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[Assistant] A flying column under Colonel Greathead came round to Agra and cleared the pandes away from it. Peace seemed to be settling upon the country.


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[Assistant] And we four were beginning to hope that the time was at hand when we might safely go off with our shares of the plunder.


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[Assistant] In a moment, however, our hopes were shattered by our being arrested as the murderers of Ahmed. It came about in this way.


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[Assistant] When the Raja put his jewels into the hands of Ahmet, he did it because he knew that he was a trusty man.


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[Assistant] They are suspicious folk in the East, however. So what does this Raja do but take a second, even more trusty servant, and set him to play the spy upon the first?


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[Assistant] This second man was ordered never to let Ahmed out of his sight, and he followed him like his shadow.


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[Assistant] He went after him that night and saw him pass through the doorway.


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[Assistant] Of course he thought he'd taken refuge in the fort, and applied for admission there himself next day, but couldn't find no trace of Ahmet.


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[Assistant] This seemed to him so strange that he spoke about it to a sergeant of guides, who brought it to the ears of the commandant.


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[Assistant] A thorough search was quickly made, and the body was discovered.


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[Assistant] Thus, at the very moment that we thought that all was safe, we were all foreseized and brought to trial on a charge of murder.


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[Assistant] Three of us, because we had held the gate that night, and the fourth because he was known to have been in the company of the murdered man. Not a word about the jewels came out at the trial.


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[Assistant] For the Raja had been deposed and driven out of India, so no one had any particular interest in them. The murder, however, was clearly made out, and it was certain that we must all have been concerned in it.


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[Assistant] The three Sikhs got penal servitude for life, and I was condemned to death.


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[Assistant] Though my sentence was afterwards commuted into the same as the others.


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[Assistant] It was rather a queer position that we found ourselves in then. There we were all foretied by the leg and with precious little chance of ever getting out again, while we each held a secret which might have put each of us in a palace if we could only have made use of it.


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[Assistant] It was enough to make a man eat his heart out to have to stand the kick and the cuff of every petty jack in office, to have rice to eat and water to drink, when that gorgeous fortune was ready for him outside, just waiting to be picked up.


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[Assistant] Might have driven me mad, but I was always a pretty stubborn one, so I just held on and bided my time. At last it seemed to me to have come.


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[Assistant] I was changed from Agra to Madras, and from there to Blair Island in the Andamans. There are very few white convicts at this settlement.


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[Assistant] And as I had behaved well from the first, I soon found myself a sort of privileged person.


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[Assistant] I was given a hut in Hopetown, which is a small place on the slopes of Mount Harriet, and I was left pretty much to myself.


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[Assistant] It is a dreary fever-stricken place, and all behind our little clearings was infested with wild cannibal natives, who were ready enough to blow a poisoned dart at us if they saw a chance.


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[Assistant] There was digging and ditching and yam planting and a dozen other things to be done.


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[Assistant] So we were busy enough all day, though in the evening we had a little time to ourselves. Among other things I learned to dispense drugs for the surgeon, and picked up a smatching of his knowledge. All the time I was on the lookout for a chance of escape.


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[Assistant] But it's hundreds of miles from any other land, and there's a little or no wind in those seas, so it was a terribly difficult job to get away.


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[Assistant] The surgeon, Dr Somerton, was a fast, sporting young chap.


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[Assistant] And the other young officers would meet in his rooms of an evening and play cards.


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[Assistant] Surgery, where I used to make up my drugs, was next to his sitting room, with a small window between us.


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[Assistant] Often, if I felt lonesome, I used to turn out the lamp in the surgery, and then, standing there, I could hear their talk and watch their play. I'm fond of a hand at cards myself, and it was almost as good as having one to watch the others.


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[Assistant] There was Major Shalto, Captain Morstan, and Lieutenant Bromley Brown, who were in command of the native troops


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[Assistant] And there was a surgeon himself, and two or three prison officials, crafty old hands who played a nice, sly, safe game—a very snug little party they used to make.


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[Assistant] Well, there was one thing which very soon struck me, and that was that the soldiers used always to lose, and the civilians to win.


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[Assistant] Mind, I don't say that there was anything unfair, but so it was. These prison-chaps had done little else than play cards ever since they had been at the Andermans, and they knew each other's game to a point, while the others just played to pass the time, and threw their cards down anyhow.


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[Assistant] Night after night the soldiers got up poorer men, and the poorer they got the more keen they were to play. Major Sholto was the hardest hit. He used to pay in notes and gold at first, but soon it came to notes of hand and for big sums.


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[Assistant] He sometimes would win for a few deals, just to give him heart, and then the luck would get set in against him worse than ever.


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[Assistant] All day he would wander about as black as thunder, and he took to drinking a good deal more than was good for him. One night he lost even more heavily than usual.


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[Assistant] I was sitting in my hut, when he and Captain Morstan came stumbling along on the way to their quarters.


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[Assistant] They were bosom friends, those two, and never far apart. The Major was raving about his losses.


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[Assistant] It's all up, Morstan," he was saying as they passed my hut. I shall have to send in my papers. I am a ruined man.


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[Assistant] Nonsense, old chap," said the other, slapping him upon the shoulder. I've had a nasty face of myself, but— That was all I could hear, but it was enough to set me thinking.


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[Assistant] Who is the proper person to whom hidden treasure should be handed over?


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[Assistant] I know where our familiar worth lies, and as I cannot use it myself, I thought perhaps the best thing I could do would be to hand it over to the proper authorities, and then perhaps they would get my sentence shortened for me.


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[Assistant] Half a million small?" He gasped, looking hard at me to see if I was in earnest.


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[Assistant] Quite that, sir, in jewels and pearls. It lies there ready for any one, and the queer thing about it is that the real owner is outlawed and cannot hold property, so that it belongs to the first comer.


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[Assistant] To government, small, he stammered, to government.


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[Assistant] But he said it in a halting fashion, and I knew in my heart that I had got to him.


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[Assistant] You think then, sir, that I should give the information to the Governor General? said I quietly.


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[Assistant] I told him the old story, with small changes so that he could not identify the places.


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[Assistant] When I had finished he stood stock still and full of thought.


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[Assistant] I could see by the twitch of his lip that there was a struggle going on within him.


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[Assistant] This is a very important matter, small," he said at last.


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[Assistant] You must not say a word to anyone about it, and I shall see you again soon.


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[Assistant] Two nights later, he and his friend Captain Morstan came to my hut in the dead of the night with a lantern.


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[Assistant] We've been talking it over, my friend here and I, and we've come to the conclusion that this secret of yours is hardly a government matter after all, but is a private concern of your own.


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[Assistant] Which of course you have the power of disposing of as you think best.


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[Assistant] We might be inclined to take it up and at least look into it, if we could agree as to terms.


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[Assistant] He tried to speak in a cool careless way, but his eyes were shining with excitement and greed.


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[Assistant] Why, as to that gentleman, I answered, trying also to be cool, but feeling as excited as he did, there is only one bargain which a man in my position can make.


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[Assistant] I shall want you to help me do my freedom, and to help my three companions to theirs.


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[Assistant] But how can we gain your freedom? You know very well that you ask an impossibility. Nothing of the sort, I answered. I thought it all out to the last detail.


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[Assistant] The only bar to our escape is that we can get no boat fit for the voyage and no provisions to last us for so long a time.


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[Assistant] There are plenty of little yachts and yawls at Calcutta or Madras which would serve our turn well. Do you bring one over?"


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[Assistant] We shall engage to get aboard her by night, and if you will drop us on any part of the Indian coast, you will have done your part of the bargain." If there were only one, he said. None or all, I answered.


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[Assistant] Small is a man of his word. He does not flinch from his friend. I think we may very well trust him.


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[Assistant] We must, I suppose, try and meet you. We must first of course test the truth of your story.


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[Assistant] Tell me where the box is hid, and I shall get leave of absence and go back to India in the monthly relief boat to inquire into the affair.


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[Assistant] Not so fast," said I, growing colder as he got hot. I must have the consent of my three comrades. I tell you that it is four or none with us. Nonsense!" he broke in. What have three black fellows to do with our agreement?


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[Assistant] Black or blue, said I, they are in with me and we are all together.


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[Assistant] Well, the matter ended by a second meeting at which Mahomet Singh, Abdullah Khan, and Dost Akbar were all present.


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[Assistant] We talked the matter over again, and at last we came to an arrangement. We were to provide both the officers with charts of the part of the Agra Fort, and mark the place in the wall where the treasure was hid. Major Sholto was to go to India to test our story.


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[Assistant] If he found the box he was to leave it there, to send out a small yacht provisioned for a voyage which was to lie off Rutland Island, and to which we were to make our way, and finally to return to his duties.


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[Assistant] Captain Morstan was then to apply for leave of absence, to meet us at Agra, and there we would have had a final division of the treasure—he taking the major share as well as his own.


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[Assistant] All this we sealed by the most solemn oaths that the mind could think or the lips utter. I sat up all night with paper and ink, and by the morning I had the two charts all ready, signed with the sign of four—that is, of Abdullah, Akbar, Mahomet, and myself.


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[Assistant] Well, gentlemen, I weary you with my long story, and I know that my friend Mr. Jones is impatient to get me safely stowed in Chokey. I'll make it as short as I can. The villain Sholto went off to India, but he never came back again.


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[Assistant] Captain Morstan showed me his name among a list of passengers in one of the mailboats very shortly afterwards.


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[Assistant] His uncle had died, leaving him a fortune, and he had left the army. Yet he could stoop to treat five men as he had treated us.


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[Assistant] Morstan went over to Agra shortly afterwards, and found, as we expected, that the treasure was indeed gone.


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[Assistant] The scoundrel had stolen it, without carrying out one of the conditions on which we had sold him the secret. From that day I lived only for vengeance. I thought of it by day, and I nursed it by night. It became an overpowering, absorbing passion with me.


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[Assistant] I cared nothing for the law, nothing for the gallows.


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[Assistant] To escape? To track down Sholto? To have my hand upon his throat? That was my one thought.


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[Assistant] Even the Agra treasure had come to be a smaller thing in my mind than the slaying of Sholo.


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[Assistant] Well, I have set my mind on many things in this life, and never one which I did not carry out. But it was weary years before my time came. I have told you that I had picked up something of medicine.


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[Assistant] One day, when Dr. Somerton was down with a fever, a little Anderman islander was picked up by a convict gang in the woods.


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[Assistant] He was sick to death and had gone to a lonely place to die


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[Assistant] I took him in hand though he was as venomous as a young snake.


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[Assistant] And after a couple of months, I got him all right and able to walk.


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[Assistant] He took a kind of fancy to me then, and would hardly go back to his woods, but was always hanging about my hut.


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[Assistant] I learned a little of his lingo from him, and this made him all the fonder of me.


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[Assistant] Tonga, for that was his name, was a fine boatman, and owned a big roomy canoe of his own.


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[Assistant] When I found out that he was devoted to me and would do anything to serve me, I saw my chance of escape.


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[Assistant] I talked it over with him. He was to bring his boat round on a certain night to an old wharf which was never guarded, and there he was to pick me up.


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[Assistant] I gave him directions to have several gourds of water and a lot of yams, coconuts and sweet potatoes.


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[Assistant] He was stanch and true as little Tonga. No man ever had a more faithful mate.


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[Assistant] At the night named he had his boat at the wharf. As it chanced, however, there was one of the convict guard down there, a vile Pathan, who had never missed a chance of insulting and injuring me.


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[Assistant] I had always vowed vengeance, and now I had my chance. It was as if fate had placed him in my way that I might pay my debt before I left the island.


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[Assistant] He stood on the bank with his back to me and his carbine on his shoulder.


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[Assistant] I looked about for a stone to beat out his brains with, but none could I see.


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[Assistant] Then a queer thought came into my head and showed me where I could lay my hand on a weapon.


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[Assistant] I sat down in the darkness and unstrapped my wooden leg. With three long hops I was on him.


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[Assistant] He put his carbine to his shoulder and I struck him full and knocked the whole front of his skull in. You can see the split in the wood now where I hit him.


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[Assistant] We both went down together, for I could not keep my balance.


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[Assistant] But when I got up, I found him still lying quiet enough.


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[Assistant] I made for the boat, and in an hour we were well out at sea.


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[Assistant] Tonga had brought all his earthly possessions with him, his arms and his gods.


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[Assistant] Among other things he had a long bamboo spear, and some Andaman coconut matting, with which I made a sort of sail. For ten days we were beaten about, trusting to luck.


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[Assistant] And on the 11th we were picked up by a trader which was going from Singapore to Jira with a cargo of Malay pilgrims.


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[Assistant] They were a rum crowd, and Tonga and I soon managed to settle down among them. They had one very good quality—they let you alone and asked no questions.


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[Assistant] Well, if I were to tell you all the adventures that my little chum and I went through, you would not thank me, for I would have you here until the sun was shining. Here and there we drifted about the world—something always turning up to keep us from London.


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[Assistant] All the time, however, I never lost sight of my purpose.


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[Assistant] I would dream of Sholto at night. A hundred times I have killed him in my sleep.


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[Assistant] At last, however, some three or four years ago, we found ourselves in England.


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[Assistant] I had no great difficulty in finding where Sholto lived, and I set to work to discover whether he had realised the treasure, or if he still had it. I made friends with someone who could help me.


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[Assistant] I named no names, for I don't want to get anyone else in a hole. And I soon found that he still had the jewels. Then I tried to get at him in many ways.


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[Assistant] But he was pretty sly, and had always two prize-fighters besides his sons and his kitmukgar on guard over him.


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[Assistant] One day however, I got word that he was dying.


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[Assistant] I hurried at once to the garden, mad that he should slip out of my clutches like that, and, looking through the window, I saw him lying in his bed with his sons on each side of him.


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[Assistant] I had come through and taken my chance with the three of them, only even as I looked at him his jaw dropped and I knew that he was gone.


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[Assistant] I got into his room that same night, though, and I searched his papers to see if there was any record of where he had hidden our jewels.


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[Assistant] It was not a line, however, so I came away, bitter and savage as a man could be.


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[Assistant] Before I left, I bethought me that if I ever met my Sikh friends again, it would be a satisfaction to know that I had left some mark of our atred.


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[Assistant] So I scrawled down the sign of the four of us, as it had been on the chart, and I pinned it on his bosom.


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[Assistant] It was too much that he should be taken to the grave without some token from the men whom he had robbed and befooled.


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[Assistant] We earned a living at this time by my exhibiting poor Tonga at fairs and other such places as the Black Cannibal.


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[Assistant] He would eat raw meat and dance his war dance, so he always had a handful of pennies after a day's work.


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[Assistant] I still heard all the news from Pondicherry Lodge, and for some years there was no news to hear, except that they were hunting for the treasure.


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[Assistant] At last, however, came what we had waited for so long. The treasure had been found.


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[Assistant] It was up at the top of the house, in Mr. Bartholomew Shalto's chemical laboratory.


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[Assistant] I came at once and had a look at the place, but I could not see how with my wooden leg I was to make my way up to it.


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[Assistant] I learned, however, about a trap door in the roof, and also about Mr. Sholto's supper hour.


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[Assistant] It seemed to me that I could manage the thing easily through Tonga.


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[Assistant] I brought him out with me with a long rope wound around his waist.


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[Assistant] He could climb like a cat and he soon made his way through the roof.


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[Assistant] But as ill luck would have it, Bartholomew Sholter was still in the room to his cost.


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[Assistant] Tonga thought he had done something very clever in killing him.


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[Assistant] When I came up by the rope I found him strutting about as proud as a peacock.


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[Assistant] Very much surprise was he, when I made at him with the rope's end and cursed him for a little bloodthirsty imp.


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[Assistant] I took the treasure-box and let it down, and then slid down myself, having first left the sign of the four upon the table, to show that the jewels had come back at last to those who had most right to them.


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[Assistant] Tonga then pulled up the rope, closed the window and made off the way that he had come.


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[Assistant] I don't know that I have anything else to tell you. I had heard a waterman speak of the speed of Smith's launch to the Aurora, so I thought she would be a handy craft for our escape.


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[Assistant] I engaged with old Smith and was to give him a big sum if he got us safe to our ship.


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[Assistant] He knew, no doubt, that there was some screw loose, but he was not in our secrets.


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[Assistant] All this is the truth, and if I tell it to you, gentlemen, it is not to amuse you, for you have not done me a very good turn.


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[Assistant] But it is because I believe the best defence I can make is just to hold back nothing, but let all the world know how badly I have myself been served by Major Sholto, and how innocent I am of the death of his son.


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[Assistant] A very remarkable account, said Sherlock Holmes. A fitting wind-up to an extremely interesting case.


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[Assistant] There is nothing at all new to me in the latter part of your narrative, except that you brought your own rope. That I did not know.


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[Assistant] By the way, I had hoped that Tonga had lost all his darts, yet he managed to shoot one at us in the boat.


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[Assistant] You had lost them all, sir, except one which was in his blowpipe at the time.


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[Assistant] Ah, of course, said Holmes. I had not thought of that.


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[Assistant] Is there any other point which you would like to ask about?" asked the convict affably. I think not, thank you, my companion answered.


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[Assistant] Well, Holmes," said Athelney Jones, you are a man to be humoured, and we all know that you are a connoisseur of crime. But duty is duty, and I have gone rather far in doing what you and your friend ask me.


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[Assistant] I shall feel more at ease when we have our story teller here safe under lock and key. The cab still waits, and there are two inspectors downstairs.


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[Assistant] I'm much obliged to you both for your assistance. Of course, you will be wanted at the trial. Good night to you.


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[Assistant] Good night, gentlemen both, said Jonathan Small.


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[Assistant] You first, small," remarked the wary Jones as they left the room, "'I'll take particular care that you don't club me with your wooden leg, whatever you may have done to the gentlemen at the Andaman Isles.'


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[Assistant] Well, and there is the end of our little drama," I remarked, after we had sat some time smoking in silence.


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[Assistant] I fear that it may be the last investigation in which I shall have the chance of studying your methods.


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[Assistant] Miss Morstan has done me the honour to accept me as a husband in perspective. He gave me a most dismal groan. I feared as much, said he. I really cannot congratulate you. I was a little hurt.


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[Assistant] Have you any reason to be dissatisfied with my choice? I asked.


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[Assistant] Not at all. I think she is one of the most charming young ladies I have ever met, and might have been most useful in such work as we have been doing. She had a decided genius that way.


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[Assistant] Witness the way in which she preserved that Agra plan from all the other papers of her father.


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[Assistant] But love is an emotional thing, and whatever is emotional is opposed to that true cold reason which I place above all things.


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[Assistant] I should never marry myself, lest I bias my judgment.


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[Assistant] I trust," said I, laughing, that my judgment may survive the ordeal. But you look weary.


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[Assistant] Yes, the reaction is already upon me. I shall be as limp as a rag for a week.


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[Assistant] Strange, said I, how terms of what in another man I should call laziness alternate with your fits of splendid energy and vigour. Yes, he answered.


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[Assistant] There are in me the makings of a very fine loafer, and also of a pretty spry sort of fellow. I often think of those lines of old Goethe.


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[Assistant] Schade, daß die Natur nur einen Mensch aus dir schuf, denn zum würdigen Mann waren zum Schelmen der Stoff.


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[Assistant] By the way, apropos of this Norwood business, you see that they had, as I surmised, a confederate in the house who could be none other than Lal Rao the butler.


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[Assistant] So Jones actually has the undivided honour of having caught one fish in his great haul. The division seems rather unfair, I remarked.


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[Assistant] You have done all the work in this business. I get a wife out of it. Jones gets the credit. Pray what remains for you." For me," said Sherlock Holmes, there still remains the cocaine bottle, and he stretched his long white hand up for it.


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[Assistant] I was putting in like a dishwasher.


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[Assistant] Lightning is part of nature, just like the rain and the wind.


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[Assistant] And it can be exciting to watch. The way it lights everything up.


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[Assistant] Please listen to this in a place where you can safely go to sleep.


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[Assistant] This is the classic fairy tale of Rapunzel. If you prefer your bedtime stories with some relaxing music,


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[Assistant] That version is also on the channel and pinned in the comments. And let's begin.


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[Assistant] There was once a man and a woman who had long in vain wished for a child.


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[Assistant] At length, the woman hoped that God was about to grant her desire.


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[Assistant] One day the woman was standing by this window and looking down into the garden she saw a bed which was planted with the most beautiful rampian and it looked so fresh and green as she longed for it and quite pined away and began to look pale and miserable


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[Assistant] Then her husband was alarmed and asked, what ails you dear wife? Ah, she replied, if I can't eat some of the rampian which is in the garden behind our house, I shall die.


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[Assistant] The man who loved her thought, Sooner than let your wife die, Bring her some of the rampian yourself, Let it cost what it will. At twilight he clambered down over the wall, Into the garden of the Enchantress, Hastily clutched a handful of rampian, And took it to his wife. She had once made herself a salad of it,


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[Assistant] If he was to have any rest, her husband must once more descend into the garden.


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[Assistant] Then the enchantress allowed her anger to be softened and said to him, if the case be as you say, I will allow you to take away with you as much rampian as you will. Only I make one condition.


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[Assistant] You must give me the child which your wife will bring into the world. It shall be treated well, and I will care for it like a mother. The man in hysteria consented to everything, and when the woman was brought to bed, the enchantress appeared at once, gave the child the name of Rapunzel, and took it away with her.


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[Assistant] Rapunzel grew into the most beautiful child under the sun. When she was 12 years old, the Enchantress shut her into a tower, which lay in a forest and had neither stairs nor door, but quite at the top was a little window. When the Enchantress wanted to go in,


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[Assistant] And then the hair fell 20 L's down and the enchantress climbed up by it.


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[Assistant] After a year or two, it came to pass that the king's son rode through the forest and passed by the tower.


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[Assistant] When he heard a song which was so charming that he stood still and listened. This was Rapunzel, who in her solitude, passed her time in letting her sweet voice resound.


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[Assistant] The king's son wanted to climb up to her and looked for the door of the tower, but none was to be found.


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[Assistant] But the singing had so deeply touched his heart.


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[Assistant] But every day he went into the forest and listened to it.


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[Assistant] Once when he was thus standing behind a tree, he saw that an entrantress came there, and he heard how she cried, Rapunzel, Rapunzel, let your hair down to me.


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[Assistant] Then Rapunzel let down the braids of hair, and the Enchantress climbed up to her. If that is the ladder by which one mounts, I too will try my fortune, said he. And the next day, when it began to grow dark, he went to the tower and cried. Rapunzel, Rapunzel, let down your hair, to me.


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[Assistant] Immediately the hair fell down and the king's son climbed up. At first Rapunzel was terribly frightened when a man


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[Assistant] But the king's son began to talk to her quite like a friend and told her that his heart had been so stirred that it had let him have no rest and he had been forced to see her. Then Rapunzel lost her fear. And when he asked her if she would take him for her husband, she saw that he was young and handsome. She thought


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[Assistant] He will love me more than old Dame Gohal does.


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[Assistant] And she said yes and laid her hand in his. She said, I willingly go away with you, but I do not know how to get down.


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[Assistant] Bring with you a skein of silk every time that you come, and I will weave a ladder with it, and when that is ready, I will descend, and you will take me on your horse." They agreed that until that time, he should come to her every evening, for the old woman came by day. The Enchantress remarked nothing of this.


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[Assistant] Until once Rapunzel said to her, tell me Dame Gothel.


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[Assistant] How it happens that you are so much heavier for me to draw up when the young king's son, he is with me in a moment. Ah, you wicked child, cried the enchantress. What do I hear you say? I thought I had separated you from all the world, and yet you have deceived me.


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[Assistant] In her anger, she clutched Rapunzel's beautiful tresses, wrapped them twice around her hand.


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[Assistant] Seized a pair of scissors with a right and snip snap, they were cut off, and the lovely braids lay on the ground. And she was so pitiless that she took away poor Rapunzel into a desert where she had to live in great grief and misery. On the same day that she cast out Rapunzel,


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[Assistant] However, the enchantress fastened the braid of hair, which she had cut off to the hook of the window. And when the king's son came, she cried.


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[Assistant] Rapunzel, Rapunzel, let down your hair to me.


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[Assistant] But instead of finding his dearest Rapunzel, he found the Enchantress.


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[Assistant] Who gazed at him with wicked and venomous looks. Aha, she cried mockingly.


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[Assistant] You would fetch your dearest, but the beautiful bird sits no longer singing in the nest.


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[Assistant] The king's son was beside himself with pain in his despair. He leapt down from the tower. He escaped with his life.


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[Assistant] But the thorns into which he fell pierced his eyes. Then he wandered quite blind about the forest, ate nothing but roots and berries, and did naught but lament and weep over the loss of his dearest wife to be. Thus he roamed about in misery for some years.


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[Assistant] And at length came to the desert where Rapunzel


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[Assistant] With the twins to which she had given birth, a boy and a girl lived in wretchedness.


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[Assistant] He heard a voice and it seemed so familiar to him that he went towards it and when he approached Rapunzel knew him and fell on his neck and wept. Two of her tears wetted his eyes and they grew clear again and he could see with them as before. He led her to his kingdom where he was joyfully received and they lived for a long time afterwards.


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[Assistant] In this exercise, you're going to create your very own decorator function.


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[Assistant] How long does it take for a particular function to run? Is my function efficient?


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[Assistant] Is it as fast as possible? Well, you'll actually get a numerical answer to that question by completing this current challenge.


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[Assistant] Try running the starting code and seeing what gets printed as the current time each time you run it. Hopefully you'll notice that that number increments each time and it represents the number of seconds since 1970. So as time goes on, that number will go up.


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[Assistant] Think about how you can use that to create a decorator function and write your decorator function inside a Speedcalc decorator.


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[Assistant] And then apply that to both the fast function and the slow function in order to see how long it takes each of them to execute.


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[Assistant] That's the goal of this challenge and remember to take a look inside the description box to see how it should behave and also look at the hints if you get stuck. Have a go now.


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[Assistant] Which is also a function. It's basically a function that we're going to be decorating.


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[Assistant] And now we can access that function inside the body of the speedcalc decorator. Next, we're going to define our wrapper function.


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[Assistant] And this wrapper function is going to be the one that's calculating the time.


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[Assistant] Inside the wrapper function, the first thing we define is the start time using time dot time. And this will give us the number of seconds since January 1st, 1970. And then we run the function that's being passed in, which is going to be the function that we decorate.


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[Assistant] And this could be the slow function or the fast function or any other function for that matter.


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[Assistant] And then after that function is completed, then we calculate the end time. Well, what is the time now since January 1st, 1970?


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[Assistant] Finally, we can write our print function and using an f string, we substitute the function.name.


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[Assistant] Which is the name of the function that got passed in, which is the function that we're decorating.


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[Assistant] And that is of course calculated by the end time subtracting from the start time.


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[Assistant] And we end up with the number of seconds that has elapsed for the function that was passed in to finish running.


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[Assistant] And then finally we return our wrapper function and that completes the definition of our decorator.


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[Assistant] Now all that's left to do is to decorate our functions that we want to apply this to. So on line 13 and 18, I'm adding this speed calculator to the fast function and the slow function.


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[Assistant] And finally on line 23 and 24, I'm actually running those functions, which will carry along with it the decorator functions.


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[Assistant] And you can see each time we're applying the speedcalc decorator to each one of them and printing out the running time.


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[Assistant] If you are pregnant, you can take folic acid to help prevent certain birth defects in your baby.


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[Assistant] Folic acid and folate are two forms of the same B vitamin.


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[Assistant] Folate is the form that occurs naturally in foods, while folic acid is the man-made form added to multivitamins and fortified foods.


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[Assistant] And DNA and RNA, the genetic material inside your cells.


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[Assistant] During pregnancy, you need even more folic acid because your baby's body needs it too.


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[Assistant] Folic acid helps your baby's brain and spinal cord develop normally.


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[Assistant] Taking folic acid can significantly reduce the risk of neural tube defects in your developing baby, called an embryo at this stage.


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[Assistant] The neural tube begins as a tiny oval-shaped piece of embryonic tissue which folds into a tube about 28 days after conception. When this tube closes properly, it continues to develop into your baby's brain and spinal cord.


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[Assistant] If the neural tube does not close due to the lack of folic acid in your diet, your baby may have a neural tube defect, such as spina bifida or anencephaly. In spina bifida, the neural tube fails to close at the bottom.


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[Assistant] As a result, the parts of the spine that enclose and protect the lower spinal cord do not completely form allowing the cord to protrude.


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[Assistant] Children with this defect often require surgery and have permanent disabilities including leg paralysis, developmental delays, and nervous system problems.


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[Assistant] In the condition called anencephaly, the neural tube fails to close at the top.


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[Assistant] Preventing large parts of the brain and skull from developing completely.


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[Assistant] Babies with this defect are unable to live and usually die before birth or soon after.


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[Assistant] Folic acid may also protect against other birth defects, such as cleft lip and palate.


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[Assistant] And atrial septal defect, which is when a hole between the right and left upper chambers of the heart fails to close during development.


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[Assistant] Because the neural tube develops so early in pregnancy, often before a woman knows she's pregnant, experts recommend all women who are able to become pregnant take a daily multivitamin containing 400 micrograms of folic acid every day.


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[Assistant] During pregnancy, the need increases to 600 micrograms of folic acid each day.


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[Assistant] Experts recommend you take this multivitamin in addition to the food folate in your diet because your body does not absorb folate found naturally in food as easily as the man-made folic acid.


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[Assistant] Caregivers will often prescribe even 1,000 micrograms of folic acid daily to assure these minimal levels are covered.


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[Assistant] Even more may be required in some groups of women, including those with previous miscarriages and those women deficient in a folic acid metabolic enzyme. Talk with your caregiver about the optimal dose for you.


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[Assistant] You don't know who the fuck I am or who you're messing around with! Where'd you get that? What are you doing?


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[Assistant] I feel like Alyssa and I should be better friends. Oh. Hi Max.


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[Assistant] Glad to see our campus is eco-friendly. There's Kate, looking sadder than usual.


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[Assistant] Victoria probably played me. I should have played her. Better get to my room before I find Warren.


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[Assistant] Go, Miss Grant. Not sure if I will, though. I'm a loser.


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[Assistant] Could be fun. Glad I'm not the only lonely nerd around here.


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[Assistant] We get it, she's missing. Somebody has gone crazy with these posters. They live.


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[Assistant] Ahoy, Captain. At least I know you've got my back.


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[Assistant] It makes me smile thinking of the day mom and I sewed that together.


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[Assistant] If I'm not looking through a viewfinder, I'm looking through a window. Always looking.


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[Assistant] I'm glad Kate let me borrow her copy of the October Country. I should have every Bradbury book for myself. It's such a pretty guitar. I need to play it more.


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[Assistant] I love that mom sent me a fat box of chocolate coconut bites for my birthday. Anne slipped in a $200 gift certificate. She sure knows how to make a sad birthday happy.


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[Assistant] Let's play a little photo Tetris and leave Victoria a message. Let's move this here. And this one there. Hmm, a pattern is emerging.


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[Assistant] Boom! This is the email I need to show Juliet. Now I have to print this fast and get the hell out of here.


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[Assistant] Dior? Givenchy? You could pay for my tuition with that wardrobe.


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[Assistant] I'm not trying to be nosy, but I heard a rumor about you. What? Who told you I was pregnant?


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[Assistant] Nobody important. They just acted like it was bullshit.


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[Assistant] In the last lesson, we had a primer on functions so that we can understand the next step, which is the Python decorator.


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[Assistant] So we've seen that functions can have functionality inputs and outputs. Functions are first class objects and can be passed around as arguments. Functions can be nested in other functions. And finally, functions can be returned as the output from another function without needing to trigger it.


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[Assistant] With these four things in mind, we're finally ready to tackle the Python decorator.


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[Assistant] First of all, I'm actually going to create a Python decorator for you so that we can talk about how it works.


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[Assistant] Here's what a Python decorator function looks like. We start off by creating just a normal function. Let's say I call it my decorator function.


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[Assistant] This function is going to take another function as an input.


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[Assistant] Now inside the decorator function, I'm going to nest a wrapper function.


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[Assistant] And this wrapper function is then going to trigger the actual function that was passed in to the decorator function.


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[Assistant] And at the end of calling all the lines in my decorator function, I'm going to return my wrapper function. But remember without the parentheses.


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[Assistant] Now basically what we've done is we've created a decorator function which can do some stuff.


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[Assistant] And it could control the calling of the function that was passed in.


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[Assistant] Remember that a decorator function is just a function that wraps another function


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[Assistant] And gives that function some additional functionality. So that's quite a mouthful, but let's take a look at an example.


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[Assistant] Let's say that I want to create a simple function, which I'll call say hello.


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[Assistant] Now this function is going to be the simplest form of function. It has no inputs and it has no outputs and all it does is just going to print hello.


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[Assistant] Now, what if I don't want to run this immediately the moment when I hit run? What if I want to add a delay to this function?


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[Assistant] Well, one way of doing that would be importing the time module and then before we print hello,


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[Assistant] We can go ahead and run time.sleep and we can sleep for a pre-specified number of seconds.


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[Assistant] So now, when I call this method, sayHello,


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[Assistant] And when I run it, you'll see that immediately nothing happens. And then after a two second delay,


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[Assistant] You can see the print hello actually gets triggered.


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[Assistant] Now, what if I wanted to create a whole bunch of functions like say bye and say greeting?


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[Assistant] And I wanted to add a delay onto each of these.


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[Assistant] Well then I would have to copy and paste this code into all three places.


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[Assistant] Now, this is a very simple example, but frequently we'll want to add some additional piece of functionality to a number of our functions. So this is where the decorator comes in handy.


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[Assistant] What we could do is before we trigger the function that's passed in to the decorator function, we can add the delay.


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[Assistant] So now, whenever we decorate a function with this particular decorator function,


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[Assistant] Then it's going to delay for two seconds before it runs the function.


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[Assistant] So let's give this decorator function a little bit more of a descriptive name. Let's call it a delay decorator.


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[Assistant] And we can call the delay decorator in front of the methods which we want to delay.


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[Assistant] So we can add it to all three of them or we can only add it to some of them. But now when I actually run my function, say hello, for example,


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[Assistant] You can see that this one because it's got the delay decorator,


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[Assistant] It's going to wait for two seconds before it prints hello.


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[Assistant] But if I call say greeting, which is the one that doesn't have the decorator above it, then you can see it gets triggered immediately.


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[Assistant] Or adding some functionality which you can do after the function.


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[Assistant] Or alternatively, you can modify the function, so maybe you want to run it twice. So now when I say hello,


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[Assistant] It's going to not only delay by two seconds, but it's also gonna run it twice.


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[Assistant] This syntax is what we saw when we were creating our Flask app. It's got the at sign and it basically decorates the function below with a decorator function. So.


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[Assistant] Putting this function through a machine that could modify the function or add some functionality before or after.


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[Assistant] Coming back to our definition, a decorator function is simply a function which wraps another function.


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[Assistant] And gives it some additional functionality or modifies the functionality.


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[Assistant] Now coming back to this syntax, this at sign, it's what's known as syntactic sugar. It's some syntax that you can write to make it easier to write an alternative line of code.


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[Assistant] So let's say we wanted to add the decorator to this say greeting.


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[Assistant] Well, if instead of using this syntactic sugar with the at sign and then the name of the function, we could also just call the name of the decorator.


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[Assistant] And then we could pass in the name of our function, say greeting.


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[Assistant] We've triggered this sayGreeting function and we've passed it through the delay decorator. But it's much easier to see at a glance what's going on by using this at sign.


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[Assistant] Because you can see now you can just trigger this method by its own name.


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[Assistant] And because the at delay decorator is on top of it, then it's going to go through that filter.


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[Assistant] I recommend spending a few minutes looking at this code, which you can access at this URL, which is in the course resources, and then trying to recreate this functionality from scratch based on your understanding of what we just talked about.


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[Assistant] And then once you're done, we head back to our Flask application. You can now see that pretty much all of the code is easily understood.


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[Assistant] We know what this underscore underscore name refers to. We know that this is a decorator, which is going to make sure that we only trigger this function if the user is trying to access the URL that is the homepage forward slash.


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[Assistant] If we wanted to create another function, let's call it,


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[Assistant] This function could also get a decorator and this decorator will check to see, well, if somebody goes to the URL forward slash buy, well, then we're going to be triggering this method effectively.


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[Assistant] The Flask framework is the one that determines which of these methods to call, and these methods are only called when the decorator says it's appropriate to do so.


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[Assistant] And we go to the URL that is our server. So this we know is the home route or the homepage. But if we go to forward slash buy, then we go to the buy route and this method gets rendered.


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[Assistant] Now, in order to make sure that you get a chance writing decorator functions yourself,


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[Assistant] To challenge you to create your own decorative functions.


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[Assistant] And it's going to be a really useful decorator function because it's going to measure the amount of time that it takes a particular function to run. So all of that and more, I'll see you on the next lesson.


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[Assistant] Girls, ooh la la. Let me check to see if the pool's heated.


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[Assistant] Too bad you made me feel like the queen of assholes because I wanted that cash stash. Tell me you're not gonna stand there watching me like a zombie.


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[Assistant] I wish Rachel was here. She would totally love being in here at night. Wish you guys had met each other.


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[Assistant] Stop being so goddamn humble. You're like the smartest, most talented person I've ever known. More than Rachel Amber? Dude, I'm not her groupie, okay? And I'm sure you have Blackwell bros all over you. Like Warren.


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[Assistant] No worries. Once you get over yourself, you're gonna make the world bow. As long as you're there with me. Don't look so sad. I'm never leaving you.


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[Assistant] Every great artist gets rejected before they get accepted. So you have to enter a photo. Even though I'm pimping the school in town you want to torch? Come on, I don't want to see Arcadia Bay burn to the shore. I just say shit like that because I've been trying to get out of here since... since you left, basically.


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[Assistant] If I could find Rachel, then pay Frank off. I'm still leaving to start a whole new life.


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[Assistant] Hey, there you go! Rachel left a bunch of her clothes with me. She's your size.


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[Assistant] You can always rewind back to your chlorine brand t-shirt and generic jeans. You suck. I like my shirt and jeans.


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[Assistant] At least you can date now, right? My dad was terrified of the day I would discover boys.


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[Assistant] Sounds like you totally crushed on Rachel. You would have too. Smart, sexy, and sassy. Like me, right? Well, duh. Maybe Rachel would have fought over both of us. You wish. Then again, maybe. You're a photographer and she wanted to be a model.


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[Assistant] Maybe not. Go on down and say hi to Joyce. Free breakfast? I have to, uh, wake and bake first. I promise not to tell. Let's not rewind and find out, okay?


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[Assistant] David, stop threatening students. He threatens them with surveillance cameras, so he can spy on everybody. Like he spies on all of us here.


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[Assistant] Thanks for letting me down again, Max. Why can't you just rewind so I don't have to see you defending that asshole?


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[Assistant] I didn't black out. I had another vision. The town is going to get wiped out by a tornado. Max, tell me everything.


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[Assistant] I knew Chloe would be all over this. So I better get moving.


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[Assistant] Are you a hungry bunny? Here, nosh on this.


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[Assistant] No doubt. I do hope your mother gets better. Let me know if you need anything.


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[Assistant] I know Samuel doesn't wear silky scarves. So, who does?


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[Assistant] I'm sorry, Principal Wells. I'm still tense after I thought Kate was going to jump today. I just needed some air and space to walk.


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[Assistant] There's no way David would get in Mrs. Grant's face. She'd shut his ass down fast.


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[Assistant] Hey Warren, you busy? Just bubble hearth, you'll be okay. Listen, I need your physics expertise stat.


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[Assistant] Warren is such a classic nerd. Let's see if I can find those items.


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[Assistant] I say one stupid thing and Mr. Jefferson gets screwed. Nice going, Max.


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[Assistant] Ew, gross. At least switch to E6.


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[Assistant] Take it easy on the door, Chloe. Let's try this instead.


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[Assistant] The company I keep. Now let's find what we want.


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[Assistant] It was also time for everyone else to go home.


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[Assistant] But little nut-brown hair had one last thing to do.


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[Assistant] This camera back in on the human.


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[Assistant] So we've been talking about perception, how to detect cats and dogs.


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[Assistant] Pedestrians, lanes, how to steer a vehicle based on the external environment.


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[Assistant] The thing that's really fascinating and severely understudied is the human side.


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[Assistant] So, you know, you talk about the Tesla, where we have cameras and 17 Teslas driving around Cambridge.


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[Assistant] Because Tesla is one of the only vehicles allowing you to, uh,


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[Assistant] To experience in the real way on the road the interaction between the human and the machine.


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[Assistant] And the thing that we don't have that deep learning needs on the human side of semi-autonomous vehicles and fully autonomous vehicles is video of drivers. That's what we're collecting.


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[Assistant] That's what my work is in, is looking at billions of video frames of human beings driving 60 miles an hour plus on the highway in their semi-autonomous Tesla. What are the things that we want to know about the human? The, uh,


[User] Please transcribe this text

[Assistant] If, if you're a deep learning therapist and we try to, uh, tear apart, uh, break, break apart the different things we can detect from this raw set of pixels.


[User] Please transcribe this text

[Assistant] We can look here from the green to red is the different detection problems, the different computer vision detection problems. Green means it's less challenging, it's feasible,


[User] Please transcribe this text

[Assistant] Even under poor lighting conditions, variable pose, noisy environment, poor resolution, red means it's really hard no matter what you do.


[User] Please transcribe this text

[Assistant] That's starting on the left with face detection and body pose. One of the best studied and one of the easier computer vision problems.


[User] Please transcribe this text

[Assistant] We have huge data sets for these. And then there is micro saccades. The slight tremors of the eye that happen in one...


[User] Please transcribe this text

[Assistant] Uh, at a rate of uh, a thousand times a second.


[User] Please transcribe this text

[Assistant] To build trust, the car needs to have some awareness of the biological thing it's carrying inside, the human inside. You kind of assume the car knows about you because you're like sitting there controlling it.


[User] Please transcribe this text

[Assistant] But if you think about it, almost every single car on the road today has no sensors with which it's perceiving you. It knows some cars have a pressure sensor on the steering wheel.


[User] Please transcribe this text

[Assistant] And a pressure sensor, some kind of sensor, uh, detecting that you're sitting in the seat. That's the only thing it knows about you.


[User] Please transcribe this text

[Assistant] That's, that's it. So how is the car supposed to


[User] Please transcribe this text

[Assistant] This same car that's driving 70 miles an hour on the highway autonomously, how is it supposed to build trust with you if it doesn't perceive you? That's, that's one of the critical things here. So if I'm, if I'm constantly advocating something, it's that we should have a driver facing camera in every car.


[User] Please transcribe this text

[Assistant] And that despite the privacy concerns, now you have a camera on your phone, and you don't have as much of a privacy concern there, is, despite the privacy concerns, the safety benefits are huge. The trust benefits are huge.


[User] Please transcribe this text

[Assistant] So let's start with the easy one, body pose, detecting body pose.


[User] Please transcribe this text

[Assistant] Why do we care? So there's a seatbelt design.


[User] Please transcribe this text

[Assistant] There's these dummies, crash-desk dummies with which, uh, which are used to design, uh, the safety system, uh, uh, the passive safety systems of our cars. And they make certain assumptions about body shapes, male, female, child body shapes. But they also make assumptions about the position of your body in the seat.


[User] Please transcribe this text

[Assistant] They have the optimal position, the position they assume you take. The reality is, in a Tesla, when the car is driving itself,


[User] Please transcribe this text

[Assistant] How often that happens? The car needs to know that you're in that position.


[User] Please transcribe this text

[Assistant] This is deep learning class, right? So this, deep learning of the rescue.


[User] Please transcribe this text

[Assistant] That takes it's input image and takes it's output. It's a regressor. It gives an XY position of the whatever you're looking for. The left shoulder or the right shoulder. And then you have a cascade of regressors.


[User] Please transcribe this text

[Assistant] That give you all these points to give you the shoulders, the arms and so on. And then you have...


[User] Please transcribe this text

[Assistant] Through time, on every single frame you make that prediction, and then you optimize, you know,


[User] Please transcribe this text

[Assistant] The temporal error from frame to frame or


[User] Please transcribe this text

[Assistant] You can just dump all the frames as if they're different channels, like RGB is three channels. You can think of those channels as in time. You can dump all those frames together in what are called 3D convolutional neural networks. You dump them all together and then you estimate the body pose in all the frames at once.


[User] Please transcribe this text

[Assistant] And there's some data sets for sports. And we're building our own. I don't know who that guy is.


[User] Please transcribe this text

[Assistant] Let's, let's fly through this a little bit. So what's called gaze classification. Gaze is another word for glance, right? It's a classification problem. Here's one of the TAs of this class.


[User] Please transcribe this text

[Assistant] Again, not here, because he's married, he had to be home.


[User] Please transcribe this text

[Assistant] There's five cameras. This is what we're recording in the Tesla. This is a Tesla vehicle.


[User] Please transcribe this text

[Assistant] There's in the bottom right, there's a blue icon that lights up automatically detected if it's operating under autopilot. That means the car is currently driving itself. There's five cameras, one of the forward roadway, one of the instrument cluster, one of the center stack, steering wheel, his face, and then it's...


[User] Please transcribe this text

[Assistant] Center stack, instrument cluster, rear view mirror.


[User] Please transcribe this text

[Assistant] And you give it millions of frames for every class. Simple.


[User] Please transcribe this text

[Assistant] Where the driver is looking. And the process is the same for majority of the driver state problems that have to do with the face. The face has so much information, uh, where you're looking, emotion, drowsiness.


[User] Please transcribe this text

[Assistant] Different degrees of frustration, I'll fly through those as well. But the process is the same. There's some pre-processing. So this is in the wild data. There's a lot of crazy light going on, there's noise, there's vibration from the vehicle. So first you have to, video stabilization, you have to remove all that vibration, all that noise as best as you can. There's a lot of algorithms, non-neural network algorithms.


[User] Please transcribe this text

[Assistant] Boring, but they work. For removing the, removing the noise, removing the effects of sudden light variations and the vibrations of the vehicle. There's the automated calibration, so you have to estimate the frame of the camera, the position of the camera.


[User] Please transcribe this text

[Assistant] And estimate the identity of the person you're looking at. The more you can specialize the network to the identity of the person and the identity of the car the person is riding in, the better the performance for the different driver state classification.


[User] Please transcribe this text

[Assistant] So you personalize the network. You have a background model that works on everyone and you specialize each individual. This is transfer learning. You specialize each individual network to that one individual. Alright. There is face frontalization.


[User] Please transcribe this text

[Assistant] Fancy name for the fact that no matter where they're looking, you want to transfer that face so the eyes and nose are the exact same position in the image. That way if you want to look at the eyes,


[User] Please transcribe this text

[Assistant] And you want to study the subtle movement of the eyes, the subtle blinking, the dynamics of the eyelid, the velocity of the eyelid. It's always in the same place. You can really focus in, remove all effects of any other motion of the head.


[User] Please transcribe this text

[Assistant] And then, you just, this is the beauty of deep learning, right? You don't, there is some preprocessing, uh, because this is...


[User] Please transcribe this text

[Assistant] You know, real world data, but you just dump the raw pixels in.


[User] Please transcribe this text

[Assistant] And uh, you know, they, they put themselves as frustrated or not. And so then we can predict, we can train a convolutional neural network to predict, is this person frustrated or not. I think we've seen a video of that. Turns out smiling is a strong indication of...


[User] Please transcribe this text

[Assistant] of frustration. You can also predict drowsiness in this way, gaze estimation in this way, cognitive load.


[User] Please transcribe this text

[Assistant] I'll briefly look at that. And the process is all the same. You detect the face. You find the landmark points in the face for the face alignment, face frontalization.


[User] Please transcribe this text

[Assistant] And then you dump the raw pixels in for classification, step five. You can use SVMs there, or you can use what everyone uses now, convolutional neural networks.


[User] Please transcribe this text

[Assistant] This is the one part where CNNs have still struggled to compete, is the alignment problem.


[User] Please transcribe this text

[Assistant] Is, this is why I talked about the cascade of regressors, is finding the landmarks on


[User] Please transcribe this text

[Assistant] The, the, the eyebrows, the nose, the jawline, the mouth.


[User] Please transcribe this text

[Assistant] There's certain constraints there and so algorithms that can utilize those constraints effectively can often perform better than end-to-end regressors that just don't have any concept of what a face is shaped like.


[User] Please transcribe this text

[Assistant] And there's huge data sets and we're a part of the awesome community that's building those data sets for face alignment. Okay, so this is again the TA in his younger form.


[User] Please transcribe this text

[Assistant] This is, uh, live in the car, real time system predicting where they're looking.


[User] Please transcribe this text

[Assistant] Exciting direction that machine learning is headed, which is unsupervised learning.


[User] Please transcribe this text

[Assistant] The less you have to have humans look through the data and annotate that data, the more power these machine learning algorithms get, right? So,


[User] Please transcribe this text

[Assistant] Currently supervised learning is what's needed. You need human beings to label a cat and label a dog. But if, if you can only have a human being label one percent, one tenth of a percent of a data set.


[User] Please transcribe this text

[Assistant] Only the hard cases. So the machine can come to the human and be like, I don't know, I don't know what I'm looking at at these pictures. Because of the partial light occlusions. We're not good at dealing with occlusions.


[User] Please transcribe this text

[Assistant] Whether it's your own arm or because of light conditions. We're not good with-


[User] Please transcribe this text

[Assistant] Crazy light, uh, drowning out the image. This is what the Google self-driving car actually struggle with when they're trying to use their vision sensors. Moving out of frame.


[User] Please transcribe this text

[Assistant] So, just all kinds of occlusions are really hard for computer vision algorithms.


[User] Please transcribe this text

[Assistant] And in those case, we want a machine to step in and say,


[User] Please transcribe this text

[Assistant] And pass that image on to the human. Be like, help me out with this.


[User] Please transcribe this text

[Assistant] And the other corner cases is, so in driving for example, 90 plus percent of the time, all you're doing is staring forward at the roadway in the same way. That's where the machine shines. That's where machine annotation, automated annotation shines. Because it's seen that face.


[User] Please transcribe this text

[Assistant] You use that to predict when something has changed. And when something has changed, you bring that to the machine for annotation. All of this is to build a giant...


[User] Please transcribe this text

[Assistant] Billions of frames, uh, annotated data set of ground truth on which to train your driver state algorithms.


[User] Please transcribe this text

[Assistant] And in this way, you can control. On the X axis is the fraction of frames that a human has to annotate. 0% on the left, 10% on the right.


[User] Please transcribe this text

[Assistant] And then the accuracy trade-off. The more the human annotates, the higher the accuracy. You approach 100% accuracy.


[User] Please transcribe this text

[Assistant] But you can still do pretty good. This is for the gaze classification task. When, uh,


[User] Please transcribe this text

[Assistant] With an 84 per, uh, 84 fold to almost two orders of magnitude reduction in, in human annotation. This is the future of machine learning. And hopefully one day no human annotation.


[User] Please transcribe this text

[Assistant] And the result is millions of images like this. Video frames.


[User] Please transcribe this text

[Assistant] Same thing. Driver frustration. This is what I was talking about. The frustrated driver is the one that's on the bottom.


[User] Please transcribe this text

[Assistant] So a lot of movement of the eyebrows and a lot of smiling. And that's true subject after subject.


[User] Please transcribe this text

[Assistant] And the happy, the satisfied, I don't say happy, the satisfied driver is cold and stoic.


[User] Please transcribe this text

[Assistant] Absolutely, that's a great question. There is a, so this is cars owned by MIT. There is somebody in the back.


[User] Please transcribe this text

[Assistant] So, uh, the comment was, my emotions might then have nothing to do with the driving experience. Uh, yes, let me continue that comment is, your emotions are often


[User] Please transcribe this text

[Assistant] You're an actor on the stage for others with your emotion. So when you're alone, you might not express emotion. You're really expressing emotion oftentimes for others. Like, you're frustrated, so it's like, ah, what the heck. That's for the passenger. That's absolutely right. So one of the cool things


[User] Please transcribe this text

[Assistant] We're doing, as I said, we now have over a billion video frames in the Tesla. We're studying, collecting huge amounts of data in the Tesla. And it's, emotion is a complex thing, right? Uhm, in this case we can, we know the ground truth how frustrated they were.


[User] Please transcribe this text

[Assistant] In naturalistic data, when it's just people driving around, we don't know how they're really feeling at the moment. We're not asking them to like enter in an app. How are you feeling right now?


[User] Please transcribe this text

[Assistant] That has to be a paper at some point. It's awesome. People love singing.


[User] Please transcribe this text

[Assistant] So that doesn't happen in this kind of data, because there's somebody sitting in the car. And I think the expression of frustration is also the same.


[User] Please transcribe this text

[Assistant] From a data set that's non-solo with a passenger. And it's very true. The tricky thing about driving, and this is why it's a huge challenge for self-driving cars, for the external-facing sensors and for the internal-facing sensors analyzing human behavior, is like 99.9% of driving is the same thing.


[User] Please transcribe this text

[Assistant] It's really boring. So finding the interesting bits is actually pretty complicated.


[User] Please transcribe this text

[Assistant] So that has to do with emotion, that has to do with... So singing is easy to find.


[User] Please transcribe this text

[Assistant] So we can track the mouth pretty well. So whenever you're talking or singing, we can find that. But how do you find subtle expressions of emotion? It's hard. When you're solo. And cognitive load. That's...


[User] Please transcribe this text

[Assistant] That's a, that's a fascinating thing, I mean similar to motion, it's, uh, it's a little more concrete.


[User] Please transcribe this text

[Assistant] In a sense that there's a lot of good science and ways to measure cognitive load.


[User] Please transcribe this text

[Assistant] Cognitive workload. How occupied your mind is.


[User] Please transcribe this text

[Assistant] Mental workload is another term used. And so, the window to the soul, the cognitive workload and soul is the eyes. So, pupil,


[User] Please transcribe this text

[Assistant] So first of all, the eyes move in two different ways. They move in a lot of ways, but two major ways is saccades. These are these ballistic movements. They jump around. Whenever you look around the room, they're actually just jumping around. When you read, their eyes are jumping around. And when...


[User] Please transcribe this text

[Assistant] Like, if all you just follow this bottle with your eyes, that your eyes are actually gonna move smoothly, uh, smooth pursuit. Somebody actually just told me today, that probably has to do with our hunting background, or as animals.


[User] Please transcribe this text

[Assistant] I don't know how that helps, like frogs track flies really well, so that you have to like, I don't know.


[User] Please transcribe this text

[Assistant] Anyway, the point is there are smooth pursuit movements where the eyes move smoothly. And those are all indications of certain aspects of cognitive load. And then there is very subtle movements which are almost imperceptible for computer vision. And these are, uhm, micro saccades. These are tremors of the eye.


[User] Please transcribe this text

[Assistant] Here, work from here from Bill Freeman, uh, magnifying those subtle movements. These are taken at, uh, 500 frames a second.


[User] Please transcribe this text

[Assistant] When it gets larger, that's an indicator of high cognitive load. But it also gets larger when the light is dim.


[User] Please transcribe this text

[Assistant] So eh, there's like this complex interplay. So we can't rely in the wild, outside, in the car, or just in general, outdoors, uh, on using the pupil size. Even though pupil size has been used effectively in the lab to measure cognitive load, it can't be reliably used in the car. And the same with blinks.


[User] Please transcribe this text

[Assistant] The, uh, when there's a higher cognitive load, your blink rate decreases and your blink duration shortens. Okay.


[User] Please transcribe this text

[Assistant] I think I'm just repeating the same thing over and over. But you can imagine how we can predict cognitive load, right? We extract video of the eye.


[User] Please transcribe this text

[Assistant] Here is the, uh, the primary eye of the, the person the system is observing. Happens to be the, the same TA once again.


[User] Please transcribe this text

[Assistant] We take the sequence of a hundred, uhm, oh it's 90 images, so that's 6 seconds, 16 frames a second, 15 frames a second. And we dump that into a 3D convolution neural network. Again, that means it's 90 channels.


[User] Please transcribe this text

[Assistant] Of 90 frames, gray scale. And then the prediction is one of three classes of cognitive load.


[User] Please transcribe this text

[Assistant] Low cognitive load, medium cognitive load and high cognitive load. And there's ground truth for that because we have people, over 500 different people do different tasks of various cognitive load.


[User] Please transcribe this text

[Assistant] And after some frontalization again, where you see the eyes are, no matter where the person is looking.


[User] Please transcribe this text

[Assistant] We find the eye, active appearance models, find 39 points.


[User] Please transcribe this text

[Assistant] Uh, of the eye of the eyelids, the iris, and four points on the pupil.


[User] Please transcribe this text

[Assistant] From the video stream, CNN runs in faster than real time, predicts cognitive load.


[User] Please transcribe this text

[Assistant] Same process as detecting the identity of the face. Same process as detecting where the driver is looking. Same process as detecting emotion. And all of those require very little hyperparameter tuning on the convolutional neural networks. They only require huge amounts of data.


[User] Please transcribe this text

[Assistant] And why do we care about detecting what the driver is doing? And I think Eric has mentioned this.


[User] Please transcribe this text

[Assistant] On the... Oh man, this is the comeback of the slide.


[User] Please transcribe this text

[Assistant] I was criticized for this being a very cheesy slide. In,


[User] Please transcribe this text

[Assistant] Can't, it's enough of that, this is better. And, uh, especially given that the, this is given today, our new president. This is pickup truck country.


[User] Please transcribe this text

[Assistant] This is manually controlled vehicle country for quite a little while. We like control. And control...


[User] Please transcribe this text

[Assistant] Being given to somebody else, to the machine, will be a gradual process. It's a gradual process of that machine earning trust. And through that process, the machine, like the Tesla, like the BMW, like the Mercedes, the Volvo, that's now playing with these ideas, is gonna need to see what the human is doing.


[User] Please transcribe this text

[Assistant] Billions of miles of forward facing data. What we need is billions of miles of driver facing data as well. We're in the process of collecting that and this is a pitch for automakers.


[User] Please transcribe this text

[Assistant] And everybody to, uh, buy cars that have a driver facing camera.


[User] Please transcribe this text

[Assistant] And through your own research, you'll find that we're in the very early stages of, of discovering the power of deep learning.


[User] Please transcribe this text

[Assistant] That the deeper the network, the better the results in a lot of really important cases, even though the data is not increasing.


[User] Please transcribe this text

[Assistant] So why does a deeper network give better results?


[User] Please transcribe this text

[Assistant] This is a mysterious thing we don't understand. There's this, there's these hundreds of millions of parameters.


[User] Please transcribe this text

[Assistant] And from them is emerging some kind of...


[User] Please transcribe this text

[Assistant] Structure, some kind of representation of the knowledge that we're giving it.


[User] Please transcribe this text

[Assistant] One of my favorite examples of this emergent concept is Conway's Game of Life.


[User] Please transcribe this text

[Assistant] For those of you who know what this is, will probably criticize me for it being as cheesy as the stairway slide. But I think it's actually such a simple and brilliant example of how, like a neuron in a neural network is a really simple computational unit.


[User] Please transcribe this text

[Assistant] And then incredible power emerges when you just combine a lot of them in a network. In the same way, this is called a cellular automata. That's a weird pronunciation.


[User] Please transcribe this text

[Assistant] And, and the rule, it's, it's, every single cell is operating under a simple rule. You can think of it as a cell living and dying.


[User] Please transcribe this text

[Assistant] It's, it's, it's filled in black when it's alive and white when it's dead and when it has two or if it's alive.


[User] Please transcribe this text

[Assistant] And it has two or three neighbors. It survives to the next time slot. Otherwise it dies.


[User] Please transcribe this text

[Assistant] It comes back to life. It has exactly three neighbors. That's a simple rule.


[User] Please transcribe this text

[Assistant] Whatever, you can just imagine, it's just simple, all it's doing is operating under this very local process. Same as a neuron.


[User] Please transcribe this text

[Assistant] It's a, it's a, it's a, or in the way we're currently training neural networks in this local gradient. We're optimizing over a local gradient. Same, local rules. And what happens if you run this system,


[User] Please transcribe this text

[Assistant] Operating under really local rules. When you get on the right, it's not, again, you have to go home, hopefully no drugs involved, but you have to open up your mind.


[User] Please transcribe this text

[Assistant] And, and see how amazing that is because what happens is it's a, it's a local computational unit.


[User] Please transcribe this text

[Assistant] That knows very little about the world. But somehow, really complex patterns emerge. And we don't understand why.


[User] Please transcribe this text

[Assistant] In fact, under different rules, incredible patterns emerge, and it feels like it's living creatures, like communicating.


[User] Please transcribe this text

[Assistant] Like when you just watch it, not these examples, this is uh, this is uh, the original, they get like complex and interesting, but even these examples, these complex geometric patterns that emerge, it's incredible, we don't understand why. Same with neural networks, we don't understand why, and we need to, in order to see how these networks will be able to reason. Okay, so what's next?


[User] Please transcribe this text

[Assistant] Uh, I encourage you to, uh, read the deep learning book. It's available online, deeplearningbook.org. As I mentioned to a few people,


[User] Please transcribe this text

[Assistant] You should, well first there's a ton of amazing papers every day coming out on archive.


[User] Please transcribe this text

[Assistant] I'll put these links up, but there's a lot of good collections of strong paper, lists of papers. There is the literally awesome list, the awesome deep learning papers on GitHub. It's calling itself awesome, but it happens to be awesome.


[User] Please transcribe this text

[Assistant] And there is a lot of blogs that are just amazing.


[User] Please transcribe this text

[Assistant] And if you're interested in the application of deep learning in the automotive space, you can come do research in our group. Just email me.


[User] Please transcribe this text

[Assistant] Jeffrey Hugh, Michael Gump, and how do you, are you here?


[User] Please transcribe this text

[Assistant] So, I, I, this is kind of incredible, so I didn't know what kind of speed we were going to be able to achieve. I thought 73 was unbeatable. Because we played with it for a while, we couldn't achieve 73. We designed a deterministic algorithm that was able to achieve 74, I believe.


[User] Please transcribe this text

[Assistant] Meaning like, it's cheating, a cheating algorithm that got 74. And so, folks have come up with algorithms that have done


[User] Please transcribe this text

[Assistant] I have beaten 73 and then 74, so this is really incredible. And, uh, the other two guys, so all three of you get a free term at the Udacity Cell Driving Car Engineering degree. Thanks to those guys for giving it, uh, giving that award and bringing their army of brilliant


[User] Please transcribe this text

[Assistant] Uh, so they have, they have people who are obsessed about self-driving cars. And we've received...


[User] Please transcribe this text

[Assistant] Over 2000 submissions for this competition. A lot of them from those guys. And they're just brilliant. So it's, it's, uh, it's really, uh,


[User] Please transcribe this text

[Assistant] Exciting to have such a big community of deep learning folks working in this field. So, this is...


[User] Please transcribe this text

[Assistant] For the rest of eternity, we're gonna change this up a little bit, but this is actually the three...


[User] Please transcribe this text

[Assistant] Neural networks, uh, the three neural, uh, three winning neural networks running side by side. And you can see the number of cars passed there. The first place is on the left.


[User] Please transcribe this text

[Assistant] Second place and third place, and in fact, the third place is almost, wait, no, second place is winning currently. But that just tells you that the, uh,


[User] Please transcribe this text

[Assistant] The random nature of competition. Sometimes you win, sometimes you lose.


[User] Please transcribe this text

[Assistant] So, there's a, the actual evaluation process runs through a lot of, a lot of iterations and takes the medium evaluation.


[User] Please transcribe this text

[Assistant] And continue submitting networks. Uh, this will run for a while and we're working on a journal paper for this, for this game. We're trying to find the optimal solutions.


[User] Please transcribe this text

[Assistant] And the first time obviously teaching this class and so thank you so much for, for being a part of it. Thank you.


[User] Please transcribe this text

[Assistant] Just write your email on the note, on the, on the index note. Thank you.


[User] Please transcribe this text

[Assistant] You found the tools? Sweet. You can sit at my desk and fix your camera.


[User] Please transcribe this text

[Assistant] No, I didn't know what to do. I don't blame you. That's some intense shit.


[User] Please transcribe this text

[Assistant] Maybe I should go to the principal. The principal? Are you still 12? That drunk jackass only cares about cash for Blackwell Academy. Don't trust him.


[User] Please transcribe this text

[Assistant] My dad would be pissed if I never used it, and now I know it'll be used awesomely. And I'll snag this picture as a symbol of our reunion. Cool?


[User] Please transcribe this text

[Assistant] Because you're such a badass, Max, let me show you my new toy.


[User] Please transcribe this text

[Assistant] I'm Price. Chloe Price. Bang! Jesus, put that thing down! Relax, sister, it's not even loaded. Yet.


[User] Please transcribe this text

[Assistant] I thought you believed in gun control. Yes, I believe I should control the gun. It's the men who need to be checked. You trust Nathan or David? Thanks for taking the heat.


[User] Please transcribe this text

[Assistant] We totally smacked his punk ass down, Max. He's no match for you and me now. That was an epic win.


[User] Please transcribe this text

[Assistant] You're giving me a look I haven't seen for a while? What's the problem?


[User] Please transcribe this text

[Assistant] You know you're gonna get busted. Or somebody like Nathan will bust me with a cap. Screw that. I need to cover my own back.


[User] Please transcribe this text

[Assistant] Your stepdad won't let you off the hook that easy. What the hell does he need another gun for? To mow down Bigfoot cheerleaders?


[User] Please transcribe this text

[Assistant] Don't keep it in your pants, okay? Max, I'm not looking for trouble. I just want protection. If anybody in this country should have guns, it should be women.


[User] Please transcribe this text

[Assistant] He freaked out on poor Kate Marsh today. I know her. She's cool. Only that prick would bully her.


[User] Please transcribe this text

[Assistant] Files? I know that look, you peeked. So, dish the dirt. Snuff videos?


[User] Please transcribe this text

[Assistant] I woke up and that perv was smiling, crawling towards me with a camera. Go on. Everything was a blur. I tried to kick him in the balls and broke a lamp.


[User] Please transcribe this text

[Assistant] If this is destiny, I hope we can find Rachel. I miss her, Max.


[User] Please transcribe this text

[Assistant] I'd like to drop a bomb on Arcadia Bay and turn it to fucking glass.


[User] Please transcribe this text

[Assistant] Hollamax, by the way, I know you saw me in the dorm.


[User] Please transcribe this text

[Assistant] Like all the other precious twee artists here, you really nailed me.


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[Assistant] Oh, sorry, Chloe. Don't let me get in the way of your bonding. I heard the gunshots and the breaking glass. It's cute that you're playing with guns. Just like me at your age.


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[Assistant] You better step back before you regret it, girl. I mean it. You want me to cut you, bitch?


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[Assistant] Ah Christ, you're more like Abbott and Costello.


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[Assistant] Nice piece. I'll consider this interest on your loan. Thanks.


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[Assistant] You have until Friday to pay me, or I'll track you down with this interest. Have a good play, kids.


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[Assistant] It was some time before the health of my friend Mr. Sherlock Holmes recovered from the strain caused by his immense exertions in the spring of 87.


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[Assistant] The whole question of the Netherlands Sumatra Company, and of the colossal schemes of Baron Mopertuis, are too recent in the minds of the public.


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[Assistant] And are too intimately concerned with politics and finance to be fitting subjects for this series of sketches.


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[Assistant] They led, however, in an indirect fashion to a singular and complex problem which gave my friend an opportunity of demonstrating the value of a fresh weapon among the many with which he waged his life-long battle against crime.


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[Assistant] On referring to my notes I see that it was upon the fourteenth of April that I received a telegram from Lyon, which informed me that Holmes was lying ill in the Hôtel du Lang.


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[Assistant] Within twenty-four hours I was in his sick room, and was relieved to find that there was nothing formidable in his symptoms.


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[Assistant] Even his iron constitution, however, had broken down under the strain of an investigation which had extended over two months, during which period he had never worked less than fifteen hours a day, and had more than once, as he assured me, kept to his task for five days at a stretch.


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[Assistant] Even the triumphant issue of his labours could not save him from reaction, after so terrible an exertion, and at a time when Europe was ringing with his name, and when his room was literally ankle-deep with congratulatory telegrams. I found him a prey to the blackest oppression


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[Assistant] Even the knowledge that he had succeeded where the police of three countries had failed, and that he had outmaneuvered at every point the most accomplished swindler in Europe, was insufficient to rouse him from his nervous prostration. Three days later we were back in Baker Street together.


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[Assistant] But it was evident that my friend would be much the better for a change.


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[Assistant] And the thought of a week of springtime in the country was full of attractions to me also.


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[Assistant] My old friend Colonel Hater, who had come under my professional care in Afghanistan, had now taken a house near Rygate in Surrey, and had frequently asked me to come down to him upon a visit.


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[Assistant] On the last occasion he had remarked that if my friend would only come with me he would be glad to extend his hospitality to him also.


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[Assistant] A little diplomacy was needed, but when Holmes understood that the establishment was a bachelor one, and that he would be allowed the fullest freedom, he fell in with my plans, and a week after our return from Lyon we were under the Colonel's roof.


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[Assistant] Hato was a fine old soldier who had seen much of the world, and he soon found, as I had expected, that Holmes and he had much in common.


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[Assistant] On the evening of our arrival we were sitting in the Colonel's gun-room after dinner. Holmes stretched upon the sofa, while Hater and I looked over his little armory of Eastern weapons.


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[Assistant] Yes, we've had a scare in this part lately. Old Acton, who is one of our county magnates, had his house broken into last Monday. No great damage done, but the fellows are still at large.