Angela Robinette: We have Michael Peruche, who is going to be talking about whistleblowers in tech.
Angela Robinette: Iana talking about artificial intelligence versus machine learning, and Bradley marker is presenting.
Angela Robinette: Digital tonight for digital ownership, Michael, would you like to start?
Michael Perusich: Sure.
Michael Perusich: Hopefully you can see that.
Angela Robinette: Yep.
Michael Perusich: Cool.
Michael Perusich: So my presentation, like she said, is over, uh, whistleblowers and some of the ethical concerns that we may come across since we're probably all going to work in tech at some point.
Michael Perusich: Umm, the first thing I wanted to bring up was that the concerns aren't always immediately obvious like in other industries, like for example, factory farming, because obviously that ones pretty easy to see.
Michael Perusich: On one hand, you're feeding a lot of people, but on the other hand, you're killing a bunch of animals that theoretically you don't have to.
Michael Perusich: If you wanted to go for a vegetarian option tax a lot different ethical concerns.
Michael Perusich: A lot of the time gets swept under the rug.
Michael Perusich: People can watch, you know, thousands of hours of content in a year and people don't see a problem with it because they put the onus on you as like a.
Michael Perusich: If you have a problem with it, your willpower should probably be more.
Michael Perusich: It's a self responsibility thing rather than a let's look at these companies that might be acting in bad faith.
Michael Perusich: Sort of thing stone the two big ethical concerns that most people find themselves hearing about are user privacy and data hoarding.
Michael Perusich: That happens with these companies.
Michael Perusich: Uh, like Facebook, for instance.
Michael Perusich: Uh, I've never used it, but if I signed up right now, they'd have a profile of me immediately and link me up to all my friends from high school or whatever to encourage me to keep using their platform.
Michael Perusich: I do use Google for Google Maps and stuff like that, and unless you deliberately turn it off in their settings, even if you don't use the map to get somewhere they track when you went there what time you arrive, what time you left and all that stuff and keep a profile of it.
Michael Perusich: Even if you didn't use their service to get there, they basically just take the data off your phone anyway, which I always thought was pretty creepy and even more companies will sell your data, they just own it and they'll sell it for a quarter or maybe $0.50 to a bunch of other companies.
Michael Perusich: That's how you get all these people cold calling you out of nowhere.
Michael Perusich: There are companies who make it their job.
Michael Perusich: Literally just to know everything about you and sell it to pretty much the lowest bidder.
Michael Perusich: A lot of the times they they're suddenly for not a lot of cash.
Michael Perusich: The second ethical concern I see a lot or algorithms on websites that lock you into certain types of content, then basically never change, which is how you get certain groups that believe things that are kind of nonsensical, like flat Earthers and whatnot.
Michael Perusich: They only see content that perpetuates those myths because the algorithms just want to keep them on the website for longer.
Michael Perusich: And when you get people that have problem with this in your organization, things might get done about it.
Michael Perusich: For instance, Francis Hogan and 2021.
Michael Perusich: This is not something I heard about very much, but she became a whistleblower after leaving Facebook and leaking a bunch of their internal documents.
Michael Perusich: And this part I want to get cleared everybody because I thought this was crazy Facebook in their own documents acknowledged that they knew they were having an effect of, like, an echo chamber on certain people that users were using Facebook to promote violence.
Michael Perusich: In certain regions like civil wars and we're using it to further human trafficking, among other things, Facebook, also in their own documents, was focusing their attention to try and get more children to use the platform, even though they emitted in their own documentation that they thought it was hurting their mental health.
Michael Perusich: And they also believed that they had a pretty big part to play in in misinformation that spread that led to the January 6th riots.
Michael Perusich: This is not something that she said.
Michael Perusich: It is something that was in the documents themselves, which I thought was a pretty big, important thing to get across.
Michael Perusich: Umm.
Michael Perusich: And we'll go back for a second.
Michael Perusich: Just to say they didn't really address any of these concerns at the time.
Michael Perusich: They basically just said she didn't work there for long enough and could not have known well enough of their internal operations.
Michael Perusich: And that basically it was a hoax, but their own documents is what we're saying.
Michael Perusich: This not her.
Michael Perusich: She went on to talk to the UK Parliament and the EU and our own government.
Michael Perusich: We'll get to that later.
Michael Perusich: Another guy who I thought was important because of mainly the things that he said was Ubers, Mark McGann.
Michael Perusich: He was a silent whistleblower for a number of years and was slowly leaking information about how they would lobby.
Michael Perusich: Governments openly break laws.
Michael Perusich: They would have concealment switches, so when police came they could delete or hide their data.
Michael Perusich: They would mislead police by giving police fake apps.
Michael Perusich: The police would request a ride.
Michael Perusich: The ride would never come, and I I'm not really sure why they did that, but a lot of misleading of police and government entities.
Michael Perusich: Uber was doing from basically 2011 to like 2016, and the documents he was leaking, or a few years after they were doing it.
Michael Perusich: Umm, he obviously in Francis Hagen also felt a moral problem was they were seeing and wanted to do something about it.
Michael Perusich: So they came forward and started leaking documents to try and enact it.
Michael Perusich: Change the big problem I see with all of this is basically nothing happened.
Michael Perusich: They they put a big target on their back.
Michael Perusich: They're never, ever going to get hired again.
Michael Perusich: Like, she's not gonna go work for Twitter and Meta owns Instagram.
Michael Perusich: So I don't know what she's gonna do.
Michael Perusich: Uber basically tells you not to worry about the bad things they did, and all of these different countries because they've changed how they operate now, which I and apparently governments are just Poole with that nobody really cared.
Michael Perusich: They went and talked to all these parliaments to try and get some laws enacted to protect people.
Michael Perusich: The companies themselves didn't change their operations very much at all.
Michael Perusich: I mean, Uber still will send their own employees to counter protest.
Michael Perusich: Taxi camp companies that are protesting Uber moving into their, you know, into their part of business and nobody really does anything about it.
Michael Perusich: So my big question at the end of all this research was, was it really worth it for these people?
Michael Perusich: The basically blacklist themselves from the industry they spent a career in to try and enact change that the governments that we have don't really care about, you know, on the flip side.
Michael Perusich: I can't say that they operated very well either.
Michael Perusich: Neither of them the Uber files in particular.
Michael Perusich: He was effective leaking things before his name was ever out there.
Michael Perusich: He didn't ever need to come forward, but chose to.
Michael Perusich: And frankly, I think the reason is so he could become a public figure and start selling books on the matter, which in my opinion, kind of.
Michael Perusich: Brings its own moral dilemma to it like you felt it was a big ethical concern.
Michael Perusich: So you decided to start leaking information on it, but also you wanted to make money on it.
Michael Perusich: So did you really care in the 1st place or did you just see the opportunity to get a story?
Michael Perusich: I guess my point with this is a lot of people become will we come a whistleblower and I'm not sure it's ever worth it because nothing really changes.
Michael Perusich: And on the other hand, I guess it's a good way to make money.
Michael Perusich: And I guess that's basically all I got for tonight.
Michael Perusich: So thank you.
Angela Robinette: Thanks, Michael.
Angela Robinette: That was great.
Angela Robinette: Yeah, brought up a lot of good points and concerns, so thank you.
Angela Robinette: Does anybody have any questions for Michael comments?
Angela Robinette: No.
Angela Robinette: OK, Ayana, Are you ready?
Ayana Green: Yes, I am ready.
Angela Robinette: All right, take it away.
Ayana Green: Right.
Ayana Green: One second.
Ayana Green: OK, so I'm doing my presentation on artificial talent intelligence versus machine learning.
Ayana Green: Umm, so AR is basically a digital computer or computer control robot that performs task commonly associated with intelligent beings.
Ayana Green: They also encompasses the idea of machine that can mimic human intelligence, and it has the ability to perceive learned reason and solve problems.
Ayana Green: So the history of artificial intelligence starts between like 280 BC and the late 1600s.
Ayana Green: Various mathematicians.
Ayana Green: The delusions, philosophers, professors, and authors.
Ayana Green: Music belt mechanical techniques, calculating machines and numeral system that all eventually lets the concept of mechanized human about and non human beings and the early 1700s depictions of all knowing machines acting to computers were more widely discussed and popular literature.
Ayana Green: Jonathan Swift novels go over travels mentioned a device called the engine, which is one of the earliest references to modern day technology.
Ayana Green: Specifically, a computer.
Ayana Green: This device intended purpose was improved knowledge and mechanical operations.
Ayana Green: To a point where even the least talented person was seemed to be skilled.
Ayana Green: All the senses and knowledge of a nonhuman mind, which is basically limiting the artificial intelligence.
Ayana Green: Umm in 1977, director George Lucas with the film Star Wars.
Ayana Green: That's was released.
Ayana Green: The film featured C3PO, a humanoid robot who's assigned as a protocol joy and as a fluent and more than 7,000,000 forms of communication, and has been companion uh RTD.
Ayana Green: R2D2, which is a small astromech Droid who is incapable of human speech, which is the inverse of seed 3PO.
Ayana Green: But instead of R2D2 communicates with electronic beeps, it's functioned includes small repairs and copilot piloting starfighters.
Ayana Green: In 1998, Dave Hampton and Caleb Chung and fainted.
Ayana Green: Furby this was the first pet toy robot for children.
Ayana Green: Umm.
Ayana Green: And then in 1999, the in line with Herbie, Sony introduced a IBO, which is artificial intelligence robot A2000 robotic pet dog, crafted to learn by interacting.
Ayana Green: This environment owners and other AI Bo it's features include the ability to understand and respond to longer plus voice commands and communicate with this human owner.
Ayana Green: In 2004, the sci-fi film I Robot, directed by Alex Priors, is released.
Ayana Green: The set year was in 2035, and humanoid robot served humankind, while one individual vehemently anti robot give given the outcome of a personal tragedy which is determined by a robot.
Ayana Green: In 2011, Apple releases Syria virtual assistance on Apple iOS operating system.
Ayana Green: Sierra uses the natural Language user interface.
Ayana Green: Refer observe answer and recommend things to its human user.
Ayana Green: It adapts a voice commands and projects as an individualized experience per user, and then in 2016, a humanoid robot named Sophia was created by Hanson Robotics.
Ayana Green: She is known as a first robot citizens and what distinguished Sophia from previous humanoids is her likeness to an actual human being.
Ayana Green: With her ability to see image recognition, make facial facial expressions and communicate through AI.
Ayana Green: Umm, So what is machine learning?
Ayana Green: Basically, machine learning is a subset of artificial intelligence that automatically enables a machine or system to learn and improve from experience.
Ayana Green: Machine learning also uses algorithms to analyze large amount of data and learn from the insights and then make informed decisions, and it also improves performance over time as there are trained and the machine learning models are the output of what the program learns from running on algorithm on training data.
Ayana Green: The history of machine learning starts with Arthur Samuel of IBM developed a computer program for playing checkers in the 1950s.
Ayana Green: The program at a very small amount of computer memory available and Samuel initiated what is called the Alpha beta pruning.
Ayana Green: The design included a scoring function used using the position of the pieces on the board and then the scoring function attempted to measure the chances of each side winning.
Ayana Green: The program chooses his next move using Umm minimax strategy, which eventually evolve into the minimax algorithm.
Ayana Green: Samuel also designed a number of mechanisms along with this program become even better.
Ayana Green: And was Samuel called Root learning his program recorded remembered all positions that had already seen it.
Ayana Green: Combined this with the value of the report function.
Ayana Green: Arthur, Samuel.
Ayana Green: Quantify phrase machine learning in 1952.
Ayana Green: In 1957, Frank Rosenblatt as the Cornell Ohh Aeronautical Laboratory combined.
Ayana Green: Donald has model of brain cells.
Ayana Green: Interactions with Arthur Samuel's machine learning efforts and create the perceptrons the Perceptron was initially planned as a machine, not a program, so the software originally designed for a IBM 700 and four 704 was installed in a custom built machine called the mark One Perceptron, which had been constructive for image recognition.
Ayana Green: This made the software and the algorithms transferable and available for other machines on the nearest neighborhood algorithm was came about in 1967 and it was the beginning of basic pattern recognition.
Ayana Green: This algorithm was used for mapping grows.
Ayana Green: It was one of the earliest algorithms used in finding a solution to the traveling salesperson problem, or finding the most efficient route.
Ayana Green: Using it, a salesperson enters a selected city and repeatedly has a program visited program visit the nearest cities until has been, uh, until all the cities have their visited.
Ayana Green: Marcello.
Ayana Green: Sorry Marcello Pellio has been given the credit for inventing the nearest neighbor rule.
Ayana Green: He intern credits the famous cover.
Ayana Green: It had paper of 1965.
Ayana Green: Oops. Ohm.
Ayana Green: With so.
Ayana Green: So in the late 1970s and early 1980s.
Ayana Green: Artificial intelligence research focus on using logical knowledge based approaches rather than algorithms.
Ayana Green: Additionally, neural network research was abandoned by computer science and AI researchers.
Ayana Green: This calls as a schism between artificial intelligence and machine learning, but until then, machine learning had been used for training training program for AI in 1990 was we came about boosting and boosting was necessary, unnecessary development for the evolution of machine learning.
Ayana Green: The boosting algorithms are used to reduce bias bias during supervised learning and include machine learning algorithms that transform weak learners into strong ones.
Ayana Green: The concept of boosting was the first presented was first presented in 1990 paper titled The Strength of Weakness, Learnability for a Robert A Shapir.
Ayana Green: So Shapir said that is set a weak learners can create a single strong learner and a weak learner that defined as classifiers that are only slightly correlated with the true classification.
Ayana Green: Which is better than random guessing.
Ayana Green: By contrast, the strong learning is is easy.
Ayana Green: Easily classified and well aligned with the true classification.
Ayana Green: Ohh so curly.
Ayana Green: Much on a speech recognition training is done by deep learning technique called long short term memory and it's a no network model described by Young and Schmidt, Huber and Self Hawk Rider in 1997.
Ayana Green: The LSTM killer task that required memory of events that took place thousands of discrete steps earlier, which is quite important for speech around the year 2007 and long short term memory, started outperforming more traditional speak Russian recognition programs.
Ayana Green: And then in 2015, the Google Speech recognition program reportedly had a significant performance jump of importing 9% using a CTC training LSTM.
Ayana Green: And so in 2006, facial recognition became reality.
Ayana Green: The facial recognition Grand Challenge was a National Institute of Standards and Technology program that evaluated the popular face recognition algorithms of all time.
Ayana Green: 3D face scans, iris images and high resolution face images were tested and they're finding suggested the new algorithms were 10 times more accurate than the facial recognition algorithm from 2011.
Ayana Green: And while you're times more accurate than those from 1995, someone will.
Ayana Green: Algorithms were able to up form human participants and recognizing faces and uniquely identify identical twins and 2012 Google X Lab developed a machine learning algorithm that can.
Ayana Green: Autonomously browse and find videos containing cats and 2014 Facebook develop a deep face and algorithm capable of recognizing or verifying individuals and photograph with the same accuracy as humans.
Ayana Green: Machine learning at its present moment is now responsible for some of the most significant advances in technology.
Ayana Green: I'm as being used for the new industry of self driving vehicles and for exploring the Galaxy as it helps and identify exoplanets.
Ayana Green: Recently machine learning was defined by sniper University as a science of getting computers to act without being explicitly programmed.
Ayana Green: Machine learning has prompted a new array of concepts and technologies, including supervised and unsupervised learning, new algorithms for robots, and the Internet of Things.
Ayana Green: The analog tools, chat bots and more.
Ayana Green: So the differences between AI and machine learning is that AI allows a machine to simulate human intelligence to solve problems, while machine learning allows machine to learn.
Ayana Green: I don't know, mostly from the past, from past data.
Ayana Green: The goal for a is development intelligence system that perform complex tasks and the goal for machine learning is to build machines that can learn from data to increase the accuracy of the output.
Ayana Green: I'm so AI is able to build systems that can solve complex task like humans.
Ayana Green: But machine learning has a limited scope.
Ayana Green: Applications.
Ayana Green: Yeah, you just technology that systems so that it mimics human decision making while machine learning uses self learning algorithms to produce predictive models.
Ayana Green: AR works with all types of data structured, semi structured and unstructured structured, while machine learning could only use structured and semi structured data.
Ayana Green: AI systems use logic and decision trees to learn reasons and self correct, so the connection between AI and machine learning is that AI is a border concept of enabling a machine or system to sense reasoning acts or adapt like humans.
Ayana Green: Machine learning is the application of AI that allows machines to extract knowledge from data and learn from it.
Ayana Green: I don't know exactly, so there's four types of artificial intelligence.
Ayana Green: The first one is reactive memory.
Ayana Green: Reactive memories are AI systems that have no memory and are task specific, meaning that an input always delivers the same output.
Ayana Green: The next type of AI and it's evolution is limited memory.
Ayana Green: There's algorithm imitates the way our brains norms work together, meaning that it gets smarter as it receives more data to train on.
Ayana Green: The first two tasks to AI reactive machine and limited memory are types that currently exist.
Ayana Green: Theory of mind and self awareness.
Ayana Green: Our API types that will be built in the future, the grand finale for the evolution of AI, would be to design systems that have a sense of a conscious understanding of their existence.
Ayana Green: But this type of AI does not exist yet, and the four types of machine learning.
Ayana Green: The first one is supervised.
Ayana Green: Supervised learning.
Ayana Green: The machine is taught by example.
Ayana Green: The operator provides a machine learning algorithm with the known data set that includes our inputs and outputs, and the algorithm was funded method to determine how to arrive at those inputs and outputs.
Ayana Green: For the operator knows the correct answer to the problem.
Ayana Green: That algorithm defines identifies patterns in data learning from observations and make predictions.
Ayana Green: The algorithm makes predictions and his corrected by the operator, and this process continues until algorithms achieve a high level of accuracy and performance.
Ayana Green: Semi supervised is learning.
Ayana Green: Semi supervised learning is somewhat to supervised learning, but instead it uses labels and unlabeled data.
Ayana Green: Labeled data is essentially information has meaningful tags so that the algorithm can use a data while unlabeled data last information.
Ayana Green: By using this combination machine learning, I Williams can learn to label an unlabeled data unsupervised.
Ayana Green: Is a machine learning algorithm studies data to identify patterns.
Ayana Green: There is no answer key or human operation operated to ride it to provide instruction.
Ayana Green: But is that the machine determines the correlations and relationship by analyzing available data in an unsupervised learning process.
Ayana Green: The machine learning algorithm and love to interpret large datasets and address the data accordingly.
Ayana Green: The algorithm tries to organize that data in some way to describe the structure.
Ayana Green: This might mean grouping the data into clusters or rain you get in a way that looks more organized.
Ayana Green: Reinforcements is focuses on regimented learning processes where a machine learning algorithm is provided with the set actions, parameters, and values.
Ayana Green: By defining the rules of machine learning algorithm then tries to explore different options and possibilities, monitoring and evaluating each result to determine which one is optimal.
Ayana Green: Reinforcement learning teaches the machine trial and error.
Ayana Green: It learns from the past experience and begins to adapt to its approaches and response to the situation.
Ayana Green: Did she the best possible results?
Ayana Green: So some examples, artificial intelligence that I've recently seen is the new feature on Snapchat, which is my AI, and I actually use my AI.
Ayana Green: Sometimes my API's Lola and you can have like conversations or ask questions.
Ayana Green: Umm, it's basically just a friendly interaction between you and a AI opposed to like when you are online shopping and you're trying to resolve something and they put you through a A a I buy or you're on the phone.
Ayana Green: They got you with a a machine operator I've heard about open AI chat.
Ayana Green: GBT, but I really never further investigated in it, but from what I've heard is that it can do anything like Ray Papers and like, do resumes.
Ayana Green: And it was a very popular topic because I know students were getting caught with it because I guess professors would run their paper to see if it was artificial, artificially written, or if it was written by a human.
Ayana Green: So that's pretty much all that I'm with machine learning.
Ayana Green: I think people get confused.
Ayana Green: Machine learning and artificial intelligence.
Ayana Green: Even though machine learning is a subset of machine learning, but here is an example of machine learning.
Ayana Green: We have younger Michael Jackson singing older Michael Jackson song, so I'll.
Angela Robinette: We can't hear it.
Ayana Green: Oh, what happened?
Angela Robinette: Did you forget to share with audio?
Ayana Green: Sure, audio.
Angela Robinette: If when you go to share content next to that is include computer sound and you have to toggle that on.
Ayana Green: Umm where?
Angela Robinette: OK, so stop sharing.
Ayana Green: Uh.
Angela Robinette: And then click the share button again.
Ayana Green: OK. Ohh.
Angela Robinette: And then next to share content, it says include computer sound and you toggle that on.
Ayana Green: And I have no idea.
Ayana Green: But sharing or not?
Angela Robinette: Oops, it isn't sharing yet, so you have to do share with computer sound and then choose your screen.
Ayana Green: Ohh cool.
Ayana Green: So this was the hopefully it plays back.
Ayana Green: And.
Angela Robinette: Yeah, we can hear it.
Ayana Green: Even.
Ayana Green: I want.
Ayana Green: So that's what machine learning does.
Ayana Green: It could do voice manipulation.
Ayana Green: I've seen other things with machine learning learning where there's one person and then a celebrity, and the real person is talking, but it sounds like the actual celebrity, so that's interesting.
Ayana Green: And as far as like algorithms and stuff, so all your likes, your tweets, your retweets, your favorites.
Ayana Green: That all gets stored and that basically depicts the type of content that you are going to see and that also depicts the type of content that like that creator is going to see what was going to put out.
Ayana Green: And that's based off of all the stored data that people are interested in.
Ayana Green: Ohh the pros and cons of AI.
Ayana Green: Wait, so the pros is that the execution of tasks by humans is more prone to make errors, so we often make mistakes while doing specific tasks, and this might be due to the variations of intellectual ability to and intellectual ability of an individual.
Ayana Green: But it's not the same case with AI based machine.
Ayana Green: The the Machinist program for in a way to accomplish a specific task.
Ayana Green: That's the accuracy.
Ayana Green: Depends on how well the AI designed and program for the machine to carry out the task.
Ayana Green: And also, unlike humans, machines do not require bakes to recover from tiredness and boost productivity.
Ayana Green: There are many day to day tasks accomplished by humans which are repetitive.
Ayana Green: The fishing of efficiency of a human reduces while continuously perform in the same job wherever it is, it affect that a human worker can productive for only 18 hours per day.
Ayana Green: But on average work, it can only invest in service for 7 to 8 hours a day.
Ayana Green: Humans need time to refresh themselves.
Ayana Green: They need to maintain work life and they cannot work 24 hours a day, one of the adventures of are another adventures of artificial intelligence is the ability to make the right decisions.
Ayana Green: There are no emotions attached to the AI based machines that help prevent hampering efficiency.
Ayana Green: The machines that are built using artificial intelligence are capable of making logical decisions as well.
Ayana Green: A human would examine a situation by considering many factors, and these factors may influence the decisions emotionally or practically.
Ayana Green: However, machine gives accurate results as they are programs to make logical decisions.
Ayana Green: Air powered machine is used a cognitive computing that helps they'll make practical decisions in real time.
Ayana Green: The cons of artificial intelligence is a creation of machines and powered with artificial intelligence.
Ayana Green: It's very costly for a large scale project.
Ayana Green: The price might reach out to millions of dollars and for a small scale project is not possible to implement AI for companies with large revenues to the cost of the development of the AR project may be felt high due to features, functionalities or scope with which is which it is designed.
Ayana Green: The cost of development also depends on the hardware and the software the companies used in the not so distant future, the rapid advancements of artificial intelligence has brought about unprecedented levels of automation, leading to profound impact on a global workforce.
Ayana Green: While AI has undoubtedly revolutionized various industries, there is an unmistakable human toll associated with its progress.
Ayana Green: And lastly, machines cannot become as creative as humans.
Ayana Green: Artificial intelligence can provide functionalities to learn from data, but cannot make the machine mimic the exact human brain and skills.
Ayana Green: The accuracy of the results from AI powered machines depends on the level of analytics used by the creator.
Ayana Green: Artificial intelligence cannot invent anything, and it can just perform this task.
Ayana Green: It's programmed for an improved by itself.
Ayana Green: By experience, the pros and cons machine learning the pros and machine learning is that it can review large values of data that discover specific trends and patterns that will not be apparent to humans.
Ayana Green: For instance, for an ecommerce website like Amazon, is searched to understand the browsing behaviors and purchase purchase histories of its users to help cater to the right products, deals and reminders relevant to them.
Ayana Green: It uses results reveal relevant advertisements as well.
Ayana Green: With machine learning, you don't have to babysit your project.
Ayana Green: I reset with the way and since it since it means giving machine the ability to learn it lets them make predictions also improve the algorithm on their own.
Ayana Green: A common example of this is an antivirus software to learn to filter new threads as they are recognized and then machine learning is also good at recognizing spam as a machine learning game experience.
Ayana Green: They keep improving and accuracy and efficiency.
Ayana Green: This lesson made better decisions and say you need to make a weather forecast model as amount of data you have keep growing algorithm learning.
Ayana Green: Your algorithm learns to make more accurate predictions faster.
Ayana Green: The kinds of machine learning is that machine learning requires massive data sets to train on, and these these should be exclusive, unbiased in a good quality.
Ayana Green: There can also be times where they must wait for new data to be generated.
Ayana Green: Machine learning is enough time to let the algorithm learn and develop enough to fulfill their purpose.
Ayana Green: With the considerable amount of accuracy and relevancy, it also needs massive resources to function.
Ayana Green: This can be an additional requirements of computer power view from you.
Ayana Green: Another major challenge the ability to accurately interpret results generated by the algorithms.
Ayana Green: You must be careful.
Ayana Green: You must also carefully choose the algorithm for your purpose.
Ayana Green: My opinion on artificial intelligence and machine learning overall is that it has as like it.
Ayana Green: It can be good, but I feel like it may be also used for bad and when I say bad I mean to the point where humans are too dependable on AI and they forget or don't know how to do basic human functions and so that is all I have.
Ayana Green: Any thoughts or questions?
Angela Robinette: While that was great, it's very good.
Ayana Green: Thank you.
Angela Robinette: Umm yeah, if you if you go back and read the chat we were very engaged.
Ayana Green: Ohh.
Angela Robinette: Does anybody have any thoughts or questions for Ayana?
Landon Moore: I have a question.
Landon Moore: I have a question of in your research, one of the subsets of machine learning is deep, deep learning.
Ayana Green: Umm.
Landon Moore: What do you think about the options of that where cause it?
Landon Moore: It's it's attempt to mimic the neuronal processes.
Ayana Green: Right.
Ayana Green: So I've actually did some measure research on like some real like extensive research on deep learning like three years ago because I was this, this was the first time I was introduced to machine learning.
Ayana Green: And at the time, I didn't even know that existed.
Ayana Green: But there's also other branches aside from deep learning.
Ayana Green: I forgot the other branches, but deep learning is probably like one of the top methods that that is used when it comes to machine learning.
Ayana Green: And so that's why not from the research.
Ayana Green: Right.
Landon Moore: Yeah, because the whole idea behind deep learning is that you're gonna mimic the neurons of the brain.
Landon Moore: And so it does.
Ayana Green: Right.
Landon Moore: It does the reinforcement training, it does all the things that the other machine learning programs use, but it's not dependent upon statistics and that's that's the real big difference.
Ayana Green: Yes.
Landon Moore: In so I was, you know, wonder if we're going to finally develop the terial cell, which is the cell of artificial intelligence.
Landon Moore: If you didn't know.
Ayana Green: Umm.
Angela Robinette: Yeah, I didn't know that.
Landon Moore: Good presentation.
Angela Robinette: Wow well.
Landon Moore: Why aren't you in analytics?
Ayana Green: Ah.
Angela Robinette: I honor you.
Angela Robinette: Stay in sofdev.
Angela Robinette: Yeah, you can.
Angela Robinette: I I mean you can do cross things if you're interested in analytics.
Ayana Green: I may take a peek at it.
Angela Robinette: But we we keep you in sofdev still.
Ayana Green: Yeah.
Ayana Green: OK, let me stop sharing my screen.
Angela Robinette: Matthew, go ahead.
Angela Robinette: No, I saw your hand raised, Matthew.
Angela Robinette: All right.
Matthew Davis: Ohh sorry, I hit mute instead of unmute.
Angela Robinette: Yeah.
Angela Robinette: OK, go ahead.
Matthew Davis: Uh, I shared a link for uh, two minute neuroscience videos.
Matthew Davis: They're real quick ways to get you a little bit more insight into how our current understanding of neuroscience works, and that's a lot of the basis on where deep learning and different algorithms may go into that.
Matthew Davis: They've built neurons and everything out of.
Landon Moore: Right.
Landon Moore: They're using all the uh, the propagation of the signal along the membrane, but if they can just figure out how to do the neuropeptide stuff, we might actually have a better solution.
Matthew Davis: Capsules.
Angela Robinette: That is scary.
Angela Robinette: What we could be capable of, but like somebody said in chat, were nowhere near that, thankfully.
Angela Robinette: All right.
Angela Robinette: We're there.
Angela Robinette: Any other comments or questions?
Angela Robinette: No.
Angela Robinette: Bradley wasn't able to make it, but he did.
Angela Robinette: He was able to do his presentation here, so let me go ahead and play that.
Angela Robinette: What?
Angela Robinette: Hello everyone and welcome to my master class presentation on digital ownership.
Angela Robinette: What is digital ownership?
Angela Robinette: There's no ownership.
Angela Robinette: Is, ah, the thought process of what control you have over your digital purchases.
Angela Robinette: Do you actually own a digital purchase?
Angela Robinette: Can it be taken from you without warning?
Angela Robinette: And if, as it does, stop allowing you to access it, do you get a refund?
Angela Robinette: And for me, the we're talking about digital ownership.
Angela Robinette: It's like movies that you buy from Amazon and store in their cloud server or video games you download from Steam, but don't keep on your computer.
Angela Robinette: Even the same thing with music that you download.
Angela Robinette: Understanding digital ownership.
Angela Robinette: What rights do you have as a purchaser of digital media, digital versus traditional ownership rights vary greatly.
Angela Robinette: Ohh, a traditional owner ship rate everyone I'm sure remembers.
Angela Robinette: Maybe not everyone now, but VHS movies used to have the FBI warnings about distributing and all that.
Angela Robinette: It's some of the ownership rights of physical media.
Angela Robinette: Digital Media has other rights, like you may only get 55 downloads of this or you cannot play it at more than one location at a time.
Angela Robinette: And so really you need to understand your ownership rights with the media you purchase.
Angela Robinette: And to start to understand that you need to understand the difference between licensing and ownership.
Angela Robinette: Licensing is like leasing your car, but with a big caveat.
Angela Robinette: It's more accurate to say is you are paying a one time purchase to drive someone else's lease car whenever you want.
Angela Robinette: However, that person or even the dealership can take that car back at a moment's notice.
Angela Robinette: And you were just out that money.
Angela Robinette: All this is spelled out in those terms and terms of services and end user license agreements that everyone sides and hardly reads, because we all know they're 20 to some 100 pages and hard to understand end user license agreement.
Angela Robinette: Specifically, what you, the purchaser, are you're an end user of that licensing agreement.
Angela Robinette: The agreements between two other people or corporations, and you are the end user.
Angela Robinette: There are limitations of digital ownership, digital rights management.
Angela Robinette: DRM is the right you have to your media, specifically your digital media and how you manage them.
Angela Robinette: So terms and services it's similar to that.
Angela Robinette: It's just more specific to your digital rights on there's geographical limitations.
Angela Robinette: As everyone knows, I'm sure they've seen YouTube add for VPNs that you can only watch certain shows in certain areas because the licensing agreements even can go.
Angela Robinette: So as far as other countries have different media restrictions of what is appropriate content and so they won't allow you to watch it because they deem it inappropriate or it needs to be edited for their viewership and also a limitations is on the platform where you can watch something on Netflix but you can't watch it on Hulu or I buy something, a digital movie on my PlayStation, but I cannot watch it on my Xbox even though it's the same format and everything.
Angela Robinette: They just don't transfer between the two and most of these rights and can be changed in limitations, can be changed at any moment.
Angela Robinette: Licensing agreements can be terminated.
Angela Robinette: They can be changed.
Angela Robinette: Uh, it's really a conversation that's happening between these two corporations that affects you, but you have no say in it.
Angela Robinette: The impact on ownership rights platforms can change or shut down at any time, just as I was previously stating.
Angela Robinette: These are conversations that are happening without you.
Angela Robinette: On each Bo is a big deal.
Angela Robinette: With that right now, as they were purging their media library, a lot of shows are just no longer watchable through any legal means.
Angela Robinette: And this actually affects a lot of creators because they it was part of their portfolio and now they can't even show it off with any legal means.
Angela Robinette: They would have to illegally get a copy of it to show us like I did create this.
Angela Robinette: I was a part of this.
Angela Robinette: Even downloads can be discontinued.
Angela Robinette: Amazon does this all the time where you buy a digital movie from them where you can download and watch wherever you want.
Angela Robinette: But then they lose the licensing agreement and you go to play it maybe a year later, maybe two.
Angela Robinette: And they're like, sorry, you can't no longer watch this.
Angela Robinette: We just don't have it anymore.
Angela Robinette: Even though you paid for it and you're just out of luck, even your own digital copy could get deleted if you digitally download it.
Angela Robinette: It does take up a lot of space if it gets accidentally deleted.
Angela Robinette: Ah, there may not be resources for you to download it a second time, and so you're just out of luck.
Angela Robinette: You it's basically accidentally deleting a file.
Angela Robinette: Would be considered throwing out a VHS or having the tape inside rip or scratch on the DVD.
Angela Robinette: It's just gone and you aren't going to get a refund for that.
Angela Robinette: 3rd ownership alternatives to digital media downloading for off use is still digital media, but you have to have a place to store it.
Angela Robinette: I know people who have external hard drives specifically for that where they download their media to and so it keeps their computer ah, free of all of it.
Angela Robinette: It's saved in one spot.
Angela Robinette: It's also one place to lose them if something ever happens.
Angela Robinette: Umm, but that is it's actually turning that digital into a physical copy.
Angela Robinette: They still do make physical copies of most media.
Angela Robinette: I shouldn't.
Angela Robinette: You can't say all because some TV shows and stuff like that.
Angela Robinette: Once that DVD boom was over, stopped making season DVDs and such.
Angela Robinette: So some of those shows are just lost to the ages.
Angela Robinette: If you can't stream them or download them, it also brings us to the issue of piracy.
Angela Robinette: Media piracy was primarily because of availability and access.
Angela Robinette: Piracy went down when streaming started, because Netflix, the big one in the beginning, pretty much had everything and people like, well, I don't need to search for something I want and steal it versus all this is convenient and there.
Angela Robinette: Or the show I wanna watch is on.
Angela Robinette: Now when I want it versus ohm weightings like well, I gotta watch 10 episodes of Seinfeld before the office is on TBS.
Angela Robinette: And so it's an example.
Angela Robinette: And so those are coming alternative to digital media.
Angela Robinette: There's also many advantages of digital ownership.
Angela Robinette: It's very convenient.
Angela Robinette: You can download a movie whenever you want, as long as it's odd.
Angela Robinette: The website you can also play it on almost any device depending on where you download it from.
Angela Robinette: There's multiple options, usually, and that makes it instant per your Internet speed.
Angela Robinette: If you have nice Fiber GB Internet, downloading a movie in high definition shouldn't be that difficult and it should happen fairly quickly.
Angela Robinette: It's also just a physical space reduction.
Angela Robinette: Ah, for those who remember having bookshelves full of VHS tapes or DVD's, or even CD towers, it's not needed anymore.
Angela Robinette: Because all the media is kept in the cloud or on tiny devices, and because of this, it can be taken almost anywhere.
Angela Robinette: You nowadays phones are essentially KVS and can stream almost any information you want at any time.
Angela Robinette: There's even portable game places now.
Angela Robinette: Steam deck, the switch, and many third party options where you can play your games handheld on the go.
Angela Robinette: So some tips for maximize your digital ownership.
Angela Robinette: I know it's a pain in the **** and can be hard to do, but you really should read your terms of services and end user license agreement.
Angela Robinette: This will detail out what you actually own of the media.
Angela Robinette: It will state out how long a notice you have to be given if they're removing your media, or if you get a refund.
Angela Robinette: Some places will refund you 100% if it's under a month, then it goes away some places.
Angela Robinette: We'll give you diminishing returns on it.
Angela Robinette: So like if you had it for three months, no refund or if you had it for two months, you'll get half a refund.
Angela Robinette: And if you can look for digital right management, free or download options or someone else is in managing your rights, you directly own this arm that still being said, websites can shut down ownership can change.
Angela Robinette: All that can happen.
Angela Robinette: So it's up to everyone to understand what their actual rights are with what they purchase.
Angela Robinette: And this is the section I have for questions, but if you're playing this recording, it means I wasn't able to make class tonight.
Angela Robinette: OK, I think I stopped sharing too soon, but yeah, that was a great presentation.
Angela Robinette: Brought up a good a lot of good points.
Angela Robinette: Does anybody have any comments questions?
Angela Robinette: Let me look at chat.
Angela Robinette: OK.
Pete Garcia: Yeah, but little comment have the back to the the issue or the the example of of the vehicle here in the beginning of the presentation where ohh there is I've I've recently heard of a motorcycle company that produces they manufacture electric motorcycles.
Pete Garcia: But The thing is that they've already include some of the features like heated.
Pete Garcia: You know he like heated handlebars or, you know, upgraded this or that.
Pete Garcia: However, you have to pay for those upgrades, which means, which begs the question, like, do you actually own it?
Pete Garcia: Can they take it away at any point after you've already purchased it?
Pete Garcia: You know where do your rights as the person spending $30,000 on a on a vehicle, you know where where do your rights lie in that?
Pete Garcia: So it's kind of, I feel like kind of the same thing and it's a interesting and a scary thought to bring up.
Angela Robinette: Right, Yep.
Angela Robinette: Umm yeah.
Angela Robinette: With software, all the vehicles and you know, mechanical things, they're all run on software.
Angela Robinette: My husband has to go in all the time with his semi and get the updates for the software to run his truck.
Pete Garcia: Yeah.
Angela Robinette: So you know what if that company goes out of business?
Angela Robinette: What if, umm, what are?
Angela Robinette: What are his brakes as a purchaser and user?
Angela Robinette: So yeah.
Pete Garcia: Or are you allowed to remove certain things on your own vehicle that you owe?
Angela Robinette: Right, right.
Pete Garcia: Yeah, it's like.
Pete Garcia: Mm-hmm.
Angela Robinette: Because if it gets repossessed, if you're making payments and it gets repossessed.
Angela Robinette: Yeah, I know that it voids warranty if you chip out your diesel trucks.
Angela Robinette: You know, like 4 dodge. Whatever.
Pete Garcia: Yeah.
Pete Garcia: And also being a comes you know you also you know or what if what about privacy you know uh privacy rights and you know things like that and like house, you know, right, what kind of price do you have for your privacy on you know those kinds of purchases.
Angela Robinette: Right, Yep.
Pete Garcia: But something fun. Scary.
Angela Robinette: Yeah, yeah.
Angela Robinette: I didn't know that about Amazon because I purchased movies.
Angela Robinette: No, I guess that's YouTube.
Angela Robinette: I I have a I pay monthly for you to so I don't have to watch all the ads and stuff, but I do purchase movies off of there, but I don't.
Angela Robinette: I don't know how long they'll be there.
Pete Garcia: Yeah.
Angela Robinette: Yep.
Angela Robinette: Right.
Angela Robinette: Does anybody else have any questions? Comments.
Angela Robinette: Landon, I saw you on muted.
Angela Robinette: And did you have anything?
Landon Moore: Oh, no.
Landon Moore: I just gonna say, uh, they can even take your stuff that you digitized yourself.
Angela Robinette: Oh, really? Yeah.
Landon Moore: Yeah, all my 8 tracks and cassettes I converted to digital and they disappeared one day.
Angela Robinette: Wow.
Matthew Davis: Wait, what?
Matthew Davis: Who are who are they?
Landon Moore: Yep, I have no idea.
Landon Moore: One day they were on my iTunes and next day they were not.
Landon Moore: So I guess Apple is the guilty party.
Matthew Davis: Ohh that's different.
Landon Moore: Yeah.
Matthew Davis: You can't count apple in anything stored on a non apple device.
Matthew Davis: You'll be fine.
Matthew Davis: You have.
Matthew Davis: You have the rights of the the physical device and you have the rights of a backup, which is a digital copy.
Landon Moore: Well.
Matthew Davis: Apple probably screws around with that by quote unquote iTunes limitations.
Landon Moore: Yeah, well, they're they're gonna make sure we don't have rights to our hardware anymore.
Angela Robinette: Wow.
Landon Moore: That that I'm not gonna go any further there.
Angela Robinette: Yeah, they just keep on shutting us down at every turn.
Angela Robinette: It seems I'm as far as the external hard drive goes.
Angela Robinette: My my husband, my brother, he had all his wedding photos and the kids when they were young on his external hard drive, something happened and he it broke or something and he lost all that data.
Angela Robinette: So now he uses OneDrive or no Dropbox, but you know there are too.
Angela Robinette: Do you own those pictures now?
Angela Robinette: Because you're using somebody's third party software or somebody else's software rather.
Matthew Davis: And I'll back up your backups.
Angela Robinette: Yeah.
Angela Robinette: When when I was going through school, I had three.
Angela Robinette: I would do it to my OneDrive, copy it to my OneDrive, my external drive and my hard drive.
Landon Moore: And your drive that you have in a secure location.
Landon Moore: Many, many, many, many miles away.
Angela Robinette: Yeah.
Angela Robinette: All right.
Angela Robinette: Great topics tonight.
Angela Robinette: Thank you everybody.
Angela Robinette: Thanks to our presenters Michael Ayana and Bradley.
Angela Robinette: And if there's nothing else, I think we can call it good.
Angela Robinette: Alright, have a good night everyone.
Pete Garcia: Have a good night.
Landon Moore: Night.
Matthew Davis: Have a good night.
Ayana Green: When I see you guys.
Sue Poole: Goodnight.
Rikki Boodram: Alright, take care.