Book II. CHAPTER XVII. It was best so for her and for us, said you meaties, after gazing long at Ledzka's touchingly beautiful, still, dead face. Then he ordered her to be buried at once, and shouted to the guards, Everything must be over on this strip of land early tomorrow morning. Let all who bear arms begin at once. Selene will light the men brightly, enough for the work. The terrible order given in mercy was fulfilled, and hunger and thirst were robbed of their numerous prey. When the new day dawned, the friends were still on deck, engaged in grave conversation. The cloudless sky now arched in radiant light above the Azure Sea. White seagulls came flying from the right across the ship, and sport of dolphins gambled around her keel. The flutes of the musicians, marking time for the rowers, echoed gaily up from the hold, and obedient to quick words of command, the seamen were spreading the sails. The voyage began with a favorable wind. As Herman looked back for the last time, the flat, desolate tongue of land appeared like a line of gray mist in the southeastern horizon. But over it hovered like a gloomy thundercloud, the flocks of vultures and ravens, whose numbers were constantly increasing. Their greedy screaming could still be heard, though but faintly, yet the eye could no longer distinguish anything, and the vast vanishing abode of horror saved the hovering whirl of dark spots, ravens and vultures, vultures and ravens. Whatever human life had moved there yesterday, now rested from bloody greed for booty after victory and defeat, mortal terror, fury, and despair. He's pointed out the quiet grave by the sea to his parents, saying, The king's command is fulfilled. Not even the one man who is usually spared to carry the news remains out of the four thousand. I thank you, exclaimed Alexander's gray-haired comrade, shaking his son's right hand.  You must leave the soil of Egypt. True it is said that to live in foreign lands, far from the beloved home, darkens the existence. Yet Pergamous, too, is Grecian soil, and there I see the two noblest of stars illumine your path, with their pure light, art and love. And his old friend's premonition was fulfilled. The story of Arachne has ended. It closed on the Nile. Herman's new life began in Pergamous. As Daphne's husband, under the same roof, with the wonderfully invigorated Myrtleis, his uncle Arceus, and faithful Bias, Herman found in the new home what had hovered before the blind man as the fairest gold of existence in our love and friendship. He did not long miss the gay varied life of Alexandria, because he had found a rich compensation for it, and because Pergamous, too, was a rapidly growing city, whose artistic decoration was inferior to no other in Greece. Of the numerous works which Herman completed in the service of the first three art-loving rulers of the new Pergomenian kingdom, Philoteris, Eumenes, and Adelaus, nothing was preserved except the head of a gall. This noble masterpiece proves how faithful Herman remained to truth, which he had early chosen for the guiding star of his art. It is the modest remnant of the group in which Herman perpetuated in Marble the two Gallic brothers whom he saw before his last meeting with Letzka, as they offered their breasts to the fatal shafts. One had gazed defiantly at the arrows of the conquerors. The others, whose head has been preserved, feeling the inevitable approach of death, anticipates with sorrowful emotion the end so close at hand. Philoteris had sent his touching work to King Ptolemy to thank him for the severity with which he had chastised, the daring of the barbarians, who had not spared his kingdom also. The gall's head was again found on Egyptian soil. Copied in Th. Scribers, the head of the gall in the Museum of Giza and Cairo, Lipsic, 1896, with appendix, by H. Kerskman. Herman also took other subjects in Pergomus from the domain of real life, though in most of his work he crossed the limits which he had formally imposed upon himself. But one barrier, often as he rushed forward to its outermost verge, he never dared to pass. Moderation, the noblest demand to which his liberty-loving race subjected themselves willingly in life as well as in art, the whole infinite, limitless world of the ideal had opened itself to the blind man. He made himself at home in it by remaining faithful to the rule which he had found in the desert for his creative work and the genuine happiness which he enjoyed through Daphne's love and the great fame his sculptures brought him, increased the strong individuality of his power. The fruits of his tireless industry, the much-admired God of Light, Phobos, Apollo, slaying the dragons of darkness as well as his bewitching arachne, gazing proudly at the fabric with which she thinks she has surpassed the skill of the goddess, were overtaken by destruction. In this statue, Bias recognized his countrywoman, Ledzka, and often gazed long at it with devout ecstasy. Even Hermon's works of colossal size vanished from the earth, the Battle of the Amazons, and the relief containing numerous figures, the sea gods, which the regent Eumenes ordered for the temple of Poseidon in Pergamos. The works of his grandson and grandson's pupils, however, are preserved on the great altar of victory in Pergamos. The power and energy natural to Hermon, the skill he had acquired in Rhodes, everything in the changeful life of Alexandria, which had induced him to consecrate his art to reality and to that alone, and whatever he had, finally in quiet seclusion, recognized as right in harmony with the Greek nature and his own, blend in those works of his successor, which a gracious dispensation of providence permits us still to admire at the present day, and which we call, in its entirety, the art of Pergamos. The city was a second beloved home to him, as well as to his wife and Bartolos. The rulers of the country took the old Alexandrian Archias into their confidence and knew how to honor him by many a distinction. He understood how to value the happiness of his only daughter, the beautiful development of his grandchildren, and the high place that Hermon and Myrtleis, whom he loved as if they were his own sons, attained among the artists of their time. Yet he struggled vainly against the longing for his dear old home. Therefore Hermon deemed it one of the best days of his life when his turn came to make Daphne's father a happy man. King Ptolemy Philadelphus had sent Laurel to the artist who had fallen under suspicion in Egypt, and his messenger invited him and Myrtleis, and with them also the exiled merchant, to return to his presence. In gratitude for the pleasure which Hermon's creation afforded him and his wife, the cause that kept the fugitive Archias from his home should be forgiven and forgotten. The grey-head son of the capital returned, with the Bafinian grass, to his beloved Alexandria, as if his lost youth was again restored. There he found unchanged the busy act of life, the Macedonian Council, the Bath, the Marketplace, the bewitching conversation, the biting wit, the exquisite feasts of the eyes. In short, everything for which his heart had longed, even amid the happiness and love of his dear ones in Pergamus. For two years he endeavored to enjoy everything as before, but when the works of the Pergamenian artists obtained by Ptolemy had been exhibited in the royal palaces, he returned home with a troubled mind. Like the rest of the world he thought that the reliefs of Myrtleis, representing scenes of rural life, were wonderful. The capture of Proserpina, a life-sized marble group by his son-in-law Hermon, seemed to him no less perfect. But it exerted a peculiar influence upon his paternal heart, for in the Demeter he recognized Daphne, in the Proserpina, her oldest daughter, Origini, who bore the name of Hermon's mother and resembled her in womanly charm. How lovely this budding girl, who was his granddaughter, seemed to the grandfather! How graceful, in spite of the womanly dignity peculiar to her, was the mother, encircling her imperiled child with her protecting arm. No work of sculpture had ever produced such an effect upon the old patron of art. Gras heard him in his bedroom, murmur the names Daphne and Origini, and therefore it did not surprise him when the next morning he received the command to prepare everything for the return to Pergamus. It pleased the Pothinian, for he cared more for Daphne, Hermon and their children than all the pleasures of the capital. A few weeks later Arceus found himself again in Pergamus with his family, and he never left it, though he reached extreme age, and was even permitted to gaze in wondering admiration at the first attempts of the oldest son of Hermon and Daphne, and to hear them praised by others. This grandson of the Alexandrian Arceus afterward became the master, who taught the generation of artists who created the Pergamanian works, in examining which the question forced itself upon the narrator of this story. How do these sculptures possess the qualities which distinguish them so strongly from the other statues of Hellenic antiquity? Did the great weaver imagination err when she blended them through the mighty wrestler Hermon with a tendency of Alexandrian science and art, which we see appearing again among us children of a period so much later? We will remain loyal servants of the truth, yet it alone does not hold the key to the holy of holies of art. To him for whom Apollo, the peer among the gods, and the Muses, friends of beauty, do not open it at the same time with truth, its gates will remain closed. No matter how much time passes by, it will be the most important thing to do, and it will be the most important Its gates will remain closed. No matter how strongly and persistently he shakes them.


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Thank you very much. So the Ecclesiastes principle is a phrase which was coined by a friend of mine, Simon Phipps, to describe a phenomenon that he was seeing in open source communities that he's worked in. So Ecclesiastes 1.9 says, what has been will be again. What has been done will be done again. There is nothing new under the sun. And what he noticed was that we were relearning lessons that have been learned in previous areas before. Now, in the open source world, it was very easy for us to think that we're trailblazers. We're going boldly where no one has gone before. Everything is new. Computing has only been around for 50 or 60 years. The web for about, what, 30 years, 25, open source, 30, 35 years. So it's easy for us to fall into the trap of thinking that we have nothing to learn from the past. And yet, like Ruth said, building software is just building stuff. We're just members of communities. And people have been doing that stuff for a really long time. And in my experience, everywhere we go, we find traces of people who have gone before. We find that there are people who've studied communities, building things, the way that people interact with each other. And we can learn from them. And so I want to give you two examples today of areas which are seemingly unrelated to open source, where I think we have lessons to learn from the past. First is from the world of architecture. And the second is from the world of city planning, which are kind of related. OK, so the first example is from a series of books called The Oregon Experiment by Christopher Alexander. It's a pattern language. Excellent book. I would recommend it to anyone. It was the inspiration for the Gang of Four design patterns later on. And what a pattern language does is he examines, Alexander does in this book, is he examines traits of successful buildings, regions, and communities. And one of the traits, one of the design patterns that he notices is the intimacy gradient. The idea of the intimacy gradient is that a building that feels comfortable goes from most public to most intimate space. You shouldn't, for example, in a restaurant, have people going through the kitchens to get to the customer toilet because they feel uncomfortable, because the kitchens are a private area. So this is my house. As it was originally, there's been some extensions since. But it's a standard three-bedroomed house with living in kitchen area, a garage, garden, and front gate leading out onto the street. And if we overlay the intimacy gradient on top of this, we can see that the house feels right. When you're coming in through the front gate, we're going through a patio area, which is kind of a little bit more intimate, and then into the living room and dining area. And we don't have to, for example, go through the bedrooms to get to the toilets, right? So communities, and the lessons I think we can learn from this, communities have their intimacy gradient as well. We have all of these ways that we can talk to people, whether it's by broadcasting on a website or on a mailing list, public forum, through to private email. And each of these has their place. Each of them needs to be kept in balance. We can't, for example, do too much in private or force people who are uncomfortable with public communication into public areas without disturbing the force in some sense. And it's also, for example, you shouldn't have people sending email to request access for a bug tracker, for example. The second example from the world of city planning, the death and life of Great American Cities by Jane Jacobs, really a pioneer in this area. I recommend this book to anybody who's interested in communities. And one of our key ideas in city planning is that to have successful neighborhoods, you need to have mixed primary and secondary uses. So primary uses are the reasons why you would go someplace, to shop, to live, to work. And those mixed primary uses are what allow you to have constant activity in an area. And that creates the demand and the constant flow of people which keeps an area safe and allows you to have that diversity of secondary uses that make neighborhoods nice to be in restaurants, theaters, parks, playgrounds, and so on. And communities also need these mixed primary and secondary uses. The classic one is professional diameter. We need, in successful communities, we need a mix of people who are working full time on the projects and a mix of people who are working on a diameter in their spare time so that you don't have the project shutting down on the weekend. Geographical diversity is also important. It allows us to avoid cultural assumptions. And it also ensures that the project is working transparently because geographically remote communities need to work on the internet for it to work properly. Also, like this pencil, and looking at the age of people who are going to get this, you need to be able to use the project in different ways. Because that keeps the project modular, keeps the architecture supple, so as the needs of your users change, your project can adapt with it. So my parting thought here is that I'm going to paraphrase George Santanaio. We have lots to learn from the past, just like Ruth said earlier. And if we fail to learn the lessons of the past, then we're doomed to repeat them. Thank you very much.


Hello this is John with Nels and in this demo on diversity of reading I'm going to be demonstrating JAWS, JAWS screen reader with Bookshelf which is a reading app by VitalSource and I'm running JAWS 2022 with Windows 11. Bookshelf version is an older version designed for Windows 7 and Windows 8. I found that the newer versions didn't work very well with this version of JAWS so I ended up using the older versions for this demo and we will demo a book created by Nels for these series of presentations that has a variety of elements that we can demonstrate how they are read by assistive technology. And once I do the JAWS demo I will do a brief demo of read aloud in another reading app called Bookworm and finish with some thoughts on some different reading apps. So we'll start going through the book here. I will not cover all the text in the book but try to give you enough context to get a sense of how these different elements are read when I read them with JAWS. So we'll start with the cover page. Cover page. And in this case all it says is cover page even though there is an alternative text description of the cover image it isn't read with this combination of green reader and the reading app. So we'll move to the next section. Document. Page has one region and no links. Title page, title page region, page I, NNELS accessible publishing summit demo dash 2020 free title page region end. And there we had the title page and JAWS is quite customizable as far as what it reads. I have adjusted it a little bit so a couple of things are indicated that are not read by default so we can get a sense of what capabilities are but most of this will apply even with default settings. So we have the text of the title page along with the page number at the beginning and that's all we have here I'll move on to the next section. Accessible publishing summit demo dash 2023 page has one region and two links copyright page copyright page region page IECC0 slash public domain accessible published publishing. And here we have the copyright page and you'll notice some of these pages say there's a region and that's just an area of text defined by the author of the book. It's possible to give a label to a region of text and for example this copyright page doesn't have a visible label in the text but that's been added as a label for this region. Some regions will have a visible heading in the text for the label it just depends on the section. And one thing about this section is there's a link further down in the check that goes to the NNELS website or just move down till we find that. No rights reserved published by colon link national network for equitable library service. And I'm just moving down by line so there we had the link and if I activated that it would run launch my browser and go to the NNELS website. Now links can be helpful especially if the reader is using maybe a touchscreen device that has we're entering URLs or copying and pasting a URL is not sufficient then with a keyboard but they are useful on computers as well to be able to open a URL without needing to manually copy an address. So I'll move to the next section. Page has no links dedication block quote page E to all readers block quote N. So we have the dedication. Let's get past this and we'll move to the first chapter of the book. Page one chapter 1 a novel dash like chapter heading level one page has six settings and no links chapter one chapter. And so I'll stop the reading there so we can go through some of it in more detail. So the first thing we have is a heading and it's two line heading but it's been quoted as the one heading element so that if I move by headings it will only land on the heading once instead of chapter one is the first line and the novel like chapter for the headings the second line but what it's quoted as one element if I move by heading it only stops once on that entire heading. And I'll move down we'll get to the level one page one chapter one heading level one the novel dash like chapter block quote left quote forget what you think you know vampires exist right quote M dash blade block quote N three May districts. There we had an epigraph in its markup as a block quote it's a little bit set it off a little bit from the text now there is a set that was on the side there is a accessibility role that can be set on a element like a block quote or a paragraph to mark it as an epigraph when using that wouldn't cause any trouble with reading systems that I've tried but it isn't supported from what I found even so it's it is available to mark an epigraph so that a screen reader could potentially indicate that it's an epigraph but it's hasn't received support to this point from my experience. So now we have the starting the text of the chapter and I'll just move through these sections this chapter is divided up by headings so I'll just move through the headings and we can read some of the different elements section with a block quote heading level two. So here we have a block quotation and Jaws as we saw with the epigraph it does indicate when a block quote starts and ends so if I move down I will now share a letter I received from my love block quote 17 Chatham Street. So now we're getting into the content of the letter and I could continue reading this as I've read normally as I read the text another thing I can do when it's marked up as a block quote as I know I want to skip past the to the end of this I can skip to the end of the element and it will skip past the block quote in this case skip to the end of it so I don't have to read the whole thing if I don't want to I can skip to the text that's after the block quote. So it's jumping to the end here and I'll keep moving down. Now we come to a context break and context breaks can be marked up visually in various ways such as an asterisk or some blank space but if they aren't marked up in the way that screen readers are able to indicate them there's no there's no indication of the context break in the case it is marked up so we'll move down. He displayed a skill in page three the choice of crown and the use of light troops and then securing his own supplies whilst he cut off those of the enemy which Kartikaya himself god of war might have envied separator. Dr. Heraklius Gloss was a very learned man although no. And so there we had the paragraph and then the context break and then the start of the next paragraph in the context break there's a separator there and that tells me there's some a change of context here between these paragraphs and also it indicated the page breaks. Now page breaks can be marked up in the text as it is here and typically I found that reading systems will either read them or they may not read them at all but they don't give you as a choice and this is one area where I'd like to see some flexibility that even though page breaks are marked up in this book if the reading system offered or a screen reader offered a choice where the break could be indicated or skipped that would give readers an option of skipping them if they didn't necessarily want to hear the page breaks in the middle of the sentence. But the fact that they are marked up does give the possibilities of being able to know exactly where the text of page starts if I wanted to go to a page number I can find out where that text starts or if I wanted to cite a passage in the book then I can tell exactly what page that's on so even though there is the issue now of screen readers reading the page numbers the mid-sentence in some cases I would say in my opinion it's still useful to have them since it does give the added benefit of knowing where the pages the page divisions are. So let's skip to the next section. So here we have a language shift heading level three with a marked up language shift which is a piece of text that's in a secondary language other than the different language other than the main text where here we have English expression refers to an inability to think of a witty comeback left parent or any sort of intelligent response right parent until it's too late to be of any use. So in this section it's had the language shift marked up in a proper French denunciation now we'll hear it without the language shift heading level three without a marked up language shift the French expression of our less but the less clearly refers to an inability to think of so in that case the reading flow is smoother but it doesn't have proper French pronunciation there and that's often the trade-off is that text-to-speech systems need to switch voices to read in another language so it does create a pause and in some cases a different voice depending on the green reader or reading system how its speech is configured. And typically it knows we advise doing it for phrases or sentences but not as much for single words and there's no hard and fast rule in some cases but it's more just something to be aware of that it does cause a voice change like this and if you have a lot of single words where there's language shifts it can be a bit jarring. So now we'll skip to the second chapter. Chapter two a more scholarly chapter heading level one. And there's the heading now the start of this is the same within the progress so I'll skip to the next heading here. Section with list heading level two. And now we have a list I'll just read through some of the texts before the list here. There we have a list of three items and I was able to arrow through them I can also move forward and backward by list items if I move backwards. In this case it's not making a much of a difference since each list item is on its own line but if it was a reference section for example with like a bibliography or a set of footnotes and notes where there was a title of the work followed by a URL it might be on multiple lines. So in that case moving by list items can be a benefit. Another thing that can help is this list also has only has one level of hierarchy so there's just one level of the three items but if let's say the first item had three sub-items that were indented underneath it it's possible to mark that up as a nested list so Jaws can indicate that the next three items are underneath this first one. And if the list is just marked up as a set of paragraphs with indentation used to show the hierarchy that hierarchy is lost with the screen reader. And I'll skip to the next section there is also a numbered list here but it's the navigation is not the same as the bulleted ones. Images section heading level two now we have images and in this section we have two images there's a regular one with some bulk text and one following that that has a longer description it needs more description so we'll review the regular heading level three regular image one day when he was strolling in the square at Blanson he saw a large wooden hut from which came the sound of terrible howling while on the platform a mount mangan coherently invited the crowd to come and see the terrible Apache tamer tomahick or humbling thunder a drawing of a shirtless white man he reaches upwards with one arm against a starry night sky text reads colon for asparagus at astrographic figure one colon illustration from finland in the 19th century and so in that case we had the text followed by the image description that was read and then the figure caption now in this case jaws isn't indicating the fact that the image and the caption are in a figure element which kind of sets it off from the text around it so it kind of loops the two together and also one more other thing to point out about the image description is that it does have some some Latin text at the end there now an image description like this is just a string of text it doesn't offer any capability of specifying markup like lists or tables or language shifts but in this case my opinion it isn't too big of a deal since I am able to go through text in detail if I want to spell words and so forth and otherwise this image description is short enough to fit in outbreaks like this so in my opinion that's it's okay we'll move over to the next image link long description that in level three a map of the camerton and limbley stoke railway in north somerset click the link below to navigate the long description graphic and so there we have the alt text and i'll move down to colon map from the map that changed the world have the figure capture long description for figure two and here we have the long description i'll activate that and this opens uh goes to another page in the ebook with the description long description for figure two link document page has two eddings and one link heading level two figure two colon map from the map that changed the world a map of the camerton and limbley stoke railway in and this is a set of paragraphs here that describes the map in more detail and i can read this just like any other second other section of text in the book so now i will tab i'll get to a link that's uh back link that returns back to the image return to figure two link if i activate that document page has one region twelve eddings and one link a map of the camerton and limbley stoke railway in north somerset click the link below to navigate the law navigate so now we're back to the image and it landed us on the text of that image and the last thing i'll show here is a table this book does also have footnotes and end notes but in this reading app they don't work particularly well in that john does read the the note reference like a star or the footnote or a number one for the end note reference but it aren't indicated as a link and also the footnote isn't read as a any different than a regular paragraph it doesn't indicate it's a footnote but some combinations of reading apps and a screen reader which is screen readers will support that better page seven section with a footnote and an end note footnote section at end note section at in level three section with a table at in level two so we're just skipping by heading there to get to the table now tables can be marked up so that the screen reader is able to properly indicate what the errors are for the the content sells as i move through so i'll move down table with three columns and five rows tells me the size name of emperor now i'm getting into the the content so i'm on the first uh cell at the top left i'll move down i can move to do this table by rows and columns so i'll move down the column augustus row two so i have the name of the emperor and if i move to the right length of reign in years 41 column two uh so that now we got the length of reign in years that's the heading for the column and it read the number after it which is the content in cell and if tables are marked up for example as an image i don't have this option to navigate the table in as much detail and in that case if it does need to be presented as an image the next best alternative would be to have a long description for that that would link to a table in a different area of the book that is coded up as a table so that i have the navigation options so that's all i will demonstrate for the moment here with this book but i will switch to the bookworm app to show read aloud and now we'll play a little bit of the read aloud of the first chapter chapter one a novel like chapter forget what you think you know vampires exist blade may third stopped so as you hear there it read the text but it didn't read any of the information about it like the fact that the chapter heading was a heading or that there was the block for the epigraph and typically read aloud can be useful especially for users that want to hear the text read out but might be able to see the visual content maybe somebody with disability like dyslexia or blind user might use this in the case where they just want to listen to the text but they don't hear so much about the format and the semantics of it but typically i would use a screen reader most of the time because that gives me more information about the text of the structure of the book and more and navigation richer navigation experience so that's what i have for the demo but i would like to end with some thoughts about some different reading apps so start with a adobe digital editions was one of them that i reviewed and it does read most of the text but it doesn't read headings very well the fact that text is a heading or image descriptions things like that that i was getting was the bookshelf experience now in some cases with unprotected epa books there is a separate view that does the more of this information most of the books i've tried in adobe digital editions don't have that option and it does sometimes tend to crash putting on the ebook when opening it with a screen reader the bookshelf app that we covered here is similar in that vital source does have a selection of books that's designed to be read in the app but it does also offer the option side of the books which is what we did here loaded the book into it now the newer version of bookshelf has found it to be a little bit more sluggish when navigating with a screen reader than the the older one here and also or certain types of navigation like activating a link or maybe going to a page you tended to throw the focus off of the reading area so i needed to navigate back to the reading area which is a little bit inefficient and also it tends to not put the focus at the desired point if for example i wanted to navigate back from a note back to the reference if the focus is thrown off i might be placed in the correct document once i navigate to the reading area but i'm not put into focus of the note reference so that's the downside in those cases when focus gets taken out of the reading area and another one i looked at was dolphin easy reader and for the most part it reads the text quite well though there are some situations such as links to footnotes and that kind of thing not working with a screen reader that now doesn't work to navigate to the link and page breaks aren't read at all with that particular app i wasn't sure whether it's a design decision or whether it's just a fact of the way the reading system is implemented that these links aren't read and another one i looked at here was bookworm that's the one we used for the read aloud this one presents the text of the book in a basically a text box and does have some options to navigate geese to move to different elements like headings and tables but it doesn't give this richer reading experience as when i was able to read the get context of the elements as i'm reading so once in a text box i don't get the the context that what i'm moving past is a heading or the table navigation now this app does have a web view to be able to view the content in a browser and that offers some more navigation possibilities but it doesn't offer the example the option for example to move between sections easily without choosing a section from the context let's think or go to page functionality another one i'll mention here was uh is google playbooks and this one works by letting you upload a book into the uh through your browser and read it in a browser um and the text was read okay but the main disadvantage here is that it presented to take one page at a time instead of typically reading applications will present a document at a time so in this case we had uh each chapter of the book in a separate document so in books if we were able to read a chapter at a time versus um when i need to read a page at a time uh i need to frequently use the option to move to the next page and then navigate to the top of the page to keep reading so it's a lot of smoother reading experience and one app that works quite well with screen leaders is thorium and the my colleague is demonstrating that you know presentation but in general i found that that works quite well for navigating pages the focus is tends to be placed correctly in most cases on the correct position in the text and the last thing i'll mention here so as you see there's a uh it depends both on having a accessible uh reading reading system combination of reading system and screen reader and also good markup in book to have uh that content read well and sometimes uh DRM or digital rights management is used which limits the uh work so that it can only be read with a particular reading system and if that system doesn't have very good accessibility then a user screen reader user is limited to uh whatever accessibility that system does provide so it can be an accessibility failure it doesn't provide a good reading experience or for example if it only provides uh read aloud like functionality where i can hear the text but not the information about it that with the structure uh then that can be uh not as useful and it's helpful if books are marked up well since if reading systems do improve in the future then that content will be available already just a matter of the reading system taking advantage of content that's marked up and as well if books are made available through systems like sila and nels where readers can read with their preferred reading system then they're able to take advantage of any accessibility that that reader reading system offers for any content that's well marked up in the book they're not limited to a particular reading app and so i hope this presentation has given you a good sense of the very variation there can be in reading systems when you're used with a screen reader and the different types of experiences that readers will have


Hello, everybody. I usually start off by saying good morning. Good day, or it's a good day. Well, it's a day Not all days are as good as Others they're not all equally good. I guess we can call this a good day Getting a new computer today not by choice The good part is we're getting a new computer. The bad part is it's not by choice So I'm having issues with my computer. It's about seven years old six or seven years old and It's come to the point where I can't upgrade it any further It's got some other problems as well as other issues all of the USB ports are fried already all of them except for the type C USB So I have an adapter where I can plug my regular USB ports into that one But if that one goes I have no way of getting files on or off of the computer myself without bringing it in somewhere So I'm on to my last last legs of this computer It's it's old and now I've updated my editing software my adobe premiere and media encoder Photo shop everything else that I use and a bunch of other programs and software that I use alongside this And this new software needs an updated driver However, my computer is not compatible with the new drivers or these new updates that I need to install to run my software They're not compatible With my old laptop anymore. So that's combined with all the USB ports being fried already not known They they need to be fixed. I Brought them in to get fixed once and they literally told me they couldn't do it So this it served me well. It's paid for itself over and over already It's an old computer. It needs to be replaced. I need to upgrade, you know in technology terms having a six-year-old computer That's old. It's not like trucks where I believe the older trucks are better than the new trucks When it comes to computers and technology the new ones are better So I'm on my way into Winnipeg right now to get a new laptop So that I can keep editing these videos and getting them out to you because it's it's not letting me edit It's not letting me render It says I need a new driver and then when I go to install the driver It says your computer isn't compatible with the new driver So they weaseled me into it and twisted my arm into going in to buy a new one Oh Motorcycle seems to be doing a pretty good speed.  It throws a punch at you You got a you got a duck and swing right back Whether you're ready or not you take a swing right back at it You want to try to shut me down life? I'll show you I was really hoping we'd be able to do this Way down the road in a few years Nope life has other plans Life says you're doing it right now Some other issues I've had with my laptop are The battery is dead. It needed to be replaced. I'm hoping that's all it was but the battery wouldn't hold a charge anymore So as soon as it got a bug, dude, just shut off. I'm starting to get nervous I get this way right before I spend a chunk of money I'm trying to go through all the options in my head Seeing if this is absolutely necessary or not can't think of any can't think of any reasons not to I Can't really argue with my wife She told me to go get a computer Just like if my truck was having problems, I wouldn't keep driving it. I'd get it fixed or get a new one Different truck and you can't just go get a new one Technology is different technology. You need you need new stuff constantly. Unfortunately trucks are more simple to me, you know Something breaks you can fix it. There are parts But with my laptop like I was saying They just stopped making the parts. They stopped making updates for it They do that on purpose and I understand their business strategy They do that to force you to get the new stuff, right so that you're always buying their stuff It's not a very good business model to design and make a computer That'll last forever. You need to make one where people will have to upgrade and Paid to upgrade. I get it. I would probably do the same thing if I was in that CEO's position, but Being in this position kind of sucks Rather be in his position There it is right over there. You see it already. I spy with my little eye best buy Here we go. Oh, it should be just over there Best buy Okay, I hope you have lots of computers in stock man. I want options. I want to see my options Better have some of that top shelf product in there somewhere Get out the step ladder. I'm gonna need to get that stuff down for me Get up behind myths mitzger mitzubishi Know how much I'm prepared to spend but I guess we're gonna find out Most people would go in there with a budget That would be smart too bad. I'm not smart I took the terrain because it uses less fuel to make myself feel better about spending money Just gonna walk in here like a boss. Oh, boy. There's lots in here. I want it all.  Well, I left without a computer They didn't have a top shelf here apparently that's at the bigger central store in Winnipeg This one carries the majority of things that you know the everyday person would need but when you're looking for the top shelf You need to go to the st. James location apparently They can order them in here, but then I got to wait for him and I want to go home with it today So we're going to the other store best buy st. James Probably 15 minutes from here Not settling on this one always got to make everything in adventure. So this is the transcona Outlet of best buy there's one two or three in Winnipeg or something and the main one is in the center of the city and say James like I was saying and That's where they keep the majority of their stock I mean, I don't know if it was possible to buy it here and then go pick it up there Feel back as the guys here gave me a lot of help I sort of wanted it. I don't know if they go on commission or not at best by I don't think they do it best by but if they do I wanted the commission to go to the guys who helped me the most, right? Oh shoot. I can't even turn left here Okay, you turn I don't know where I'm going or what I'm doing Just because I'm a professional driver doesn't mean I know where I'm going Honestly, the guys here they there's about three or four guys helping me because I had specific questions of what I needed and They went and got all the experts here, which they were very helpful. I was very thankful for that But it turns out the laptops that I need aren't here So I'm just like Kate if the laptops aren't here. I'm sorry, but I've got a bail I've got to get out of here and go pick it up. It's all we can order it to you and like have it delivered. I Need it today. I'm sorry. I need it today.  It's the one on the left the taller one on the left there I don't know. That's a condo bill. It looks like condos because they got balconies, right the newest addition of the Winnipeg Skyline love to go inside and see what it actually looks like in there I wonder how much it costs to build a building like that You know, we're looking at a building our house, right? And we're looking at the costs of building our house on a shop on our land in a garage and stuff And we're like whoo. All right, things cost money. Can you imagine building that like that's what a 40 30? 40-story building. It's not the tallest in the world. Not by far, but for Winnipeg, it's pretty pretty cool pretty tall Wonder how much money you'd have to have that how much money would you make off it? Like if they're all condos and you sell every condo, how much money are you like how much profits in there? It's got to be a lot. I mean a lot of money in real estate The only problem is in order to make a lot of money. You have to have a lot of money to start Takes a lot of money to make a lot of money There it is right there that one on the left there New building I wonder what the condos are being sold for in there I got so many questions Because everything is crazy right now, right? I Bet you it costs several hundred thousand dollars. Oh, I bet anything or if they're renting it I bet your rent is like five thousand dollars a month I'm just like spitball and you're just throwing something out there and see does anybody know Maybe I should Google it and see what they're selling for I don't think the interior is done yet But you know, maybe I should call in act like a Perspective condo buyer. Oh, this is Josh. Yes, I have I have a Swimming pool filled with money that I don't know what to do with. Can you help me? How much for one of your fancy condos downtown Winnipeg? Yes, I've got money. Why are you talking to me like that? Hello They probably just hang up on me. I was just gonna say I've never had that many green lights in a row in Winnipeg Wow, we almost made it all the way through downtown Okay, same store same city Different location I hear they have the top shelf stuff we'll find out Get out your stepladders Hope they got some tall employees Reach those top shelves for me. Oh This this store is much bigger much bigger We want to go to the back here. Let's see what they got Looking for the aces section. This is a sir looking for aces We're here Ta-da, so we went with we went to the top shelf Went all the way up to the top and one down like I usually do This is in another aces laptop. I'll maybe do an unboxing video at home It's called the ROG Strix and the ROG Strix. I don't know how you pronounce it. It is Second only to the the number one And they assured me that this will do everything I needed to do and more Good news is I spent less than I thought I would Because I was shooting for the top shelf, but I like to rain myself back just a little bit usually I like to go for the best of the best but One step down is always okay too the best of this this one was about a thousand dollars more than a thousand I was fifteen hundred dollars cheaper than the the top top shelf one. This one was still on the top shelf. It was just Just underneath the top one fifteen hundred dollars cheaper So this way this one will do everything I needed to do because I'm not a big gamer I don't need to play fortnight. I don't play fortnight But this will handle fortnight. No problem. They assured me. I was like that's great information to know I Hate fortnight there. I said it. I shouldn't say that. I don't know anything about fortnight I don't hate fortnight cake fortnight guys forgive me forgive me I don't hate fortnight, but if I wanted to play it. I can play it no problem now But it will do everything I needed to do has all the latest drivers installed in it already It'll do all my editing for me It is another aces gaming laptop. I like that just because they have More technology in there to keep it cool. They're built to work hard and when I'm rendering videos The computer does work really hard. So anyways, let's go home and I'll show you once I get there There it is. Oh I'm gonna knock at the door Who goes there? There's one to come in see my computer. I've got a new computer Well, let's check it out Cut off all the seals already You see this So this here's the old laptop. I'm trying to get it to work to render my videos. I've been trying all day But it's not gonna work because I've this is like the sixth time I'm trying doesn't have the right drivers But this computer will still be good for like tasks here at home Once we get it all cleaned up and dust it off Here's the new one another aces. I really like this aces. So it's done very well with me So I went and got another one and you get this plastic off of it here Here's the first time that I'm opening it If you do this one hand, I don't really want oh There's a little protection on there very nice very nice. You can go over there and cover up all that dirt and dust Okay, so I went with a 17.3 inch screen or is it a 17 and a half And you can tell it's already quite a bit thinner Then this one that I've got here Very thin that's another computer underneath there. It's the backup This is now the backup and that's the backup for the backup and this is the primary computer so The one thing I like better about that computer already is that all of these outlets like for the internet the wired internet the HDMI cable everything else this This is the only USB that worked remember that so I had everything plugged in through here All of these USB ports didn't work anymore, but instead of having all of these on the side has a few USBs on the side here but the main ones Are all here on the back the internet the HDMI Everything's at the back of the computer and out of the way of my my dealings You're just gonna have to excuse the mess today was my day to clean everything up in here and alas I Had to go and run into the city for an emergency laptop purchase Yay Well, you see this it paused itself already. I didn't pause you Weird stuff like this. Now see I'll tell it to continue It's old it's almost time for an upgrade. Let's get this whole area cleaned up. This is driving me nuts It's got a multicolored keyboard that changes color a little underglow here. That's pretty cool So, yeah, this is my My new ASUS laptop. It's got the Ryzen 5000 series 7 or the Ryzen 7 5000 series however you want to say that And that GE Force RTX graphics card and it's nothing too crazy It actually doesn't have as much storage as my old one here This old one had 1.2 terabytes of storage in there. This only has 512 gigabytes Which is a little disappointing They did tell me that when I was there, but I didn't realize how much space I use up with all my footage and my vlogs But that's fine Like I said, I have an external hard drive that I put all of my stuff onto anyways I just have to be a little bit more diligent at getting it off of the computer and putting it onto the external hard drive And we can do that. This is way faster Here's all my like my clips from today and I can play them at full Apparently that's at the bigger central store in Winnipeg See this one carries and there's no No skipping or no lagging for the top shelf. You need to go to the st. james location apparently And that was at full quality Uh in the preview on my old computer full quality would not be able to uh Play like that. It wouldn't be smooth It is a lot faster and I like it. It's a little bit more compact a little bit easier to Pull around with me. It doesn't have a Wi-Fi camera built in and I like that this one does it always makes me nervous Having a camera look at me. I usually have a piece of tape over it I have an external One right here. Actually, I want to pull that out because I want to take it with me on the road Just in case we want to do some live streaming Where is it here?  It's a bit a bit of chaos. All right, this is my Wi-Fi. It's uh razor It's got the light ring light built right into it and it just hooks right in there and There you go That should work fine. Uh, I believe it hooks on there like To figure this out with you guys here I believe it goes on like No, I couldn't go on like that. That wouldn't that wouldn't be right. Oh like Like this there you go there And it's not touching the screen not touching the screen good. So then it just uh Sits there like that top of the screen while I'm live streaming Or I can put it on its own stand wherever I want I don't see how that works. I'll give that a test another day. I'm tired. I need to get to bed I need to be up early tomorrow. We got to go and pick up a load in Winkler And head back to Lang Banks's sketch one There comes the yawns already and I got to get all my stuff ready to go. I got my bag behind me here I'm packing it up for the week It's gonna be good time. That was a fun day today. Uh spending money Didn't really want to spend but I did spend less than I thought I would And um, uh, hopefully I I didn't go too low I wanted the top shelf, but yes Quite a bit more expensive. I think I'll be happy with this. It'll serve my purpose well judging by my system information on the computer here Uh I can sort of tell you what the computer is It will serve my purpose It's an asus tech computer. Uh, the system model is the rog Strix System type is an x64 base pc. It's got a for the processor It's got an amd ryzen 7 5800 h with radian graphics 32 01 mhz. Does that make it hurt? Uh eight cores and 16 logical processor I hope this means something to some of you because it means nothing to me I go clicky clicky and it goes and it makes videos. It's all I know Uh, what else can I say it's got yeah 512 gigs of storage on the The drive itself I'm guessing it's probably made in china Uh, what else would you like to know about it? That's it's got microsoft windows 10 on it right now, but apparently I can upgrade to windows 11 Uh right away And so far I mean it's it's been working well for me. I'm sorry. I can't really I sort of sound ignorant when I talk about computers because because I am and I can admit that I don't know a lot About how they work, but I like using them. They serve a good purpose It's just if it breaks I'm lost Thanks for watching today everybody. I really appreciate it tomorrow. We're back on the road I'm actually picking up a load together with my father-in-law. It's his first trip at keystone And I'm going out with him. We're picking up two loads One for me one for him in winkler and headed out to line bank together That way if he has any questions, I'm right there I can explain the e-log to him because uh, he's an old school trucker as well. He doesn't Uh, know the whole e-log system that well. I mean, he he just got out of training. So he knows He knows how it works, but if there's any problems, I'll be right there. I can help him out And uh, you know just to get the ball rolling Get him used to everything make sure that everything goes good and uh, I can show him how to uh How we Collect the paperwork and we send it in after we're done delivering it stuff like that. It'll be a lot of fun So I gotta be up early and meet him at work at like 6 a.m It's time to go to bed I'll talk to you later guys. Thanks for hanging out with me today. It's been a weird day


This is TechSoupConnect. My name is Jerome Scriptunis, and I run the chapter called TechSoupConnect for Time Banking Organizations. Today's topic is Everyday AI for Everybody. Our guest speaker is Ken Mahalak. He's a computer science lecturer at Ocean County College and he's been working in collaboration with Intel to develop a curriculum around artificial intelligence. And we're very fortunate that he is sharing some of his first day off in a very long time, I think, to help us understand and appreciate from the perspective of people who are largely not technology developers, but technology consumers. And we need to be smart, mindful and aware, especially in the spectrum of our work where we're working with individuals from all walks of life, all stages and ages of human development. And we want to be able to communicate, appreciate and understand. And that's one of the goals for this morning's, let's call it information sharing on increasing our awareness of how artificial intelligence surrounds us. And it's, say, maybe the weather or it's in our environment and it's to our benefit to be mindful of that and know how to work with it. And to know it when we see it, although it is a big field. Ken, I've known Ken for several years, one of the nicest people to communicate with because Ken has a deep appreciation and understanding for how people are affected by technology and an extraordinary patience for teaching technology. And so he's a wonderful person to have as a guest speaker. Ken, thank you very much for giving us your time and the benefit of the tremendous amount of work you've put in recently on AI. So tell us a little bit about your life and help us get excited or your life with AI.  No, let's keep it. I'm a Gemini. And then get us excited about how we could be just a little bit wiser and smarter with technology. Sure. Yeah, by the way, thanks for that wonderful introduction. Everything Jerome said was flattery to 100% level. But anyways, yeah, let me just spend a few minutes with you guys, just talking about AI, give you a feel for it and maybe where it's headed. I'll take questions at any time. Okay. And rather than you guys typing in, I got some things I can show you. We just wouldn't get my mouse here, okay? So do this. Go to nearpod.com, all right? So open up a browser and just type nearpod.com, okay? And this way I can push out information to y'all that are long and stuff. You just gotta click on the button. And then we- Yeah, put nearpod.com in the- Oh, in the chat, awesome. In the chat. Okay. You may have to resize your screen if you're working with one screen or just what's the, don't expand. I'm gonna see if I can do, exit full screen. So that's what I did on my display screen. I have a laptop and I exited full screen mode and that opens up a little space so that if you click on nearpod.com and then enter L-Y-X-I-E, do I have that right? Yep. Yeah, I should be showing it on my screen.  So Cora, if you could please try that, Sheena, Audrey, Valerie, Ken, if y'all could give that a try. And I'm actually, while we're doing that, I'm gonna try it on my phone and see if it works, see what happens. So I got nearpod coming up on my phone. And then when I go there, I click what join a lesson, is that what it is? I think I said, yeah.  And then I click join. So it's slightly different on the phone, okay. And then I could put in my name and my nickname, okay. Okay, I'm putting that in and I'm in, okay. And there's also an app, there's a nearpod app, which you can also try as you like, but we got enough going on. I don't want to flood myself. Yes. Okay, Ken, back to you. Okay, no, it sounds good. So if everybody is on nearpod, it's just easier. I can push some information out to you. Okay. It's just easier. Okay, yeah, this is what I'm gonna talk to you about, but it'll be brief. I sent you a roam like over honors. Yeah, so it's probably more than obviously we can cover today, but I just want to give you a feeling for we're, AI is everywhere already now. You use it every day. You may not think about it, but you use it every day. If you do any type of talking with Alexa or Siri or face facial recognition to unlock your phone, or if you do any texting and words coming up for you to help you predictive texting or autocomplete, that's all AI. All right. So yeah, so let me ask a question that does AI mean it's predictive, it's forecasting, it's a decision like a guided decision making capability? It can be. There's several domains in AI. I was gonna cover them and I can talk about machine learning and neural network stuff like that, but... Okay, go ahead. So I was gonna talk about what is AI? I was gonna briefly talk about the domains and talk about some of the technology behind AI. By the way, it's all mathematics. If you're interested in this field, the things you wanna learn is statistics with a little bit of calculus. And of course, if you really wanna get into it, the Python programming, which is a must. Data analysis skills are good too. If you really wanna get into it, but there's a lot of no code platforms. In fact, I just finished an introduction AI course where we use a no code platform that's like drag and drop. And there's a lot of platforms out that Google has, Amazon, Microsoft and all that where you don't really need the code. You can upload your data and do stuff. So... Yeah, I think what, as we see on the screen, you're sharing that we wanna arrive at is some guidance from you can, towards the end, on ethics in AI. Oh, the ethics, yep. Can we trust it? Should we be uncomfortable about anything? Well, let me be blunt. Can you trust it? It really depends on that it's all about the data. In fact, AI is all about the data you use to train these models.  Like, I definitely will talk about that. So let me just move forward here, if I can. There we go. Okay, so a gentleman, if you're really into AI, a gentleman I like to follow is Andrew A. I mean, his credentials speak for itself, but he's good. He teaches at Stanford. He started his own company. That actually advises a lot of other companies. So if you wanna look at practical AI type applications and you wanna see what's really going on in industry, he is an excellent gentleman to start with. But one of his, he's got a bunch of courses on Coursera. That kinda kicked off the, at least helped start the revolution. In fact, he was a co-founder of Coursera if you ever used that training platform. He had a machine learning course, which was a big hit. But he has this AI for everyone course that anybody can take, really. And this is a slide. I borrow from him that says AI is the new electricity and it's gonna transform businesses. And he's right. It's already doing it. Applications in healthcare, X-ray scanning, MRI scan, agriculture, John Deere is a big AI proponent. They bought several companies and they're designing equipment that talk about self-driving vehicles, self-driving farm equipment. All right, it's a little bit easier to model that. So it's already transforming these industries but he's a good gentleman to follow. And he, I use this slide because he compares deep learning to superpower. So we'll talk about that. Like I said, he has some really good courses. The AI for everyone course is awesome. Okay, so that's my plug for Andrew and me. Let me just go to this. So like I said, you're already using it every day. If you talk to Siri, if you got Google, if you got Alexa at home and you say, hey Alexa, I haven't Alexa, but I call it, I named it something different. I use it pretty much only to play music, but I do have friends that use it to control their lights and all that stuff. But when you talk to those things, behind the scenes is a neural network that is recognizing your speech and translating into actions. So if you're using that, if you're using your face to unlock your iPhone or any other thing, that's called the convolutional neural network. That's actually artificial intelligence. Anything that will auto capture photos, if you go to Facebook and it identifies you, that is artificial intelligence. In fact, Facebook, the big tech companies, Google, Facebook, Amazon, Microsoft, they are the investment in a, it's incredible. And Facebook actually is a big investor just because you think of all the post-state deal with, and I forget how many post-state deal with a minute, but they have to analyze that for offensive text or misleading videos and stuff like that. So they put a lot of research into it here. Assisting you, like I said, when you text, if you're doing predictive text, if you're in Gmail, they have a feature where it finishes your sentences for you and stuff like that. That's all artificial intelligence. Behind the scene, Google uses it. Obviously everybody's familiar with autonomous vehicles being tested by Uber and Waymo and even Tesla, right? All these safety assist features. And more is coming. And after a while, there's this funny quote that says, AI, after a while, you don't even realize you're using it, you just accept it as part of your daily life. And this kind of frustrates AI researchers because everybody's expecting these talking robots and all that stuff. And they're like, yeah, but what about all this great stuff we did with your facial recognition is good, but when are you gonna give me that robot? So anyways, you're using this every day and Jerome alluded to that New York Times article. That's nice, it's maybe a two-page article that goes into a little bit more details how you're using this in your daily life. So if you think it's not here, you're wrong, it's here, you're using it, all right? And you know what, it's just gonna be adopted more and more by companies, right? During COVID, those are very challenging times. They still are. Companies in some cases weren't able to find people so they had to adapt their businesses. So COVID has also accelerated the adoption and the research intake. We can just see some of the statistics that comes from a Harvard Business Report. 55% of companies says they accelerated their strategy, almost 90% is saying it's becoming a mainstream technology. So it's here and if you're interested in this type of field, it's a great career, great future for anybody, students and existing industry professionals. This is another thing, yeah, go ahead, Joel. Do you think it will get to the point where there's going to be a warning label on applications informing the consumer that this application is using AI algorithms based on data sets collected from such and such. Is that, as you said, when you get to the point maybe on the ethics of it depends on the data set and the data training. Sure, sure. So that depends on I think legislation. I think companies will not offer that information up but recently the European Union is very active in making sure citizens know that AI is being used and New York City has just recently passed some laws that a lot of times AI is used for resume screeners. They make people aware that we are using an AI application to look at your resume and you have the right to have a human intervene. Big push on AI, there's a couple of issues with AI besides the data. One is always having humans in the loop to make sure you don't have an AI that goes slightly rogue and does things that you don't intend it to do and there's great examples of that. One is Amazon built a hiring application and they trained it with the resumes of existing engineers. Who are the engineers? Largely it's a male population. Largely it's white male and Asian. So anyways, what did this Amazon hiring AI based assistant learn how to do it? Learned how to discriminate against women. So it found a way when a woman submitted a resume it detected if it was a female by looking at just descriptions or primarily or predominantly women's colleges and it excluded those resumes. It's kind of garbage in garbage out. You put data in, it's not really learning anything like we learn things. If you feed it discrimination and data you're gonna see that as a result. So you gotta be careful. Yeah, so Ken then are the data sets mostly historical data sets or I thought that artificial intelligence was a learning. It was gathering real time data. It can, it depends on the application you're building. Some can be based on historical data. Like maybe if you're training something to recognize breast cancer or something like that. We could look at, there's a very popular data set from the University of Wisconsin. You can train based on that. But so you might have to upgrade it to include recent data. That's something you have to deal with when you talk about deploying. How about like with that article from the New York times about how photographs on my phone are organized or put together in a story or the facial recognition. There's no data set on that that's historical. It's built as I'm using the phone, is that correct? Yeah, but along the way, typically what has happened behind the scenes is Google or Apple has trained that neural network to recognize certain things, certain patterns with millions of images actually. And they can recognize dogs, cats, people, faces, so there's a lot of training that has to be done. Anyways, this slide just says the same thing. Businesses are adopting it and there's a changing landscape.  Some of these jobs are gonna disappear. So there's another impact to society where it's like, hey, we won't need maybe accounting bookkeepers, things like that. We'll be able to actually build AI applications so I can do a lot of this routine type work. Not necessarily take the place of all the type of work that it's counting on, but accounting does, but maybe some of them mundane items. So Ken, as you're giving us these examples and going through the slides, if there's something that comes to mind, have you heard any stories about how AI helps people in the community? For example, if someone is homeless or if there's human trafficking or if, I don't know, you have the, what I did just recent article about Beagles and airports that sniff out contraband or something like that. Does AI have a way to sense information other than through like numerical data? Can it pick up like vibrations, smells or is that still in the future? That type of smells and vibrations? That's definitely possible, but there's a lot of practical AI applications that have assisted. They use a lot of AI in pharmaceuticals already. They're being used to come up with new drugs to treat diseases. They're using AI in particle physics to uncover new particles, recognize new planets, things like that. Largely what goes on there is there's a few forms of AI. You can train and primarily 95% of all AI applications today are what's called supervised learning-based applications, which means you're training them, okay? Yeah, it actually can sense facial emotions too. China has an application that they recently put out that does that type of stuff, senses when you're happy, when you're sad, when a class is not paying attention, things like that. Oh, that's interesting. No, that's very interesting that if you lose engagement that would be tremendously valuable in human services. Sure, but there's another form of AI called unsupervised learning where you got a ton of data and you just don't know what's going on there. So you'll get that data, you'll throw it into an AI-based application that will find clusters of information and be able to relate. I didn't know that when somebody goes in on Friday night to buy beer, they also buy diapers. That's a famous case where I forget if it was Walgreens or Walmart kind of put that together. Now that is unsupervised learning still, that's a holy grail. If you can figure out how to find that type of stuff, you're on your way to general intelligence. But let me just mention this, this is really, Intel's really behind all this and they're working aggressively with community colleges and vocational schools to bring these skills and this knowledge out to a sector. Traditionally AI was offered in junior or senior master type courses in college. And so now they're bringing this down to community colleges. A lot of the materials I use and a lot of the information I got is really due to Intel and some of the courses and information they sent me. But my school, I teach at Ocean County College. We were one of the early adopters of Intel's program and we've rolled out a degree in courses and there's more information. If you're curious, they have a program called the workforce AI workforce program. I'm gonna push that out to you now. So if you see that, if you click on your near pod you'll get more information about their AI for workforce program. And it shows the schools there. So you can take a look at that and if, and by the way, that's worldwide. And then over here, I'm just pushing out our artificial intelligence page for Ocean County College just to get a little marketing. So that's it.  Okay. So with that, buy my new book, buy my new CD. That's fine. See my new movie. Okay. So the AI for workforce information, is that something that might be interesting for like high school students or if someone wants to dabble around a little bit? Yeah. In fact, Intel originally launched this with high schools in Singapore from what I understand. And in fact, the class I finished up teaching on Tuesday, I had nine students, eight of them were from local high schools in Ocean County College. And the intro to AI, I go into a little bit of math, but the whole goal is to not be math heavy, expose people to the various areas. And if they're interested, they can proceed with the curriculum. But like those courses are meant for anybody. So in the fall, I have some business majors that are taking your AI course. It's really meant for anybody. Yeah. All right, let's continue. Yeah. So I always ask class, what do you think AI is? What is the definition? And John McCarthy, who is viewed as the father of AI, he did a lot of things too, time sharing, invented the list language, but he defined it as the science and engineering of making intelligence. So in other words, can we get machines to operate with the same level of intelligence that we have?  And they expected they would have fully functioning robots like you see in movies by early 1970s. And we still don't have it. And because it's incredibly hard thing to do to replicate human intelligence. In fact, they don't even know how human intelligence works to some extent, to a large extent actually. And some people are predicting maybe this is still a couple hundred years off from this type of technology, but who knows? But that's John McCarthy's definition. This is great. I just put this slide in here because this chief decision scientist at Google, Cassie Kuzrakoff, she has a making friends with machine learning, but she is very good, succinctly lays out a lot of information on various machine learning algorithms, things like that, AI what it is. But she always says, AI succeeds a very complicated test that programmers can't write instructions. And that's it. A lot of times, if you come from a software development background, you're given a set of rules, you write up some software and it does it. And AI, if you think of how we think, you can't easily write a lot of rules. Maybe you can write a thousand rules and stuff like that, but there's definitely, we're just more sophisticated by that. We understand context and we know you can't. For example is, it takes about 15 hours for a person to learn how to drive a car, okay? And famous scientist, a gentleman named Jan Lacoon says, to basically teach a AV, autonomous vehicle, not to drive off a cliff, would take 10,000 training exams before it figures out, okay, I can't drive off a cliff. To us, it's obvious. These things that they're smart, you have to train them and they're just nowhere at the level of intelligence. So the things on movies and the things you read from Jan Lacoon is, they're a little bit, I think, extreme in terms of AI is gonna destroy us. Maybe at some point, but we got a few years. So what AI do we have today? We really have what's called weak AI. These applications are very brittle. They'll do one thing and one thing. Okay, that's it. General AI, if you guys saw the Iron Man movies, Jarvis and stuff like that, years away, 2050 to 2075 is probably an optimistic guess. Some people think it's 200, 300 years. Some people think we'll never achieve that type of intelligence, all right? By the way, there's a link there. It's a great book called Architects of Intelligence where you can read about all the pioneers. They interview the pioneers and the leading experts in the field. They ask a bunch of questions and you get a lot of interesting opinions from them. These are the level of AI body. We're in the first one, narrow. We train it to solve one problem. That's it, very brittle. The second one would be equivalent to Jarvis where we could sit down and we could have a conversation with a AI-based application. That's called general intelligence. And then there's super intelligence, which this is the point where these machines are much smarter than us and maybe they're making the decisions about what we can do. Maybe they're managing us, I don't know, but that's way off in the future. I like, go ahead, I'm sorry, is there a question? Yeah, one came in from Cora asking about AI chatbots. Would those be stage one or stage two? Yeah, big time stage one. Chatbots are very brittle. In fact, OCC has a chatbot called Reggie. It was funny in class. I had the kids playing with it, asking if it was sentient and stuff like that. And the minute I finished class, I had an email from the manager who's responsible for that, asking me, hey, did you have the class? That's a round of it, but yeah, chatbots will, yeah, they're getting better, but they'll only be, they're really only developed to answer a limited set of questions. If you ever spend time with a support chatbot, it doesn't take you long to realize, I gotta talk to real you, Bob. We've got about 15 minutes left, and I know we're gonna talk about some ethics, and I wanna make sure we get into the rock, paper, scissors. Oh yeah, I'll go through this pretty quick, don't worry about it. Okay, okay. So I do like to talk a little bit about the history. People think it's happened overnight. They've been working on this really since the 40s. It's been here a long time. Neural networks was first proposed in 1943, so it goes back on. And the whole goal of neural, or the whole basis of neural networks is something called the perceptron, which is meant to mimic a neuron, but it's a mathematical modeling.  Like, okay, tell me if this is a cat or a dog. Won't be able to do much beyond. So it's been around a long time, and there's been what's called these AI winters where you get this, every other article is about AI, you get this hype, and then they realize we really can't do all the things we talked about, and so funding dries up. So there's been a couple of that, and for all we may be through a hype stage right now, although Andrew Aang doesn't think so, and with some of the advances in deep learning, most people think not, that this growth will continue. All right, and what called the resurgence? Well, few things, massive amounts of data, the data you are now generating with your phone and your internet, your purchases on Amazon and stuff like that, it's unbelievable, you guys are predictable, you're generating patterns, and from those patterns, I can start predicting what you're gonna do. The second thing is, let's face it, our CPUs, GPUs, we've just gone to the point where compute power has increased amazingly, and finally the research on deep learning has paid off with the data, because you need millions of data samples to train this, and you need GPUs, graphic processing unit to really crunch, because it's just a bunch of math that you're going out behind the scenes, right? 2012, this famous researcher just killed what's called the ImageNet competition, just decimated with a neural network, that sort of caused the resurgence and caused industry and the academia to say, wait a minute, maybe there's some stuff here. So that kind of caused, I'm gonna skip some of this and just go right to, how is AI use today? And I talked about that, computer vision, right? Natural language processing, chat box. I mean, somebody mentioned chat box, a lot of times what they'll do is they'll do what's called sentiment analysis, where they'll look at reviews and like Yelp or Google reviews, where they'll tell you this restaurant's good to eat, not good to eat. Machine translation, Google translates get pretty good. Speech, we had our smart assistants, when you start seeing this in robotics with smart driving cars or John Deere farm equipment that is recognized disease crops and treating it, right? So you're seeing this used all over the place. Can I jump in with the question? I saw something, I think it was on 60 minutes about how video technology and computing can represent someone doing something in a scene, but it's not actually that person, but it looks so real. And that's used, I don't know, in advertising or something. That's, yeah, deep fake. Deep fake, yeah, that's a branch. I don't go into that much. That's a branch called general adversarial networks, but yeah, that's a concern with generating fake video and posting it on some social media platforms and all that stuff. But yeah, that's an ethical area that kind of spot deep fakes things. Yep. But I personally don't own that much about that technology. Yeah, so I can't comment too much of it. So the quick domains and we'll get to demos, right? These are really the big domains, machine learning, neural networks, which, if you say neural networks now means deep learning, what does deep learning mean? It just means you have a bunch of layers that kind of are set up to mimic the brain. And then of course, computer vision and natural language processing is you interacting with your phone with Surrey or Alexa or doing things like that, okay? So machine learning looks funny. I always say, if you like the game, you're perfect for AI because this whole field started largely because Arthur Samuel liked to play checkers and he developed the program to do that.  And what machine learning is, in the old days we had input data, we were right to program. And that was traditional and you get output. Now what goes on is you've got data, you got input and outcomes, the program from a machine learning application. Now what is the program? The program in a majority cases is gonna be some type of mathematical formula or form. That's what's going on. It's looking at patterns and then coming up with a formula that you can use to classify or make a prediction on that, all right? I'm not gonna show, but there's a two minute video on machine learning that if Jerome distributes the slides, it's really good and succinct and gets to really the crux of it really quickly, all right? Let me jump up to that slide. I'll put it in and could you, which page was that on? I'll call you after this and I'll tell you once. Okay, all right, all right. So you guys gotta remember this is all about data. I spend like a good couple lessons in class talking about data. It is important when you talk about working on AI applications and all that stuff, so little time is spent on the modeling. Most of the time is spent on getting the data, storing the data, getting rid of noise, analyzing it. That's 80 to 85% and it's all about the data because you train these things and if you got bad data, you're gonna get bad results. All right, here's our first example. Okay, Cora and I found the video, the two minute video. Oh, good, okay, awesome. Okay, so I just pushed that rock, paper, scissors and I can, this is a fun machine learning application. So if you click that link, it's a game that was developed by Affinity and what this game is doing is it's looking at your patterns. Now, you're gonna have to play this like a thousand times, maybe more, but after a while, so I always just scroll down on this main page and I know thanks play with mouse, or you can let it use your camera, but I don't do that. So I just say no thanks and when you do that, it'll bring up round one. You're gonna play the machine where you pick, that's paper, scissors, rock. And if you just click on that, you can see, okay, I did rock, he did scissors, I won. But if I keep on repeating and I always tell my students in class, cause we don't have a lot of time, just repeat your sequence, all right? Just keep on doing that. And you'll see after a while, you can't learn. You can't beat this thing, all right? And the thing that's going on by, so you guys got kind of a fun playing that, what's going on behind the scenes is, humans are very predictable. Even though you think you're being random unless you really go out of your way, you're gonna have a certain pattern. And after thousands of examples, and it's not five or 10 or 20, right? After thousands of examples, maybe millions, I'm gonna be able to predict what exactly you're gonna do probably within the 80 to 90% range. It's all about probability at this point. And that's exactly what's going on with machine learning, right? With machine learning, what's going on is there's a family of models. I talked about supervised learning. That is the primary application these days. There's unsupervised learning and there's reinforcement learning. And this is what you're doing. You guys are generating data. And when you're looking at predictions, it's just going down into a regression problem or a classification problem. Do you have cancer? Do you not have cancer? Regression. All right, I'm gonna sell my house. What do I think I'm gonna get for the sale of that house? Or clustering, right? Clustering is unsupervised. That's where you don't know what's going on with your data. You throw it into an AI application and there's a few algorithms that look at this type of stuff. And it equates. It says, hey, look, I saw that people when they go into a store on Sundays. They're buying milk, but they're buying this thing. So let's put it together. Those are patterns that there's just too much data for humans to analyze. So you rely on machine learning, right? And there's a lot of them. Here's all the machine learning algorithms. And I don't cover all of them in my course, but I cover a few of them. Just some of the more common just to give students a background. We look at really a no-coat do something like nearest neighbor, which is very simple algorithm. When you classify a point, what were my neighbors classify? Some of these things are pretty simple. Some of these things are a little bit more complex. All right, hopefully you're having fun with that game. Neural networks, it's really based on your brain, how they think it functions. They think it boils down to the connections between the neurons where the information is stored. And by the way, we have 86 billion neurons. And in our neurons, in our brain is all that knowledge that we spend a lifetime learning. And humans are incredibly adaptable. We learn pretty quickly. We learn pretty quickly that, hey, that dog may bite, where it might take 10 million data rows to train a neural network so it could walk away and figure out this dog will bite versus it. So humans are just not at that level yet. This is a neural network, by the way.  And you typically have layers. You have an input layer where you feed input. That would be data. You've got these hidden layers that are meant to represent our brain. And if you have more than a couple hidden layers, that's what's called a deep network. And then you have output, which we'll tell you is a dog, is this not a dog? Is this bone broken? Is it not broken? So here's an example of a deep neural network. Usually when the hidden layers get above three, that's cool. And these are really classic applications. When you do speech recognition, facial recognition, when you're talking with chatbots, recommendation systems, in not all cases, but when Amazon's recommending things to you based on previous purchases or things you purchased, those are all neural networks behind it. There's a fun game I like to use that I'm gonna push out to you from Google. And let me push this out to you guys. Okay, it's, you can doodle. You can say, let's draw. And it'll tell you to draw a cake. And if you got your voice turned on, okay, I got it. You can start drawing. It'll tell you, I think you're drawing. I see pond. Yeah, so it's fine. Or pillow. Or hockey puck. Let's see if I get cake. Or birthday cake. Yeah, here we go, birthday cake. Yeah. See, so it's kind of, it's a neural network behind it. And actually when you do this type of stuff, for all these things, you're actually helping to train this thing. For Google. Sorry, I couldn't guess it. So that's a fun game that I like to do in class. Okay. Can you flip that slide for just a few seconds that showed the brain is 60% fat? Like I went over that. Is there you coming up with that? Oh, I actually got rid of that. Yeah. Yeah, I got rid of that. All right. So you told me I was a limited time schedule, but in my case, my brain is more than 60% fat. Okay, this is good. I'll do computer vision. Look, we see this, right? We see faces on two children. They look like they're male. They're smiling. They got nice greased back here. Looks like they're wearing the same shirt. Computer see this. They just see a bunch of numbers. We see three dogs running. I don't know what kind of type of dog breed that is, but right? Computer see numbers. All right. So what you do is you have something called a convolutional network and you define layers that basically flatten the image, meaning they take away color because analyzing black and white versus color is a lot easier from a computational perspective. You have these layers, convolutions that recognizes circles. And then you get down to recognizing faces and on the output will be this is a person, this is a dog, things like that. In fact, here's a good example that shows a picture of, I actually think one of the deep learning researchers' wife and you can see the various layers, the first hidden layer, the second hidden layer, right? What these layers are recognizing. And then at the end, it predicts a car, a person or an animal. That's how these things work. The issue with neural networks though, and there's research being put into this is it's not explainable. And so people want explainable AI. When I go to my doctor's office and the doctor says, you have cancer. And I go, how do you think that? He goes, I couldn't tell. I sent it to a neural network and it said it, but I don't know why it said that, but just a bunch of computations and it said you had cancer. There's a big push towards explainable AI. And there's some algorithms like decision trees. You can see how a computer arrived at that conclusion. Neural networks aren't there. So there's some controversy there. I'm gonna skip Dolly. People I've heard about Dolly before. If you haven't, it's a Google application. I'll push it out to you. That can, if you type in, I'm gonna skip some of this stuff. Yeah, there you go. So if you type in a description, it's got a huge, it's been trained with millions of images. If you type in a description, it will try to generate that picture for you. And there was just a recent article where somebody typed in the last selfie and it generated these apocalypse-type pictures where it showed a person in like a nuclear bomb line. Hold on, that was a sad article. But anyways, Dolly, earlier I was messing around it. And I asked to do Hulk getting a pedicure. And you can see the Hulk got his feet up there. They're not green though. This is probably a better picture of Hulk's feet getting a pedicure. And there's Hulk. Looks like Hulk's actually doing something there. I'm a big fan of Hulk. Ken? This is fascinating. And can you spend one minute or so and then on ethics? Yeah, yeah. Was that the machine net? Is that one the moral machine or? Moral machine is, yeah, it's fascinating, but I'm just gonna quickly, what will be the impact society? There are gonna be job impacts. Is universal income referred about universal income?  I recommend if you have Netflix. There's a great documentary called Coded Bias that just shows some of the problems with this stuff. I talked about Amazon discriminating against hiring women engineers. Jay Balamwani, a researcher at MIT, she was using a facial recognition application and it didn't recognize her face because she was African-American. And so why?  So it's... On Twitter, like Google Photos recognized they were black. I barely recognized their faces of monkey and like, really? That was... Yes, yep, that was another thing. Morified at the same time. I'm sorry, I missed that question. Was that a question? Laughing, but they were also like, or but like, that came out. Yep. But I'll say this is a good, if you have Netflix or if you don't get to roam the host a party and show this because it's... You gotta watch out for that. You gotta watch out for adversarial attacks. You can change a pixel on a stop sign and guess what, that vehicle's flying right through. So, you know, and just some top AI application failures. Microsoft put a chat bot, this was 2016. They put a chat bot out there to learn from Twitter. And with a matter of, I think 12 hours, this thing was uttering the most racist, sexist slurs there were because it was learning from Twitter, which... Probably not a good place to learn. There's a lot of failures. You're only as good as the data. What's nice is there is an ethics canvas when you're building any applications, six areas, six principles that you could consider. Is it explainable as one of them? Is there bias? Is this including everybody? So, this is something we stress when we teach the course. Moral machines is a great exercise. And I'm gonna push that out. Let me just make sure. I think I have the link on that as we'll put this in as the last. I just put the link in. I'm putting them in the chat. Sure. And so we'll get a copy of the chat that can be sent out to the people who registered along with the slides. I'm just going to put that in one more time. Sure. And... But this is an interesting thing to play if you haven't seen it.  Do you kill, is this a little picture? Do I kill the five cats that are, and they may be walking across the street illegally. Obviously cats don't know what to stop. Yes or no. Or do I swear to avoid the cats and do I take out, it looks like business people walk across the street. So there's no one scenarios, but at the end you get a rating as to how did you do compared to other people in terms of they were used over elder people, things like that. I'll finish real quick. So there's a big push towards explainable. Again, if you think of yourself as a patient, you wouldn't want somebody coming in and giving you a diagnosis and not being able to explain it. So that's a current very active area of research for AI engineers and researchers. The failure rate is high on these things. So you gotta be very patient. You see it's 85%. You just gotta be realistic about this. There's all these predictions about the future. Some of the things I talked about, hey, when do you think AI could take over and be a surgeon, for example? And you can see over there, it looks like it's about years from 2016, maybe 40 years in the future. All right. And you see this quote, researchers think there's a 50% chance AI outperforming humans in all tests at 45 years. That might be a little bit optimistic. I always like to end with a little comic that says, are you concerned about the increase in artificial intelligence? No, but I'm concerned about the decrease in real intelligence. So that's funny. I like to share with the class. And again, thank you. We do have a program at Ocean County College, really with sponsorship and Intel as a partner. When we go into this a lot more detail, but thank you for your attention and your participation and any questions. We're gonna let people put them in the chat. This is fascinating. Every single part of it was really fascinating. And I wanna thank you, Ken, for your time. And for the tremendous effort that you put into the slides that you shared with us. I know there are things you used in your class, but this is just extraordinary. And some others who were not able to join today, they will get a copy as well. And this is fascinating. It's breaking new ground for our TechSoup Connect topics. And I think it's gonna stimulate some ideas, particularly maybe having a group session where we watch, what is it? No coded bias. Coded bias, yeah. Coded bias. Yeah, that's good. That's a good idea. And just being aware of some of these articles, the games, the moral machine, to help us examine how we're approaching things in our work and learning about AI-based tools that might help us out. Ken, thank you. You're welcome.