Welcome to the FAA Production Studios and the FAA Safety Team's National Resource Center, located in the Sun and Fund Complex in Lakeland, Florida. I'm your host, Walt Shammel. And our next presenter is an FAA Safety Team Manager for Mississippi, holy cow, he's got a whole long list of things over here, FSO, IP, CP for the District National Resource Inspector and Helicopters doing a lot of different jobs for the FAA. He served 28 years in the U.S. Navy and retired in 1997 from the Naval Air Station in Meridian, Mississippi. Then he flew corporate, owned an FBO, operated a flight school in Meridian, Mississippi and also served as a pilot examiner with the FAA and a safety counselor for the Jackson FISDO. He holds an ATP and CFI with fixed wing and rotor wing certificates. He was an operations inspector for the Jacksonville, Mississippi FISDO for nine years and then he joined the safety team in December of 2007. His topic is airport surface safety. Let's welcome Theo Mavridoglu. Theo? Thank you, Walt, appreciate it. Thank you for the warm welcome. Beautiful weather outside. I'm sure you're going to enjoy the air show later on today. Airport surface incident mitigation strategy. So we obviously have a problem with surface incidents in order to apply mitigation strategies to reduce those accidents. Those accidents are both costly in terms of life and limb and in cost of airplanes and such aircraft in general. This is what happened after the aircraft collided in the air. This and similar other accidents have occurred even in the state of Mississippi where I reside and work whereby Delta State University, and this is no secret, they were in a pattern. They were clear for landing on the same runway. They were given enough space between base and final, yet they still found each other if you will in the air and landed safely, if on my head, on the ground known was hurt. But you can see some of the issues here, high wing, low wing, and so on so forth. We'll talk more about that in a minute. This is another surface incident that occurred, not enough clearance obviously was granted or observed between the two aircraft taxing. And so this is a multimillion dollar expenditure, there probably fear in the eyes of the passengers if there were passengers inside the time when the aircraft were taxing. So we're going to talk about, I was in the Navy for 28 years, no apologies for that although we have some Marines here in Air Force. I was told to tell them what you're going to tell them, then tell them and tell them what you told them. I'm going to follow this routine that I've been using for years. So we're going to talk about the goals of the airport surface incident mitigation strategy. We'll talk a little bit about the background, how this thing, the concept started, and were developed into what we have today as a presentation and those teams that are working out there to mitigate those accidents and incidents. The components, statistics, and I'm not going to bore you too much with statistics because I know lots of numbers make people confused and they don't really serve a purpose in my opinion. What are the common factors and of course prevention strategies that we all need to be involved in in order to mitigate those surface incidents. Here are our goals, reduce surface incidents at GA airports. So the question is why GA airports? Well we have more GA airports than we have 139 airports and there's more GA aircraft flying than we have commercial aircraft flying. What I mean commercial, I mean 121 carriers and so on and so forth. So plus in the GA airport environment, more likely to not have a tower and a control tower than in the 139 airport of course which requires an ATC airport traffic. Assuring improvement, knowledge and understanding. We need to be crystal clear as to what it takes to be involved in a surface incident and how to avoid a surface incident. And of course achieve support from aviation organizations. Who are those organizations? Of course the FAA were sold on this because this is our goal, is promote safety. I as an aviation safety inspector, my middle name is Safety and of course this is a safety program. So what are the other organizations that need to be involved? U, AOPA, HAI, ALPA and all those other alphabet groups as we call them that need to be involved and they are involved. They have formed partnerships with us in order to reduce these accidents. And runway safety has been around for several years as you know. You probably went to safety seminars and you heard people standing up talk about runway safety and they developed runway safety action teams in order to reduce these accidents. And that's a major concern because that's where life is lost and that's where money is wasted if you will in terms of aircraft damage. A little bit about the background. Call to action, the FAA made this a priority several years ago to promote and reduce runway incidents as a high priority. Fast team, that's what I am. I'm an FAA safety team manager for the state of Mississippi as Walt had mentioned and one of my goals in my flight plan if you will, excuse that term because that comes from a higher up, is to reduce surface incidents and accidents. And also the GA in general, the general aviation has an effort to reduce accidents, that's mitigation and methods to reduce the accidents and incidents. What are the components of this program? Collaborate with industry. Who is industry? All of you. If you work for an operator commercially or if you fly on your own, you're part of the aviation industry. Study runway incident, human factors. This is where the bugaboo is, human factors. If we reduce the human errors from the aircraft operations, whether it be helicopters, airplanes, sea planes, whatever, we're going to reduce probably 65 to 70% of all accidents and incidents. But how can that be? You'll say perhaps now we have some unmanned aerial vehicles. Well, the military has to start developing those to some extent for a war effort but not necessarily for general aviation or for commercial aviation. I hope I'm not alive when we have to enter into an airliner to go from Lakeland, Florida or from Tampa, Florida to Chicago and there's not a human being in there. Yet, we know that human beings are the cause of many of the incidents and accidents. It's just nature. That's how we are. We're going to talk about human factors in a minute. Incidentally, if you have any questions between now and when we finish, just hold that thought for a minute until we get a mic to you and then we can just discuss that issue that you have. Everything inside the fence, what do we mean by that? Well, imagine an airport that has a fence. Most airports do have a fence now since September 11th specifically.  I was flying with a student some years ago in an airport just south of Merida, Mississippi, and we made an approach to land and I saw something in front of me that didn't look like it belongs there and it was a cow and so we had to call the FBO, please get the cow out of there, but anyway, so those things will happen. And all those cause incidents on the surface. Propose additions to the PTS. We know what a PTS, right? Practical test standards. How many here are private, instrument commercial, ATP, Kingshaw Hand? Private? Okay. Commercial? Okay. And ATP? Okay. Very good. I don't know how much, it's been a while since I've given a certification right. Maybe we could do other rides, check rides, but I have not done one lately with a private and perhaps a commercial applicant, but I feel, and I know you probably agree too, that the PTS needs to have a little bit more requirements when it comes to surface incidents and runway safety. I mean, we have that little placard that shows the markings and signs and we ask a couple of questions and perhaps we as CFIs, if we have any CFIs here, are not doing as good of a job, in my opinion, to promote runway safety when we are preparing pilots and candidates for the next rating. And since we know this is where the buck stops with the CFI level, I think we need to pay more attention there. And I'm hoping that in the near future that the PTS will include some additional elements for ACIMS, mitigation strategies for surface incidents and runway safety. And develop new products. We're already doing that. You may see some in the back or in the FISDO or with your safety teams, you may see that they have products that we need to do more of that and promote aviation safety in terms of surface again in terms of CDs, DVDs, trifolds and anything else that we can use to promote this concern. Collaborate with the FAA line of business. What are the line of business? ATC, of course, plays a major role when we are at an airport that there is a control tower or a ground control. We, pilots, you need to be in that business as well. What are the line of business? Airport district office. It's another branch or line of business of the FAA that designs airports, taxiways, intersections, and so on and so forth. Well, those folks as well need to be part of this effort. And they are part of this effort. To make those taxiways such that they're clear to us who are flying or taxiing or operating a vehicle around the airport. So we don't place ourselves in a predicament. So airport district office, the engineers who design airports and modify them, airport traffic control, FISDO, of course, the people who enforce the rules and the rest of us who fly. CFI and DPE initiative. Well, the CFIs, we're going to touch on that a little bit. The CFIs need to take this upon themselves. Do we have any CFIs here? Very good, sir. And the CFIs, and I do a little instructing on the side for relatives, if you will, need to take this as a charge because these are the people who are going to be able to influence today's and future pilots to be more aware of surface incidents and mitigate accidents and incidents. And of course, the DPE, designated pilot examiner, who gives the examination after the CFI has finished preparing the candidate, the student, to take the examination, either for the initial rating of private or for commercial and up the ratings, they need to ensure that the candidates have the knowledge, the adequate knowledge, to perform an aircraft on the ground, of course, in the air as well, and to perform in a safe manner when they're on the ground. Office of runway safety. I think Dan Sealy has been here, and he's probably going to be again. He works in this particular area, runway safety. And if you have not heard Dan speak, make it a point to do because he's a very dynamic speaker and he has a lot of information. That office as well, both at the national level in DC and at a regional level, where I am in Southwest region, in Dallas, Fort Worth, we've taken that effort, not only on the fast side, like I said, but ATC, ADO, and all those other lines of business within the FAA. And national and local ATC personnel. ATC is one of the most important parts of the FAA which can ensure or help us ensure with an E that is, not with an I, okay, ensure separation enough on the ground and understanding of what routes we need to take in order to taxi from point A to point B. We'll talk a little bit more about that. Human factors, study regarding runway safety. Again, this is gonna come over and over and over again. Human factors is number one. If we reduce the human factor errors when it comes to runway safety or surface incidents, we've reduced 65 to 68% of all incidents on the ground. Identify typical runway incidents. What are the ones that are most common? If you put them on a bell curve, where is the first, the most common area where we see them? And what are they? We're gonna talk a little bit about this later on in the presentation. But think about this while we're talking about what are the things that you saw or you have been involved in and how you dealt with this. Categorize human factors common to runway incidents. And Dan Sealy's office, runway safety, has done that. And again, I'm gonna pitch for you to come back to listen to Dan talk because he's got a lot of good information on runway safety. And incorporate this information into product development. And again, we've done a fairly good job in the FAA doing this but we need to do a little bit more in terms of CDs, DVDs and presentations outside this particular forum back in our homes. Promotional campaign, that's what it's all about. Promote safety. Signage identification. Now when was the last time, and this is a rhetorical question, don't raise your hand. What was the last time you looked at signage in one of those pamphlets? Things have changed. They try to make it more clear. The single straight line, yellow line with a broken yellow line now has become a double continuous line with a double broken line. Did you know that? So they're trying to do make it obvious to all of us who fly or operate on the airport surface where we need to stop or where we can go across and stop and then do the rest of the items that we need to do prior to taxing to our destination. So signage is important. Not in terms of what is on the ground as well because we may be in a tailwheel airplane or an airplane that's pretty high. After raining, maybe dust or sand on the taxiway, it will see a scenario that happened up in Denver of how an airliner almost got into trouble because of snow on the ground and some other factors too. That's later on the presentation. But also the sign is also the ones that you see in color black, red or yellow. You knew there were three colors, right? Surface maps, 121 carriers, airlines and 135 operators, we used to call them air taxi operators have a requirement under the FARs, Federal Aviation Regulations, to have the taxi chart open before they start taxi, before they release the brakes. Why we in the part 91 world don't do the same? I don't know. Maybe we can discuss this later on. But that's a requirement for 135 operators. It's a requirement for 121 operators. And I have several 141 schools that I have a certain influence on to increase their safety to a higher level because safe enough is not safe enough. And so trying to elevate that level of safety for 141 schools, which we certificate if you will, to have the requirement to have the charting on the ground in front of them as their taxing. But before the tax you review it. We'll talk a little bit more about this, hold that thought. Operational planning, what do we mean by that? Well, I know you plan to leave Lakeland, Florida and to go down to Miami. You find out the runway lengths, how much fuel you need, weight and balance, what else are you gonna go based on the winds and all those other variables that you must be informed in order to conduct a safe flight. But do you, and don't answer this question, do you plan your taxi route? Well, you tell me Theo, taxi route, this airport is a simple airport, one runway, maybe one taxiway. I know this airport, I don't need to do this. Well, yes you do. Because the other person who's operating in the airport approximately at the same time either landed and taxing back or is about to take off as well, taxi to take off may not know the airport well. May not be able to read the tetrahedron or the windsock or the wind is not significant enough for him to have a tailwind long enough runway to take off the opposite direction. Not knowing that there's another operator at the airport.   So operational planning is not at the time when you put the throttle forward and start rolling down the runway but it starts when you're chalked before you taxi. Human factors, we're gonna talk about that as well. Lots of errors, most errors occur because of humans making mistakes. Now how are we gonna deal with this? We cannot eliminate a human, okay? We cannot transfer that, that's another presentation on itself. Transfer, some people say, well the insurance company will take care of it or my medical insurance will fix me up if I have an accident or the insurance company will fix my airplane but that's not transferring of the error or the risk. So we need to mitigate and manage the risk in situational awareness. When we are taxing we have to, have to and have to three times know exactly where we are, okay? This is not the time to pulling out charts, punching in the GPS which has been a problem ever since it became with a nice screen and a little TV there. I catch myself watching those things myself. It's just a human condition. So when we're taxing we need to be looking at the task at hand. Additions to the PTS, that's another objective that we have. Emphasize critical nature of ground operations at the instructor level, of course a DP for checking. Incorporate ground operations as a primary phase of flight. When does the flight begin? Does it begin when we actually leave the ground? No, it does not. When the aircraft starts to move on its own power. So unless you're pushing the aircraft you get the engine starting or the engine starting the chocks are off and you're moving those power levels or throttles forward the flight has begun and I'll tell you why. The NTSB agrees with the FAA in that particular description because the NTSB says it's an aircraft accident or incident if the aircraft was to conduct a flight or returning from a flight and something happened on the ground. So both the NTSB and the FAA agree to that. I think we all need to agree that if it's on its own powers moving then we started the flight. So here we are. So it's a primary phase of flight. Establish standards for ground operation performance. Standards, what are the standards? Well, one example that I gave earlier was the chart, the taxi chart in front of us as we're ready to taxi not only for the professional pilots because we're no less professional than 121 carriers or 135 carriers. New product design. Design of new products for FY09 and beyond. This is part of the flight plan for the FAA. DVDs and storyboards and airport specific awareness graphics posters and brochures. Now you see the products that we have in the back or in the FBOs that we leave products there both of the safety team and the FAA in general and they're generic to all airports. We advocate that we make the signage specific to that airport that you operate on. So you can go to the FBO, order your fuel and get a poster that shows the taxiways, the numbers, the names of the taxiways, the runways and so on and so forth and ingress and egress points. I don't know what's about to happen here, if anything. But, now is that C-17 taking off? Is he landing? Is this a runway incursion? I don't know. I'm not sure anybody does. But I wouldn't want to be the soldier that stands right there. Have you seen a C-17 fly? That's one big airplane. I guess, again I told you, I'm not gonna bore you with statistics too much but we need to put some of them in here. We know that 58% of all operations with this ground operations or flight operations are general aviation. There's more of us than there are commercial pilots. And we're one of the last countries, incidentally, that have that privilege. Even Canada, our country to the north doesn't have that ratio. In Europe, of course, it's almost nonexistent. 65% of all pilot deviations are general aviation. Why would you say that happens? Is it because we're less professional? I don't think we're less professional. Maybe we're not practicing the professionalism we should when it comes to surface incidents. However, the airports that we operate out of, in many cases, are non-towered airports. Therefore, we don't have the assistance of ATC to give us guidance from here to there. One serious incursion happens every day. That's pitiful. That's 365 a year. And here's some more pitiful information. Most happen during the day in good visibility. Why do we think big sky, small airplane theory?  I hope not because there's a lot more of us flying these days in spite of the gas prices because we still can and we enjoy it, we love it. Pilot experience makes no difference. 300-hour private pilot, 10,000-hour commercial or ATP makes no difference because those individuals with the ATPs in the 10,000 hours have to deal with complacency. They've been there, they've done this, they know how it's done. The other people perhaps don't have that experience level. You catch the error, this is a teacher's union, incidentally. So we have our embarrassing moments, too. Some of them we just discussed and we're gonna discuss a little bit more. Here's some of the errors. Now, we can sit here for hours, talk about errors that we're gonna giggle and laugh and joke, but I know that some of us have been involved in some of these things or observe some of these errors occurring, okay? And you can read this yourselves. Enter runway after acknowledging hold short instructions. Happens all the time. Unfortunately, happens all the time. Taxi wrong route and enter runway. Crossed runway without clearance. Given position and hold instructions, then departed without clearance. So his taxi in the position hold gave him or her the clearance to take off as well, at least in their minds. Did you know the term have changed? It is no longer position and hold. IKO is trying to change that term.  Landed and departed without communication or clearance. And given clearance to land on runway, landed on a taxiway. Now, why would anybody do that? Unless the taxiway, again, this is back to engineering, ADO, airport district office, which is part of the FAA. Perhaps the runway was not marked as well and the taxiway was. And so it's full. Now, how many of these happened? Not too many. However, the engineering part is also, needs to get involved in reducing, if you will, these incidents. I feel sorry for that. What is this? It's a beach product, is it not? Is it a Cherokee? Okay. Wow. Look at the wing. Now we know what happened here, right? The truck drove into the wing because the airplane couldn't have done that. So, not only the incidents occur because of aircraft, helicopters or airplanes, but also vehicles. When we are taking our, what do you call it, our car over to the aircraft that we rented or we owned and we parked it. We need to park it in such a way as not to, obviously things have things like that. And also when we walk across a taxiway in certain airports, you can still do that. And that's unfortunate we can. You need to be careful. This happened up in Memphis, Tennessee. Okay, this is FedEx and UPS. Now, don't raise your hand, but I know that we are taxing sometimes faster than the runway to take off because we see another aircraft starting to tax, taxi himself or herself, okay? We need to be the first ones out. Conversely, coming into an airport that's busy in the pattern, we need to sneak in there on base leg instead of entering in a 45, left on wind, base and final on land, see if we can cut that person. Don't do it. Just like Nike says, don't do it. Competition is fun, but not to this extent. Okay, common factors on these incidents, surface incidents. First is memory. We all know that we can only remember seven related items. That's human. I mean, the psychologist came up with these numbers. I accept them as sets. I was told in instructional management school in Quantico, Virginia that on a slide, you never put more than seven items there. So maybe that's what it is. Or when I was an officer in the Navy, it said your span of control should be no more than seven, okay, and so on and so forth. So anyway, seven is a number that we use as far as memory. Stress can influence this memory now. So if we're stress, if we're thinking about something else, and when I mean stress, I don't mean negative stress. Something happened to family, house burned down, had a car accident, but also positive things. Your daughter is getting married. You just won the Florida Lottery. And if you did, I'm your best friend, incidentally. So positive events are stressors just as negative events are stressors.   Of course, I'm joking. We remember what we want to remember. Future tasks are hard to remember. When you were getting your instrument rating, you remember your instructor was saying, remember what you're gonna do very next and the one after that, just two items. Anything more than that, and we kinda lose that memory. Attention, focused attention allow us to filter out irrelevant information. When we're taxing, we're taxing, we're taxing. That's all we need to be doing. If we're gonna be running radios, charts, talking to people, a passenger behind us, we need to stop on the taxiway, take care of those issues, and then taxi again. Assuming we can't, we don't have two or three aircraft behind us. But one thing at a time, folks. Too much attention on one thing can lead to error. You agree with that? Because you focus on one thing to the exclusion of other things that are relevant to the tasks that you're doing. Brief the airport diagram. Brief it. What do you mean? Theo, I'm flying by myself. Well, you can talk to yourself. I do it all the time. My wife says I do. So, brief the plate. Where am I? I'm at the FBO. I need to turn right on Charlie, taxi to X-ray, make a left on X-ray, and so on and so forth. When you say it, how many senses are involved?  The more senses are involved, the more memory you'll be able to capture. So talk to yourself. I know as an instrument pilot, you do this. And if you don't, please do. Brief the approach plate to yourself if you're by yourself. Remain alert even in familiar locations. Again, this is the complacency issue that I mentioned earlier. Communications, another major issue, probably one of the biggest ones. I count for most runway incursions. Failure to read back. Now he'll say, taxi to the runway 3-4, hold short of Charlie. And what do we say many times? Roger. Well, Roger does not say anything, he says that you heard something. You did not hear everything. Taxi to runway 3-4, hold at Charlie, 2-2 Alpha Golf. But if you say Roger, it means that you heard a communication, but not necessarily the communication. Blocked or stepped on communication. The airlines have a nice feature. They wait for about 30 seconds after communication before they key the mic, okay? Of course you may not be able to, if you're flying in Chicago or Miami or Orlando, have all the time, but beware of that. Accepting clearance of another aircraft. My aircraft tail number is 2-2 Alpha Golf. Well, there's another operator, a piper there, that's 2-1 Alpha Golf. Well, that sounds a lot the same in the air when you're busy and some of the other people talking to you in the aircraft and so on and so forth. And misunderstanding instructions. So what do we do when we misunderstand instructions? Say again, thank you, sir. Fatigue, major issue with us, because we're humans, lack of sleep. I did a presentation with Dr. Bill, I forgot his name, who is the Human Factors PhD at a conference in Mississippi. And we talked about that. And he talked about the circadian rhythm and the two o'clock in the afternoon and the two o'clock in the morning where we are less attentive and how much sleep we think we're getting. We always overstate how much sleep we have gotten. You go to sleep at 10 o'clock, it doesn't mean you're gonna fall asleep instantly. You're probably gonna think for a while before you get any REM sleep and so on and so forth. So be aware of that before I trip. Fatigue can lead to decreases in memory and attention. Caffeine. Well, it works for me for about 15 minutes, then there's a down slope to that. So be careful with caffeine unless you have it intravenously, okay. 20 minute power naps may help. My wife is a good example of this. She can actually take a 10 minute nap and she is up and at it like she woke up seven o'clock the next morning. I go down for 10 minutes, I'm not waking up till tomorrow morning. So know thyself. Take frequent breaks, even if it's just relaxing, closing your eyes, putting your feet up. Teamwork, what do we mean by that? ATC, ground personnel, pilots, and so on and so forth. That's the team. That's how we're gonna solve this problem. Fail to coordinate or clarify with whomever you're talking to. Some of the problems are don't catch any read back errors. And ATC has that burden in that they give you direction. You repeat it, but your repeat is not exactly as they give you the direction. But they're not catching that error. Okay, and we have ATC involved as well. Forget what you were told. Now, some of us are getting old. I wanna see what time it is. So I'm gonna look at my watch and it says about seven after nine. And at 30 seconds later, I forgot what I saw. Does it happen to you? Don't raise your hand. So we tend to forget. Not only because of age, but because we have some other things in mind as well. The recognize that it's a system problem, not a pilot or an ATC. It's just is. It is what it is and we just need to deal with it. Humans cannot be eliminated, just managed. We know that. Eventually, I guess in 3000 AD, there may be flying without people in there, but I'm not gonna be around. I don't know who's gonna win this battle here. What can we do? This is the meat and potatoes. Communications, think and listen first. I know these are things you already know. We all know, we all try to practice, but we're gonna discuss them again, okay? Think about what you will say. Think about it before you key the mic. Who are you talking to? Who are you? Where are you? Relative to who you're talking to. And what are your intentions are? If you're announcing in the blind, are you talking to ATC? And of course, use standard phraseology. That's another good presentation. If there is in the FA Safety.gov, standard phraseology, I recommend that course highly. Be concise and precise. Don't spend too much time on the radio. Finding out where the pizza place is when you land. When you land, find out where the pizza place is. Okay, not in the air. Identify and write down all count frequencies. Now, back in the days when I was flying, we had a knee board, VFR, IFR, you name it. I don't see that very much. I don't see it even in 141 schools. Well, perhaps because the GPS has now have a bank of 10 frequencies, both calm and nav, and we can just flick through it, but guess what? It takes our attention outside and in the cockpit when we're doing these things. Where a glance at the knee board was I think much better. Check CTaf, non-toward airports, other airports using same frequency. So you may be in a vicinity of an airport that uses a similar frequency. ATC does their best not to assign frequency that they're so close for one to the other for airports that are proximal to each other, but sometimes it happens. So be careful. Listen to approach, control frequency. You can find out a lot more. You go on into, let's say, pick an airport, Naples. If at 15, 20, 30 miles out, you can put that on number two, calm, and listen to that and see what's happening in the pattern. As opposed to all of a sudden ATC drops you, it just says change to your local frequency and now you're the newcomer and you have no idea what's happening at the airport you're about to land. So listen way back then, maybe perhaps in number two. Listen to approach, control frequency. Understand ATC instructions. Do you? Were the instructions confusing? Yes, many times they are. I'll give you an example, but I think we're gonna be running short of time here. Check with ATC if actual instructions are against expectations. If you expected to receive this and you were given something different, you may wanna clarify this and please do because the instruction may have been intended for another aircraft and not for you because they're humans too and they make mistakes. You hear about this, right? ATC makes a mistake? Paradise. Mechanic makes a mistake? Paradise. We make a mistake, we die. Waiting longer than one or two minutes. If you have not gotten a response back from ATC after you made a call, then you may wanna check the frequency or call back again. In a position hold, this is something that I have a pet peeve with, an instrument departure, they put you on the runway waiting and I don't know what's happening behind me, who's coming on top of me. I don't like that I would rather wait at the hold short line and I can make a quick takeoff rather than be on the runway, but that's me. The instructions don't make sense. If they don't make sense, call them back. Anytime you're unsure, never assume. You know what happens when you assume. If in doubt, ask. If unsure, verify. Hold short instructions. Again, make sure you understand those and you comply, position the whole instructions. There's a difference now, right? Active runway crossings. Read back all instructions. Are you on the correct frequency? If you're using an old chart, you may not be. If your database in the GPS has not been updated, you may not be. Two-way communication, what does that mean? Cause and effect. You talk, they respond. We both understand it's not one way. Communication is two-way. Dialogue. Repeat heading, altitude and airspeed on a clearance because those are the three things that'll keep you away from other aircraft when you're in the air. Beware of similar call signs, similar frequencies, parallel runways, left, right, center, and intersections or full length clearances. The assumption is you're 152 and it's a 10,000 foot runway. You can go to the very end of it and take off and you still take off and land three times and maybe that's the expectation of ATC but you're taxing all the way to the end to take all 10,000 feet. Which is advisable. Airport familiarity. Review and use airport diagrams, plan taxi routes, know where you are and know where you're going. And how you're gonna get there. As for progressive taxi. Now, you need to ask for progressive taxi once or twice. If you're gonna be going to that airport many times, please study the environment so you don't have to do that. Thank you. All right, this is, timing is good. I'll show you a video, Denver. Oops. Well, I may show you a video. Here, trouble hole bar, what's up? Got 4216 north on mic, enter the ramp at alpha, alpha for three, four. Marked on mic, alpha for three, four, kilo on 4216. The assertion. The approach is for Delta for 31. 628, Denver, grass out on Lima, enter the ramp at alpha, November. South on Lima, alpha for the map for 2620.  I understand there was some snow there and perhaps the taxiway was not clearly marked. That's not a, that's not clear. That's a 106, you know it's on mic. 6106, enter the ramp at alpha, November, then ramp there. Up to that by like 736. Notice the ceiling, visibility, half a mile and 600 feet fog and snow. Another airways is landing. He was supposed to turn, okay? The aircrew deviated from the assigned taxi clearance of mic. They saw two, not three, perhaps because of the snow or fatigue or unfamiliar with the airport, whatever the reasons were. So he thought there were two versus three. Lima, mic and the runway. So he crosses the whole short line. And you know what the whole short line looks like. There's some more markings and signs that tells him where he is. That he thinks on mic taxiway taxing down. Clues, yellow versus white. Runway markings versus taxiway markings. 236, 12 ramp there. ATC does not know where he is right now. We're going to go around 209, we're going to Bravo 19. You have 529 into the ramp at Robin, November in the ramp there. And Elon 42-16, where are you at right now? Signature. Sagan, the frontier 297 go around. We're just coming up with the signature. D-Ride, that's you on the runway. That's just that thing. Yes, sir. 32-16, thanks. Which one of these is correct? Taxi two.  Which one would you say it is? Excellent, we're going to skip this one because we're going to be running out of time here. But yes, it is, sir. Absolutely right. Okay, what can we do to mitigate some of this threat? Cockpit procedures. Clean the windscreen. As simple as that. Some of ours are wearing glasses. The windscreen is, what do you call it? It's dirty, it's hazed, perhaps clean that. Remove charts from the windscreen. I have a pet peeve with that too. Do a CFI checkride or an ATP checkride and you put those charts right there and it makes a reflection. And you cannot see not only this part out of the windscreen, but this part out of the windscreen. Use current charts and AFDs. Install see-through sun visors. Well, let me tell you. I put those in my airplane and sometimes when I fly into the sun, I wish I had those solid ones. So, for what it's worth. Install see-through sun visors. Use all lights. Landing lights on the runway like the airlines do. When they take the runway clear for takeoff, all the lights come on. Position three feet, they say. Left or right of the center line so you don't blend in with the markings of the runway. I think it's a good idea, especially in big airports. And sterile cockpit. Now if the airlines have a 10,000 foot sterile cockpit, we should at least have a what? A 4.4 nautical mile or a five-statue mile from a geographical center of the airport where we talk about nothing but the operations of the aircraft at that time. Not where we're gonna go get that burger. Sterile cockpit from engine start to cruise altitude. For us, sometimes it's only 2,000 feet, 3,000 feet. So let's make that a geographical boundary versus an altitude like the airlines. Yes, sir? Position, the gentleman's asking about positioning your aircraft. There's lines in the center of the runway, center of what do you call it? Center line markings. If we position a white aircraft, most of us have white aircraft or a fly, position a little bit left or right of it, that kind of shows that there's some anomaly between what you should see when you approach an airport as you're landing. Yes, sir, that's exactly right. It's for the benefit of someone else. Yes, sir. Thank you. Use all aircraft lights, especially low visibility conditions. Goes without saying. Beware of fatigue and low visibility conditions as you're taxing in low visibility, you're squinting, you're paying more attention. Therefore, fatigue comes in a little bit sooner. Assign tasks to other crew members. Believe it or not, passengers who are not pilots like to have some responsibility. We all do. We like to contribute. So even if you have a young child, left, right seat, back seats, give them responsibility to look at, you know, outside for traffic and make sure we're sitting in the center of the runway and so on or taxiway. If lost contact ATC, immediately stop where you are and touch base with them. Teamwork. What I mean by that is ATC, us pilots and other CFIs and other groups, ADO as I mentioned earlier, need to be involved in order to mitigate this issue that we are dealing with. Any questions?  Well, I appreciate your attention. Thank you for coming. I know it's an early start for presentations, but I appreciate you coming. It's gonna be a great air show today, 86 degrees. I hope everybody has fun in a safe drive or flight home. Thank you. Okay. Thank you, Leo. Leo, Theo. You brought up some good points about communication. I work with a lot of flight instructor applicants and I never know what they're gonna say. And here they are, they're supposed to be the pros. They've got all this flight time, they've worked with ATC a lot and I'll get airplane so-and-so ready to taxi. And the tower says, ground control says, okay. And they look at me and they said, what do you mean by that? And I said, what are you supposed to tell them? Who you are, where you are and what you wanna do. Exactly. And then you get the, I'd like to taxi to the runway and the tower sent a note out that says, do not ask for our permission. Tell us what you want. Isn't that interesting? I thought ATC ran everything, right? Well, 9123 says that we will comply with clearances and instructions. Exactly. But I like that. If the pilot makes a mistake, pilot dies. If ATC makes a mistake, pilot dies. There's something wrong with this picture. So, situational awareness, positional awareness. Absolutely. Things you talked about. Very important. Where you going from here? I'm gonna amend the booths, FAA safe.


Hi, welcome back, I am Tony, your General Advisor on the Virtual Linguistics Campus, and I am here with our monthly update. You know that we now have more than 10,000 users, and as usual, here are the statistics with the top 10 countries. So what's new? First of all, we welcome more than 30 new users from the AECC College in Bournemouth, England. They are using a customized version of our Speech Science course. Welcome and good luck! And secondly, as started in December, more and more language samples are coming in from the Philippines. Great! Finally, our astronomy course is becoming part of the curricula of specific grammar schools. And probably most importantly, our first microcredential course on corpus linguistics will be activated this month. So stay tuned! Okay, that's it for now, see you next month, I'm Tony.


Okay, well I said I'm going to talk about computational language and artifacts from the future. So I'm going to try and summarize about 40 years' worth of work and thinking in 30 minutes, so I'm not sure how well that's going to work, but let me give it a try. So I think this idea of computation is probably the single most important intellectual achievement of the past century, and it's something that's really defined a lot of what we do today, and I think it's going to be even more important in the future. So what I'm going to talk about is kind of how one interacts with computation and how one thinks about it. So let me start off by actually showing something here. So let's imagine you walk up to your computer and the question is does your computer have kind of computational intelligence built into it? Back when computers were first out and about, you didn't really expect much from your computer, it was just raw machine code. Then there were early languages and operating systems, networking and so on. What we can expect today is to have computational intelligence built into our computers, and I've been working on this for many years now and millions of people use the things we've produced. But let me kind of show you what do I mean by that? I don't know, let's say I say, you know, prime of a thousand, what's the thousandth prime? Okay, we know that. Let's say we want to make a table of the first, let's say we make a table of a thousand primes. Okay, we know how to do that. Let's say we want to make, I don't know, let's make a plot that shows those prime numbers. That's not terribly exciting. Let's say we want to make, we want to work out differences between those prime numbers. Kind of the idea is we should be able to just walk up to our computer and have it know the things it needs to know so that we can take questions that we want to ask and immediately start to do computations with them. So for example, that was something with numbers. Let's say we want to do something with words. Let's say we should expect our computer to know all the words in English. So there's a list of words in English. Let's say we want to just take, let's say we want to work something out about these words. Let's say we want to take the first letter of all those words in English. There we got the result. And now let's say we want to make a word cloud that shows us what's the most common letter, what are the, what are the frequencies of different letters here. So these are kinds of things. Okay, let's do that. Let's see. Let's do that for Spanish just for fun. Who knows the answer to this already? It's like what pages of a Spanish dictionary will be the most used. Okay, it looks like C and followed by A are the most popular words. Or we could say let's take, let's pick up, let's take an image or something. And let's say we take an image there. We could say let's let's let's some edge detect that image. Or let's say we can expect to just compute things based on what we have here. So actually, let's do something different. Let's say we want to blur that image. And let's say we want to let's do the following. Let's say nest list. So we're going to make a nested sequence sequence of images where we're progressively blurring more and more. So let's make 10 successfully blurred images. Okay, so we're losing the tiger here. Let's say we want to let's let's just see what happens if we try and image identify what's in these pictures see how far it can get and still know it's a tiger. So let's see. The percent just means the most recent thing we got. Let's see what it can do with this. Oh, boy, it did really well there. Wow, I'm impressed. It must tigers tigers are not as well camouflaged as we think. Let's go to 20 steps here. Let's see what it does then. I think it's going to lose it eventually. It's going to say it's something crazy and different. Oh boy, tiger is very obvious there apparently. Okay, well in a case so so kind of the notion is we should just be able to immediately know things about about things in the world. So let's say we make to make a graph. This might be a graph showing relationships between people or something. We can just pick up that graph and we say let's make a plot of communities in that graph or we could say we want to know something about about the world. Let's say we say Madrid for example here. Okay, we know that that's a city and we could for example say what's the population of Madrid. We could say that's okay. So let's say we want to know what's the can we work out what the population has been over the course of time. Let's see how much data we have about that. Okay, so that only 34 data points but let's say we can say let's make a plot that shows the population of Madrid as a function of time. Okay, there it is. Oh, for example, we could say we're asking how far is it from where I am to where you are. I think you guys are in Madrid. Let's see what the geodistance between Madrid and where I am. Let's see, I'm actually in Concord, Massachusetts and the geodistance is 3400 miles. Let's see, I should convert that into kilometers and there it is 5500 kilometers. Let's say let's just do one more computation here. Let's say let's get the capital cities in Europe for example. And let's get then this is something where we're using natural language to specify what we want, but then what we're getting is this kind of symbolic representation in this case of cities. And we could say for example let's say find the shortest tour between those cities. Let's see if we can do that. Okay, so now that tells us that there's a so we could for example make a picture which says let's take what we've got there. I should have put this all together in one one line, but let me let me not do that. What I'm going to do here is I'm going to make a plot that shows hope. Yeah, there we go. There's a there's a kind of shortest tour that visits all the capital cities in Europe. Okay, so this is a little bit of an example of kind of what what I mean by by sort of computational intelligence. You can kind of just walk up to your computer and have it do these kinds of things. Have it already know lots of things about the world and already be able to compute lots of things from what it knows. And by the way for a practical level you can you could always say if I wanted to I could say cloud deploy something here and make this into an API that I can let let's say for example let's do this let's just for fun let's just let's do something very practical here let's cloud deploy an API function that takes let's say takes the city and something which is going to be a city and then let's say we want to compute this thing let's see hash city here and then we want to say let's um actually let me go ahead I can do the following let me just say I'm going to make that something that shows up on the web like that actually let me just go ahead and publish this and then everybody can use it um let's say cloud publish uh this API and now hopefully let's see what happens here um okay now we get this this object in the cloud we can go there and um oh I should have said whoops let me actually do something different let me let me make a form not an API this is an API that will be called by an external program this is a form intended for humans to use so now I can go and um uh um and get oh and I made a mistake here um I should have said I can change my code here I should have said that we go there okay so now I've got a page on the web if I could only get this to move to the right screen here hold on uh there we go okay so now I've got a page on the web and now I could say city boston and now it'll go and do the computation and give me a result there so this is something I just created this this thing and I deployed it here and in fact if you wanted to you could take um let's see uh let's just get the url out of that and let's say oops no that's not what I wanted to do um let's just get the url from this and then I could say something like um uh barcode image of that um as a qr code and now hopefully if you go and point your phone at that um you can um uh you can get to that particular api that I just created um I could also actually take this notebook that I've been creating here um that this kind of the concept here is this is sort of I can make kind of a computational essay here so I can go and say something like um uh cities in Europe or something here and I can take this and write out what I'm doing and then write it in in a language English Spanish whatever and then also also represent what I'm doing computationally okay so this is a little bit of a kind of what I mean by sort of computational intelligence and computational language let me now talk a little bit more about the the kind of the underpinnings of this what what to how to think about what we're seeing here so in a sense we can start with thinking about sort of what is computation what is what is going on when we do computation computation is about following rules by setting up rules that define how something should work and then following those rules so a long time ago I got interested in seeing how general is this notion of computation to what where can we apply this notion and so one big place that I got interested in is applying it to the natural world to understanding natural processes can we think about natural processes as computations if so what kinds of programs might nature be running well the kinds of programs we're used to writing are these big complicated things that we carefully construct for our particular purposes but if we just look at sort of programs at random what might they actually do so a type of program that I looked at a lot of things called cellular automata let me show you an example of one so here the idea is this is a very simple rule that just says you've got a line of black and white cells and at each step you update the color of each cell according to this rule depending on the neighbors of that cell so let's take a look here at what this particular rule does okay let's so let's start it off in just one black cell at the top and let's um let's run it for 40 steps and let's just put a mesh so we can see the cells um oops and if I do this right there we go oops uh what did I do here um let's see what did I do here um oh that's big mistake um okay so uh so this is just starting off with this very simple rule we start off with one black cell here we get just very simple things to happen okay so long ago I got interested in well what happens when we use other kinds of rules and we can just sort of imagine kind of looking at this computational universe of possible rules kind of turning our computational telescope at that universe of possible rules and just seeing what's out there so let me let me do that computer experiment for you again here so let's say I just look at um uh lots of different let's say I look at the first um let's do this let's look at uh the first um um 64 rules of this type okay oops okay so this is now experiment I first did back in the 1980s and uh looking at what happens with different kinds of possible programs possible rules so what we see is sometimes the behavior is really simple we just get one black dot in the middle sometimes we get this more complicated kind of nested pattern here um and uh but if we keep going the most surprising thing I've I've ever discovered is what happens when we get to this rule 30 here so let's take a look at what rule 30 is so let's just uh let's just copy these down here um rule 30 it's just another one of these simple solid automaton rules this is the rule and um let's see what it does so it seems to be doing something kind of complicated let's keep going a little bit longer let's see if it sort of resolves to something simple let's run it let's say for 400 steps um and uh um this is what it does so this is really a remarkable thing because we're just starting off with that very simple rule we just start off with that simple black dot and we're generating all of this complicated behavior there's a certain amount of regularity over on the left but many aspects of this pattern look for all practical purposes completely random and uh so it's sort of a fundamental thing that in the computational universe it's very easy to make very complicated things we're used to in doing engineering that if we want to make something complicated we have to go to lots of effort we have to set up very complicated plans and so on but that's not what happens in the computational universe in the computational universe you just look out there with the sort of computational telescope and you immediately start finding all of this amazingly complex behavior so how does this relate to for example what we know about nature well nature shows us a lot of kinds of complexity and what seems to be the case is that the kind of secret that it's using to produce all that complexity is it's just running programs from the computational universe why haven't we seen more of these programs in what we do in technology well it's because in doing technology we want to be able to foresee what the the programs that we set up are going to do so we don't want something we haven't wanted something whose behavior is as complicated as this we want something where we can readily say okay this is going to behave in some straightforward way that we can readily understand something like this for instance instead but what we learn is that out in the computational universe there is sort of amazing richness in what's possible and in a sense we already knew a phenomenon like this we knew that for example if you take let's say pi there's a simple procedure for generating the digits of pi but once they're generated they seem for all practical purposes random and that's the same kind of thing that we're seeing happen here okay so what's the significance of this well there are there are many things that this implies this provides kind of a new set of raw materials for making models of the of the world both social world and natural world in the last probably 15 or 20 years there's been really a transition from what was happening for the last 300 years which is if you want to make a model of the world you use a mathematical equation to if you want to make a model of the world use a program and that's been sort of a a silent transition in the world of science that's happened in the last 15 or 20 years to this kind of computational paradigm but there are also implications for example we can ask questions like how difficult will it be to foresee what's going to happen in the system how difficult will it be to foresee what's going to happen in some system that we model in the world well one of the things that we discover is this phenomenon I call computational irreducibility that it's really not possible to work out what's going to happen in a system like this much more efficiently than just by running the system and seeing what happens there's no way that we can figure out there's no way we can sort of succeed in being smarter than the system and figuring out what it's going to do more efficiently than just by running it and that's something very different from what we've been used to in science but it's something that we increasingly have to understand the implications of for science and many other things is this phenomenon of computational irreducibility so one of the questions that comes up from realizing that such simple rules can produce such complicated behavior and that it relates to nature is what does this mean for kind of our whole universe and for physics and so on so long ago I long ago I used to be a physicist and but I there's sort of been a long term question just how simple can the underlying rules for the universe actually be and a big surprise in the last year has been the fact that we've discovered that there is actually a way to understand how the universe works in terms of very simple rules and I'll just sketch this I'm not going to go into it in much detail this is kind of a visual summary of what of what's going on there the the idea in a cellular automaton you've got this kind of line of cells and it sort of predefined what space and time are like what we think is going on in an actual in the actual universe is that there's sort of an an infrastructure underneath space it's just like we think of space as just this continuous thing where we can put a point anywhere we want just like we might think of a fluid like water as being a continuous thing where we can pick any place in the water we want but actually we know for water that it consists of discrete molecules bouncing around it's just it appears continuous on the scale we look at it well we think the same thing is true of space and that actually space is composed of sort of individual discrete points kind of atoms of space and the only thing that's true about those kind of atoms of space is that they are sort of abstract elements with a certain identity and they have certain connectivity to each other and so in general we form this this hypergraph that represents the relationships between these sort of atoms of space and the remarkable thing is that when you kind of grow this hypergraph you can just like what we saw in the case of cellular automata you can do something like um you could uh see if I can find one no that's not the right one that's more like the right one um let's say we just grow this hypergraph um how do I have a good example here sorry um um the uh uh let's find you a better example um uh that's a good example here um this should be an example yeah here we go so this is this is just starting off from a very simple graph and showing how by applying very simple rules you eventually grow this much larger graph well turns out the graphs you grow here's an example of one um the graphs you grow on a large scale can behave like continuous space and so that's kind of how we understand that space like something like water is made up of discrete points uh we think that the actual scale of those discrete points is something vastly smaller than anything we've known so far it's probably more like 10 to the minus 100 meters and so on well turns out so so in this model one can understand the how space arises time is a rather different kind of thing from space time is sort of the inexorable progress of computation but it turns out that as a result of a phenomenon called causal invariance it turns out that uh one still reproduces kind of the the notions of spacetime that we've come to that to to think of in relativity theory and so on well then the next surprising thing is that this notion of space the in the abstract it's just a bunch of points connected together we don't even know that it's formed into something that we can think of as three-dimensional or whatever else but it turns out you can think of this as being limiting to something like a uh a finite dimensional space and then it turns out you can also have curvature in space and then the first big surprise is that from this underlying very simple model that involves just these these atoms of space and these hypergraphs being rewritten and so on you can reproduce Einstein's equations you can reproduce the the all the phenomena that we know about gravity and you can reproduce things like black holes and so on that that's sort of one branch another branch that's important is that you can reproduce quantum mechanics and in fact from from these models we can now get a much clearer idea of how quantum computing works we can actually compile existing kind of quantum computing methods into the underlying structure of these models and we can kind of see to what extent it's possible to really get advantages from quantum computing and so on but the main point here is that what we've discovered in the last year or so in the last last six months since we we announced it um is that we're really on a track here it seems to actually find the fundamental theory of physics and to discover that our universe is in some fundamental sense computational and that kind of motivates understanding more about just what is out there in the computational universe of possible programs okay well so now the question is we've got this giant universe of possible programs they do all kinds of different things now the issue is is this useful to us so what what what is technology about technology tends to be about the idea of taking um taking things that exist in the world perhaps in the natural world and harnessing them for some human purpose or another and so for example we might find out that there's you know magnetic materials in the world and then we realize that we can harness that for the human purpose of making magnets and so on um or we might find that there are liquid crystals and harness that for the purpose of making displays and so on so now the question is in the computational universe how can we harness the things we find for some useful human purpose so for example in the case of this rule 30s tell your automaton we've used this for many years as a random number generator it's really good at that but that's that's a particular purpose that we've managed to harness this thing for but now out in the computational universe there's just an amazing amount of sort of computational capability there and now the the key question for us is how do we connect that sort of ocean of computational capability with the things that we humans actually want to do and in a sense I I've spent a large part of my life trying to build that bridge between what's possible in the computational universe trying to understand what's possible in the computational universe and on the other hand trying to understand how we represent what we humans actually want to do and that's where computational language comes in the whole idea of this thing we call well from language is to create that bridge between what is what is possible in the computational universe and what um uh and what we humans want to think about so you know like in in a typical natural language there might be some tens of thousands of of words in more than language there are about six thousand sort of core things that you can think about doing in terms of geometry or sound or geography or finance or engineering or whatever else um and the idea is to have this this computational language that represents whatever we want to think about in computational terms so that we can make use of the power of the computational universe to be able to do the things we want to do now in a sense the objective is is very different from what's been done in kind of the long history of programming languages programming languages are about taking what computers do and providing wrappers around what computers do so that we can build programs that uh uh potentially quite large programs that kind of ultimately just tell the computer step by step what to do what we've been trying to do with our computational language is to build something where we can represent the way we think about things computationally and by the way we can then get at computers to help us do that thinking so kind of the notion of well from language is not only to make something that computers can understand but also to make something that us humans can understand to essentially provide a notation for thinking about things computationally so you know we might go back here and just look at this and see and see this kind of computational notation that tells us what to do so the way I think about this sort of in terms of sort of the historical implications is the following back if you go back 400 years people were doing math of various kinds but they was very hard to go very far with math because you ended up having to talk about math using words and that was not a very streamlined or sort of portable way to do it then mathematical notation was invented plus signs time signs things like that and suddenly it was possible to use math in a mainstream way and sort of mathematical thinking really took off and one got algebra and calculus and all the various mathematical sciences in a sense what we're trying to do with computational language now is to make this this notation this medium in which one can represent computational thinking in which one can can represent the things one wants to think about in computational terms and this provides and just as mathematical notation kind of enabled the mathematical sciences that brought us a large chunk of what modern science is today so the idea of computational language is to enable kind of computational x for all x I mean I think that the for every field one can imagine from sort of archaeology to zoology there either is now a computational version of the field or there will be and that's kind of the version of the field that is going to take over in the course of the 21st century so so kind of the idea is this is a sort of full-scale computational language that's trying to represent all these kinds of things in the world in such a way that we can make use of the sort of the the computational knowledge that our civilization has developed we can package that all up and make use of it in this computational language to be able to represent things in the world computationally and do the computations we want to do so that's kind of the the mission that we've been on and what's really fun to see is is that our computational language is used in lots of places it powers Wolfman for for example which powers the computational knowledge of things like Syrian Alexa and so on it also is used in lots of kinds of corporate settings behind lots of kinds of systems in the world that people might say are doing AI or or machine learning or but or just dealing with knowledge or dealing with with pure computation but underneath is Wolfman language running and what's really fun for me to see is that in a sense this the the concept of a full-scale computational language is something that's been kind of a separate branch in development from what we've seen in for example programming languages and so on but it has allowed lots of people including me to build things which I think we can talk of and think of as sort of artifacts from the future they're things that we kind of imagine can be done computationally but it has not been possible it's people it's been hard to conceptualize how would you do that computationally well the notion here is to provide this computational language which lets people think about things computationally and and then be able to get the computer to help them actually do those things and build large systems based on that this is obviously important also at an educational level for letting people do the thing which is sort of the paradigm of the 21st century think computationally to get to the point where you can think about things computationally without learning all of the sort of details of low-level computer science and never getting to the kinds of things where you actually get to compute about interesting things in the world well just to there's there's there's vastly more to say about all this I'll just mention one more thing that I think I said I would talk about which has to do with with how to think about this sort of the role of computational language in the future of things outside of of sort of pure the things we usually think of as computation so one one area is things like computational law if you want to write a contract today you would write a contract in natural language English Spanish whatever else but you try and write it in kind of a legalese to make it a bit more precise than standard natural language but in fact what computational language allows one to do is to start thinking about writing that contract not in a natural language but in a computational language which is possible to be autonomously executed and where there's a precise definition of what it means and one of the more important computational contracts that we have to write is the kind of the contract with the ai's so to speak it's kind of the the ultimate ai ethics question you know what is it that we really want to tell the ai's we want them to do we want to say be nice to humans we have to kind of define that more precisely and the way I think we define it is to write in computational language what we mean by what we're saying there we need to take the things we imagine that we're thinking about and turn them into computational language which is then executable and understandable at a computational level so I should probably wrap up there and I think the the main main thing I wanted to communicate was this this notion of computational language this idea of being able to represent things in the world computationally the you know we built this very practical system that has tried to collect as much sort of computational intelligence as possible so that it's so that one can expect us just sort of walk up to one's computer and immediately expect it to to be able to apply computational intelligence to lots of kinds of things and the fact that this is sort of connected what what makes that really powerful is that there's this kind of ocean of computational possibility that we've discovered from the basic science of computation and by the way we now know we now have really good evidence that our whole universe is making use of that kind of idea of computation because that's how fundamental physics seems to work and so this this notion that so we're kind of the the idea is there's this sort of ocean of computational possibility and we're gradually getting to mine little pieces of it and what computational language does is it goes from the things we want to think about to the kinds of things that it's possible to to execute in this computational universe of possible computations all right I should stop there I hope I left time for some questions and discussion thank you very much thanks a lot stefan we have three questions we have three questions it's the first one I think it will be easy for you it's how do you foresee the combination of algebraic computing with the statistical numerical computations in matcha learning algorithm so how do you foresee this combination of algebraic computation and statistical numerical computation yeah yeah right so so I mean what's what's interesting is if we take something like the there are things that we do which are very precise which are very well defined there are things we do which are instead based on things like machine learning the thing that I think is the most powerful is to combine them together so for example let's say we're we're dealing with our tiger here and I was really surprised that um that it was able to identify this but let's say we want to say let's say um image identification network there we go okay so this is now going to let's let's actually take a neural net here let's take the neural net that does the identification and we can well we'll see that that's just a a function here that is a function in our language so we it looks like kind of a a symbolic kind of function but if we wanted to we could take this network apart we could see what was inside there everything that's in there is kind of defined in a kind of symbolic way but um but what we get is is this thing that we can use machine learning on so so for example here let let's say we want to take our tiger and instead of looking at the complete output let's just look at five layers of the neural net and apply that to the tiger see what we get um we'll probably get the the internal thoughts okay we need to make that into an image we'll get sort of the internal thoughts of the neural net as it was doing this analysis and so what's interesting here is just sort of the way in which we can kind of smoothly combine these things which are in a sense precise computations with things that I did a feature space plot looking at how these different pieces of the thoughts of the neural net um were arranged in feature space and so on so I think the the greatest power comes from making use of things like machine learning as components in this bigger picture of doing overall computation I mean one thing I might say is that uh you could ask well why don't you just use um natural language let's say plot a you know cosine curve and purple I don't know if that's going to work um you just say why don't you just write code by by using um uh by by um um by by just giving natural language and so what one finds happens is that when when the thing one's asking to do is fairly simple it's just sort of one sentence that can work quite well when one wants to build up this more elaborate story about what computation one wants to do the idea of just sort of tell the computer what to do in words stops working and that's where the power of computational language and having the sort of organized well-designed coherent symbolic language becomes important where you get to put together all these different pieces so I think that's that's kind of the story that that um what uh sort of things like machine learning end up being powerful components in this bigger picture where you build up the sort of whole stack of computational capabilities in the in the notion in the in the context of symbolic language we have another another question for the for the audience is uh if you could please clarify what is the difference between computational language and making traditional language more high level right so so the big point is that um uh it's a language which already knows a lot of things about the world so I mean in you know if you if you imagine sort of a typical programming language I don't know I don't know too many programming languages but we could start up here we could uh let's see we could we could start up a python cell here and we could start typing something in python but you know we don't expect that python will know let's say I don't know uh who Vincent van Gogh was uh we don't expect that that um uh we don't turn we don't know you know we could say what were the notable artworks of Vincent van Gogh we don't expect that um that it will know something like that uh we don't expect that we could take I don't know we could take let's take a random collection let's let's take a random um uh random collection of five of these we don't expect that um uh we don't expect to be able to do computations immediately with these so let's say for example let's get um um let's see if we've got images for these we may or may not have images for these but let's see okay there we go we don't expect that now we could take one of these images and make um I don't know we could say let's make a um uh a chromaticity plot out of these let's make 3d chromaticity plots from each of these we don't expect that these are things that our language will intrinsically know how to do now what's the importance of having a language that intrinsically knows how to do these things well it allows one very quickly and very effectively to do real computations relevance in the world and the thing that has been kind of my activity for the last 40 years is maintaining kind of a coherent design of everything that's here it's not something where you start with a little language and you build all these libraries and you have to fit libraries together and you have one library and it's incompatible with some other library and there's a library that does this and library that does that the idea is to have a single coherent language where it's designed so that all these different things whether we're dealing with machine learning or image processing or numerical computation or things about artworks or whatever but all of that fits together that's kind of the idea of having a full scale computational language a representation of the world in computational terms that's kind of the notion of it and you can think of it as kind of the the unique case where one sort of built a library that knows about the whole world so to speak and did that in a coherent way so all the pieces fit together but but another you know you may notice that there are features of wealth and language which are really rather different from what one's seen elsewhere you know for example the fact that there is a notion of an object that a symbolic entity that represents a Van Gogh painting that's that's something that is the kind of the whole notion of symbolic programming is something that is fundamental to what what's going on here in fact the the idea of symbolic computation and the idea of kind of you know you define a function let's say I want to define a function I don't know I might define let's say I'll define a function with two like this that takes does I don't know what what might we do here we might do some function that and let me not but but basically what I'm doing is I'm I'm I'm specifying what the the way the language works is it's defining rules for what to do with patterns and these patterns can represent both things that are traditional kinds of things that one might see in a typical programming language but they can also represent this whole spectrum of other kinds of things in the world including you know artworks or whatever else another question final question because there's there's some related ones about the future about how you foresee the future of the facts the the future of computational languages the main trends you identify how do you foresee the future well I mean the main thing the main immediate thing is what do you expect your computer to be able to do and you know we have gone through over the last 60 years from it's just a raw computer to it's got some low-level language to it's got an operating system it's got networking it's got a user interface what we expect to have in the future is it's got computational intelligence built into it all of that computational knowledge that's been accumulated by our civilization whether it's about you know properties of planets or characteristics of movies or whatever else to have all of that knowledge immediately accessible and immediately computable that's that's something that I expect now what does that mean that means that you get to build a lot of magic things I mean today there are this is why I sort of talk about artifacts from the future today you know if you look at sort of people winning hackathons or startups doing amazing things you'll you'll you'll find lots of them where at the bottom of it is our wolfman language system being able to sort of bring computational intelligence to bear on on kind of a particular a particular problem or or task and I think the most significant thing is that you can expect to see kind of a sort of a big jump in the level of computational ability that you can expect to build things out of now what what it also is something that you can expect is a level of of sort of a new way of thinking about the world where you think about the world computationally where when you sort of formulate a question where you wonder about something about your personal analytics tracking or something you know have I had more cups of coffee while whatever is happening or whatever and you've got the data for it being able to routinely take that and using computational language express what you want and and get results I think that the the notion of kind of thinking about things in the world thinking about the ways that one sets up businesses and so on in these computational terms this is very powerful and in order to kind of fix those ideas in order to have a way of thinking about those things coherently we need some language to think in and that's what computational language provides for us now going forward there are just so many kinds of things that that computational language is important for I mean one one example I've been a little bit involved in is questions about when when one wants to define something like I'm sorry can we lose it no it's fine it's fine it's fine you can continue yeah yeah I'm sorry the um no I was I was just going to say that that lots of kinds of things about sort of you know expressing our preferences for for things is something we can expect to do we might do that by just telling somebody in words what our preferences are for you know what we should see in our news feed or some such other thing but we can also express that in computational language and it becomes a medium just like when when literacy for for ordinary natural language became decently common 500 years ago there is a moment that will eventually happen when there's sort of literacy in computational language that becomes common and where people expect to be able to communicate things in computational language just like at least some part of the world expects to communicate things in mathematical language today but it's a much broader thing to communicate in computational language another thing that one realizes as soon as one realizes that sort of everything is becoming computational is that these kind of principles about how computational things work become things that are important in everyday life so for example from 300 years ago with people like Galileo and Newton and so on we got ideas about the way basic physics works and ideas about things like energy and momentum and so on in mechanics and now we'll often think about energy and momentum as metaphors for lots of kinds of things in the world well similarly with sort of computation as a foundation for our understanding of lots of things including as it happens physics there are kinds of concepts that come in like computational irreducibility that are notions that then we realize are important generally in the world and so for example computational irreducibility is something that we gradually need to understand more of it's about it's kind of computational irreducibility is kind of explaining the limitations of things like science people have sort of imagined you have science you can predict what's going to happen you can predict how the pandemic is going to play out you can predict this or that thing it's the thing that one realizes is that one one has been the particular ways that science has been used are making use of kind of the pockets of computational reducibility that people have found but the fact is that a lot of what's out there in the kind of scientific world is computationally irreducible that is the only way you can work out what's going to happen is essentially to simulate it or play out what what actually happens and see what happens and it's this this phenomenon so this is something we kind of learn from the fact that sort of it's computational all the way down is we have to take seriously these kinds of concepts like computational irreducibility as things which are have an effect on all the kinds of things that we see in the world and the way that we think about things in sort of everyday life and the question of sort of can we to what extent can we expect that we can just sort of rely on science to tell us what's going to happen that from within the sort of the foundations of science is this phenomenon of computational irreducibility that kind of describes the limitations of science it both describes the existence of computational irreducibility both tells one that computation really achieves something it achieves some sort of irreducible amount of work but it also tells one that there are limitations on what one can say about what that computation will do so those are a few of the few of the kind of indications of of of kind of the where things will go i mean computational language is really the bridge between what is computationally possible and what we humans want to think about and one of the things that's always interesting in the evolution of language is the way that that what one talks about in language one then can think about and build on top of so for example there was a time before the word blog existed and one might have said well i'm writing these pieces and i'm putting them on the web and so on and so on and so on but then after there are enough of those kinds of things the term blog gets invented and then one can start talking in terms of blogs and one can start thinking about sort of how one builds things up from that and it's the same way with computational language once one has sort of taken these concepts and made them into something that one has sort of organized in a coherent way in a computational language one gets to start thinking in terms of those concepts and i know over the last 34 years that i've been building well from language what's happened is that their set of concepts and as we understand them better we can see further and we can build more and more and and sort of incorporate more and more into this kind of computational language to represent the world but i think that that's um that's the thing that i can we can expect to see happen is as computational language gets better understood there are these concepts that come into existence that when then one can reason in terms of and build other concepts on top of them it's interesting to me over the past 30 years or so that there are ideas that i wanted to put into the early versions of well from language particularly to do with higher order functions and functional programming and things like that which were hard to put in because there was not good ambient understanding of these things in the world at large so you put them in there but nobody understands how to use them and that's not a worthwhile language and over the course of time as sort of the sort of one's been able to build these layers of understanding it's possible to go further and further and to sort of make use of the kind of conceptual framework that's been built with computational language so thanks a lot stefan a real honor having you here to to close this this edition of business conference so it's thanks so much take care and keep in bye


Hi everyone, Joe for Jaspy's CaseBrakes.com coming at you with our last case. We had a ton of this stuff, but we had our last case of 2020, Gold Rush Autograph Full Size Baseball Helmets. Really fun stuff. We will play Who Am I with the last four boxes here for bragging the rights. I'll give you some clues you guys can guess and see if any of you ladies or gentlemen were able to figure it out. Random player break number 17, you know how this works by now. We sell 26 spots. We multiply that by two, so you get two spots for one, two players for one spot. So there's this group right here. Good luck boys and girls, let's double it up. And there is the huge list of players right here. Let's roll it, let's randomize each list one and a four five times. One, two, three, four, and fifth and final time. After five times, we got Ben all the way down to big boys 007. All right, so one and a four five times for the players, one, two, three, four, and good luck fifth and final time. After five, we got Jose Ramirez down to Carlton Fisk. Jose Ramirez all the way down to Carlton Fisk. Now their team, their teams should be on the helmet, so that's until you know which version of these players are going to be. All right, so I'm just going to scroll through this list just as is and then I'll alphabetize by your first names and then give everybody a chance to see and collect their thoughts on who they have. It's just for the recording, just so people want to watch. Later they can make sure everything's fine. All right, let's sort by column A, so Aaron Pettit, that's your group of teams right there, including that guy. Ben, Chris, big boys 007, Edwin, Gary, Ian, Jim, Josh, Nick, Nick F, Nick S, Richard, and Spencer. There you go, so I'm going to pause the video when we come back, we're going to see if there's any trades and then off the break, stick around. All right, welcome back folks. There were no deals done, but the list actually that I've alphabetized by player name now remains the same. Let's just scroll back through it one more time. And if you're rewatching the video, you can always pause to make sure you've got the players that you got originally from the randomizer. There's the case right up there. We're going to pop that open right on that second camera above my head. Bring each individual box here to the main camera. We'll play a quick round of who am I for all four of the full-size helmets. And we'll go from there. Some of them are almost like toy helmets that have the autographs in it. Every once in a while, you get a really nice full-size helmet. So we'll see which one is which. There's four opportunities of who am I. I need like a theme song for who am I. You can repeat a couple of the records from like North Carolina. I know. Really 2000s. I think it's family-friendly, maybe. If you could get an end to it. There's the full-size helmet right there. All right, so let's pop this open. Let's see who this is going to be. Almost revealed the name. I feel like a little sticker inside the name there. All right, I see it. All right, put your thinking caps on, folks. In previous instances, no, I've not been able to tell the difference, whether it's a real helmet or kind of a cheaper helmet. You'll see what I mean when you see it. Okay, who am I? I'm a Hall of Famer, a Cy Young Award winner, an eight-time All-Star, a World Series winner, a Silver Slugger, an NLCS MVP, and a Rolades Relief Award winner. I should start to give it away. But when I won the World Series, I don't think it was as a reliever.  It was not. No, not Siever. I don't think Siever was ever a reliever. No, not Otani. Although I think he should be turned into a closer. Not Glavin. He never closed. Got it. John Smoltz won a Rolades Relief Award, probably in the season where he had 55 saves in 2002 with the Braves. What a great turnaround for him. Not too many players who have 213 wins in their career and 154 saves in their career. All right, so there's the helmet inside here. There you go. So you can kind of see what I mean about the sort of cheaper helmets here. That kind of, you know, that kind of interior. But it's got the JSA card inside there, if you can see. It's kind of hard to see. So I just pull it out for you. There it is. There's the JSA card right there. And there's the Smoltz autograph right there with the brace on it. Should I just take it out? Sure. Might as well. Digging all into it anyway. There it is. Nice. Good looking autograph. Any other authentication right here? There's the JSA sticker on the back. So almost not quite a batting practice helmet, but you know what I mean. You've seen these helmets before. All right. Who has John Smoltz? Scroll down to John Smoltz. Spencer Barclay. Nice. There you go, Spencer. Congrats to you. Let's put this right back inside here. See if they have the helmet inside first. And then the brown paper. I don't know if we reuse these boxes. I have no idea how we've been doing this, but they ship in a different box, the group says. Okay. It shows you how much I know about my own business. There's Spencer, random player 17. They just say, Joe, you just look pretty. Get on camera and open up things. All right. All right. Fine. Fair enough. All right. Next box. Good luck. We have another hurler here. This guy's also pretty good. Another Hall of Fame hurler. A three-time Cy Young Award winner. A six-time All-Star three-time World Series winner. Four-time Gold Glover and a two-time ERA title champion. Born in New York, but grew up in Scottsdale. Went to Scottsdale High School in Scottsdale, Arizona. And then went to Arizona State University in Tempe. Well, he's also credited with Townsend University. I don't know what happened there, but maybe there's a transfer in there somewhere. But Hall of Famer with 411 out of 444 votes. What is that? I don't know why baseballreference.com just doesn't calculate the percentage from it. 411 divided by 444 with 92.56% of the vote. It's pretty good. Three-time World Series winner back in the day when this team was really good. He has 268 wins over his 19-year career with just one team. With just one team. And a career 2,212 strikeouts. Back when this team was really good. They haven't been good since the last time they won a World Series in 1983. But he won one in 1970 and he won one in 1966. Which is pretty amazing. Long career there. Baltimore knocking one out in three different decades. They just didn't win. No, they won in the 70s. Yeah, they won one in the 60s with him on the team. I think they won. Back when this team was good. Yeah, Chris Gill has been nailing him. Jim Palmer, 19 years on the same team. 268 wins. Three 20-win seasons. I mean a lot of seasons with under a three ERA. One was a 209 ERA. He's pretty darn good. Yeah, I mean he's pretty darn good. Three World Series. The Orioles beat my Dodgers in 1966. I remember that. That was a heartbreaker. They beat the Reds in 1970. That's big red machinery I think. And then they beat the Phillies in 1983. Jim Palmer, this guy was pretty, pretty good. So let's see who has it. That would be Big Boys 007 with the old Orioles legend. I always liked that Orioles logo. As a West Coast kid I always wondered what would have been like to grow up with that little goofy guy there. There's the JSA card inside there. The corresponding sticker on the back right there. Pretty nice stuff. Kind of wish they had the white panel on the front. But it's still a nice looking helmet. Back inside the box here. The shipping team can reuse whatever they want to use at their discretion. This goes to Big Boys 007. In RP, Random Player Break number 17. There it is right there. Big Boys 007 written on the box. Two more to go. Empty box. And the next one. Good luck. Alright, next one. Another old school guy.  This time we've got a middle infielder, a second baseman, a right handed second baseman, a Hall of Famer, 10 time All-Star, 2 time World Series winner, 8 time Gold Glover. And what is this award? TSN Major League Player of the Year, I guess when that was the thing. He was voted in by the Veterans Committee into the Hall of Fame in 2001. Not Fisk, not Morgan, not Smith. He won two World Series. Now here's the clue that'll give it away. He hit one of this team's most epic home runs in not only this team's history, but in Major League history in a dramatic ninth inning blast that decided the entire World Series. This remains the only walk-off home run in a seventh game of a World Series. Yep, Obi-Wan's got it. Yep, Chris Gill's got it. Maz. Bill Mazurowski. Yeah, still he's 83 years young right now. He's got a birthday coming up on September 5th. Okay, so this is a nicer helmet right here. Still Game 7 of the World Series, 1960 World Series where the Pirates beat the Yankees in seven games. He also hit 320 in that series. Two home runs, one of which was the famous one. Five RBIs, a couple doubles right here. There's his gold ink on the Pirates black right there, which is nice. You got an ear flap here. So these are one of the nicer designs and some extra padding in here by Rawlings. I don't think this is a helmet that you can use. No, it says on-field equipment. You can read all the safety disclaimers right there. There it is. Very nice. And we got the PSA DNA sticker and the corresponding card in the bag right over here. So who's got old Bill? Bill Mazurowski. Let's go back to the list. I'm going to go back to the list. Nicholas Stone. There you go. Nice. This is a gold ink right on the black. That's a sharp-looking helmet right there. Let's see what's in the last one. So we got three old Hall of Famers. So far, will the fourth be a youngin'? Will we see a youngster? Oh, he won't say they didn't have ear flaps back in the day. This might not historically accurate. I wonder if old Bill looked at this helmet and I didn't wear it. We didn't have ear flaps on our helmets. All right, two Nicholas. Probably said it's something more colorful. Stone. Probably. What else do you want me to wear? A skirt? Right. The old 83-year-old Bill Mazurowski signing helmets. And he's just like, what is this? What's going on here? What do you want me to do? Baseball helmets. I got speakers in these things? Kids aren't collecting cards anymore? What's going on here? All right, last one. Where's the sticker? Oh, down here? Okay. Another old-school guy. Another old-schooler. I think he's also a Hall... Are these all Hall of Famers? I think they might... I think they might be, unless they're youngsters. Okay, I am a Hall of Famer, 12-time All-Star, another person who's won multiple World Series, two World Series, 10-time Gold Glover, 4-time Silver Slugger, an ALCS MVP, and an All-Star MVP. The winner throws right, Puerto Rican, and got 523 votes out of 381, which I think if I did the math right. I feel like Obi-Wan... Well, okay. I was going to say, you probably can't answer this question. All right, started his career with one team, with San Diego, played most of his career, I think with this team. That's what color his helmet is in here. Then went to Baltimore for a few years, Cleveland for a few years, Mets and White Sox for a season, and then Arizona and Chicago White Sox for a season. 300 lifetime, 210 home runs, over 1100 RBIs, 474 strikeouts, almost a 3,000 hit club guy, 2,724, a lot of doubles, 504 doubles in his career, 80 triples. He was an excellent glove guy as well. His World Series were both with Toronto. I think Obi-Wan and Big River have got it. Toronto won in 1992, and then the year right after that, 92 and 93. One of the back-to-backers. It is Roberto Almar. Excellent player. What was that one unfortunate incident? No, it's Cleveland edition. I just saw a blue helmet. Wow. Fooled. I got trolled here. He spent a few years in Cleveland. He was an All-Star in those seasons too. It was pretty good. Cleveland, Roberto Almar. I thought it was going to be Blue Jays, Roberto Almar. There you go. He's got a nice auto, silver ink on the Cleveland right here. JSA, JSA card inside the bag. All that will ship, of course. Not fooled. I kind of peeked in there. I saw a little bit of blue. I couldn't tell the hue, but it's going to be that. We got Gary Tashima with the Roberto Almar. There you go. That was our fourth and final helmet. I'm Joe for jaspyscasebreaks.com. Thanks everyone for getting into the action. I will see you next time for the next one. Bye-bye.