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[Assistant] Who is this KKK? And why does he pursue this unhappy family?


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[Assistant] Sherlock Holmes closed his eyes and placed his elbows upon the arms of his chair with his fingertips together. The ideal reasoner, he remarked,


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[Assistant] Word, when he had once been shown a single fact in all its bearings, deduced from it not only the chain of events which led up to it, but also all the results which would follow from it.


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[Assistant] As Cuvier could correctly describe a whole animal by the contemplation of a single bone,


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[Assistant] So the observer, who has thoroughly understood one link in a series of incidents, should be able to accurately state all the other ones, both before and after.


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[Assistant] We have not yet grasped the results which the reason alone can attain to.


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[Assistant] Problems may be solved in the study which have baffled all those who have sought a solution by the aid of their senses.


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[Assistant] Carry the art, however, to its highest pitch. It is necessary that the reasoner should be able to utilise all the facts which have come to his knowledge, and this in itself implies, as you will readily see, a possession of all knowledge.


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[Assistant] Which even in these days of free education and encyclopedias is a somewhat rare accomplishment.


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[Assistant] It is not so impossible, however, that a man should possess all knowledge which is likely to be useful to him in his work, and this I have endeavoured in my case to do.


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[Assistant] If I remember rightly, you on one occasion in the early days of our friendship defined my limits in a very precise fashion.


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[Assistant] Yes," I answered, laughing. It was a singular document. Philosophy, astronomy, and politics were marked at zero, I remember. Botany variable, geology profound as regards the mud-stains from any region within fifty miles of town,


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[Assistant] Chemistry eccentric, anatomy unsystematic, sensational literature and crime records unique, violin player, boxer, swordsman, lawyer, and self-poisoner by cocaine and tobacco. Those, I think, were the main points of my analysis." Holmes grinned at the last item.


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[Assistant] Well," he said, I say now, as I said then, that a man should keep his little brain-attic stocked with all the furniture that he is likely to use, and the rest he can put away in the lumber-room of his library, where he can get at it if he wants it.


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[Assistant] Now, for such a case as the one which has been submitted to us to-night, we need certainly to muster all our resources.


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[Assistant] Kindly hand me down the letter K of the American Encyclopedia, which stands upon the shelf beside you. Thank you.


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[Assistant] Now, let us consider the situation and see what may be deduced from it.


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[Assistant] In the first place we may start with a strong presumption.


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[Assistant] That Colonel Openshaw had some very strong reason for leaving America.


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[Assistant] Men at his time of life do not change all their habits and exchange willingly the charming climate of Florida for the lonely life of an English provincial town.


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[Assistant] His extreme love of solitude in England suggests the idea that he was in fear of someone or something, so we may assume, as a working hypothesis, that it was fear of someone or something which drove him from America.


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[Assistant] As to what it was he feared, we can only deduce that by considering the formidable letters which were received by himself and his successors. Did you remark the postmarks of those letters? The first was from Pondicherry, the second from Dundee, and the third from London. From East London. What do you deduce from that?


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[Assistant] They're all seaports. That the writer was on board of a ship. Excellent.


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[Assistant] We have already a clue. There can be no doubt that the probability—the strong probability—is that the writer was on board of a ship. And now let us consider another point.


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[Assistant] In the case of Pondicherry, seven weeks elapsed between the threat and its fulfilment.


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[Assistant] In Dundee it was only some three or four days. Does that suggest anything? A greater distance to travel?


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[Assistant] But the letter had also a greater distance to come. Then I do not see the point.


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[Assistant] There is at least a presumption that the vessel in which the man or men are is a sailing ship.


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[Assistant] It looks as if they always send their singular warning or token before them when starting upon their mission.


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[Assistant] You see how quickly the deed followed the sign when it came from Dundee.


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[Assistant] If they had come from Pondicherry in a steamer they would have arrived almost as soon as their letter. But as a matter of fact seven weeks elapsed.


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[Assistant] I think that those seven weeks represented the difference between the mailboat which brought the letter, and the sailing vessel which brought the writer. It is possible. More than that, it is probable.


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[Assistant] And now you see the deadly urgency of this new case, and why I urged young Openshaw to caution.


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[Assistant] The blow has always fallen at the end of the time which it would take the senders to travel the distance. But this one comes from London, and therefore we cannot count upon delay.


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[Assistant] Good God! I cried. What can it mean, this relentless persecution?


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[Assistant] The papers which Open Shore carried are obviously of vital importance to the person or persons in the sailing ship.


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[Assistant] I think it is quite clear that there must be more than one of them.


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[Assistant] A single man could not have carried out two deaths in such a way as to deceive a coroner's


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[Assistant] There must have been several in it, and they must have been men of resource and determination.


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[Assistant] Their papers, they mean to have, be the holder of them who it may.


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[Assistant] Have you never," said Sherlock Holmes, bending forward and sinking his voice, have you never heard of the Ku Klux Klan? I never have. Holmes turned over the leaves of the book upon his knee. Here it is, said he presently.


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[Assistant] Ku Klux Klan A name derived from the fanciful resemblance to the sound produced by cocking a rifle.


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[Assistant] This terrible secret society was formed by some ex-Confederate soldiers in the Southern States after the Civil War, and it rapidly formed local branches in different parts of the country, notably in Tennessee, Louisiana, the Carolinas, Georgia, and Florida.


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[Assistant] Its power was used for political purposes, principally for the terrorizing of the negro voters, and the murdering and driving from the country of those who were opposed to its views.


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[Assistant] Its outrages were usually preceded by a warning sent to the marked man in some fantastic but generally recognised shape—a sprig of oak leaves in some parts, melon seeds, or orange pips in others. On receiving this the victim might either openly abjure his formal ways, or might fly from the country.


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[Assistant] If he braved the matter out, death would unfailingly come upon him, and usually in some strange and unforeseen manner.


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[Assistant] So perfect was the organisation of the society, and so systematic its methods, that there is hardly a case upon record where any man succeeded in braving it with impunity, or in which any of its outrages were traced home to the perpetrators.


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[Assistant] For some years the organization flourished in spite of the efforts of the United States government and of the better classes of the community in the South.


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[Assistant] Eventually in the year 1869 the movement rather suddenly collapsed, although there have been sporadic outbreaks of the same sort since that date.


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[Assistant] You will observe," said Holmes, laying down the volume, that the sudden breaking up of the society was coincident with the disappearance of Openshaw from America with their papers. It may well have been cause and effect.


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[Assistant] It is no wonder that he and his family have some of the more implacable spirits upon their track


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[Assistant] You can understand that this register and diary may implicate some of the first men in the South, and that there may be many who will not sleep easy at night until it is recovered.


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[Assistant] Then there are successive entries that A and B cleared or left the country, and finally that C was visited with, I fear, a sinister result for C.


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[Assistant] Well, I think, Doctor, that we may let some light into this dark place, and I believe that the only chance young Openshaw has in the meantime is to do what I have told him.


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[Assistant] There is nothing more to be said or to be done to-night, so hand me over my violin, and let us try to forget for half an hour the miserable weather and the still more miserable ways of our fellow-men.


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[Assistant] It had cleared in the morning, and the sun was shining with a subdued brightness through the dim veil which hangs over the great city. Sherlock Holmes was already at breakfast when I came down.


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[Assistant] You will excuse me for not waiting for you," said he. I have, I foresee, a very busy day before me in looking into this case of young open shores. What steps will you take? I asked.


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[Assistant] It will very much depend upon the results of my first inquiries. I may have to go down to Horsham after all. You will not go there first.


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[Assistant] No, I shall commence with the city. Just ring the bell and the maid will bring up your coffee.


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[Assistant] As I waited I lifted the unopened newspaper from the table and glanced my eye over it.


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[Assistant] He spoke calmly, but I could see that he was deeply moved.


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[Assistant] My eye caught the name of Openshaw and heading tragedy near Waterloo Bridge. Here is the account.


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[Assistant] Between nine and ten last night, Police Constable Cook of the H Division on duty near Waterloo Bridge heard a cry for help in a splash in the water.


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[Assistant] The night, however, was extremely dark and stormy, so that in spite of the help of several passers-by, it was quite impossible to effect a rescue.


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[Assistant] The alarm, however, was given, and by aid of the water police the body was eventually recovered. It proved to be that of a young gentleman whose name, as it appears from an envelope which was found in his pocket, was John Openshaw, and whose residence is near Horsham.


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[Assistant] It is conjectured that he may have been hurrying down to catch the last train from Waterloo Station, and that in his haste and the extreme darkness he missed his path and walked over the edge of one of the small landing-places for river steamboats.


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[Assistant] Holmes more depressed and shaken than I had ever seen him.


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[Assistant] That hurts my pride, Watson, he said at last.


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[Assistant] It is a petty feeling, no doubt, but it hurts my pride.


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[Assistant] It becomes a personal matter with me now, and if God sends me health, I shall set my hand upon this gang.


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[Assistant] That he should come to me for help. That I should send him away to his death.


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[Assistant] He sprang from his chair and paced about the room in uncontrollable agitation, with a flush upon his sallowed cheeks and a nervous clasping and unclasping of his long thin hands.


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[Assistant] They must be cunning devils, he exclaimed at last. How could they have decoyed him down there?


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[Assistant] The embankment is not on the district line to the station. The bridge no doubt was too crowded even on such a night for their purpose. Well, Watson,


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[Assistant] We shall see who will win in the long run. I am going out now. To the police.


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[Assistant] No, I shall be my own police. When I have spun the web they may take the flies, but not before.


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[Assistant] All day I was engaged in my professional work, and it was late in the evening before I returned to Baker Street. Sherlock Holmes had not come back yet.


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[Assistant] It was nearly ten o'clock before he entered, looking pale and worn.


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[Assistant] It had escaped my memory. I have had nothing since breakfast. Nothing? Not a bite. I had no time to think of it. And how have you succeeded? Well, you have a clue? I have them in the hollow of my hand.


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[Assistant] Young Openshaw shall not long remain unavenged.


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[Assistant] Why, Watson, let us put their own devilish trademark upon them. It is well thought of. What do you mean?


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[Assistant] He took an orange from the cupboard, and tearing it to pieces, he squeezed out the pips upon the table.


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[Assistant] Of these he took five and thrust them into an envelope.


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[Assistant] On the inside of the flap he wrote SH for JO. Then he sealed it and addressed it to Captain James Calhoun, Bark Lone Star, Savannah, Georgia.


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[Assistant] That will await him when he enters port," said he, chuckling. It may give him a sleepless night. He will find it as sure a precursor of his fate as Openshaw did before him.


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[Assistant] The leader of the gang. I shall have the others, but he first. How did you trace it, then?


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[Assistant] He took a large sheet of paper from his pocket, all covered with dates and names.


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[Assistant] I have spent the whole day," said he, over Lloyd's registers and files of the old papers, following the future career of every vessel which touched at Pondicherry in January and February in 1983.


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[Assistant] There were thirty-six ships of fair tonnage which were reported there during those months. Of these, one, the Lone Star, instantly attracted my attention, since although it was reported as having cleared from London, the name is that which is given to one of the states of the Union. Texas, I think.


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[Assistant] I was not, and am not sure which, but I knew that the ship must have an American origin.


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[Assistant] What then? I searched the Dundee records, and when I found that the Buck Lone Star was there in January 85, my suspicion became a certainty.


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[Assistant] I then inquired as to the vessels which lay at present in the port of London. Yes?


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[Assistant] A lone star had arrived here last week. I went down to the Albert Dock and found that she had been taken down the river by the early tide this morning, homeward bound to Savannah.


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[Assistant] I wired to Gravesend and learned that she had passed some time ago, and as the wind is easterly, I have no doubt that she has now passed to Goodwinds, and not very far from the Isle of Wight. What will he do then?


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[Assistant] Oh, I have my hand upon him. He and the two mates are, as I learn, the only native-born Americans in the ship. The others are Finns and Germans.


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[Assistant] I know also that they were all three away from the ship last night. I had it from the stevedore who had been loading their cargo.


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[Assistant] By the time that their sailing ship reaches Savannah the mailboat will have carried this letter, and the cable will have informed the police of Savannah that these three gentlemen are badly wanted here upon a charge of murder.


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[Assistant] There is ever a flaw, however, in the best laid of human plans.


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[Assistant] And the murderers of John Openshaw were never to receive the orange pips which would show them that another, as cunning and as resolute as themselves, was upon their track.


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[Assistant] Very long and very severe were the equinoctial gales that year.


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[Assistant] We waited long for news of the Lone Star of Savannah, but none ever reached us.


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[Assistant] We did at last hear that somewhere, far out in the Atlantic, a shattered stern post of a boat was seen swinging in the trough of a wave, with the letters L.S. carved upon it.


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[Assistant] I'm, uh, honored to be with you today for your commencement from one of the finest universities in the world.


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[Assistant] And uh, this is the closest I've ever gotten to a college graduation.


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[Assistant] Today, I want to tell you three stories from my life. That's it. No big deal. Just three stories. The first story is about connecting the dots.


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[Assistant] I dropped out of Reed College after the first six months, but then stayed around as a drop-in for another 18 months or so before I really quit. So why'd I drop out? It started before I was born. My biological mother,


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[Assistant] She felt very strongly that I should be adopted by college graduates. So everything was all set for me to be adopted at birth by a lawyer and his wife.


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[Assistant] Except that when I popped out, they decided at the last minute that they really wanted a girl.


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[Assistant] So my parents who were on a waiting list got a call in the middle of the night asking


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[Assistant] We've got an unexpected baby boy. Do you want him? They said, of course.


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[Assistant] My biological mother found out later that my mother had never graduated from college and that my father had never graduated from high school. She refused to sign the final adoption papers.


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[Assistant] She only relented a few months later when my parents promised that I would go to college. This was the start in my life.


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[Assistant] And all of my working class parent savings were being spent on my college tuition. After six months, I couldn't see the value in it. I had no idea what I wanted to do with my life and no idea how college was going to help me figure it out.


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[Assistant] So I decided to drop out and trust that it would all work out okay.


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[Assistant] I could stop taking the required classes that didn't interest me.


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[Assistant] And much of what I stumbled into by following my curiosity and intuition turned out to be priceless later on. Let me give you one example.


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[Assistant] Reed College at that time offered perhaps the best calligraphy instruction in the country.


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[Assistant] Because I had dropped out and didn't have to take the normal classes, I decided to take a calligraphy class to learn how to do this.


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[Assistant] I learned about serif and sans serif typefaces, about varying the amount of space between different letter combinations, about what makes great typography great. It was beautiful, historical.


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[Assistant] Artistically subtle in a way that science can't capture. And I found it fascinating.


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[Assistant] But ten years later, when we were designing the first Macintosh computer, it all came back to me.


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[Assistant] And we designed it all into the Mac. It was the first computer with beautiful typography.


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[Assistant] If I had never dropped in on that single course in college, the Mac would have never had multiple typefaces or proportionally spaced fonts.


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[Assistant] And since Windows just copied the Mac, it's likely that no personal computer would have them.


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[Assistant] If I had never dropped out, I would have never dropped in on that calligraphy class, and personal computers might not have the wonderful typography that they do.


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[Assistant] But it was very, very clear looking backwards ten years later.


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[Assistant] Again, you can't connect the dots looking forward. You can only connect them looking backwards.


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[Assistant] So you have to trust that the dots will somehow connect in your future.


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[Assistant] You have to trust in something. Your gut, destiny, life, karma, whatever. Because believing that the dots will connect down the road will give you the confidence to follow your heart even when it leads you off the well-worn path. And that will make all the difference.


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[Assistant] We worked hard and in 10 years, Apple had grown from just the two of us in a garage into a $2 billion company with over 4,000 employees.


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[Assistant] We just released our finest creation, the Macintosh, a year earlier and I just turned 30. And then I got fired. How can you get fired from a company you started? Well,


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[Assistant] As Apple grew, we hired someone who I thought was very talented to run the company with me. And for the first year or so, things went well.


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[Assistant] But then our visions of the future began to diverge, and eventually we had a falling out. When we did, our board of directors sided with him. And so at 30, I was out. And very publicly out.


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[Assistant] What had been the focus of my entire adult life was gone, and it was devastating. I really didn't know what to do for a few months.


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[Assistant] I felt that I had let the previous generation of entrepreneurs down, that I had dropped the baton as it was being passed to me.


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[Assistant] I met with David Packard and Bob Noyce and tried to apologize for screwing up so badly.


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[Assistant] I was a very public failure and I even thought about running away from the valley. But something slowly began to dawn on me. I still loved what I did. The turn of events at Apple had not changed that one bit. I'd been rejected, but I was still in love. And so I decided to start over. I didn't see it then.


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[Assistant] During the next five years, I started a company named Next, another company named Pixar, and fell in love with an amazing woman who would become my wife.


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[Assistant] And is now the most successful animation studio in the world.


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[Assistant] Apple bought next, and I returned to Apple, and the technology we developed at next is at the heart of Apple's current renaissance. And Loreen and I have a wonderful family together.


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[Assistant] Sometimes life, sometimes life's gonna hit you in the head with a brick. Don't lose faith.


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[Assistant] You've got to find what you love. And that is as true for work as it is for your lovers.


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[Assistant] Your work is gonna fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work.


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[Assistant] And the only way to do great work is to love what you do.


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[Assistant] As with all matters of the heart, you'll know when you find it.


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[Assistant] And like any great relationship, it just gets better and better as the years roll on. So keep looking. Don't settle.


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[Assistant] It made an impression on me. And since then, for the past 33 years, I've looked in the mirror every morning and asked myself, if today were the last day of my life, would I want to do what I am about to do today?


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[Assistant] And whenever the answer has been no for too many days in a row, I know I need to change something.


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[Assistant] Remembering that I'll be dead soon is the most important tool I've ever encountered to help me make the big choices in life. Because almost everything


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[Assistant] All external expectations, all pride, all fear of embarrassment or failure. These things just fall away in the face of death, leaving only what is truly important.


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[Assistant] Remembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart. About a year ago I was diagnosed with cancer.


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[Assistant] I had a scan at 7.30 in the morning and it clearly showed a tumor on my pancreas. I didn't even know what a pancreas was.


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[Assistant] My doctor advised me to go home and get my affairs in order, which is doctor's code for prepare to die. It means to try and tell your kids everything you thought you'd have the next 10 years to tell them in just a few months.


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[Assistant] It means to make sure everything is buttoned up so that it will be as easy as possible for your family. It means to say your goodbyes.


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[Assistant] But my wife who was there told me that when they viewed the cells under a microscope, the doctors started crying.


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[Assistant] Because it turned out to be a very rare form of pancreatic cancer that is curable with surgery. I had the surgery and thankfully I'm fine now.


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[Assistant] This was the closest I've been to facing death. And I hope it's the closest I get for a few more decades.


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[Assistant] Having lived through it, I can now say this to you with a bit more certainty than when death was a useful but purely intellectual concept. No one wants to die.


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[Assistant] Even people who want to go to heaven don't want to die to get there. And yet, death is the destination we all share. No one has ever escaped it.


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[Assistant] And that is as it should be, because death is very likely the single best invention of life.


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[Assistant] It's life's change agent. It clears out the old to make way for the new. Right now, the new is you.


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[Assistant] But someday, not too long from now, you will gradually become the old and be cleared away. Sorry to be so dramatic, but it's quite true.


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[Assistant] Your time is limited, so don't waste it living someone else's life.


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[Assistant] Don't be trapped by dogma, which is living with the results of other people's thinking.


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[Assistant] Don't let the noise of others opinions drown out your own inner voice.


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[Assistant] And most important, have the courage to follow your heart and intuition.


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[Assistant] They somehow already know what you truly want to become. Everything else is secondary.


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[Assistant] There was an amazing publication called The Whole Earth Catalog, which was one of the Bibles of my generation.


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[Assistant] It was created by a fellow named Stuart Brand, not far from here in Menlo Park. And he brought it to life with his poetic touch.


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[Assistant] It was sort of like Google in paperback form 35 years before Google came along. It was idealistic, overflowing with neat tools and great notions.


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[Assistant] And then, when it had run its course, they put out a final issue.


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[Assistant] On the back cover of their final issue was a photograph of an early morning country road.


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[Assistant] The kind you might find yourself hitchhiking on if you were so adventurous. Beneath it were the words, stay hungry, stay foolish. It was their farewell message as they signed off. Stay hungry, stay foolish. And I've always wished that for myself. And now, as you graduate to begin anew, I wish that for you. Stay hungry, stay foolish. Thank you all very much.


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[Assistant] Hello everyone and welcome back to English with Lucy.


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[Assistant] Today I have got a video for you, as you can tell by the title, of ten expressions that use the verb, the verb, the verb, expressions using the verb to have, and I know that to have


[User] Please transcribe this text

[Assistant] Is a verb, well, it's an auxiliary verb. It's a verb that can be really, really confusing for you guys because...


[User] Please transcribe this text

[Assistant] I think most of them would be used in the US and in other English speaking countries as well. So they're pretty universal, but they're seriously, seriously useful. So let's get started with the lesson.


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[Assistant] Quickly, before we get started. Nah, kidding, not today. Let's get started with the lesson.


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[Assistant] Number one, my first expression using have is to have a laugh.


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[Assistant] To have a laugh. Now, to have a laugh means to kid around or to be joking.


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[Assistant] Uhm, or even to have a joke can be said as well. So if I say to you, are you having a laugh? I'm essentially saying, are you joking? Or, sorry I was just having a laugh. Sorry I was just joking. I wasn't being serious.


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[Assistant] To have somebody on, so it's a separable, and this one is always separable, phrasal verb. In this context,


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[Assistant] In this context, it must always be separated. It is a separable one for this meaning. It sort of means to persuade somebody that something is true, when in reality it isn't.


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[Assistant] So if I don't believe something that somebody is saying to me, I'll say, are you having me on?


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[Assistant] Are you trying to persuade me that something's true when it actually isn't in reality?


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[Assistant] See, it's much, much easier just to say, are you having me on, rather than are you trying to persuade me something's true when it actually isn't in reality.


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[Assistant] Do you know, I've only had one coffee today and it's like 5pm.


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[Assistant] Also for those of you who don't know, I spend a lot of my time on a farm and I'm filming in a barn right now and


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[Assistant] I think something's going on with some sort of animal poo outside and there are flies everywhere. So if you see any, it's not me, it's the farm.


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[Assistant] Now the next one, we're still talking about laughing and joking, and this one is a lovely one. This one is to have the last laugh.


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[Assistant] And this is such a satisfying feeling to have the last laugh. To have the last laugh means to finally be more successful than somebody else who was unpleasant to you.


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[Assistant] Anna was bullied for being a nerd at school.


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[Assistant] Obviously these other people were nasty to Anna. This isn't true by the way, I've made it up. The other people were nasty to Anna.


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[Assistant] But she had the last laugh because she got the highest grade in the school. She was more successful than those other people who were unpleasant to her.


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[Assistant] Totally fake. I'm sure she was very successful at school. She's a very intelligent woman, but I've just, I've made it up. Number four. To have a sweet tooth.


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[Assistant] Now I'd really like to know if this one translates into your language at all because I can't remember if it is in Spanish.


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[Assistant] I don't know if it translates. Let me know, obviously, which language you speak and...


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[Assistant] If it's the same in your language or if there's a slight variation, but in English to have a sweet tooth means to be a big, big fan of sweet foods. So the person with the biggest sweet tooth that I know is my grandma, my mum's mum. She loves sweet foods and anything sugary.


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[Assistant] You can see it in her face when she sees them. She's really, really excited about it. So yes, my lovely grandma has a sweet tooth. Yeah, comment down below. Let me know.


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[Assistant] I love hearing about similarities between languages. In one of my previous videos, which I'll put up there in the card, uhm, to hit two birds with one stone, so many of you came back and you told me so many different things, uhm, and it turns out that it's a phrase that is quite common in many different languages, but with slight variation, so thank you everyone who got involved in that.


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[Assistant] Now number five, this is a phrase that my mum used to say to me when I was a child and I always knew that I had done something wrong. And this is to have a bone to pick with someone.


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[Assistant] So to have a bone to pick with someone. And my mum always used to say, Lucy, I have a bone to pick with you. I know if she's watching now, she'll be laughing because she knows exactly how she says it. Lucy, I have a bone to pick with you. And basically, when somebody has a bone to pick with someone,


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[Assistant] It means that they have a problem or issue that they want to discuss with that person. So it would normally be because I hadn't kept my bedroom tidy and organised. This was a problem for my mum and she wanted to talk to me about it. Lucy, I have a bone to pick with you.


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[Assistant] It's quite a nice diplomatic way, in my opinion, to confront an issue with someone. Instead of saying, I want to talk to you about a problem, I have a bone to pick with you. I think it's a bit nicer, a bit softer.


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[Assistant] Now, number six is, is it number six? Yea, it's number six. Number six is something that I actually had myself.


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[Assistant] It is to have a change of heart and if you have a change of heart, it means that you change your attitude.


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[Assistant] Or your opinion about something, normally a long-standing opinion or attitude about something, and usually it's a change from a negative point to a positive one, but it's all relative.


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[Assistant] And my change of heart was that I thought that I wanted to work in marketing. My dream was actually to work in L'Oreal. I really, really, really, really wanted to work in the marketing department of L'Oreal.


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[Assistant] For some reason, I had it in my head. But suddenly, I had a change of heart. I realised that I wanted to continue teaching. And more than that, I wanted to start teaching on YouTube.


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[Assistant] Was more important to me than a typical marketing career. So I had this big change of heart. I had worked for four years to achieve my degree. All I thought about was marketing and suddenly my attitude changed. I had a change of heart.


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[Assistant] Number seven is to have a crack at something. To have a crack at something. To have a crack at something. Distracted by the beautiful sounds of English. Uhm, to have a crack at something is to try something. Or to try to do something.


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[Assistant] There's a really hard- I always struggle with this. You know like a jar of pasta sauce or gherkins? I love gherkins. Pickles in America. Uhm, especially the ones from the Polish section in the supermarket. They are better than the English gherkins.


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[Assistant] Anyway, I digress. Uhm, the jar, the lid is really, really tight. And you, I always go and ask a man in the house to open it.


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[Assistant] Or to have a turn at trying to do something.


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[Assistant] So you could use it in a situation like, you know, would you mind taking my picture?


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[Assistant] It's a common tourist situation and you could say, I'll have a crack at it, I'll give it a go, I'll try. So yes, that's a lovely phrase for you guys to use.


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[Assistant] Now number eight, number eight is a romantic one. This is to have a crush on someone. To have a crush on someone. So if you have a crush on someone, it means you fancy them


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[Assistant] You really quite like them in a romantic sort of way. So I, for a very, very long time, had a crush on Chris Pratt. I'm not sure if you know who he is. He's a very, very funny American actor. And when I saw him in Jurassic Park,


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[Assistant] I had, I developed this huge, huge crush. And to this day, I have a crush on Chris Pratt. Now, crushes are normally meaningless. They're just that initial kind of, oh, I like him or I like her or I like them kind of thing. So yes, I'm sure we all have crushes on people.


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[Assistant] Now number nine is one that's very relevant to you guys actually.


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[Assistant] It is to have a good command of something. To have a good command of something.


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[Assistant] And this means to know something well or to be good at something. So we really, really, really frequently use it with languages. So if I say I have a good command of Spanish, it means I know Spanish well, I am good at speaking Spanish. I am more than capable of conversing.


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[Assistant] So if you guys are applying for a job or you want to update your CV, you don't want to say, I'm fluent in English, but you know, you want to say that you are good at it. You can say, I have a good command of the English language. I have a good command of English. Really nice one to use.


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[Assistant] As in, I am tempted to confront them. It doesn't mean that I'm going to do it, but it means that the temptation is there. So you might receive a piece of work from your teacher or your university lecturer or your boss, and they've returned it to you with a bad mark or a bad review. And you might say to your colleague or classmate,


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[Assistant] I have a good mind to go and speak to them about it, like I am tempted to. It's kind of like, I know I shouldn't, but I kind of want to.


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[Assistant] Right guys, those are my 10 have expressions. There are loads more. I really, really enjoyed making this video. I don't know if you can see.


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[Assistant] How much I'm enjoying filming today, but I really really am loving it.


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[Assistant] But for some reason, I really enjoyed making this video. I think it's because I got to relate a lot of the, um, the expressions back to me in my life. I always like to do that.


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[Assistant] Uhm, so if you enjoyed it, please comment down below. Definitely say if there's a similarity with sweet tooth. I am just obsessed with hearing about similarities between languages on opposite sides of the world.


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[Assistant] And uhm, yeaah. Subscribe to my channel. Don't forget to connect with me on all of my social media. I've got my Facebook, I've got my Instagram, and I've got my Twitter. And I will see you soon for another lesson. Mwah!


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[Assistant] Those little white worms you found earlier are glow worms.


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[Assistant] Well, it's not really gold, but I still think it's special.


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[Assistant] Those little white worms you found earlier are glow worms.


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[Assistant] It's alright, little field mouse. Stay where you are and keep talking. I'll follow the sound of your voice. What should I say? Anything. Just keep talking. Uh, it's misty in the meadow. The flowers have gone away. Misty in the meadow, which makes it hard to play.


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[Assistant] And I spy with my little eye, definitely wouldn't work. We can't spy anything. I know. How about we play, I don't spy with my little eye. Okay, how does it work?


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[Assistant] I don't spy with my little eye something orange. Orange? A Dahlia flower? No.


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[Assistant] Spoken about writing as far as I know. So uhm, this one has been highly requested. I know a lot of you are doing your IELTS exams. So I have got seven top tips that are going to help you improve your writing.


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[Assistant] Quickly, before we get started, I'd just like to thank the sponsor of today's video, Lingoda. Lingoda is an amazing online language academy. They don't just teach English, they also teach Spanish, German and French. You can subscribe to Lingoda on a monthly package basis.


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[Assistant] And you will gain access to a combination of both group and private video lessons with real qualified native teachers.


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[Assistant] It's actually highly relevant to this video because you, you really ideally want somebody to check through your writing.


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[Assistant] So in your private lessons on Lingoda, you can mention that you'd like them to look at your writing and that's what you can focus on for that lesson. Lingoda has given all English with Lucy subscribers a special discount. You can get $50 or 50 euros off your first month at Lingoda. All you have to do is click on the link in the description box.


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[Assistant] Let's get started with the lesson. So my first tip for improving your writing is keep it simple. Now I know this seems really, really obvious, but I always say to my students, simple done accurately is better than complicated done inaccurately.


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[Assistant] In other words, it's better to keep things simple and do it well.


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[Assistant] Rather than overcomplicate everything and do it badly.


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[Assistant] So in English writing exams, you want to show off your English, but you want to show off your accurate English, not all this English that you don't know or haven't learnt yet. Now that leads me on to point number two. In order to avoid making mistakes in your writing, it's a really, really good idea to have a collection of set phrases and keywords that you can use in that exam.


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[Assistant] So my tip number two is prepare key phrases. Now you do have to be careful with this.


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[Assistant] You don't want to rehearse what you're going to write, but you do want to have a nice selection of things that you could possibly say if the time arises. Now especially on the IELTS exam, and I know a lot of you are studying for this, but this really goes for any academic writing exam.


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[Assistant] You want to have linking words and key phrases prepared. Now I'm going to give you a list with two examples for each point. It doesn't cover everything that you need to know, but if you study everything in the list,


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[Assistant] You will be much, much more prepared for your English writing exam.


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[Assistant] So pay attention, and if there's any point that you think, I really could expand on that, go and do your research and prepare yourself.


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[Assistant] You want to make sure that you have a variety of different ways to compare in the same way at the same time, contrast,


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[Assistant] From my perspective, from my point of view, generalize, overall,


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[Assistant] In summary, in short. So that's just a very brief list showing you exactly what you need to study to be fully prepared for any English writing exam.


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[Assistant] Now, point number three and definitely, definitely, definitely for academic writing, i.e. English tests or university pieces, do not use contractions. Or is that don't use contractions? So, cannot to can't.


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[Assistant] These are all examples of contractions and in your writing exams you want to avoid these. I know that you guys really, really, really want to keep your word count down because you are restricted and you want to show off as much as you possibly can.


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[Assistant] But I've got lots of tips for reducing your word count coming up, so make sure that you do not feel tempted to include contractions in your academic writing. So, tip number four!


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[Assistant] Reduce your word count. Use as few words as possible. Completely contradictory because I've just told you that you should not use contractions, but I've got some really, really good mini tips within this big tip.


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[Assistant] To help you keep your word count down and your English level


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[Assistant] The first one is avoid these dead weight words like very, so, a lot, really. I have mentioned this before in a video, but if you want to show off your English, avoid these. They're pointless. They are empty calories in English. Replace them instead with a strong word.


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[Assistant] So instead of saying really happy, I would say I am elated.


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[Assistant] Instead of saying so scared, I would say...


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[Assistant] Okay, so by using strong words and avoiding these boring words, you can reduce your word count by a fair bit. Now, another way to really reduce your word count but really improve the quality of your writing is avoiding there is and there are. We want everything to be as clear as possible.


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[Assistant] And sometimes there is and there are can act as fillers. They add extra words to our sentences. So I'm going to give you an example so you can see this and bear it in mind next time you're doing an academic piece of writing. Sentence one. There are many problems that we may encounter. There are many problems that we may encounter. Sentence two. We may encounter many problems. We may encounter many problems.


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[Assistant] The first sentence has eight words. The second sentence has five words. You have reduced your word count and you have increased the quality and the clarity of your writing. So make sure you apply this in your next exam. Number five, read.


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[Assistant] I know many of you like to read in your spare time, you like to do it for fun, and a lot of you will read books in English which is amazing for improving your spelling and improving your vocabulary as well.


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[Assistant] But if you're trying to get a good score in an academic writing exam, you might not be reading the right kind of thing. Look at the potential pieces of writing that they're going to ask you to do. Are they asking you to write articles, reports, essays? Maybe they're asking you to write all three.


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[Assistant] If they are, read all three. You need to go online or go to your library and read articles.


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[Assistant] Read reports, read essays, read reviews. Do everything you can to familiarise yourself with all the styles of writing.


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[Assistant] Number six is one that you will have heard in school so many times, but I've worked as a teacher for many, many years and I get so frustrated with silly, stupid mistakes that I know that my students know. They don't make these mistakes when they're speaking, so why do they make them when they're writing?


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[Assistant] So many times I've gone and I've prepared a lesson because I've seen that there were some very clear mistakes in my students' writing.


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[Assistant] And I prepare a lesson to correct this mistake, and when I go to teach it, they say, but teacher, we already know this, this is baby stuff. And it's because they don't check their work.


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[Assistant] I have a clear example of this. I used to work as a proofreader. I used to check through documents before they got sent off to publishers to make sure that they were all correct, that there was no spelling mistakes, there were no grammar mistakes, there was nothing, nothing wrong, they were all perfect. And I was amazing.


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[Assistant] At doing this I could get every single mistake just by looking at a document from a distance I could pick out those errors.


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[Assistant] However, if you follow me on Instagram, you will see that I make spelling and grammar mistakes all the time. Why?


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[Assistant] Because I don't check. I don't check my writing. I am a native English speaker and I make spelling mistakes. Granted.


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[Assistant] Many of them are due to autocorrect on my phone, which I'm battling with, but a lot of them are my fault.


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[Assistant] And I don't check them, so I don't see them. I know when you finish an exam you're like, ah, I'm done, I want to move on to the next question, but you can avoid so many point deductions.


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[Assistant] Just by checking through your work, so please, please, please make it your absolute priority. I personally think it's much better if you write less and check more. Fewer words, higher accuracy.


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[Assistant] And I know normally I promote fluency over accuracy, but not in writing exams. In writing exams, you want perfection. Now, number seven is perhaps the most relevant and the most difficult of all, especially if you're in really remote parts of the world.


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[Assistant] Number seven is write and get it checked. Write and get it checked. And unfortunately,


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[Assistant] The getting it checked thing is a lot harder than it seems. You want to write as much as possible, checking it of course. It used to really annoy me when my students would come in with five essays, but they- I was so happy that they'd done that, but then I saw that they'd made loads and loads of stupid mistakes, and I just thought, ugh.


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[Assistant] Why didn't you check it? I'm just correcting the same thing over and over again. But, write as much as you can, check it,


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[Assistant] And then see if you can find somebody to correct it. Now I'm not saying find a complete stranger, it's got to be in their interest to correct it as well. I personally think it's very unlikely that you're going to find a random teacher on the internet that will be willing to check your writing for free.


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[Assistant] However, there are lots of services and penpal sites. If it's like a mutual exchange, if you find somebody who wants to learn your language and they can help you with your English, then you can do that. Or like I mentioned, on services such as Lingoda, and you can just have a straight hour of analysing writings that you've already done and already sent them. But I will say, especially to my younger viewers,


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[Assistant] Really, really, really be careful when you're contacting people online, uhm, to practice your English.


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[Assistant] Make sure it's a trustworthy person because there are so many predators that want to talk to especially young girls, uh, to practice their English and it doesn't actually turn out to be a English practice. It turns out to be something quite different so please, please, please be careful.


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[Assistant] If you've got the funds and if you've got the time, get a proper teacher and pay them to help you. It's the only way that you're going to be guaranteed the proper skills and the proper qualifications and also the proper incentive. Why would a random stranger on the internet want to help you with your writing? What's in it for them? Always think about that.


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[Assistant] Right guys, that's it for today's lesson. Don't forget to check out Lingoda. You can take advantage of this voucher code by clicking on the link in the description and applying it at checkout when purchasing your first month subscription.


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[Assistant] Don't forget to connect with me on all of my social media. I've got my Facebook, my Instagram, and my Twitter, and I'll see you soon for another lesson. Mwah!


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[Assistant] Yeah, it's all about British culture, we've got some accent tutorials, things like that, anything British.


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[Assistant] That's the thing, you shouldn't ever worry if you're making mistakes when this is a second language, like when people for whom it's their first language, like even we get stuff wrong.


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[Assistant] Hello and welcome to this fairy tale by sleep cove for the podcast and YouTube channel to get a great night's sleep.


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[Assistant] Please listen to this recording in a place where you can safely go to sleep. This recording does not have any relaxing music in the background, but if you'd prefer the recording to have relaxing music, please check out the link in the top pinned comment for that video. And let's begin. Hands in Luck by The Brothers Grimm.


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[Assistant] Some men are born to good luck. All they do or try to do comes right. All that falls to them is so much gain.


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[Assistant] All their geese are swans. All their cards are trumps. Toss them which way you will. They will always, like poor puss, alight upon their legs, and only move on so much the faster. The world may very likely not always think of them as they think of themselves, but what care they for the world? What can it know


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[Assistant] About the matter. One of these lucky beings was neighbor Hans.


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[Assistant] Seven long years he had worked hard for his master.


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[Assistant] At last he said, Master, my time is up. I must go home and see my poor mother once more. So pray pay me my wages and let me go. And the master said, You have been faithful and a good servant, Hans. So your pay shall be handsome. Then he gave him a lump of silver as big as his head. Hans took out his pocket tanker chief


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[Assistant] Put the piece of silver into it and threw it over his shoulder and jogged off on his road homewards. As he went lazily on, dragging one foot after another, a man came in sight, trotting gaily along on a capital horse. Ah, said Hans aloud, what a fine thing it is to ride on horseback. There he sits as easy and happy


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[Assistant] As if he was at home In the chair by his fireside He trips against no stones


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[Assistant] Save shoe leather and gets on he hardly knows how.


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[Assistant] Hands did not speak so softly, but the horseman heard it all, and said, Well friend, why do you go on foot then? Ah, said he, I have this load to carry. To be sure it is silver, but it is so heavy that I can't hold up my head.


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[Assistant] And you must know it hurts my shoulder sadly. What do you say of making an exchange? said the horseman. I will give you my horse and you shall give me the silver, which will save you a great deal of trouble in carrying such a heavy load about with you. With all my heart said Hans, but as you're so kind to me, I must tell you one thing.


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[Assistant] And said, when you want to go very fast, smack your lips together and cry chips.


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[Assistant] Hans was delighted as he sat on the horse, drew himself up, squared his elbows, turned out his toes, cracked his whip, and rode merrily off. One minute, whistling, a merry tune, and another singing.


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[Assistant] No care and no sorrow, a fig for the morrow.


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[Assistant] After a time he thought he should like to go a little faster, so he smacked his lips and cried, Jip! Away went the horse full gallop, and before Hans knew what he was about he was thrown off and lay on his back by the roadside. His horse would have run off if a shepherd who was coming by, driving a cow, had not stopped.


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[Assistant] Han soon came to himself and got upon his legs again, subtly vexed, and said to the shepherd, This riding is no joke.


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[Assistant] When a man has the luck to get upon a beast like this.


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[Assistant] That stumbles and flings him off as if it would break his neck. However, I'm off now once and for all.


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[Assistant] I like your cow now a great deal better than the smart beast that played me this trick and has spoiled my best coat you see in this puddle. Which, by the by, smells not very like a nose game.


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[Assistant] One can walk along at one's leisure behind the cow, keep good company, and have milk, butter and cheese every day into the bargain.


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[Assistant] If you are so fond of her, I will change my cow for your horse. I like to do good to my neighbours, even though I lose by it myself. Done, said Hans merrily. What a noble heart that good man has, thought he. Then the shepherd jumped upon the horse, wished Hans and the cow good morning, and he rode away.


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[Assistant] Hands brushed his coat, wiped his face and hands, rested a while, and then drove off his cow, quietly, and thought his bargain a very lucky one. If I only have a piece of bread, and I certainly shall always be able to get that, I can whenever I like, eat my butter and cheese with it, and when I am thirsty, I can milk my cow, drink the milk, and what can I wish for more?


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[Assistant] When he came to an inn, he halted, ate up all his bread


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[Assistant] And gave away his last penny for a glass of beer. When he was rested, he set off again driving his cow towards his mother's village.


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[Assistant] But the heat grew greater as soon as noon came on, till at last he found himself on a wide heath that would take him more than an hour to cross. He began to be so hot and parched that his tongue claved to the roof of his mouth. I can find a cure for this, thought he. Now I will milk my cow and quench my thirst. So he tied her to the stump of a tree.


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[Assistant] And held his leavened cap to milk into, but not a drop was to be had. Who would have thought that this cow, which was to bring him milk and butter and cheese, was all that time utterly dry? Hans had not thought of looking to that while he was trying his luck in milking and managing the matter very clumsily. The uneasy beast


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[Assistant] began to think him very troublesome, and at last gave him such a kick on the head as he knocked him down, and there he lay, a long while senseless. Luckily, a butcher soon came by, driving a pig in a wheelbarrow. What is the matter with you, my man? said the butcher as he helped him up. Hans told him what had happened,


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[Assistant] How he was dry and wanted to milk his cow, but found the cow was dry too. Then the butcher gave him a flask of ale, saying, There, drink and refresh yourself. Your cow will give you no milk. Don't you see she is an old beast, good for nothing but the slaughterhouse. Alas, alas, said Hans, who would have thought it?


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[Assistant] What a shame to take my horse and give me only a dry cow. If I kill her, what will she be good for? I hate cow beef. It is not tender enough for me. If it were a pig now,


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[Assistant] Like that fat gentleman you are driving along at ease. One could do something about it.


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[Assistant] It would at any rate make sausages. Well, said the butcher, I don't like to say no when one is asked to do a kind, neighbourly thing. To please you, I will change and give you my fine fat pig for the cow. Heaven reward you for your kindness and self-denial, said Huns as he gave the butcher the cow. And taking the pig off the wheelbarrow, drove it away, holding it by the string that was tied to its leg.


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[Assistant] So he jumped, and all seemed now to go right with him. He met some misfortunes to be sure, but he was now well repaid for all.


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[Assistant] How could it be otherwise with such a traveling companion as he had at last got? The next man he met was a countryman carrying a fine white goose. The countryman stopped to ask what was the time. This led to a further chat and Hans told him all his luck. How he had so many good bargains and how all the world went gay and smiling with him.


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[Assistant] The countryman then began to tell his tale, and soon he was going to take the goose to a christening. Feel, said he, how heavy it is, and yet it is only eight weeks old. Whoever roasts and eats it will find plenty of fat upon it. It has lived so well. You're right, said Hans, as he waited in his hand. But if you talk of fat,


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[Assistant] You seem to be a good sort of fellow, so I can't help doing you a kind turn. Your pig may get you into a scrape.


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[Assistant] In the village I just came from, the squire has had a pig stolen out of his stye. I was dreadfully afraid when I saw you that you had got the squire's pig. If you have, and they catch you, it will be a bad job for you. The least you can do will be to throw it into the horse pond. Can you swim? Poor Hans was sadly frightened. Good man, cried he.


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[Assistant] Craig, get me out of this scrape. I know nothing of where the pig was either bred or born, but he may have been the Squires, but alt I can tell you, you know this country better than I do. Take my pig and give me the goose.


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[Assistant] I ought to have something into the bargain. So the countrymen give a fat goose for a pig indeed.


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[Assistant] However, I will not be hard upon you as you are in trouble.


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[Assistant] Then he took the string in his hand and drove off the pig by a side path. While hands went on his way, homewards free with care. After all, thought he, the chap is pretty well taken in. I don't care whose pig it is.


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[Assistant] But wherever it came from, it has been a very good friend to me. I have much the best of the bargain. First, there will be a capital roast.


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[Assistant] Then the fat will have me in goose grease for six months and there will be all the beautiful white feathers. I will put them into my pillow and then I am sure I shall sleep soundly without rocking. How happy my mother will be. Talk of a pig indeed. Give me a fine fat goose instead.


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[Assistant] As he came to the next village, he saw a scissor grinder with his wheel working and singing. Over the hill and over Dale, so happy I roam, work light and live well. All the world is my home. Then who is so blithe, so merry as I?


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[Assistant] Hans stood looking on for a while, and said at last, You must be well off, Master Grinder. You seem so happy at your work. Yes, said the other. Mine is a golden trade. A good grinder never puts his hand into his pocket without finding money in it. And where did you get that beautiful goose? I did not buy it. I gave a pig for it. And where did you get the pig?


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[Assistant] I gave a cow for it. And the cow? I gave a horse for it. And the horse? I gave a lump of silver as big as my head for it. And the silver? Oh, I worked hard for that seven years long. You have threw them well in the world hitherto, said the grinder. Now, if you could find money in your pocket, whenever you put your hand in it, your fortune would be made.


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[Assistant] Very true, but how is that to be managed? How? Why you must turn a grinder like myself, said the other. You only want a grindstone, the rest will come of itself. Here is one, but it is but little worse for wear. I will not ask more than the value of your goose for it. Will you buy? How can you ask, said Hans? I should be the happiest man in the world.


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[Assistant] If I could have money whenever I put my hand in my pocket, what could I want more? There's the goose. Now, said the grinder.


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[Assistant] As he gave him a common rough stone that lay by his side. This is a most capital stone. Do but work it well enough and you can make an old nail cut with it. Hands took the stone and went his way with a light heart. His eyes sparkled with joy and he said to himself,


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[Assistant] Surely I must have been born in a lucky hour. Everything I could want or wish for myself comes true. People are so kind. They seem really to think I do them a favour in letting them make me rich, giving me good bargains. Meanwhile, he began to be tired and hungry too, for he had given away his last penny in his joy at getting the cow.


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[Assistant] At last he could go no farther, for the stone tied him sadly, and he dragged himself to the side of a river, that he might take a drink of water and rest a while. So he laid the stone carefully by his side on the bank, but as he stooped down to drink,


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[Assistant] You forgot it. Pushed it a little and it rolled down plump into the stream.


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[Assistant] For a while, he watched it sinking into the deep clear water, then sprang up and danced for joy, and again fell upon his knees and thanked heaven.


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[Assistant] With tears in his eyes, for his kindness in taking away his only plague, the Oakley heavy stone. How happy am I, cried he. Nobody was ever so lucky as I. Then he got up with a light heart, free from all his troubles, and walked on till he reached his mother's house.


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[Assistant] And told her how very easy the road to good luck was.


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[Assistant] And so I started thinking with a fellow journalist, Jeremy Hay, about how we might practice our craft differently. How we might go to the heart of divides, to places of conflict like journalists always have, but then once there, do something really different.


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[Assistant] We knew we wanted to take the core tools of our craft, careful vetting of information, diligent research, curiosity, a commitment to serving the public good, to serving our democracy, and do something new.


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[Assistant] And so we mapped out this process, what we call dialogue journalism, for going to the heart of social and political divides. And then once they are building journalism-supported conversations between people on opposite sides of polarizing issues.


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[Assistant] But how actually to do this in a world that's so divided, so deeply divided, when we live in a world in which we, cousins and aunts and uncles, can't talk to one another. When we often live in separate and distinct news ecosystems. And when we reflexively and habitually malign and dismiss those with whom we disagree. But we wanted to try.


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[Assistant] And so right after the 2016 election, in that time between the election and the inauguration,


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[Assistant] We partnered with the Alabama Media Group to do something really different.


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[Assistant] We brought 25 Trump supporters from Alabama together in conversation with 25 Clinton supporters from California.


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[Assistant] And we brought them together in a closed, moderated Facebook group that we kept open for a month.


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[Assistant] What we wanted to do was to give them a place to engage with genuine curiosity and openness.


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[Assistant] And then we wanted to supply facts and information, facts and information that they could actually receive and process and use to undergird their conversations.


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[Assistant] And so as a prelude to this conversation, the first step in what we call dialogue journalism, we asked


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[Assistant] What they thought the other side thought of them. So when we asked, uh,


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[Assistant] The Trump supporters from Alabama, what they thought, the Clinton supporters in California thought of them, this is some of what they said.


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[Assistant] They think we are religious Bible thumpers, that we're backwards and hickish and stupid.


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[Assistant] They think that we all have Confederate flags in our yards, that we're racist and sexist and uneducated. They think we're barefoot and pregnant with dirt driveways.


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[Assistant] And then think we're all prissy butts and we walk around in hoop skirts with cotton fields in the background.


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[Assistant] And they said this, that we're crazy, liberal Californians that we're not patriotic, we're snobby and we're elitist, we're godless and we're permissive with our children.


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[Assistant] And then we're focused on our careers, not our family. And then we're elitist pie-in-the-sky intellectuals, rich people, whole food eating, very out of touch.


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[Assistant] So by asking questions like this at the start of every conversation, and by identifying and sharing stereotypes, we find that people, people on all sides, begin to see the simplistic and often mean-spirited caricatures they carry. And in that, after that, we can move into a process of genuine conversation.


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[Assistant] So in the two years since that launch California-Alabama project, we've gone on to host dialogues and partnerships with media organizations across the country.


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[Assistant] And they've been about some of our most contentious issues, guns, immigration, race, education. And what we found


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[Assistant] Remarkably, is that real dialogue is in fact possible. And that when given a chance and structure around doing so, many, not all, but many of our fellow citizens are eager to engage with the other.


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[Assistant] Too often journalists have sharpened divides in the name of drama or readership or in service to our own views.


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[Assistant] And too often we've gone to each side, quoting a partisan voice on one side and a partisan voice on the other, with a telling anecdotal lead and a pithy final quote, all of which readers are keen to mine for bias.


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[Assistant] But our dialogue-based process has a slower pace and a different center.


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[Assistant] And our work is guided by the principle that dialogue across difference is essential to a functioning democracy.


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[Assistant] And that journalism and journalists have a multifaceted role to play in supporting that.


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[Assistant] So how do we work at every stage where it's transparent as possible about our methods and our motives?


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[Assistant] At every stage, we take time to answer people's questions, explain why we're doing what we're doing. We tell people it's not a trap.


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[Assistant] No one's there to tell you you're stupid. No one's there to tell you your experience doesn't matter.


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[Assistant] And we always ask for a really different sort of behavior, a repatterning away from the reflexive name-calling so entrenched in our discourse that most of us on all sides don't even notice it anymore.


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[Assistant] And they, they, they say things like, how can you believe X and how can you read Y and, and you believe that this happened?


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[Assistant] But generally, in this miracle that delights us every time, people begin to introduce themselves and they begin to explain who they are and where they come from. And they begin to ask questions of one another.


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[Assistant] And slowly over time, people circle back again and again to difficult topics, each time with a little more empathy, a little more nuance, a little more curiosity.


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[Assistant] And it's not about scoring political victories with question traps.


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[Assistant] Is that our state of discord is bad for everyone. It is a deeply unhappy state of being. And people tell us this again and again. They say they appreciate the chance to engage respectfully.


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[Assistant] With curiosity and with openness and that they are glad and relieved for a chance to put down their arms.


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[Assistant] And so we do our work in direct challenge to the political climate in our country right now. And we do it knowing that it is difficult, challenging work to hold and support people in opposing backgrounds and conversation.


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[Assistant] And we do this work by putting community at the heart of our journalistic process, by putting our egos to the side to listen first, to listen deeply.


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[Assistant] To listen around and through our own biases, our own habits of thought, and to support others in doing the same.


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[Assistant] And we do this work knowing that journalism as an institution is struggling and that it has always had a role to play and will continue to have a role to play in supporting the exchange of ideas and views.


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[Assistant] So for many of the participants in our groups, there are lasting reverberations. Many people have become Facebook friends and in real life friends, too, across political lines. After we closed that first Trump-Clinton project,


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[Assistant] About two-thirds of the women went on to form their own Facebook group, and they chose a moderator from each state. And they continue to talk about difficult and challenging issues.


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[Assistant] And people tell us again and again that they are grateful for the opportunity to be a part of this work. Grateful to know that people on the other side aren't crazy.


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[Assistant] Grateful that they've had a chance to connect with people they wouldn't have otherwise talked to.


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[Assistant] And a lot of what we've seen and learned, despite the fact that we call ourselves spaceship media, is not at all rocket science. If you call people names, if you label them, if you insult them, they're not inclined to listen to you.


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[Assistant] Genuine communication takes practice and effort and restraint and self-awareness.


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[Assistant] There isn't an algorithm to solve where we are.


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[Assistant] It's to which degree we can teach systems to act, to perceive and act in this world from data.


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[Assistant] So let's take a step back and think of what is the full range of tasks that an artificial intelligence system needs to accomplish. Here's the stack.


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[Assistant] From top to bottom. Top the input, bottom output.


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[Assistant] The environment at the top, the world that the agent is operating in.


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[Assistant] Sensed by sensors. Taking in the world outside and converting it to raw data, interpretable by machines. Sensor data. And from that raw sensor data, you extract features.


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[Assistant] You extract structure from that data such that you can input it, make sense of it, discriminate, separate, understand the data.


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[Assistant] And as we discussed, you form higher and higher order representations, a hierarchy of representations based on which the machine learning techniques can then be applied.


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[Assistant] Once the machine learning techniques, the understanding, as I mentioned, converts the data into features into higher order representations and into simple, actionable, useful information, we aggregate that information into knowledge. We take the pieces of knowledge extracted from the data,


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[Assistant] Through the machine learning techniques and to build a taxonomy.


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[Assistant] A library of knowledge. And with that knowledge, we reason. An agent is tasked to reason.


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[Assistant] To connect pieces of data seen in the recent past or the distant past to make sense of the world it's operating in.


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[Assistant] And finally to make a plan of how to act in that world based on its objectives. Based on what it wants to accomplish. As I mentioned, a simple but commonly accepted definition of intelligence.


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[Assistant] It's a system that's able to accomplish complex goals.


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[Assistant] So a system that's operating in an environment in this world must have a goal, must have an objective function, a reward function, and based on that it forms a plan and takes action.


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[Assistant] What kind of task can an artificial intelligence system learn as we understand AI today?


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[Assistant] We will talk about the advancement of deeper enforcement learning approaches in some of the fascinating ways it's able to take much of this stack and treat it as an end-to-end learning problem.


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[Assistant] But we look at games. We look at simple, formalized worlds. While it's still impressive, beautiful, and unprecedented accomplishments, it's nevertheless formal tasks.


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[Assistant] Can we then move beyond games and into expert tasks of medical diagnosis, of design,


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[Assistant] And into natural language and finally the human level tasks of emotion, imagination, consciousness.


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[Assistant] Let's once again review the stack in practicality in the tools we have. The input for robots operating in the world from cars to humanoid to drones.


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[Assistant] Is LiDAR camera, radar, GPS, stereo cameras, audio, microphone, networking for communication, and the various ways to measure kinematics with IMU.


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[Assistant] It's then processed, features are formed, representations are formed, and multiple higher and higher order representations. That's what deep learning gets us before neural networks, before the advent of


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[Assistant] Before the recent successes of neural networks to go deeper and therefore be able to form high order representations of the data.


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[Assistant] That was done by experts, by human experts. Today, networks are able to do that. That's the representation piece. And on top of the representation piece, the final layers of these networks are able to accomplish the supervised learning tasks, the generative tasks, and the unsupervised clustering tasks.


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[Assistant] Through machine learning, that's what we talked about a little in lecture one and we'll continue tomorrow and Wednesday. That's supervised learning.


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[Assistant] And you can think about the output of those networks as simple, clean, useful, valuable information. That's the knowledge.


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[Assistant] And that knowledge can be in the form of single numbers, it can be regression, continuous variables, it can be a sequence of numbers, it can be images, audio, sentences, text.


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[Assistant] Speech. Once that knowledge is extracted and aggregated, how do we connect it in multi-resolutional ways? Form hierarchies of ideas. Connect ideas.


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[Assistant] The trivial silly example is connecting images, activity recognition and audio for example.


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[Assistant] If it looks like a duck, quacks like a duck, and swims like a duck, we do not


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[Assistant] Currently have approaches that effectively integrate this information.


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[Assistant] To produce a higher confidence estimate that is in fact a duck. And the planning piece.


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[Assistant] The task of taking the sensory information, fusing the sensory information and making action, control and longer term plans based on that information as we'll discuss today.


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[Assistant] Are more and more amenable to the learning approach, to the deep learning approach, but to date have been the most successful and non-learning optimization based approaches, like with the several of the guest speakers we have, including the creator of this robot, Atlas, in Boston Dynamics.


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[Assistant] So the question, how much of the stack can be learned end to end from the input to the output? We know we can learn the representation.


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[Assistant] And the knowledge from the representation into knowledge even with the kernel methods of SVM and certainly


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[Assistant] With, with neural networks mapping from representation to information.


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[Assistant] Mapping from raw sensory data to knowledge.


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[Assistant] That's where the success, the automated representation learning of deep learning has been a success. Going straight from raw data to knowledge. The open question for us,


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[Assistant] Today and beyond is if we can expand the red box there of what can be learned end to end. From sensory data to reasoning, so aggregating forming higher representations of the extracted knowledge.


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[Assistant] And forming plans and acting in this world from the raw sensory data. We will show the incredible fact that we're able to do learn exactly what's shown here end to end with deeper enforcement learning on trivial tasks.


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[Assistant] In a generalizable way, the question is whether that can then move on to real world tasks of autonomous vehicles, of humanoid robotics, and so on.


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[Assistant] That's the open question. So today let's talk about reinforcement learning. There's three types of machine learning.


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[Assistant] Unsupervised are the categories at the extremes.


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[Assistant] Relative to the amount of human input that's required. For supervised learning, every piece of data that's used for teaching these systems is first labeled by human beings. And on supervised learning on the right is no data is labeled by human beings.


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[Assistant] In between is some sparse input from humans. Semi-supervised learning is when only part of the data is provided by humans, ground truth. And the rest must be inferred, generalized by the system. And that's where reinforcement learning falls. Reinforcement learning, as shown there with the cats,


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[Assistant] As I said, every successful presentation must include cats. They're supposed to be Pavlov's cats.


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[Assistant] And, uh, ringing a bell, and every time they ring a bell, they're given food and they learn this process.


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[Assistant] The goal of reinforcement learning is to learn from sparse reward data, from learn from sparse supervised data and take advantage of the fact that in simulation or in the real world there is a temporal consistency to the world. There is a


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[Assistant] Temporal dynamics that follows from state to state to state through time.


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[Assistant] And so you can propagate information, even if the information that you received about the supervision, the ground truth is sparse. You can follow that information back through time to infer something about the reality of what happened before then, even if your reward signals were weak.


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[Assistant] So it's using the fact that the physical world evolves through time.


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[Assistant] Some sort of predictable way to take sparse information and generalize it over the entirety of the experience that's being learned.


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[Assistant] So we apply this to two problems. Today we'll talk about deep traffic.


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[Assistant] As a method, as a way to introduce deeper enforcement learning. So deep traffic is a competition that we ran last year and expanded significantly this year.


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[Assistant] And I'll talk about some of the details and how the folks in this room can, on your smartphone today, or if you have a laptop, train an agent while I'm talking. Training a neural network in the browser. Some of the things we've added are we've added the capability


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[Assistant] We've now turned it into a multi-agent deep reinforcement learning problem. Where you can control up to 10 cars within your own network. Perhaps less significant.


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[Assistant] But pretty cool is the ability to customize the way the agent looks.


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[Assistant] So you can upload, and people have, to an absurd degree have already begun doing so, uploading different images instead of the car that's shown there.


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[Assistant] As long as it maintains the dimensions, shown here as a SpaceX rocket.


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[Assistant] The competition is hosted on the website, selfdrivingcars.mit.edu slash deep traffic. We'll return to this later.


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[Assistant] And a paper describing some of the fundamental insights.


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[Assistant] That will help you win at this competition is an archive.


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[Assistant] So, from supervised learning in Lecture 1 to today.


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[Assistant] Supervised learning we can think of as memorization.


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[Assistant] of ground truth data in order to form representations that generalizes from that ground truth.


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[Assistant] Reinforcement learning is, we can think of as a way to brute force propagate that information, the sparse information.


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[Assistant] Quality reward to state that does not directly have a reward.


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[Assistant] To make sense of this world when the rewards are sparse but are connected through time. You can think of that as reasoning.


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[Assistant] So, the connection through time is modeled in most reinforcement learning approaches.


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[Assistant] Very simply, that there's an agent taking an action in a state and receiving a reward. An agent operating in an environment executes an action, receives an observed state, a new state, and receives a reward. This process continues over and over.


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[Assistant] In some examples, we can think of any of the video games, some of which we'll talk about today, like Atari Breakout as the environment. The Agent is the paddle.


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[Assistant] Each action that the agent takes has an influence on the evolution of the environment. And the success is measured by some reward mechanism. In this case, points are given by the game. And every game


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[Assistant] It has a different point scheme that must be converted, normalized into a way that's interpretable by the system. And the goal is to maximize those points, maximize their award.


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[Assistant] The state is the angle, the angular speed, the position, the horizontal velocity.


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[Assistant] The actions are the horizontal force applied to the cart. And the reward is one at each time step if the pole is still upright.


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[Assistant] All the first person shooters, the video games, and all StarCraft. The, uh, strategy games.


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[Assistant] In case of first person shooter in Doom, what is the goal? The environment is the game. The goal is to eliminate all opponents. The state is the raw game pixels coming in. The actions is moving up, down, left, right, and so on. And the reward is positive when eliminating an opponent and negative when the agent is eliminated.


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[Assistant] We've been packing with a robotic arm. The goal is to pick up a device from a box and put it into a container.


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[Assistant] The state is the raw pixels of the real world that the robot observes. The actions are the possible actions of the robot, the different degrees of freedom and moving through those degrees, moving the different actuators to realize the position of the arm. And the reward is positive when placing a device successfully and negative otherwise.


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[Assistant] Everything can be modeled in this way. Markov decision process.


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[Assistant] As a state, as zero, action, a zero, and reward received.


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[Assistant] A new state is achieved. Again, action reward state, action reward state. Until a terminal state is reached.


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[Assistant] And the major components of reinforcement learning is a policy.


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[Assistant] Some kind of plan of what to do in every single state, what kind of action to perform, a value function


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[Assistant] Some kind of sense of what is a good state to be in, of what is a good action to take in a state.


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[Assistant] And sometimes a model that the agent represents the environment with.


[User] Please transcribe this text

[Assistant] Some kind of sense of the environment it's operating in, the dynamics of that environment that's useful for making decisions about actions. Let's take a trivial example.


[User] Please transcribe this text

[Assistant] A grid world of three by four. Twelve squares. Will you start at the bottom left?


[User] Please transcribe this text

[Assistant] And they're tasked with walking about this world to maximize reward. The reward at the top right is a plus one and a one squared below that is a negative one. And every step you take is a punishment or is a negative reward of 0.04. So what is the optimal policy in this world?


[User] Please transcribe this text

[Assistant] Now when everything is deterministic, perhaps this is the policy.


[User] Please transcribe this text

[Assistant] When you start at the bottom left, well, because every step hurts, every step has a negative reward, then you want to take the shortest path to the maximum square with the maximum reward. When the state space is non-deterministic,


[User] Please transcribe this text

[Assistant] You go left and point one, you go right. Unfair. Again, much like life. That would be the optimal policy.


[User] Please transcribe this text

[Assistant] What is the key observation here? That every single state in this space must have a plan.