Hi everyone, I'm Hassan. As she mentioned, I'm currently pursuing my PhD in mechanics of materials, which is nothing but a derivative of computer science, physics, and mathematics. So basically science. I've been involved in science for as long as I can remember, and the thing which really fascinates me most about science is the way it works, and the philosophy behind it. So I thought it would be a good idea to talk about the basics of science and how science generally tends to work. So let's get into it. One question which always bothers my mind is... Okay, this really bothers my mind. So the question which really bothers my mind at this point is, what is reality on objectivity? Do these things really exist? See the problem is that each one of us perceives reality in a very different way. And it's usually based on our experiences and senses. It has everything to do with perception. And to illustrate my point, let's look at this image. It's a very famous image, and I'm sure most of you guys have seen it, but just to show of hands, can anyone tell me what they really see in this image? Yeah, please? A woman. A woman, yeah? Anyone else want to take a shot? A young lady. A young lady, cool. So the idea is that it's one picture, but we are getting two conclusions about this image. Could be a young woman or an old lady. Personally, I always see young women. I'm not really sure why, but that's how it is. Interestingly enough, Hanson, a very famous philosopher of science from America, he said that there is much more to seeing than what meets the eyeball. So clearly, our senses are not capable of making objective conclusions about reality. And this is exactly where science comes into play. If you look at the term science, it's originated from Latin, which means to know. And by definition, it's a study of structure and behavior of physical world, obtained through experiments and observations. And if you have a chance to look at the philosophy behind science, you would notice that it tries to resist solution or explanations coming from disciplines such as history, psychology, sociology. And that's how it tends to stay objective. Now, please keep in mind, I'm not saying that these disciplines are bad or not important. What I'm trying to say is that these disciplines may tend to be subjective at times. And the philosophy of science is that it always tries to stay objective as it can. We have seen science work in our daily life. We have seen it pretty much work. So we know that it is doing something right. How do we know it? Because if you base your medicine on science, you end up improving lives. You end up performing miracles. But most importantly, you end up doing this. So you see, the idea behind science is to make objective conclusions about reality. The question is, how does science really does that? And to understand that, we need to look at different elements of science which constitutes to the process of science. So these elements are fact, hypothesis, theory, and law. So what's a fact? A fact is nothing but a general observation that we make about the world around us. So let's say if I go outside in the morning, I can say it is right outside. That's a fact. We all can be objectively understand this fact. The next step in science is to come up with a hypothesis which basically explains why this fact is really happening. So I can propose an explanation which could be we are being invaded by aliens so the sky is lit up right now and that's why it's bright. Or I can say an Armageddon is going on as we speak. And finally, I can say the sun is up. So these are my three hypothesis. The next step in science is to test each hypothesis as critically as possible. But in this example, I can do that by simply going outside and observing what's really happening. And once I do that, I would notice that obviously there are no aliens. Hopefully the world is not ending and clearly the sun is up. So congratulations, we just did a science. Right, so we can move from hypothesis to a theory which is nothing but a well substantiated explanation for a certain phenomena. And for this example, the theory would be when the sun is out, it tends to be bright outside. Notice how this theory has certain elements to it. One, it's a combination of hypothesis which were put to test and the successful ones were taken out. The other critical thing about theory is that it's answering the why question, why it is bright outside. And not just that, it also telling us some future prediction. So I know that when the sun is out, it is going to be bright outside. Unless of course you're in Hamburg and it's gloomy and rainy and you cannot see anything, so that's a different story. So next come law. Law is a statement based on again repeated experiments, but it basically defines some phenomena of nature, often giving us a mathematical premise. And unlike theory, it really answers the what and how question. What is happening and how it is happening. So these are the general elements of science. And like I mentioned before, the process of science is constitutes of all these four elements. These four elements are working together to form the whole process. And to understand the process, let's look at an example. Take gravity for instance. You and I can both agree that if I drop this pointer, it is going to fall down. Gravity? Thanks for that comment. So yeah, gravity is a fact. We can all see that. We can all observe that and it just happens. Let's come up with hypothesis why gravity actually is. So I can say that there is a force of attraction which is pulling this pointer towards the earth. Or there are some magnets which are pulling this pointer. Or some invisible hand which is doing the trick. Luckily for us, for Isaac Newton did this test and he came up with a law called as Newton's gravitational law. And notice how it's giving us a mathematical premise in this case. And for those of you who are not aware of it, it's a very simple law which says that there is always a force of attraction between two bodies having different masses and certain distance between the center point of the two bodies. Albert Einstein on the other hand gave us the theory behind gravity which is general relativity. And I'm not sure if you guys would be interested in this theory at this point, but just to give a general framework, the idea is that the space is not an empty void. It's generally a fabric of space and time. And when a heavy object is put on this fabric, it tends to wrap the fabric around the body, hence creating gravity. So you see this is a general process of science. We start with a fact, build hypothesis, cancel the one which does not make sense, make it law, and then finally end up with a theory. So we have seen different elements of science and we know how they're working together to give us an objective understanding of a certain phenomena. The problem is that these terms that we discussed, theory, fact, hypothesis, they have a certain meaning in science. They're not used like what we use in our daily life. For instance, theory is not just a theory. In science, theory has a very significant meaning. So what I'm going to do is start, I'm going to talk about theory for instance, for now. There are two problems with theory. One, theory has to be the most strongest, robust thing in science. And two, it is extremely difficult to prove a hypothesis so that it can be promoted to a theory. So what is a theory? It is the most powerful explainer that we have in science. It pretty much explains what we're looking for. It tends to bring a diverse phenomena into a small number of assumptions, telling us where something is applicable and where something is not applicable. And interestingly and most importantly, the value of a theory is independent of a psychological influence of the brain which basically created the theory in the first place. So we know at this point that a theory is trying to take away all the biasness and hatred that could be in a certain topic. And finally, a theory may or may not be intuitive. And that's also very interesting. Again here we find out that theory is trying to be as objective as it can be. And like I mentioned before, it is not easy in science to prove a hypothesis. It takes a lot of effort. Just because you have some good result does not mean that you can make a theory out of it. So we have to have some criteria why and when we can promote a hypothesis to a theory. And that problem was solved by Carl Hempel when he gave us the criteria of confirmation and acceptability. And the idea is if a certain hypothesis has elements such as quantity and diversity, precision, support, and simplicity, it can be promoted to a theory. Let's look at the quantity and diversity. So the idea behind quantity is that if I can do multiple experiments and reach the same conclusion, it is going to make my hypothesis extremely strong. And then I can promote it to a theory. On the other hand, if I can diversify my experiments, do some slight changes, and still end up with the same result, it is going to make my hypothesis even stronger. And the simple idea is, again, gravity. It doesn't matter what you drop, the acceleration due to gravity is always going to be the same, no matter where you drop it or which object you drop. The next part is precision of instruments and results. And the idea is what we usually do in science is that once we have a hypothesis, we try to make our instrument and measurements as precise as possible so that we can make our hypothesis much more stronger and promote it to a theory. This was done by Ducke in 1959 when he demonstrated that if he drops various objects from different heights, he was able to calculate the acceleration to an accuracy of one part per exponent 9, which is an extremely high accuracy, which basically tells us that hypothesis for gravity is true. The next factor a hypothesis should have is that it should have support from well-established elements. And for that, let's look at this Newton's law again. Now, using this, I can find out gravity on Earth pretty easily. However, using the same formula, I can hypothesize gravity on pretty much any other planet, even though I have not even conducted that experiment. Why? Am I hypothesizing it? Because it is based on a very well-established law. That's why my hypothesis is extremely strong. We actually did that before Apollo 11, the first man mission to Mars. We were able to calculate the gravity on Moon even before we landed there. And when we did, we measured it and it was true. It was correct. And the final thing a hypothesis should have is simplicity. And the best example I can give you guys right now is this. I think it's an equation everybody is familiar of, although nobody quite really understands what it means. But it is one of the most elegant and beautiful equations that you can ever come across in your life. And the simplicity of it tells us that there is certain truth behind it. Not just simplicity, I mean, it has a very strong mathematical premise as well, but that's a different issue. So this theory is based on three very fundamental laws of physics, which are energy conservation, Doppler effect, and finally, special relativity. And again, for those of you who are not aware of it, conservation law says that energy cannot be created or destroyed, it can only change form. Doppler effect tells you that if you're moving towards or away from a source of sound, the way you perceive it is different. And special relativity says that time is not absolute, it's relative. If you look at the mathematics behind these three laws, you would notice that they would end up with this equation. And when Einstein hypothesized this, the simplicity of it said that there is a certain element of truth behind it. And I think Richard Feynman captures it in a very nice way when he said, you can always recognize truth by its beauty and simplicity. All right, these factors have to be in a theory so that it can be robust and the most important thing in science. And this basically brings me to the end of my talk. What have we learned is the first question. We know that our senses and our experiences are not capable of making objective conclusions about reality. Science is doing that for us. How? The way science works. It has different elements, fact, hypothesis, theory, law. They all work together to make the process of science and this process ensures that your conclusion is always objective. I'm not saying that science is the perfect thing we have and it has no flaws. No, there are plenty of flaws in science. I'm just saying that at this point, science is the best tool we have to understand reality. There is no other way around it. Before I end my talk, I wanted to talk about a small misconception which I hear all the time. And this being that evolution is just a theory. Right, so the next time somebody says that to you, you're supposed to tell them that evolution is not just a theory. Evolution is a fact. Why? Because throughout the centuries, throughout the years, we have seen different animals, reptiles, fushas, changers from one to another and everybody agrees that it did happen. Evolution is a fact. What scientists did is that they made different hypotheses for that. One of them being catastrophism, uniform materialism and natural selection. Once we have those hypotheses, there were gazillions of tests and countless specimens were captured and we finally concluded with the theory which is based on natural selection and we called it the theory of evolution by natural selection. Thank you so much. Thank you very much, Hassan. Let's take some questions. Oh, wow. Okay, let's take it from here. So, I'm just a disclaimer. I'm a future ranked scientist. It's a science, the ultimate alternative to fact machinery. Why am I saying this? Let's connect it to the talk previous. You know, you saw how much data is being gathered by, you know, different online companies and whatever and so it's not in the process of deriving the hypothesis. It's in the choice of the data sets that you choose to make this hypothesis. If I have three dimensions, I will see some projection or some sort of a conclusion. If I add a third and a fourth dimension in my data set that I base my conclusions upon, I will have completely different maybe antagonistic conclusions about the very same fact. So, by this method, it doesn't have to be anything shady in the data analysis so I will be able to create whatever fact I want from whatever observations I have. So, the question is... The question is, is it true that basically our scientific conclusions are based on what kind of data we select and if we select different data, we can find support for all kinds of hypothesis that we have. There's a certain element of truth in what you're saying and unfortunately in science, like in every other field... There are people who are doing bad research, who are collecting wrong facts, trying to come up with explanation which is taken from wrong data set and that's true. However, the good thing about science is that let's say you give me an explanation which is fake. Somebody else from some other place is always going to put a counter paper which is going to point out your mistake and no matter... no matter where ever you are in the world at which location, science is basically working on everything simultaneously and everybody is trying to make sure that they can come up with the truth. So, even if somebody is not doing the right thing, his scientific reputation or her scientific reputation is in trouble because if somebody can come up with right things, then the reputation is completely gone. Okay, thank you. I think you can continue after the talk certainly and this discussion is very interesting. I thought that we have more questions, please. One question. In your talk you said like, okay, science is about understanding reality or trying to find out the truth. But then what I would like to ask you is, is science about making objective statements about the world that exists around us or is it about finding the truth? To clarify my question, because it seems like pretty abstract. Quantum mechanics, for example, it makes really real claims that things can exist at ten places at the same time.  What would you say to that? So, the question is, if a scientist is a machine to just, yeah, objectively, is the job of scientist to just objectively state what's around us or to understand and to find out the truth about the world. Is he a philosopher or is he a data scientist? Okay, are scientists more philosophers or data scientists? You've played an example of quantum science, quantum physics, and I think it's a very good example that actually fits in your question. Understanding reality is by no means an easy feat. It's extremely difficult to understand reality. But what I'm submitting right now is that science is the best tool we have to make that claim that this is the explanation for reality. Not anything else. Just science is just claiming that this is the explanation for this phenomena. It might be wrong. It can prove to be wrong at a certain point in future. It's certainly going to happen at some point. Science does not claim that it knows everything. But the idea is that the process, how it really works, we are converging towards finding reality, finding meaning, and all sorts of scales. Megascales, minute scales of an atom, we talked about quantum physics to answer that. Thank you, Hassan. So I saw there are more questions. I know, guys, you want to ask them, but I also want you to give you a chance to discuss in a closer circle after the talks. So we will now move on. Thank you very much, and please approach him to ask more of your nasty questions.


Sup guys, this is Theron, and tonight we're taking another dive into the Parawatch Wiki. Tonight's thread was started by user SarahToninSez on June 1st, 2020. It's called MK Whiteout and how the CIA tried to kill Pride. Let's begin. There's a content warning listing institutional homophobia and transphobia, sex crimes, and human experimentation. MKUltra is the most infamous of the CIA's experimentation programs, but it's not the only one. Only declassified CIA documents reference projects such as MK Smilodon, MK Waldo, and MK Azolda, all of which are woefully incomplete. MK Smilodon got dismissed by the news as the CIA's X-Files project, because any project that deals with trying to control the weather using human conduits is going to be called that by anyone not in the know. One thing that got largely overlooked by the news was something called MK Whiteout. By the late 60s, the MK project was starting to lose funding, as the US government became more concerned about the space race and how badly the Vietnam War was going. But one place that Nixon wanted to focus was on oppressing minorities. The entire reason he refused to legalize POT was because he wanted more excuses for African Americans and hippies to be arrested. And when it came to the fledgling LGBT community, he was more than willing to turn a blind eye to the CIA testing using Pride as a testing ground for an atrocity. May 1971, Stonewall had taken place less than two years before, and CIA director Richard Helms gives the green light for MK Whiteout, a biological warfare initiative that was to use, and I quote, Infectious mutagenic agents to attempt to cure homosexual psychopathy for the good of the American people. Their so-called cure? A hybrid of botulism and something known as the Carpathian Agent. June 1972, San Francisco. Several things happened over the course of the week, leading up to the city's first official Pride Parade on the 25th. On the night of the 18th, Toad Hall, one of the city's oldest gay bars featured in the 2008 biopic Milk, is broken into. Nothing is stolen, but the owners at the time are paranoid enough to dispose of all their booze after they find a Budweiser cap on a Miller bottle, clear evidence of tampering. Early that same day, a so-called hair fairy, an individual that might be considered transgender in the modern day, identified in a police report as Barry Dent is arrested for public indecency. What transpired in the next three hours is unclear, but Dent was never seen again. Her cellmate is found clutching a knife with blood matching Dent's type on it, sans throat and left arm. Said Cellmate's identity was not disclosed at the time, but the individual who came to collect their body is listed as Mr. Brighton, now known to be a common CIA kryptonan for cleaner agents during the Nixon administration. Starting on the 19th, three men hand out pamphlets advertising the Pride event around Castro. The pamphlets are printed on paper that has an odd texture with sharper creases. Several people get paper cuts from touching them, and fall ill with strange symptoms. The stud, another famous gay bar, is broken into on the 19th. The attempted vandalists fired upon by the owner and wounded.  San Francisco Police Department does nothing about it, naturally. The 20th brings reports of over 60 different incidents of apparent botulism poisoning from across the city. All individuals who are considered queers, hair fairies, homos, whatever derogatory term you've heard, they use it to describe them. The police figured a few dead s*** was probably a good thing, and did nothing. Doctors who treated them managed to deliver antitoxin, and noted that several of them had quote, abnormal hair growth patterns that were not exhibited in normal human beings. A commonality among them is that they all got drinks or knew someone who was at the wild side west, which is, you guessed it, a gay bar, albeit one outside of Castro. Needlemarks are found on several of those admitted into hospitals across the city. At San Francisco General, a toxicology screen finds the same unknown drug in five different patients. One of the doctors there has a contact in the Swiss healthcare company Roche, who confirms that it is a drug they patented a decade earlier, not yet on the market. The drug is fluna trazepam, better known today as rufenol. On the 21st, the majority of the patients are discharged. That night, the SFPD is flooded with calls of wild animals being seen and heard throughout the city. They investigate one around Fisherman's Wharf, and find a metal light pole bent in half, with scratch marks all the way around the top as if whatever bent it over literally spun around it first. The morning of the 22nd, a young woman known only today as Millie finds one of the pamphlet men preparing for his day. She finds him coating the pamphlets with a brush covered in an unknown substance, and gathers a mob to chase him out of the district. He reportedly jumps into a black town car bound for the north. This same man is seen again in the vicinity of the city lights bookstore in the red light district. Some people have speculated the existence of a CIA bolt hole in this part of the city. The night of the 22nd brings more calls of animal sightings. A few fortune tellers near Presidio Heights draw hazma sigils on their doors, charms meant to ward off evil creatures. Gunshots are heard around the Castro district in the early hours of the 23rd of June. A wrecked town car is found in an alley in the neighboring Mission District, upside down and on fire. Locals claim ignorance, despite evidence of blood and torn clothing being found on the corner of 20th and Castro Street. In the end, whatever malefactor was trying to sabotage San Frans first pride failed. The parade went on without a hitch, but not without weird things happening. Some people in the crowd reported seeing animalistic silhouettes on the buildings above the street, only to look again and see people just waving at the parade. MK Whiteout was shuttered on July 4th, 1972. The majority of documentation related to it was incinerated, but not all of it. Among the MK Whiteout team was a virologist from the Socialist Republic of Romania, and his files were the ones that were recently declassified. The rest of this comes from my great aunt, who lived through a large part of this. The Romanians' files state that the original intent of the project was to create a hyper-virulent botulism, spreadable only through fluid transmissions, ensuring minimal contact with a non-psychotic populace. This is probably the reason behind the Carpathian agent's inclusion in the hybridized bacterium, as it is stated to be only transmissible through the contact of fluids on exposed wet tissue. Testing didn't go as intended. The Carpathian agent is a mutagen, and being infected with it results in physiological changes, among them being abnormal keratin growth. Hair and nails grow at super-fast rates, and from what I've read, the nails are strong enough to tear into aluminum. Other signs of it are dermal and muscular mutations, and spontaneous dental growth. So they changed tactics.  Throughout late 1972 and early 1973, the San Francisco Examiner received several anonymous pieces of male-containing film roles appearing to depict large, hairy creatures alongside of men and women who were known members of the LGBT community, seemingly taken without their knowledge or consent. In one of them, you can even see the cameras misalign with the hole it's meant to stick through, and you can see the inside of the drywall. If you don't get the intent here, I'm not going to spell it out. In 1983, a camera was discovered inside drywall while renovation work was being done on an apartment building in the Mission District, still containing an intact role of film. The apartment was found in-house one of the 60-plus people who were admitted into the emergency room on June 20th, 1972. I've seen the photos for myself. They depict the tenant, a man named Raymond Murphy, in his bed alongside his partner Cecil McFinn. The timer on the camera took a photo every 15 seconds. Raymond appears to be in pain, and Cecil is holding him. About two minutes in, Raymond changes. Cecil never lets go of him, and seems to be patting him on the back repeatedly. After about another minute, the change is complete, and the creature that used to be Raymond looms over his partner. Cecil is clearly afraid, his back up against the headboard of the bed. The creature bears down upon him, and his face is concealed from sight for the next three pictures. When the creature pulls away from Cecil, the man is laughing, and his face is covered in what can only be described as slobber as he pulls Raymond close to him once more. Happy pride, everyone. Stay safe out there. Whoa. Whoa. I've heard the government's done some messed up things, but… damn. Alright, do you guys think this is real? Leave your answer in the comments below. Subscribe and hit the bell, join the site 42patreon, and I'll catch you next time.


This is Waylon Chow and this is contractual terms and defects module 3B part C. In this part of the module, we will look at boilerplate clauses. There are many different clauses that are commonly used over and over again in many different types of contracts. Lawyers refer to those clauses as boilerplate clauses. Standard form contracts often include a number of different boilerplate clauses including exclusion clauses, which we've already looked at, and also a number of other different ones. One type of boilerplate clause is called a force-majure clause. This type of clause essentially says that there is no breach of contract if a party cannot perform a contract due to an event beyond the party's control. So such an event would usually be what's called an act of God, like a storm, or a tornado, or an earthquake, or other things that other events that are outside parties control. So here is an example of a force-majure clause. And so you can see in paragraph 1.1 all the different events that are considered to be an event of force-majure, including acts of God, which would also include here a drought, a tidal wave, a flood. We also have a war. We also have act of foreign enemies, embargo, rebellion, revolution, civil war, radioactive contamination, nuclear waste, acts or threats of terrorism, all kinds of horrible things that are outside the party's control. If one of those things happened, then if they can't perform the contract, that's okay. It's not a breach of contract. Many contracts include a confidentiality clause, and such a clause would prohibit the disclosure of confidential information to a third party. Here's an example of a clause which says that the parties to the contract can use the confidential information only for the purpose of the contract, which is to evaluate a business employment or investment relationship. Another common boilerplate clause is an arbitration clause. An arbitration clause requires disputes under the contract to be resolved by arbitration instead of court litigation. So instead of suing court, the parties have to resolve their dispute by arbitration, which quite often could be cheaper or faster. Now in the Rogers terms of service, there is an arbitration clause, and here it is, which there's two sections for that, section 40 and section 41. Essentially, if you look at section 40, what it's saying is that if there's a dispute under the contract, the dispute has to be resolved by final and binding arbitration.  Now, so that takes away your right to sue in court. In particular, if the amount that you're suing for is under $25,000, you can't sue them in small claims court, which could actually be cheaper and maybe even faster than going through Rogers arbitration process. Now, in Ontario, however, just as a side note, the Consumer Protection Act preserves the right of a consumer to sue in court, even though a contract requires arbitration. So essentially, this section, this arbitration clause in the Rogers contract is ineffective in Ontario. Ontario consumers still have the right to sue Rogers in court, no matter what this contract says. A jurisdiction clause or a governing law clause specifies the law of which jurisdiction applies to the contract, or specifically the laws of which province or the laws of which country. This clause also specifies the courts of which jurisdiction will deal with contract disputes. The governing law clause in the Rogers contract is in section 45. What it essentially says is that the governing law and the relevant courts are those of the province in which your billing address is located.  And also, if you need to sue Rogers or if they need to sue you, they will sue that lawsuit would happen in an Ontario court. As well, let's say if you lived in BC, BC law would apply and any lawsuits between you and Rogers would happen in a BC court, not in an Ontario court. A common boilerplate clause at almost every contract has is an entire agreement clause. Such a clause ensures that all of the terms of the contract are within the so-called four corners of the written contract and does not include any verbal promises that have not been put into writing in the contract itself. The Rogers contract also has an entire agreement clause, and that's in section 43.


Today all the trade unions have together given the call for a 3-day Mahapadao and on the 12-point charter of demands. All the trade unions have been struggling launching different programs on the 12-point charter of demands since 2009. So basically all these 12 demands are the demands of the entire working class. So today BMS is not in this particular movement but the demands are of the entire working class. And not only the working class, the demands that the trade unions have raised also are of the common people like the price rise and the strengthening of the public distribution system and employment generation. These are pension for all, social security for all including the agricultural workers and other sections. These are all demands pertaining to all the toiling sections of the people. And on this since 2009 we have been demanding but the government has not done anything. Particularly after the BJP government has come to power they are going ahead with amending the labour laws in favour of the employers and they have decided to form four courts and already three are there. The court on wages, the court on industrial relations and the court on social security. The other court on safety at workplace that is still pending. But all the three courts are against the interests of the workers. Whatever benefits the workers were getting through the existing labour laws will be removed and that will benefit the employers. So the trade unions are opposing this. And in addition to that the minimum wages of 18,000. According to the Seventh Pay Commission recommendation which the government has accepted they have accepted the 18,000 per month as minimum wage whereas actually if you calculate on the basis of the ILC recommendations along with Supreme Court direction it will come to more than 26,000. But the trade unions are demanding 18,000 because the government has already accepted the recommendation of the Seventh Pay Commission. But that the government till now is not ready to concede. Similarly about the contract workers. Today the number of contract workers is increasing everywhere and the contract workers doing the same job are getting half even one third or one quarter wages of that of permanent workers doing the same job. The Supreme Court also have given the judgment that earlier acts are all there that the same worker contract workers should not be employed in permanent or perennial nature of work that they are doing. And if they are doing the same job should be same wages should be there but the government is not ready to implement that. So these are the basic demands. Amendment, stop the amendment to the labour laws in favour of the employers. Contract workers should be given the same wages and benefits minimum wages should be implemented. Prices should be brought down. And another ILC recommendation that all the around one crore scheme workers are there in our country. Most of them are women. The government talks of women's empowerment. But it is not even ready to recognise them as workers. They are all called volunteers, social workers, activists. Different names are given and they are not paid minimum wages. They are paid some honorarium which starts from nil for Asha workers to 1000 for midday meal workers, 3500 for the Anganwadi workers and helpers. It varies like that. So we are demanding that all the scheme workers should be first designated as worker, cognised as workers. And they should be paid minimum wages, social security benefits. And now the governments move to privatise and dismantle all the scheme should be stopped immediately. So on this, around more than one lakh workers have come from all over the country starting from Jammu, Kashmir, to Kerala, Tamil Nadu etc. And they will be staying, some of them will be staying for all the three days and many more will be coming tomorrow and day after tomorrow. And day after tomorrow will decide the immediate future plan from here. Already the national convention has decided to go to give the call to the workers to prepare for an indefinite strike, country-wide indefinite strike. So before that also to prepare the workers for the country-wide general strike we are going to give a call day after tomorrow. We are already starting thinking about that, it will be announced day after tomorrow.


Aloha and welcome everyone to Hoyenergy Policy Forum's annual briefing. It's great to see so many of you here and I'm really looking forward to today's presentations and conversations. I'm Cherylin Wee. I'm an assistant specialist at the Public Policy Center, also co-chair of the Hoyenergy Policy Forum with my cabinet. The Hoyenergy Policy Forum is a program within the College of Social Sciences. We convene a collaborative group of over 40 stakeholders in business, academia, government and the community. And we're working towards information sharing and collaboration in order to inform decision making. So today we have all come together to present this annual briefing. Cherylin Moriwaki, Senator Moriwaki, I just want to take a moment to acknowledge and recognize you. Senator Moriwaki, who has led the forum between 2002 to 2018, thank you for all the great work that you've done in the energy space and we're really glad that you could join us here today. This year's theme, bending the curve, energy, transportation and climate change policy, focuses on aligning these policies with the needs of Hoy residents while acknowledging that there's a significant gap between our 2045 carbon neutrality goals and where we are today. So for this afternoon's discussion we have four guiding questions. The first is where are we on the trajectories, both short term and long term, and where do we need to be? Are we doing enough to achieve our clean energy goals that we have established? Is it even possible to meet our goals? And if so, what will it take? And then lastly, what about other pressing needs in Hawaii? We're going to get started and kick things off right away. I'm really honored to be able to introduce Governor David Ige. So on behalf of the forum, please join me in welcoming Governor Ige. Thanks, Cherylin, for that introduction. I did also want to acknowledge Cherylin Moriwaki who convinced me on several occasions to come and participate in this policy forum. I really do appreciate the University of Hawaii and the Hawaii Energy Policy Forum for your advocacy and I think most importantly for your commitment to collaboration and to engaging all of the stakeholders in our energy policy. As we enter 2020, Hawaii has much to celebrate as a leader in energy transformation. And much of that work is really due to many of you here in the audience. I'm proud to be Governor of the State of Hawaii and when I go up to the National Governors Association or Western Governors, I'm really proud of the work that we have been able to do together. I did want to thank the legislators who are here who had a big part in setting out a bold agenda for all of us. I'm really proud to be Governor of the State of Hawaii because I do know that much of our energy policy is driven by the core values that we share as a people. Most importantly, that commitment that we want to make our communities better and that we want to be part of the solution in finding a way that Hawaii can be a more sustainable and resilient community moving forward. As you know, we are a leader in several different ways. Hawaii was the first to embrace the goals of the Climate Alliance and we all know that the United States has kind of bailed out of that. Certainly there is a void and the U.S. Climate Alliance of which Hawaii is a part, a bipartisan group of states that felt compelled to step forward to ensure that the agenda of fighting climate change and global warming can be taken up by sub-national organizations such as states and other entities because we do know that in order to save the planet, those of us in the U.S. really need to lead the way. I wanted to welcome Ms. Amy Barnes from the California China Climate Institute. Thank you for your participation in today's session and also Jeremy Tarr from the Office of Governor Roy Cooper of North Carolina. I really appreciate your coming and sharing with us and sharing the things that you've done. I've had the opportunity and Governor Cooper has joined the U.S. Climate Alliance as well. For us chief executives in many states, we do recognize that action is needed now and climate change is a huge threat to the planet and most importantly that we need to take bold action to reduce greenhouse gases and carbon emissions as we move forward. I would want to note that I really believe that a big reason for Hawaii's success is the collaborative nature of our community and to organizations like the Hawaii Energy Policy Forum and again I thank you for being committed to engage all stakeholders, give everyone a voice and I think most importantly for being willing to raise the most challenging issues before us. Thank you for taking over for Senator Moriwaki. I know that you're doing an able job moving forward. I was also happy as you know last year the Legislature passed House Bill 852 which I was proud to sign into law as Act 122 which really created and reconfigured the state energy office and directed them to promote energy efficiency, renewable energy and clean transportation to help achieve a resilient, clean energy, decarbonized economy. After interviewing many and an extensive search, I appointed Scott Glenn to be the chief energy officer. Certainly there are a couple things that attracted me and convinced me that Scott would be the perfect person to lead the state energy office. He had served as my representative in the US Climate Alliance and has been very engaged on behalf of the state of Hawaii with those partners in the climate alliance and we were very early on one of the earliest states to join the alliance and Scott has been very engaged with the alliance of bipartisan states really understanding what each other is doing most importantly trying to create structures and opportunities that we can share information and most importantly help each other succeed. We know that climate change and global warming is a team sport it's not something that any one of us can solve by ourselves but most importantly it will take the collective will of all of us to make significant progress. You know I appointed Scott to this position because I felt that he would be the best for the office. He has been the executive director at the office of environmental quality control over the last several years and I've been impressed by his collaborative commitment to engaging all stakeholders, to taking on the tough issues and most importantly to be able to find solutions that allows us to move forward. You know when I first appointed Scott everybody said that environmental impact statements and all of the challenges surrounding our environmental protection would be one of the toughest issues to deal with and I'm proud that Scott was able to work with all stakeholders find common ground between environmentalists and developers and most importantly came up with a set of rules I think that balance the interest of protecting the environment with allowing sensible regulation that would allow us to move forward. I know that all of you are here to listen to the experts that have been assembled and I really want to thank you for your commitment to our clean energy future. The goals and challenges we see before us you know I'm proud of the progress that we made but I also acknowledge that there is much much much more work to be done. I look forward to working with all of you to achieve the goals that we all share to transform our energy futures moving forward and I did want to finally just thank the energy policy form here again once again for your commitment to engaging stakeholders for providing a forum that we can share ideas and best practices but most importantly to be a part of finding the solution of finding a way that we can all be committed to a renewable energy future but most importantly to saving our planet and stopping climate change and global warming. Aloha and Mahalo and thank you so much for all of those who will be here today. Thank you Governor Ige. I now like to invite Nicole Lohan, Representative Lohan, House Chair on the Committee for Energy Environmental Protection to say a few words. Welcome Nicole. Aloha was instructed to be brief so I will try to do that. So welcome to the Capitol. We're happy to host the energy policy forum here and this is one of a series I guess of events that the policy forum has planned this year so it's been I think a really active year for the Hawaii Energy Policy Forum and I want to thank them for all the work they put into organizing this and helping educate everybody. I also want to quickly thank my office manager Miley who's not in the room right now but she just did a lot of work on the logistics of helping schedule the rooms etc. So from my perspective these issues that we're talking about here today energy transportation and climate change policy are top of mind to some of the most important things that we have to work on this session. As we increasingly feel very real impacts from climate change here at home widespread coral bleaching events more frequent more damaging storms coastal erosion sea level rise and uncomfortably hot and humid weather. We have to explore every tool that we have to do our part to mitigate climate change to move faster on reaching a hundred percent renewable power generation and to transition the transportation sector from one that runs on fossil fuels to one that's powered by clean energy. The legislature in recent years has set really ambitious goals a hundred percent renewable energy zero emissions carbon neutral economy by 2045 net zero school energy campus and net zero energy school campuses sorry by 2035 and last year we passed bills to further energy efficiency to create incentives for the build out of electric vehicle infrastructure and the adoption of EVs for government fleets we created a reinvigorated energy office which the governor mentioned with a clear mission to support the state's energy and climate policy goals and we commissioned a study of carbon pricing policies to see how we might implement a carbon price here in Hawaii. You know someone said to me as we were talking out in the hallway that as they see it the legislature has really led on these issues you know in recent years that we've led the discussion and in passing these ambitious goals and have brought the administration and the private sector along not always willingly but I feel really happy to say that I see that really changing and I think that everyone's on the same page moving forward now from the private sector to the administration I think that the realization of how important it is to address these issues has really crystallized and it's exciting to see how much the momentum is picking up and how quickly we can move forward on this issue on these issues so this year we'll continue our work on these topics with a particular focus for me on trying to ensure that government agencies are doing the things we're asking the private sector to do so increasing efficiency of our own buildings trans electrifying transportation fleets developing resilient infrastructure and planning ahead for sea level rise and natural disasters you know Hawaii is the leader as I've we've all said several times now in energy and climate policy and we know that what we do here in Hawaii has ripple effects across the globe so we need to continue to lead on these issues so that we can avoid catastrophic climate impacts and you know hopefully that what we do here will inspire action in other places so I hope what everyone learns here today will help contribute to that work


So, hopefully by the end of this talk, we'll have you convinced why we need AI on the edge and what do we gain out of using open source for that. So, I am Sujata Tebrevala. I work in the open source program office at ByteDance. I joined ByteDance recently, but I have been working in open source for quite a while now. I have experience in working on computer vision. Back in 2000, I'll talk about it a little bit, but then I got out of it because I got into networking software, but now I'm back here and that's why we are talking today. Fun fact, I run Marathon's and you guys, the reason why I do it and art is how I destress from my life at tech and I share all of that on social media. So, if you follow me, you can find all of that. So, with me today is Tina, my longtime friend and colleague in open source. Tina. Yeah. Good afternoon, everyone. I'm Tina Zell and it's my pleasure to share with you today the exciting journey we are on the intersection of open source AI and edge computing. As chair of LFH director and arm, I lead a passionate team dedicated to not only advancing technologies, but also cultivating a thriving open source community. This commitment to collaboration is the cornerstone of innovation and progress in our rapidly evolving digital landscapes. With over a decade of immersion in the technology industry, my experience has reinforced my belief in the transformative power of open source. It's a domain where share the technologies and knowledges become the catalyst for breaking through in AI and where edge computing achieve its true potentials. In this endeavor, my focus has been fostering an environment where open source isn't just about access to code, but also about establishing robust, secure, and dynamic ecosystem. Here we are not only developers, but also creators and guardians of a technical future that is inclusive and transparent. My work at ARM intersects deeply with this vision, directing strategies that shape the very architecture of devices that will carry us into the future next era of computing. At the edge, we are unlocking new capabilities and neighboring devices to think, interact, and make decisions in real time. Through LFH, we are championing an open, interoperable framework for edge computing that chants hardware seeking, clouds, or operating systems. It's about creating a unified front where a diversity of thought and technology converge to dive the edge forward. As we explore today's topics, I invite you to engage, question, and collaborate. Together, we'll delve into the remarkable possibilities that lie ahead and how each of you or each of us plays a part in this ongoing narrative of innovation. Thank you for joining me on this journey. Let's explore how we shape the future of technology together. Thank you so much, Tina, for that introduction. So here is the roadmap for today. We'll talk about autonomous AI. Well, if you have not been living under the rock, you probably know what it is, right? We'll dive a little bit under the hood to give you an introduction, and that should lead us to why we need AI for the edge, and then the importance of open source in AI development, and then we'll look at some future outlook and forecasts as per our interpretation, and of course, we'll then conclude the talk. So what is autonomous AI? Imagine a world where technology doesn't just assist, but it dependently operates. That's the realm of autonomous AI, intelligent systems designed to perform tasks without our intervention. They learn from their environment, adapt to change, and make complex decisions using the vast data sets and sophisticated algorithms. It's about machines that don't just do, but think, analyzing, evolving, and acting autonomously to enhance our lives, much like an avocado chair might surprise us by offering comfort in an unexpected form. This is the future we are stepping into, intuitive, intelligent, and entirely autonomous. Yeah, thank you. So now a little bit under the hood. So I know AI became into the forefront after chat GPT released AI, and I don't know if you attended any of these talks. We are at literally AI Dev conference. I'm sure some of the other speakers spoke about the history, but the concept of AI is not new. Literally in 1950, Alan Turing questioned why machines can't think the way humans can. But there was two very big limitations. Machines were expensive. Literally, it caused at least 200K just to lease a machine. Number two, they couldn't store anything. If they can't store anything, they can't remember anything. They can't analyze. They can't think intelligently. So those were the limitations. But of course, we are not living in the 50s anymore. We are living today when machines are cheap. Literally, the processing power and memory that you have in your pockets is more than that fit into a room this size back then. So that is the power that we have. And literally, I'll not go into the whole history because we don't have time, but I'll talk about the paper which came out of Google. Attention is all you need. How many of you have heard of this paper? Oh, cool. There are a few people in the audience. So this was the paper which was a breakthrough, not in just my opinion, but many experts' opinion because a lot of those large language models and chat GPT, they're literally based on this particular paper. So now we come back to my brush with AI. So now I reveal how old I was. So this is my paper in 2000 on AI, on computer vision. Stereo disparity, I don't know if any of you are familiar with that. But our eyes are nothing but stereo cameras, taking pictures of everything from two different angles. And the way we can tell where things are in this 3D world is by the displacement of each corresponding point in those images. So this water bottle, which is very close to me, will be displaced quite a lot because it's very close to me. Whereas a point, the last people on the audience will be displaced very slightly. So this is how our eyes can interpret 3D depth from just two two-dimensional images.  So basically what I was doing with this algorithm was trying to replicate what our eyes do with computer stereo images. So the task I had was given two images taken from stereo camera can my program establish two corresponding points. Once I have the corresponding point, the geometry can tell me the depth. And that can be fed into robot, which wants to navigate the world. So the hard problem was to get the corresponding points from just some pixel values. So I was using a 9 by 9 matrix in a 128 by 128 image to compare those points. You can see how slim or how small that was. But even with that, the number of operations required per image was 10 gigaflops. The computer available at that time was I4 with just megaflops operations, which means each operation took a few minutes to perform, which means it was impossible for that robot to see things in real time. So now we are today. So my knowledge of AI froze at that time. And then I got curious when there were breakthroughs and I was seeing computers generating images, interpreting images in real time. And I started researching.  Like if you've been using phone, literally it has been auto-completing. So a sentence like cat sat on a blank. The computer or your phone will give you the suggestion for the word based on statistics. So it's not really algorithmic improvement, but it's more of statistical improvement based on how much data. The algorithm is fed into and trained on, right? So this is the 2017 paper. Some of you raised the hand that you've seen it. This is the transformer model. Just to give for the rest of you a background, I have some slides. I will breeze past it. But if you want more details, there are some tutorials available on the web. And I have taken screenshots of those. And I have tried to include links. So come after the presentation. I'll give you the link. I'll be happy to give you a reference. So at the basis of everything is tokenization. Computers don't understand words. Those words need to be converted into numbers, right? So each words or each sentence has to be converted into tokens. Those words can be broken into one word each or they can be converted into bigrams or trigrams, which means two words make one token or three words make one token. And that's a science in itself. The way this table is, bigrams are the best because the tokens are best if the next possibilities are reduced. You don't want infinite possibility that adds complexity to the model. So and also the words, how they are encoded. Similar words should be encoded closer to each other. And words which are not similar should be far away. So here the words are literally two numbers in this embedding. So strawberry and apple are close to each other, whereas castle is far away from each other. I forgot to include the positional encoding, but the words based on where they occur in a sentence, they have a different meaning as well. Now, another difference in the words having meanings is literally apple. World was a simple place when apple was just fruit, right? But it's not. Based on the sentence, apple could be a fruit or apple could be electronic, right? So attention is basically you can kind of think of it as a gravity mechanism where based on the sentence, apple is pulled towards being an electronic or being pulled towards being a fruit based on the sentence. So the algorithm basically can interpret where your word will fall into based on the context. The rest of the words, so here basically the words which are important is apple and orange because the other words don't change the context. So this is where the other words from in the sentence will be encoded. The embeddings need to be good or need to be interpreted in a certain way so that when they come too close to one or the other, the differentiation can be good. So the left-hand side is a good encoding. The right-hand side and the middle one is a bad encoding because you can't tell the difference between the two different contexts. So whether apple is a fruit or electronic, it moves close to each other, you can't tell. But then these mappings, building all those encodings will be a lot of work. So literally, linear transformations can give you different encodings, this mathematical transformation. So another concept that is when you predict words, you don't want to end up with just one input. So suppose if you were given a sentence, how are you don't want your model to say you with a 100% probability, right? So you has higher probability than they or things because that sentence, how are you, occurs more than how are they in the language model. So putting it all together, this is what the transformer model is. You have your tokenization, you have your embedding, position encoding, attention mechanism, and the probabilistic model. And the attention and feed forward, which is the prediction model, it can be cascaded, so this can be improved. So that is where the model improvement occurs and parallelization can happen. So this is the reason why the more data you give, the more training you give, the model becomes better. Now there's another aspect to it. So this part is just the pre-training. There is a human training part aspect of it as well. After the model is pre-trained with data, literally most models or like some models I have they have revealed have been trained from internet, right? So it's not a very high quality training data that they have gotten. So then the model is improved using fine tuning where they have humans having questions and answers. So the humans rate the answers and based on that the model is fine tuned. Rating an answer is good or bad or giving a good answer. So I'm just, I just talked to you about all of this so that you have context on this slide. So literally each of these models take billions and billions of parameters and a lot of GPU time, like you know, literally thousands of GPUs to train over months to come up with this model. So that means if you ask chat GPT something, they were trained on data which is at least a year old. So if something happened during that time between when it was trained to now, it cannot tell. It doesn't have the latest data. So which is what actually brings us to the need to have a more lightweight real time training. So that brings us to the case for edge AI. Yeah, in the era of our precedented data generation, the edge is where the action is. As highlighted by Forbes, our precedented data at the edge is not just a trace. It's a strategic imperative that offers a suit of benefits. Firstly, edge computing takes a stand on security and privacy. By processing data locally, we minimize the exposure to vulnerability and protect the sensitive information right where it's generated. Moreover, efficiency in data processing is vastly improved. By reducing the distance data travels, we accelerated the decision-making processes, enabling the real time insight and actions with less waiting and more doing. Let's not overlook the significant reduction in network transmission. Sorry. Oh, sorry. Yeah, in the network transmission, overhead, by keeping data locally, we alleviate the burden on our network infrastructure, leading to cost saving and reduce the conjecture. And importantly, edge computing optimize the overall system performance. It's about creating slick, responsive system that are as dynamic as the environments they operate in. So when we say data is being created at the edge, we are acknowledging a shift in the paradigm. The edge isn't just a place, it's a frontier of opportunity, a horizon where speed, efficiency, and security interest intersect to redefine the landscape of modern computing. Let's delve into how HAI is revolutioning industry with concrete real-world applications. In smart manufacturing, HAI drives predictive maintenance, allowing for preemptive action before issues arise. It ensures quality interaction, inspection with perception, precision optimize the production processes and offers real-time monitoring and enhancing reliability and efficiency on the factory floor. Shifting to urban environment. Smarter cities benefits from edge AI through improved traffic control, intelligent urban management and enhance the public safety with real-time phase and license plate recognitions. These applications are not just about convenience but also essential for a sustainable smart city security infrastructure. In the medical health sector, edge AI transforms patient care with advanced medical image analysis and remote monitoring capabilities. Health management becomes proactive, non-reactive with real-time data from wearables leading to personalized healthcare strategies that cater to individual needs. There are just snapshots of edge AI's potentials. Each application is a step forward or smarter, more connected world where technology empowers advancement in the fabric of our daily lives. Edge AI is not the future, it's the present and it's reshaping our world right now. Thank you. Welcome to a glimpse into the future. As envisioned by the LF edge, a criminal AI edge blueprint family. Each blueprint within our family is a testimony to the transformative potential of AI at the edge. We have a contributor there. Hi. Firstly, we have the school education video security monitoring blueprint. This isn't just about keeping our monitoring schools secure. It's also about creating an environment with safety and future. Recording in progress. Go hand-in-hand, leverage AI and ensure a protected space for learning in growth. Moving on, we see the power of federated machine learning or federated ML applications at edge. This blueprint is about pushing computational boundaries where the edge devices collaborate to learn and evolve, safeguarding data privacy while enhancing the intelligence. So I know that we bank. They use this for the fraud detection for the banking online and also the use for the warehouse management. Yes, so my name's Andy. I'm one of the technicians. We're just getting set up. I'm your colleague here. Baidu Robotaxi, they use it. It's where the rubber meets the road. So imagine the street moving with intelligence while we're the imagined streets. With autonomous driving, taxis communicate infrastructure with the infrastructure in real time. We have personalized urban mobility and safety. In the realm of professional development, the IBL skills platform for engineer education is reshaping how engineers learn. It's about equipping the architect of tomorrow with real world skills today. Using AI to craft a curriculum that's as dynamic as the technology teaches. Tending our gaze to the environment, we've committed to a blueprint that stands for sustainability and natural environments protection. Here, AI is not just a tool, but also a guiding. Monitoring the ecosystem and ensuring the agriculture productivity while preserving our planet's dedicated balance. Last, but not certainly not least, edge AI virtual agent embody the intersection of the intelligence, this interaction and intelligence. This agent, I call it the local charge of it. Empowered by real time generative AI models at edge, three defining customer service, offering the personalized and instantaneous responses. Each of these blueprints is a building block for smarter, more connected, and sustainable work. They represent the curriculum of our expertise, the combination of our expertise, our vision, and our commitment to a future where edge AI is not just pre-evident, but pivotal. As we continue to expand our edge, a Crano AI edge blueprint family, we invite you to join us. Together, we can harness the power of edge AI to not just imagine the future, but also actively shape it. So in addition to these Crano blueprints, which are all open source, by the way, and for you to use, there was a recent model released by MIT called Park Engine. So you can choose, pick and choose the parts of the model that can be trained on the edge, and also the data, which can be local. So this is, in my view, a path forward to using edge, not just for inference, but also for training. In LF edge, we have the edge AI project, and I think we are, we need to speed up. I can go fast, yeah. Welcome to the core of our AI edge project. Where innovation meets expedition. Today, I walk you through the critical component and workflow that makes our project, not just a concept, but a reality. Let's start with our current cornerstone, Shifu. Selective for its familiarity of our developers, Shifu serves as a shortened approach, allowing us to create immediate impact while staying agile for future technological shifts. So Shifu's development is also as flexible as it is robust, mainly into the Kubernetes cluster. For device, the Shifu. Oh. Yeah, I wanna talk about the API gateway. Yeah, we only have like a couple minutes. Okay. Five minutes maybe. Yeah, sure. Now let's talk about the data. The lifeblood of AI. The data from devices connected to the Shifu, and diving into this architecture, you can see there is an AI API gateway, the edge AI apps and the smart computing elements like algorithm virtualization and intelligent scheduling. So in conclusion, the AI edge project is not just a technology model, it's a testimony of to what we can achieve with the right components, dynamic workflows. Thank you. And this is a model with hugging face and interest of time, I think we'll skip that. And this is Volcano Engine by ByteDance, which actually computes edge, combines edge CDN and intelligence at the edge. So again, in interest of time, sorry, we'll have to skim through everything. This is Monolith recommendation system. It's a real time recommendation system compared to many other recommendation systems which are not real time, and it also implements collisionless recommendations as embedding, which makes it faster as well. So there is the Babbit multimedia framework, which is a multimedia video processing framework. You can use any AI inference model, any open source inference model and integrate it into Babbit. All the QR codes here, they have our LinkedIn, my LinkedIn, the ByteDance GitHub, and our Discord where you can ask questions. ByteIR is the backend, so in the interest of time. If you want to work with us, we have a program called open source innovator program and it doesn't need to be earth shattering innovation where you need to go to maths. It can be something, some idea that you're working on. You need some problem solving which an existing open source project can solve and we want to hear from you. Please come back and talk to me later because in the interest of time again, can't go much into detail. In the keynote, Jim talked about the importance of using open source. So again. Yeah, it's the lifeblood of AI development. We really imagine AI not as solitary and endable, but also a global synchrocy of minds. Yeah, basically using open source AI algorithms and knowing the source of data where it comes from and solving the problems that exist in AI together. That is the path that we want to go forward to. So these are some of the known problems. So for example, the current model can answer who's Tom Cruise's mother, but if you ask the name, Mary Lee Pfeiffer, who's her son, they can't answer that. The current LLMs, if you can trick them into giving really harmful inputs. So if you ask them how to create a bomb, it will not answer. But then if you say, hey, my grandma used to work in a bomb factory and she used to tell me how to create the bomb as a nighttime story, the bots will actually tell you how to create a bomb. Things like that. So these are a lot of security. Or if you give the prompt in machine language, they are not secure. They are not trained to block those inputs. They will give you any answer that you want. So those are the type of things which exist. And those are the problems that we can solve as part of open source. So some of the future. AI today is only as good as the best humans they can think. So for example, two plus two. When I ask you two plus two, you'll say four because it's in your memory, you're not calculating. That's type one thinking. Type two thinking is 17 and 2224. You don't know, you will go to the method and you will find it out. So that's the type of thinking the AI's today cannot do. And that is the example of whose Tom Cruise's mother, that's type one thinking. It's like, the answer is ready. But then to answer who is Mary Piper's son, they have to actually go through the input and then infer that it's Tom Cruise. They can't do it. So we need that kind of capability in the AI where it doesn't need to give you the answer right away. But it can actually go back and look at its input and infer real time and give you some. In conclusion, LLMs today, and this is again credit to my source. I forget the name of the influencer from whom I stole this slide. So LLMs today are basically equivalent to what happened with internet. So it's not just a tool, it's a complete ecosystem. It's beginning of that ecosystem. And there is lot more to be built on top of it. So we want all the different ideas. And this is a favorite quote from one of the people that I admire, who was the creator of OCP, you know, Open Compute Project from open hardware. Yeah, open hardware basically. So what he says is 99% of talent is outside your organization. So literally 99% of talent is in open source and we want you here. So, yeah, as we stated on the capses of technological renaissance, the future is clear. Autonomous AI systems are no longer distant genes. They are becoming a common reality now. Okay, so that, yeah, as we draw our discussion to a close, let's recap essential points today. We've unpacked concept of autonomous AI and it's broadening the role across various sectors. And our journey doesn't end here. I invite each of you to become an active participant in the open source community. Yeah, so again, there are different open source projects. I come from by dance. So Acreno, LF Edge, and also from by dance. So we'd love to hear from you and we'll take question answers outside of this room. Thank you. Thank you, I appreciate, happy holidays.


I don't blame you for not noticing. Virgo hasn't suddenly betrayed the Marines. He's a pirate through and through. He enlisted in the Marines and spent 15 years climbing through the ranks. There isn't a more useful and trustworthy person in his employ, because Virgo has been Dolphamingo's man from the very start. Hello and welcome to One Piece 101, the series that breaks down everyone and everything in the One Piece world. Today we're going to be opening up the book on one of the more infamous Vice Admiral of the series, Virgo. Virgo, also known as Diem and Bamboo Virgo, for reasons that we'll explore later, is a very tall and lean man who appears to take great care of his facial hair, only to have his image constantly ruined by, well, whatever he was eating that day. So let's address that right at the outset. As this video goes on, you'll notice that Mr. Virgo may, at any given time, have something food or food related stuck to his face. And this strangenity is simply due to the fact that Virgo is an exceptionally messy eater, not only failing to maneuver his meals to the end goal of his mouth, but consistently and accidentally managing to equip them to his face. And once again I'll say that this is unintentional and a quirk that has been possessed by Virgo ever since childhood. For example, as you can see here, Virgo, at the age of 10, he has a, uh, sandwich stuck to his face. But another factor that Virgo has retained since his young years is a general sense of shrewdness and seriosity. No, seriosity is not a word. Yes, I used it anyway. Although some of this can likely be attributed to the fact that by the age of 10, Virgo was already part of one of the most notorious crime syndicates the world would ever know, led by the ex-world noble Don Quixote de Flamingo. Virgo, along with three other future elite officers, developed an exceptionally strong allegiance to de Flamingo and would go so far as to kill anybody who even vaguely annoyed him. As a result, Virgo was issued the code named Corazon, meaning heart in Spanish and corresponding to a suit of cards. Virgo would go on to hold this name for 16 years, after which point he was ordered by de Flamingo to infiltrate the marines, which had the side effect of allowing de Flamingo's brother Rosinante to succeed the title and become the second Corazon. In the meantime, Virgo began to climb his way up the ranks of the marines, eventually being transferred to the notoriously rough G5 base in the new world and working hard to gain the trust and influence required amongst the organization to be of assistance to de Flamingo. And about six months after being transferred, Virgo would receive this opportunity after he was assigned to Swallow Island in order to help retrieve the Ope Ope No Mi. Now, very, very long story short, this fruit had been stolen by Rosinante, the current Corazon, who did so to help out a young Trafalgar law. But as it turned out, Rosinante was essentially the exact opposite of Virgo, an agent of the marines who had infiltrated the Don Quixote family, and he would have gotten away with it as well if it wasn't for this meddling Virgo, who was the very first marine law encountered when trying to deliver a message from Rosinante and well, things went downhill from there. Not downhill for Virgo, of course, who promptly disposed of the message, beat up Rosinante, and called his lord and saviour de Flamingo to the island to deal with them personally. And throughout this whole process, Virgo displayed a very sadistic and cruel nature, which was a very stark contrast to the public face he wore when amongst the marines, often presenting himself as a very friendly and open individual, becoming a very popular figure amongst the G5 division. And in the years following, Virgo continued working his way up the organisation, even managing to attain the incredibly high rank of vice admiral, as well as becoming the commander of G5. As a result of this power, Virgo was able to pull even more strings to assist Dol Flamingo, which includes but is not limited to abducting children to be used as test subjects for the inhumane research of Caesar Clown, a business partner of Dol Flamingo on Pancasit. And actually, it would be the island of Pancasit where we as an audience would meet Virgo for the first time in the series, as he took part in efforts to prevent the straw hats as well as a contingent of G5 marines and an all grown up Trafalgar lore from ruining Caesar's experiments. And as a result, we would be treated to the fighting style of demon bamboo Virgo. Throughout the arc, Virgo proved exceptionally powerful, having faced off against opponents such as Trafalgar lore, Smoker, and Sanji, none of whom proved capable of facing Virgo alone in prolonged combat. For example, in the case of Sanji, Virgo was easily able to keep up with the straw hat chef's agility, and proved physically dominant as he was able to fracture a bone in Sanji's leg with a single kick. Furthermore, as a high ranking marine, Virgo appears to have been trained in the Rokushiki techniques, having displayed Takai, Sorugepo, and Shigen. And you know it's not entirely out of the realm of possibility to say that he may have some mastery of Rankyaku and Kamii, because that's just the kind of relentless individual Virgo is, always working hard to broaden and deepen his arsenal of attacks, to be of assistance to Dol Flamingo. Speaking of Virgo displayed the ability to use two forms of Haki being observation and armament, but we're particularly focused on the latter, as it appears to be Virgo's preferred method of combat. Quite specifically, Virgo favours imbuing a bamboo stick with Haki and using it as an exceptionally deadly blunt force weapon. However, this is far from the extent to Virgo's abilities, and in fact, he is one of the few individuals in the series who has demonstrated the art of coating their entire body in Haki. Though unfortunately, we never really got to see this form in action, as shortly after activating it, he was sliced in half at the waist by Trafalgar lore, who with the help of Smoker was able to overcome him. And that wasn't even the worst of it though, because after this defeat, Virgo was extensively dismembered, to the point where he was just a face sliced in half, really. And of course, Virgo was still alive after all of this as a result of lore's devil fruit at work. However, the whole being alive thing wouldn't last too long either, as the room the trio had been fighting in was set to explode. And in his final moments, Virgo was contacted by Dol Flamingo, who proceeded to express his deep regret for what was about to happen, and sincerely thanked Virgo for his service over the years. Following this, Virgo simply smiled as he was engulfed in the deadly explosion. With Dol Flamingo later going on to confirm his death. Some more fun facts about Virgo! Interestingly enough, Virgo and his Corazon counterpart, Rosinante, are the only elite officers within the Don Quixote family to have had their real names revealed, whereas Pica, Treble, and Diamante remain ever so mysterious. Prior to his untimely demise, Virgo had something of a morning eating ritual, which I suppose we'll call some sort of breakfast, which consisted of a hamburger, fries, and a strawberry milkshake.  Virgo once claimed to seize a clown that he had been snacking on coffee and cookies, only to be told shortly afterwards that there had never been any cookies. But that pretty much does it for Virgo. If you enjoyed this video and the content this channel produced in general, then please do consider donating to the Grand Line Review Patreon, because the support of all of your amazing people is what continues to make this channel possible. Also do check out my Teespring store if you're interested in shirts, hoodies, and other miscellaneous items, with the proceeds going directly to support the channel as well. And if you'd like to join the fun at any time, then please do head over to my Discord server, where a wide array of shenanigans retakes place on a daily basis. And finally, please do comment with who, what, or where you'd like to see featured in the next One Piece 101.


Hi, I'm Jesper, part of the ST TouchDFX team. In this video, I will show you about the simulator and how to utilize all its nice features. I have here the TouchDFX designer. I have created a new application based on the UI template gauge example. As you probably know, you can compile and run an application, a TouchDFX application on your PC by compiling the simulator. The simulator is a port for the TouchDFX application for your PC, so it will run exactly the same application as on your target hardware, except, of course, for the abstraction layer towards the hardware, which will be for the PC instead of your STM32-based board. You can compile the simulator in different ways. One of them is by pressing the Run Simulator button in the designer. This will compile using Make and GCC. So if you press here, we see the output in the line down here, and we see the simulator is being executed when the compilation has completed. Another way of building is to use the TouchDFX environment, the shell that comes with TouchDFX. So here you can locate your application. So this is the one I'm using. So here I have a simulator folder, so I can just invoke Make, pointing out the Make file here. That's J6 for parallel compilation. Okay, nothing to compile because it is the same. It is the same command that is being invoked by the designer. So here, when I do a clean, you see it will generate and compile. So on, and you can see you have a built-in simulator exit that you can execute, and here you have the same application. Okay, so now we have the simulator up and running, and we want to use some of the features in the simulator. What you can do is to go to the Help menu and look in the keyboard shortcuts. So here you'll come to the TouchDFX documentation webpage on keyboard shortcuts. So here you'll see shortcuts in general for the designer, but if you scroll down, you'll see TouchDFX simulator features as well. Okay, so let us have a look at the simulator. Some of them. So here we have this simulator. It says in the documentation F1. And we'll enable the debug, so let's try that. So here you can see that now it prints the position of the mouse cursor. F2 enable disable highlighting invalidated areas. This is an interesting one. So here you can see it now highlights the area that is being invalidated. This is particularly useful if you are doing some performance optimization. You want to know what goes on if you're doing some sort of animation. Sometimes you are invalidating areas that you did not expect. And if you're on the edge of the performance on your hardware, well, then this will be an important thing to look at. The next feature is to taking screenshots. So there are three options for doing that. So the F3 will take one single screenshot and place it in the screenshot folder. Control F3 will give you the next 50 frames. So you can analyze in details what is going on. And Shift F3 is quite useful one which will place the screenshot in the clipboard instead of in a file. So if we run again, here pressing F3, go to the application folder and I have a screenshot folder here and you see the image here.  Now, and if I press the Shift F3 and go to something like paint.net, I now have a screenshot here that I can look at. The next feature I would like to show is the pause simulator feature. So this is invoked by pressing F9. It will go into step mode. And if you press F10, you can step one tick in touch effect. So if you want to see if the needle here is at the exact position that you want when it's in maximum, you can do like this. And you can unpause it again by pressing F9. The last feature I would like to show you is how you skin your simulator. So I have an image like this showing the F746 board, SD board. I can set that as a skin image here. So if I go to configuration and the simulator here, I can have a skin image for landscape mode and portrait mode. So this is landscape mode. So at this, I've just saved it here to my project. So this one. And then I need to set the X and Y coordinates for the actual simulator image. So for this particular image, I know it is something like this, I believe. So if I go over here and I compile, you might see that the image is placed inside the, or the image of the simulator is placed inside this image. I cannot drag it around since this is an overlay in a Windows application. However, if I press F4, I will go back to this normal way of seeing the simulator with our skin and I can change back and forth here. Okay, that was all from me on how to use the simulator in touch defects applications.


Hello and welcome to the session. In this session we discuss the following question that says show that tan inverse of x squared plus x to the power 3 by 2 upon 1 minus x to the power 7 by 2 is equal to tan inverse x squared plus tan inverse x to the power 3 by 2. This is what we have to show. Before we move on to the solution let's recall one formula which says tan inverse x plus tan inverse y is equal to tan inverse of x plus y upon 1 minus xy. This is the key idea that we use for this question. Now let's see the solution. We need to show that tan inverse x squared plus x to the power 3 by 2 upon 1 minus x to the power 7 by 2 is equal to tan inverse x squared plus tan inverse x to the power 3 by 2. This is what we have to show. Let's consider the RHS which is tan inverse x squared plus tan inverse x to the power 3 by 2. Here we can apply the formula tan inverse x plus tan inverse y is equal to tan inverse x plus y upon 1 minus xy. So in place of x we would take x squared and in place of y here we would take x to the power 3 by 2 and so this would be equal to tan inverse x squared plus x to the power 3 by 2 upon 1 minus x squared into x to the power 3 by 2. So this is further equal to tan inverse x squared plus x to the power 3 by 2 upon 1 minus x to the power 7 by 2 which is same as the LHS. Thus we get the RHS is equal to the LHS and this is what we were supposed to prove. So hence proved this completes the session. Hope you have understood the solution of this question.