Ladies and gentlemen, good morning. So artificial intelligence has been part of our daily lives for many years now. It is used by the maps when giving us predictions, by the online search engines, answering our questions. And it is the algorithm feeding us targeted content on our social media platforms. We only perhaps started calling AI, AI, in our casual conversations when chatGPD appeared almost a year ago and stunned us with its ability to produce human-like text when it blew our minds with its ability to create it. From deep learning came the foundation models, powering generative AI applications, models of artificial networks inspired by the billions of neurons connected in the human brain. They process extremely large and diverse sets of unstructured data to perform their tasks. So latest studies suggest that generative AI could raise global GDP by 7%. And that's a significant effect for any single technology. Which industry could see the best or the biggest impact as a percentage of their revenues start coming from generative AI? Can this revolutionary technology empower us to admit persistent economic headwinds to tackle our most pressing challenges? How do we balance creativity and control? I personally feel that the generative AI discussion couldn't be more exciting and disturbing at the same time. And I honestly feel a bit schizophrenic when I get all hyped up reading about protein engineering through language, large language models and how it could one day end the suffering of cancer and Alzheimer patients. But then I find quite unsettling everything. I read about black boxes, hallucinations, emergent properties and of course the unintended consequences of generative AI to become terrified, reading that GPT4 shows parts of human intelligence. With all this, how do we make sure we are enhancing our humanity, not losing it? I'm sure you all want to hear what our panelists have to say about all of this today. I am honored to be joined by Mary Cummings, the director of Mason Autonomy and Robotics Center at George Mason University, Pascal Fung, J-professor in the Department of Electronic and Computer Engineering at Hong Kong University of Science and Technology, Jay Lee Clark, distinguished professor and director of the Industrial AI Center at the University of Maryland and Jeremy Jenkins, the managing director of the World Economic Forum. If you choose to share insights from the session, please use the hashtag AMGFC with you with some strong promises of adding value and growth to the global economy. Are we talking about making our industries, our sectors better or about even creating whole new industries? I think that AI can make some industries better, for sure, I mean, we've already seen it. I do think that we're going to see a lot of new jobs created and I think it's hard for us, you know, you hear the mantra over and over again that jobs are gonna be taken away, people, you're not gonna need journalists anymore and you know, it may be true that generative AI can potentially replace small amounts of jobs but if you really know how this technology works, one of the issues of generative AI is it regresses to the mean, it's always giving you the average response and so if you ever look at any text generated by any of the large language models, what you find is it's actually quite boring, it's very predictable, it's very average, right? So I, as a professor, I don't worry about AI at all, I encourage my students to use it, go for it because generative AI produces like BC level work, it's not innovative, it's not creative and so I think what generative AI is actually going to do is it's going to make us better. Do you think we're gonna have new industries? Oh, absolutely, there will be a whole, I call it the crime scene investigators division. If you're a company and you put chat GPT or any of the other large language models into your company, you're gonna have to hire a whole division of people to correct it. That's interesting. So Jeremy, let's talk about real life examples now of sectors that have actually started benefiting and unlocking value by adopting generative AI. What comes to mind? Sure, let me give you a positive example here again building on Mrs. Example. We often think of AI as displacing labor and that's ultimately a choice, whether we use it for automation or augmentation and so in a lot of the developed countries, it's natural to think of labor shortages, okay, how do we get more efficiency, how do we displace labor? But if we look in emerging markets and we've seen really positive examples, for example in India, here we're actually using AI and agriculture to augment the labor there, right? You're not actually gonna replace the worker on the farm and we recently ran pilots in the state of Telangana, 7,000 chili farmers where we looked at say, okay, how do we use digital technologies including AI to improve the utilization of water, reduce the usage of pesticides, improve time to market and increase the yields on the food production. And what we saw is that we could actually generate around 15% improvement in yield, improvements in profitability at around $700 per hectare. Now if you take into account that roughly 50% of the Indian population is in agriculture today, slightly over and 85% of their smallholder farmers with just only a hectare or two, making a $700 increase in your yield that you get from that hectare with these digital tools is absolutely huge. It will help contribute to what you said, you know, that's kind of 7% growth in the economy. So it's up to us, it's a choice whether we use it for augmentation or automation and I think my biases we'll see a lot of use cases that we use it to augment human labor, human capabilities and that's what will help drive those improvements. Other sectors, do you see growth coming mainly from in the short term? I mean, you mentioned agriculture, what other industries? Yeah, another one I think I'll again pull from the developed markets example is in healthcare. All right, we have these debates around, okay, is the AI radiologist better than the human radiologist? You know, is it 95% accuracy, 97% accuracy? But if you're in a country like Rwanda where you have a dozen radiologies for a population over 10 million people, it's not a choice between the human radiologist or an AI radiologist, it's a choice between none or the AI. And then it's quite clear, I'll take the AI radiologist, whether it's at 95% or 97% and actually bring that in. And so I think that's again another positive example of the dissemination of these technologies and then they benefit by increased connectivity, increased utilization of digital devices. And so I see a really positive effect here. Jay, when you hear about these examples, you get the feeling that AI, generative AI is there and ready, filling all the gaps, being able to do everything. Does that apply to industry? Or are there still gaps that need to be filled? Well, certainly, I mean, AI is not just generative AI, right? So certainly it's a one new trend from the text and images and language model. But the industry, we're talking about large knowledge model, not just large language model, which means we have a historic data, lots of data. We've got machine data, sensor data, humans, you know, judgment data and also model simulation data. Those are the critical. So industry, we're talking about 3W, work reduction, reduce a lot of redundant work. For example, if I repeat work, well, why repeat? One time finished, right? Second, waste reduction. Well, all industry want to continuously remove the waste, imagine better emission, better carbon footprints, eventually you have carbon free, right? That's what it is. Third, worry reduction. We talk about industry, a lot of work, a lot of worries, people worry, you have alarm, you have a downtime, you have an accident. Those worries can be predicted, can be prevented. That's why you have a better, even like manufacturing industry, you wanna attract younger workforce coming in, you better make the systems very, very exciting, not just a dark, boring, dangerous, right? So you wanna make the workplace is more fun, more exciting and more systematic, yeah. But you will essentially need more specific models. Well, a domain-driven model, not just open source data. But industry, domain source, not shareable sometimes. Of course, often, semiconductor, no way, right? Not shareable. So you gotta build your vertical, domain vertical AI. Just improve the localized efficiency, quality, and safety, yeah. Maskal, in the longer view, what do you see AI contributing when it comes to the more complex human challenges and problems? Yeah, so I think it's necessary for me to spend a little bit of time explaining the fundamental difference between the generative AI models we're seeing today and the future. Yes, we will continue to have domain-specific, industry-specific, context-specific applications and AI tools. But the fundamental difference is this. People always think that CHI GPT or GPT-4 can do everything. We actually have no understanding of what's the limit of what they can do and also the limit of their capabilities, the mistakes they can make. However, they are what we call general learners. What's important to understand about these models that they can learn everything under the sun from the text data, from audio, from image. So there are multi-model, large models coming up that they will learn everything and then they will be embodied also in robots so they can learn from the physical world. So these are general learning models. They can learn everything from us. So once they've learned all this, the future of AI, sorry, I'm being a bit like a lecture, but the future of AI is that we're gonna make downstream AI models, classifiers and generators or creative tools. They can be very creative downstream from these foundational models, all right? And then we need to have safety measures and so on into this, so to solve complex problems. So they have the amazing ability. A scientist, before you have a scientific theory, you need to have hypothesis. You need to explore the different possibilities. For example, for drug discovery, for cancer treatment, you need to have the hypothesis space, exploration of such space. And these models can do a much more scalable job than a single scientist or a number of scientists. So that's how they're gonna help us solve complex problems. They can help us with the exploration. They can help us with the learning from all the data and from multi-model data in the world. Missy, do leaders in the business community understand the technology as is being put by Pascal? Do they understand what it is about, what AI is, and how is this affecting adoption? So I think the biggest threat to national security in the United States is the technical illiteracy that is happening about AI, which is much bigger than generative AI. But people think that they hear AI and they immediately think, chat GPT. I wanna be really clear, and I'm gonna be like Pascal and take on the professorial tone, but a little contrary to Pascal. Generative AI knows nothing. It does not know right from wrong. It does not know truth from a lie. It only knows what's in the data set. And if the data set is corrupted, then it knows corrupted things. The vertical AI is, I think, perhaps the best implementation because you narrowly constrain it. And I worry and I lecture around the world that companies who buy into the hype of generative AI are putting themselves at real risk. And if you have a software company that doesn't touch a safety critical system, then fine. But what I see is generative AI and versions of generative AI showing up in safety critical systems, and this is a real risk.  Self-driving cars are making mistakes that end up in injuries. Pseudo self-driving cars like Teslas that with a lot of AI in them are killing people. So we need to make sure, and I think it starts with the C-suite. The C-suite needs to stop with what I call the world's word salad. I do not put blockchain, digital twin, and AI in the same sentence, because it tells me that you don't know what you're talking about. So what I would implore people is to make sure you surround yourselves with people who really know what AI is so that you can leverage the real benefits without putting your company at risk. Let me take this to Jeremy and about forward-thinking leaders. Do you need strong leadership in order to transition into the AI world in order to transform your business? What do strong leaders need to rethink? Yeah, I think we definitely need leaders to actually understand the fundamentals. And I agree with Missy here that there's a lot of misunderstandings what happens. So people look at this, they say, okay, let's just roll out chat GPT. And if you come back to the example and I think I'm biased along with Jay Lee here, I think we'll end up with a number of vertically specialized models. And if we come back to the health example from earlier, I don't necessarily want to get my health AI data from TikTok and Reddit and X, versus if I build a maybe a small language model, which can still be quite large with all the data we have from sensors, from doctor's reports, et cetera, we can still have massive amount of data, use generative AI techniques to apply it within a specific domain and then get improved healthcare, eventually personalized medicine. But it'll need to be done with intention. It'll need to be done with understanding of the trade-offs. And here I think this is where the role of dialogue is important. And sometimes you can actually, you know, you move slowly to move smoothly and you move smoothly to move fast. And if you just rush into, okay, let's roll out these models because everybody else is, you actually do introduce these risks in. So I think it does require deliberate leadership, conscious understanding of both the opportunities of the goals that we want to pursue, but as well some of the risks that come with it. Askar, when we talk about enhancing productivity, are we also talking about changing workflows within an organization, changing values within an organization? So coming back to this misunderstanding of generative models again. So it's very important to understand that these models are general learners. So we use them to learn about general human capabilities that, so for example, the ability to do logical thinking, reasoning, the ability to plan, the ability of multiple languages. These are the abilities that we learn common sense and so on. There are benchmarking papers that we publish to measure these abilities of these models. So what you want to use them in industry is that you have to first of all understand whether in your application you need any such human abilities. You don't need a GPT if you are doing safety some kind of a classification of, I don't know, downtime or something. You don't need that human like common sense, right? When you don't need that, don't use it because then it also has a possibility of hallucinating things that you don't want. But if you really need human capabilities, you start with these models and then you fine tune with your applications with human in the loop. So even with these models, there's human, you know, RRIF is human in the loop. It's by no means perfect. So the models are not perfect. So with that being mindful of its pitfalls, when you're using your company's workflow, you need to understand where I need such abilities, where I don't need it. Maybe customer service, I need a chat bot, then I will use these models and I need to fine tune it. So make sure it's safe, make sure this is exactly what I want and that this will follow the company scenarios. If you don't need, it's not, if you don't need that, you are doing some kind of analysis, you might not need foundational models. So it is important to have this, what you guys call the air literacy, to know what to use or what not to use. Jay, would you say that it would really depend on how much the technology is purpose driven and that will determine the sort of return on investment you make in adopting AI? Yeah, and we in general in industry or in many sectors, right, we have, I call this three P issue, problems, processes and purpose, right? Engineer love problems, right? Yeah. But management love processes, SOP, SOP, but customer care about purpose, right? Value, evidence. So from that perspective, when you make a product, you wanna provide the evidence to customers, oh, I do save you energy. See, this is the evidence. It's not about the assumption about the improvements. Evidence, so people buy evidence by purpose, right? So eventually you want to embed those good method, good practices, could be machine learning, could be a good model, but if the system very complicated, well, we can use a surrogate model like the response service approximation to measure the large inputs versus a describable output. The many method can be used, right? So I call the metrology system. How do you measure the evidence so customer willing to pay? That's very important. That's the baseline. So another thing we have to understand the industry, in many way, we have to understand the baseline and bottom line. Baseline is what are you comparing with? It's not open space intelligence. The baseline is here. Then your bottom line, if I invest X dollar, a number of people, how much I got out of it? Can you sustain that bottom line? Only one time. So AI is not always intelligent, has to be actual implementation. So you spoke about the knowledge-based systems for industry, and then there are the general models that make a lot of mistakes, or have you seen it? Do you see them talking to one another one day in a factory? I would say, generally AI is a new way of using a large open resources. Like Missy was saying, the data quality is in question, and you cannot use those to do the actual things, right? But industry, but those data are controlled by the company. Energy data, semiconductor data, even the mini machinery data. So we will cherry and pick and label the data in such a way. The data are useful, and usable. The large industry data is useful, sometimes not usable, because you do not have a label with a good background and no baseline attached to it. So I would say, generally AI can also add the user element in there. For example, I have workers, I want to do fast training. Ah, instead of, you teach you one to one, right? I can, oh, you can self-learn systems in general space. And of course domain-specific space takes time. But general space, I can skip very quickly. So for that customer service, like you described, which is a good tool, right? And right now, if you call many, many airlines, sometimes if like I cancel, very hard to reach people, right? So somehow that should be improved the customer service aspect. Missy, do you feel sometimes that we're putting too much hope on a technology that is still being developed and does that sort of put pressure for releasing new technology that perhaps did not really mature enough? Yeah, I heard you say at the beginning that you were both excited and terrified with Chinertive AI. And I thought to myself, oh, I'm just exhausted with it. It's the latest and greatest new tool. So this is not my first time with World Economic Forum. Pascal and I were many years ago on the Robotics and AI Council. And this was like 2017. And this is, we were on the Robotics and AI talking about, we were talking about the wave of AI that was gonna come. And one of the messages that I delivered personally to Klaus was there's too much hype. It's going out of control. And here we are seven years later. And I'm actually thinking about moving to a desert island because I can't get away from it, right? It's a tool in a toolbox. It is not the only tool as the other speakers have said. You need to know when is the right time to apply it and when it's not the right time to apply it.  And this is particularly bad in the United States.  The United States is looking into whether or not we can replace research scientists with generative AI. It's insane. It's not ever going to happen, at least not with the tools that we have right now. But they're thinking about committing hundreds of millions of dollars into trying to make this happen. This is a mistake. We need to augment humans with generative AI. If we go down this path of trying to make research, independent research scientists with artificial intelligence in the form of generative AI, I can assure you that would be the end of the United States being world leaders in this space. That's a very strong message. So Pascal, and I mean, yeah, I started talking about AI when I felt that I had an assistant I could ask questions to. And then realized that it was sometimes very confidently giving me totally wrong answers. Now, I understand you call that hallucinations. Are these solvable problems? What is the story of not being able to reverse engineer an output that is coming from a system of generative AI? What do all these problems tell us Pascal and are they being solved? Yeah, so first of all, I do remember our discussions in the sea seven years ago, but I also want to say that me, along with many scientists in AI, AI scientists, I had this recent exchange with Yosha Benkyu. We today, we say that we never imagined this day will come in our lifetime. This time is different. I'm sorry to be the bearer of good news and bad news. This time it is different for us who have been AI in this line of research for 30 years and more. These models are reaching a point that we are very concerned about its safety having a real, you know, real impact in the world, including hallucination. So coming back to hallucination, so the term came from the computer vision area where people saw the generative image models. Let's not forget, there's not just the charge of BT, the tax models, there's also the image models. Actually as a visiting professor at the Central Academy of Fine Art in Beijing, I teach artists and design students to use these models. They actually are very good at being creative. That's why they're called generative AI models. They create things that have not been seen in a database. So initially, you know, if you use these image models for mid-journeys, label diffusion, so on, it will create images sometimes at the beginning. In the original, many years ago, seems like a lifetime ago when you have image models that would generate cats and dogs in the sky. It looks very much like human visual hallucination. So that's where the term came from, computer image hallucination, because they look really like human hallucination. So today we use that term to describe in general the same phenomenon of creativity of these models, but when it creates something we don't want, we call that hallucination. So for example, nonfactual answers, undesirable toxic content, discriminatory content, we classify all these things undesirable creativity under the term of hallucination. And there are active, it is active research area in the field, a lot of researchers are working on identifying hallucination and mitigating hallucination. The difficulty of identifying hallucination is that it's exactly same as creativity. So it has the same confidence, because not just it sounds confident, but that internally the model itself does not have an uncertainty measure against things that are not true, because that's how the model came about. It was supposed to generate things that was not seen in a database. So it doesn't know. So what we are doing today is to actually link these models, ground, we'll call them grounding, ground these models in large knowledge base. So don't give me the answer like this, you know, you check first, okay? We're incorporating knowledge base, knowledge sources, structure and then structure knowledge sources into the training process, into the decoding process, and also by mitigating the fine-tuning database. So the general database has already been trained. Now when we fine-tune, meaning we retrain it with curated database, that's factual. And there are really new approaches. For example, one that's been proposed by Yellowcomb Meta is an approach to incorporate this kind of self-reflective reasoning to say, hey, am I telling the truth here? In the inference process. So when it's generating answers, it's supposed to check itself. So there are new research coming out and will come in the coming years for us to solve this problem. I think we can solve this problem in different ways. Whether we're combining these models with another model, or we are making this model self-reflective, those are different approaches. I believe we can solve this problem. But it's a bit worrying that we're solving problems of systems that are already there and working, they're not in labs. Yes, so we also think that people need to have, we need to have a general framework of governing the regulating the outcome. So the outcome, how you use the system in the financial domain, in the medical domain, in health domain, should be subjected, in the legal domain, should be subjected to the existing regulations in these industries already. So you should not, as a lawyer, be using charge of BT to come up with previous cases. That's violating actually the legal profession's own regulation, right? So humans should not be using these models thinking to treat them as another human being. They're machines. You use them as tools. So just like any other tool, you cannot just take its output for granted. You curate it, you look at it, you verify it. So that's the usage that we need to regulate. Jeremy, speaking of toxicity, which Pascal mentioned here, the social media story and all the unintended consequences of what seemed like an innocent, like or targeted feed. Have we lost control there, first of all? And what have we learned and how did we change things accordingly? Yeah, so I don't believe that we've collectively lost control. I think it's important that we don't overly anthropomorphize AI. We retain agency. I think what we've done is we've delegated to algorithms things that probably shouldn't have been delegated, right? And so I think this is really important to just remember that we do have agency. These are decisions that we take around when we use the tools, when we don't. And this is one of the reasons the World Economic Forum's established in AI Governance Alliance, which Missy's also joined me on. We have a number of the large technology companies at the forefront of using these models. We have some of the new companies that are challenging the incumbents. We have regulators coming in. And I think to borrow from the alliterative frameworks of Professor Jay Lee, we think about the three Gs. One, the guardrails that Pascal's discussing there. It is important to discuss, when should we not be using these technologies? We need to understand them better. Emission, critical functions, things that are around life and death. What is the governance that we need around these models? How does that play out in different contexts? Again, the model I want in a manufacturing floor or in a delivery system will be different than in healthcare or a nuclear power plant. And then what are the guidelines that we can propose for companies and institutions on how to think about these things? So I think this kind of guardrails, governance guidelines, it's an important way to consider that. And then if you have this framework, then depending on the use case, depending on the purpose and the intent, you can look at. But I think within that, again, it's important to retain agency, to be conscious of that, and not just assume that there's some autonomous system out there that's doing this, right? We're still responsible for what we put in place. Jay, speaking of life and death, is it a kind of an evolution?  You mean human or? Hopefully no. AI. Well, I would say if you look at the certain thing, right, in many industrial space, we human beings have a great knowledge, but also we have many areas we're not incapable to do. The time window, when you're in the very fast things, we're not good at it. We have emotions involved, right? So certain things, for example, in certain things you wanna make decisions for certain, we are limited by what we see, what we know, and what is the emotional influence. But the augmentation from the AI system can help you look at different type of priority, rationale, and the risks, I think that is a good tool. I mean, let's use an example of an ICU in hospital. We work in ICU AI, right? Today, if people in ICU, people die every day there, unfortunately, right? They have a great equipment, all connected, right? You have a ventilator or the pulse and everything. No intelligence. Everyone's waiting for alarm happen. Alarm happen, nurse going into it, right? That's what happened. So eventually, like a brain, you have traumatic injury, TBI we call. There are a lot of risks we cannot see. Nurse only waiting for alarm happen. So you can really see what is transient risk is coming. Help to prioritize the time. If you have a 26 patient, you'll equally spend 26. No, pay attention to those high risk patients first, right? Let's see, is this... Let's save lives. Is this when we talk about feeding AI systems, mental human models and doing as much of that as possible? Well, it kind of goes back to what Pascal said. The real success of these systems are not going to be just a generative AI model. It will be a generative AI model plus model-based AI, plus combining with go-fi, good old-fashioned AI, right? So I think that we need to move away from... I feel like people desperately want some magic in their lives and somehow generative AI has... There's got some magic to it and I think we love to have magic, right? But the sad part about this is generative AI is kind of like that really bad old-fashioned Las Vegas magician. They've got a couple of good tricks but it's really not that hard to see how they're doing the trick, right? And so I would like us to kind of back away from the religious fervor of AI and the magic of AI to really start looking at the hard issues which are the real engineering of AI, how to make them safe, the hallucination problem, if you think it's bad for tech space AI, all self-driving cars, Teslas, plus all real self-driving cars have hallucinations in for real. They see things that are not there and they slam on their brakes and this has led to countless accidents and some deaths, right? So I hope that we really concentrate on getting our hands dirty in the AI to try to make it work and to make it work. We're gonna have to build an infrastructure around it. So in that sense, to what extent do you think regulations should be focused on the actual engineering rather than the national considerations or national priorities? I have to tell you, the United States is so bipolar right now. I recently finished a year with the Biden administration helping them do regulation for self-driving cars and it's just the technology we refuse to regulate. So despite the fact that there's been a lot of deaths and lots of problems, we're not gonna regulate self-driving cars. Okay, then like half a mile over in DC is everyone trying to ban large language models in chat GPT and as a scientist, it drives me insane because the technology they wanna ban for large language models is the same as what's in the cars and I've said that more than once the administration.  It's the same and this is one of the reasons I run around screaming that everyone's an idiot, you need to go back to school and learn about what these technologies are because you cannot just start making sweeping grand regulation. It's gonna have to be in the verticals. You have to regulate self-driving cars. You have to separately from medical devices, separately from financial industries because the application is you cannot get away from the fact that it's data specific and so instead of trying to have grand regulation and sweeping policies, we need to look at this each in an individual case. Pascal, how does it affect the rolling out of technologies when we speak about different geographies with different regulations, say in the East and Europe and the US and then you talk about one company that is operating in all of these spaces. So today the reality is that every jurisdiction has different regulatory framework on AI and technology, the use and privacy and so on. Some of it, you know, EU can be more stringent on some aspects and China is more stringent on other aspects and so on. So for, I think all the global companies operating in these different jurisdictions, they have to complying with all of them, right? So that's a challenge for global companies. However, I think there is something that's really important to all the other countries and all people and I think it is time we start talking about international treaty or AI safety. If we have learned any lessons or I was, as I mentioned, I met with the grandson of Robert Oppenheimer last week in Geneva, there's this Oppenheimer project and they asked this question, what lessons have we learned from the Manhattan project? So what we have today is that every major country has a Manhattan project on AI and nobody knows what the other is building and everybody's scared about the red lines. So why don't we get together between nations to talk about the red lines that we all do not want to cross? For example, you mentioned earlier that putting AI in operation of nuclear weapons as an example. For example, the rights of humans should proceed that of any machine, no matter how human-like they become in the future. So, and other things, right? So I think this is time, not only we have all these multi-stakeholder, multi-national discussions on AI regulations, but also really precisely on the red lines we should not cross now and in the future because again, it is not hype. This is a new era of AI from these models because they are universal learners. We did not build them to learn A, B, and C. They learn everything on their own with a very simple objective function of autoregressive self-learning. What are the red lines for you, Pascal? The red lines for me, so one of it, which is that the human rights should always superset off machine. No matter what happens, any conflict will come between an anonymous human being to a super intelligent machine that's super human-like that human should always have more rights. And also, I'm concerned that we should not build AI or use AI in any fashion that will violate the universal declaration of human rights, which all these countries have already signed on to. So, non-discrimination, equity, inclusivity, human dignity, respecting human dignity, and so on. Jeremy, the European AI Act that is going to be enforced under this year, how significant is this on this path? It has to be careful commenting on these elements. If you look at the regulation that's actually developed before the current trend there, so I think it'll still require some adaptation to the current environment and the new technologies. I think if we go back this 2017 kind of seminal paper, attention is all you need from some of the Google researchers that actually drove this kind of transformer framework, still relatively new. What I do see is us needing to move towards actually policies and regulations that understand the risks rather than trying to regulate the technology or these cases, actually that's trying to regulate for the risks. And within this, we're sometimes feel like we're torn between this surveillance capitalism or surveillance corporatism, however it might be, and the state. Actually, if you start looking at policies that put citizens first, and you talk about the human aspect there, and I do see positive examples of that. If I think if we look at some of the work that's been done around digital public goods in India, for example, they have a baseline of digital identity with the Aadhaar program there. They have their UPI program in financial services. They're now developing regulations for technology stacks in healthcare and agriculture. Designed, again, with intent, you can have public goods that emerge from these capabilities. You can create entrepreneurial ecosystems that different players can plug in, and you can actually expand the market that also the incumbent and traditional players can benefit from services. But these require dialogue. They require interaction. And I think the most important thing we can do is continue the discussions to share an exchange and avoid that we think there's any single solution in place or that there's a software solution, all these, but rather discuss it to develop the policies and regulations there. Whenever we have a session about AI, generative AI, any type of AI, and we have one more question left, it has to be about general intelligence, general artificial intelligence. So, Jay, is it wrong to think that the development in generative AI has pushed up considerably the timeline to artificial general intelligence? Well, no, yes or no, because in some unknown space that can help us improve the knowledge acquisition, improve the extent to extend what we don't know, right? The learning system, which is good. But for a opportunity space, for example, we have many, many things in industry. We need more efficiency, more a carbon-free environment. We need to apply good method. Doesn't matter as AI or whatever, but if I do use AI, well, I want to make sure we have more precision things. But in generative AI, I give you many possibilities. But in industry, we're looking for precision, the different culture, right? So we're not talking about possibility. Precision, precision, precision. Precision semiconductor, seven nano, five nano, three nano, two nano, right? For aerospace, you have a much fewer efficiency and better, you know, the safety, all the precision precision. So I think the future, we need to look at the, how we make the data quality better. And trustworthiness is very important. Certain area you're talking about beside the red line area, but you need also understand gray line area, right? The news, fake news.  Fake plus. But in a critical sensitive timeframe, in some election, all those big, bad things to happen. So that's why I'm saying how you have a good method to have a trustworthiness of the things, right? Pascal from the very specific to the very general, with general artificial intelligence via challenge to our humanity, to our own existence. It's challenging our existence right now, isn't it? I talk to artists, I talk to, actually I'm going to talk to artists next week in Milan again. I talk to scientists even, and I talk to policymakers, writers, so on. And every time I talk to people, they feel that these tools are doing something that they are used to doing. So it's already a challenge, but it's a challenge back. I think the challenge we are facing today is pushing us to ask the question, the question of us, who are we? What are humans? What's our humanity? And I have talked to people, and this is how I explain where we're going to go with AI. AI will become more and more powerful, and it's going to be multimodal, it's going to be embodied, it's going to be in robots. You will see robots in a few years that will go around learning on its own, physical tasks, including intellectual tasks. So these machines will be able to do not just the stuff we are doing physically, that we are used to, right? They can fly higher than us, they can run faster, but they are going to be thinking better than humans. The thinking, so the term thinking has been a threat to a lot of people. What are human beings but thinking machines? Are we just thinking machines though? Humans are not just thinking machines. We're not just the ones who produce PowerPoints or write analyst reports or even somebody who produces algorithms because today we're using Generative AI to produce methods to check the safety of Generative AI. So all these thinking process can be also assisted and augmented or even replaced by AI. I cannot think in all domains. I know very narrow scope of knowledge. So there are things I don't know. I can ask my AI to help me think, right? So we feel threatened because we always thought that's what makes us humans unique, our language abilities, our ability to speak, our ability to emote, our ability to think. These seem to be all doable by machines. So what's our humanity? That's a challenge to us. I think we need to focus on our humanity. So the way we respect each other, the way we treat each other with understanding, empathy between different cultures to learn about each other, right? To understand the kind of questions we need to ask. What are the big questions we need to ask today? What are the big problems we need to solve? AI helps us solve problems. We ask the questions. We ask the important questions. Yesterday I learned this term Ubuntu from a participant here from Africa. I love it. I am who you are. I am who everybody is. That's human. That's not machines. Machines are not who we are. We are who we are. Thank you, Pascal. Jamie, are you worried we have very little time? Very little time. I'm actually quite a bit more optimistic. I'm much more concerned around the immediate existential risks of geopolitics, of climate change, of misinformation, election. Then I am around necessarily killer robots, so I realize there's a few of them in the cars going around, but the way it's sometimes portrayed. But I still see more opportunities than not. I think I see far more green areas. And the generative AI can actually help raise the base level of capabilities for a number of individuals, actually empower them, and then augment the capabilities of the strongest or the most thoughtful people to be more creative and more applied. So overall, I'm optimistic on this, but we still need some relation and governance along the way. Missy, final thoughts on the general architecture? I would, Jeremy. I'm just going to go out there and say it. I do not think we are any closer to AGI today than we were six months ago, than we were a year ago. I think that we are, many people are going down the wrong path. It's not happening. There's no intelligence in these systems. There are no sparks of intelligence in these systems. These are man-made, and they're gonna, and now I'm not gonna rule out that something big could happen in the next, but I do not think that, I'm not worried at all. I sleep very well at night because I know just how brittle these systems are, and they are not performing like you really think they're performing. But I will tell you, I really do worry about the lack of deconstructionist thinking by people and their desire to believe it's true, and because of the desire, people are going to go down a path to try to make this happen, and in the end, we're gonna be left with some serious consequences. Some very powerful statements. I told you this is going to be both exciting and terrifying. Thank you very much to our audience, to my guests, Mary Cummings, Pascal Fung, Jay Lee, and Jeremy Jiggins. Thank you very much. Thank you.


First off, my mother had a propensity to emotionally abandon myself and my siblings and my father during the early stages of my upbringing. And what happened is she would emotionally abandon us and I would feel like I did something wrong. So to get my mother's attention, I'd be like, mom, look at me, look at me, look at me. And I'm saying this, you know, kind of not literally speaking. And in that, I became an anxious attachment style. What I mean to say is I was, you know, there was an abandonment of love and then I kept chasing it. So that became where I needed it to feel safe in that moment and it didn't occur.


We have spent the past decade talking about Justin Verlander, Max Scherzer, Clayton Kershaw, Jacob Dubrov, all these like big arms in MLB DFS. And tonight we get to feature a lot of the new faces. A lot of guys who haven't been like established as being high strikeout, high upside guys or DFS, but who I think we can pretty definitively say are in that tier right now. We're gonna break down which of those guys I like most for tonight, which ones we should buy into, which ones might wanna be a bit more skeptical of, let you know where we should turn for tonight in Daily Fantasy Baseball. Welcome on into the solo shot. That's right here on the FanDuel Podcast, Network and Numberfire.com. My name is Jim Sonis. I am a senior writer and analyst for Numberfire. You're gonna break down tonight's eight game main slate with lockstep for 7.05 PM Eastern for tonight. The one weather spot is in Chicago and could be multiple aspects of this that are noteworthy. That's the cubs and the cardinals. There is rain in the forecast before first pitch. Looks like it may be out of there before first pitch. So might not impact the start of the game, but if they play, winds are in from center at 13 miles per hour and temperatures are just 53 degrees. So it is a downgrade to batters as a result of the temperature downgrade to batters because of the wind being in a wriggly field. All that stuff does negatively impact batters. So I would downgrade batters and upgrade pitchers in a big way, should they play. So make sure you check back on the timeline of that weather to ensure they will in fact play for tonight. We'll dig into the implications of that, whether that pushes any of those guys into the top of the pitching pool and much more in just one second. But first a reminder to make sure you're subscribed to the solo shot in the Numberfire Daily Fantasy podcast feed. Wherever you get your podcast, just search for the Numberfire Daily Fantasy podcast feed on Spotify, Apple podcasts, wherever it may be, search it there, find it, hit subscribe if you like what you hear. Leave us a five star rating as well. And as always these podcasts do go up in video form over on the Fandall YouTube page as well as subscribe there and give us a thumbs up if you like what you hear. The NBA playoffs are in full swing and you can get in on the action right from first tip with Fandall. Right now all customers can get a no sweat same game parlay every weekend when you bet the NBA playoffs, that's right. Just plays a three plus leg same game parlay or same game parlay plus on any NBA playoff game. You'll get bonus bets back if you don't win. There's no better place to bet the all the playoff action than America's number one sports book. Head to the Fandall app and get a no sweat same game parlay every weekend at the NBA playoffs. Fandall official sports betting partner on the NBA must be 21 plus and president select states. Fandall's offering online sports wagering in Kansas under an agreement with Kansas Star Casino LLC. Bonus issued is non with trouble bonus bets that expire seven days after a seat. Restrictions apply state terms at sportsbook.fandall.com. Gambling problem call 1-800-Gambler or visit fandall.com slash RG and Massachusetts Hope is here. Gambling help line MA.org or call<PHONE_NUMBER> for 24 seven support. And at New York, 1-877-8 Hope and Why or Text Hope and Why. In Arizona 1-800-NEXT-SEP or Text NEXT-SEP to 533-42. In Connecticut 1-888-789-7777 or visit ccpg.org slash chat. In Indiana 1-800-9 with it in Wyoming and Kansas 1-800-522-4700 or in Kansas kscamblinghealth.com Leeds, Vienna is 1-877-770 stop in Maryland and the gamblinghealth.org and in West Virginia, go to 1-800-gambler.net. Pitching preview for this Monday mainstay Zach Gallin headlines things. He comes in here at $11,300 followed by Anthony DiSclifani at 10-2. Freddie Peralta is $10,000. Marcus Drowman 98, Hunter Brown 96 with Logan Gilbert coming in at $9,500. Nestor Cortes is 93 against the A's. Dylan Cease at 91 with Tony Gonslund and Patrick Sand of all the others at $8,000 or higher. Last week, I was on Zach Gallin and that did not work out because he had his first dud this year. But he's back at home tonight and he's in an elite matchup. And I'm going right back to Zach Gallin with no reservations for this slate. In that start last week, Gallin had a three-year in runs across five winnings and had just six strikeouts. The velocity in that game was five though, which I think allows us to look at a larger sample and the larger sample on Gallin this year is sick. He has a 2.47 skill interactive ERA. His strikeout rate is 35% with a 3% walk rate. The skill interactive ERA and strikeout rate bolt tops in the slate by a wide margin. He's facing the Marlins at home for tonight. The Marlins against Ritey so far this year have an 87 WRC plus with a 24% strikeout rate and no power at all. So it's a great spot. It's also a revenge game for the former Marlin and Zach Gallin. They traded him away, you know, they got the jazz chism, but hey, maybe a little bit of juice is flowing there for Gallin as a result. I haven't projected for 8.5 strikeouts for tonight, which is the top number on the slate by a wide margin. Obviously the year of engineering plays a massive factor into that, but either way, Zach Gallin, I'm more than happy to rank this guy first. Salaries high at 11-3, but with a lot of the stacks I want for today, I can make that work. So Zach Gallin to me, the clear cut number one pitcher on tonight's slate. I've also gotten bit a couple of times by using Logan Gilbert this year. And his ERA does reflect that, it's at 4.01. But he's facing the Rangers tonight, and I like him a lot here at $9,500. The ERA for Gilbert, as mentioned, is not great, but that's kind of the one bad thing in this profile. Everything else is really good. He's throwing a splitter this year and throwing more curve balls as well, which is leading to a lot of strikeouts. Gilbert has a 30% strikeout rate across six darts, with a 5% walk rate leading to a steel interactive ERA of 3.18. That likely means that the results for Gilbert should be better than what they've been so far. It's not the easiest matchup tonight. The Rangers have a 115 WRC plus against Reides. They got plenty of power too. And they're definitely a team that I don't actively target by any means. They do strikeout a bit though, and Gilbert is at home. I haven't projected for 6.9 strikeouts tonight, which ranks behind just Gallin and Freddie Peralta on this slate. But Peralta's facing the Dodgers, that's not great. Marcus Strowman is in play due to the weather. We'll talk about him and things to watch. But the strikeouts here, good for Gilbert, the matchup not as bad. So I will go Logan Gilbert number two, with a hair differentiating him between Marcus Strowman and above Freddie Peralta as well. Now, when I'm looking at the pitchers below $9,000 searching for our value play, I find nothing. There are no pitchers below 9,000 I wanna use tonight. So I'm not gonna talk about them. And instead, I'm talking about Dylan Cease. He's right at 91, so almost a value play. We'll talk about him instead as the quote, unquote, value for tonight. He's facing the Royals, which helps a bunch here. They have a 77 WRC plus against Reides. That was even lower before they got to face the A's bullpen for a full series. Like it was like 64 at one point. So the A's bullpen did boost them here. 25% strikeout rate for them, 7% walk rate. They're a great matchup for a Reide. And honestly, Cease could use that boost right now. His velocity was back up a bit in his last time out, which was a good thing. But even with the velocity being closer to what it was last year, he still walked four guys and let up four earned runs in five innings. For the full season now, Cease has a 12% walk rate. He's letting up a ton of hard contact. So he needs to face a less powerful, less patient team. Luckily, he gets that tonight here against the Royals. He still has a 26% strikeout rates. That's kind of the one thing Cease has still been doing pretty well. And that's why I haven't projected for 6.5 strikeouts tonight. That's not bad for $9,100. Even if it does come with some downside with the walks in the hard contact, it's even better if the velocity is up again for Dylan Cease here. I prefer Gilbert and Gallon above him despite the salary. So I'm gonna go with those guys, one, two, Gallon one, Gilbert two. I think that Cease is in the mix with the guys like Peralta and like Strowman. So everyone at 9,000 range pretty similar for you tonight, but I'll go Gilbert two above them. Stacking for tonight is a bit difficult because it's gonna force us to take some leaps of faith and be okay with sub-optimal situations. One of those sub-optimal situations is the Yankees lineup. It's bad right now. It's real bad. So in order to stack them, I need a pretty elite situation. But I think we get that for tonight and I'm willing to stack them here. And that's because they're facing the A's. We talked a lot about how bad their bullpen is. That is a major factor here, honestly. And honestly, it might be the primary factor. The opening matchup isn't bad either though. They're facing JP Sears who's a lefty and that means a lot of the primary guys who are still healthy here do get the platoon advantage. Sears was not terrible. His ERA this year is 5.06, but it comes with a 4.08 skill interactive ERA. That's not bad, but it's letting up a lot of fly balls and that can work in Oakland, but it's less likely to work in New York at that park. So I think the Yankees have enough guys who can hit lefties. It's not fun by any means, but against this bullpen, I think we have to do it on this slate. So I will go the Yankees to the top stack despite the fact makes me a little bit queasy to do that given the state of their offense right now. The Yankees did just get Harrison Baderback. He already has two of them run since the return. He was not jaw-droppingly good in his rehab stint, but in his career, 228 iso against lefties, I don't think they can afford to not have him in there. So I'd expect Bader to play. He's $2,700. You know, he's looked good so far. So I'll go for him. I think that he's probably one of the focal points in this Yankee stack. We'll go with it. It's not a fun offense, but I think they make sense for tonight. Second stack I feel better about, but it's not in a great park. That is in San Francisco, stacking the Giants. They're facing the Nationals and Jake Irvin. Temperature in San Francisco, 57 for tonight, which is pretty low even for this slate. It's the second lowest behind just that Cubs game. So weather is not good here. I still think it might be the number two stack though. Jake Irvin made his debut for the Nats last week, and he got through that okay. Let up one or run across four to third innings. The rest of the data was not as fun though. In that game, Irvin had four walks compared to three strikeouts. His swinging strike rate was 4.9%. And based on previous data, I think that was kind of to be expected. In triple A this year, Irvin had a 20% strikeout rates with an 11% walk rates. Last year in double A he was five, but that's double A. And he also let up a 42% fly ball right there. He's facing the Giants tonight as mentioned, and they've had a great start to this year against righties with a 124 WRC plus. Their ISO is 225. So there's a lot of power in this lineup right now. It's not the best park factor that stakes, but that 225 ISO, a lot of that has come in San Francisco. So I still think we should be hiding them despite that even though, again, like the Yankees doesn't feel great to stack at San Francisco, but like I still think it makes sense for tonight. And we talked about JD Davis. I think it was last week within this Giants stack. And typically he's the guy we use against just lefties. And then thinking healthier, maybe he's not playing every day against righties, but against righties this year, 338 ISO for JD Davis. That's awesome. He should play against righties, frankly. He's not gonna steal bases. We do want that this year, but he's viable against righties and lefties right now. I think the righties part very noteworthy, especially with Davis's salary still at $3,000. So just dongs Davis. Finally, the nickname has become truth. We were just six years too early on calling him that when he was with the Mets. So we'll take it now. JD Davis could play for tonight in DFS. If you want a more contrarian stack, I think the Mariners will wind up qualifying there. They're facing John Gray. Gray's ERA this year is 4.40, which is not terrible, but his peripherals are rough. It may not work, but I think it does give us a window to stack and see what happens. I thought Gray pretty well last year his first year outside of course field, finished the full year of 3.96 ERA and a 26% strikeout rate. But this year, the velocity is down a bit. And that's especially true on a slider. I'm not sure if he's doing that intentionally like he couldn't be just throwing it differently, but he's also throwing that pitch less often instead leading more in his changeup. Last year, the slider was easily Gray's best pitch. It is still his best pitch this year, but you know, the velocity is down regardless it's been good. So throwing it less is concerning for multiple reasons.  He could reverse course and throw more, but the usage on that pitch has decreased in three straight games and it was its lowest of the year by five percentage points last time out. It's possible Gray just doesn't have a feel for that pitch right now for some reason. In that game with the fewest sliders he has thrown this year, he let up two home runs, four earned runs allowed. He may get better, but as of now, I think it's a worthwhile stack. So I think the Mariners make a lot of sense. I think Gray, again, he could turn things around that's always the risk here, but for right now I'm willing to stack against him. I do want to favor the lefties here. It's not a ton of guys in this Mariners lineup, but I think it makes it easy to rank Jared Kelmick and Cal Raleigh at the top of our stacking list. And honestly, neither guy's super high salary. Kelmick is 34, Raleigh 29. Both those salaries are very reasonable and both those guys have shown a lot of upsides so far this year. So start with the lefties and Kelmick and Raleigh, then filter in the righties and go with that. But to me, they're the building blocks here due to the issues Gray has had against lefties both last year and this year. Things to watch for tonight. Do want to touch a bit on Marcus Strowman. The weather does boost his appeal there. He is just not as big of a strikeout guy as Gilbert and Gallinar. I had Strowman projected for 5.6 strikeouts tonight, which is pretty good if we can assume he lets up minimal or runs. I just like their upside more. I don't blame you if you want to do it, but the strikeouts are my rationale. The reduced number of strikeouts for Strowman relative to them is while go with those guys above him despite the good weather at Wrigley Field Force Night. The A's are facing Nester Cortez who's actually been weird this year where he's been letting up a ton of fly balls. He cut back on his cutter used in the second start and in the five start sense, his fly ball rate is 70%. The A is bad team, but they've actually been pretty good against lefties in a very small sample this year. So I don't mind it. I don't think it's the worst idea to consider stacking here. Honestly, for like a single entry term, I might just do it. The A's are plus 170 to win or 172 to win right now. So like, I don't think people will be in them for stacking, but I think it's worth a shot. It's not safe by any means, but I think it's worth a shot to stack the A's and tournaments for tonight. Finally, the White Sox roster is super depleted, which means I can't stack them, but you could consider one-offs here if you like anybody against Zach Renke. Renke, lots of fly balls, lots of hard contact. So again, not a ton of guys here, you really want to use in DFS, but I think that the White Sox really is viable if you find somebody you like within that lineup. Let's finish up here with the Dinger calls for today. Both these will be at Yankee Stadium and the Boring homerun call, which is wild to say, given where he was at the beginning of the year, where he was last year, which is like 16 different teams, but the Boring homerun call is Brett Rooker. Brett Rooker has been awesome so far this year. Brett Rooker, when he was coming out of college, I know that was like one of the first draft classes where they had like stack cast data from some of the stadiums. The stack cast data I remember was like phenomenal in college, which is why the twins took him pretty early. So it's not a huge shock to see him doing this, but 10 homeruns so far this year, his salary is 42, but like, you know, I get it. So Boring homerun call is Brett Rooker for tonight. The fun one is in that same game, Harrison Bader, if he does play, I think again, maybe it's not boring because he's gone deep in two of his four games so far this year, but he's always had lefties pretty well at Yankee Stadium, basing a lefty, let's up some fly balls. So homerun calls for today, both the Yankee Stadium, the Brett Rooker and Harrison Bader. That is all that we have here for today on the Solo Shop, but as a reminder, make sure you're subscribed to the Number Fire Daily Fantasy podcast feed. Wherever you get your podcasts, we put these up every weekday here on the Number Fire Daily Fantasy podcast feed and over on the Fanduul YouTube page. So also make sure you're subscribed over there. If you have any questions for me, I am on Twitter at Jim Sonnis, J-I-M-S-A-N-N-E-S. You can also follow the Fanduul podcast network at Fanduul Podcast. I want to thank you all for tuning in for today. Good luck to you with your MLB DFS lineups for tonight. We'll talk to you once again tomorrow to break down Tuesday's slate. This has been the Solo Shot right here on the Fanduul podcast network.


How'd you guys this day? It's going man. Yeah, it's just going Oh, man, what do you have? What do you have tea today?  Maybe that's one steepie nuka Post right what's happening right now? She's here, too Yeah, she's since She's gotta be sand side. Hey, let's go brother. Judge is so busted man It's kind of actually broken. It wasn't plucking right


Mystic about what could happen in the future, but we'll just have to wait and see Okay, well, thank you and thank you for being a an intern We pay them four dollars an hour Okay All right, oh Wow, this is you. Yeah This is our whisper room and what we do here is we record our podcast and four days a week We have a direct ISDN line into Hawaii public radio the conversation with Beth and Kosovitch and Chris Vander Cook So we do that live four times a week I do a podcast of my own called the pond squad and here We've also been recording our offshore series award-winning podcast series race politics native Hawaiians So we went ahead and purchased this nearly soundproof Studios to do all our work here because the buses can be really noisy along Waili Avenue So what is your topic here you're doing? Well, there's no topic right here, but today Stuart was on that was him eating over there You probably had him in the background and he was talking about a rail again What's the better route to go? Is it the general excise tax? Is it the transient accommodation tax? Do we look at property taxes and history looks particular how GT is regressive and it hurts a lot of the more lower-income Middle-income folks and so maybe that's not the best way to go But we'll let the legislature decide that I'm for the hotels paying for it That's all right. Yeah, let's go out here and show you more. Yes, please What kind of a meeting is this gonna? Now this you're the one that was doing the podcast is that it is you said I he was on the radio with HPR today Yes, hi And your name is my name Stuart your turn. Yes, and so you're doing the podcast and about the The what the special session will do. Yes, it was we looked at tax it we looked at taxes the three taxes they're talking about using to raise money for rail and the Point of this story was really to discuss. There's no Perfect way each of these taxes has a problem associated with it You have the general excise tax, which is It is it is sort of hidden so people don't really realize it, but it's also very unfair It's a regressive tax the most regressive. You've got the property tax, which is less regressive But people will see it more clearly as on their bill and finally you've got the hotel tax the Transient accommodation tax and that's on our big industry. The tax is already high So the legislature is gonna have a tough decision to make and that's what I wrote about that's what I talked about today Where all the magic happens, okay She's been here a couple of times back when she and our publisher had a partnership they since moved on but Speaking of people that are cutouts come in here This is mayor Caldwell, oh dear good light. It's too dark. Isn't it? Yeah, let's get it back here. You know, you can see Basically the mayor declined our invitation to interview during the election last year and so we interviewed Charles Dejeu But the mayor decided he didn't want to do a video interview So we just took him around town in various places and videotaped him and the mayor actually told me later that he thought it was kind of funny I said we should have gone live because that's what we're all about. But anyway, we have our creativity here at civil These people are talking to patty over here. We talked to patty I'm still trying to figure that out because we have a lot of unmanageable people No, I Help oversee the news operation and set some of our long-term priorities edit a lot of stories And civil beat is the kind of place where everybody Sort of chips in and does a lot of different sorts of things But I get involved in helping develop stories edit stories work with reporters And help patty set some of our long-term strategy and priorities


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Hey there friends, thanks for checking in at the range now and I decided to bring out the 350 Legend Ruger American Ranch rifles been a while since I shot it very much excited to do that and I brought a bowling ball This is called a Brunswick Rhino now every time I feature a bowling ball It's been a while someone will tell me everything about that bowling ball that I don't know Nonetheless, we're going to fire this from a hundred yards with the 350 legend We'll talk more about that after we set up. I got a target set up here to make sure of my diamond back my vortex diamond back is Zeroed in I know it's zeroed in we'll see it's been a while and then we're gonna fire this from a hundred yards And just see what the effect is with the 350 legend. Let's get this set up All right, here we are at a hundred yards I took a couple shots at my blue target and I'm grouping a little bit high Not sure why there is a lot of wind going on out here. I'm using the 350 legend a hundred and eighty grain It's a deer load. It's one of the heavier bullets in the 350 legend Ruger American Ranch rifle 16 inch barrel vortex diamond back three to nine by 40 millimeter So I'm gonna take a few shots at that target that I'm gonna attempt to hit that bowling ball and see what The effects are those two hit pretty dead nuts. Let's see how I do with that bowling ball I'm expecting it to hit and then possibly roll down that hill But nonetheless if I could hit it we'll look at the effects. I'm feeling very confident now I've got my tactical frog blanket that it's actually become quite popular and I like that because not only does it give me confidence But you guys seem to like it as well. Let's see how I do with the bowling ball Not sure what happened again. I have a camera down there, so we'll get a closer look I don't know if I hit it or not. It didn't seem to move Let's try that again that time I know I hit and Looks like it just took the covering off but We'll see hundred eighty grain bullet a heavier 350 legend a lot of people use that hundred and fifty grain Thirty more grains will make a difference. I think at least out of bowling ball. I'm sure it would on a whitetail Let's try that one more time All right Let's take a look at and make sure I'm unloaded. Yep We'll take a look at that bowling ball and see what the 350 legend did to it All right, here is my blue target wasn't as bad as I thought it was from a hundred yards, okay? First couple were right in this group and then the last two right here That's actually a real nice shot from a hundred yards and that's what you'd expect to shoot But there is quite a bit of wind they had to deal with that no problem I'll have to check the video But my buddy who's with me told me that I hit the bowling ball three times it did not penetrate at all now Here is the outer covering. This is heavy and and really thick. So that's what we're looking at there It did create a crack over here. So the Energy of that round cracked it up quite a bit definitely definitely ruined it But it did not penetrate anywhere near what I thought it would but there again You don't know what's inside these things whether it's asbestos or some type of composite combination of everything but that's what a 350 legend with the 180 grain bullet will do from a hundred yards three shots and I don't know if I'm impressed if I'm not impressed. I think it's pretty good But I will say this It sure was fun getting the Ruger American ranch rifle back out and shooting the 350 legend Yeah, if you like videos like this, please subscribe and sure I always appreciate thumbs up button Thanks for watching and you guys be safe


Well, I am very thankful and I greatly, greatly appreciate everybody who's helped me at Crop Ship Relay for all their work. If you've got any Crop Ship Relay that you're on this fundraiser, I want to thank you for your help too. And I'm just very thankful but very grateful and pleased.


Hi everyone. So I'm John. I'm from the Open Science MOOC. MOOC stands for massively online open course. But what we don't want to build is just like another unidirectional educational service that reinforces like the role of an advisor or stereotypical sort of lecturer. What we really want to build is like a community platform that empowers individual learners to communicate with each other and learn from each other in a very open space as part of a community. We're very much in development at the moment. If you think 95k is a small amount for a budget, as is even less about 5% of that. Yeah, it's in development. And I think we're partnered with about half of you in the room at the moment. And what we really want to do is provide like the educational foundation for the global open science movement. So thanks.


Welcome back to Tennis Talk. My name's Cam Williams and the Davis Cup has now finished its group stage And we have the finalists set for November, which is at the end of the season meaning that eight teams got through the group stage Let's go have a look at the teams that got through and which teams missed out There's four groups of four teams so 16 teams total and in group a we had Italy and Croatia both getting through with Sweden And Argentina both missing out Italy won all three of their matches and Croatia one two out of their three So Baratini, Sinner, Chorich, Chilich, they're all playing in the finals where Sweden and Argentina both missed out Having a group B now and Spain got through with Canada So Alcaraz, Ozealia Seym getting through and you can see there the two teams missing out Serbia and Korea Now Serbia didn't have Djokovic this time, but no Djokovic loves playing for his country So that's a big out from the Davis Cup at the end of the year no Djokovic and no Serbia having a look at the group C now and Germany without Alexander Zverev still won all their matches along with Australia getting through as well meaning that France and Belgium both missed out So Zverev if he does come back at the end of the year will be able to represent Germany in the Davis Cup in Australia Without Kirios still got through so Dimin or a Kokonak is getting the job done there and in group D We had the Netherlands going through undefeated along with America as well So the United States and the Netherlands are featuring in the finals But Great Britain and Kazakhstan both missed out Andy Murray Cam Norrie Dan Evans all missing out from Great Britain So here is the finals now and it turns out to be a knockout tournament at the end of the season So the group stage just happened knockout tournament in November with a quarterfinals set Italy versus America We'll play the winner of Germany versus Canada with a huge matchup with Felix Ozealia Seym possibly playing Zverev at the end of the year Baratini versus Fritz, I mean Cine versus Fritz some crazy matchups potentially there Australia will take on the Netherlands and Croatia will take on Spain So we might get Chilich versus Elcores like we did at the US Open or maybe even Chorich versus Nadal Which we got in Cincinnati. So some massive matchups there potentially you can see there We've got Italy versus Canada Italy versus Germany in the semi-final Australia versus Spain. I mean there's some massive matchups there along the way But now it's a knockout tournament Davis Cup is set So you have it Davis Cup is set for the year and that'll be happening at the end of the year after the seasons complete in November But no Djokovic. Djokovic will not be playing because team Serbia didn't make it through and of course We're not gonna get the Russian team either because of what's happened over in the Ukraine So no team Russia either who are the defending champions, but what do you think? It's a pretty good lineup Let me know down in the comments below. What's your favorite team from the Davis Cup to get through are you excited about Spain? I'm actually having Nadal and Elcores both playing for team Spain That'll be unbeatable at the end of the year But you know you've got Baratini Cine doing it for team Italy So there are some solid teams outside of the Spanish team the Davis Cup is set the quarter finals ready to go in November


Everyone I'm Bianca Renee and you're watching Bianca Renee Today and welcome back to another Foundation Friday and I'm so excited about today's foundation because I got the Dermacol full coverage foundation that you've probably seen on Instagram. Now just like that Instagram sticky bra I told myself if I saw this foundation one more time I was going to buy it. So I did. Now if you have seen this foundation on Instagram it's usually seen to cover tattoos. That's how full coverage of stuff is. I don't have any tattoos to show you but I do have this scar so we could try with this. Now before I demonstrate how I put it on my face and how well it covers scars let me tell you a little bit more about it. It's called the Dermacol Film Studio. What brand of prog? Don't know what all that means but it says make up cover this is straight up for covering everything tattoos scars blemishes dark under eye circles whatever you want to hide this is going to do it. This is also waterproof so that's going to be kind of cool to test this summer you know they say take her in the pool on the first date. We'll see about that SPF 30 that's a really high SPF for a foundation I've always seen like 10 15 but 30 that's pretty intense. So when you do take it out the box it comes in this little gold container it looks like a big thing of superglue. This does have an expiration date on it it expires in 2020 so I have some time. I am wearing the foundation right now and as you can see my face is beat it is covered all flaws and blemishes with ease. Now before I show you how I cover up my scar let me show you exactly how I apply this foundation to my entire face. Alright let's do this my face already has on moisturizer but I'm going to get my veil mineral primer by Hourglass this is my new favorite primer so this is what I will be using in my foundation Fridays because I know it works. Now I am primed let me go ahead and open this foundation I'm so exited and I actually tried to take a peek at the color but it is sealed shut so you have to use your top and then you're going to poke it and there's our color okay so it's already starting to kind of pop out. I'm going to do a little like color test what a blemish be sure oh wow this is creamy this shouldn't even be used on the face well you want coverage there you go alright got a little ahead of myself let me actually get my beauty blender and apply a little bit of this on here I think applying it with my finger actually gives me even more full coverage this is kind of blending it out a little bit thinner you know what let's use this as a concealer I'm going to grab my little concealer brush and put a little bit on here and let's get these dark circles one time oh yes what dark circles what bags the only bags I have a designer since I want to keep the full coverage of this foundation I think I'm just going to keep applying to the other side with my finger so I'm going to grab a good little dollop it's basically like a concealer for your entire face that's all we're doing is concealing all flaws okay so first impression I really like it I'm someone who loves full coverage because I've always dealt with acne and acne scars so if I can cover that up you know fool the world I'm down for it now that I'm satisfied with my foundation let me go ahead and put on the rest of my face and we're back so now that my face is complete as you could see I'm extreme full coverage my face is beat there is no time for imperfections or flaws at all now since this was a random impulse buy I did buy it from Instagram from this random website called shop of beauty boutique.com I'll put that information below in the description box and I bought it for looking at my receipt $15 $15 foundation plus $5 for shipping so basically this foundation cost me $20 the Dermacol Foundation comes in a total of 13 colors to choose from and unfortunately I am their darkest skin tone this is number 224 I literally just bought the darkest one that I could and fortunately it did match me really well but if you are darker than me or if I get a tan there's nothing else for us to choose from so I really hope that the Dermacol Foundation comes out with more darker skin tones I would not wear this foundation on an everyday basis this is just like way too much perfection for a normal day like at the grocery store but if I had a very special occasion or if I was doing like a photo shoot or even filming on camera this would be a great foundation to just make you look flawless but before I do my full day foundation test let's go ahead and cover up the scar so here's my burn scar I burned it on the oven when I attempted you know be a good wife one day and like cook and stuff that was dumb all right so I got my foundation it's gonna dab oh look at that so the first problem is my arm is darker than my face thanks to the summer and tanning but we could try to blend this out a little bit I mean it definitely covered everything but now let me just try to blend so now I'm just gonna grab a little bit of my powder and kind of set it there you go I mean it's not my best work I do have a bunch of bright lights on it but it's like a natural normal casual day you might not notice it when I'm talking because it's covered but hey this looks a lot better than that big old scar so this foundation went on at 11 30 a.m I am going to wear it for the rest of the day so I'll be back in a couple hours and we'll see how well this foundation actually holds up on my oily skin and I'm back it has now been eight hours of me wearing this foundation and this is how shiny I am so it's not too different from a regular drugstore foundation in my opinion I'm always getting shiny right here by my nose a little bit on my forehead I always think it looks better until I come in front of all these lights and film but I'm like hmm a little shiny it is starting to kind of rise a little bit but let's blot so like always in my foundation Friday videos I always grab some oil absorbing sheets and we see how much oil comes off so I'm just gonna press on the oil areas there is my oil so a lot of oil came off on this oil absorbing sheet and so did a little bit of the foundation but I still do have a pretty good amount of coverage overall I'm pretty satisfied with this derma coal foundation I was expecting it to be horrible after eight hours but for being so full coverage on my very oily skin it did a pretty average job of keeping me matte so that is everything I have to say about the derma coal full coverage foundation if you've tried this yourself please leave me a comment let me know your thoughts if you have normal dry or oily skin and make sure you come back next week for more videos I post foundation reviews every Friday and curly hair videos every Sunday so I will see you all next week or right here on Beyond Green Day today


For more videos on people's struggles, please subscribe to our YouTube channel. Donald Trump is out. Joe Biden has been sworn in as the President of the United States. The end of the Trump era is being hailed as the end of a dark age. What's going to be different under Biden? Some of his executive orders do reverse some of the more contentious stances that Trump took. This includes joining back the Paris Climate Accord, stopping the withdrawal of the United States from the World Health Organization, stopping the construction of the Keystone pipeline, etc. His cabinet nominations however suggest that no fundamental change will take place, nothing that could upset the corporate elites and other such friends of the Democrats. Who all has Biden nominated? Let's look at the foreign policy and national security team. Biden's cabinet is being applauded as the most diverse in US history. These nominations may be diverse demographically, but offer no diversity in terms of opinion. The people Biden wants to bring in, his cabinet have campaigned for wars and have benefited from wars. Most were part of the Obama administration as well, during which a shockingly high number of drone strikes and deportations were conducted. All of these policies could make a comeback under Biden. Things at the domestic front also look similar. There may be some diversity demographically, but the people Biden is planning on instating in his cabinet will carry out the same pro-corporate business friendly policies that establishment Democrats have in the past. The Biden administration intends to defend corporate interests even at the expense of people's lives and the planet. These are just a few of the many such characters that Biden plans on bringing with him. Biden owes his victory to both the widespread anti-Trump sentiment and the massive resentment among people against the current living conditions in the US. However, his nominations are people who are committed to fulfilling his promise to his rich donors that nothing will fundamentally change.


How can I easily art resin many pieces at once? The key to art resigning many pieces at once is to be prepared.  So line all of your work surfaces with plastic. Get each of your pieces propped up on stands and level before you start resigning. Have some dust covers like cardboard boxes or plastic containers on hand to fit each piece. So if you're working on eight or ten pieces all at the same time, you may choose to actually mix up only enough art resin to do two or three pieces at a time before you move on to the next two or three and the next two or three.  So measure, mix, pour, torch, cover, get it done, move out of there. And work smarter, not harder.


India is undoubtedly the jewel in the crown of ancient sculpture. And although Rome is home to the Renaissance, an attraction which lures enormous numbers of people there every year, India is unquestionably home to sculpture, which would put even the most efficient of the Italian masters to shame. However, conveniently, academia and thus most of the modern world overlook these astonishing feats of ancient art in favor of less controversial artistic wonders. One of our tried and tested methods of establishing whether an ancient artifact, or indeed an ancient ruin, attributed to a less capable, more modern imposter, is actually evidence of forgotten lost knowledge, is finding the puzzling accomplishments often hidden within the architecture or construction. One of the many examples of these is polygonal masonry. These very old walls, created without the use of mortar, are compelling examples of a fragmented technique either borrowed, or possibly intriguingly, leftover memories of a now forgotten technology. And although these more modern attempts range in age stretching far into thousands of years, the lesser capability of the builders is clear for all to see. Our point being that when these ancient walls stretching far before the Romans are compared to Mesoamerica, Peruvian, and indeed ancient Indian ruins, the exquisite polygonal architecture, the precise carving and stone building present, are clear, strong, controversial evidences of a forgotten civilization. How did these ancient builders acquire such a sophisticated knowledge and awareness of stone shapes, and the subsequent placement of each stone, perfectly placed against one another, forming impenetrable barriers which have stood the tests of the ages? We feel that regardless of what academia claims as the truth, pertaining to the origin and creators of these ancient wonders, the skills required to create them are thankfully beginning to become apparent to the majority, rather than the few. This ancient, forgotten people clearly attained a level of stoneworking and construction knowledge we are yet to acquire, clearly a far more advanced and capable people than we are today, let alone the modern historical imposters academia claims as the culprits. We feel, regardless of others claims, the evidence to suggest an intercontinental, highly advanced technologically superior civilization, once flourished here on our planet, is highly compelling. The Sea of Galilee Although not a real sea, it has remained named as such due to the staunch traditions, mainly religious which have grown and flourished from around its shores. The first century historian, Flavius Josephus for example, was so impressed by the areas surrounding the Sea of Galilee, he once wrote, quote, one may call this place the ambition of nature. Reporting a thriving fishing industry around the lake, with well over 200 boats regularly working the waters, archaeologists have since discovered only one such fishing vessel, found in 1986, it has been nicknamed the Jesus boat. According to Christian religion, much of the ministry of Jesus Christ himself actually occurred upon the shores of the Sea of Galilee, and a recent discovery within the waters themselves has continued to perplex specialists within the area, astounding all who have been exploring, said discovery. The mysterious structure is cone shaped, made of unhewn base salt cobbles and boulders, and weighs an estimated 60,000 tons according to researchers. An astonishing size, making it much heavier than any of our modern day warships. Rising nearly 32 feet out of the ancient sea's sediment, it also has a diameter of about 230 feet. Stonehenge for example, which is an impressive ancient structure in its own right, has an outer stone circle diameter of only half that. First discovered in 2003 using sonar exploration of the southwest portion of the sea, divers have since been down to investigate the presumably ancient structure, writing regarding their finds within the latest issue of international journal of nautical archaeology. Researcher Yitzhak Paz, antiquities authority, and Ben Gurion University believes it could date back more than 4,000 years. Quote, the more logical possibility is that it belongs to the 3rd millennium BC, because there are other megalithic phenomena from that time that are found close by, Paz told LiveScience.com in an interview, noting that those sites are associated with fortified settlements. Could it be that this is where the peoples of Bet-Europe buried and honored their dead? Is this a proverbial city of the dead, or something else entirely? As more research is undertaken, it is only a matter of time before we understand this amazing structure for what it truly once was. A few months ago we did a video regarding an enigmatic mountain which rests within modern day Tibet. We touched upon the amazing legends, speaking of the mountain actually being that of an ancient man-made pyramid, which according to such legends is placed at the center of the universe. They spoke of a mysterious giant eye placed upon the top of the mountain, an eye which according to said legends will reveal itself when the ice and snow within the area melts away. Akin to a story containing the eye of Mordor, yet hopefully not as malevolent. Although Mount Kailash can be found within modern day Tibet, its location is very close to the borders of India, a place which few know possesses one of, if not the most amazing ancient structure to have ever been discovered, or indeed built upon our planet. A structure which dwarfs the Great Pyramids, and indeed the Great Sphinx with artistic wonder. Actually known as the Kailash Temple, it is an exquisitely cut series of supposed preying temples and other communal buildings which was, many thousands of years ago, carved straight out of an enormous horseshoe-shaped rock resting within a hillside. According to mainstream academia, Kailash Temple was somehow built by a primitive people using primitive tools during a duration of 400 years, from 200 BC to 600 BC. However, no one seems to be able to explain how such a primitive culture could have possibly created something so awe-inspiring, something so artistically accurate and wonderful, something we would indeed struggle to recreate today. A structure not only architecturally accurate, but also drenched in a masterpiece of sculpture. Largely accepted as a flawless piece of art, no less than 200,000 tons of stone was masterfully carved away, creating several separate temples each drenched in tiny artistic detail. Rediscovered in 1819, is it possible that the Hindu decorations found within were merely later additions? Additions to a relic left actually built by a civilization far more advanced and far more ancient than we are allowed to publicly believe? It is understandable for one to wonder, how did a primitive civilization create such a wonder with primitive tools, attaining such a perfection, such refined finish to each tiny detail? It is conveniently unexplained just how they managed to cut into this single block of rock with such precision and indeed vision, adorning the structure with thousands of animals. It seems as if it were a tribute, a gift depicting what can be found on our planet. Is Mount Kailash, as legends say, really the center of the universe? Is this mind boggingly detailed, most intricately built ancient temple by a long way actually a tribute to this fact? Made up of temples which are all now perceived to be shared between three faiths, Buddhist, Hindu, and Jain. Are these multiple faiths further evidence of a re-inhabitation rather than a construction? The 200,000 tons of rock, for example, is nowhere to be found. And as previously covered in the Kailash video, the same is seen with the apparent enormous excavation found around the base of Mount Kailash itself. Compelling evidence for manipulation of the landscape, giving credence to the legends of it being, in fact, an enormous pyramid. Regardless of this, the fact that the temple carries the same name as this mysterious and still unclimbed mountain within Tibet, we find highly compelling.