[User] Please transcribe this text

[Assistant] Because you can't, because then the non-deterministic aspect of the control


[User] Please transcribe this text

[Assistant] You can't control where you're going to end up, so you must have a plan for every place. That's the policy. Having an action, an optimal action to take in every single state.


[User] Please transcribe this text

[Assistant] Now suppose we change the reward structure, and for every step we take, there's a negative, uh, our reward is a negative two. So it really hurts. There's a high punishment for every single step we take. So no matter what,


[User] Please transcribe this text

[Assistant] If we decrease the reward of each step to negative point one, the policy changes.


[User] Please transcribe this text

[Assistant] Were there some extra degree of wandering encouraged?


[User] Please transcribe this text

[Assistant] Every step is, increases the reward. Then there's a significant incentive to, to stay on the board without ever reaching the destination.


[User] Please transcribe this text

[Assistant] So the value function, the way we think about the value of a state or the value of anything in the environment is


[User] Please transcribe this text

[Assistant] The reward we're likely to receive in the future. And the way we see the reward we're likely to receive is we discount the future reward because we can't always count on it.


[User] Please transcribe this text

[Assistant] Further and further out into the future, more and more discounts decreases the reward, the importance of the reward received.


[User] Please transcribe this text

[Assistant] And a good strategy is taking the sum of these rewards and maximizing it. Maximizing the discounted future award. That's what reinforcement learning hopes to achieve. And with Q learning, we use


[User] Please transcribe this text

[Assistant] Any policy to estimate the value of taking an action in a state.


[User] Please transcribe this text

[Assistant] We move about the world and use the Bellman equation here on the bottom.


[User] Please transcribe this text

[Assistant] To continuously update our estimate of how good a certain action is in a certain state.


[User] Please transcribe this text

[Assistant] So we don't need, this, this allows us to operate in a much larger state space and a much larger action space. We move about this world through simulation or in the real world, taking actions and updating our estimate of how good certain actions are over time.


[User] Please transcribe this text

[Assistant] Of the reward received by taking action S, uh, action A in state S.


[User] Please transcribe this text

[Assistant] And the maximum reward that's possible to be received in the following states. Discounted.


[User] Please transcribe this text

[Assistant] That update is decreased with the learning rate. The higher the learning rate, the more value we, the faster we learn, the more value we assign to new information.


[User] Please transcribe this text

[Assistant] That's simple. That's it. That's Q learning. The simple update rule allows us to, to explore the world and as we explore,


[User] Please transcribe this text

[Assistant] Get more and more information about what's good to do in this world. And there's always a balance in the various problem spaces we'll discuss. There's always a balance between exploration and exploitation.


[User] Please transcribe this text

[Assistant] As you form a better and better estimate of the Q function of what actions are good to take, you start to get a sense of what is the best action to take.


[User] Please transcribe this text

[Assistant] But it's not a perfect sense. It's still an approximation. And so there's value of exploration. But the better and better your estimate becomes, the less and less exploration has a benefit.


[User] Please transcribe this text

[Assistant] So usually we want to explore a lot in the beginning and less and less so towards the end. And when we finally release the system out into the world and wish it to operate its best, then we


[User] Please transcribe this text

[Assistant] Have it operate as a greedy system, always taking the optimal action according to the Q value function.


[User] Please transcribe this text

[Assistant] And everything I'm talking about now is parameterized and are parameters that are very important for winning the deep traffic competition.


[User] Please transcribe this text

[Assistant] Which is using this very algorithm with a neural network at its core.


[User] Please transcribe this text

[Assistant] So for a simple table representation of a Q function, where the y-axis is state, four states, s1, 2, 3, 4, and the x-axis is actions, a 1, 2, 3, 4,


[User] Please transcribe this text

[Assistant] We can think of this table as randomly initiated or initiated, initialized in any kind of way.


[User] Please transcribe this text

[Assistant] That's not representative of actual reality. And as we move about the world and we take actions, we update this table with the Bellman equation shown up top.


[User] Please transcribe this text

[Assistant] The approximation becomes the optimal Q table.


[User] Please transcribe this text

[Assistant] The problem is when that Q table becomes exponential in size.


[User] Please transcribe this text

[Assistant] When we take in raw sensor information as we do with cameras with deep crash.


[User] Please transcribe this text

[Assistant] Potential state space, the number of possible combinatorial variations of what states is possible is extremely large. Larger than, we can certainly hold the memory, and larger than we can, uh, ever be able to accurately approximate through the Bellman equation over time through simulation.


[User] Please transcribe this text

[Assistant] So this is where deep reinforcement learning comes in. Neural networks are really good approximators. They're really good at exactly this task of learning this kind of Q table.


[User] Please transcribe this text

[Assistant] So as we started with supervised learning, where neural networks help us memorize patterns using supervised ground truth data, and we move to reinforcement learning that hopes to propagate outcomes to knowledge,


[User] Please transcribe this text

[Assistant] Deep learning allows us to do so on much larger state spaces, on much larger action spaces, which means it's generalizable.


[User] Please transcribe this text

[Assistant] And it does so because it's able to learn the representations as we discussed on Monday.


[User] Please transcribe this text

[Assistant] The understanding comes from converting the raw sensory information into simple useful information based on which the action in this particular state can be taken in the same exact way.


[User] Please transcribe this text

[Assistant] So instead of the queue table, instead of this queue function, we plug in a neural network, where the input is the state space, no matter how complex.


[User] Please transcribe this text

[Assistant] And the output is a value for each of the actions that you could take.


[User] Please transcribe this text

[Assistant] Input is the state, output is the value of the function. It's simple.


[User] Please transcribe this text

[Assistant] At the core of the success of DeepMind, a lot of the cool stuff you see about video games, DQN or variants of DQN are at play. This is what, at first, with a nature paper from DeepMind,


[User] Please transcribe this text

[Assistant] The success came of playing the different games, including Atari games.


[User] Please transcribe this text

[Assistant] So how are these things trained? Very similar to supervised learning.


[User] Please transcribe this text

[Assistant] And the discounted expected reward from future states.


[User] Please transcribe this text

[Assistant] The loss function here for a neural network, the neural network learns with a loss function. It takes the reward received at the current state.


[User] Please transcribe this text

[Assistant] Does the forward pass through a neural network to estimate the value of the future state?


[User] Please transcribe this text

[Assistant] Of the best action to take in the future state and then subtracts that.


[User] Please transcribe this text

[Assistant] So you take the difference between what your Q estimator, the neural network, believes the value of the current state is and what it more likely is to be.


[User] Please transcribe this text

[Assistant] Based on the value of the future states that are reachable based on the actions you can take.


[User] Please transcribe this text

[Assistant] Input is the state, output is the Q-value for each action, or in this diagram, input is the state in action and the output is the Q-value. It's very similar architectures. So given a transition of S, A, R, S'


[User] Please transcribe this text

[Assistant] S current state taking an action receiving a reward in achieving S prime state.


[User] Please transcribe this text

[Assistant] The, the update is do a feedforward pass to the network for the current state. Do a feedforward pass for each of the possible actions taken in the next state. And that's how we compute the two parts of the loss function.


[User] Please transcribe this text

[Assistant] Again, loss function, backpropagation is how the network is trained.


[User] Please transcribe this text

[Assistant] This has actually been around for much longer than Deep Mind. A few tricks made it, made it really work.


[User] Please transcribe this text

[Assistant] So as the games are played through simulation, or if it's a physical system, as it acts in the world,


[User] Please transcribe this text

[Assistant] It's actually collecting the observations into a library of experiences and the training is performed by randomly sampling the library in the past.


[User] Please transcribe this text

[Assistant] By randomly sampling the previous experiences and batches.


[User] Please transcribe this text

[Assistant] So you're not always training on the natural continuous evolution of the system. You're training on randomly picked batches of those experiences. That's a huge...


[User] Please transcribe this text

[Assistant] It's a, it's a, seems like a subtle trick, but it's a really important one.


[User] Please transcribe this text

[Assistant] Again, subtle trick, as in a lot of deep learning approaches, the subtle tricks make all the difference, is fixing the target network. For the loss function, if you notice,


[User] Please transcribe this text

[Assistant] You have to use the neural network, the singular neural network, the GQN network to estimate the value of the current state and action pair.


[User] Please transcribe this text

[Assistant] And the next. So you're using it multiple times.


[User] Please transcribe this text

[Assistant] And as you perform that operation, you're updating the network.


[User] Please transcribe this text

[Assistant] Which means the target function inside that loss function is always changing.


[User] Please transcribe this text

[Assistant] So you're, the very nature of your loss function is changing all the time as you're learning. And that's a big problem for stability.


[User] Please transcribe this text

[Assistant] That can create big problems for the learning process. So this little trick is to fix the network and only update it every say thousand steps.


[User] Please transcribe this text

[Assistant] So, as you train the network, the network that's used to compute the target function inside the loss function is fixed.


[User] Please transcribe this text

[Assistant] It produces a more stable computation on a loss function. So the ground doesn't shift under you as you're trying to find a minimal.


[User] Please transcribe this text

[Assistant] For the loss function. The loss function doesn't change in unpredictable, difficult to understand ways.


[User] Please transcribe this text

[Assistant] And reward clipping, which is always true with general, the systems that are operating, uh, uh, seeking to operate in a generalized way is for very, for these various games.


[User] Please transcribe this text

[Assistant] The points are different. Some points are low, some points are high, some go positive and negative, and they're all normalized to a point where the good points or the positive points are a 1 and negative points are a negative 1. That's reward clipping. Simplify the reward structure.


[User] Please transcribe this text

[Assistant] And because a lot of the games are 30 FPS or 60 FPS and the actions are not


[User] Please transcribe this text

[Assistant] It's not valuable to take actions at such a high rate inside of these particular Atari games that you only take an action every four steps, while still taking in the frames as part of the temporal window to make decisions. Tricks, but hopefully gives you a sense


[User] Please transcribe this text

[Assistant] of the kind of things necessary for both


[User] Please transcribe this text

[Assistant] Seminal papers like this one, and for the more important accomplishment of winning deep traffic, is the, is the tricks make all the difference. Here on the bottom is...


[User] Please transcribe this text

[Assistant] The circle is when the technique is used and when it's not. Looking at replay and target. Fixed target network and experienced replay. When both are used for the game of breakout, river raid, sea quest and space invaders. The higher the number, the better it is. The more points achieved.


[User] Please transcribe this text

[Assistant] So when, it gives you a sense that when replay and target both give significant improvements in the performance of the system.


[User] Please transcribe this text

[Assistant] Order of magnitude improvements. Two orders of magnitude for breakout.


[User] Please transcribe this text

[Assistant] And here is pseudocode of implementing DQN, the learning.


[User] Please transcribe this text

[Assistant] The key thing to notice, and you can look through the slides, is the, the, the loop, the wild loop.


[User] Please transcribe this text

[Assistant] Of playing through the games and selecting the actions to play.


[User] Please transcribe this text

[Assistant] It's not part of the training. It's part of the saving.


[User] Please transcribe this text

[Assistant] Uh, the observations, the state, action, reward, next state observations and saving them into replay memory into that library. And then you sample randomly from that replay memory to then train the network based on the loss function.


[User] Please transcribe this text

[Assistant] And with probability up, up top of the probability epsilon, select a random action. That epsilon is the probability of exploration that decreases. That's something you'll see in deep traffic as well.


[User] Please transcribe this text

[Assistant] Is the rate at which that exploration decreases over time through the training process. You want to explore a lot first and less and less over time.


[User] Please transcribe this text

[Assistant] So, this algorithm has been able to accomplish in 2015.


[User] Please transcribe this text

[Assistant] It's for the first time that raw sensory information was used to create a system that acts and makes sense to the world. Makes sense to the physics of the world enough to be able to succeed in it from very little information. But these games are trivial.


[User] Please transcribe this text

[Assistant] This DQN approach has been able to outperform a lot of the Atari games. That's what it's been reported on. Outperform the human level performance. But again, these games are trivial.


[User] Please transcribe this text

[Assistant] What I think, and perhaps biased, I'm biased, but one of the greatest accomplishments of artificial intelligence in the last decade.


[User] Please transcribe this text

[Assistant] At least from the philosophical or the research perspective.


[User] Please transcribe this text

[Assistant] It's DeepMind's system that beat the best in the world in a game of Go. So what's the game of Go? Simple.


[User] Please transcribe this text

[Assistant] I won't get into the rules, but basically it's a 19 by 19 board, shown on the bottom of the slide.


[User] Please transcribe this text

[Assistant] For the bottom row of the table. For a board of 19 by 19.


[User] Please transcribe this text

[Assistant] The number of legal game positions is 2 times 10 to the power of 170.


[User] Please transcribe this text

[Assistant] It's a very large number of possible positions to consider. At any one time, especially the game evolves, the number of possible moves is huge. Much larger than in chess. So that's why AI community thought that this game is not solvable.


[User] Please transcribe this text

[Assistant] When AlphaGo used this, used human expert position play.


[User] Please transcribe this text

[Assistant] To seed in a supervised way, reinforcement learning approach. And I'll describe it in a little bit of detail in a couple of slides here. To be the best in the world.


[User] Please transcribe this text

[Assistant] And beat the best in the world in an extremely complex game. This is not Atari.


[User] Please transcribe this text

[Assistant] This is a much higher order difficulty game. And the quality of players that it's competing in is much higher. And it's able to extremely quickly here to achieve a rating that's better than AlphaGo.


[User] Please transcribe this text

[Assistant] And better than the different variants of AlphaGo. And certainly better than the best of the human players. In 21 days. Of self play.


[User] Please transcribe this text

[Assistant] So how does it work? All of these approaches, much, much like the previous ones, the traditional ones, they're not based on deep learning.


[User] Please transcribe this text

[Assistant] Are using Monte Carlo Tree Search, MCTS.


[User] Please transcribe this text

[Assistant] Which is, when you have such a large state space, you start at a board and you play and you choose moves.


[User] Please transcribe this text

[Assistant] With some exploitation, exploration, balancing.


[User] Please transcribe this text

[Assistant] Choosing to explore totally new positions or to go deep in the positions you know are good until the bottom of the game is reached until the final state is reached and then you back propagate the


[User] Please transcribe this text

[Assistant] Quality of the choices you made leading to that position. And in that way you learn the value of, of board positions and play. That's been used by the most successful Go playing engines before and Alpha Go since.


[User] Please transcribe this text

[Assistant] But you might be able to guess what's the difference with AlphaGo versus the previous approaches.


[User] Please transcribe this text

[Assistant] They use the neural network as the intuition, quote unquote, to what are the good states, what are the good next board positions to explore.


[User] Please transcribe this text

[Assistant] And the key things, again, the tricks make all the difference. That made alpha go zero.


[User] Please transcribe this text

[Assistant] Work, and work much better than AlphaGo, is first, because there was no expert play, instead of human games.


[User] Please transcribe this text

[Assistant] AlphaGo used that very same Monte Carlo Tree Search algorithm, MCTS, to do an intelligent look ahead based on the neural network prediction of what are the good states to take.


[User] Please transcribe this text

[Assistant] It checked that, instead of human expert play, it checked how good indeed are those states. It's a simple look ahead action.


[User] Please transcribe this text

[Assistant] That does the ground truth, that does the target, uh, correction that produces the loss function. The second part is the multi-task learning, or what's now called multi-task learning, is the network is, is, uh, quote unquote, two-headed.


[User] Please transcribe this text

[Assistant] In the sense that first it outputs the probability of which move to take, the obvious thing, and it's also producing a probability of winning.


[User] Please transcribe this text

[Assistant] And there's a few ways to combine that information and continuously train both parts of the network depending on the choice taken. So you want to take the best choice in the short term and achieve the positions that are at the highest likelihood of winning for the player that's whose turn it is. And another big step.


[User] Please transcribe this text

[Assistant] Is that they updated from 2015, they updated the state of the art architecture, which are now the architecture that One ImageNet is the residual networks. ResNet for ImageNet.


[User] Please transcribe this text

[Assistant] Those, that's it. And those little changes made all the difference.


[User] Please transcribe this text

[Assistant] So that takes us through deep traffic and the 8 billion hours stuck in traffic.


[User] Please transcribe this text

[Assistant] America's pastime. So we tried to simulate driving, the behavioral layer of driving.


[User] Please transcribe this text

[Assistant] So not the immediate control, not the motion planning, but beyond that on top.


[User] Please transcribe this text

[Assistant] On top of those control decisions, the human interpretable decisions of changing lane, of speeding up, slowing down, modeling that,


[User] Please transcribe this text

[Assistant] In a micro traffic simulation framework that's popular in traffic engineering, the kind shown here.


[User] Please transcribe this text

[Assistant] We apply deep reinforcement learning to that. We call it deep traffic.


[User] Please transcribe this text

[Assistant] The goal is to achieve the highest average speed over a long period of time, weaving in and out of traffic.


[User] Please transcribe this text

[Assistant] For students here, the requirement is to follow the tutorial and achieve a speed of 65 miles an hour.


[User] Please transcribe this text

[Assistant] And if you really want to achieve a speed over 70 miles an hour, which is what's required to win. And perhaps upload your own image to make sure you look good doing it.


[User] Please transcribe this text

[Assistant] What you should do, clear instructions. To compete, read the tutorial.


[User] Please transcribe this text

[Assistant] You can change parameters in the code box on that website, cars.mit.u.s.g of traffic. Click the white button that says apply code, which applies the code that you write. These are the parameters that you specify for the neural network.


[User] Please transcribe this text

[Assistant] It applies those parameters, creates the architecture that you specify and now you have a network written in JavaScript living in the browser ready to be trained. Then you click the blue button that says run training and that trains the network.


[User] Please transcribe this text

[Assistant] Much faster than what's actually being visualized in the browser. A thousand times faster.


[User] Please transcribe this text

[Assistant] By evolving the game, making decisions, taking in the grid space, as I'll talk about here in a second. The speed limit is 80 miles an hour.


[User] Please transcribe this text

[Assistant] Based on the various adjustments we made to the game, reaching 80 miles an hour is certainly impossible on average. And reaching some of the speeds that we've achieved last year is much, much, much more difficult. Finally, when you're happy and the training is done, submit the model to competition.


[User] Please transcribe this text

[Assistant] You can click the request visualization specifying the custom image and the color.


[User] Please transcribe this text

[Assistant] Okay, so here's the simulation. Speed limit 80 miles an hour. Cars, 20 on the screen. One of them is a red one in this case. That one is controlled by a neural network. It's allowed the actions to speed up, slow down, change lanes, left, right, or stay exactly the same.


[User] Please transcribe this text

[Assistant] The other cars are pretty dumb. They speed up, slow down, turn left, right, but they don't have a purpose in their existence. They do so randomly. Or at least purpose has not been discovered.


[User] Please transcribe this text

[Assistant] The road, the car, the speed. The road is a grid space. An occupancy grid that specifies


[User] Please transcribe this text

[Assistant] And when there's other cars that are going slow, the value in that grid is the speed of that car. That's the state space. That's the state representation. And you can choose how much, what slice of that state space you take in. That's the input to the neural network.


[User] Please transcribe this text

[Assistant] For visualization purposes, you can choose normal speed or fast speed for watching the network operate.


[User] Please transcribe this text

[Assistant] And there's display options to help you build intuition about what the network takes in and what space the car is operating in. The default is no extra information is added. Then there's the learning input which visualizes exactly which part of the road


[User] Please transcribe this text

[Assistant] Here's a safety system. You can think of this system as ACC, basic radar ultrasonic sensors, helping you avoid the obvious collisions to obviously detectable objects around you. And the task for this red car, for this neural network, is to move about this space


[User] Please transcribe this text

[Assistant] Uh, is to move about the space under the constraints of the safety system.


[User] Please transcribe this text

[Assistant] The red shows all the parts of the grid it's not able to move into. So the goal for the car is to not get stuck in traffic.


[User] Please transcribe this text

[Assistant] The input, like DQN, is the state space, the output is the value of the different actions, and based on the epsilon parameter through training and through inference evaluation process, you choose how much exploration you want to do. These are all parameters. The learning is done in the browser, on your own computer.


[User] Please transcribe this text

[Assistant] Giving you some of the variables here. Perhaps you go back to the slides to look at it. The brain quote unquote is the thing that takes in the state and the reward takes a forward pass through the state and produces the next action.


[User] Please transcribe this text

[Assistant] Forward length and backward length. Lane side, number of lanes to the side that you see. Patches ahead is the patches ahead that you see. Patches behind is patches behind that you see. New this year can control the number of agents that are controlled by the neural network. Anywhere from one to ten.


[User] Please transcribe this text

[Assistant] And the evaluation is performed exactly the same way. You have to achieve the highest average speed for the agents.


[User] Please transcribe this text

[Assistant] The very critical thing here is the agents are not aware of each other. So they're not jointly, jointly planning.


[User] Please transcribe this text

[Assistant] The network is trained under the joint objective of achieving the average speed for all of them. But the actions are taken in a greedy way for each. It's very interesting what can be learned in this way.


[User] Please transcribe this text

[Assistant] Because these kinds of approaches are scalable to an arbitrary number of cars.


[User] Please transcribe this text

[Assistant] And you can imagine us plopping down the best cars from this class together and having them compete in this way. The best neural networks.


[User] Please transcribe this text

[Assistant] Because they're full in their greedy operation, the number of networks that can concurrently operate is fully scalable. There's a lot of parameters.


[User] Please transcribe this text

[Assistant] The layers, the many layers, types that can be added. Here's a fully connected layer with 10 neurons. The activation functions, all of these things can be customized as is specified in the tutorial.


[User] Please transcribe this text

[Assistant] Regression given the value of each of the five actions. And there's a lot of more specific parameters, some of which I've discussed. From gamma to epsilon to experience replay size to learning rate and temporal window.


[User] Please transcribe this text

[Assistant] The optimizer, the learning rate, momentum, batch size, L2L1 decay for regularization and so on.


[User] Please transcribe this text

[Assistant] There's a big white button that says apply code that you press that kills all the work you've done up to this point, so be careful doing it. You should be doing it only at the very beginning.


[User] Please transcribe this text

[Assistant] And the network state gets shipped to the main simulation from time to time. So the thing you see in the browser as you open up the website is running the same network that's being trained. And regularly it updates that network. So it's getting better and better even if the training takes weeks for you.


[User] Please transcribe this text

[Assistant] It's constantly updating the network you see on the left. So if the car for the network that you're training is just standing in place and not moving, it's probably


[User] Please transcribe this text

[Assistant] Time to restart and change the parameters, maybe add a few layers to your network.


[User] Please transcribe this text

[Assistant] Number of iterations is certainly an important parameter to control.


[User] Please transcribe this text

[Assistant] And the evaluation is something we've done a lot of work on since last year to remove the degree of randomness, to remove the, the incentive to submit the same code over and over again to hope to produce a higher reward, uh, a higher evaluation score.


[User] Please transcribe this text

[Assistant] The method for evaluation is to collect the average speed over 10 runs about 45


[User] Please transcribe this text

[Assistant] Seconds of game each, not minutes, 45 simulated seconds. And there is 500 of those, and we take the median speed of the 500 runs. It's done server side, so extremely difficult to cheat. I urge you to try.


[User] Please transcribe this text

[Assistant] You can, uh, try it locally. There's a start evaluation run, but that one doesn't count. That's just for you to feel better about your network.


[User] Please transcribe this text

[Assistant] That's, that should produce a result that's very similar to the one we produce on the server. It's to build your own intuition.


[User] Please transcribe this text

[Assistant] And as I said, we significantly reduced the influence of randomness. So the, the score, the speed you get for the network you design should be very similar with every evaluation.


[User] Please transcribe this text

[Assistant] Loading and saving. If the network is huge and you want to switch computers, you can save the network. It saves both the architecture of the network and the weights on the network and you can load it back in.


[User] Please transcribe this text

[Assistant] Obviously, when you load it in, it's not, uh, saving any of the data you've already done. You can't do transfer learning with JavaScript in the browser yet.


[User] Please transcribe this text

[Assistant] Submitting your network, submit model to competition, and make sure you run training first. Otherwise, it'll be initiated, the weights are initiated randomly and will not do so well.


[User] Please transcribe this text

[Assistant] You can resubmit as often you like and the highest score is what counts.


[User] Please transcribe this text

[Assistant] The coolest part is you can load your custom image, specify colors, and request a visualization.


[User] Please transcribe this text

[Assistant] We have not yet shown the visualization, but I promise you it's going to be awesome. Again, read the tutorial, change the parameters in the code box, click apply code, run training.


[User] Please transcribe this text

[Assistant] Everybody in this room, on the way home, on the train, hopefully not in your car, should be able to do this in the browser. And then you can visualize, request visualization, because it's an expensive process. You have to want it for us to do it, uh, because we have to run it server-side.


[User] Please transcribe this text

[Assistant] Competition link is there, GitHub starter code is there, and the details for those that truly want to win is in the archive paper. So the question,


[User] Please transcribe this text

[Assistant] That will come up throughout is whether these reinforcement learning approaches are at all or rather if action, planning, control is amenable to learning.


[User] Please transcribe this text

[Assistant] Certainly in the case of driving, we can't do what AlphaGo Zero did. We can't.


[User] Please transcribe this text

[Assistant] Learn from scratch from self-play because that would result in millions of crashes in order to learn to avoid the crashes. Unless we're working like we are with DeepCrash on the RC car or we're working on simulation.


[User] Please transcribe this text

[Assistant] So we can look at expert data, we can look at driver data, which we have a lot of and learn from. It's an open question whether this is applicable. To date, and I bring up two companies because they're both guest speakers.


[User] Please transcribe this text

[Assistant] Deep IRL is not involved in the most successful robots operating in the real world.


[User] Please transcribe this text

[Assistant] Control and planning, like in this robot.


[User] Please transcribe this text

[Assistant] Does not involve learning approaches, except with minimal addition on the perception side. Best of our knowledge. And certainly the same is true with Waymo, as the speaker on Friday will talk about.


[User] Please transcribe this text

[Assistant] Deep learning is used a little bit in perception on top, but most of the work is done from the sensors and the optimization based, the model based approaches. Trajectory generation and optimizing which trajectory is best to avoid collisions. Deep IRL is not involved.


[User] Please transcribe this text

[Assistant] And coming back and back again, the unexpected loco pock is a higher award, which arise in all of these situations when applied in the real world. So for the cat video, that's pretty short where the cats are ringing the bell and they're learning that the ring of the bell


[User] Please transcribe this text

[Assistant] I urge you to think about how that can evolve over time in unexpected ways that may not have a desirable effect.


[User] Please transcribe this text

[Assistant] Where the final reward is in the form of food and the intended effect is to ring the bell.


[User] Please transcribe this text

[Assistant] That's where AI safety comes in. For the artificial general intelligence course in two weeks, that's something we'll explore extensively. It's how...


[User] Please transcribe this text

[Assistant] These reinforcement learning, planning algorithms will evolve in ways that are not expected.


[User] Please transcribe this text

[Assistant] And how we can constrain them, how we can design reward functions that result in safe operation.


[User] Please transcribe this text

[Assistant] So I encourage you to come to the talk on Friday at 1 p.m. as a reminder. So 1 p.m. not 7 p.m. in Stata 32123 and to the awesome talks in two weeks from Boston Dynamics to Ray Kurzweil and so on for AGI.


[User] Please transcribe this text

[Assistant] Now tomorrow we'll talk about computer vision and psychfuse. Thank you everybody.


[User] Please transcribe this text

[Assistant] This animation is brought to you by Skillshare, an online learning community with thousands of inspiring classes for creative people. Explore new skills, deepen existing passions, and get lost in creativity.


[User] Please transcribe this text

[Assistant] Skillshare is an online learning community with thousands of inspiring classes for creatives. It's curated specifically for learning, meaning there are no ads. And they're always launching new premium classes.


[User] Please transcribe this text

[Assistant] So you can stay focused and follow wherever your creativity takes you. And it's less than $10 a month with an annual subscription.


[User] Please transcribe this text

[Assistant] If you want to learn how to use the same software programs we use at Nucleus, Skillshare has hundreds of courses for you on Cinema 4D, Adobe After Effects, and ZBrush.


[User] Please transcribe this text

[Assistant] They offer courses on how to create micrograph images in C4D so your cellular animations look realistic and cinematic and much, much more.


[User] Please transcribe this text

[Assistant] Take advantage of Skillshare's free trial for premium membership by clicking the link below in the video description. Sign up and explore your creativity.


[User] Please transcribe this text

[Assistant] I hope you check the perimeter, as my step-ass would say. Now, let's talk bidness.


[User] Please transcribe this text

[Assistant] Oh boo hoo, poor little rich kid. I know you've been pumping drugs and shit to kids around here.


[User] Please transcribe this text

[Assistant] I bet your respectable family would help me out if I went to them. Man, I can see the headlines now.


[User] Please transcribe this text

[Assistant] To look at me you would not know that my electricity was cut off last week for non-payment.


[User] Please transcribe this text

[Assistant] These days I'm buying the $1.99 trial-sized jug of Tide to make ends meet. I bet you didn't know laundry detergent came in that size.


[User] Please transcribe this text

[Assistant] You invite me to the same expensive restaurants the two of us have always enjoyed, but I order mineral water now with a twist of lemon, not the $12 glass of Chardonnay.


[User] Please transcribe this text

[Assistant] I am frugal in my menu choices, meticulous. I count every penny in my head.


[User] Please transcribe this text

[Assistant] I demurred dividing the table bill evenly to cover desserts and designer coffees and second and third glasses of wine I did not consume. I am tired of trying to fake appearances.


[User] Please transcribe this text

[Assistant] A friend told me that I'm broke, not poor, and there is a difference. I live without cable, my gym membership and nail appointments. I've discovered I can do my own hair.


[User] Please transcribe this text

[Assistant] Uhm, I exhausted that long ago. There is no expensive condo to draw equity and no husband to back me up.


[User] Please transcribe this text

[Assistant] Before expressing polite sympathy for my plight and then demanding payment arrangements I can't possibly meet.


[User] Please transcribe this text

[Assistant] Friends wonder privately how someone so well educated could be an economic freefall.


[User] Please transcribe this text

[Assistant] I'm still as talented as ever and smart as a whip, but work is sketchy now, mostly on and off consulting gigs. At 55, I've learned how to fake cheeriness.


[User] Please transcribe this text

[Assistant] I don't remember exactly when it stopped, but I cannot deny now having entered the uncertain world of formerly and used to be.


[User] Please transcribe this text

[Assistant] I'm not sure anymore where I belong.


[User] Please transcribe this text

[Assistant] What I do know is that dozens of online job applications seem to just disappear into a black hole. I'm wondering what is to become of me. So far my health has held up.


[User] Please transcribe this text

[Assistant] But my body aches, or is it my spirit?


[User] Please transcribe this text

[Assistant] I wrote this piece a year ago. It's a composite of my story and other women I know. I wrote it because I was tired of pretending I was alright when I wasn't. I was tired of faking normal.


[User] Please transcribe this text

[Assistant] I wasn't seeing myself in the popular press. Nobody I knew was traveling the world or buying a condo in Costa Rica.


[User] Please transcribe this text

[Assistant] My friends, many in their 50s and 60s were looking at a downward mobility, a work for life proposition, just a job loss, medical diagnosis or divorce away from insolvency. We may not have hit rock bottom.


[User] Please transcribe this text

[Assistant] Doesn't take much. The median household in the U.S. only has enough savings to replace one month of income.


[User] Please transcribe this text

[Assistant] 47% of us cannot pull together $400 to deal with an emergency. That's almost half of us. A major car repair. And we're standing on the abyss.


[User] Please transcribe this text

[Assistant] You wouldn't know it to look around you. I'm not the only one in this situation. There are people in this room who are in the same predicament. And if it's not you, it is your parents or your sister or maybe your best friend. We get good at faking normal. Shame keeps us silent and siloed. When I first


[User] Please transcribe this text

[Assistant] Why on earth would we draw down our 401k plan to cover the shortfall on our mother-in-law's nursing home care?


[User] Please transcribe this text

[Assistant] We're accused of being poor planners and deadbeats, all that money we spent on lattes and bottled water.


[User] Please transcribe this text

[Assistant] To shame and blame is so deliciously tempting.


[User] Please transcribe this text

[Assistant] Many of us don't even wait for others to do it. We're so busy doing it to ourselves.


[User] Please transcribe this text

[Assistant] I say let's own our part. We all could have saved more. I know I could have saved more. And if you were to rifle through my life over the last 30 or 30 years, you would see more than one dumb thing I have done financially. I can't change that now. And neither can you. But let's not mix up


[User] Please transcribe this text

[Assistant] Individual isolated behavior with the systemic factors that have caused a 7.7 trillion dollar retirement income gap.


[User] Please transcribe this text

[Assistant] Millions of boomer age Americans did not land here because of too many trips to Starbucks. We spent the last three decades dealing with flat and falling wages and disappearing pensions and through the roof cost on housing and healthcare and education.


[User] Please transcribe this text

[Assistant] Savings and pension and social security. Well, that stool has gone wobbly. Take savings. What savings? For many families, there's just nothing left to save after the bills have been paid.


[User] Please transcribe this text

[Assistant] The pension leg of the stool has also gone wobbly. We can remember when many people had pensions. Today, only 13% of American workers are employed by companies that offer them.


[User] Please transcribe this text

[Assistant] So what did we get instead? We got 401k type plans. And suddenly responsibility for retirement planning got shifted from our companies to us.


[User] Please transcribe this text

[Assistant] We got the reins, but we also got the risk. And it turns out that millions of us just aren't that good at voluntarily investing over 40 years. Millions of us just aren't that good at managing market risk.


[User] Please transcribe this text

[Assistant] And really the numbers tell the story. Half of all American households have no retirement savings at all. That would be zero. No 401k, no IRA, not a dime.


[User] Please transcribe this text

[Assistant] Now a hundred and four thousand dollars does sound better than zero, but as an annuity it generates about three hundred dollars. I don't have to tell you that you can't live on that.


[User] Please transcribe this text

[Assistant] With savings down, pensions becoming a relic of the past, and 401k plans failing millions of Americans, many near retirees are dependent on social security as their retirement plan.


[User] Please transcribe this text

[Assistant] But here's the problem. Social Security was never supposed to be the retirement plan. It's not nearly enough. At best, it replaces something like 40% of your pre-retirement income.


[User] Please transcribe this text

[Assistant] Things have changed a lot from when Social Security was introduced back in 1935. Then a 21 year old male had a 50% chance of living until he was 65.


[User] Please transcribe this text

[Assistant] It'd be dead within five years of receiving benefits. That's not the pattern today.


[User] Please transcribe this text

[Assistant] If you're in your late 50s and good health, you're gonna live easily another 20 or 25 years. That's a really long time to make ends meet if you are broke.


[User] Please transcribe this text

[Assistant] To have a shot at something other than being old and poor in America.


[User] Please transcribe this text

[Assistant] We're going to have to save ourselves and each other.


[User] Please transcribe this text

[Assistant] And I'm inviting you to do so as well. I'm not gonna tell you that it's not.


[User] Please transcribe this text

[Assistant] Easy. I ventured though to tell my story because I thought it would make it a little easier for people to tell theirs. I think it's only through our strength in numbers that we can begin to change the national lala conversation that we are having on this retirement crisis.


[User] Please transcribe this text

[Assistant] With so many of us shell shocked and adrift about what has happened to us, we're gonna have to build up from the grass roots.


[User] Please transcribe this text

[Assistant] Forming what I think are resilient circles. These are small groups of people coming together to talk about what has happened to them. To share resources and information and to begin to figure out a way forward.


[User] Please transcribe this text

[Assistant] I believe from this base that we can find our voices again and sound the alarm, start pushing our institutions and policy makers to go hard on this retirement crisis with the urgency it deserves.


[User] Please transcribe this text

[Assistant] We're gonna have to adopt a live low to the ground mindset. Drastically cutting back on our expenses. And I don't mean just


[User] Please transcribe this text

[Assistant] What is called for now is to, in a much deeper way, ask ourselves what it really means to live a life that is not defined by things. I call it smalling up.


[User] Please transcribe this text

[Assistant] Smalling up is figuring out what you really need to feel contented and grounded. I have a friend who, uhm, drives really beat up raggedy cars, but he will scrimp and save.


[User] Please transcribe this text

[Assistant] $15,000 at one point to buy a flute because music is what really matters to him. He smalled up.


[User] Please transcribe this text

[Assistant] Sent in one more CV or applied to one more job online or attended one more networking event that surely I'd get the kind of job I was used to having. Surely things would return to normal. The truth is, I'm not going back.


[User] Please transcribe this text

[Assistant] In this new place that we are, we're going to be asked to do things that we don't want to do. We're going to be asked to take assignments that we think are beneath our station and our talent and our skill. I have had to get off my throne.


[User] Please transcribe this text

[Assistant] Last year a good friend of mine asked me if I would help her with some organization work. I assumed she meant community organizing along the lines of what President Obama did in Chicago. She meant organizing somebody's closet.


[User] Please transcribe this text

[Assistant] I said, I'm not doing that. She said, get off your throne. Money is green.


[User] Please transcribe this text

[Assistant] It's not easy being part of the advanced team that is ushering in this new era of work and living.


[User] Please transcribe this text

[Assistant] Bridge work is also letting go of this notion that our worth and our value depend on our income and our titles and our jobs. Bridge work can look crazy or cool depending on how you were rolling when your personal financial crisis hit.


[User] Please transcribe this text

[Assistant] Bridge work doesn't mean that we don't want, uh, to build on our past careers, that we don't want meaningful work. We do.


[User] Please transcribe this text

[Assistant] I've also learned to think strategy not failure when I'm sort of processing all these things that I don't want to do.


[User] Please transcribe this text

[Assistant] If you need to take in a boarder to help you pay your mortgage or pay your rent, do it. If you need to get food stamps, get the darn food stamps. AARP says only a third of older adults who are eligible actually get them.


[User] Please transcribe this text

[Assistant] Do what you need to do to go another round.


[User] Please transcribe this text

[Assistant] Know that there are millions of us. Come out of the shadows. Cut back, small up. Think strategy, not failure.


[User] Please transcribe this text

[Assistant] Investing billions of dollars in the diagnosis, treatment and management of disease. It's not enough to just live a long time. We want to live well.


[User] Please transcribe this text

[Assistant] We haven't invested nearly as much in the physical infrastructure to ensure that that happens. We need now


[User] Please transcribe this text

[Assistant] Guidance and ideas about how to live a richly textured life on a much more modest income.


[User] Please transcribe this text

[Assistant] So I am calling on change agents and social entrepreneurs, artists and elders and impact investors. I'm calling on developers and disruptors of the status quo. We need you to help us imagine the


[User] Please transcribe this text

[Assistant] My journey has taken me from a place of fear and shame to one of humility and understanding.


[User] Please transcribe this text

[Assistant] I'm ready now to link shields with others to fight this fight. And I'm inviting you to join me. Thank you.


[User] Please transcribe this text

[Assistant] Story number eight. The Adventure of the Speckled Band.


[User] Please transcribe this text

[Assistant] On glancing over my notes of the 70 odd cases in which I have during the last eight years studied the methods of my friend Sherlock Holmes,


[User] Please transcribe this text

[Assistant] I find many tragic, some comic, a large number merely strange, but non-commonplace, for working as he did rather for the love of his art than for the acquirement of wealth, he refused to associate himself with any investigation which did not tend towards the unusual and even the fantastic.


[User] Please transcribe this text

[Assistant] Of all these varied cases, however, I cannot recall any which presented more singular features than that which was associated with the well-known Surrey family of the roilots of Stoke Moran.


[User] Please transcribe this text

[Assistant] The events in question occurred in the early days of my association with Holmes, when we were sharing rooms as bachelors in Baker Street.


[User] Please transcribe this text

[Assistant] It is possible that I might have placed them upon record before, but a promise of secrecy was made at the time, from which I have only been freed during the last month by the untimely death of the lady to whom the pledge was given.


[User] Please transcribe this text

[Assistant] It is perhaps as well that the facts should now come to light, for I have reasons to know that there are widespread rumours as to the death of Dr. Grimsby Roylott, which tend to make the matter even more terrible than the truth.


[User] Please transcribe this text

[Assistant] It was early in April in the year 83 that I woke one morning to find Sherlock Holmes standing fully dressed by the side of my bed.


[User] Please transcribe this text

[Assistant] He was a late riser, as a rule, and as the clock on the mantelpiece showed me that it was only a quarter past seven, I blinked up at him in some surprise, and perhaps just a little resentment, for I was myself regular in my habits.


[User] Please transcribe this text

[Assistant] But it's the common lot this morning. Mrs Hudson has been knocked up. She retorted upon me and I on you. What is it, then, a fire?


[User] Please transcribe this text

[Assistant] No, a client. It seems that a young lady has arrived in a considerable state of excitement, who insists upon seeing me. She is waiting now in the sitting-room.


[User] Please transcribe this text

[Assistant] Now, when young ladies wander about the metropolis at this hour of the morning, and knock sleepy people up out of their beds, I presume that it is something very pressing which they should have to communicate.


[User] Please transcribe this text

[Assistant] Should it prove to be an interesting case, you would I am sure wish to follow it from the outset.


[User] Please transcribe this text

[Assistant] I thought at any rate that I should call you and give you the chance. My dear fellow, I would not miss it for anything.


[User] Please transcribe this text

[Assistant] I had no keener pleasure than in following Holmes in his professional investigations, and in admiring the rapid deductions, as swift as intuitions, and yet always founded on a logical basis with which he unraveled the problems which were submitted to him.


[User] Please transcribe this text

[Assistant] I rapidly threw on my clothes and was ready in a few minutes to accompany my friend down to the sitting-room.


[User] Please transcribe this text

[Assistant] A lady dressed in black and heavily veiled, who had been sitting in the window, rose as we entered.


[User] Please transcribe this text

[Assistant] Good morning, madam," said Holmes cheerily. My name is Sherlock Holmes. This is my intimate friend and associate, Dr. Watson, before whom you can speak as freely as before myself.


[User] Please transcribe this text

[Assistant] I'm glad to see that Mrs. Hudson has had the good sense to light the fire.


[User] Please transcribe this text

[Assistant] She raised her veil as she spoke, and we could see that she was indeed in a pitiable state of agitation, her face all drawn and grey, with restless frightened eyes like those of some hunted animal.


[User] Please transcribe this text

[Assistant] Her features and figure were those of a woman of thirty, but her hair was shot with premature grey, and her expression was weary and haggard.


[User] Please transcribe this text

[Assistant] Sherlock Holmes ran her over with one of his quick, all comprehensive glances.


[User] Please transcribe this text

[Assistant] You must not fear," said he soothingly, bending forward and patting her forearm. We shall soon set matters right, I have no doubt. You have come in by train this morning, I see? You know me then.


[User] Please transcribe this text

[Assistant] No, but I observed the second half of a return ticket in the palm of your left glove. You must have started early, and yet you had a good drive in a dog cart, along heavy roads, before you reached the station.


[User] Please transcribe this text

[Assistant] The lady gave a violent start and stared in bewilderment at my companion.


[User] Please transcribe this text

[Assistant] There is no mystery, my dear madam," said he, smiling.


[User] Please transcribe this text

[Assistant] The left arm of your jacket is spattered with mud in no less than seven places.


[User] Please transcribe this text

[Assistant] The marks are perfectly fresh. There is no vehicle save a dog-cart which throws up mud in that way, and then only when you sit on the left-hand side of the driver.


[User] Please transcribe this text

[Assistant] Whatever your reasons may be, you are perfectly correct," said she.


[User] Please transcribe this text

[Assistant] I started from home before six, reached Leatherhead at twenty past, and came in by the first train to Waterloo.


[User] Please transcribe this text

[Assistant] None save only one who cares for me, and he, poor fellow, can be of little aid.


[User] Please transcribe this text

[Assistant] I have heard of you, Mr. Holmes. I have heard of you from Mrs. Farintosh, whom you helped in the hour of her sore need. It was from her that I had your address.


[User] Please transcribe this text

[Assistant] Oh sir, do you not think that you could help me too, and at least throw a little light through the dense darkness which surrounds me? At present it is out of my power to reward you for your services, but in a month or six weeks I shall be married, with the control of my own income, and then at least you shall not find me ungrateful.


[User] Please transcribe this text

[Assistant] Holmes turned to his desk and unlocking it, drew out a small case book which he consulted.


[User] Please transcribe this text

[Assistant] Farentosh, said he. Ah, yes. I recall the case. It was concerned with an opal tiara. I think it was before your time, Watson.


[User] Please transcribe this text

[Assistant] I can only say, madam, that I shall be happy to devote the same care to your case as I did to that of your friend. As to reward, my profession is its own reward, but you are at liberty to defray whatever expenses I may be put to at the time which suits you best.


[User] Please transcribe this text

[Assistant] And now I beg that you will lay before us everything that may help us in forming an opinion upon the matter."


[User] Please transcribe this text

[Assistant] The very horror of my situation lies in the fact that my fears are so vague and my suspicions depend so entirely upon small points, which might seem trivial to another, but even he to whom of all others I have a right to look for help and advice looks upon all that I tell him about it as the fancies of a nervous woman.


[User] Please transcribe this text

[Assistant] But I've heard, Mr. Holmes, that you can see deeply into the manifold wickedness of the human heart.


[User] Please transcribe this text

[Assistant] You may advise me how to walk amid the dangers which encompass me. I am all attention, madam. My name is Helen Stoner.


[User] Please transcribe this text

[Assistant] And I am living with my stepfather, who is the last survivor of one of the oldest Saxon families in England.


[User] Please transcribe this text

[Assistant] The family was at one time among the richest in England, and the estates extended over the borders into Berkshire in the north and Hampshire in the west.


[User] Please transcribe this text

[Assistant] In the last century, however, four successive heirs were of a dissolute and wasteful disposition, and the family ruin was eventually completed by a gambler in the days of the Regency


[User] Please transcribe this text

[Assistant] Nothing was left save a few acres of ground and the two hundred year old house which is itself crushed under a heavy mortgage.


[User] Please transcribe this text

[Assistant] The last squire dragged out his existence there, living the horrible life of an aristocratic pauper.


[User] Please transcribe this text

[Assistant] But his only son, my stepfather, seeing that he must adapt himself to the new conditions


[User] Please transcribe this text

[Assistant] Obtained an advance from a relative, which enabled him to take a medical degree and went out to Calcutta.


[User] Please transcribe this text

[Assistant] whereby his professional skill and his force of character he established a large practice.


[User] Please transcribe this text

[Assistant] In a fit of anger, however, caused by some robberies which had been perpetrated in the house, he beat his native butler to death, and narrowly escaped a capital sentence.


[User] Please transcribe this text

[Assistant] As it was, he suffered a long term of imprisonment, and afterwards returned to England a morose and disappointed man.


[User] Please transcribe this text

[Assistant] When Dr. Roylott was in India, he married my mother, Mrs. Stoner, the young widow of Major General Stoner, of the Bengal Artillery.


[User] Please transcribe this text

[Assistant] My sister Julia and I were twins, and we were only two years old at the time of my mother's remarriage. She had a considerable sum of money, not less than one thousand pounds a year.


[User] Please transcribe this text

[Assistant] And this she bequeathed to Dr. Roylott entirely, while we resided with him, with a provision that a certain annual sum should be allowed to each of us in the event of our marriage.


[User] Please transcribe this text

[Assistant] Shortly after I returned to England, my mother died.


[User] Please transcribe this text

[Assistant] She was killed eight years ago in a railway accident near Crewe.


[User] Please transcribe this text

[Assistant] Dr. Roylott then abandoned his attempts to establish himself in practice in London, and took us to live with him in the old ancestral house at Stoke Moran.


[User] Please transcribe this text

[Assistant] The money which my mother had left was enough for all our wants, and there seemed to be no obstacle to our happiness.


[User] Please transcribe this text

[Assistant] But a terrible change came over our stepfather about this time.


[User] Please transcribe this text

[Assistant] He shut himself up in his house, and seldom came out, save to indulge in ferocious quarrels with whoever might cross his path.


[User] Please transcribe this text

[Assistant] Violence of temper approaching to mania has been hereditary in the men of the family.


[User] Please transcribe this text

[Assistant] Two of which ended in the police court until at last he became the terror of the village, and the folks would fly at his approach, for he is a man of immense strength and absolutely uncontrollable in his anger.


[User] Please transcribe this text

[Assistant] Last week he hurled the local blacksmith over a parapet into a stream.


[User] Please transcribe this text

[Assistant] He had no friends at all save the wandering gypsies, and he would give these vagabonds leave to encamp upon the few acres of bramble-covered land which represent the family estate.


[User] Please transcribe this text

[Assistant] And would accept in return the hospitality of their tents, wandering away with them sometimes for weeks on end.


[User] Please transcribe this text

[Assistant] He has a passion also for Indian animals, which are sent over to him by a correspondent


[User] Please transcribe this text

[Assistant] And he has at this moment a cheetah and a baboon which wander freely over his grounds and are feared by the villagers almost as much as their master.


[User] Please transcribe this text

[Assistant] You can imagine from what I say that my poor sister Julia and I had no great pleasure in our lives.


[User] Please transcribe this text

[Assistant] No servant would stay with us, and for a long time we did all the work of the house. She was but thirty at the time of her death, and yet her hair had already begun to whiten, even as mine has. Your sister is dead, then? She died just two years ago, and it is of her death that I wish to speak to you.


[User] Please transcribe this text

[Assistant] We were little likely to see anyone of our own age and position.


[User] Please transcribe this text

[Assistant] We had, however, an aunt, my mother's maiden sister, Miss Honoria Westvale, who lives near Harrow, and we were occasionally allowed to pay short visits at this lady's house.


[User] Please transcribe this text

[Assistant] Julia went there at Christmas two years ago, and met there a half-pay major of Marines, to whom she became engaged.


[User] Please transcribe this text

[Assistant] My stepfather learned of the engagement when my sister returned, and offered no objection to the marriage.


[User] Please transcribe this text

[Assistant] But within a fortnight of the day which had been fixed for the wedding the terrible event occurred which has deprived me of my only companion.


[User] Please transcribe this text

[Assistant] Sherlock Holmes had been leaning back in his chair with his eyes closed and his head sunk in a cushion


[User] Please transcribe this text

[Assistant] But he half opened his lids now and glanced across at his visitor.


[User] Please transcribe this text

[Assistant] Pray be precise as to details, said he.


[User] Please transcribe this text

[Assistant] It is easy for me to be so, for every event of that dreadful time is seared into my memory.


[User] Please transcribe this text

[Assistant] The manor house is, as I have already said, very old, and only one wing is now inhabited.


[User] Please transcribe this text

[Assistant] The bedrooms in this wing are on the ground floor, the sitting rooms being in the central block of the buildings.


[User] Please transcribe this text

[Assistant] Of these bedrooms, the first is Dr. Roylott's, the second my sister's, and the third my own.


[User] Please transcribe this text

[Assistant] Dr. Roylott had gone to his room early, though we knew that he had not retired to rest.


[User] Please transcribe this text

[Assistant] For my sister was troubled by the smell of the strong Indian cigars which it was his custom to smoke.


[User] Please transcribe this text

[Assistant] She left her room, therefore, and came into mine, where she sat for some time, chatting about her approaching wedding. At eleven o'clock she rose to leave me, but she paused at the door and looked back. "'Tell me, Helen,' said she. "'Have you ever heard anyone whistle in the dead of the night?' "'Never,' said I.


[User] Please transcribe this text

[Assistant] I suppose that you could not possibly whistle yourself in your sleep? Certainly not. But why?


[User] Please transcribe this text

[Assistant] Because during the last few nights I have always, about three in the morning, heard a low, clear whistle. I am a light sleeper and it has awakened me.


[User] Please transcribe this text

[Assistant] I cannot tell where it came from, perhaps from the next room, perhaps from the lawn. I thought that I would just ask you whether you had heard it. No, I have not. It must be those wretched gypsies in the plantation. Very likely, and yet if it were on the lawn, I wonder that you did not hear it also. Ah, but I sleep more heavily than you. Well, it is of no great consequence at any rate.


[User] Please transcribe this text

[Assistant] I think that I mentioned to you that the doctor kept a cheetah and a baboon.


[User] Please transcribe this text

[Assistant] We had no feeling of security unless our doors were locked. Quite so. Pray proceed with your statement. I could not sleep that night.


[User] Please transcribe this text

[Assistant] A vague feeling of impending misfortune impressed me


[User] Please transcribe this text

[Assistant] My sister and I, you will recollect, were twins, and you know how subtle are the links which bind two souls which are so closely allied.


[User] Please transcribe this text

[Assistant] It was a wild night. The wind was howling outside and the rain was beating and splashing against the windows.


[User] Please transcribe this text

[Assistant] Suddenly, amid all the hubbub of the gale, there burst forth the wild scream of a terrified woman. I knew that it was my sister's voice.


[User] Please transcribe this text

[Assistant] I sprang from my bed, wrapped a shawl around me, and rushed into the corridor.


[User] Please transcribe this text

[Assistant] As I opened my door, I seemed to hear a low whistle, such as my sister described.


[User] Please transcribe this text

[Assistant] And a few moments later a clanging sound as if a mass of metal had fallen.


[User] Please transcribe this text

[Assistant] As I ran down the passage my sister's door was unlocked and revolved slowly upon its hinges.


[User] Please transcribe this text

[Assistant] I stared at it, horror-stricken, not knowing what was about to issue from it. By the light of the corridor lamp I saw my sister appear at the opening, her face blanched with terror, her hands groping for help.


[User] Please transcribe this text

[Assistant] But at that moment her knees seemed to give way and she fell to the ground.


[User] Please transcribe this text

[Assistant] She writhed as one who is in terrible pain, and her limbs were dreadfully convulsed.


[User] Please transcribe this text

[Assistant] At first I thought that she had not recognized me.


[User] Please transcribe this text

[Assistant] But as I bent over her, she suddenly shrieked out in a voice which I shall never forget. Oh my God! Helen! It was the band! The speckled band!


[User] Please transcribe this text

[Assistant] There was something else which she would fain have said, and she stabbed with her finger into the air in the direction of the doctor's room.


[User] Please transcribe this text

[Assistant] But a fresh convulsion seized her and choked her words.


[User] Please transcribe this text

[Assistant] I rushed out, calling loudly for my stepfather, and I met him hastening from his room in his dressing gown. When he reached my sister's side, she was unconscious.


[User] Please transcribe this text

[Assistant] And though he poured brandy down her throat and sent for medical aid from the village, all efforts were in vain.


[User] Please transcribe this text

[Assistant] For she slowly sank and died without having recovered her consciousness.


[User] Please transcribe this text

[Assistant] Are you sure about this whistle and the metallic sound? Could you swear to it? That was what the county coroner asked me at the inquiry.


[User] Please transcribe this text

[Assistant] It is my strong impression that I heard it, and yet


[User] Please transcribe this text

[Assistant] Among the crash of the gale and the creaking of the old house I may possibly have been deceived. Was your sister dressed? No, she was in her nightdress.


[User] Please transcribe this text

[Assistant] In her right hand was found the charred stump of a match, and in her left a match box.


[User] Please transcribe this text

[Assistant] Showing that she had struck a light and looked about her when the alarm took place. That is important. And what conclusions did the coroner come to?


[User] Please transcribe this text

[Assistant] He investigated the case with great care, for Dr Roylott's conduct had long been notorious in the county.


[User] Please transcribe this text

[Assistant] But he was unable to find any satisfactory cause of death.


[User] Please transcribe this text

[Assistant] My evidence showed that the door had been fastened upon the inner side.


[User] Please transcribe this text

[Assistant] And the windows were blocked by old-fashioned shutters with broad iron bars which were secured every night.


[User] Please transcribe this text

[Assistant] The walls were carefully sounded and were shown to be quite solid all round, and the flooring was also thoroughly examined with the same result.


[User] Please transcribe this text

[Assistant] The chimney is wide, but is barred up by four large staples.


[User] Please transcribe this text

[Assistant] It is certain, therefore, that my sister was quite alone when she met her end. Besides, there were no marks of any violence upon her. How about poison? The doctors examined her for it, but without success. What do you think that this unfortunate lady died of then?


[User] Please transcribe this text

[Assistant] It is my belief that she died of pure fear and nervous shock.


[User] Please transcribe this text

[Assistant] Ah, and what did you gather from this allusion to a band, a speckled band?


[User] Please transcribe this text

[Assistant] Sometimes I have thought that it was merely the wild talk of delirium.


[User] Please transcribe this text

[Assistant] Sometimes that it may have referred to some band of people, perhaps to these very gypsies in the plantation.


[User] Please transcribe this text

[Assistant] I do not know whether the spotted handkerchiefs which so many of them wear over their heads might have suggested the strange adjective which she used.


[User] Please transcribe this text

[Assistant] Home shook his head like a man who is far from being satisfied.


[User] Please transcribe this text

[Assistant] These are very deep waters, said he. Pray, go on with your narrative. Two years have passed since then.


[User] Please transcribe this text

[Assistant] And my life has been until lately lonelier than ever.


[User] Please transcribe this text

[Assistant] A month ago, however, a dear friend whom I have known for many years has done me the honour to ask my hand in marriage. His name is Armitage, Percy Armitage.


[User] Please transcribe this text

[Assistant] The second son of Mr. Armitage of Crane Water near Reading.


[User] Please transcribe this text

[Assistant] My stepfather has offered no opposition to the match, and we are to be married in the course of the spring.


[User] Please transcribe this text

[Assistant] Two days ago some repairs were started in the west wing of the building.


[User] Please transcribe this text

[Assistant] When last night as I lay awake thinking over her terrible fate,


[User] Please transcribe this text

[Assistant] I suddenly heard in the silence of the night the low whistle which had been the herald of her own death. I sprang up and lit the lamp, but nothing was to be seen in the room. I was too shaken to go to bed again,


[User] Please transcribe this text

[Assistant] So I dressed, and as soon as it was daylight I slipped down, got a dog-cart at the Crown Inn, which is opposite, and drove to Leatherhead.


[User] Please transcribe this text

[Assistant] From whence I have come on this morning with the one object of seeing you and asking your advice. You have done wisely, said my friend, but have you told me all? Yes, all.


[User] Please transcribe this text

[Assistant] For answer Holmes pushed back the frill of black lace which fringed the hand that lay upon our visitor's knee. Five little livid spots, the marks of four fingers and the thumb, were printed upon the white wrist. You have been cruelly used, said Holmes.


[User] Please transcribe this text

[Assistant] The lady coloured deeply and covered over her injured wrist. He is a hard man, she said, and perhaps he hardly knows his own strength.


[User] Please transcribe this text

[Assistant] There was a long silence during which Holmes leaned his chin upon his hands and stared into the crackling fire.


[User] Please transcribe this text

[Assistant] This is a very deep business," he said at last. There are a thousand details which I should desire to know before I decide upon our course of action. Yet we have not a moment to lose. If we were to come to Stoke Moran today, would it be possible for us to see over these rooms without the knowledge of your stepfather?


[User] Please transcribe this text

[Assistant] As it happens, he spoke of coming into town today upon some most important business.


[User] Please transcribe this text

[Assistant] It is probable that he will be away all day, and that there would be nothing to disturb you.


[User] Please transcribe this text

[Assistant] I have one or two things which I would wish to do now that I am in town.


[User] Please transcribe this text

[Assistant] But I shall return by the twelve o'clock train, so as to be there in time for your coming.


[User] Please transcribe this text

[Assistant] And you may expect us early in the afternoon. I have myself some small business matters to attend to. Will you not wait in breakfast? No, I must go.


[User] Please transcribe this text

[Assistant] My heart is lightened already since I have confided my trouble to you. I shall look forward to seeing you again this afternoon.


[User] Please transcribe this text

[Assistant] She dropped her thick black veil over her face and glided from the room.


[User] Please transcribe this text

[Assistant] And what do you think of it all, Watson?" asked Sherlock Holmes, leaning back in his chair. It seems to me to be a most dark and sinister business. Dark enough and sinister enough.


[User] Please transcribe this text

[Assistant] Yet if the lady is correct in saying that the flooring and walls are sound, and that the door, window, and chimney are impassable,


[User] Please transcribe this text

[Assistant] Then her sister must have been undoubtedly alone when she met her mysterious end.


[User] Please transcribe this text

[Assistant] What becomes then of these nocturnal whistles, and what of the very peculiar words of the dying woman? I cannot think.


[User] Please transcribe this text

[Assistant] When you combine the ideas of whistles at night, the presence of a band of gypsies who are on intimate terms with this old doctor,


[User] Please transcribe this text

[Assistant] The fact that we have every reason to believe that the Doctor has an interest in preventing his stepdaughter's marriage, the dying allusion to a band, and finally the fact that Miss Helen Stoner heard a metallic clang


[User] Please transcribe this text

[Assistant] Which might have been caused by one of those metal bars that secured the shutters falling back into its place, I think that there is good ground to think that the mystery may be cleared along those lines. But what then did the gypsies do? I cannot imagine. I see many objections to any such theory.


[User] Please transcribe this text

[Assistant] And so do I. It is precisely for that reason that we are going to stoke Moran this day. I want to see whether the objections are fatal, or if they may be explained away. But what in the name of the devil?


[User] Please transcribe this text

[Assistant] The ejaculation had been drawn from my companion by the fact that our door had been suddenly dashed open, and that a huge man had framed himself in the aperture.


[User] Please transcribe this text

[Assistant] His costume was a peculiar mixture of the professional and of the agricultural, having a black top hat, a long frock coat, and a pair of high gaiters, with a hunting crop swinging in his hand.


[User] Please transcribe this text

[Assistant] So tall was he that his hat actually brushed the crossbar of the doorway, and his breath seemed to span it across from side to side.


[User] Please transcribe this text

[Assistant] A large face, seared with a thousand wrinkles, burned yellow with the sun, and marked with every evil passion, was turned from one to the other of us, while his deep-set bile-shot eyes and his high, thin, fleshless nose gave him somewhat the resemblance to a fierce old bird of prey. "'Which of you is Holmes?' asked this apparition.


[User] Please transcribe this text

[Assistant] My name, sir, but you have the advantage of me, said my companion quietly.


[User] Please transcribe this text

[Assistant] It is a little cold for the time of year, said Holmes.


[User] Please transcribe this text

[Assistant] What has she been saying to you? screamed the old man furiously.


[User] Please transcribe this text

[Assistant] But I have heard that the crocuses promise well," continued my companion imperturbably.


[User] Please transcribe this text

[Assistant] You put me off, do you? said our new visitor, taking a step forward and shaking his hunting crop.


[User] Please transcribe this text

[Assistant] Holmes chuckled heartily. "'Your conversation is most entertaining,' said he. "'When you go out, close the door, for there is a decided draft.'


[User] Please transcribe this text

[Assistant] He stepped swiftly forward, seized the poker, and bent it into a curve with his huge brown hands.


[User] Please transcribe this text

[Assistant] He snarled, and hurling the twisted poker into the fireplace, he strode out of the room.


[User] Please transcribe this text

[Assistant] He seems a very amiable person," said Holmes, laughing. I'm not quite so bulky, but if he had remained I might have shown him that my grip was not much more feeble than his own.


[User] Please transcribe this text

[Assistant] As he spoke he picked up the steel poker and with a sudden effort straightened it out again.


[User] Please transcribe this text

[Assistant] Fancy as having the insolence to confound me with the official detective force.


[User] Please transcribe this text

[Assistant] This incident gives zest to our investigation, however, and I only trust that our little friend will not suffer from her imprudence in allowing this brute to trace her.


[User] Please transcribe this text

[Assistant] And now, Watson, we shall order breakfast, and afterwards I shall walk down to Doctor's Commons, where I hope to get some data which may help us in this matter.


[User] Please transcribe this text

[Assistant] It was nearly one o'clock when Sherlock Holmes returned from his excursion. He held in his hand a sheet of blue paper, scrawled over with notes and figures.


[User] Please transcribe this text

[Assistant] To determine its exact meaning, I have been obliged to work out the present prices of the investments with which it is concerned.


[User] Please transcribe this text

[Assistant] The total income which at the time of the wife's death was little short of £1,100 is now, through the fall of agricultural prices, not more than £750.


[User] Please transcribe this text

[Assistant] Each daughter can claim an income of £250 in case of marriage.


[User] Please transcribe this text

[Assistant] It is evident, therefore, that if both girls had married this beauty would have had a mere pittance, while even one of them would cripple him to a very serious extent.


[User] Please transcribe this text

[Assistant] My morning's work has not been wasted, since it has proved that he has the very strongest motives for standing in the way of anything of the sort.


[User] Please transcribe this text

[Assistant] And now, Watson, this is too serious for dawdling, especially as the old man is aware that we are interesting ourselves in his affairs. So, if you are ready, we shall call a cab and drive to Waterloo.


[User] Please transcribe this text

[Assistant] I should be very much obliged if you would slip your revolver into your pocket.


[User] Please transcribe this text

[Assistant] And Ely's number two is in an excellent argument with gentlemen who can twist steel pokers into knots. That and a toothbrush are, I think, all that we need.


[User] Please transcribe this text

[Assistant] At Waterloo we were fortunate in catching a train for Leatherhead, where we hide a trap at the station inn and drove for four or five miles through the lovely Surrey Lanes. It was a perfect day, with a bright sun and a few fleecy clouds in the heavens.


[User] Please transcribe this text

[Assistant] The trees and wayside hedges were just throwing out their first green shoots, and the air was full of a pleasant smell of the moist earth.


[User] Please transcribe this text

[Assistant] To me, at least, there was a strange contrast between the sweet promise of the spring and this sinister quest upon which we were engaged.


[User] Please transcribe this text

[Assistant] My companion sat in the front of the trap, his arms folded, his hat pulled down over his eyes, and his chin sunk upon his breast, buried in the deepest thought.


[User] Please transcribe this text

[Assistant] A heavily timbered park stretched up in a gentle slope, sickening into a grove at the highest point.


[User] Please transcribe this text

[Assistant] From amid the branches there jutted out the grey gables and high roof tree of a very old mansion.


[User] Please transcribe this text

[Assistant] Yes sir, that be the house of Dr Grimsby Roylough, remarked the driver.


[User] Please transcribe this text

[Assistant] There is some building going on there," said Holmes. That is where we are going.


[User] Please transcribe this text

[Assistant] There's the village, said the driver, pointing to a cluster of roofs some distance to the left.


[User] Please transcribe this text

[Assistant] But if you want to get to the house, you'll find it shorter to get over this stile and so by the footpath over the fields. There it is, where the lady is walking.


[User] Please transcribe this text

[Assistant] And the lady, I fancy, is Miss Stoner, observed Holmes, shading his eyes.


[User] Please transcribe this text

[Assistant] Yes, I think we had better do as you suggest.


[User] Please transcribe this text

[Assistant] We got off, paid our fare, and the trap rattled back on its way to Leatherhead.


[User] Please transcribe this text

[Assistant] I thought it as well," said Holmes, as we climbed the stile, that this fellow should think we had come here as architects, or on some definite business. It may stop his gossip.


[User] Please transcribe this text

[Assistant] Good afternoon, Miss Stoner. You see that we had been as good as our word.


[User] Please transcribe this text

[Assistant] Our client of the morning had hurried forward to meet us with a face which spoke her joy.


[User] Please transcribe this text

[Assistant] Dr Roylott has gone to town, and it is unlikely that he will be back before evening.


[User] Please transcribe this text

[Assistant] He is so cunning that I never know when I am safe from him. What will he say when he returns?


[User] Please transcribe this text

[Assistant] He must guard himself, for he may find that there is someone more cunning than himself upon his track.


[User] Please transcribe this text

[Assistant] You must lock yourself up from him tonight. If he is violent, we shall take you away to your aunt's at Harrow. Now, we must make the best use of our time, so kindly take us, at once, to the rooms which we are to examine.


[User] Please transcribe this text

[Assistant] The building was of grey, lichen blotched stone, with a high central portion and two curving wings, like the claws of a crab, thrown out on each side.


[User] Please transcribe this text

[Assistant] In one of these wings the windows were broken and blocked with wooden boards, while the roof was partly caved in. A picture of ruin.


[User] Please transcribe this text

[Assistant] The central portion was in little better repair, but the right-hand block was comparatively modern, and the blinds in the windows, with the blue smoke curling up from the chimneys, showed that this was where the family resided.


[User] Please transcribe this text

[Assistant] Holmes walked slowly up and down the ill-trimmed lawn and examined with deep attention the outsides of the windows.


[User] Please transcribe this text

[Assistant] This, I take it, belongs to the room in which you used to sleep.


[User] Please transcribe this text

[Assistant] The centre one to your sisters, and the one next to the main building to Dr Roylott's chamber. Exactly so, but I am now sleeping in the middle one.


[User] Please transcribe this text

[Assistant] Pending the alterations, as I understand. By the way, there does not seem to be any very pressing need for repairs at that end wall. There were none. I believe that it was an excuse to move me from my room.


[User] Please transcribe this text

[Assistant] Now on the other side of this narrow wing runs the corridor from which these three rooms open. There are windows in it, of course. Yes, but very small ones, too narrow for anyone to pass through.


[User] Please transcribe this text

[Assistant] As you both locked your doors at night your rooms were unapproachable from that side.


[User] Please transcribe this text

[Assistant] Now, would you have the kindness to go into your room and bar your shutters?


[User] Please transcribe this text

[Assistant] Miss Stoner did so, and Holmes, after a careful examination through the open window, endeavoured in every way to force the shutter open, but without success. There was no slit through which a knife could be passed to raise the bar. Then with his lens he tested the hinges, but they were of solid iron, built firmly into the massive masonry.


[User] Please transcribe this text

[Assistant] Hmmm, said he, scratching his chin in some perplexity.


[User] Please transcribe this text

[Assistant] My theory certainly presents some difficulties. No one could pass these shutters if they were bolted.


[User] Please transcribe this text

[Assistant] Well, we shall see if the inside throws any light upon the matter.


[User] Please transcribe this text

[Assistant] A small side door led into the whitewashed corridor from which the three bedrooms opened. Holmes refused to examine the third chamber, so he passed at once to the second, that in which Miss Stoner was now sleeping, and in which her sister had met with her fate.


[User] Please transcribe this text

[Assistant] It was a homely little room with a low ceiling and a gaping fireplace, after the fashion of old country houses.


[User] Please transcribe this text

[Assistant] A brown chest of drawers stood in one corner, a narrow white counterpane bed in another, and a dressing-table on the left-hand side of the window. These articles, with two small wicker-work chairs, made up all the furniture in the room save for a square of Wilton carpet in the centre.


[User] Please transcribe this text

[Assistant] The boards round and the panelling of the walls were of brown, worm-eaten oak, so old and discoloured that it may have dated from the original building of the house.


[User] Please transcribe this text

[Assistant] Holmes drew one of the chairs into a corner and sat silent, while his eyes travelled round and round and up and down, taking in every detail of the apartment.


[User] Please transcribe this text

[Assistant] Where does that bell communicate with?" he asked at last, pointing to a thick bell-rope which hung down beside the bed, the tassel actually lying upon the pillow. It goes to the housekeeper's room. It looks newer than the other things. Yes, it was only put there a couple of years ago. Your sister asked for it, I suppose. No, I never heard of her using it. We used always to get what we wanted for ourselves.


[User] Please transcribe this text

[Assistant] Indeed, it seemed unnecessary to put so nice a bell-pool there. You will excuse me for a few minutes while I satisfy myself as to this floor.


[User] Please transcribe this text

[Assistant] He threw himself down upon his face with his lens in his hand, and crawled swiftly backward and forward, examining minutely the cracks between the boards.


[User] Please transcribe this text

[Assistant] Then he did the same with the woodwork with which the chamber was panelled.


[User] Please transcribe this text

[Assistant] Finally he walked over to the bed and spent some time in staring at it and in running his eye up and down the wall.


[User] Please transcribe this text

[Assistant] This is very interesting. You can see now that it is fastened to a hook just above where the little opening for the ventilator is. How very absurd! I never noticed that before.


[User] Please transcribe this text

[Assistant] Very strange, muttered Holmes, pulling at the rope.


[User] Please transcribe this text

[Assistant] There are one or two very singular points about this room. For example, what a fool a builder must be to open a ventilator into another room when, with the same trouble, he might have communicated with the outside, sir. That is also quite modern," said the lady. Done about the same time as the bell-rope? remarked Holmes.


[User] Please transcribe this text

[Assistant] Yes, there were several little changes carried out about that time.


[User] Please transcribe this text

[Assistant] With your permission, Ms. Stoner, we shall now carry our researchers into the inner apartment.


[User] Please transcribe this text

[Assistant] Dr Grimesby Roylott's chamber was larger than that of his stepdaughter, but was as plainly furnished.


[User] Please transcribe this text

[Assistant] A camp bed, a small wooden shelf full of books, mostly of a technical character, an armchair beside the bed, a plain wooden chair against a wall, a round table, and a large iron safe were the principal things which met the eye.


[User] Please transcribe this text

[Assistant] Only once, some years ago, I remember that it was full of papers. There isn't a cat in it, for example. No, what a strange idea. Well, look at this. He took up a small sorter of milk which stood on top of it. No, we don't keep a cat, but there is a cheetah and a baboon.


[User] Please transcribe this text

[Assistant] Ah, yes, of course. Well, a cheetah is just a big cat, and yet a saucer of milk does not go very far in satisfying its wants, I dare say. There is one point which I should wish to determine.


[User] Please transcribe this text

[Assistant] He squatted down in front of the wooden chair and examined the seat of it with the greatest attention.


[User] Please transcribe this text

[Assistant] The object which had caught his eye was a small dog lash hung on one corner of the bed.


[User] Please transcribe this text

[Assistant] The lash, however, was curled upon itself and tied so as to make a loop of whip-cord. What do you make of that, Watson?


[User] Please transcribe this text

[Assistant] It's a common enough lash, but I don't know why it should be tied. That is not quite so common, is it?


[User] Please transcribe this text

[Assistant] Ah me, it's a wicked world, and when a clever man turns his brains to crime it is the worst of all.


[User] Please transcribe this text

[Assistant] I think that I have seen enough now, Miss Stoner, and with your permission we shall walk out upon the lawn.


[User] Please transcribe this text

[Assistant] I had never seen my friend's face so grim, or his brow so dark as it was when we turned from the scene of this investigation.


[User] Please transcribe this text

[Assistant] We had walked several times up and down the lawn, neither Miss Stoner nor myself, liking to break in upon his thoughts before he roused himself from his reverie.


[User] Please transcribe this text

[Assistant] It is very essential, Miss Stoner," said he, that you should absolutely follow my advice in every respect. I shall most certainly do so. The matter is too serious for any hesitation. Your life may depend upon your compliance. I assure you that I am in your hands.


[User] Please transcribe this text

[Assistant] In the first place both my friend and I must spend the night in your room. Both Miss Stoner and I gaze at him in astonishment. Yes, it must be so. Let me explain. I believe that this is the village inn over there. Yes, that is the crown.


[User] Please transcribe this text

[Assistant] Very good. Your windows will be visible from there. Certainly.


[User] Please transcribe this text

[Assistant] You must confine yourself to your room on pretence of a headache when your stepfather comes back. Then when you hear him retire for the night,


[User] Please transcribe this text

[Assistant] You must open the shutters of your window, undo the hasp, put your lamp there as a signal to us, and then withdraw quietly with everything which you are likely to want into the room which you used to occupy. I have no doubt that in spite of the repairs you could manage there for one night. Oh, yes, easily. The rest you will leave in our hands. But what will you do?


[User] Please transcribe this text

[Assistant] We shall spend the night in your room, and we shall investigate the cause of this noise which has disturbed you.


[User] Please transcribe this text

[Assistant] I believe, Mr. Holmes, that you have already made up your mind," said Miss Stoner, laying her hand upon my companion's sleeve. Perhaps I have. Then, for pity's sake, tell me what was the cause of my sister's death. I should prefer to have clearer proofs before I speak. You can at least tell me whether my own thought is correct, and if she died from some sudden fright.


[User] Please transcribe this text

[Assistant] No, I do not think so. I think that there was probably some more tangible cause.


[User] Please transcribe this text

[Assistant] And now, Miss Stoner, we must leave you, for if Dr. Roylott returned and saw us, our journey would be in vain.


[User] Please transcribe this text

[Assistant] Goodbye, and be brave. For if you will do what I have told you, you may rest assured that we shall soon drive away the dangers that threaten you.


[User] Please transcribe this text

[Assistant] Sherlock Holmes and I had no difficulty in engaging a bedroom and sitting-room at the Crown Inn.


[User] Please transcribe this text

[Assistant] They were on the upper floor, and from our window we could command a view of the avenue gate, and of the inhabited wing of Stoke Moran Manor House.


[User] Please transcribe this text

[Assistant] At dusk we saw Dr. Grimesby Roylott drive past, his huge form looming up beside the little figure of the lad who drove him.


[User] Please transcribe this text

[Assistant] The boy had some slight difficulty in undoing the heavy iron gates, and we heard the hoarse roar of the doctor's voice, and saw the fury with which he shook his clinched fists at him.


[User] Please transcribe this text

[Assistant] The trap drove on, and a few minutes later we saw a sudden light spring up among the trees as the lamp was lit in one of the sitting rooms.


[User] Please transcribe this text

[Assistant] Do you know, Watson," said Holmes, as we sat together in the gathering darkness, I have really some scruples as to taking you to-night. There is a distinct element of danger. Can I be of assistance? Your presence might be invaluable. Then I shall certainly come. It is very kind of you.


[User] Please transcribe this text

[Assistant] You speak of danger. You've evidently seen more in these rooms than was visible to me.


[User] Please transcribe this text

[Assistant] No, but I fancy that I may have deduced a little more. I imagine that you saw all that I did.


[User] Please transcribe this text

[Assistant] I saw nothing remarkable save the bell-rope, and what purpose that could answer, I confess, is more than I imagine. You saw the ventilator too?


[User] Please transcribe this text

[Assistant] Yes, but I don't think that it's such a very unusual thing to have a small opening between two rooms. It was so small that a rat could hardly pass through.


[User] Please transcribe this text

[Assistant] I knew that we should find a ventilator before ever we came to Stoke Moran. My dear Holmes. Oh yes, I did.


[User] Please transcribe this text

[Assistant] You remember in her statement she said that her sister could smell Dr. Roylott's cigar. Now, of course, that suggested at once that there must be a communication between the two rooms.


[User] Please transcribe this text

[Assistant] It could only be a small one, or it would have been remarked upon at the coroner's inquiry. I deduced a ventilator. But what harm can there be in that?