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[Assistant] He's also my friend, and I don't want us to lie to him.


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[Assistant] He has that right. If he throws you out, I'll go with you.


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[Assistant] Dear Papa, All social ties are finished. Now we only have ties of family. I have a husband now. If you can give us some money, I will move far away.


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[Assistant] No one will know my husband's name, Mr. Sorel. I was the first to love. I seduced him. There is no finer man, and I will go with him.


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[Assistant] If he dies, I will die too. Maybe you'll be happy.


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[Assistant] But first, I'll be his widow in public. I won't be afraid.


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[Assistant] Stay with Father Perard. I'll handle everything here. You must trust me.


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[Assistant] I have waited six weeks out of respect for you, but soon I will go to Father Perard to be married.


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[Assistant] I'm afraid people will use this against you though. If you come, everything will be fine.


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[Assistant] Mr. de la Verne will be very happy, but please don't forget about me. I won't send your letter until you promise we will have a public marriage.


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[Assistant] Write your own training loop in PyTorch. In this video,


[User] Please transcribe this text

[Assistant] Well, look at how we can do the same fine tuning as in the trainer video, but without relying on that class.


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[Assistant] This way, you'll be able to easily customize each step of the training loop to your needs.


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[Assistant] This is also very useful to manually debug something that went wrong with the Trainer API. Before we dive into the code, here is a sketch of a training loop. We take a batch of training data and feed it to the model. With the labels, we can then compute a loss. That number is not useful on its own,


[User] Please transcribe this text

[Assistant] It is used to compute the gradients of our model weights. That is, the derivative of the loss with respect to each model weight.


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[Assistant] Don't hesitate to take a refresher on your favorite deep learning course.


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[Assistant] We'll use the Clue Armor PC dataset here again, and we've seen how to process the data using the datasets library with dynamic padding. Check out the videos linked below if you haven't seen them already.


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[Assistant] With this done, we only have to define PyTorch data loaders.


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[Assistant] Which will be responsible to convert the elements of our dataset into patches.


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[Assistant] We use our data collator for panning as the collate function, and shuffle the training set to make sure we don't go over the samples in the same order at each epoch. To check that everything works as intended, we try to grab a batch of data and inspect it. Like our dataset elements, it's a dictionary.


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[Assistant] But this time, the values are not a single list of integers, but a tensor of shape batchized by sequence length. The next step is to send the training data in our model. For that, we'll need to actually create our model.


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[Assistant] As seen in the model API video, we use the fromPreTrain method and adjust the number of labels to the number of classes we have on this dataset. Here, 2.


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[Assistant] Again, to be sure everything is going well, we pass the patch we grabbed to our model and check there is no error.


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[Assistant] As the labels are provided, the models of the Transformers library always return the loss directly.


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[Assistant] We'll be able to do Lust.backward to compute all the gradients, and we'll then need an optimizer to do the training steps.


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[Assistant] We use the AdamW optimizer here, which is a variant of Adam with proper weight decay. But you can pick any pine torch optimizer you like.


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[Assistant] Using the previous loss and computing the gradients we've lost at backward,


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[Assistant] We check that we can do the optimizer step without any error.


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[Assistant] Don't forget to zero your gradient afterwards, or at the next step, forget to add it to the gradients you computed.


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[Assistant] We could already write our training loop, but we'll add two more things to make it as good as it can be.


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[Assistant] The first one is a learning rate scheduler to progressively decay your learning rate to zero. The get scheduler function from the transformers library


[User] Please transcribe this text

[Assistant] It's just a convenience function to easily build such a scheduler. You can again use any PyTorch learning rate scheduler instead. Finally,


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[Assistant] If we want our training to take a couple of minutes instead of a few hours, we'll need to use a GPU. The first step is to get one. For instance, by using a collab notebook.


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[Assistant] Then, you need to actually send your model and training data on it by using a Torch device.


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[Assistant] Double check the following line sprite to create a device for you, or be prepared for your training to last more than an hour. We can now put everything together.


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[Assistant] First, we put our model in training mode, which will activate the training behavior for some layers, like dropout.


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[Assistant] Then we go through the number of epochs we picked, and all the data in our training data loader. Then we go through all the steps we've seen already.


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[Assistant] Send the data to the GPU, compute the model output, and in particular, the loss. Use the loss to compute gradients, then make a training step with the optimizer.


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[Assistant] Update the learning rate in our scheduler for the next iteration, and there are the gradients that we optimized earlier.


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[Assistant] Once this is finished, we can evaluate our model very easily with a metric from the datasets library.


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[Assistant] First, we put our model in evaluation mode to deactivate layers like dropout, then go through all the data in the evaluation data loader.


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[Assistant] As we've seen in the trainer video, the model outputs logits, and we need to apply the argmark function to convert them into predictions.


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[Assistant] Symmetric object then has an add-batch method we can use to send it for intermediate predictions.


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[Assistant] When the evaluation loop is finished, we just have to call the compute method to get our final results.


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[Assistant] Congratulations! You have now fine tuned the model all by yourself.


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[Assistant] I don't know my guy, I don't know. Uhm, belly ache though, like that was crazy, like we were just sitting in my garage and we were just like...


[User] Please transcribe this text

[Assistant] As we do, just hanging out in the garage. As we do. And we were just like playing random stuff, we were just like, you know, strumming and making up random melodies as you do to write a song. We didn't even mean to write a song, we just were like there and that was, it was happening and uhm, we were just like riffing and Phineas was like sitting all alone, mouth full of gum.


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[Assistant] Okay, so I came up with the- Dude, see the thing is that we write- we forget because we write so 50-50 that-


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[Assistant] We kind of both feel like we wrote the whole song because we like wrote it equally, you know? So it's like, I don't know. Anyway, anyway, uh, I wrote that first part and then Phineas was like, my friends aren't far.


[User] Please transcribe this text

[Assistant] In the back of my car are their bodies, like we're like driving around the town with our friends in the car. No. I was like, I was like in the back of my car lay their bodies. I just killed all of them.


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[Assistant] I also grew up being really, we both grew up really being guilty about stuff, so like all, everything we do we just get a bellyache because we were guilty. Like, I used to steal stuff when I was like four. I would just like steal random things I saw, and then I'd feel horrible.


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[Assistant] Yeah, uhm, but yeah, that, I mean, and like a big part of our writing process is we like to write, whether it be uhm, from our perspective or from nobody's perspective, like, it's, you can write about anything, you don't have to go through it, you know?


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[Assistant] As long as you're not like, lying, like some people are, they really talk about things they really have no idea about, but it's like, it's different when you try to be a character, you know, when you put yourself in somebody else's shoes and write from their perspective, you know, I've, I've like written a song from the perspective of somebody I hurt, you know, mentally, so then,


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[Assistant] It was one of the ones I was just talking about, the horrible ones that we recorded before Ocean Eyes. Yeah, uh huh, yep. When I was 12, yeah I was 12, I uhm, my mom was teaching me how to write songs and she had this little like songwriting class and I was in it and the, the kind of like the


[User] Please transcribe this text

[Assistant] And I just wrote down a bunch of stuff and I wrote a song called Fingers Cross about the zombie apocalypse. But,


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[Assistant] Nobody actually knows that's what it's about, except when I tell them. Because it sounds more like a longing, unrequited love, you know, it sounds like...


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[Assistant] Photos or being the manager. Yeah. Have you ever thought of doing that?


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[Assistant] Why is he reading books when I am dying? Oh, how I wish I had never married him.


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[Assistant] I wish I was a girl again, and out on the moors. Open the window, Ellen. Now!


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[Assistant] No, it will make you cold and you will become even sicker.


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[Assistant] That's my room, with the candle in it, and the other candle is in Joseph's room. He's waiting for me to come home. Well Joseph, you must wait longer.


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[Assistant] To find Wuthering Heights, we must pass by the Gimmerton Church.


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[Assistant] Heathcliff, we've seen the ghosts in that churchyard, haven't we?


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[Assistant] We used to tell each other to stand in the churchyard when we were children and call to the ghosts, He, Cliff, I'm dying, and I might become a ghost in that church soon. Will you come if I ask you? If you do, I'll keep you with me forever. My love, I'll not lie under that ground by myself.


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[Assistant] The people might bury me twelve feet under the ground.


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[Assistant] But I won't rest until you are with me Heathcliff. I never will.


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[Assistant] Oh sir, Miss Catherine is sick and will not stay in bed.


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[Assistant] I'm going to a new home, a little home in the churchyard, under the ground. I'm dying. Edgar, I don't want you now. Go back to your books. Sir, she does not know what she is saying.


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[Assistant] She's gone! I had to go into Gimerton this morning, and I found out that Heathcliff's taken her away!


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[Assistant] With my confidence restored, it all sounded pretty good to me, and I was anxious to try out this wonderful plan. I thanked him, and taking the brace and nails, I left the store.


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[Assistant] By the time I reached home, it was too late in the day to start making my traps. That night, I talked the idea over with Papa.


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[Assistant] I've heard of coons being caught that way, he said, but I never paid much attention to it.


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[Assistant] Your grandfather should know, though, for he was quite a coon hunter when he was a boy. From what he told me, I said, it never fails. Papa asked if I wanted him to help make my traps. No, I said, I think I can do it myself.


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[Assistant] I didn't sleep too well that night. I bored holes, drove nails, and fought coons practically all night.


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[Assistant] Early the next morning, I went to the trash pile. As I stirred around in the rusty old cans, I thought of another time I had searched for a can.


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[Assistant] Finally, I found the one I wanted. It was bright and shiny.


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[Assistant] Everything was going along just fine until mama caught me cutting out the circles of tin with her scissors.


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[Assistant] I always swore she could find the biggest switches of any woman in the Ozarks. That time she overdid it. I was almost to the river before the stinging stopped.


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[Assistant] It wasn't hard to find places for my traps. All along the river, large sycamore logs lay partly submerged in the clear blue water.


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[Assistant] On one where I could see the muddy little tracks of the ring-tails, I bored a hole, dropped in a piece of tin, and drove my nails.


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[Assistant] On down the river I went making my traps. I stopped when I ran out of nails. Altogether I had fourteen traps.


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[Assistant] That night Papa asked me how I was making out. Oh, all right, I said. I've got fourteen of them made.


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[Assistant] The next morning I was up with the chickens. I took my pups with me as I just knew I'd have a big ring-tail trap and I wanted them to see it.


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[Assistant] I was a disappointed boy when I peeked out of a cane break at my last trap and didn't see a coon. All the way home I tried to figure out what I had done wrong. I went to Papa. He put his thinking cap on and thought the situation over.


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[Assistant] Maybe you left too much scent around when you made those traps, he said. If you did, it'll take a while for it to go away. Now, I wouldn't get too impatient. I'm pretty sure you'll catch one sooner or later.


[User] Please transcribe this text

[Assistant] Papa's words perked me up just like air does a deflated inner tube. He was right. I had simply left too much scent around my traps. All I had to do was wait until it disappeared, and I'd have my coon hide.


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[Assistant] Morning after morning it was the same old disappointment, no coons.


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[Assistant] When a week had gone by and still no results from my traps, I gave up.


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[Assistant] What little patience I had was completely gone. I was firmly convinced that coons didn't walk on sycamore logs any more, and bright shiny objects had about as much effect on them as a coon hound would.


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[Assistant] One morning, I didn't get up to run my trapline, I stayed in bed. What was the use? It was just a waste of time.


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[Assistant] When the family sat down to breakfast, I heard my oldest sister say, Mama, isn't Billy gonna get up for breakfast?


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[Assistant] Why is he in his room? Mama asked. I didn't know. I thought he was down looking at his traps. I heard Papa say, I'll go wake him up. He came to the door and said, You better get up, Billy. Breakfast is ready. I don't want any breakfast, I said. I'm not hungry.


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[Assistant] Papa took one look at me and saw I had a bad case of the ring-tailed blues. He came over and sat down on the bed.


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[Assistant] What's the matter? he asked. You having coon trouble? Grandpa lied to me, Papa, I said. I should have known better. Who ever heard of anyone catching a coon with a brace and a bit and a few horseshoe nails?


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[Assistant] I wouldn't say that, Papa said. I don't think your grandpa deliberately lied to you. Besides, I've heard of coons being caught that way.


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[Assistant] Well I don't think I've done anything wrong, I said. I've done everything exactly as he said and I haven't caught one yet. I still think it's that scent, Papa said.


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[Assistant] You know, someone told me or I read it somewhere that it takes about a week for scent to die away. How long has it been since you made those traps? It's been over a week, I said.


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[Assistant] Well the way I figure it's about time for you to catch one.


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[Assistant] Yes, I wouldn't be surprised if you came in with one any day now. After Papa had left the room, I lay thinking of what he had said. Any day now. I got up and hurried into my clothes.


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[Assistant] As soon as I was finished with breakfast I called my pups and lit out for the river. The first trap was empty. So was the second one.


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[Assistant] That old feeling of doubt came over me again. I thought, it's no use, I'll never catch one, and I so need the skin to train my pups.


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[Assistant] On the way to my third trap I had to walk through a thick stand of wild cane. It was tough going and my pups started whimpering. I stopped and picked them up.


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[Assistant] We'll be out of this in a few minutes, I said, and then you'll be alright.


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[Assistant] I came plowing out of the matted mass and was right on the trap before I realized it. I was met by a loud squall. I was so surprised I dropped the pups. There he was, my first coon.


[User] Please transcribe this text

[Assistant] He was humped up on the sycamore log, growling and showing his teeth. He kept jerking his front paw, which was jammed deep in the hole I had bored. He was trapped by his own curiosity.


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[Assistant] On seeing my movement, the coon let out another squall. It scared me half to death. I froze in my tracks and started yelling again at my pup.


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[Assistant] The girl pup worked around behind the coon and climbed up on the log. I screamed at her. She paid no attention to me.


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[Assistant] The girl pup saved him. Like a cat in a corn crib she sneaked in from behind and sank her needle-sharp teeth in the coon's back.


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[Assistant] It was too much for ol' Ringy. He turned the boy pup loose, turned around, and slapped her clear off the log.


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[Assistant] She came running to me, yelping her head off. I grabbed her up in my arms and looked for the boy-pup.


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[Assistant] When the coon had turned him loose, he too had fallen off the log. He was trying to get back to the coon. I darted in and grabbed him by the hind leg.


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[Assistant] With the pup under each arm and running as fast as I could, I lit out for the house. Coming out of the bottoms into a fresh plowed field, I set my pups down so I could get a little more speed. I started yelling as soon as I came inside of the house. Mama came flying out with my sisters right behind her. Papa was out by the barn harnessing his team. Mama yelled something to him about a snake.


[User] Please transcribe this text

[Assistant] He dropped the harness, jumped over the rail fence, and in a long lope started for me. Mama reached me first. She grabbed me and shouted, Where did it bite you?


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[Assistant] Fight me, I said. My mama, I'm not bit. I've got him. Mama, I've got him. Got what? Mama asked. A big coon, I said. The biggest one in the river bottoms. He's this big, Mama. I made a circle with my arms as big as a twenty gallon keg.


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[Assistant] Mama just groaned way down deep and covered her face with her hands. Some big tears squeezed out between her fingers. Almost in a whisper I heard her say, Thank God! I thought she was snake-bitten.


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[Assistant] My sister's seeing mama crying, puckered up and started bawling. He needs a whippin', the oldest one said. That's what he needs, scaring mama that way.


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[Assistant] Something busted loose inside me and I cried a little too.


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[Assistant] I didn't mean to scare mama. I sniffed. I just wanted everyone to know I caught a coon.


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[Assistant] Within these stone walls, 3,512 men are paying their debt to society. Every day new men enter. Every day old ones leave. This is the story of one who left in a coffin.


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[Assistant] Here are the deposit slips you asked for, Mr. Jefferson. They're all stamped? Yes, sir. Anything else? That's all.


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[Assistant] Ladies and gentlemen of the jury, the state demands the death penalty. The jury's been out four hours. Keep this line open.


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[Assistant] Ladies and gentlemen of the jury, have you reached a verdict?


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[Assistant] Your honor, I ask you to set bail large enough to guarantee the appearance of this prisoner for trial. Will $20,000 be sufficient?


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[Assistant] Afraid, Floyd? Sure you're afraid. Because your time's up.


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[Assistant] You know now that no man can put himself above the law. No man is smarter than the law. Like countless others who have paid the supreme penalty, you've learned too late that crime can be no more perfect than man himself.


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[Assistant] The supervisor said, I don't think women can work organized crime. He said, you'll be the only woman on this squad. And I was. Welcome to New York.


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[Assistant] I twisted myself closer into him, quite an accomplishment actually, to make it clear what I was talking about. That's difficult to say. Everyone is different and, well, so far you're the very most different of all.


[User] Please transcribe this text

[Assistant] The average young vampire is too obsessed with thirst to notice much else for awhile. That doesn't seem to apply to you. With the average vampire though, after that first year, other needs make themselves known.


[User] Please transcribe this text

[Assistant] Neither thirst nor any other desire really ever fades. It's simply a matter of learning to balance them, learning to prioritize and manage. How long? He smiled, wrinkling his nose a little. Rosalie and Emmet were the worst.


[User] Please transcribe this text

[Assistant] Took a solid decade before I could stand to be within a five mile radius of them. Even Carlisle and Esme had a difficult time stomaching it. They kicked the happy couple out eventually. Esme built them a house too. It was grander than this one, but then Esme knows what Rose likes. And she knows what you like. So after ten years then, I was pretty sure that Rosalie and Emmett had nothing on us. But it might sound cocky if I went higher than a decade.


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[Assistant] Everybody is normal again, like they are now. Edward smiled again. Well, I'm not sure what you mean by normal.


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[Assistant] You've seen my family going about life in a fairly human way. But you've been sleeping nights. He winked at me.


[User] Please transcribe this text

[Assistant] There's a tremendous amount of time left over when you don't have to sleep. It makes balancing your interests quite easy. There's a reason why I'm the best musician in the family. Why, besides Carlisle, I've read the most books, studied the most sciences, become fluent in the most languages. Emmett would have you believe that I'm such a know-it-all because of the mind reading. But the truth is that I've just had a lot of free time. We laugh together.


[User] Please transcribe this text

[Assistant] It's my first time in person also anywhere since the beginning of the pandemic and I have to admit, you know, I woke, I was telling Sean this, I woke up this morning, I was like, why did I sign up to do in person again instead of just logging on Zoom and rolling?


[User] Please transcribe this text

[Assistant] Uh, but it's also like super invigorating and exciting to do something out of the ordinary instead of just, um, you know, being on zoom all day. So great to see faces in the audience again.


[User] Please transcribe this text

[Assistant] Uhm, okay, thanks for the introduction. I'm Kosa Gachir Lambert again. So faculty in mechanical engineering at Berkeley, not too far away. Uhm, and then also really involved in our design institute there. So think of a mirror kind of like to the d.school here. We have the Jacobs Design Institute at Berkeley.


[User] Please transcribe this text

[Assistant] So, a quick, just about me, uh, so at Berkeley I direct the co-design lab, which stands for cognition and computation and design.


[User] Please transcribe this text

[Assistant] Uh, so we're interested in the wide range of problems that kind of span these topics around human behavior and design. Uhm, so we run controlled experiments that we use both behavioral and computational methods.


[User] Please transcribe this text

[Assistant] We also do some, I would say, HCI-adjacent work. Uh, so we're thinking about interactive prototypes and making new tools. Typically these are things for, uh, engineers and designers to use. And then we also study, you know, real designers, quote unquote, in, in the flesh, like, while they're making. So this can be both students and professionals.


[User] Please transcribe this text

[Assistant] Uh, so the first one is come join us at Berkeley no matter who you are. Uh, so I'm hiring, uh, both, um, PhD students and postdocs.


[User] Please transcribe this text

[Assistant] If you're a senior PhD student postdoc, uhm, we're also hiring in the mechanical engineering department, uh, tenure track position in data driven design, uh, due November 15th. So if you're in this room, I presume you're interested in design and maybe one of these two things, uh, relate to you. Okay. So.


[User] Please transcribe this text

[Assistant] When I think about design, right, where there's lots of topics that we're engaged in. And, uh, you know, when we talk about design computation, we might think about things like this. This is, uh, some folks, my friends at Autodesk.


[User] Please transcribe this text

[Assistant] Uh, this is their new generative design platform. So making new tools that designers and engineers might use to help realize ideas. And then as we start to move down, we're thinking about interactions and ways that people are utilizing these tools. So what makes it helpful or useful to them?


[User] Please transcribe this text

[Assistant] And when we think about behavior, we're thinking about people making in the design process. This can be an individual.


[User] Please transcribe this text

[Assistant] What makes an individual creative or successful? Or we can think about teams. How can we enable teams of people to collaborate more effectively to create better together?


[User] Please transcribe this text

[Assistant] So a lot of the work in my lab revolves around studying designers. And typically, you know, what we're trying to do here is better understand fundamental processes involved in the design.


[User] Please transcribe this text

[Assistant] You know, some popular focuses that we have would range from studying differences between experts and novices in the design process. So what makes someone an expert at design?


[User] Please transcribe this text

[Assistant] Thinking about more nuanced topics like different brainstorming or ideation methods. Uh, thinking about, uh, something I'll talk about a lot today, which is analogical reasoning and creativity in design.


[User] Please transcribe this text

[Assistant] This whole space and ecosystem that I play in, uhm, is really at the intersection of a few different topics. So we like to bring together perspectives on human behavior.


[User] Please transcribe this text

[Assistant] And I think laying at the intersection of these different techniques we can answer some really interesting questions because each one of these methods really allows us to learn something different about design behavior.


[User] Please transcribe this text

[Assistant] So this is a future vision of what may be designed to look like in the future from some of our friends in the UK here. And I really like this vision of


[User] Please transcribe this text

[Assistant] Kind of this future of interaction and immersion with some of our design tools. And you can see how some of these ideas come to life even in this quick picture, right? So how we're getting these real time insights that might come from cognitive processes or neural signals that we're measuring.


[User] Please transcribe this text

[Assistant] Right, how someone might have an interface. And I saw that, you know, you saw, uh, had a talk here on, uh, VR and AR the other week. So, you know, how might someone then be, you know,


[User] Please transcribe this text

[Assistant] Uhm, and whole host of questions such as emotional response, national gestures, you know, emotion recognition, etc. So how does this kind of thing become a reality?


[User] Please transcribe this text

[Assistant] I think we have a ways to go, and I'll share some perspectives of some of the work that we've been doing, uh, related to some of these questions, specifically in the context of how to inspire people, so how to find examples for someone automatically based on their design work in real time.


[User] Please transcribe this text

[Assistant] I call this future scalable design support tools and we need to do a few things, right? So we need to measure people's cognition, their brain activity in some way.


[User] Please transcribe this text

[Assistant] Then maybe we want to reorient them in this global space of many design solutions. Maybe give them a design that's more sustainable or more feasible to the one that they currently have.


[User] Please transcribe this text

[Assistant] So I'm going to get to these topics again, but we're going to take a little bit of a detour first, uhm, and talk about analogical reasoning in engineering design. So, uhm, the whole talk is really about finding good examples for people, and one way to do this is through an analogy. So, uhm, whether you know this really well,


[User] Please transcribe this text

[Assistant] When we talk about analogical reasoning, we're talking about the process where in information from one application, the source is applied in a new way. We call that the target. So I'm trying to design a chair. I might draw inspiration from other pieces of furniture or maybe something in nature.


[User] Please transcribe this text

[Assistant] You know, maybe a tree inspires the new back of my chair.


[User] Please transcribe this text

[Assistant] So we study this more formally in the design research community because tons of studies and this is like


[User] Please transcribe this text

[Assistant] And lots of open questions, so uhm.


[User] Please transcribe this text

[Assistant] How should we give these examples to people, right? Like, should I give it to them at the beginning, in the middle, at the end of problem solving? Uhm, how does someone's past experience or expertise impact the way that they might use such a thing? And then...


[User] Please transcribe this text

[Assistant] Why are these analogies helpful in the first place? You know, can we actually measure brain activity while people are using these sources of inspiration to learn about, uhm, like mental representation of analogies and then do a better job at kind of answering these other sub-questions? And so here's some


[User] Please transcribe this text

[Assistant] So this is a close-up view of Velcro being inspired by these cockleberry plants that get stuck on your pant leg and then


[User] Please transcribe this text

[Assistant] Uhm, I love this one, so this is for my colleague, Liyue Lin at Berkeley. This is a small cockroach-inspired robotic device.


[User] Please transcribe this text

[Assistant] Uh, and the difference between a near versus far field analogy, so.


[User] Please transcribe this text

[Assistant] We'll keep it pretty simple here. You can imagine this spectrum that analogies can fall on. Uh, a near analogy being something that shares a lot of surface, uh, attributes with the problem. So I quickly mentioned offhand.


[User] Please transcribe this text

[Assistant] I'm a furniture designer making chairs. Maybe other chairs share a lot of surface level properties with what I want to do. That would be a near field analogy. A far field analogy is something that has mostly non-surface level connections. So a good example of this are ones from nature.


[User] Please transcribe this text

[Assistant] So prior work has kind of demonstrated this idea that there's this quote unquote sweet spot of analogies that are most effective. And uhm,


[User] Please transcribe this text

[Assistant] This idea is that there's not too, you know, not too hot, not too cold, same concept here. So not too near, not too far is where it's kind of like the optimal point of examples to present to people.


[User] Please transcribe this text

[Assistant] Being the, you know, researcher that's curious, uh, we kind of identified one big stumbling block here, which is that it's really, really hard to even, like, come up with a set of examples for a given problem. So, the first research study that I'm going to show,


[User] Please transcribe this text

[Assistant] Uhm, is where we use, uh, crowdsourcing technique to actually try and generate a really large database of analogies for problems. So, we started with identifying kind of the standardized set of problems from the design research literature in my community.


[User] Please transcribe this text

[Assistant] We then crowdsourced a high volume of people on Amazon Mechanical Turk to provide solutions to these problems. We then used some natural language processing to extract common ideas and analogies that were helpful at these different distances, so some near and some far away.


[User] Please transcribe this text

[Assistant] Uh, and then we tested this, so we ran a human subject study where we recruited a hundred and fifty students, uhm, who were in a design engineering class and we gave them different, uh, analogies and we saw the impact on problem solving.


[User] Please transcribe this text

[Assistant] You know, design a new measuring cup for the blind to a device that can collect energy from human motion.


[User] Please transcribe this text

[Assistant] Alright, so as I mentioned, we put these online. Uh, we had 1300 people who solved these problems, came up with ideas to them.


[User] Please transcribe this text

[Assistant] And what we did was we took the solutions and we separated them into different categories. So one was really crude based on word frequency. One was more nuanced based on the semantic distance of the actual content of their ideas.


[User] Please transcribe this text

[Assistant] So then we get to kind of the fun part is, you know, running these cognitive studies with, with people in order to like giving them the, the different concepts. So, uh, we recruited 111, uh, students in mechanical engineering.


[User] Please transcribe this text

[Assistant] Who then each got four problems and they had ten minutes to work on them. So across these there were different conditions, but they basically saw one condition with common words near, one with the medium, one with far, and one where we're just controlling. We're giving them no new information. They're just getting words from the problem statement.


[User] Please transcribe this text

[Assistant] So we can see here like one problem is a device to remove the shell, uh, from a peanut in areas with no electricity. And common ideas would be like crank, blade, squeeze, conveyor, right? Things that you might automatically start thinking of when you're trying to solve that problem. Okay.


[User] Please transcribe this text

[Assistant] So, these, you know, 100 plus mechanical engineering students came up with a ton of concepts, right?


[User] Please transcribe this text

[Assistant] 1600 plus ideas, uhm, and so what do we do with those ideas? So uhm, as some of you might be aware, you know, it's common to, to write these manually, which is another huge burden on poor graduate students, right? Uhm, so these were rated, uhm,


[User] Please transcribe this text

[Assistant] And this, uh, ICC is the kind of agreement of the raters. Uhm, and so what the raters are doing is they're assessing ideas like these that the students came up with in the class, uh, across four different categories. So, how novel is the idea? How feasible is it? Is it useful?


[User] Please transcribe this text

[Assistant] And what's its overall quality? So this kind of mega metric.


[User] Please transcribe this text

[Assistant] Okay, and then this is just showing the relationship between, uh, some of those attributes. Alright, so with this...


[User] Please transcribe this text

[Assistant] We see some interesting things and I'm just kind of quickly summarizing across what we found in this first experiment. So one is we see that these near examples improve people's ability to generate more useful and feasible concepts.


[User] Please transcribe this text

[Assistant] And the second is that these more abstract, these distant examples help improve their overall novelty.


[User] Please transcribe this text

[Assistant] We also looked at another thing, which is, you know, just asking people how useful were these, uh, different


[User] Please transcribe this text

[Assistant] You know, words for you, these ideas as you were generating problems and, uh, you know, how did you perceive...


[User] Please transcribe this text

[Assistant] Alright, so this is like a, it's a nice study, it was like a lot of fun doing this crowdsourcing work, uhm, but it left us with a lot of questions, right?


[User] Please transcribe this text

[Assistant] We don't really know anything about how people were using them, why these, uh, different pieces of stimuli were helpful for individuals. And that, like, opened up this whole, uhm,


[User] Please transcribe this text

[Assistant] Trying to answer these questions, open up this whole, like, rabbit hole of work that, uh, myself and my research group now, we've been on, uhm, at the intersection of design and neuroscience. And so, one experiment that I want to talk about mirrors what I showed, except now, we did this inside of a fMRI. So,


[User] Please transcribe this text

[Assistant] We had a little bit of a pared down experiment, so we had just the near, just the far condition, 12 problems.


[User] Please transcribe this text

[Assistant] My 32nd, uh, fmri primer for people who might have, like, no clue what this is. So,


[User] Please transcribe this text

[Assistant] With the MRI, what we're trying to do is at a high level link tasks that are, that people are performing while they're inside to areas of their brain that are active while they're doing that task.


[User] Please transcribe this text

[Assistant] And so what we do is we measure changes in the oxygenated hemoglobin, uh, within the brain and we're comparing activation between different experimental conditions that we have. So was there more brain activity? So this is like a old sample experiment, but like, was there more?


[User] Please transcribe this text

[Assistant] Brain activity while people are watching movie compared to at rest or doing the task. So for us, what does brain activity look like?


[User] Please transcribe this text

[Assistant] When the participants were inside of the MRI, it looked kind of like this. So they would get some instructions, they would see the design problems, so.


[User] Please transcribe this text

[Assistant] They have one minute to think of solutions.


[User] Please transcribe this text

[Assistant] And then they took a quick break, 20 seconds to do a separate task that did not relate to this brain activity or regions that we are interested in, interested in. And then they came back, they got two more, uhm, words and they kept on solving the problem for another one minute.


[User] Please transcribe this text

[Assistant] And then at the end we asked them for some ratings. So using that thumb pad, our keypad, they answered how useful were the words, how relevant, how novel did they think their solutions were during that.


[User] Please transcribe this text

[Assistant] So first at just like behavioral level, just looking at the raw data.


[User] Please transcribe this text

[Assistant] We see that participants are generating more ideas, uh, in the early phases of ideation. So I don't think this is particularly surprising. Immediately people came in.


[User] Please transcribe this text

[Assistant] And it's like new idea, new idea, new idea. So lots of ideas are being generated early on.


[User] Please transcribe this text

[Assistant] And then in the second half, the second one minute this, you know, starts to fall off a cliff. And the interesting thing to us is that


[User] Please transcribe this text

[Assistant] We see that the stimuli when they're getting these examples that's having an overall positive effect on how many ideas they generate. So people generate more ideas overall in this second period when they're getting examples and that's more pronounced for the near


[User] Please transcribe this text

[Assistant] So overall the near condition is providing, uh, the most help in terms of stimuli that are generated. Yes.


[User] Please transcribe this text

[Assistant] In short, it's not great to have, you can only measure brain activity for a certain amount of time, uhm, before you like want those, those regions that you're interested in to return to a baseline level.


[User] Please transcribe this text

[Assistant] So we, even the 60 second period that we have here is like at the upper bound of what you would want for this.


[User] Please transcribe this text

[Assistant] Uhm, and so in order to break that up, so this is a problem with fMRI. So this is why you might see other neuroimaging modalities like EEG or FNIRs being more prevalent for this type of work. Uhm, you want to let these regions related to


[User] Please transcribe this text

[Assistant] Critical thinking, problem solving, return to baseline.


[User] Please transcribe this text

[Assistant] So with this one back test, they're like,


[User] Please transcribe this text

[Assistant] There's less ideas but we're seeing a bitter impact of the, uh, the stimuli that we're being provided to people. Okay?


[User] Please transcribe this text

[Assistant] And so we see this if we, so these graphs are showing for like, it's modeling every instance in time across the conditions.


[User] Please transcribe this text

[Assistant] And so we see here like this outlier of not having the inspirational stimuli in that second problem solving block. But like basically all the other ones are almost like


[User] Please transcribe this text

[Assistant] So what we're doing here is we're looking at the brain activation levels that are specifically locked around


[User] Please transcribe this text

[Assistant] Love would love to talk, uh, neuroimaging with those who are interested and we can chat after, but I just wanted to sum up at like a high level, I know this is a broad audience, uhm, a few of the findings and like in terms of the interpretation of what those brain regions mean.


[User] Please transcribe this text

[Assistant] And then more interesting to us, this process of directing your attention internally, so.


[User] Please transcribe this text

[Assistant] Specifically trying to recall things from memory, as well as


[User] Please transcribe this text

[Assistant] One other result that I just want to quickly show is more about impasse. So like,


[User] Please transcribe this text

[Assistant] And so what we see is that this, like what we call the impasse network is most present in that control condition.


[User] Please transcribe this text

[Assistant] And we're linking this here, uhm, in the impasse and the control condition to there being an increase in visual processing, uh, and we believe that this is really attributed to, like, people continuing to, like, search for insight, inspiration, but practically,


[User] Please transcribe this text

[Assistant] When people aren't coming up with ideas, they're just looking at the screen and there's not a lot going on.


[User] Please transcribe this text

[Assistant] Which is using a method called hidden Markov models to try and identify like these reoccurring brain states uhm, that people revisit while they're problem solving. So we want to see like, do these brain activation networks that are


[User] Please transcribe this text

[Assistant] Kind of hot off the press stuff. Uhm, but it's a really like, I think, fun blend between neuroscience, a method that you might be familiar with from another area, which is hidden Markov modeling.


[User] Please transcribe this text

[Assistant] Okay, so some takeaways overall from like these first collection of studies that I showed. So I think overall,


[User] Please transcribe this text

[Assistant] These results are informing behavior during uhm, the design process and it really reinforces


[User] Please transcribe this text

[Assistant] And shows, I think, this direct, uhm, evidence for these common theories that we hold.


[User] Please transcribe this text

[Assistant] Uhm, and the second is this evidence again of the sweet spot. So we saw in the work here that


[User] Please transcribe this text

[Assistant] So we believe that this, kind of what we thought of as the nearer common set, is actually a little bit shifted on that spectrum that I showed, and more akin to a sweet spot example.


[User] Please transcribe this text

[Assistant] Whereas the far condition that we had was likely too far away, like too dissimilar for people to make sense of.


[User] Please transcribe this text

[Assistant] We also were really, you know, excited by and inspired by the neuroimaging, uh, element of this work.


[User] Please transcribe this text

[Assistant] And helping to uncover these two distinct, uh, search patterns as people were exploring the design space. And so, uh, I alluded to these, but one of them was the successful use of the inspirational stimuli.


[User] Please transcribe this text

[Assistant] I want to spend my last 15 minutes or so talking about kind of where we've been taking some of these ideas more recently.


[User] Please transcribe this text

[Assistant] Uh, and so less relation to like cognitive science, neuroscience in the next portion.


[User] Please transcribe this text

[Assistant] More on, uh, I guess design computation and design behavior.


[User] Please transcribe this text

[Assistant] So uhm, the first is kind of a question that uhm, my graduate student Nanya has been working on.


[User] Please transcribe this text

[Assistant] Database of other design ideas, so showing here we have like lots of patents.


[User] Please transcribe this text

[Assistant] Uhm, how should I make the comparison? Like, there's so many different similarity methods that exist to do this task. What's the best one and why? So that was like the high level goal here.


[User] Please transcribe this text

[Assistant] Basically a functional decomposition of products. So there's a product library that has many commonly used objects that are all represented this way. And it, you know, describes, uh, the flow of energy and materials through the system at a very kind of like nice granular fashion.


[User] Please transcribe this text

[Assistant] And we use, uhm, a whole host of methods that are kind of described here, so some are vector-based methods, like cosine similarity, uh, some are network-based methods, thinking about, you know, graph edit distances, and, uh, other ones that you might be less familiar with, like the delta con distance.


[User] Please transcribe this text

[Assistant] We're basically exploring these six different, like commonly used, uh, metrics for similarity and seeing what's the best one.


[User] Please transcribe this text

[Assistant] The core insight is, as research goes, sometimes there's not really a right answer here. Uhm, but I think it's important to note that


[User] Please transcribe this text

[Assistant] They're returning really different stuff. So often times you'll see as a, and we've experienced this in my own group.


[User] Please transcribe this text

[Assistant] As researchers, you kind of just select whatever you want at this stage. A lot of people select that cosine distance metric. Uhm, but we're seeing, you know, they're skewed in opposite directions. So having and deciding which method here to select has a really big impact.


[User] Please transcribe this text

[Assistant] And it's that there's a user interface and people are actually interacting with this system and trying to retrieve stuff. Uh, this matters and it matters a lot. And so one of the outputs here is we started to map these methods.


[User] Please transcribe this text

[Assistant] Along what we believed they might be better suited to. So we're looking at kind of complex systems to less complex systems and better for exploring a design space versus maybe being in a more convergent state of exploitation where you want to really like dive in. So that was one piece of this study. More recently, what we wanted to do was see


[User] Please transcribe this text

[Assistant] How do all of these like algorithmic techniques of measuring similarity compare to


[User] Please transcribe this text

[Assistant] How a human, if you just ask them, is A more similar to B, actually relate to one another. So, I won't go in depth through, uhm, this figure here because I'm looking at the, at the clock. Uhm, but I think this is more useful. So, basically we showed people,


[User] Please transcribe this text

[Assistant] Uh, different representations of those same products that we had initially the functional models for. And we showed them a few different representations of those items. So,


[User] Please transcribe this text

[Assistant] And one, we're showing people like high level descriptions about like how that design works. So here's a toy plane and we're saying that the function is to pump, you know, pressurize air into the plane and then, you know, it creates translational motion. So that's one.


[User] Please transcribe this text

[Assistant] How it works representation or level of abstraction.


[User] Please transcribe this text

[Assistant] And the other one is high level product purpose. So, you know, this nerf gun is to export the ammo, let's say, right? And so we have a whole data set of like,


[User] Please transcribe this text

[Assistant] Things from water pumps to printers to game controllers across these two abstraction levels.


[User] Please transcribe this text

[Assistant] And we're running a study where in one condition, or one version, we're showing people online


[User] Please transcribe this text

[Assistant] Different variations like this and asking them to say,


[User] Please transcribe this text

[Assistant] This level of representation really affects, you know, what's returned.


[User] Please transcribe this text

[Assistant] We see here, you know, these two items, the nerf gun and the plane were close together because they have this shared pneumatic mechanism that people probably picked up on from reading the description. But when we compare that to product purpose, we see that the


[User] Please transcribe this text

[Assistant] Uh, we also were conducting this work where we were


[User] Please transcribe this text

[Assistant] Trying to give people examples like in real time. So we were letting people come into the lab, giving them a design problem. They would work on that problem.


[User] Please transcribe this text

[Assistant] We would map their solution into this design space representation, and then we would give them a near or far example, uhm, from where they currently were within this design space. And everything here is based on text, right? So they're describing their idea, the ideas that they're getting back are actually the, uhm,


[User] Please transcribe this text

[Assistant] Amazon crowd, uh, Amazon Mechanical Turk like sourced ideas. So we're just finding like textually similar concepts from this large database that we have.


[User] Please transcribe this text

[Assistant] But that wasn't so good, like I was kind of underwhelmed by that. It always bugged me that we were like using these really simple, uh, text-based representations for.


[User] Please transcribe this text

[Assistant] Like this whole process that's really dynamic in design, right? Like we want to let people do...


[User] Please transcribe this text

[Assistant] Whatever they want. We want to let people sketch. We want to let people make stuff in CAD. We want to also support the text examples and so,


[User] Please transcribe this text

[Assistant] Uh, this is something that two students, so Alisa and Forrest, have been working on, which is trying to realize this vision of, like, this multi-purpose platform that can take any type of input


[User] Please transcribe this text

[Assistant] Can take it in text, can take it in sketch or 3D model, and then return something to you that's either near or far away from what you're working on. So.


[User] Please transcribe this text

[Assistant] I will say off-front there's like, we have some really exciting stuff, but I think there's like so much room to go, uh, and so many really, really challenging questions to explore that relate to, uhm, this topic.


[User] Please transcribe this text

[Assistant] Uh, high level, we're training like these two, uh, different networks, one that's focusing on form, uh, so we call this the appearance network, and one that's focusing on function.


[User] Please transcribe this text

[Assistant] Uhm, I won't go into the details but the function one I think is in particular like the harder challenge in my opinion because it's really, really difficult.


[User] Please transcribe this text

[Assistant] To know like what type of functional representation to use, uh, for a given problem. So we can support these two different ways, searching ideas versus that are look like what you have and searching ideas that behave like what you currently have.


[User] Please transcribe this text

[Assistant] So at Stanford, so I have to give you the plug for the dataset that we, that we started out with. Actually, before we used, uh, PartNet.


[User] Please transcribe this text

[Assistant] We tried to use a dataset called the ABC dataset that some of you might have seen, uh, which is a re- ABC is a really large open source repository of


[User] Please transcribe this text

[Assistant] Uh, three-dimensional CAD models. Uh, but there's no structure there whatsoever. So like, anyone can go online, make a CAD model and submit it to this repository. And there's like, you know,


[User] Please transcribe this text

[Assistant] If you have seen PartNet or ImageNet or anything similar, there's a really nice ontological structure where, you know, we know not only like the labels, like this is a bag, but the bag is then segmented into handle and, uh, you know, fastener and all of the subcomponents as well.


[User] Please transcribe this text

[Assistant] So, we have over 25,000 CAD models across 24 different object categories that are searchable within this prototype interface that we built.


[User] Please transcribe this text

[Assistant] And so just to give you a quick kind of snapshot on the types of things that you can get back. So if you're querying our system based on this, you know, table support based on appearance, you're getting things that


[User] Please transcribe this text

[Assistant] You know, don't relate to tables at all necessarily, but visually you can imagine how this, like, faucet base looks like that. Or for this, uhm, dresser, how that flat component looks like a tabletop or whatever else is shown. Compare this to parts that are retrieved based on function. I think more debate here.


[User] Please transcribe this text

[Assistant] You know, other supports across different chairs. I like this one with the chair legs.


[User] Please transcribe this text

[Assistant] We see other legs, but then we see like a table support or a cabinet support that share that same purpose of supporting another object.


[User] Please transcribe this text

[Assistant] As I mentioned, this interface that we made supports three high level types of search. So you can search based on a keyword, like give me a container.


[User] Please transcribe this text

[Assistant] Give me, you know, this is the part I want to find something that's high or low in appearance similarity or high or low in functional similarity. And then we also have this, like,


[User] Please transcribe this text

[Assistant] Workspace area where you can draw, you can drop some of the CAD models into, you can expand them or manipulate the models.


[User] Please transcribe this text

[Assistant] So, I don't know, so this is like just a little quick video that, it's like six seconds, but you can just see.


[User] Please transcribe this text

[Assistant] Showing the different like areas where you can enter keywords.


[User] Please transcribe this text

[Assistant] Is trying to unpack different types of search modes that are available to people. So searching based on appearance or function and seeing how those decisions shape


[User] Please transcribe this text

[Assistant] The design space or the area that they're covering. So do people explore more broadly in the design space when using the functional search or keyword search or what have you?


[User] Please transcribe this text

[Assistant] Uhm, so again, I think a long way to go here, but this is another really nice fusion, I think, between like design behavior, design computation, because to enable


[User] Please transcribe this text

[Assistant] Any of these methods, right, like there's a lot of, uhm, computational work that's underpinning this.


[User] Please transcribe this text

[Assistant] And so, uh, some initial results are that we're seeing the most coverage. So people are exploring the most, the most broadly.


[User] Please transcribe this text

[Assistant] I think a little bit disappointing to see in some respects. Uhm, but also there's more flexibility and imagination that you can express in language. And often times as designers when we commit to sketching or commit to making a CAD model, we're very much more like set in that representation or that form.


[User] Please transcribe this text

[Assistant] I think there's a lot of challenges. And so just with this project, right, we see there's a big challenge, I think with like the data.


[User] Please transcribe this text

[Assistant] How do we even get this like high quality design data? Like it's really taxing to do these studies with professionals or with students and have them come in and make designs and relate them to one another. Uhm, so that's a huge challenge.


[User] Please transcribe this text

[Assistant] There's also this like whole issue of function that


[User] Please transcribe this text

[Assistant] You can ask me a question about it, but I will say I don't have good insight necessarily.


[User] Please transcribe this text

[Assistant] But trying to learn what something is for is like so challenging. Like if there's any like computer vision folks in the room, like it's just like such an immense challenge across like any application area and we see it in mechanical design as well.


[User] Please transcribe this text

[Assistant] To be quite honest, this was not like something that used to be on my mind. And now it is super present in my mind. Like how do we even design an effective interface that can serve as this mediator between human and data that's in the background? Uhm, and so we're kind of working on now like iterating on the interface I quickly showed.


[User] Please transcribe this text

[Assistant] Uhm, starting to engage professional designers to like use this tool to give us their feedback, uh, and to see how it's impacting their processes. So, going back to the beginning, right, we talked about this vision of infusion of behavior.


[User] Please transcribe this text

[Assistant] Beginning to scratch the surface on like, on every single one of these, like areas, behavior, cognition, computation. And I tried to show a kind of spectrum of work that all shared the flavor of how do we give, find, or sorry, how do we find good examples for people and how do we present that to them? And there's


[User] Please transcribe this text

[Assistant] Tons of other ones, right, that lay at this intersection. So uhm,


[User] Please transcribe this text

[Assistant] I'm so curious about this like handoff problem for design tools, so.


[User] Please transcribe this text

[Assistant] Like, when should we, like, allow, have a tool cue somebody that they want to provide feedback and how does that interaction, uh, at, what does it actually look like? So, if you're an HCI person, I think some of these will look very, uh, common and familiar to you.


[User] Please transcribe this text

[Assistant] So I want to leave some room for questions. It looks like we have like 10ish minutes. Uhm, here are some coordinates for me. So please do reach out if, uh, I don't get to your question or you just want to connect or if you're at Berkeley and want to come say hi.


[User] Please transcribe this text

[Assistant] Uhm, and also, if you want to talk about any of this stuff, uh, please let me know. Uh, and with that, thanks for having me and happy to take questions.


[User] Please transcribe this text

[Assistant] Yeah, really great question. I have to admit too, it's like kind of eerie in the room here to like have the people on Zoom echoing from the side.


[User] Please transcribe this text

[Assistant] So, to be honest, that was not something that we were doing a very good job of to start and I think running that like last steady and building that interface really showed us, uhm,


[User] Please transcribe this text

[Assistant] How we need to change our perspective, so uhm.


[User] Please transcribe this text

[Assistant] I think we do a good job at like finding forms that are similar, but as I alluded to in the talk


[User] Please transcribe this text

[Assistant] We're really struggling with the functional side of things, uhm, and part of that is because within our like


[User] Please transcribe this text

[Assistant] Anything that exists in the world, we're comparing this against a data set that's already been segmented into different components. So if I fi-


[User] Please transcribe this text

[Assistant] So if we have a shape that looks like a chair leg, it's going to find other chair legs and we're going to know something about that object's function or purpose already. It's not uhm, you know, completely open.


[User] Please transcribe this text

[Assistant] Moving this to be completely open in the, like, version that you just described is something that we, like, honestly really haven't started yet and is going to take a long time.


[User] Please transcribe this text

[Assistant] Yeah, yeah, yeah, great question. So that was our like main, so that was what we wanted to look at, right? Is like really at a fine-grained level, understand how are these like near concepts different from the far concepts? Uhm, the truth of it is,


[User] Please transcribe this text

[Assistant] These two processes when they're useful are much more similar than different. And because of our experimental design, which if I was now to go back and do it again, I probably would have designed that experiment a little bit differently.


[User] Please transcribe this text

[Assistant] Uhm, we didn't have the ability to answer that question. So we were basically not getting any effects when I tried to, when we tried to look at, you know, the near activation versus the far activation. And that's why we went for that more global pooling effect of looking at, okay, what's the effect of example versus no example? So I don't know, but I'm really curious. Yeah.


[User] Please transcribe this text

[Assistant] Uhm, that wasn't something that was well controlled for in that experiment, honestly. So uhm, there's, you know, obvious exclusion criteria that range everywhere from types of medication that people are using to what types of glasses do they wear, like their vision.


[User] Please transcribe this text

[Assistant] To background, like we wanted people who had a background in design, uhm, either industrial design or mechanical design at the time.


[User] Please transcribe this text

[Assistant] And we didn't explicitly control for gender, like in the, uh, in the paper, we report say gender differences in a variety of other demographic information, but it wasn't something that we were trying to control for at that time. And if I was to do the study again, I think that's another


[User] Please transcribe this text

[Assistant] Well, okay, I'll answer it in two ways. Uh, first is like my general comment that I think


[User] Please transcribe this text

[Assistant] Uh, a good research project usually leaves more questions unanswered than answered. Uhm, so there's a lot that I would now change about this study. Uhm, but to be quite honest, I don't know how much of some of the questions I still have.


[User] Please transcribe this text

[Assistant] I think, you know, fMRI is an incredibly powerful, uh, brain imaging technique that gives you really, really good spatial resolution of brain activity, but it's not particularly well suited to a really dynamic and active tasks like is present in design. And so that's actually something that we're looking at now is.


[User] Please transcribe this text

[Assistant] Individual, like, ideas. So let it be flexible in time, but only measure until someone generates a concept, and then stop it, have them do a different task, come back until they generate a new concept, stop it, etc. So uhm,


[User] Please transcribe this text

[Assistant] The design that we used was pretty simplistic, right? Like one minute set for, and then one minute again for all participants. We could have done a better job at being more fluid there, allowing like flexible amounts of time that varied from, I don't know, 10 seconds to 25 seconds or so.


[User] Please transcribe this text

[Assistant] I think in general, like a really good follow on research topic that we haven't looked at yet is like being more explicit at linking concepts that we're giving to


[User] Please transcribe this text

[Assistant] Whether or not said concepts are showing up in solutions that are being generated and like


[User] Please transcribe this text

[Assistant] How, the extent to which they are, uh, my completely like anecdotal just, you know, being a fly on the wall and watching some of these things is that


[User] Please transcribe this text

[Assistant] The FAR examples are kind of, they're either incredibly


[User] Please transcribe this text

[Assistant] Helpful, like you just get it, you draw that connection.


[User] Please transcribe this text

[Assistant] Or it's not useful at all and ignored. And when we do this at scale across, you know, a hundred people or a thousand people, the, the times where it was just the most helpful thing in the world, right? Like I saw that cockroach and then I came up with this like little micro robot kind of gets washed out and the overwhelming effect, which is most people


[User] Please transcribe this text

[Assistant] Great question. Yeah, no, the participants were totally blind. They were not given anything like that. We just asked them to, uhm,


[User] Please transcribe this text

[Assistant] So they described the first idea that came to mind, and then uhm, they described the second idea that came to mind. Right, like, there was no prompt on, give me your most creative idea, or give me your most feasible idea. Uhm, those were evaluation criteria that are,


[User] Please transcribe this text

[Assistant] And there's pretty systematic ways to, uh, for human raters to make those evaluations.


[User] Please transcribe this text

[Assistant] I was as proud of it as Papa would have been if someone had given him a dozen Missouri mules.


[User] Please transcribe this text

[Assistant] Mama said afterwards that she wished she hadn't made it for me, because in some way wearing that cap must have affected my mind. I went coon crazy. I was out after ring-tails every night. About the only time I didn't go hunting was when the weather was bad, and even then Mama all but had to hog-tie me. What wonderful nights they were.


[User] Please transcribe this text

[Assistant] Running like a deer through the thick timber of the bottoms, Tearing my way through stands of wild cane, Climbing over drifts and jumping logs, Running, screaming, and yelling, Woo-wee! Get em, boy, get em! Following the voices of my little hounds,


[User] Please transcribe this text

[Assistant] It wasn't too hard for a smart old coon to fool old Dan, but there were none that prowled the riverbanks that could fool my little Anne.


[User] Please transcribe this text

[Assistant] As Grandpa had predicted, the price of coonskins jumped sky high. A good size hide was worth from four to ten dollars, depending on the grade and quality.


[User] Please transcribe this text

[Assistant] I kept the side of our smokehouse plastered with hides. Of course I would spread them out a little to cover more space. I always stretched them on the side facing the road, never on the back side. I wanted everyone in the country to see them.


[User] Please transcribe this text

[Assistant] The money earned from my furs was turned over to my father. I didn't care about it. I had what I wanted. My dogs.


[User] Please transcribe this text

[Assistant] I suppose that Papa was saving it for something because I never saw anything new turn up around our home. But like any young boy, I wasn't bothered by it and I asked no questions. My whole life was wrapped up in my dogs. Everywhere I went, they went along.


[User] Please transcribe this text

[Assistant] There was only one place I didn't want them to go with me, and that was to Grandpa's store. Other dogs were always there, and it seemed as if they all wanted to jump on old Dan.


[User] Please transcribe this text

[Assistant] It got so about the only time I went to see my grandfather was when I had a bundle of fur to take to the store. This was always a problem. In every way I could I would try to slip away from my dogs. Sometimes I swore that they could read my mind. It made no difference what I tried, I couldn't fool them.


[User] Please transcribe this text

[Assistant] One time I was sure I had outsmarted them. The day before I was to make one of my trips, I took my furs out to the barn and hid them.


[User] Please transcribe this text

[Assistant] The next morning I hung around the house for a while and then nonchalantly whistled my way out to the barn. I climbed up in the loft and peeked through a crack.


[User] Please transcribe this text

[Assistant] I could see them lying in front of their dog house. They weren't even looking my way.


[User] Please transcribe this text

[Assistant] Taking my furs, I sneaked out through a back door, and walking like a tomcat, I made it to the timber. I climbed a small dogwood tree and looked back. They were still there, and didn't seem to know what I'd done.


[User] Please transcribe this text

[Assistant] Feeling just about as smart as Sherlock Holmes, I headed for the store. I was walking along, singing my lungs out when they came tearing out of the underbrush, wriggling and twisting, and tickled to death to be with me.


[User] Please transcribe this text

[Assistant] At first I was mad, but one look at dancing little Anne and all was forgiven. I sat down on my bundle of fur and laughed till I hurt all over. I could scold them a little, but I could no more have whipped one of them than I could have kissed the girl. After all, a boy just doesn't whip his dogs.


[User] Please transcribe this text

[Assistant] Grandpa always counted my furs carefully and marked something down on a piece of paper. I'd never seen him do this with other hunters and it got the best of my curiosity.


[User] Please transcribe this text

[Assistant] One day while he was writing I asked him, Why do you do that, Grandpa? He looked at me over his glasses and said, kind of sharp, Never mind, I have my reasons.


[User] Please transcribe this text

[Assistant] When Grandpa talked to me like that, I didn't push things any farther. Besides, it didn't make any difference to me if he marked on every piece of paper in the store.


[User] Please transcribe this text

[Assistant] I always manage to make my trips on Saturdays, as that was Coon Hunter's day.


[User] Please transcribe this text

[Assistant] I didn't have to stand around on the outside of the circle anymore and listen to the coon hunters. I'd get right up in the middle and say my piece with the rest of them.


[User] Please transcribe this text

[Assistant] I didn't have to tell any whoppers, for some of the things my dogs did were almost unbelievable anyhow.


[User] Please transcribe this text

[Assistant] Oh, I guess I did make things a little bigger than they actually were, but I never did figure a coon hunter told honest-to-goodness lies. He just kind of stretched things a little.


[User] Please transcribe this text

[Assistant] I could hold those coon hunters spellbound with some of my hunting tales.


[User] Please transcribe this text

[Assistant] Grandpa would never say anything while I was telling my stories. He just put it around the store with a silly little grin on his face.


[User] Please transcribe this text

[Assistant] Once in a while, when I got too far off the beaten path, he would come around and cram a bar of soap in my pocket. My face would get all red. I'd cut my story short, fly out the door, and head for home.


[User] Please transcribe this text

[Assistant] The coon hunters were always kidding me about my dogs. Some of the remarks I heard made me fighting mad. I never saw hounds so small, but I guess they are hounds, at least they look like it.


[User] Please transcribe this text

[Assistant] I don't believe little Anne is half as smart as he says she is. She's so little, those old coons think she's a rabbit. I'll bet she sneaks right up on them before they realize she's a dog.


[User] Please transcribe this text

[Assistant] Some of these nights big old Coon's gonna carry her off to his den and raise some little Coon puppies.


[User] Please transcribe this text

[Assistant] I always took their kidding with a smile on my face, but it made my blood boil like the water in mama's tea kettle. I had one way of shutting them up.


[User] Please transcribe this text

[Assistant] Let's all go in the store, I'd say, and see who has the most hides in there.


[User] Please transcribe this text

[Assistant] It was true that my dogs were small, especially little Anne. She could walk under an ordinary hound. In fact, she was a regular midget. If it had not been for her long ears, no one could have told that she was a hound.


[User] Please transcribe this text

[Assistant] Her actions weren't those of a hunting hound. She was constantly playing. She would play with our chickens and young calves, with a piece of paper or a corn cob. What my little girl lacked in size, she made up in sweetness. She could make friends with a tomcat.


[User] Please transcribe this text

[Assistant] Old Dan was just the opposite. He strutted around with a belligerent and tough attitude.


[User] Please transcribe this text

[Assistant] Although he wasn't a tall dog, he was heavy. His body was long and his chest broad and thick.


[User] Please transcribe this text

[Assistant] His legs were short, big, and solid. The muscles in his body were hard and knotty. When he walked they would twist and jerk under the skin.


[User] Please transcribe this text

[Assistant] He was a friendly dog. There were no strangers to him. He loved everyone. Yet he was a strange dog.


[User] Please transcribe this text

[Assistant] He would not hunt with another hound other than little Anne, or another hunter, not even my father.


[User] Please transcribe this text

[Assistant] The strangest thing about old Anne was that he would not hunt even with me unless little Anne was with him. I found this out the first night I tried it.


[User] Please transcribe this text

[Assistant] Little Anne had cut the pad of her right foot on a sharp jagged flint rock. It was a nasty cut. I made a little boot of leather and put it on her wounded foot. To keep her from following me, I locked her in the corn crib.


[User] Please transcribe this text

[Assistant] Two nights later I decided to take old Dan hunting for a while. He followed me down to the river bottoms and disappeared in the thick timber. I waited and waited for him to strike a trail. Nothing happened. After about two hours I called to him.


[User] Please transcribe this text

[Assistant] Coming up through the barn lot I saw him rolled up in a ball on the ground in front of the corn crib.


[User] Please transcribe this text

[Assistant] I immediately understood. I walked over and opened the door. He jumped up in the crib, smelled little Anne's foot, twisted around in the shucks and lay down by her side. As he looked at me I read this message in his friendly gray eyes. You could have done this a long time ago.


[User] Please transcribe this text

[Assistant] The last time I had heard my dogs, they were downstream from me. I walked on, listening.


[User] Please transcribe this text

[Assistant] I hadn't gone far when I heard old Dan. What I heard froze the blood in my veins. He wasn't bawling on a trail or giving the tree bark. It was one long continuous cry.


[User] Please transcribe this text

[Assistant] In his deep voice there seemed to be a pleading cry for help.


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[Assistant] Scared, worried, and with my heart beating like a churn dasher I started toward the sound. I almost passed him, but with another cry he let me know where he was. He was out on the ice pack. I couldn't see him for the fog. I called to him and he answered with a low whine. Again I called his name. This time he came to me.


[User] Please transcribe this text

[Assistant] He wasn't the same dog. His tail was between his legs and his head was bowed down. He stopped about seven feet from me. Sitting down on the ice, he raised his head and howled the most mournful cry I had ever heard. Turning around, he trotted back out on the ice and disappeared in the fog. I knew something had happened to little Anne. I called her name.


[User] Please transcribe this text

[Assistant] She answered with a pleading cry. Although I couldn't see her, I guessed what had happened. The coon had led them to the river. Running out on the ice, he had leaped across the trough. My dog's, hot on the trail, had followed. Old Dan, a more powerful dog than little Anne, had made his leap. Little Anne had not made it.


[User] Please transcribe this text

[Assistant] Old Dan, seeing the fate of his little friend, had quit the chase and come back to help her. A smart old coon had pulled his trick, and a deadly one it was.


[User] Please transcribe this text

[Assistant] I had to do something. She would never be able to get out by herself. It was only a matter of time until her body would be paralyzed by the freezing water.


[User] Please transcribe this text

[Assistant] Laying my axe down, I held my lantern out in front of me and stepped out on the ice. It started cracking and popping. I jumped back to the bank. Although it was thick enough to hold the light weight of my dogs, it would never hold me. Little Anne started whining and begging for help. I went all to pieces and started crying.


[User] Please transcribe this text

[Assistant] She cried again, begging for the one thing I couldn't give her, help.


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[Assistant] I thought, if only I could see her, maybe I could figure out some way I could help. Looking at my lantern gave me an idea.


[User] Please transcribe this text

[Assistant] I was looking for a stand of wild cane. After what seemed like ages I found it. With the longest one I could find I hurried back. After it was trimmed and the limber end cut off, I hung the lantern by the handle on the end of it and started easing it out on the ice.


[User] Please transcribe this text

[Assistant] I saw old Dan first. He was sitting close to the edge of the trough, looking down. Then I saw her. I groaned at her plight.


[User] Please transcribe this text

[Assistant] All I could see was her head and her small front paws. Her claws were spread out and digging into the ice. She knew if she ever lost that hold, she was gone.


[User] Please transcribe this text

[Assistant] Old Dan raised his head and howled. Hound though he was, he knew it was the end of the trail for his little pal.


[User] Please transcribe this text

[Assistant] I wanted to get my light as close to Little Anne as I could, but my pole was a good eight feet short. Setting the lantern down, I eased the pole from under the handle. I thought


[User] Please transcribe this text

[Assistant] No better off than I was before. In fact, I'm worse off. Now I can see when the end comes.


[User] Please transcribe this text

[Assistant] Little Anne cried again. I saw her claws slip on the ice. Her body settled lower in the water. Old Dan howled and started fidgeting. He knew the end was close.


[User] Please transcribe this text

[Assistant] I didn't exactly know when I started out toward my dog. I had taken only two steps when the ice broke. I twisted my body and fell toward the bank. Just as my hand closed on a root I thought my feet touched bottom, but I wasn't sure. As I pulled myself out, I felt the numbing cold creep over my legs.


[User] Please transcribe this text

[Assistant] I got behind it, anything to blot out that heartbreaking scene. Little Anne, thinking I had deserted her, started crying. I couldn't stand it.


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[Assistant] I opened my mouth to call old Dan. I wanted to tell him to come on and we'd go home as there was nothing we could do. The words just wouldn't come out. I couldn't utter a sound.


[User] Please transcribe this text

[Assistant] I lay my face against the icy cold bark of the sycamore. I thought of the prayer I had said when I had asked God to help me get two hound pups.


[User] Please transcribe this text

[Assistant] I knelt down and sobbed out a prayer. I asked for a miracle which would save the life of my little dog. I promised all the things that a young boy could, if only he would help me.


[User] Please transcribe this text

[Assistant] Still saying my prayer and making promises, I heard a sharp metallic sound.


[User] Please transcribe this text

[Assistant] I waited for an answer, all I could hear were the cries of little Anne. Again I hollered,


[User] Please transcribe this text

[Assistant] I shivered from the freezing cold of my wet shoes and overalls. A straining silence settled over the river.


[User] Please transcribe this text

[Assistant] A feathery rustle swished by in the blackness. A flock of low flying ducks had been disturbed by my loud shouts.


[User] Please transcribe this text

[Assistant] I strained my ears for some sound, now and then I could hear the lapping slap of the ice cold water as it swirled its way through the trough.


[User] Please transcribe this text

[Assistant] It couldn't have made the sound as it was too close to me. The noise had come from out in the river. When I looked at my lantern.


[User] Please transcribe this text

[Assistant] I knew that it had made the strange sound. I had left the handle standing straight up when I had taken the pole away. Now it was down.


[User] Please transcribe this text

[Assistant] For some unknown reason the stiff wire handle had twisted in the sockets and dropped.


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[Assistant] As it had fallen, it had struck the metal frame making the sharp metallic sound I had heard.


[User] Please transcribe this text

[Assistant] I wanted to take Joe Carp and Frank Riley out to lunch to celebrate.


[User] Please transcribe this text

[Assistant] I guess it'll take time for them to get used to the changes in me. Everyone seems frightened of me.


[User] Please transcribe this text

[Assistant] When I went over to Gimpy and tapped him on the shoulder to ask him something, he jumped up and dropped his cup of coffee all over himself. He stares at me when he thinks I'm not looking.


[User] Please transcribe this text

[Assistant] Nobody at the place talks to me anymore, or kids around the way they used to. It makes the job kinda lonely.


[User] Please transcribe this text

[Assistant] It's me, and yet it's like someone else lying there, another Charlie. He's confused, rubbing his head, staring up at Frank, tall and thin, and then at Gimpy nearby.


[User] Please transcribe this text

[Assistant] Massive, hairy, gray-faced gimpie with bushy eyebrows that almost hide his blue eyes. Leave the kid alone, says Gimp. Jesus, Frank, why do you always gotta pick on him? It don't mean nothing, laughs Frank. It don't hurt him, he don't know any better. Do ya, Charlie? Charlie rubs his head and cringes. He doesn't know what he's done to deserve this punishment, but there's always the chance that there will be more.


[User] Please transcribe this text

[Assistant] But you know better, says Gimpy, clumping over on his orthopedic boot. So what the hell you always picking on him for? The two men sit down at the long table.


[User] Please transcribe this text

[Assistant] The idea seems to appeal to Gimpy, who turns to stare at Charlie. Maybe you got something there. Hey, Charlie, come here a minute. As he usually does when people are talking about him.


[User] Please transcribe this text

[Assistant] Charlie has been keeping his head down staring at his shoelaces. He knows how to lace and tie them. He could make rolls.


[User] Please transcribe this text

[Assistant] He could learn to pound, roll, twist and shape the dough into the small round forms. Frank looks at him uncertainly. Maybe we shouldn't Gimp. Maybe it's wrong.


[User] Please transcribe this text

[Assistant] Charlie stares at him, the smile melting from his face. He understands what Gimpy wants, and he feels cornered. He wants to please Gimpy, but there is something about the words learn and teach.


[User] Please transcribe this text

[Assistant] Something to remember about being punished severely, but he doesn't recall what it is.


[User] Please transcribe this text

[Assistant] And the gleaming gold disc rotates slowly, catching the light of the fluorescent bulbs. The pendant is a brightness that Charlie remembers, but he doesn't know why or what. He doesn't reach for it.


[User] Please transcribe this text

[Assistant] He knows you get punished if you reach out for other people's things. If someone puts it into your hand, that's alright, but otherwise it's wrong. When he sees that Gimpy is offering it to him, he nods and smiles again. That he knows, laughs Frank. Give him something bright and shiny.


[User] Please transcribe this text

[Assistant] Who has let Gimpy take over the experiment leans forward excitedly.


[User] Please transcribe this text

[Assistant] As the bakers set to the task of teaching Charlie, others from the shop gather around. Frank clears an area between them on the table.


[User] Please transcribe this text

[Assistant] There is talk of betting on whether or not Charlie can learn to make rolls. Watch this carefully, says Gimpy, putting the pendant beside him on the table where Charlie can see it. Watch and do everything we do. If you learn how to make rolls, you get this shiny good luck piece.


[User] Please transcribe this text

[Assistant] Charlie hunches over on his stool, intently watching Gimpy pick up the knife and cut off a slab of dough. He studies each movement as Gimpy rolls out the dough into a long roll, breaks it off and twists it into a circle, pausing now and then to sprinkle it with flour.


[User] Please transcribe this text

[Assistant] Gimpy holds his elbows out as he rolls the dough, like a bird's wings, but Frank keeps his arms close to his sides.


[User] Please transcribe this text

[Assistant] Gimpy keeps his thumbs together with the rest of his fingers as he kneads the dough, but Frank works with the flat of his palms, keeping thumbs apart from his other fingers and up in the air.


[User] Please transcribe this text

[Assistant] Worrying about these things makes it impossible for Charlie to move when Gimpy says, go ahead, try it. Charlie shakes his head. Look, Charlie, I'm gonna do it again slow. Now you watch everything I do and do each part along with me, okay? But try to remember everything so then you'll be able to do the whole thing alone. Now come on, like this.


[User] Please transcribe this text

[Assistant] Charlie frowns as he watches Gimpy pull off a section of dough and roll it into a ball. He hesitates, but then he picks up the knife and slices off a piece of dough and sets it down in the center of the table.


[User] Please transcribe this text

[Assistant] Slowly, keeping his elbows out exactly as Gimpy does, he rolls it into a ball. He looks from his own hands to Gimpy's.


[User] Please transcribe this text

[Assistant] Alright now, says Gimpy, now we make a roll. Awkwardly but carefully, Charlie follows Gimpy's every move.


[User] Please transcribe this text

[Assistant] Occasionally, a twitch of his hand or arm mars what he's doing, but in a little while,


[User] Please transcribe this text

[Assistant] He's able to twist off a section of the dough and fashion it into a roll. Working beside Gimpy, he makes six rolls.


[User] Please transcribe this text

[Assistant] Charlie stares at the huge slab of dough, and at the knife that Gimpy has pushed into his hand. And once again, panic comes over him. What did he do first? How did he hold his hand? His fingers? Which way did he roll the ball?


[User] Please transcribe this text

[Assistant] And to get the bright good luck piece that Gimpy has promised him.


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[Assistant] He turns the smooth heavy piece of dough around and around on the table, but he cannot bring himself to start.


[User] Please transcribe this text

[Assistant] But how does it get to be a roll like the ones in the tray? That's something else. Give him time and he'll remember. As soon as the fuzziness passes away, he'll remember. Just another few seconds and he'll have it.


[User] Please transcribe this text

[Assistant] He wants to hold on to what he's learned for a little while. He wants it so much. Okay, Charlie. Size Gimpy, taking the cutter out of his hand. That's alright, don't worry about it. It's not your work anyway.


[User] Please transcribe this text

[Assistant] Go ahead, Charlie. Go sit down and look at your comic book. We gotta get back to work.


[User] Please transcribe this text

[Assistant] Charlie smiles at him and goes back to the flour sacks in the corner near the mixing machines.


[User] Please transcribe this text

[Assistant] He likes to lean back against them while he sits on the floor cross-legged and looks at the pictures in his comic book. As he starts to turn the pages, he feels like crying, but he doesn't know why. What is there to feel sad about? The fuzzy cloud comes and goes.


[User] Please transcribe this text

[Assistant] And now he looks forward to the pleasure of the brightly colored pictures in the comic book that he has gone through 30, 40 times. He knows all of the figures in the comic.


[User] Please transcribe this text

[Assistant] And he understands that the strange forms of letters and words in the white balloons above the figures means that they are saying something. Would he ever learn to read what was in the balloons? If they gave him enough time, if they didn't rush him or push him too fast, he would get it. But nobody has time.


[User] Please transcribe this text

[Assistant] Letting it swing and twirl around so that it catches the light. Here, he says gruffly, tossing it into Charlie's lap. And then he limps away.


[User] Please transcribe this text

[Assistant] Like looking out of the kitchen window early when the morning light is still gray. I've come a long way since then.


[User] Please transcribe this text

[Assistant] But what must Frank and Gimpy think and feel now? Seeing how I've changed.


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[Assistant] People at the bakery are changing. Not only ignoring me, I can feel the hostility.


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[Assistant] The rotten thing is that all of the pleasure is gone because the others resent me. In a way I can't blame them.


[User] Please transcribe this text

[Assistant] They don't understand what has happened to me and I can't tell them. People aren't proud of me the way I expected. Not at all. Still, I've gotta have someone to talk to.


[User] Please transcribe this text

[Assistant] I'm going to ask Miss Kinian to go to a movie tomorrow night to celebrate my raise. If I can get up the nerve.


[User] Please transcribe this text

[Assistant] But there are things I can't put down unless I can keep them private. At least for a while.


[User] Please transcribe this text

[Assistant] I'm allowed to keep back some of these more personal reports, but before the final report to the Wellberg Foundation,


[User] Please transcribe this text

[Assistant] I dropped by the office earlier this evening to ask Dr. Strauss or Professor Niemer


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[Assistant] Because people have always spoken and acted as if I weren't there, as if they never cared what I overheard.


[User] Please transcribe this text

[Assistant] I've already informed the convention committee that we will present the paper at Chicago. Then I heard Dr. Strauss' voice. But you're wrong, Harold. Six weeks from now is still too soon. He's still changing. And then Neamer. We've predicted the pattern correctly so far.


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[Assistant] We've succeeded. It's all positive. Nothing can go wrong now. Strauss.


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[Assistant] This is too important to all of us to bring it out into the open prematurely. You're taking the authority on yourself, Neamer.


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[Assistant] You forget that I'm the senior member of this project. Strauss.


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[Assistant] And you forget that you're not the only one with a reputation to consider. If we claim too much now, our whole hypothesis will come under fire. Neamer, I'm not afraid of regression anymore.


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[Assistant] Thousands of neurosurgeons all over the world would be using his methods, but at this point


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[Assistant] I realized I no longer had the right to stand there outside the office and listen to them without their knowing it.


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[Assistant] They might not have cared when I was too feeble minded to know what was going on, but now that I could understand, they wouldn't want me to hear it. I left without waiting for the outcome. It was dark.


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[Assistant] I'll wait until tomorrow to ask them about taking Miss Kinney into a movie to celebrate my raise.


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[Assistant] I wish I could sit and talk with them over coffee and the campus bowl luncheonette.


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[Assistant] When they get together to argue about books and politics and ideas, it's exciting to hear them talking about poetry and science and philosophy.


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[Assistant] About Plato and Hegel and Kant and all the other names that echo like great church bells in my mind. Sometimes I listen in on the conversations at the tables around me and pretend I'm a college student, even though I'm a lot older than they are. I carry books around and I've started to smoke a pipe. It's silly, but since I belong at the lab, I feel as if I'm a part of the university. I hate to go home to that lonely room.


[User] Please transcribe this text

[Assistant] April 27th. I've made friends with some of the boys at the campus bowl.