for 22 to 25% of diseases, there are other causes of acute pain, gynecologic, bowel, 10% pylonephritis, pneumonia, constipation, gastrointestinal disease, 10% of alternative diagnoses when appendicitis is suspected, and the diagnoses include bowel disease, Crohn's disease, infection, hematocinal and purpore, mechal diverticulum, mesenteric adenitis, and endosusception, and we do not see the diseases that we see in adults. So Crohn's disease, even infection, simply thick walled bowel, one layer of the bowel measures greater than three millimeters, which is the normal cutoff, typically between four and six millimeter thickness. Both sides are effective, but when we do measurements, we're talking about measuring one wall. It's slightly compressible, it doesn't have a blind ending, and it's hypervascular. Looking for the blind ending is key, and you may see nodes. This is Crohn. Infectious enteritis is another cause of right lower quadrant pain. It can be viral or bacterial. All for some, usually normal and viral infection, and bacterial infection, you may see wall thickening in nodes. You cannot exclude it from Crohn's disease, there are many cultures. So how about this, 15 year old girl with diarrhea and pain? This is bowel. You can see the walls are thick, some air and aluminum, lots of color. This turned out to be infection, E. coli enteritis, but it certainly could be Crohn's as well. We're going to identify bowel wall thickening, not necessarily the same with the causes. This is another one. Hinaxialum purpura affects young children in the first decade. It's a small vessel vasculitis. It affects the skin, the inner rash, small bowel, kidney, joints, and it can cause extreme abdominal pain due to a hematoma or inter-susception, or just the fact there's a vasculitis and it's involved the bowel wall. So once again, you get thickened bowel, it looks like the other cases I showed you, and it involves the duodenum, the genome, the ilium, any place, and it's very vascular. Now the clue here is that it's a younger patient, so you might think about Hinaxialum purpura and look for the rash, but certainly infection could look like this in a rare case of Crohn. Mechal dive reticulum to mimic appendicitis, it's the most common anomaly of the GI tract affecting 2-3% of the population. 60% of patients, 60% with mechal dive reticulum, present in the first decade, and they present with pain due to inflammation, gets inflamed, or maybe inter-susception. And the dive reticulum is a remnant of the amphilomessenteric duct, and the duct in utero connects the bowel to the umbilicus, and normally it disappears, but if the proximal end stays open, you've got a mechal dive reticulum. It arises from the terminal ilium, not the cecum, within 10 centimeters of the ilial cecal valve. It is blind ending, this is it, you know, do a target sign on the short axis, and it likes the right lower quadrant, or it may be midline. So this should be, it's not 72, 12-year-old girl with right lower quadrant pain and vomiting. And we saw this too, it's blind ending, but when you traced it back, it came from the ilium, and we did a CT to prove it, here is the cecum. This is the dive reticulum coming from the ilium, and here's another one, target sign, this is laying it out, this is a blind ending tube coming from the ilium. So when you see a blind ending tube, even the appendix, trace it back, make sure it does come from the cecum. Mesenteric adenitis mimics appendicitis, it's self-limited, normally they're given something for pain, it will disappear in a couple of days, it's a benign inflammation of lymph nodes within the mesenteric, it likes young children and young adults, sort of like the same age, ages of appendicitis, often viral, but occasionally bacterial. And it's nothing more than a collection of lymph nodes. The strict diagnosis requires seeing more than three lymph nodes, and the right lower quadrant on the mesenteric root, and they have to be greater than five millimeters in short axis, and if the normal appendix and bowel are seen, one lymph node that's enlarged does not allow the diagnosis of mesenteric adenitis. And if you turn color on, you see the characteristic flow in the central highland, just like any other lymph node. And finally, interceception is a cause of acute pain. It's prolapse of one segment of the bowel, the interceceptum, into another, and it causes acute pain in early childhood, okay? Early childhood, under three years, 90% are iliocolic. The remainder may be iliocolic, coliocolic or iliolio, but this is what you're commonly going to see. 90% are iliopathic and have no leap point, and they're just due to hypertrophy of prior patches, small nodes.  Ilium going into colon, and the total length is usually 8.5 centimeters. And on ultrasound, you're going, and typically we see it because it's ilium going into the colon and the right abdomen. You're going to see multiple layers. The target sign, the bull's eye sign, and if you lay it out, you've got the pseudo kidney sign, okay? Oh boy, it's sort of like a kidney, at least that's what they call it. Here's another one. The target sign, the right abdomen, and you'll lay it out, you've got the pseudo kidney sign. That is interceception. Turn color on. If the bowel is viable, you're going to see lots of color. And it's sort of helpful if you're going to go ahead and try to reduce this because it tells you that you have a viable bowel, and this is usually reducible. Here's another one. There's no flow. We made an attempt, a reduction, it didn't work, and that was the schemic bowel surgery. Lead points, 10 percent of patients were common at older children. The lead points include mechal diverticulum, polyp, hematoma, duplication, cysts, and lymphoma. And ultrasound, you're just going to see a mass and the interceception. Here is the interceception, a multi-layered mass, right abdomen, right upper quadrant, not right lower quadrant. This echogenic part is the mesentery that goes along with the bowel. This is a mass. That was a hematoma. This is an interceception target sign, multiple layers. The echogenic part is the mesentery oementum. And this was a polyp. Here's another interceception, multiple layers. There's an anechoic mass in this interceception that's a duplication system. The ultrasound factors that predict that you're going to have difficulty reducing this, if you see absent blood flow, I showed you that. If you have a large amount of fluid in the central interceception, it's a bad sign. If you see a lot of trapped lymph nodes in the interceception and if there's a lead mass. Sensitivity, 94 to 100%, specificity up to 100%, occasionally fecal contents, inflammatory bowel disease, hematoma will mimic interceception. This patient had acute pain. We thought maybe that was interceception. Did an enema? This turned out to be a colitis. And the differential is the other interceception, transient interceception. It occurs in the left upper quadrant, a right subgenum. It's very short compared to the standard interceception, which is much larger. It resolves spontaneously. And it's only the one lesion. Don't touch it. It's going to go away. So these patients had pain. We do look at the right lower quadrant, but we also look, as I mentioned, in the entire abdomen. If we don't find something in the right lower quadrant, this was in the left upper quadrant. It's a target appearance. It's an interceception. It's small. Another one, it's small. Those are transient interceceptions. We feel comfortable with that. We leave it alone. As I mentioned, there are a few other diseases you occasionally may see, pylonephritis, pneumonia, constipation. We're not going to see this well in ultrasound, but we do scan the entire abdomen. We look at both kidneys. We look at the liver, the gallbladder, pancreas. And we trace the bowel. This is a patient with acute right-sided pain. And the reason is pylonephritis. Here's an echogenic area in the kidney and no flow. On Doppler, that's acute pylonephritis. So to sum it up, we discussed some of the common causes of acute right lower quadrant pain. Appendicitis, 20%, 25% of the diagnosis, blind immune to both lots of color. Once it becomes ischemic, you see secondary findings and you may lose flow. We talked about other causes, mimics, bowel disease, and mesenteric anitis, a cluster of lymph nodes, bowel disease, we did mention. We talked about Crohn's disease. This is Crohn's disease. But we said infection could look the same. We talked about the diverticulum, the mechal diverticulum, and the fact that it arises from the ilium and not the cecum. And finally, we brought in into this inception the world target appearance, a classically right upper quadrant because it's ilium going into colon. So to finish this talk, appendicitis is always a diagnosis that's requested for us in the examination. But you'll see that up to 25% of the time. You'll see other diagnosis related to bowel or GYN. We're going to look at GYN shortly. And then most cases, many cases, you're not going to make a diagnosis, probably viral. Well done ultrasound. It's a great tool. And it does allow a correct diagnosis in the majority of patients. It's just a matter of looking for the clues.


Hello everyone and welcome to IOM, the Regional Office of Asia Pacific for our first webcast to celebrate and commemorate the World International Day against Trafficking. I'm joined by several experts from around the region. I'll introduce them as we start talking to them and we're going to hear a bit about what's going on in trafficking, in anti-trafficking activities from IOM's offices and IOM special projects around the region. We're lucky to have such high-caliber people with us from Bangkok, from Indonesia, from Hong Kong, Cambodia, Timur Lechte and indeed Papua New Guinea. I'll start with Tara Dermott who works in the Regional Office here like myself. I'm Joe Larry, by the way, I'm the Regional Spokesperson and Senior Communications Officer in the Regional Office for IOM Bangkok. Tara is a colleague of mine here working on leading a project, a mainstream project called IOMX which uses celebrity music and media to try and draw up awareness about trafficking. Tara, what have you been doing recently? Thanks for the intro, Joe, and for organizing this conversation today. So, as you mentioned, IOMX is the Regional Office's innovative campaign to promote safe migration and to encourage public action to stop exploitation and trafficking. Now, IOMX aims to move beyond simply raising awareness and to actually affecting behavior change through applying a communication for development approach. So, this means that we take an evidence-based participatory approach to designing our materials so that they are most likely to achieve the actual impact that we desire. So, we launched in early 2015 and since then we have been prioritizing our campaign efforts on two industries. We have been looking to prevent the exploitation of men and boys in the fishing sector and also women and girls in domestic work. So, in 2015, we launched the I Care Do You campaign where we tapped into our strong network of celebrities, musicians, and we produced video content that reached 60 million people. And I'm happy to say that in post-impact surveys, we found that of the viewers of the programs, of Thai viewers of the programs, more than half of them said that they would support ethical companies. So, this is really great for the counter-trafficking community's ongoing efforts to not only protect aspirant migrants before they leave, but to actually address, you know, institutional problems within supply chains of seafood industries. This is really great. With domestic work, earlier this year we launched the IOMX Happy Home campaign and this was around not only increasing the general public's understanding of domestic worker rights and trying to provide a platform for domestic workers to voice their priorities, but to also encourage all employers and homeowners and neighbors to make sure that their homes, their homes, their homes are supported by domestic workers and that we that they have our language contracts. And it's really important because the local staffs show that a domestic worker, so this is really relevant to the world today. Yeah, and so IOMX, we've been focusing on fishing and domestic work, but we're going to be expanding the work that we do to other sectors, so I encourage everyone to visit IOMX.org, follow us online and stay tuned for more exciting work that's coming out. Thanks very much, Tara. Elizabeth, in Dili, can you tell us about the work you've been doing in helping victims of trafficking, helping the police to be more sympathetic towards victims of trafficking? What are you doing in Dili to make that happen? In Timor, we are helping not only police, but the immigration service here in Timor, like to explain to them what human trafficking is because this is very new, like we mentioned in Timor. So we are helping them to identify the case and then the police officer is mostly here or work through intimidation and usually consider victims of trafficking as guilty. So also there is like, we are like giving their awareness of specific needs for victims of trafficking in here, like dealing with the person who suffer from like physical and mental abuse in here. And also currently last week, we are organizing like CSS training with the local national NGO in here, like how to encourage the police, how to give them, like how to deal with the victim of trafficking here. That's great. Thanks, Elizabeth. A few audio challenges there, but I gather you're helping, you're using actors to help the police to understand what it's like to be victim of trafficking and get the police to be more sympathetic, which is an amazing approach. Narul, you are a well-known anti-trafficking activist and worker in Indonesia. You've moved recently to head up IOM's office in Hong Kong. Can you put some numbers on this? How many people in your experience are still in slavery, in fishing vessels and in sex work and other forms of exploitation in this region? Well, it's difficult to come up with the real figure, but you know, when I'm speaking about trafficking in Indonesia, particularly for the fishing industry, there are estimate that around 200,000 Indonesian fishermen actually still in water and exploited, you know, just in like trafficking situations. This figure actually come up from our Indonesian government. They believe on this number and therefore they are actually approaching IOM on how we could actually intervene and help them in additions that we have already been assisted, offer, you know, 2000 foreign fishermen, particularly from Cambodia, Myanmar and some number of Lao and Thai in Indonesian water. So, you know, we work closely with IOM Cambodia, with IOM Myanmar to assist with their return and by now they all already go back home. But you know, we believe that there are still small scale of numbers happening because just recently the Indonesian government through the Ministry of Marine Affairs and their fisheries and marine affairs, they have contacting us, you know, small scale of number like 150 or 20 people, you know, stranded in Indonesian water again and of course they actually also been exploited in a fishing industry. So, that's if it is talking about fishermen, you know, exploited in a fishing industry as a trafficking. But when we're speaking about trafficking in Indonesia, in addition of fishermen itself, foreign fishermen or Indonesian fishermen, we have also big numbers of Indonesian micro workers who are actually exploited outside Indonesia, working as domestic worker, working as, you know, worker in a plantation, particularly palm oil plantations, construction worker, and also, you know, of course, including in some other sectors. And locally itself, we have local domestic workers being trafficked, you know, or also being exploited within Indonesia. That's the situation. We have also foreign women's actually from Moldova, Colombia, Uzbekistan, trafficked in a forced prostitution in a big cities of Indonesia, including Jakarta as a, you know, prostitution. So, this is these situations actually are quite complex. Trafficking in Indonesia is quite complex. And if you're asking me to come up with some how many estimate at the moment, it's difficult to be honest. Yeah. That's true, of course. It's a very shady, shady industry. And it's impossible to tell exactly the numbers that we're talking, you know, hundreds of thousands of people work as modern day slaves in this region. And somebody who actually has to deal with that problem when people get home is Brett Dixon, who works in our office in Cambodia as a project manager there. Brett, you're seeing these people when they get home, when they get back to Cambodia, when they've been rescued by IOM in Indonesia, they get back to Cambodia. What's the state are they in? And what are the long lasting scars that they would bear from the awful exploitation that they endure with working 24 hours a day with beatings and with violence and little or no pay? What are you seeing? Thanks, Joe. Yeah. So, last year, IOM in the region, particular IOM, Indonesia, IOM, Cambodia, Myanmar worked together to assist thousands of men stuck in the fishing industry, enduring these conditions, forced labour conditions. And this has been possible through our presence in the region, also our strong relationships with the government and the embassies who played a big role in identifying and assisting with the voluntary return. So, here in Cambodia, IOM plays a key role in providing post-arrival immediate assistance. So, this entails meeting the men at the airport, helping them through immigration. We also work in the land borders as well to assist men and identify men who have been in that condition and to provide the care they need. We either have acknowledged that there's a lot of health consequences of trafficking. So, medical care and psychological care is a key. So, we provide a lot of medical checkups and treatments. We also have psychologists on board to provide that care and cancelling. Many of the men returning back with behavioural issues such as drug abuse that they picked up on the boats, heavy drinking. It's a very highly violent sector that they work in on fishing boats and they're subjected to regular beatings, as you mentioned. So, this has a huge impact on the psychological well-being. And this can inhibit finding secure work and meaningful employment if they're suffering from depression or a drug abuse. So, we work with a strong network of NGOs to provide reintegration support, which is really key to their recovery, to providing support to their families and helping the families to support those loved ones that have returned. We had some successes in a few cases, finding employment, working with local employers to develop apprenticeship programs to help the men to develop skills and eventually open up their own businesses, such as motorbike mechanics. That's great. So, there can be some good news and there can be resolution for these people, but as you said, there are deep lasting psychological scars. We're hearing that trafficking is exploitative and it's wrecking lives. What's the experience in Calille in Papua New Guinea? What's the work as IOM doing there and what's the trafficking phenomenon like in the country where you are, which not many people would know much about? Thanks, Joe. Good day, everybody, listening in. And what I wanted to say in for trafficking within PNG, the phenomenon itself is very little awareness has been done within the country. A lot of people don't understand what trafficking is and that's everything from both domestic to international trafficking. So, IOM has been engaging with the community and the government for the past six, over six years since 2010 to raise awareness and to also develop legislation to combat the crime. One of the biggest, one of the most significant achievements within the last six years has been the official recognition of trafficking as a crime within the country, which was developed in 2013 by IOM in assisting the Department of Justice and it's now criminalized the act with a charge of 20 years for adult trafficking and 25 years for trafficking of minors. So, that has been a huge achievement for the country and alongside that IOM has also been involved in the technical aspect of it, so helping the government to identify victims of trafficking. We're working with the police, we're working with immigration, we're working with customs officials to go through the process of doing a victim identification assessment and as well as bringing together both the government side as well as the civil society side to provide a holistic approach to protection for potential victims of trafficking. Within the country itself, domestic trafficking is a very, very big issue largely because it is embedded so much in culture and the lack of understanding does not bode well for this. Many people say, well, we've been doing it for this amount of years and there's never been anything wrong. Why is it now domestic, why is it now considered trafficking? So, getting the understanding of what it is at a very, very local village level is key to combating it. The issue of forced marriage, of underage forced marriage and paying off of bride pricing is a very big factor within the country and it's something that needs to be addressed and this is what IOM is trying to do. Great, thanks for almost all the time. It's a really, very complex phenomenon of human trafficking and it can touch all our lives. No one is immune from being duped into trafficking from the villages in the Highlands of Papua New Guinea to the metropolises of Bangkok. I want to ask you all one last question and a quick answer, one word or one sentence and I'll start with you, Khalil. I'll go back in reverse order. Khalil, what is needed in one phrase to make an impact against human trafficking? I think we need to work on the demand side and curb it on the demand side. Okay, great. Brett, how about you? What do you think? One thing that we need to do, society needs to do to curb human trafficking. I think we need to empower migrants to make proper decisions to give them the information so they can migrate safely. Excellent, thanks. How about you and Nurul? What would be the one thing you would say on this anti-trafficking day to try and curb trafficking? Nurul, can you say it again with your microphone on? Nurul, what's the one thing you would do to curb trafficking? Law enforcement is definitely important to combat trafficking in person. In addition, of course, promoting safe migrations, preventing awareness tracings, but I think law enforcement is playing also very significant roles. Thank you. And back to you, Elizabeth, in Dili, what's the one thing that should be done to help counteract a modern day slavery? From me, it's like it's because the trafficking is very new in Timor-Leste in here. So it's like maybe give more education, not only for the police officer and immigration service, but this more education to young generations here in Timor-Leste, so they need to know what is trafficking is really. Thank you, Elizabeth. And finally, Tara, what do you say? The one thing that needs to be done by the world to help reduce or stop trafficking? We need a global movement. We need for every individual to understand how human trafficking is relevant to their lives and to act accordingly. Thank you all very much for joining. Thanks for your time to discuss this important subject. It's World Anti-Trafficking Day, the day when people take action against human trafficking. Spread the message. Log on to www.io-m.int. Find out more. Talk about the subject. It's not going away. It's a huge phenomenon. Thanks again for listening and I'm Joe Larry in the IOM Regional Office in Bangkok.


I'm called Rose Kaguwa from Uganda and I'm an industrial chemist by profession but working for the last 24 years with a water utility, serving in different capacities starting as a water quality analyst and now growing up into leadership and heading one of the directors. Starting as a young girl and having a passion for sciences, at one point you would wonder whether you've made the right career choice, serving in an organization for 24 years without moving but beginning to see the impacts that you have in terms of service delivery for water and sanitation helps you know that every day you wake up you're actually serving and it's a God-given job to give and provide to people what they really need. Coming from Africa where as many people may not know many women spend half that day and children fetching water, carrying it on their heads, looking for places for decent sanitation, when you can contribute to that change it overwhelms me and the award is so meaningful especially at this time as we are looking at the SDGs and trying like in Uganda the target that the government has said for us is that by 2020 it should be 100% coverage for water and you know that it's a tall order but basically we cannot do it without the women. We need to bring them in at all levels and this award also inspires us to continue contributing as women to the sector and knowing that we have a very big role to play.


Hey guys, this is Coin Telegraph, the market report. Welcome everyone for another great show. I'm Marcel Pechman, I'm a writer here at Coin Telegraph. I used to work for multiple banks, including Deutsche and UBS, so I used to be a trader and I joined the crypto industry in 2017 and since then I've been a full-time trader and analyst and I'm joined today by our expert, Sam Borgie. Yeah, Marcel is the reform banker, right? From Wall Street, from the banking sector to the crypto sector, right? From one DGEN to another DGEN, apparently, given the way the industry has gone recently. Yeah, hi everyone, my name is Sam Borgie, business editor at Coin Telegraph. I mostly focus on a lot of the business news around the crypto and blockchain industry. I wrote my first Bitcoin article back in 2012, I think it was, around the whole Silk Road fiasco, so I've been in this space a long time, but nothing could prepare me for what's happened this year, especially over the past three weeks or so, never a dull moment in the sector, as we're gonna see with the articles this week. That's a good question, Sam. I've been here yesterday, I heard that the recent, the drop that we've seen over the past year or so was worse than what we saw in 2014 at Montgox. Do you think this is correct, saying like, well, Bitcoin is down 72% in one year. I think it's down like 76 or 77% from the peak. Do you think it has any resemblance to what I've seen on Montgox in 2014? Yeah, there definitely is a resemblance. Now, Montgox had a bigger impact on the industry as a whole at the time, given how small Bitcoin and crypto trading was, and the majority of the volume at the time was on MT Gox. Now, with FTX, the actual size of the losses are bigger in terms of how much money was lost given the size of FTX, but overall, the industry is more diverse and bigger than it was back in 2014, so logic would dictate that FTX going down shouldn't completely annihilate the sector, but the issue we're gonna have now moving forward is the contagion effect, and we've already started to see that several companies have come forward and stated that, you know what, we have considerable funds locked up on FTX, so that's really where the concern is, is that what's the next domino to fall? Is there gonna be another domino to fall? That's really where the real concern is right now. Okay, so for those who are unfamiliar with the case, I wasn't here in crypto industry back in 2014, but I know that MT Gox had like a 70 to 80% market share, so comparing to what FTX had at its peak, which I think it was like 25% of the volume, it is small, irrelevant, some might say, but the point is in 2014, we did not have so much derivatives market, like the open interest for the Bitcoin futures at FTX, it was like 25% of the market, and when you have such a big loss for derivatives traders, I think that the impact for the industry, I'm not saying for the holders of Bitcoin or the holders of Ethereum who have long-term mentality, what I'm saying is for institutional investors, traders, lenders, and all the ecosystem that has been formed, the venture capitalists that has been pouring money on altcoins and exchanges themselves, I think that money is going away maybe for a long time. I don't think that any reasonable institutional investor will see, okay, I understand that FTX was just an exchange, I understand that Genes, we're gonna get into that, was just a lending company, so I don't see there's a risk in crypto. I think it's just the opposite. The market retail investors and institutional investors will be scared at least for 12 months, so do you think we're gonna see a longer winter, crypto winter because of that? Yeah, you know, Marcel, you raised a lot of really good points about just how short investor sentiment is at that time, at present. In terms of the length of crypto winter, right now, for me, the case remains that we are in four year cycles, and unless I see otherwise, I am expecting things to eventually turn around, leading into the halving, and then eventually another market recovery, but the situation going on right now cannot be overstated. Anybody who comes and says they saw FTX crash coming, you know, that's mostly a load of nonsense. I was always a bit puzzled as to how Sam Bankman freed rose to prominence so quickly and why FTX was so popular, but there's no denying the massive impact that they had on the ecosystem. And it was a largely positive impact if you take a look at the sponsorship deals, how much they were responsible for pushing crypto and Bitcoin into the mainstream. To have them basically go belly up over a 48 hour period is basically what it took, is very concerning. So yeah, I do think that as CZ even said to the CEO of Binance, this probably pushes back the industry a few years, and I'm still waiting for the dust to settle. I don't think the dust has settled just yet. If you take a look at price action, it looks like we could still be headed lower, but overall, I think that investor sentiment has been shattered to the core, and the FTX one in particular, that one is the one that stung the most because very few people saw it unraveling the way that it did. There were a few people who raised concerns about FTX, but the chain of events, nobody could have predicted that. Basically SBF was a fraud, and what he was doing was a complete and utter fraud. No one can really sit there and say they expected that to happen. Those who were advertising FTX US, which is a regulated exchange, they did their due diligence in the sense that they were advertising a regulated exchange. Did they know that the founders were involved in an Enron-level scam or an Enron-level fraud? Unlikely. So, yeah, I think investor sentiment is gonna take a long time to come back. Right now, you basically have the Bitcoin hodlers, the guys and gals have been in the industry for a long time, we're hodling. And then you have a few projects that are building, but beyond that, right now it's not looking good. I agree 200%. I think Celsius and Luna, we can say, well, those were like VC companies or it was a crypto project that was kind of risky. It was an algorithm stablecoin that changed in the way. It was completely centralized. So one can say, okay, those were not directly related to Bitcoin and Ethereum, so should have less impact. But FTX, as you said, it was one of the main gateways fit on an off-ramp. And as I said, one of the main derivatives exchange. But the topic for this week, quick shout outs to the guys on the YouTube chat. We have Renee Montenegro. Epic crash to $10,000 is a week away. We have Capybara. We have Yana. We have FTX by SA. Thanks guys for joining us. It's a pleasure being here. We also have Cardano Green on chat. But Sam, so the topic of this week's show is the Grailscale Bitcoin Trust, the Genesis and GBTC and the event or collapse of those funds being the next, next one event. So first I'm gonna give the viewers what I understand that what DCG and Genesis is. And then you jump in to correct me if I'm wrong or add other stuff. So DCG, DCG, digital group or digital currency group is a holding company, right? They have a bunch of companies owned by Barry Silverth. And they have coin desk website. They have Genesis trading, which is a market maker, OTC and landing firm. They also have the Grailscale funds, which is kind of like an ETF because it's a fund which is traded on OTC markets on nice and US markets. And this fund only holding is either Bitcoin or Ethereum or other cryptocurrencies. So it's kind of an ETF, but it doesn't really have redemption. Once the owner of the funds buys in, the only way to get out is selling to another people on the open markets. So it's not really a good product. It charges 2% per year, but at the peak, it had over $30 billion worth of Bitcoin and over I don't know, but $4 or $5 billion worth of Ethereum. Of course, as the market went down, so did the value of the BTC and ETH on those funds. So Grailscale, the company administers those funds, the GBTC and the Ethereum fund. But the problem is that Genesis, another company from DCG, the market making company is underwater. We don't know, maybe they lost a billion, two billion dollars. So there's a lot of speculation going here that because of Genesis, the company is going bankrupt or it's missing money, it's insolvent. The assets on the Grailscale fund might be sold. So causing a black swan event. Is that what I understood correctly?  And I know that it's been reported that even at DCG's recent board meeting, the topic of a potential liquidation of GBTC was brought up. That's what's been reported. I don't want to flood or anything like that. But at least it's been discussed. And obviously that would have a pretty severe impact on the broader market. Yeah, so that's basically where we're at. I know that also DCG recently led a fundraising effort. Reports claim they're trying to raise about a billion dollars to cover the massive hole in their balance sheet that's caused by what's going on with Genesis as you described. So where there's smoke, there's fire. And apparently it seems like I received news that even the about us page on DCG was basically purged team page. So that for me was an eye opening. We can't find the team. Now this was as of a couple of days ago. I haven't checked now. But from what I saw, the team page was purged. So not looking good, I mean, not looking good at all. Adrian Danilo, can you share the news of the GBTC? Black Swan, the one posted on the counter. Okay, so here's the news post on November 21. So yesterday, GBTC, which is the fund owned by Grayscale administered by Grayscale. Is it the next Bitcoin price next one? So can you scroll down a little bit, Danilo, please? Yeah, so there we have a contagion in the world. Everyone leaps as November grinds on just like the terror collapse. The victims were a voyager or Celsius. And now the FTX contagion turns to GBTC. So my questions and what I don't understand is if a fair manager goes bankrupt for whatever reason, what does the fund contents have to do with that? Or is it a special case for Grayscale? What happened here that made people think, hmm, if Genesis go down, the contents of the GBTC fund, the 650,000 Bitcoins might be sold. So what's the difference here? Yeah, I mean, I think that that's a really good point because I know that DCG has time and time again stated that their holdings are secure. They even had a coin-based attestation showing that the contents of GBTC are secure. I think the issue comes from Genesis being a liquidity provider halting withdrawals due to what they called, I think, unprecedented market turmoil. So I think when you have the liquidity provider is potentially bankrupt, I think that's where the issues are arising. But yeah, at the end of the day, it would require someone to come in and actually start selling off their GBTC shares. And I think there was a recent report from Bloomberg, a note that was released very, very recently saying that that's unlikely to happen right now.  So the connection isn't very clear, but what seems to be clear is that within the DCG umbrella of companies, there are some issues right now, and that's having some kind of trickle effect or contagion effect on the broader company. That's based on the reporting that I'm seeing. So that's the overview of where we are right now. It looks like between Genesis and GBTC and why everyone is concerned. And why exactly is the funds trading below the contents, the BTC value? It has been going for, I don't know, at least 10 months or 11 months that the GBTC shares were trading below par. Of course, the discount is widening. I think it reached 50%, but do you think that investors are adding you in advance that Genesis had a problem? So what's your opinion here? I don't think they knew. I certainly didn't think there was any issue with Genesis up until recently. I just think this is all a symptom of the brutal bear market that we're in and just a function of supply and demand. I mean, this has been a down only market. This has been a mean market, basically. It's been down only for the past 10 months. I mean, you know better than anyone that during bear markets, we get massive bounces, right? We even caught them back in 2009 during the financial crisis. And Bitcoin and crypto, it's literally been down only. Like if you would have told me a year ago that Bitcoin is gonna be basically a straight down shot for 10 months, I would have thought that would be completely unrealistic. Nothing falls the way that Bitcoin and crypto have fallen this year. So I think that the effects of that are being felt across various asset classes. So I think right now we're starting to see just the downstream effect of a brutal bear market even by crypto standards. I agree. Well, I think that grayscale funds are the elephant in the room right now and an eventual liquidation of those 650,000 bitcoins would trigger further panic. I have no doubt here. What I doubt is that anyone knows the correct answer to what's gonna happen if Genesis goes down. It goes down because Genesis is one company. In grayscale, the fund administrator is another. So one thing is for sure. Managing the GBTC fund is extremely profitable. They are in 2% fees per year. Doesn't matter if Bitcoin goes up or down. So having said that, I think it makes sense for grayscale to sell some of their assets in companies. For example, they could sell grayscale, the fund administering company. But in that case, I don't see why it would trigger an auction for the bitcoins or force them to liquidity to sell the GBTC content. So I don't think that this doomsday scenario is gonna happen. But do you think there could be another company relevant in the industry popping up as a dead body because of FTX? Are there other potential victims out there? Well, we've seen some smaller scale companies. I know that the whole BlockFi fiasco was part of that. Right now, I can't really predict who else could be exposed. I know that it's creditors, it's top 50 creditors if FTX have basically lost billions. And that could take years before that whole bankruptcy process goes through the motions. So I can't predict which other companies can go down. All I know is that the effect of FTX going down has been basically an earthquake on the industry. And the tremors will probably be felt for a while longer. So that's just my general takeaway. It's hard to predict in these kinds of, I think FTX could maybe be looking at an actual black swan in the sense that nobody saw it coming. So, or very, very few did. So that in of itself, the second largest crypto exchange in the world going down, I think is hopefully this is the end of it. You know, hopefully this is the end of it. Or I don't know if I can do another year of this, Marcel. I don't know if I can do another year of this. My body can't take it no more. I can't take more sleeping pills for more than a month. I'll be dead. But in my opinion, the biggest risk right now is the mining sector, because one, those guys are sitting on a ton of hash rate. And if they eventually shut down because of those firms becoming insolvent, I think other players in the ecosystem will also collapse because the miners typically post as collateral for funding to pay their bills and even to pay for acquisition of the ASIC miners. They put those ASIC miners as guarantees as collateral. So once the company goes down, you're gonna hand out those machines to the lender. And what the lender is gonna do with 10,000 ASIC machines in a bear market, nothing. It's basically worthless. So I don't think that crypto prices can suffer even more if those events unfold. So it's gonna drain liquidity from the system and add pressure on the liquid assets, maybe triggering sales on Bitcoin and Ethereum. But in the longterm, it's gonna, I think it could be meaning that miners will no longer be able to operate using leverage, which is gonna be good. But in the short term, lower hash rate will be seen as negative. Do you agree with that? Yeah, yeah, I agree. I mean, I think that the relationship between hash rate and price has always been, there's always been like a debate about that. But yeah, in the short run, definitely. And crypto right now in Bitcoin, we're in the midst of the great deleveraging effect right now, everything is being delevered. A lot of the excess is being taken out of the market. And that also applies to the mining sector as well. So I do think Bitcoin, of course, will survive and Bitcoin is battle tested. The network will survive and thrive. But in the short term, for holders, for investors, for those optimistic about the space, Bitcoin just invites attack over and over again. Because it's very, very rare that somebody can look at an asset like Bitcoin and just be neutral about it. The more you learn about it, the more you develop a very strong opinion one way or the other. So in terms of sentiment, I think the mining issues going on that you described will have a short term impact, but longterm, I think that Bitcoin is still on track. Quick shout out to YouTube floors here. We have Neuromancer. Big question is, what is the next CIFI to upload? Well, Sam already said his opinion is really hard to predict even FTX if we ask it any analyst a month ago, the odds of FTX imploding, no one would say FTX. So I don't think it's worth doing such an exercise. Have Luca Zarifi, great show on discussions today. Congratulations. Crypto Glenn, we are par for the course for the four year cycle. Don't panic, just continue to build positions and projects with great utility. And in 2025, you'll be fine. Exactly what Sam did, we're doing the four year cycle. I think the Bitcoin halving is like 400 days away or so. So the best time historically to buy it's a year ahead of the halving, which is gonna happen over the next 90 days. So I think it's a good time to reposition. Crypto Glenn, the news is doing the thing they do down here, talk about, okay, so some guys are asking for more data. And if I had something to present, well, I can prove that Tether is solvent. I can prove that Binance is or is not insolvent. We would be doing that. And we would be earning a lot of money. But the problem with cryptocurrency, especially with centralized entities is the lack of transparency. A subject that I wanna discuss over the show, I think it's set for later on, it's the proof of reserves, but we can discuss that right now. So recently we saw some exchanges doing the proof of reserves and Binance, for example, had 62 billion dollars, including the BNB token and the BSD, that the stablecoin issued by Paxus or whatever, but held under Binance cold wallets. And this could be seen as a step towards transparency as the viewers are requesting. So yes, we can discuss about it and we can express our opinions on what those exchanges are doing. But it's not the ultimate growl. That's not what's gonna tell us if the exchange or the stablecoin is solvent or not. So what's your take here on crypto's exchanges proof of reserves and? Yeah, like you mentioned, it doesn't really say anything about solvency per se. I think there were even some reports about exchanges sending crypto back and forth to one another to take snapshots to prove their reserves. I think proof of reserves as a concept is very intriguing. I think it's useful as a concept. People wanna know that the assets they have on their centralized exchanges are actually being held in cold storage, for example. They wanna know that they're actually collateralized as opposed to doing what FTX did, which is taking client deposits and basically sending them off to their sister firm Alameda to do whatever they want with it. So proof of reserves as a concept, I think is really important. I think you're gonna probably see more pressure on that moving forward. But what does it say about actual liquidity, about the company's actual health? That's still up for debate, especially the shenanigans we saw last week with certain exchanges sending crypto back and forth to each other around the same time that everyone was talking about proof of reserves. That was a bit peculiar to me. Yeah, Adrian Danilo, can you pull up the news from Cointelegraph? Like CoinMarketCap launches proof of reserve tracker for crypto exchange. I think it's gonna be very helpful for the users, at least to know where to look for the information. I think that's the job that Cointelegraph and us as analysts and editors and writers are doing. Had we, if we had a crystal ball to say, well, this data is good or this data is not reliable, it would be feeble to reach, but it's simply not possible to do that with a lack of information because of centralized entities. So there we got the news from altered by Savannah 40s four hours ago. CoinMarketCap launches proof of reserve tracker for crypto exchanges. The tool allows users to monitor exchanges reserves through displays of total assets and public wallet addresses, along with the balance and the value of wallets displayed. Yeah, so there's the CoinMarketCap post on Twitter. So the new feature of the exchange proof of reserves, I believe that other providers such as Nansen had already been doing that for a couple of weeks, but shout out to the industry. I think it's the path forward, but there's a point that I wanna highlight. Let's take a step back here. So why are those centralized crypto exchanges needed in the first place? We already have smart contracts. We already have digital assets. So sure, one might say they facilitate the fiat on and off ramps, meaning the ability to convert fiat currency into crypto and vice versa. The problem is those exchanges rely mostly on stable coins and external payment gateways. So theoretically, a user can buy USD coin or die or Paxos was in PayPal and traditional fintechs. And from that point on, exclusively rely on decentralized markets, the dexes to trade. So we need to understand that most traders, they are addicted to the benefits of centralization, namely the fake liquidity and the derivatives like we saw on FTX. Even after the second biggest exchange collapsed and was proved Ponzi. So I think that those guys are simply addicted to centralization, even if there are benefits on decentralization. So what's our take here, Sam? Do you think that those centralized exchange will continue be a key driver for the industry over the next couple of years? Yeah, I do think there will be, Marcel. Like I said, centralized exchanges, I mean, look, centralization isn't always horrible. I mean, there's certain benefits to working on a centralized exchange or certain conveniences. I know it's a lot more intuitive and easier for a lot of traders and investors to simply buy Bitcoin on a centralized exchange, especially when there's such a clear fiat on ramp. It's a lot easier, for example, to buy on Coinbase than to create a wallet, a browser wallet, and then connect to a decentralized exchange. So I think the ease of use aspect of it will make centralized exchanges still popular moving forward.  I mean, they're all centralized and there's not a lot of issues around that. I mean, obviously Bitcoin and crypto, there's a different spirit that guides Bitcoin that's a centralization.  That would be my way of using a centralized exchange. I use centralized exchange. I have a few exchanges that I use, but I'm not storing my Bitcoin on them or if I did have some, they're all coming off now or they've all been off after what we've seen the past few weeks. So use centralized exchanges at your own risk, but at least have a plan. Like if you're gonna use them because it's an easier way to buy Bitcoin, consider taking your Bitcoin off the exchange at some point. So the real issue I think though is gonna be the centralized lending platforms, the yield, the promising. Imagine giving up your keys to earn 5% API, right? Like we saw the risk of that with BlockFi.  Well, look what happened. So I would never touch any of those platforms. And if you can't answer the question, how do they get the yield? Know that you are the yield. So yeah, just remembering the audience that everything that's said here is our own exclusive options. It does not reflect what the company thinks or about the interest to grow on any competitors. It's just our own opinion here. So for the proof of reserves, I think yeah, it's better than nothing but unless each user, each depositant has a hash to verify their own account within the exchange balance, the mechanism doesn't work. So I think it's better than nothing but you can't really say that, yes, financing is completely solvent. It has 62 billion dollars on its cold wallets. Okay, so how much do the clients deposit? 62, 90, 50, we don't know about that. So I see that guys on the chat are asking for some data and charts and I'm gonna show you on the next segment. There's a news analysis that I did yesterday. Data shows traders are slightly bullish even as crypto total market cap falls $800 billion. So Danilo Adrian, can you share the news on Convalier, please? So the total crypto market capitalization has dropped at $800 billion but that highlights a few reasons why some traders are bullish. So can we show the chart of the total capitalization? Yeah, if you can increase the chart please, Adrian and Danilo so the viewers can, yep, yep, there you go. So we've seen that the $800 billion level has been holding for the past three weeks and today we saw another mini pump to 3% getting us above the $800 billion market cap capitalization line. So it seems an important level to keep an eye on, okay? So the causes of the collapse are FTX, Alameda, then DCG, the Genesis, which is the main topic of today's shows that great scale funds with $650,000 Bitcoin and all of these risks of the fund contents being sold. So thank you Danilo, can you scroll down the news a little bit please? A little bit, yeah, a little bit down. So we can show the balance leverage between bulls and bears, a little bit down further please. The next chart, yeah, there you go. So the perpetual conflicts, so the futures also known as inverse swaps, they have a funding rate, which is charged usually every eight hours. So how do professional traders use such data, the funding? So can you increase the size of the image please? Yeah, so if the funding rate is positive, it means that the buyers, the longs are paying the fee. So in order to buy using leverage, you're gonna pay a fee to the seller. If the funding rate is negative, it means that the shorts, those betting on the price downside are paying the fees. So what we saw over the past weeks, this is seven day accumulated data, is the prevalence of negative funding rate. So we saw bears doing leverage bets that the market would go down. So initially we can say, yeah, well, looking from the perspective of the funding rate, we can say that markets were bearish, but that's not the only data point you need to analyze. Because for instance, if I'm a buyer, but I think that the exchanges are insolvent, I'm gonna remove coins from exchange and reduce my leverage. So the buyers could have gotten scared and run away. So to confirm if those investors are really bearish, you should also look at the Bitcoin options markets. So can we show the next chart, please, Danilo, Adrienne? Yeah, there you go. So the options put and call Rachel, that's the chart. What's an option, a call option? It's a right to acquire, to buy the cryptocurrency at a later date. So it's a bull, neutral to bull instrument. And the put is a sell option. So it gives the buyer a right to sell. So it's a protection, it's like you're buying insurance. If the price goes down, you get money from the option. So whenever the chart goes up, it's a put to call Rachel. So whenever the chart goes up, it means traders are getting more bearish. So currently there's like 40% more calls. So more rights to acquire Bitcoin at a later date. So in general, the market is not bearish. So what we saw of the negative funding rate was likely caused by the FTX insolvency and collapse itself. It's more of a systematic risk because the current Rachel of the put and call options does not tell us that the markets are bearish. Yes, it went from 0.53 to 0.55, but still we have more prevalence of call bullish. So the neutral to bullish call options. Thank you, Danilo. So on aggregate, what the article says and it shows data is that there's some resilience among the professional investors. Nobody is willing to short below $19,000. It doesn't seem to make sense on a risk reward Rachel. Do you agree with those professional traders, Sam, or do you think that they could have gotten it wrong? Well, I mean, they could always get it wrong, but the value proposition of Bitcoin continuing to fall below 19K, I guess diminishes. And yeah, I mean, it depends on what your strategy is. I think that we're probably gonna bottom out sometime soon. And where that bottom is exactly, I'm not sure. It could be in the 14s. It could be in the 13s, maybe as low as 10. I'm not making a price target. I am a bit surprised to see a reluctance to short below 19,000, to be very honest, given where we are in the market and where we are in the cycle. But as an investor, someone who's not trading it, I don't really see any reason to nitpick on where Bitcoin could potentially bottom. But I guess these are traders. So I just find it interesting to see that they're unwilling or more unwilling to short below 19K, even though it seems like there's still quite a bit of room to fall before we capitulate for the cycle. Next one event, I don't know if it's gonna be the Grailscale funds or it's gonna be Tetra or it's gonna be Binance or another centralized exchange. If there's another backs one event, we can definitely see even Bitcoin and Ethereum going down 30, 40% from here. But I think that the biggest risk lies on altcoins. I'll give you an example. Solana at the peak had $10 billion of TVL, the value deposits on DeFi and remaining smart contracts applications. And the number went down to $260 million. So from $10 million to $260 million. So the risks for the altcoin is simply losing 50%, losing 90%. I don't think the same risk can happen to Bitcoin and Ethereum right now. I think Ethereum has problems of its own. It's gonna unfold over the next years if they do deliver on or not the promises of low fees and low latency and high security using proof of stake. It's still gonna be proved and Bitcoin has problems of its own like the recent week we saw a huge spike on the network fees because centralized exchange was gathering a bunch of addresses and consolidating in a single wallet. So that can give some users some idea of, well, Bitcoin doesn't work. We need Lightning Network to start working right now. And Lightning Network is not ready right now. Lightning Network is Bitcoins, one of Bitcoins scaling solution. But it's not ready. You can test it, you can play it with a little bit of $100, $200, but no one, no developer would recommend you, yes, put all your money on Lightning Network, it's safe. We just saw some bugs, some major bugs, no money was lost, but the network itself got shut down for a couple of hours. So, but my point is, altcoins currently have much downside risks than Bitcoin and Ethereum. So for the last topic of today's show, Sam, there's Adrian Danilo, if you can show the fidelity news. You asked senators, huge fidelity to reconsider its Bitcoin offers after FTX blows up. So Sam, can you explain to the viewers what is fidelity and what is their relationship with Bitcoin and what are the senators asking here? Yeah, I think this was a table by Elizabeth Warren, if I'm not mistaken, which that's all you really need to know about this. But fidelity is one of the, it's a major mainstream institutional investment firm and under Abigail Johnson, they've been pro-Bitcoin for a long time. I mean, they've been exploring the space, exploring mining, exploring digital assets for several years. And now apparently some congresspeople, some senators are using the FTX debacle to warn fidelity against further expanding its Bitcoin offerings or to seriously reconsider their Bitcoin offerings. Despite the fact that the fiasco that happened with FTX was less about crypto and Bitcoin and more about fraud, okay? That we have laws in place that are supposed to prevent people like Sam Bankman-Fried from doing what they did, right? From doing what they did to customer funds, from doing what they did with a sister firm that is trading funds from another firm. So we have laws that protect investors against fraud, right? But what they're using, they're using the whole FTX fiasco to go after Bitcoin, which is what I predicted was gonna happen. How successful they're gonna be, I'm not quite sure, but the issue that we saw with FTX goes beyond Bitcoin and crypto. There are several crypto-specific risks that we have in the market. The massive spate of hacks that we've seen, for example, that's a huge problem. There are a lot of crypto-specific risks, but the shenanigans of SBF go far beyond that. So it looks like they're trying to urge fidelity to reconsider its Bitcoin offerings and its pro-Bitcoin stance because of what happened at FTX. I guess we'll see if they heed that advice, but I think probably not. Yeah, I think that the main view that we should give to our viewers' audience is that, yes, fidelity is a giant like $7 trillion worth of funds management, but fidelity crypto, it's a separate business, it's small. I don't think fidelity holds more than $200 million for their funds, for their clients in Bitcoin and cryptocurrencies, but I think it's a good thing for them to have the option. Yes, we wanna invest in real estate. Yes, we wanna invest in Bitcoin, in Ethereum, in gold. So the more investment options available for the general public, I think it's an extra positive. Of course, they shouldn't be exposed to scams and there should be some kind of regulations because they are not buying the cryptocurrencies directly, they are using the ETF or the fund, so there should be some kind of regulations for those kind of investors, people that don't want to invest in cryptocurrencies directly. So, but I don't think that, yeah, I agree with Sam, I think it's Elizabeth Warren and a couple of legislators that have always been anti-crypto, no matter if it was trading at $70,000 or $16,000, they have always been anti-crypto. And I wanna thank the audience for sharing your time with us. I really honestly hope that we answered all of those questions and I wanna advise you guys to log in to pro.cointelegraph.com, it's a full powered trading station, so CoinTelegraph Markets Pro, it's really easy to use, it has a complete dashboard and it uses the same technology of the institutional investors. There's alerts, there's news and there's, you're gonna be a flash science when there's buying opportunity when the coin gets listed. So log in to pro.cointelegraph.com and make sure to use it and get to know the trading station and how it works. Feel free to chat us on Twitter if you have questions, just tag CoinTelegraph or either me or Sam, we're gonna be glad to be able to help you with that using CoinTelegraph Pro. So Sam, any closing talk for today? In a bear market like this, the key is to survive and if you survive, you make it through, there is always light at the end of the tunnel and you'll see in a year from now, maybe longer, we're looking back at this period as a formative period in crypto and in Bitcoin, but survive and you'll be okay. Yeah, I agree with Sam. It's a long-term investment. I do think that governments will run out of money because they are trying to reduce the taxes to stimulate economies or they're trying to keep the rates artificially low so it doesn't cause panic, it doesn't cause market crash, but inflation and energy prices are not going away.  So even if you don't love Bitcoin, even if you think it's not the best solution in the world, it's what we have right now. Yes, there are other cryptocurrencies that offer some different experience and some different benefits. If you want and if you study, you should also have exposure, but at least if you're gonna invest in crypto, at least 30% exposure in the biggest cryptocurrency, which is Bitcoin. So that's my tip for today. Thank you guys, thank you guys, thank you everyone. Thank you Danilo, thank you Adrian and thank you everybody at Cointelegraph that makes this show happens. Make sure to like and subscribe. We're here every Tuesdays. Thanks for joining us.


Hi, everyone. Again, so today, I will be talking about responsible AI and explainable AI. It's more like a one-on-one in terms of responsible AI and explainable AI and how they are both connected. So I will start talking about responsible AI, of course, what are the principles, what are the main developer phases, how it connects with explainable AI. And at the end, I will showcase a paper that it's making a new approach in terms of explainable AI. It's more like related with human explainable AI. So that will be mainly the talk. So first, I know it's 11 AM, so we should be OK to see a pizza now. We are not so hungry. So the idea is to use the analogy of making a pizza to explain responsible AI. So we have principles. When we would like to use responsible AI or when we need to meet something using responsible AI, we have principles. Of course, in the papers and all the documentation that you can find, there are multiple principles. I prefer to highlight the main four principles that you can see in most places because the documentation is pretty new. And the concepts, you can find multiple concepts and multiple different concepts everywhere. So the first one is fairness. The pizza, if we're talking about the pizza, it talks about having the toppings equally distributed in each slice. In this case, I think it's arugula and cheese, yes. Hopefully, it's not pineapple. I don't know if you are big fans of pineapple. I'm not a big fan. But in that case, it will be equally distributed. So the same amount of toppings to each particular person, each particular slice. In AI, it is if you would like to do... This is mainly related with bias. So you can have a data set that is biased and you would like to avoid it. You should be avoiding training your model with a biased data set, of course. You have tools that we will see later. Second one is transparency, which is in the pizza analogy, is if you would like to go to a place, you should know how they cook the pizza, how they prepare the pizza, what is the recipe. If it's a Neapolitan pizza, or if it's an American pizza, or if it's... I don't know. They have different kinds of pizzas. So you should know or they should publish you or they should show you how they are preparing this pizza. So you will trust in this pizzeria because they are sharing the recipe. In terms of AI, it's sharing the models that they are using or how they are working with the data or how they are dealing with the data that it could be from the preprocessing, from the modeling, from the training, the modeling, and of course, how to use the data in the model. The third one is accountability. In the pizza, if you receive a pizza that it's overcooked, you need to know that someone is in charge of this pizza. They would like to replace you. The pizza they would like to give you a new pizza. Or probably if you don't like the pizza, you can ask for a new one. So someone has to be responsible for making this pizza and to give you a different pizza with different toppings. You'll probably ask for pepperonis, and if you get pineapple, you won't like the pineapple, so you would like to ask the pepperonis. And in AI, of course, someone has to be responsible of this application of this model. Now we are all seeing the LLMs hype, and the question here is who is responsible for this model. It's the person that is training the model, it's the person that is implementing the model, the person that is doing the fine-tuning or the company that is doing the fine-tuning. So someone should be the owner of this application. The last one is privacy and data protection. In the PICS analogy, it is to protect the data of the employees, so you don't know the names of the people that are working there because it's protected, or you probably don't know the exactly cheese provider or the exactly person that is providing the cheese. So this is internally protected. In the AI, it's more about protecting how you work with the data. For instance, if you are training a model to detect, to make a fraud detection system, you don't want to share the data of your customers, so I like to keep the data hidden and to protect the data. This could be related to something with GDPR, but this is more in that line. So what you will get at the end is if you go to a PICS idea and the PICS idea is following these four principles, you would choose this principle. You will go to buy a PICS in that place. The same can happen with AI. If you will trust to a model or to an application, you will give your data to this application if they are following the four main principles, four main principles or more principles. But now, that's OK with the PICS, and so we are done with the PICS, and we are mainly developers. So if you would like to build a model, it's OK with the principles, but you need tools to build this model to meet each particular four principles. So without going into details, the first one is you need to check if your dataset is biased. The way that you check it is if you know the data, you can see or you can do some query and you can see if it's biased by gender, age or whatever. And you have some tools, what is the IBM i360 or the Google, what if they are open source tools that you can go there and you will give, and these tools will give you if the dataset is biased or non-biased. And they can also de-biased the dataset if it's oriented to one particular gender or whatever. The second one is you have to detect or you should detect proxy variables, which is sometimes it's not implicit, the bias for instance. It's easy if you like to detect gender or age or country or whatever, but sometimes you can get this information through other information or through different information.  Or if you have a geographic patterns, you can see the ethnicity of this person. So you would like to avoid that to make your dataset less biased. The last three or the main ones is you should have some, you should have the dataset documented, how you treat the data, where the data come from in LLMs for instance, or it could be LLMs or computer vision or any use case is, which was your dataset to train the model, like in LLMs, you train the dataset for a particular use case. So you train for finance. So you get the data from particular banks and you train your model or healthcare or whatever, but where the data came from, you should have it documented because I didn't talk yet, but probably you can use it to make your model better, to work better, or it could be some regulations that you need to meet, that you will need to explain when you're making a decision or the model is taking a decision, you should explain why the model did that. The last two is explainable AI that will go deep later. That is, you can have the principles and you can have everything, but you need tools or you need something to help you to explain why the model is taking that decision. There are family, it's very advanced and there are multiple algorithms and multiple ways to do it, but explainable AI is basically that what you need. You need something to give you insights about the decisions. And the last one, which is pretty important, that is, while you are implementing your model and it can help on the data protection part and in the privacy part, let's suppose that your application is centralized or working in multiple devices, you need to protect the data or you need to protect the model when the model is working. So in that case, you can use tech techniques like PPML, which is privacy protection machine learning. So it's encrypting the machine learning process. Of course, I will not go in detail by just to let you know that we have that. And OpenFL, which is a federated learning framework. So when you're doing, when you're training a model in a federated way, you should protect the data of each particular node. So there's another technique that could be useful, but you also should be aware of, okay, it's not just following the principles, I need to protect the data when I'm working with the data or when you are training this model. So just to show an example of the first one to detect bias, this is just an example of something that I wanted to highlight. This is the case of the IBM. So you have a data set. In this case, it's the German credit scoring. And you will like to see if it's bias or if it's not bias. So what happened here, here it's saying that you have multiple features that equal opportunity index and so on. And you can see, you can visually see if the data set that you are working, it's biased or not. This is just a visualization tool, but you can go and step ahead and you can say, okay, could you please de-biased this data set for me? And this tool has the chance to do it. But this is a step before training the model. It's something that you do before that also. So it's not just one tool, it's not just one thing that you say, okay, I have one tool, I put everything here and I'm protected about responsible AI, explain it by AI, and I can get everything in just one place. It's not the case, you have to use multiple tools and depending on your use case and your need, you will probably use different toolkits. So once you have a model, I suppose that you have a model trained to detect if an email hits a fraud or it's not a fraud. And you can have some questions. Let's suppose that you have a model and it's saying that yes, this email, it's fraud. But you can make some questions.  Based on what? Based on what? Features, variables, why not something else? Because you don't know. And the second one is how do I correct an error that probably if you're not an AI user or if you're not developing the model, you would like to say, okay, you said that it's spam, but it's not fraud. So how can I correct that? Or how can I modify that to make it more trustable? And the last one is should I trust it? Which is more like a human perception because at the end of the day, we are humans using these models. So we need to create models that are trustworthy for people, basically. So why do we need, explainable AI to try to answer those questions? This is a quote that I think that's pretty useful. If you go to the papers and so on, the terms could be used interchangeably. You can say explainability, you can see interpretability, you can see multiple things. But basically what is explainability, it was providing size to a targeted audience to fulfill their need. So it's a targeted audience. You are doing something to a particular audience, which means is that if you have a solution that works for healthcare, the explanations that it will give to some particular verticals will be different. I mean, it could be different or it could not be different, but for sure it should be different because the audience is different, the use case is different, the explanation that they need that they are different and probably for some people is different, probably some people need some kind of explanations and some other people needs other kinds of explanations. So as I said, it's more related with perspectives. It's to who we are explaining that. So we have five angles that could be more, but here I wanted to highlight five angles. The first one is the regulatory perspective. So you need to write the explanation, like GDPR, you are probably, you should be forced to explain why the model did something. If you are using Uber or if you are using a fraud detection system in Europe, that GDPR is used, you need to do it. So it's not that you are forced to have an explanation on that topic. So you need to find something that can give this kind of explanations to regulations, which is could be understandable, could not be understandable, but it's what they need. And the other explanations could be related with scientific or for the person that is developing the model. If you are working with LLMS, you probably will need to understand why the attention layer or why the detail of the model did something. And this insight will help you to improve your model, to make it better and to improve your model. So if I go to these explanations to an end user, probably the end user will not understand what is an attention layer, of course. But for an AI developer of this scientific perspective, it's pretty important. And last one, there are the end users. So as I said, the people that is consuming your application, people that has no idea about AI that they need to understand or visually understand, okay, why did you do that? So as you can see, there are multiple ways to explain a model and you need to find the right one for each particular use case. This has a lot of details, of course I will not go into details, but the main idea to explain here is that you have an explanation, you would like an explanation. Okay, it's not working. You like an explanation, right? And this explanation, it's mainly divided in three parts. The first is data explainability. The thing goes, explain the data, as we said with the bias and so on, which is previously of your, it's before you are training your model, so you need explanations about your data, related with your data. And you have a family of things that the main idea is to remove the bias, remove the overfeeding, or try to avoid these kind of problems that you can have on your training model. Second one is the model explainability. Everything, as I said, related with the model, going in details of the model, trying to understand how the model works and how to make the model to perform better. And the last one, that is the one that I will go in details later, is the post hoc explainability. Once you have the model trained, once you have a model with node bias, once you have a model perfect, someone will need to know, okay, why did you do that? Or why did the model did that? So this is the post hoc explanations. So it seems confusing at the beginning, but it was pretty easy to read. But now, you probably know that the models that are out there, decision trees, regressions, transformers, GPT, neural networks. And the challenge that we have is if you like to get better or what is going on in the last years, is that we are improving in accuracy.  Compared with five years ago, it was pretty different. So now they are performing much better. But the thing is that the interpretability or the explanation that you can get from those models, those models, it's almost impossible to understand what's going on inside.  We can understand how they work internally, but it's pretty hard to really go into details. So the transformers or even the neural networks, you cannot see what's going on inside. And the easiest part is the decision trees. So decision trees is something that you see. So you know which part of the model, which leaf of the model, the algorithm is following to give you an answer. So it's pretty visual. So we have a challenge. We need to explain these models are the most complicated models that most people is starting to use, like deeper learning. If we start talking about the 200, the 2000s, most people started to use neural networks. We started to use more neural networks compared to the decision trees because we like to solve a problem. So we are, most of the time, we try to find algorithms that solve our problems. If they are complicated, we'll see later, but they solve our problems. So the challenge that we have is what we can do with all these models that we have or that we are using. And this is another complicated slide. Seems to be complicated, but it's not complicated. So as I said, the Potshawk, which is you have the model and you have multiple approaches to try to find an explanation. You have mainly, we can separate in two families. One family is I have the model and for me it's like a black box and I would like to understand not how the model works, but how the model took that decision. So I will not go into details of the model. I will not touch the model. I will try to find a way to find an explanation using the inputs and the outputs. Just looking at that thing. This is what shop is. I will explain later, but this is this part of, but this is not working. It's this one, right? Shop. And the other common or most common use is lime. Lime, it's a bit different. Instead of having, as I showed before, these complicated models, let's suppose that you have a transformer or you have a neural network, the attribution models or lime or this family that is up there, they try to create a parallel model that is simple, that is understandable, that could be a decision tree, to try to explain what the main model is doing. So instead of using BERT or a transformer or a neural network, you can probably get the same results using a very explainable model. So and since the model is explainable, you can use this model to do the predictions instead of using the neural network. It could be more challenging because sometimes you cannot get the same results, but it works well, let's see. But these are two approaches. And within these two approaches, you have tons of multiple variations that some of them they use parallel models with shop and parallel models with an alternative models and sample of models. But this is our main concept or I go in detail of the model or I just see input and output. And this is what shop is. You treat the model as a black box, so you just see the input and the output. You have features, of course, in this case, HX, BP, but I don't know what it is, and BMI. And what you will get is the weight of each feature for one particular prediction, you can say. So in this case, I wanted to know something and it says that H was the most important feature and sex agenda was the less important feature in that case. But this is just technical, right? What you can do after, once you have this information, this is going to the beginning that is it important for the end user? Probably not, probably it could be useful, it could not be useful, but this is a kind of explanation that you can use. If you like to go deep in this shop prediction that is not using the model, we have a toolkit that is open source with its Intel explainable AI tools, which is, we try it, because one of the challenge that you have with this is that if you like to use Lime, you need to go to the Lime site or download the Lime GitHub. If you like to use Shop, you have to go to Shop. If you like to use any others, you have to go to each particular GitHub. What we are trying to do here is we are trying to centralize everything so you can use just in one place, you can use Shop, you can use Lime, and you can use multiple use cases. I wanted to do a demo, but I didn't have the time to do it because it could take time, but this is an example that is, let's suppose that we would like to know what the heart disease system, and we would like to know which features it was important to make that classification. So these are the inputs, and the label is at the end, which is if the heart disease is fixed, normal, reversible, which I don't know what it is, but it's the kind of labeling, right? So you train a model, as you normally do, you use TensorFlow, you compile a model, you fit your model, this is as usual, could be TensorFlow, could be PyTorch, could be PsychoTrain or whatever. This is the model that you get, of course, this is rated with TensorFlow, so it's as usual. And what you do after that is once you have a model trained, you ask for the explainer. So you use the API, and you send the model, and you send a particular part, or you send them the line of the code that you would like to get explanation. And what you get is the answer. As I showed before, we can see what was more important and what has less importance. So three table spoons we have. Know what it is, what's the most important part, or the most influenced feature in that particular decision, and old peak was less important part, what was the less important feature. And same as before, so it's just information that you can use or you cannot use, but it could be important to, even for you if you are training a model, you can say that these features are important, or some other features are not important. But it also works with BERT, or with GPT or these other models, as it's a black box, and you are not going in this black box, and you can use whatever model. You can use the latest model, whatever. In that case, I would do the same with BERT, so you train your BERT model, you download your BERT trained model, you invoke for the explainer, same thing. You send the data set and you say everything. This case is a text classifier, so you say it's like a sentiment analysis if it's positive or negative. So what you will get is, you get which parts of the paragraph or the text the model took as positive and the model took as negative. In this case, the red ones are the negatives, you see not bad, not good and so on, it's negative, and the positives are the blue. So this part of the, and the model usually waits this to see, okay, this phrase of this part is a positive or is a negative part, but this is also important to know if the model is performing well. In text, if you are doing that things, it's not able to detect ironies and so on, so you can see here if it's working well, the model with the ironies or it's not working so well. But this is another way to get explanations. But as I said at the beginning, we have everything that we developed is by ourselves, by ourselves. It's we need to create an explanation for us and we create those explanations based on what we think that could be useful for the audience. It could be finance, healthcare or whatever. But this is a human interaction, so you need to put the human interaction or the human-computer interaction in the middle of the wave. So you can have the best part of the best model to explain something, but if it's not useful for the end users, or they will not trust your model or they will not use your model for whatever you are trying to sell. So you need to add the human part here. And how we can do that is by as simple as just interviewing your audience, as simple as that, trying to see what they like, what they would like to see if it's useful or not. And this is what this paper did, which is a paper that this is from Intel Labs and it's an interview that they did and they picked this Merlin application. It's pretty useful. I mean, I tried to use it here and it's pretty useful. It's an application to detect birds. So in that case, I have a goose, a Canadian goose, I guess I'm from Canada. And it detects the goose, right? And the idea is to talk to the end users of this application and give them explanations and try to see why they think about those explanations. So the questions are if it's important, how they would like to use the explanations and if it's useful for them. They show these four explanations. But for me, for instance, the last one is the better, but because I'm more close to AI, but they propose heat map, example basis or concept basis or prototype basis. Heat map is like in computer vision, you can have the same explanations. So you will get that heat map off with the most important parts that the model see to take that decision or to classify or to identify the bird in this case. So they give the heat map examples. They said, okay, I took that decision because this bird is similar to these other birds. Concept base is similar to a shop. You get values, you get numbers by each feature. And the last one is, this is why it's what I like the most because it's highlighting which zones of the bird were important to classify the bird. Here we can see, okay, sometimes probably it's not important for the user, but this is what we can see. And the perception where they divided into people, into kind of knowledge people between high knowledge of AI and low knowledge of AI. And you see that the results were different. So for high, if you have a high AI, you think that the heat map is intuitive because it is for us, it is. But for a person that has no idea about the AI, it's related to weather for them because the blue, the red, so. And the other one is same with the concept bases. Concept base, this is great for us, for me because you have data, you have the features, you have the value and you have everything. But it's not so useful for people that has no idea about it. So stuff like this will go right over my head and make no sense because it's numbers and so on. So what we get at the end is we get a gap. We get a huge gap in the middle. And we have one solution, but we have multiple end users or multiple consumers of the solution. And we need to find a way to fill this gap. For instance, the creators and the end users, if they both know about AI, of course they are satisfied with explanations. But the users with low AI, they don't care about explanations or they would like to see practical examples and because they would like to collaborate with the system, okay, I see that the model is detecting that it is a particular kind of bird. Okay, I would like to help the model to improve or to make it better.  So we can say two things from this slide. The first one is it's perfect for people that have an AI knowledge. So we are doing something or we are usually building software or models that are good for us, not for the end users or for the people that has no idea about how to use AI. And mainly we need to fill this gap between the AI knowledge and the low AI knowledge. But we need to see it and the only way to see it is we need to talk to people, we need to understand what they need and something that for us is really easy to understand or really useful for some people it's not and they will be the consumers of our application. So we need to be aware of that. And okay, recapping, how does it fit everything? So you have the principles that I talked at the beginning with the pineapple pigs and so on. We have fairness, privacy. Once we have these principles, but explainable AI is something that it's like feeding those principles. And once you have everything in the middle, once we are working with everything almost, you will get the responsible AI or we will meet the responsible AI goal. As I said, it depends. Sometimes most, some of them are important. Some of others are not relevant for the use case that you are working. But you need to know that at least there are some steps that you should follow. We didn't talk about ethics. Ethics is another huge topic, but we didn't know any detail, but it also goes within this explanation. Conclusion, so we have it's known fits all solution. As I said, you need to find the right solution for you, for your use case, for your audience. The real use cases that have happened in this use case, in this example, can expose pitfalls in explainable AI existing methods. So probably we don't see it, but we need to do that. One of the most important is explainable AI has to answer the why, not the what. So we need to know why the model is taking that decision, why the model did that, not what or not how, because the users probably don't care about that. And the explanations, which is the last one, is very important, are part of your value proposition. Even if we need to meet with some requirements of GDPR, which is good, because we need to meet it, but if you're all building a solution, your value proposition is, okay, my model, I am totally transparent with you, the data set that I'm using, how I trained, it's not biased, and it's part of the value proposition when we are explaining that. Call to action, of course, we encourage the community to try to reduce, to help us to reduce the gap between the last gap, between the high AI knowledge audience and the low AI knowledge audience. As I said, there are multiple models, some of them works for healthcare, some of them works for finance, some of them works for multiple verticals, but what I used to see is they are not too much collaboration between most of them, so they are working, you have the finance or you have the healthcare community, but probably some healthcare problems were solved using finance models, so they can work for both of them, and there is no one centralized place. As I said, that could be a good project to try to centralize, but really to centralize everything in one toolkit, toolkit or place or project or something like that. And the collaboration here is also, I mean, of course, is key. And that's it, thank you so much. Any questions? Thank you for your time. What I was asking myself is for large language models, like JetGBT, which have a large variety of use cases, how can this topic be applied? I think it's a bit harder, and also expectations might be very different from different users. Yeah, usually with large language, good question, thank you. And I think for large language models, we have, when we try to use large language models, we download something that's generic, and we don't know what it was trained. Even if it says the model card, if you download for high defense, for instance, they say that, okay, it was trained on one particular data set and so on, but these models, they could be racist or they could, because they were trained in the entire data set of the world, for instance. And probably the ethics in that part is important. You need to be careful about, you need to add an extra layer, because even if the model would like to comment something that could be racist, you need to put this layer to try to avoid that. And it could be racist or it could be bias or whatever, because the models they are prepared for, they answer. And I see that this part is very important to use it. There are some toolkits that you can use for that to try to avoid that. And after that, if you like to use it in your environment, finance, healthcare, you probably will need to fine-tune that. And this part of the remove the bias and remove everything is also important to have your model just for your data, for your environment. But I believe that the most important part is to set the guard rules of those models, because you will check with all the models if you try it with all of them. They can all be biased, and they are biased. And they have a disclaimer that they say, okay, this model can be racist, so it's up to you if you like to use it or how you use it. So even if the model is great, you need to build this extra layer of security. Did I answer your question? Yes. All right, thank you. All right, all right. Thank you so much for your time and enjoy the rest of your day. Thank you.


So, I talk about ETL made easy with a patch hop, who knows a patch hop already? No one knows, okay? Okay. And I'm a founder of a patch hop user group Japan and Neo4j users group Tokyo. And the Japanese team. And I love Neo4j graph database. So, graph committee MVP on some Neo4j speaker, Neo4j Ninja. Okay. Today's agenda is here. What is a patch hop? And community. And hop concept. And hop versus cattle. Do you know cattle? ETL too. No? Okay. And the plugin and the connection and languages and getting started with a patch hop, how to download, how to use. Okay. And demo of a patch hop and conclusion and QA. Okay. What is a patch hop? A patch hop is the short name of the hop orchestration platform. HOP. Okay. And this is open source data integration platform. It's easy to use. It's free. A patch hop level project and open source and a patch version two license. This is a fork from a cattle. A history is here. 2019 hop initially started as a folk of the cattle. Who knows? Pentaho? No? Oh. Okay. And 2020, we started a patch hop incubating. And 2021. So we start a patch hop, a patch talk level project. And so delete incubating. So just a patch hop. This year, patch hop 2.4.0 released. Community. Here. This is user group in Japan. So meet up here. Facebook here.  Please join us. Okay. And this is YouTube. You can see YouTube about the patch hop. Okay. A concept is here. A patch hop visual development. Easy to use. Drag and drop interface. Just you can make workflow and pipelines. And design once run anywhere. Like Java. Okay. And it's run on native hop runtime. And a patch spark. A patch link. And Google data from. Okay. A patch hop life cycle has a life cycle management. Hop projects, environments, runtime configuration here. And manage Git versions. Hop tools is here. Go away, call encrypt, run, search, server, translator. Hop versus kettle. Kettle is a base of the hop. So a little bit, a lot of different user interface and the name. A feature here. And the very, very different name we use. So most common, I think. Okay. And graphically using interface name and kettle spoon. We use a hop glue. And hop run. And hop run. It's easy to use. Easy to understand. Here. Easy to understand. Do you think so? And unit testing is here. And we support unit testing. And the patch. We support Flink and Google Cloud. Features here. We add a lot of features to kettle. Hop has a lightweight server. Lady like Ruby or Python. Yeah. 8081. Just. You can use the lightweight server. This is a pipeline. Pipeline together with workflows. The main building blocks in hop. Okay. And. Oops. Okay. And next workflows. Workflows are one of the core building blocks in the patch hop. So where pipelines do heavy data lifting orchestration work? So we can control a separate pipeline. The one one workflows from one one workflows. We have a lot of plugins. A lot of servers. A lot of database. It's one of the action plugins. SH, SS, SFTP and pop. We can. We can get. From. From SFTP pop. We can use. From. The data and my SQL support. And. If you also support. A lot of vision database connections have. So driver. Use a driver, JDBC driver and native driver. How did he is here? Uh. 2.4. Add. We add. That TV. You know. That TV. A lot of TV here. That TV TV too. And. Derby. So. Just access. Postgres or Oracle. And my secret. A lot of database. We can read. Okay. Transform is here. Add checks. Add. Seconds. XML join output. And. We can control. Deep. Open. Or read. External plugins here. We have. Drop. Dropbox. Input and output. And Excel output. And Google analytics. And. Google seats input and output. And. Like with. LDIF. Interface and MQTQ. Input output. Here. This is a. A sample of Postgres. Interface. We have. JDBC driver for. 42 4.3. And like. Like. You can. Enter the. Like this. Okay. You can use Postgres. And. We have a lot of. Languages. One is simplified Chinese. We add simplified Chinese. And. English. Oh. Japan also. It's better about you. Languages here. English and Chinese. Diane France. So. Not beta version. Beta version here. A lot of languages. Beta versions. Japanese and Korean. Something. Okay. You can download. From. Website. 2.4.0. Or 2.5. Snapshot. From here. And you can. Also. Okay. Oh. Can I use demo? You might. Okay. Show demo. And. This is a Docker version of. Apache hop. Here. And. You can access. This is a top. Level. Screen. Apache hop. And. This is a. This is a screen and. So. Please imagine. If you. Create. Fake. Fake data. Of. For. Your application. Okay. And. We make a pipeline. Okay. Yeah. So. Generate. Okay. Now. This is a create. Of data. Just. Just 10. 10 records. Okay. 100. Okay. And. That sequence. There it goes. And. No. I can't use demo with your. Okay. Spray. Yeah. Yeah. Yeah. Kafka. Yeah, yes. Yes. Yeah. I love Kafka. I'll be. I think that there. I just downloaded it. Yeah. Yeah. Okay. Yeah. It doesn't work. With the. Screen. screen presentation software. Yeah, I think there is some compatibility issue between the streaming software and this. But yeah, I just tested it on my machine. It does work. Do we have questions for Koji-san?