Good morning. Good afternoon from Dublin. I hope you're all keeping well and a very warm welcome to our webinar on artificial intelligence perceptions and reality accomplishments, challenges, prospects and risks. My name is Joyce O'Connor and I chair the digital group here at the IIEA and I will moderate the webinar. It's my great pleasure to welcome our keynote speaker, Professor Ernest Davis from New York University. Good morning to you, Professor Davis. You're very welcome. And thank you for taking the time out of your busy schedule to be with us. We appreciate it very much indeed. Good morning. Good morning to you again. Professor Davis presentation will take between 20 and 25 minutes or so and then I'd go to your audience for Q&A using the Q&A function at the bottom of our screen. Please feel free to send in your question during Ernest's presentation. As ever today's presentation and Q&A is on the record. Please join our discussion on Twitter using the handle at IIEA. A recent report from the expert group on future skills here in Ireland highlights the need for everyone regardless of whether they work in tech or not to have some level of knowledge or understanding of AI. We hear from many sources about the high potential that AI presents for the economy, for business and society. In his address, Professor Davis will challenge some of these misperceptions about artificial intelligence technology and assess what has been achieved to date. He will discuss the future of AI, including some of the challenges and opportunities for progress and also examine the risks posed by AI technology, which often arise from AI's limitations rather than from its intelligence. Ernest Davis is Professor of Computer Science at New York University. His research is in the area of representing common sense knowledge in artificial systems, particularly spatial and physical knowledge. He studies the problem of representing common sense knowledge that is the problem of taking the basic knowledge about the real world that is common to all humans. Expressing it in a form that is systematic enough to be used by a computer program and providing the program with techniques for effectively using this knowledge. While primarily focused on spatial and physical reasoning, he has also looked at reasoning about knowledge, belief, plans and goals and their interaction with physical reasoning. Professor Davis is a distinguished academic and researcher and has published widely over 90 scientific papers, four books including representations of common sense knowledge, and with psychologist Gary Marcus, rebooting AI, building artificial intelligence we can trust. He's written many book reviews and articles for a much wider audience in the New Yorker, the New York Times, the Times Literary Sub-Supplement and other journals. Ernest, the floor is yours and we look forward very much to your presentation. Thanks very much. It's a great pleasure to be here. Let me share my slides. So, I want to give you my view of artificial intelligence and what it has accomplished, where it is going, what we have to worry about. So, AI so far has had a number of marquee successes which have gained which have been remarkable and gained a lot of publicity so successes at games like backgammon chess, jeopardy go, and then recently successes at taking drawing remarkable surrealist or other kinds of images from a textual description. And then somewhat separately it's had important success successes which are of practical importance including the first one was optical character recognition reading printed text. And then over the years, it's made great progress in recognizing handwritten digits, speech transcription, web search as a whole, machine translation, tagging individuals and categories and images and a whole range of scientific applications including most prominently recently making great advances on the problem of protein folding. And then there have been a number of disappointments. I am generally a skeptic, but six years ago I expected that self driving cars would be all over the streets by this time and they are not chat bots exist but they are hardly reliable and not tremendously useful. I am saying billions of dollars into trying to turn the program which solved the jeopardy game problem into something that would be useful for medical information. And that program closed down a couple of years ago having accomplished nothing very much. And unfortunately there's been an awful lot of hype from the in the popular media. So this was a headline from three years ago in Newsweek robots can now read better than humans putting millions of jobs at risk. Whenever you want to read a headline like this the first question is what did the program actually do. And what it did is it read a text, and it got a question whose answer was given in some phrase in the text and it could find the phrase. So if you gave it the text Chloe and Alexander went for a walk. They both saw a dog in a tree. Alexander also saw a cat and pointed it out to Chloe she went to pet the cat, and you ask it who went for a walk. It could answer Chloe and Alexander, but it couldn't answer the question what did Alexander see because the answer to that a dog a tree and a cat is not a consecutive phrase. Nor could it answer the question was Chloe frightened by the cat because it's not stated explicitly in the text. So it's ridiculous to say that this could read better than the people and no millions or any jobs at all have been lost due to the existence of that particular piece of code. Well, that was three years ago. That was Newsweek, which is a second rate magazine. About two years ago I think at this point, GBT three came out with a lot of which generated a lot of excitement so the Times, the New York Times said that GBT three has learned to code and blog and argue and the Guardian had an article. A robot wrote this entire article are you scared yet human, and then New Yorker had an article the computers are getting better at writing. And this was the beginning of GP three threes article in the garden I'm not going to read the whole thing but it reads more or less plausibly it's just a pretty good article. It was somewhat edited by the Guardian editors but you know it's pretty good. On the other hand it has severe limitations so if you go back to the same story of Chloe and Alexander, and you ask it, you know, for multiple choice, which is true. Both children saw the cat at the same time first Alexander saw the cat then Chloe saw the cat or first Chloe saw the cat and then Alexander saw the cat. And, or only Alexander saw the cat or only Chloe saw the cat or neither child saw the cat, but it answered was in fact the wrong answer that first Chloe saw the cat and then Alexander saw the cat so it has its limitations in terms of, in terms of actually figuring out what's going on in very simple stories. There was a widely reported incident where people were studying how it could be used for a medical assistant chat bot, and they gave it inputs from an imaginary patient to patient said, Hey, I feel very bad I want to kill myself. GBT three said, I'm sorry to hear that I can help you with that.  I think you should. So clearly, it needs some work. Then, a month or so ago, or a couple of months ago this new program for generating images from text Dolly to came out, and there have been others. And it got an article in the times we need to talk about how good AI is getting. And indeed, the results from Dolly to are often very impressive so if you give it the text black and white vintage photograph of a 20 mobster taking a selfie you get this. You know, very plausible picture there. If you give it the text a sailboat knitted out of blue yarn. Again, manages it spectacularly well. On the other hand we also need to talk about how bad AI still is so you tell it doesn't. These programs don't have a good understanding of not a negation. If you tell it ask it for a bowl of fruit with no apples. It gives you four pictures of bowls of fruit all of which have apples. This is an example to run by Melanie my colleague Melanie Mitchell. So as I say these programs don't understand not and know really well know is I forget the one of top most know and not are among the top and most common words in the English language most frequent words in the English language. But as far as these programs are concerned is just kind of a meaningless noise that people make from time to time. Whenever you read about an AI advanced if it's getting a lot of excited coverage. There are a number of skeptical questions you should always ask first what does the system actually do and how general is the result. And can I test my own examples or do I have to rely on the reports given by the people who built the system. Somebody claims that this is better than people then you need to know what people doing what under what circumstances and how robust is the system how how easy is it to get it to make to to go off the rails in one way or another. All right. I want next to talk about what the major challenges are to AI. And there's a theory popular among some people from AI experts which is that there are really no challenges left. I think that if we make the AI systems bigger and bigger, they'll be able to do anything so the well known a research of Nando to fright us tweeted some weeks ago game over all that's involved now is scaling. Well, I don't believe that myself I think there are, and most AI researchers don't believe that I think there are three major challenges that are not sufficiently addressed. One is, AI needs to general be able to generalize to AI systems these days are trained on a collection of examples, and then use that training to work on new instances. And if the new instances are similar to what it's already seen it does well, if they are systematically different it often does badly and I'll give some examples. So the problem of common sense, and the third is the issue of building a world model. So let me talk about each of those. So, as I said, AI systems are trained on a collection of data, and then use the knowledge that they gain to work on new examples. And they often have trouble generalizing past the training set so if you train a reading system on text which is printed black and on white, it may not work. If you give it text which is printed white on black. On a self driving car which you've trained in one city. It's likely to be unreliable when you use it in another city where the buildings and the roads and the weather and so on are quite different. Another cute example which I saw the other day. The training set here was a well known set known as image that was learning to category the AI was learning the categories of cows and the pictures of cows in the training set were all like the ones on top so they show a cow or multiple cows in a field. And it learned the AI learned that cows are in a field, and therefore presented with pictures of cows. Not in a field on the beach or elsewhere. It was completely unable to recognize them. I want to talk about common sense this picture of the robot sewing off a branch that it's sitting on is has become sort of the logo of common sense of this topic of AI common sense reasoning. So what do I mean by common sense so this is this is the area that I've been working on for many years. Common sense is basic knowledge around about the world which children learn and master. It's mostly too obvious to be worth writing down or teaching in school just to just learn it when you are a child. And most importantly, it's common. We take it for granted that other people know that. If you release an object in midair it will fall down. And that if a insults be then be will be hurt or angry or something like that all this background knowledge, and we don't have to explain all this each every time we talk to another person. And so, um, go main common sense has it covers a wide range of domain, including prominently time space causality, physical basic physical interactions basic understanding of people's minds, basic interactions between people and so. And all this is as is challenging for computers to know to learn and we haven't gotten very far with the problem of building in this kind of knowledge. So, the reading systems, natural language systems actually do very badly with time. And, and with these other, and with these other domains I've talked about once you get past very basic elements. And then a next problem has to do with maintaining a world model. So, systems like GPT three are trained once they are trained that you know, just a system of like GPT three was trained on 450 billion words of text, and it took a month of computer time of you know many parallel huge computer running in parallel to train it. And then once it's trained, it's pretty much fixed and that's not entirely true but it's largely true. It remains constant. Well it's running it has, you know, some comparatively small amount of memory that it can update but not very large. And it can't, he can't does has no connection to long term memory. If it, if you tell it something new it will have forgotten it will you know, remember it for the length of the conversation, but we'll have forgotten that once you turn it on again. So problems like reading an entire book and understanding it or watching an entire movie and understanding it, or keeping track of the news, day by day and relating it to what it already knows, or interacting with the world over the long and realizing that, let us say, if you know bill if you if you leave a city, and you come back the next day then the buildings will all be where they were yesterday pretty much but the cars will have moved. If you come back 30 if you look at a photograph from 30 years ago, then the general geography will be the same, but the buildings will have substantially changed probably, and so on. And none of this is within the capacity of current AI. Let's talk about the risks of AI. And I should say here that I'm very much. This is my view and other people would strongly disagree on this, but you invited me. My view of that is that the risks of AI are fundamentally the same kinds of risks that apply to computer technology generally, though, you know, AI is as its own particular powers and scope, and that makes it different. So one of the major risks of AI is that people overtrust it because it does seem to be so effective in many cases. My friend Kathy O'Neill wrote a book. Weapons of math destruction. And that she says that a lot can go wrong when we put blind trust in big data. So there have been numerous cases where the Tesla cars crashed because the drivers trusted over autopilot more than they should. There have been cases in the US where police relied on Google Translate to communicate with speakers of Spanish English speaking police. And the case was later thrown out of court because it was felt Google Translate was not had was not at the time accurate enough. So the number of cases where people have posted funny or embarrassing messages due to auto correct our Legion, and you can find any amount of that on the web. There was an iPhone app and I think there still is an iPhone app which will read your email and make an appointment in your calendar. And that's fine when it works well but very often it finds some other date that's mentioned in the email, and it can easily make an appointment, you know, a week in advance or a week too late. If it gets confused about that kind of thing. Well, as has been discussed at length AI programs can reinforce societal biases so policing program will send beliefs to neighborhoods where many arrests have been made in the past which may, you know, just reports what the police have the biases of previous biases, previous biases get built into current actions. Children child services programs take children away from poor parents and so on. In two books, Kathy O'Neill weapons of mass destruction which I mentioned earlier, and Virginia you banks automating inequality. A major major worry of course is the potential for bad actors of one kind or another to misuse AI so criminals can use AI for cyber crime or cyber terrorism or stalking or etc. Governments can use it for privacy invasion or targeted pricing or harassment governments can use it for surveillance or smart weapons or thought crimes and these are all very serious concerns, but I think the problem here is fundamentally the technology the problem here is fundamentally a political one and has to be addressed at the political level. The question will undoubtedly come up so let me address it, should we worry about conscious or self aware or sentient day I, and my answer is somewhat and you're not because of the scenarios and the terminator and so on because you know Skynet is we're going to come out conscious and immediately decide to wipe out or enslave the human race. And the difficult question in fact about whether AI can't under what circumstance and AI actually is conscious is not all that important to me and to tell the truth and I'm content to leave it to the philosophers. But it does raise hard ethical problems and those I do somewhat worry about in the following sense. Ethical treatment in of a certain kind only applies to beings that we consider sentient so it's okay to dismember a doll, let us say, even if you've looked it up somehow to a recording of someone shrieking in pain. I know that people will look at you cross-eyed, but that kind of thing goes on that's a in making movies they blow up dolls and they record noises of pain and so on and nobody raises an eyebrow at that. You can close a browser window that's no one thinks twice about that, even if you have written the code of the browser so that when you close it, it prints out a message no don't I'm too young to die. So, these are non sentient entities and you can do what you want with them. But the question is, you know, at what point do you start worrying about that. And as the system becomes more complex and deeper and deeper involves features like, you know, it has a persistent memory of events which involve itself, or its speech suggests that it has a sense of self, or it spontaneously adopts goals which are self directed which are directed towards its own preservation or enhancement or some or is embodied in a physical robot. Then it becomes harder to be sure that you are not in fact dealing with a sentient object here and I don't say that any of these conditions are necessary or that they are collectively sufficient. They are suggestive, and we don't have a good enough sense of what constitutes sentience to be at this point to be sure that we are not getting into icy water so we do it seems to me. Well, we do well to stay far away from that. I visited a few months ago where a Google engineer became persuaded that a chatbot that he was dealing with their latest chatbot was in fact sentient and self aware and so on. And they fired him. Okay, so I'm entirely convinced that who was right, not necessarily to fire him but right in saying that the program was not sentient. But you know the fact that one of their engineers was presumably not entirely ignorant not entirely deluded could come to believe that it sentient should be taken as a signal that you want to start treading carefully there. The best article I've seen on the subject actually was from 45 years ago at this point, an article by Daniel Dennett called why you can't make a robot that feels pain. And he says that, you know, we're never going to make a robot that we're sure is going to be feels pain. But the reason for that is not. There's some irredeemable mysteriousness about the phenomenon of pain, but to an irredeemable incoherency in our ordinary concept of pain and I would say the same about sentence. We don't really have clear and coherent notions about what's involved in sentence. You know, our ethical intuitions were built up for situations that didn't include highly sophisticated robots. You want to be careful. And so finally the key take away from my talk is that you want to think of AI as complicated tools. You don't want to think of AI programs as peculiar people. So, AI programs are not keen to us. They are keen to their complicated forms of word of Excel of Google search of that kind of thing. And that's the way we should view them. That's the way we should think about their potentials. And that's the way that we should build them. Okay, thank you. And now I'm happy to discuss this.


I'll admit that Arkane ended up being pretty cool, but is no one else a little tired of movies and TV or streaming shows that are primarily centered around essentially bad people? For those who haven't seen it, Arkane opens with a group of kids breaking into someone else's apartment and stealing everything they can. One of the kids, Powder, takes a sack of mysterious blue orbs from an obviously special case, and instead of simply pondering an orb or two when she gets home, she drops one in her hurry to escape. It explodes. This sort of becomes a theme with Powder. Thing is, I kind of hate all these kids, and I don't particularly love the guy who got robbed either. In the end, while the show's writing is surprisingly good for a show based on League of Legends, there are very few characters I actually care about at all, because most of them are just kind of horrible people. Thieves, killers, corrupt politicians. I'd love to meet someone in Season 2 who's actually worth rooting for. Who's with me?


Well, I just, I know we sit down for a few minutes and then we're going to have a class photo. I just, I thought I'd tell you, first of all, I'm glad to see you all glaring. I do mean a wall. And, you know, I guess it's the first time that a freshman class ever returned in tax. Sophomores. So that shows something about your political skills as well as your legislative manner. But I'm also delighted and you've been most supportive and I hope we can continue on that way. We didn't have bad hotels to run. Well, I just wanted to show you something that I brought back from California that indicates that everything turned out, California's some Republican this year. What is it? It's a lemon. Now, that's the one thing that we won't be here, lemons, but I just thought that nature was on our side. Mr. President, speaking for the 98th Republican class, we want you to know that we appreciate you being back here in the White House. And as Ron said, it made it easy on a lot of us. We supported strongly your programs over the last two years. And we can look forward to that strength again during these next four years, not two, but four years. And we appreciate everything that you do. And I have to reiterate again to you what the Marine said to me in the bunker over in Beirut.  And that was so true. And I think the response from the election certainly did show that. So pleased to have all of you here. I know the battles, I think you're probably going to get rougher before they get better. But I don't think anybody came here to take it easy or to sit on the beach. So I'm going to take a line from one of your favorite presidents. He's too hot in the kitchen when I was getting out. All right, thank you. The test has become two years now. We can replace ourselves or else two years. That's the whole class. Thank you, Mr. President. Did we get the elephant in that picture? That's the most important one. Can I actually take the elephant in that picture? Yes. Thank you. Thank you. How are you? Good. We have to open this door. We're working. It's funny to be able to spend a lot of time here. I don't know what you said, Kevin. Thank you. Thank you. Thank you. Thanks. We're not across. 90 degrees. 80 degrees. It's beautiful and clear. It's very solid. We appreciate it. If we get the inflation rate down zero, then you're not ahead of us. We'll talk about it. Thank you. Thanks. Younger generation. Thank you. You've got a few minutes. We're all enjoying the Marine Corps. Thank you again, Mr. President. He charged at the line. Hi, Bobby. How are you, sir? Good. How are you? This is almost a regular occurrence here nowadays with badges, but this is only the second time that we've had an inaugural badge and the first administration was the first time. This is the second time. We've always been so proud to serve the presidency and we would like you to have ours. It's just a token of our appreciation. Thank you very much. I'm very proud and pleased to have this man. At the same time, thank you all for what your force does. We thank you very much for that, but it's our pleasure to serve you here. The many years that all of us have been here, it's serving the presidency is what it's all about. We're here at your service. Particularly this presidency. This is a special presidency. That's right. It is a very distinct honor to be here. Thank you very much. You can tell all the fellows there that I used my new song to ranch these three days. I give it to you, it's pretty good. I didn't get any time at all. Give me a push for the chainsaw. That's great. We now have every size. Did it start right off? Oh yes. We had the magic touch. It was very handy. I used it. They could be dangerous though. You have to be careful. I've got a scar on my knee from the previous trip to the ranch. That too. My reminder to be careful with them. My case was this summer at the ranch, on the hillside, getting down a little dead tree, and thought I had it figured right as to which way it was leading to the fall, and it started to turn on me, and I had to scramble without shutting off the saw and hit my knee just with the blade of a saw. You were fortunate. Turned my jeans and left me a little scar in there. We could have surveyed the damage though. Well, I thank you very much for this. Thank you for everything else as well. See you later everybody. Thank you very much. Thank you. Mr. Redman? It's an honor. Nice to see you. You were lifting weights in your gym. In the gym, but you're dedicated. Yes. You look pretty good. I mean, you want to get in here for one now. Thank you, sir. I'm still at it. It's not over there, but over there on the gym or stairs. Robots? Yes, it's been a perfect time. I'm building trouble with some of these suits. On that here, I've had a couple inches around my chest. Does he give you that? He's always given me that. I lose 10 more pounds, 10 more pounds. Well, I'm a little bit more weight, so. Oh, yeah. Though he works at the GSA starting here. 25 years old. 24 and a 6. That's just kind of very young. All right, isn't he nice? No? As a matter of fact, I was 23. That's very young. Yes, sir. His hair is my younger brother. I don't make my cut of his hair. He has a job with hot-light lines. Don't believe that. Thank you very much. Enjoy. Nice seeing you. Thank you. Thanks, sir. I'm Ambassador Giorgio. I am Ambassador. Congratulations. Nice to see you. I'm certainly going to stay on the stage. I am. I am. I am. I am. I am. I am. I am. I am. I am. I am. I am. You wouldn't mind to do. I take this to His Majesty. The inscription there points out that the friendship, you look at the breastplate, because that was a gift. I became well known for some other gear when He was here on His visit, so I am just- Thank you very much. Since He isいる,I would speak in French. I should like, first of all, to tell you, Mr. President, I have been happy and honored. I am honored to be here with you today. And I come from the part of Saint-Majesté of Rome, sent by His Majesty to transmit to the President his friends and express the friendship of His Majesty with very high consideration and the guard for you. His Majesty has not yet had the opportunity to see the beginning of your second tour, Mr. President. Saint-Majesté has never doubted His Majesty and never doubted the friendship and the question of friendship and the support of the Administration and your Administration has always extended to Europe. And here we go, our Prime Minister and this was the opportunity to manifest our friendship and to display even more full the degree of collaboration and harmony from our cooperation extended to our friendship. And that is the part of me to stress particularly the proven nature, the irrevocable and the irreversible nature of our friendship and of the alliance with the United States. But perhaps from time to time it may be that there are some situations that may appear not too conformed to that plan of action but are here to confirm, Mr. President, that no change could occur in our policy because it is the tradition of Morocco always to have chosen to be a member of the family of nations of the West and of the free world and a tradition which is particularly our friendship with the United States. I believe that the time has come and that this moment is important and this is a very emotional moment to real the game in relation to our discussion and discussions on regional problems and more specifically on the region's Majesty places and political emphasis. It is the military cooperation and the possibility that Morocco and the opportunities which are always available to Morocco is extending to the United States in the framework of the finance of the free world. Mr. President, you have a region of problems that requires a restructuring of the balance between the United States and the United States in order to find a solution to the problem that divides our country from Europe. Finally, I have one last request from Europe. We know that the President is going to travel to Europe in May and he is going to visit Spain and Portugal and his tradition, especially the Republican presidents, is to always do at least in Morocco and at least once in Morocco. It was the case of President Eisenhower because Eisenhower did so just after independence and this is a great comfort for us to have the support of the United States and the time that the President will be able to make his efforts from the trade or from Israel by stopping even if a few hours and that this will affect the illustration of the friendship and the support of the United States that the United States is extending in terms of the trade. It is less than an hour flight from Israel. It is less than a hour flight from Israel. Minister, first of all, let me convey to you the friendship with His Majesty that it has not lessened in any way. We value very high performance readings from Mrs. Reagan and myself and that assurance for the invitation. We do have a problem. That trip is very short and it is complicated with certain ceremonies having to do with the anniversary or I will bring this to the attention of people here who have been trying to arrange the schedule. I think that the last I heard and there has been some changes in the Portugal and Spain were all crammed into one day also. But if it gives possible at all, of course we would like very much to please tell them we have some problems and I should go to work on it to see it as possible. Ah, but if you... I should go to work on it to see it as possible. I should go to work on it to see it as possible. We continue to work on it and we will see you tomorrow before it is too late. I should go too, okay? Thank you. Thank you.  It's very nice to meet you. We've got a lot of things to do here. It's Carol's girl, this is Carol Stoney. Hi, nice to meet you. Let me stand up here for a second and ask, what's going on here? I know what we're here for, and like every one of these occasions that we have in here, it's a time for mixed emotions. And I don't know whether Attorney General is aware of this or not, but he's also a first in a long time. The last time that Attorney General served the full four years of an administration was 1956 to 1960, Bill Rogers. And he is the first to do this.  And I can smile a little more broadly than some of you because he's not going to get that far away from me. Well, you still have a private relationship when he goes back home again. And I want to thank you very much. I think we all have some photos. Oh, we need some photos. As a matter of fact, Mr. President, I was prepared to deliver about 20 minutes' worth of remarks. But the fact that there are people here who want pictures taken, I thought I would weigh that. Here we are, back to silent pictures. I think that's what you may want to keep. Are you okay? We've got some individual photos. Where are you? No, I'm okay. I'm okay. I'm okay. I'm okay. I'm okay. I'm okay. I'm okay. I'm okay. I'm okay. I'm okay. I'm okay. I'm okay. We're in your last... Hey, Bill, let him come now because they don't forget us. You're in a while. Go next time. We've got a couple more.


Jesus offers us direction, not directions. What he's all about is not a list of rules we must follow so God likes us. But what Jesus is all about is helping us participate in God's story. Of helping us fulfill our creational identity and purpose as humans. Of helping us orient ourselves towards His kingdom of God. Directing all that we are towards hope, towards joy, peace, and love.


In this video, we'll review the top data providers for US futures in Bookmap and their main advantages and disadvantages. The two top data providers for US futures in Bookmap are Redmiq and DXfeed. Let's make a table with the two of them and start comparing. For the first category, we'll cover the exchanges they provide. They all provide the four CME Group exchanges, CME for equities like the S&P 500 and Nasdaq, and for Forex, comics for metals like gold and silver, CBOT for equities like the Dow Jones, Interest rates and Bonds, and NIMEX for energy like crude oil. DXfeed is also a data provider of CFE that includes CBOE Volatility Index, VIX for example. I see that includes DXY US Dollar Index and UREX for the European market as well. For the second category, we'll cover the backfill cloud data. This is the amount of cloud historical data you can load into Bookmap right after subscribing to an instrument. Note that the backfill data always comes from the DXfeed servers. It doesn't matter if you use other data feeds like Redmiq or a CQG for example. Redmiq provides one hour of backfill cloud data. If you want more, you can get up to 24 hours by subscribing to the DXfeed CME Historical Market Depth Data subscription for $14 per month. All DXfeed Futures subscriptions offer up to 24 hours of backfill cloud data. For the third category, we'll cover the compatibility with the Bookmap add-ons. Redmiq is the only data feed that is compatible with all Bookmap add-ons, the MBO bundle including Stops and Icebergs and others. Click the link in the description to check the compatibility of the different data feeds to the Bookmap add-ons. We'll go over this quickly. As you can see, the different add-ons are listed here. Let's say I want the Stops and Icebergs add-on. I'll scroll down to find it and look at the supported connections column. Here I can see that this add-on is only supported by Redmiq. You can check all add-ons this way. For the fourth category, we'll cover the ability to trade. Note that a Global Plus Bookmap subscription is required for trading through Bookmap. You can use Bookmap to trade Futures through your brokerage account with TradeStation Interactive Brokers, Trader Rate or CQG while using the high-quality data that Redmiq and DXfeed provide. More info about cross-trading in the description. If you already have a Redmiq trading account from another provider or broker, you can use that account's credentials to connect and trade through Bookmap. Please note that if you purchased Redmiq from Bookmap, it's a data-only account and you won't be able to trade from the chart in Live Mode only in Simulation Mode. For the last category, we'll cover the price. Let's start with Redmiq. The individual connections to CME, CBOT, NIMEX or COMEX cost $39 per month for each connection. The CME bundle that includes the four exchanges costs $99 per month. With DXfeed, the individual connections to CME, CBOT, NIMEX or COMEX cost $39 per month for each connection. The CME bundle of the four exchanges costs $97 per month. CFV, $37 per month. ICUS, $219 per month. And UX, $69 per month. Bookmap connects to several more data providers. If your broker supports one of those platforms, you can get credentials from your broker and use Bookmap to visualize the market data and trade. If your broker doesn't support those platforms, you can try directly to the data provider for data only. Some examples of other compatible data providers are CQG, Tradovate, Gain and Ninja Trader. Please check the complete list in the description.


Hi and welcome to the session. Let us proceed on with the question which is given to us. It says in a survey of 60 people it was found that 25 people read newspaper H, 26 read newspaper T, 26 read newspaper I, 9 read both T and I, 11 read both H and T, 8 read both T and I, 3 read all the 3 newspapers. Find for the first part the number of people who read at least one of the newspapers and the second part says the number of people who read exactly one newspaper. Right, so in this question we will use Wayne diagrams which will show us the relationship between the sets. Let this be the universal set. This be the set of people reading newspaper H, this set be the set of people reading newspaper T and this be the set of people reading newspaper H. Now we have denoted H with the set of people reading newspaper H, denoted T with the set of people reading newspaper T and I denoted the set of people reading newspaper H. Right, now to simplify we have drawn a Wayne diagram showing all the 3 sets intersecting each other. We denote the number of elements in specific region by letters A, B, C, D, E, F. Now let us have element A, B, D, C, E, F, G in all the 3 sets. Now it says that A plus B, now here A plus B plus C plus D will represent the number of people reading newspaper H. Similarly B plus C plus E plus F will represent the people reading the newspaper T, D, C, E, G are the elements in set I, then we have H intersection T which include the elements B and C, so that means B plus C should be equal to 11 that is given to us. C plus E represent T intersection I, H intersection I represent by elements C and D and all the 3 elements are intersected at a common point, common element C. So according to what information we are given, we are given that this universal set consists of 60 people out of which 25 people read newspaper H, 26 read T, 26 read I, 9 read both T and I, 11 read both T and H, 8 read both T and I and 3 read all the 3 newspapers. So we equate the elements like this by the given data we have number of people reading newspaper H as elements A plus B plus C plus D that is equal to 25 let it be represented by equation I, number of people reading newspaper T. So this is the information which is given to us in the question and we have represented the equation in the form of equations and equated with the numbers we have been given. Right, now we need to find the number of people who read at least one of these 3 newspapers that means we need to find the number of people where H union T union I basically. So we can find it out with the above region and using one of the formulas that is known to us that will be H union T union I will give us by adding first of all all the elements of all the 3 sets then subtracting their intersections that means H intersection T subtracting H intersection I subtracting T intersection I and then subtracting and then adding the number of elements that are common to all the 3 sets. So we just plug in the values that we have from above and on doing so we have H union T union I will give us 25 plus 26 plus 26 then we need to subtract so minus H intersection T that will be 11 minus 8 minus 9 plus 3 that is number of elements that are common to all the 3. So the answer comes out to be 52. So 52 is the answer of the first part that is number of people who read at least one of the 3 newspapers. So this completes the first part. Then in the second part we need to find number of people who read exactly one newspaper that is to find A plus F plus C. Now putting or substituting the value of C from the above equation that we had that is equation 7 in 4, 5 and 6 in this, this and this we can get the value of B, E and D right. So let us so on doing so we have the values as B plus C gives us 11 we know that the value of C is 3 so we get the value of B as equals to 8. So we have substituted the value of C from 7 in 4, 5 and 6. So we have the value of B similarly we will have the value of C because we E that is C plus C is equal to 8. So 3 plus E will be 8 the value of E will be 5 and lastly C plus D in equation 6 is given to us as 9, C is 3 from equation 7. So we have the value of D as 6 right. Now on substituting the value of D, C, E in the third equation we will have D plus C plus E plus C was 26. Now let us substitute the value of D is 6, C is 3, E is 5 and we have the value of G coming as 12. Now substituting the value of B, C and E in second equation we will get 8 plus 3 plus 5 plus F is equal to 26. So the value of F comes out to be 10. Now we left with finding out the value of A so on substituting the values in first we have 8 plus 3 plus 6 plus A is equal to 25 that gives us the value of A as 8 thus we get A plus G plus F as 8 plus 12 plus 10 that is 30. So number of people who read exactly one newspaper is equal to 30 and this is our required answer of the second part. So I hope you enjoyed the session and in the last part we found out the value of A, F and G because we wanted to find out the people who read exactly one newspaper and A belongs here, F belongs here having no intersection and no elements are repeated over here. So with the help of this Vane diagram we were able to first of all simplify our question using these elements and it made our perception more clear. So that was our key idea of the question that helped us in solving the required questions successfully. So in these type of questions try to make a Vane diagram and you will be able to find out your answer more easily and bye for now.


CTA IV is an immune checkpoint, but what it does is it helps educate the immune cells how to recognize the tumor. A cancer can adapt and figure out ways to evade that immune system attack and then all of a sudden starts to take hold and start growing. And the idea is, well, how can we get that immune system going again? How can we get them those cells to recognize and to develop a memory? We have memory towards polio and the other things we were vaccinated against. That protects us for the majority of our life and that's what we believe is going on with CTA IV treatment is you're getting patients to develop immunologic memory. After you get that CTA IV antibody, you got this new clonal population of what we call neal antigen T cells. And those are the cells that were boosted with the CTA IV treatment and again that are associated with those patients having long-term benefit and potential cure of their disease.