Compuco

compuco

A majority of the individuals we’ve interviewed for have been to a university and graduated with a bachelor’s or master’s degree in computer science (or a related field). Thinking Cap During and/or after their studies, they were accepted into internships which helped them kick off their professional career. But theirs is not the only path. For , most of his knowledge of software development came from reading books and taking online courses - all on his own. Today, he is the CEO of his own startup. Damian Philips-King Damian’s journey begins during his teenage years in Barbados, where he first developed an interest in technology and the intellectual freedom that computers brought to the country. “I come from a developing country, which is Barbados, and getting education outside of the basic fundamentals like math and English; technology is something that's not pushed heavily in that society. So, just being able to get that information from the internet was huge for me. And that was kind of like, Yo, this is something that's cool. ‘Cause, like, we can be anywhere in the world, and we can learn to do this stuff and we can generate new jobs and stuff based off of this stuff.” With his mother’s help, Damian enrolled in a vocational school where he learned how to build computers from the ground up. “That's what sparked my interest [in computer science],” he said. “From then on, it was like slowly adding onto that curiosity.” Unlike many computer science students, Damian doesn’t have a strong math background or even strong math skills. He considers himself more creative, as he likes to draw and come up with new ideas. Damian knew that he would have to obtain some sort of post-secondary education to gain entrance to the computer science industry. He chose a school called CompuCollege in Halifax, Nova Scotia, Canada because they offered a degree path that could be completed in two years instead of the traditional four years. His reasoning was that he wanted to be able to work in the field as soon as possible. Damian considers attending CompuCollege a “necessary evil” because, in truth, he doesn’t believe that you need to go to school for 2-4 years to learn how to become a developer. (To become good at something …) “‘Cause at the end of the day we learn on the job, and we learn by making many, many mistakes. I always look at the light bulb. Thomas Edison famously said that he didn’t fail 10,000 times but successfully found 10,000 ways that will not work. And he created the light bulb, something that we use in our lives day in, day out.” Despite his misgivings, he was determined to stay focused on his goal. “Your whole life is ahead of you,” he told himself. “You're here for a reason to start your own life, but also to be able to give back to your family back in Barbados. And you know, they sacrificed a lot for me, especially my mom. So, I had all of that motivation.” Right out of CompuCollege, he started working at Blackberry in tech support. This was an eye-opener for him because it was his first real job and he had to work many long hours. Despite that, he kept learning on his own time. His new focus was programming, which is the bedrock of software development. “Fairly recently after I joined Blackberry, I realized, oh, there's like a whole new like area and I wanted to be part of it. The only way you can learn is just get knowledge and I wasn't in the position to go back to school. So, I just started buying a bunch of books.” Sacrificing sleep was no sacrifice at all for Damian. “And the cool thing is I was working shift work,” he said. “So, in the daytime, instead of sleeping, I was working on understanding that stuff [programming]. ‘Cause the day is when I work my best. That's pretty much what I did. And I did that for several years before I was confident enough to say, alright, I understand this shit.” After learning about programming for several years until he felt confident in the subject, he started applying for jobs outside of Blackberry. But, since he didn’t follow the traditional university education route for computer science students, many of his applications were rejected. “I was applying for jobs left right and center. I've been going to Toronto, and I was turned down a lot. Obviously, because it's like, here's this guy, he has zero computer science background, like he's junior and we want a senior. I wasn't able to get an internship or go through natural channels.” Finally, a shop called Blazing the Agency that built web applications for companies on contract gave him a chance. His position gave him the industry experience he needed to qualify for other, better jobs down the road. It also taught him a lot about software development, which is the area of computer science that piqued his interest above all others. At his next job at , Damian was finally able to pursue his curiosity in software development and flex his programming skills in that area. He continued to do so at a series of startups that he worked at over the following few years, including and . Igloo Software Terminal MioVision At Miovision, he met his future business partner, Kurtis McBride, and together they would start a company called , which is a platform for health professionals. Formicidy Today, Damian is CEO at Formicidy, as well as CTO at a development shop called Strata Research. His idea for this platform, from which health professionals can run and expand their businesses, was born out of his determination to become healthier and more fit. “I love front end development. I love all of this stuff, but also like paying attention to my health. So, I was like, how can I marry all of the things that I love and build something that can fund my life, not just like, I wanna be able to work in something that I do every day and I wanna be able to share that with other people.” For future high school students looking to become entrepreneurs in computer science, Damian advises them to follow his example and discover their passions. “Find something that you want to do that you love and you are passionate about and build that… Because if you like it, you can probably find a hundred million people that [like] the same thing, too. But you have to be passionate… It has to be something that’s true to you and resembles you… And that's what investors look at. They don't ever invest in a company. They invest in you as a person.” Damian’s journey is a powerful reminder to high school computer science students that passion and creativity are just as powerful as the ability to solve algebraic equations. You don’t necessarily have to follow a traditional path to get your foot in the door in this industry. There are more self-starters like Damian to read about at Thinking Cap! Also published here

A Conversation With Damian Philips-King: A Self-Taught Developer and CEO

Cloud computing involves the provision and usage of computing resources like hardware, storage, databases, networking, software, processing, etc. over a network (e.g., the internet) on a subscription basis. It is a groundbreaking computing paradigm that has been around for some time and is transforming the way computers are used. This transformation boils down to the fact that cloud computing pulls down the hardware barrier that limits peoples usage of computing resources. This means that cloud computing minimizes the limitations of a given computer device by granting it remote access to software running on a server somewhere, and also allowing it to utilize the storage, processing and networking resources of the server. Hence, a physical computing device is virtually extended in functionality by a server. Though shared computer resource usage dates back to mainframe computers, this ability to virtualize and thus, cut the cost of computing resources has brought cloud computing to the limelight and according to comparethecloud.net, cloud computing has been hailed as the champion of the information age. Cloud computing is permeating the modern day tech space, however its success has also been facilitated by other technologies. One important example of such facilitating technologies is Linux OS. Sisense reports that Linux was running about 90 percent of the public cloud workload in 2017. Also, Alibaba cloud reports that 75% of cloud-enabled enterprises use Linux as their primary cloud platform. This article explores the relationship between Linux and cloud computing and also illuminates the revolutionary re-emergence of shared computing as cloud computing (hence the term ‘re-evolution’). Understanding the Cloud Computing ‘Re-evolution’ ‘Cloud Computing’ as a term was first used in 1997 but the concept of cloud computing dates back to the 1950s when mainframe computers were being used. The bulky and expensive mainframe’s hardware acted as a centralized computing platform accessed by multiple end users via ‘dumb terminals’. This sharing of the mainframes computing power, storage systems, and memory represents the basis for cloud computing. The introduction of personal computers in the 1980s was highly celebrated as it delivered such useful technology to firms and households and before long computers became widespread in usage. However, this created a problem as individual computers were quite limited in computing capability (e.g storage, processing, networking etc.). These limitations became more apparent as more sophisticated software were continually being released and people sought to enjoy these new software on their personal computers. Also, running businesses online was quite difficult and expensive because a dealer would first have to purchase extended computer infrastructure (e.g servers, networking etc.) and hire skilled technical experts before they could do business online. Hence, online businesses were difficult to scale. However, as more large tech companies developed and the internet became widespread, the companies soon realized that just like how mainframe computers had shared usage, they could sell access to some of their massive infrastructure to internet users who needed them. This is how cloud computing was born. The term ‘Cloud’ refers to servers where data can be stored and different software and applications are running. Cloud technology provides on demand access to infrastructure, platforms, and software (applications) on the server in the form of computing services with the help of an internet connection. For example, one can log in to their social media account on Facebook or Instagram from any device, and access their posts, images, videos etc. This is possible because such data is stored on the cloud. The widespread usage of the internet coupled with the reducing cost of computers and other internet enabled devices, resulted in the increased use of web based applications. With this web popularity and the ever increasing demand for access to data and applications through multiple devices, cloud computing appears to have a very promising future. The image below shows some of the largest cloud computing platforms in recent times; Cloud computing is a very broad topic. The image below summarizes the key concepts in cloud computing; Virtualization is probably the most important concept in cloud computing because it is how the physical resources of a server are transformed into virtual utilities that can be accessed remotely on demand. Virtualization facilitates efficient usage of a server by aggregating multiple operating systems and applications on a single shared computer to be accessed by different users. The deployment models and delivery models are self explanatory. The private, public, hybrid or community models describe the kind of network used in cloud computing while SaaS, PaaS and IaaS models describe if the resource available for cloud access is a software application, software platform or infrastructure respectively. Importance of Cloud Computing Cloud computing is important because it reduces computing costs and requirements, enhances scalability, increases software accessibility, supplies backup storage, promotes flexibility, increases computing speed and productivity etc. Cloud computing basically, minimizes hardware limitations in technology usage. Linux and Cloud Computing Linux is considered to be one of the best Operating Systems for cloud computing systems with widespread adoption across industries and emerging technologies (75% of cloud-enabled enterprises report using Linux as their primary cloud platform). Its been reported by Sisense that nine of the top ten public clouds run on Linux. Also, Every time someone uses Google, their search query goes through a machine running a Linux kernel. This correlation is not just coincidental but is due to the fact that Linux has many desirable characteristics (e.g speed, security, low cost) that makes it a perfect choice for a cloud computing environment. In-fact, in many ways Linux can be said to have facilitated the huge success of cloud computing. Major cloud computing platforms like AWS, Microsoft Azure, and Google Cloud Platform all use some form of Linux OS. According to Alibaba cloud, integrating complete Linux and open-source based cloud infrastructure provides the following benefits: 35% of lower infrastructure costs. 16% fewer unplanned outages. 39% lower productivity loss due to unplanned downtime. 38% more efficient IT infrastructure teams. 17% more productive development teams. 26% faster application development cycles. These benefits make Linux OS not just recommended but integral to the success of a cloud computing enterprise. The victory of Linux Linux OS is so successful in the field of cloud computing because it solves many of the main problems encountered in the field. This is the fundamental reason. Some of these critical problems are; Difficulty in handling very large amount of information Problems with securing such large volumes of digital data Management costs can be very expensive Reliability and availability concerns Licensing issues with management software Portability and Interoperability Software Maintenance How Linux OS solves the problems of Cloud Computing 1. Difficulty in handling large data: Cloud computing involves gathering large volume of data from a multitude of customers a round the world and organizing their data in an orderly manner using the same server or the same groups of servers. This is a big problem of volume and complexity. The software required for such task must not be heavy (the data is already too big), complicated or limited in capability. Linux with its light weight, simple user interface and robust command line instruction set (bash) is a perfect candidate to solve this problem. Unlike heavy Operating Systems like windows, Linux doesn’t overload the server and provides the operator with a versatile instruction set to handle any data complexity. Bash instructions are also extensible, allowing a server operator to create more programs to handle special needs. 2. Problems with securing large volumes of digital data: Security is a major concern with cloud computing as it basically involves maintaining large data repositories which are accessible over the internet. This creates concerns that such data could be vulnerable to cyber attacks. Linux also comes to the rescue in this scenario being one of the most secure OS currently available. Compuhoy.com reports that contrary to the 60,000 known viruses for windows, Linux only has 40 known viruses. This considerable invulnerability of Linux to cyber attacks is mainly due to the fact that it is open source and maintained by thousands of developers around the world who are always alert for vulnerabilities and quickly fix them. 3. Management costs can be expensive: Management costs is a major concern in cloud computing because more and more resources (e.g software and storage) need to be purchased to keep up with the ever increasing volume of data. With Linux however, software costs are easily sliced off management costs because Linux is free to use and extend which makes it a cheap software for cloud computing. 4. Reliability and availability concerns: A cloud computing platform needs to be reliable because customers may need to access their data at any time. Hence, software used for cloud computing must be fast (to avoid lagging) and secure (to avoid crashing) in order to prevent down time. Linux easily meets these speed and reliability requirements. 5. Licensing issues with management software: Certain proprietary software are restricted in usage by the people that created them. Hence, any software used in cloud computing must be properly assessed before adoption to prevent future limitations on the platform. Linux is free to use with an open license, hence, any needed adaptation can be made at any time to a cloud computing platform running Linux OS. 6. Portability and Interoperability: One core principle of cloud computing is making computer resources available to any device trying to access it. Hence, the software for cloud computing platforms must have multiple device support. Linux is basically an ubiquitous OS because it runs on desktop, mobile, web, servers, embedded systems, etc., which makes it the perfect solution to this problem. It is very portable and very efficient on any device it is running on. 7. Software Maintenance: Any software used in cloud computing needs to be continuously maintained to keep the platform secure and agile. It may be expensive to maintain a team that will continue updating such software but outdated software leads to vulnerabilities issues and an insecure cloud computing platform is basically a failed platform. Linux is a perfect solution to this software maintenance problem because there’s practically an army of open source developers constantly creating new Linux software for free. Conclusion Cloud computing is a groundbreaking computing paradigm that is revolutionizing the information technology industry. It will possibly become the dominant method of using computers in the near future and will have massive impacts on the application industry, gaming industry, hardware industry and the world economy as a whole due to its virtualization of computing resources. Being the dominant OS in cloud computing, Linux is the platform from which cloud computing scales over the internet, to the heights of utopia. This is one great victory for Linux OS, a humble gift of goodwill, lifted to the heights of prominence by hands raised together in selfless opensource service.  The cloud is heaven, tech lovers aspire to shed limiting hardware skins to merge with the cloud, and Linux holds it all up, the enduring pillar of heaven.

Pillar of Heaven : Linux and the Cloud Computing ‘Re-evolution’

As part of my data-science career track bootcamp, I had to complete a few personal capstones. For this particular capstone, I opted to focus on building something I personally care about - what better way to learn and possibly build something valuable than by working on a passion project. Additionally, I believe too much time is spent trying to impress other people, and life is too short to not work on things you personally find interesting. In light of this, I decided to build a boxing prediction web app. One that would show would be users the probability of a given outcome in a fight. To replicate the reality of boxing were people in different weight classes cannot fight each other, the fight in question has to be between fighters in the same weight class. In my previous article I wrote about the process of acquiring most of the data necessary to train and build the model to carry out these predictions. I also got into the seemingly laborious but quite interesting process of cleaning up the data, presenting it in the format of an interactive dashboard and deploying it on Heroku. The full article is available on . I also chose to build a custom top ten ranking for each division based on wins and to a smaller extent the caliber of opponents a given boxer beat. I did not however use an elo-rank type scoring system, which in all fairness is probably a significantly more realistic representation of the boxing landscape. Hackernoon More clean up and transformation For the purpose of building the model, I had to transform the data I already had in such a way that i would have a row for each bout and the relevant stats for Boxer A and the opponent. This process involved using list comprehension to create a list of all the column names relevant to a specific stat or opponent. cols = [ +str(i) i range( , )]
two = [ +str(i) i range( , )]
... 'secondBoxer' for in 1 85 'secondBoxerWeight' for in 1 85 For each list of columns for each stat related to a bout, I used unpivoted the columns using the boxer's name, sex, division and global id as the identifier variables and then concatenated these dataframes into one. With the end result being a 'long dataframe' with each row showing data related to each fight a given fighter fought along with the opponent's stats and stats relevant to that particular bout. concated = pd.melt(data,id_vars=[ , , , ], value_vars = cols,var_name= ,value_name= )
concated_two = pd.melt(data,id_vars=[ , , , ],value_vars=two,var_name= ,value_name= ).drop(columns=[ , , , ])
concated_three = pd.melt(data,id_vars=[ , , , ],value_vars=three,var_name= ,value_name= ).drop(columns=[ , , , ])
... fully_merged = pd.concat([concated,concated_two,concated_three,concated_four,concated_five,
          concated_six,concated_seven,concated_eight,concated_nine,concated_ten,
          concated_eleven,concated_twelve,concated_thirteen,concated_fourteen,concated_fifteen],axis= ,sort= )
fully_merged = fully_merged.set_index( ) 'name' 'global_id' 'sex' 'division' 'label' 'opposition' 'name' 'global_id' 'sex' 'division' 'weightb_label' 'opp_weight' 'global_id' 'name' 'sex' 'division' 'name' 'global_id' 'sex' 'division' 'last6_label' 'opp_last6' 'global_id' 'name' 'sex' 'division' #merge all 1 False 'name' A lot of sklearn models typically require features to be numerical as opposed to categorical, as such I found it preemptive to convert all categorical variables to numerical variables. In one column for example, I had the opponent's results from their previous 6 matches. In such scenarios I searched for the string win. I counted the total win strings I found and multiplied the result by 10, by 5 for each string containing the word draw and 0 value for each loss, giving me a numerical score as a representation of the opponent's last 6 bouts. fully_merged[ ] = fully_merged.opp_last6.str.count( )* +fully_merged.opp_last6.str.count( )* +fully_merged.opp_last6.str.count( )* #converting last 6 fights to points 'opp_last6' 'win' 10 'draw' 5 'loss' -5 One of the columns I had contained a list of lists, with each nested list containing the judges' scores for both the boxer and the opponent. Cleaning this specific column, involved extracting all numerical values, un-stacking the column into 6 columns for each score, renaming the multi-level columns created and then assigning the columns to the 'main dataframe'. I followed the same logic in unpacking the number of rounds won for each boxer. ref_points =fully_merged.judge.str.extractall( ).unstack().reindex(fully_merged.index)      
ref_points.columns = ref_points.columns.map( .format)
fully_merged[[ , , , , , ]] = ref_points[[ , , , , , ]] r'(\b\d+\b)' '{0[0]}_{0[1]}' 'judge1boxer' 'judge1opp' 'judge2boxer' 'judge2opp' 'judge3boxer' 'judge3opp' '0_0' '0_1' '0_2' '0_3' '0_4' '0_5' While its useful to have stats for each boxer and potentially use those as features for my model, I also wanted to get the same attributes and stats for each opponent. In excel there is a function referred to as a vlookup, with its purpose being to retrieve a value from a specific column. For example, we can look for the name of a person, or a string that resembles this name and then return any value associated with that person's name in other columns. Using similar logic. I used the map key to 'map' the opponent's name to the name column. For each match I retrieved the weight, height and other stats associated with the matching name. dataset[ ]= dataset[ ].map(dataset.drop_duplicates( ).set_index( )[ ])
dataset[ ]= dataset[ ].map(dataset.drop_duplicates( ).set_index( )[ ]) dataset[ ]= dataset[ ].map(dataset.drop_duplicates( ).set_index( )[ ])
dataset[ ]= dataset[ ].map(dataset.drop_duplicates( ).set_index( )[ ])
.... 'opp_KO ratio' 'opposition' 'name' 'name' 'KO ratio' 'opp_KnockedOut ratio' 'opposition' 'name' 'name' 'KnockedOut ratio' #get opponents age, height and bmi type ratio 'opp_age' 'opposition' 'name' 'name' 'age' 'opp_height' 'opposition' 'name' 'name' 'height' Enriching my dataset with more data While I already had quite a fair amount of data to work with, as an avid boxing fan I knew that there was more data out there that could potentially help me a better model. The number of punches thrown and landed in a given fight could be aggregated into interesting features showing both the overall accuracy of a given boxer and how well the boxer's defense typically stands against other opponents- judged by the percentage of punches landed against said boxer. What made this process a little tricky at first is that, to get the punch stats for each boxer. I initially envisioned simulating clicking on the view/download button which would reveal the boxing stats for each fight. In order to do this I experimented with using selenium to input a given boxer's full name and then simulate clicking through the view/download stats button and reading the text with the punch stats for each bout a given boxer fought. However, I eventually opted to use the request library to retrieve data from the secondary web address with the punch stats for each fighter, this proved to be a more feasible option. I used the requests library to firstly create a list of id's for each boxer available in the data source and then iterate through the list and return, for each boxer id, the fights recorded along with the punch stats for each fight. While I could have broken the punch stats by round, I chose to focus on the totals; the total jabs, power punches and overall punches thrown and landed in each fight. final_rounds_df = pd.DataFrame()
    final_df = pd.DataFrame()
    stats_pattern = re.compile( ) index, row df.iterrows():
        ... dataload = { : row[ ], : row[ ], : row[ ], : row[ ], : row[ ]
                    }
        ... stats = re.findall(stats_pattern, r.text)
        slice1 = [] no range( ):
            slice1.append( )
        data_input = iter(stats)
        stats = [list(islice(data_input, elem)) elem slice1]
        slice2 = [ , , , , , , , ]
        input2 = iter(stats)
        stats = [list(islice(input2, elem)) elem slice2] idx, fighter enumerate(stats[ :]):
            total_df = pd.DataFrame(fighter) total_df[ ] = row[ ] idx % == :
                total_df[ ] = row[ ] :
                total_df[ ] = row[ ] total_df[ ] = [ , , ] final_df = final_df.append(total_df) final_df.rename(columns={ : , : }, inplace= ) final_df.drop_duplicates(inplace= ) final_df # get punch stats per fight : def punch_stats (df) '\d+\.?\d?(?=%)|\d+\/\d+' for in # create the data/parameters for each request "event_id" 'event_id' "fighter1_id" 'fighter1id' "fighter2_id" 'fighter2id' "fighter1_name" 'fighter1ln' "fighter2_name" 'fighter2ln' # scrape all the round data from the response for in 78 2 for in 12 12 12 12 12 12 3 3 for in # final punch stats for in -2 #add the event_id 'event_id' 'event_id' #fighters name if 2 0 'fighter' 'fighter1ln' else 'fighter' 'fighter2ln' #add stat titles 'punch_stat' 'Total Punches' 'Jabs' 'Power Punches' #append dataframes to the corresponding dataframes #renaming columns 0 'punches' 1 'pct_landed' True #dropping duplicates True return For brevity's sake I did not include all the entire code I wrote and replaced some parts with ellipsis. As always this step was followed by a cleanup and transformation process. This involved a mixture of pivoting the data from long to wide to easily aggregate punch stats for each boxer, dropping columns with information that would not be useful for either my model or interactive dashboard. Rather unfortunately, after merging this with my data, I found that CompuBox had punch stats on around 16 - 20% of the boxers in my dataset, reducing the overall impact these stats would have on my model. In order to build a more visually appealing web app, I also decided to use the beautifulsoup library to scrape pictures of all the boxers in my dataset. The idea behind that was to make sure that if a user selected a boxer the picture of the boxer, assuming it was available would be shown right below the user's selection. path = file = pd.read_csv(path) index,row file.iterrows():
    sleep( )
    site = row[ ]
    response = requests.get(site)
    soup = BeautifulSoup(response.text, )
    pics = soup.find( ) :
        pic_url = pics[ ]
        urllib.request.urlretrieve(pic_url, + str(site.split( )[ ])+ ) :
        image = urllib.request.urlretrieve(image, +str(site.split( )[ ])+ ) 'https://raw.githubusercontent.com/EmmS21/SpringboardCapstoneBoxingPredictionWebApp/master/boxingdata/df2.csv' for in 2 'players_links' 'html.parser' 'img' try 'src' 'C:\\Users\\User\\Documents\\GitHub\\SpringboardCapstoneBoxingPredictionWebApp\\pictures\\' '/' -1 '.jpg' except 'https://chapters.theiia.org/central-mississippi/About/ChapterOfficers/_w/person-placeholder_jpg.jpg' 'C:\\Users\\User\\Documents\\GitHub\\SpringboardCapstoneBoxingPredictionWebApp\\pictures\\' '/' -1 '.jpg' From inspecting the elements of each profile I noticed that only the profile picture was encapsulated in the img tag, using this knowledge, I iterated through the dataset and for each URL link to the boxer's profile, I found every URL within the tag 'img' and then retrieved each picture from the url extracted, using try and except to capture any exceptions that may occur. For example capturing a case were there is no profile picture in a given page. Building the model In building the actual model to carry out the predictions I initially opted to use a Random Forest Classifier. To explain it simply, think about how humans typically make decisions. Let's use the example of deciding to go to the gym. We can form a decision tree to emulate this process. I first ask myself if my body is still tired from my previous workouts. This initial question branches into two nodes; Yes and No. Assuming I am tired, I would then ask myself if my body is genuinely tired or if I  simply do not feel like going to the gym. Two branches would then stem out of this node. As this process continues on all nodes each branch would ultimately generate its own conclusion/end result. I could either opt to go to the gym, choose to workout indoors at home, choose to carry out some other form of physical exercise or chose to rest. We can think of each node as a feature or a combination of features. Random Forest is essentially multiple decision trees. However, with each tree an element is randomness is introduced, each tree takes a random set of features and samples with replacement (for each item sampled, the item drawn will be returned into the dataset before the next sample is drawn). The model then makes a prediction based on the majority prediction derived from the trees. However, long time boxing fans can attest to the fact that draws in boxing are few and far between, this was evident in my data. I was therefore dealing with highly imbalanced classes, with the word classes referring to possible outcomes in a given fight. As a result of this, evaluating my confusion matrix revealed significantly lower accuracy when it came to predicting draws. In order to 'balance' these predictions out, I decided to assign individual weights to each class in my dataset, assigning a significantly greater weight to draws. Through this process, hyperparameter tuning using GridSearch and filtering for the most important features, I was eventually able to push the accuracy of my draw predictions up to a little over 50%. After further research and conversations with my mentor, I opted to test out the categorical boosting model, or . Interestingly, this model can handle both categorical and numerical values without requiring the user to convert categorical features to numerical values in the pre-processing stage. Much like other gradient boosting algorithms CatBoost implements decision trees, but appears to reduce overfitting and requires much less parameter tuning than say XGBoost. There are articles that better explain how this model works, such as the one I have attached in this article. 'CatBoost' Through CatBoost I was able to build a model with much better performance than my RandomForest. Assessing my confusion matrix revealed accuracy of 70-77% for wins and losses and 73% for draws. Sharing the model Now let's assume hypothetical Tommy is at home about to watch Wilder v Fury 2 along with all the undercard fights on the 22nd of February 2020. Out of curiosity he wants to use this model to predict the outcome of a few fights on that night. Will the hard hitting Deontay Wilder add Tyson Fury to his highlight reel by knocking him out as easily as he did Bermane Stiverne in their rematch? Or, will Fury be on fire and leave Wilder's defence terrified and not only retain his lineal 'title' but acquire the WBC heavyweight championship? In order to facilitate this process I decided to build a web app that would use the model I built to generate probabilities of a fight ending in a particular way. To reuse the catboost model in my shiny app, I saved it as a pickle file. Since I would essentially need to call Python code in R, I required needed the . This allows for interoperability between Python and R, letting me interface to Python. The process of building and deploying this app to shinyapp.io can be divided into three steps. Firstly setting up the virtual environment to the desired python version for the app, and explicitly install python libraries that I would need to use on shinyapps.io reticulate package virtualenv_create(envname = )
virtualenv_install( , packages =c( , ))
use_virtualenv( ,required = ) "python_environment" "python_environment" "pandas" "catboost" "python_environment" TRUE The second step involved building the user interface of the app. This can either be created as a separate file or a function inside a single file app. Here I set the background image, added styling tags, created the output tags that would react to user inputs and dropdown menus to allow users to filter boxers according to their weight class, fluidRow(column(offset = , width = ,align= ,
                                titlePanel(h5(selectInput( , ,choices=unique(boxing$division)))))),
                fluidRow(column(offset = , width= ,
                                wellPanel(
                                  fluidRow(
                                    uiOutput( ),
                                    uiOutput( )))), ... 5 2 "center" "dropdown" "Select Boxer Weights" 3 3 "Names" "boxerA" ... Lastly, I created the server function that define the logic of the app. For example this defined the process of filtering the boxer dataset and the names shown to the user based on the weight class the user selected in the dropdown option pertaining to weight class and rendering images matching the IDs associated with the boxers selected. output$Opponent <- renderUI({
    req(input$dropdown)
    df <- boxing %>% filter(division % % input$dropdown)
    selectInput( , ,choices = df$name) #filter opponent names based on selection in "names2" "Opponent" Unfortunately, there are current limitations with the data I used. Boxing fans will notice that quite a few big names are missing. However, I will be updating my data regularly and should have more boxers included very soon. For those interested the app is available here - . Free to send any feedback or advice to help me improve my app on my Twitter at Emmoemm. https://thebeyonder.shinyapps.io/boxingapp2/

I Built a Boxing Prediction Web App on Shiny, Here's How

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Whether you love it or hate it, working from home is the new normal. But as we welcome people virtually into our dusty living rooms filled with unused gym equipment and jealous cats, there's a lot of room for the professional masks we put on to slip in unexpected ways. This Slogging thread by Akasha Rose, Amy Tom, David Smooke and Justin Roberti occurred in slogging's official #future-of-work channel, and has been edited for readability. Akasha Rose Mar 6, 2021, 1:09 AM What is your funniest (or most cringe-worthy) working from the home moment? Have you had to tell anyone that you are "not a cat"? Amy Tom Mar 8, 2021, 10:16 PM ❤ 1 I don’t think I’ve had a truly cringeworthy moment yet. I’ve been frozen in the middle of a blink or animated conversation on Zoom for sure… many times. 😂 BUT on a side-note I’m obsessed with the cat lawyer. “I’m not a cat” is exactly what a lawyer pretending to be a cat would say Akasha Rose Mar 9, 2021, 7:22 AM 💯 1 That video had me absolutely paralysed with laughter whenever I recalled it for the next six days. I was actually in a lawyer's office when I first saw it and was in spasms showing all the staff. What kind of world do we live in now if an esteemed lawyer has to convince people he really isn't a cat? It was like something out of a Miyazaki film, Howls Moving Castle maybe. My own "cat" moment was recently when my cat was walking over my keyboard when the mic was off, only to step on the mouse and click it right when it was positioned over the mic icon, switching the mic back on just as I was saying "Oh not the cat's butt" very loudly to an audience of about 50 people who didn't know me thankfully. David Smooke Mar 10, 2021, 8:55 AM Only 10M views! that video deserves more! It's a national treasure in my book. I bet 394th District Court of Texas could get some major cash if they listed that video for sale as an NFT! I do also wonder how much they've already made from it via licensing it the BBCs and CNNs of the world .. https://www.youtube.com/watch?v=KxlPGPupdd8 David Smooke Mar 10, 2021, 9:03 AM 🔥 1 😄 1 It wasn't too cringy but I did recently do something that would make you shocked to learn I run a technology company...  I was just starting to speak at Mozilla Love the Internet Hackathon, and as to not be distracted I x-ed the Hopin discussion, made my slides full screen and dove right in. Couple minutes in, I see my phone vibrating, it's the organizer calling. "You're on mute." Sure enough, the chat is like "he's muted, "still muted and he doesnt know," and "Now he's just taking phone calls..." I had  clicked share video and not share audio although I thought the button  said share video and audio. I just started from the beginning and did the thing again. Justin Roberti May 14, 2021, 4:08 AM 🔥 1 :slogging-slack-blogging: 1 💚 1 I had a cringeworthy moment yesterday -- a prominent billionaire crypto investor/VC was on a Zoom call early for a podcast I produce. His usual assistant was on vacation, Calendly didn't update when we agreed to push it back 30 minutes, so I had to fill 20 minutes and keep him on the line or I was afraid the podcast wouldn't happen. I had to fill 20 minutes so I jumped on to say hi because I've corresponded with him for at least 3 years. It's been a long time WFH so I really would have preferred to shave before this video call, my hair is large and unmanageable, but I did manage to throw on a nice shirt. I said hi and explained we kind of "know" each other (he did not remember me at all) and he looked at me and said, "So, what are we doing here?" Like a guest that would like to not have his time wasted, so I thought "OK, we are doing an interview now." And I kind of choked. So I started with "So... what's up with Dogecoin?" Like for that moment my muse had left me and I just had nothing. He responded, "I don't know... it's got a dog. People like dogs. Dogs are cute." He was that level of interviewee where you can essentially do no wrong because he will launch into what he talks about pretty much no matter where you start. It was fine, the host got on and we did the podcast. He even gave me some thoughts on Dogecoin beyond the dog. But... i was seriously not prepared and for a very long second I was feeling it. Some day I will make an NFT of this moment Akasha Rose May 15, 2021, 4:32 AM When is the interview out? Justin Roberti May 15, 2021, 3:46 PM Akasha Rose TBH i didn't think i had anything I could use. But I got some comments on BTC and that turned out to be useful: https://www.benzinga.com/markets/cryptocurrency/21/05/21126509/bitcoin-gets-a-carbon-black-eye-from-musk-but-draper-stands-by-price-predictions Akasha Rose May 17, 2021, 1:08 AM What is it with cats and zoom calls, anyway? As soon as I start having a zoom call on my laptop, the cat thinks it is a personal invitation to sit on my lap. Or shoulder. Or back of the chair. And of course, it's not just any cat, it's the fluffiest cat there is.  I put him in the other room but he literally climbs up the door with his claws hanging from it and howling, smacking his body against the door panels. Having him on the laptop or my head is actually far less distracting than his pitiful mews. A friend sent me this meme as the only solution.

Cats, Computers, & Working From Home Fails

Every June, tech bros, pension portfolio managers and Twitter threadbois the world over turn their eyes to Santa Clara as hosts its annual Worldwide Developer Conference (WWDC, or ‘dub-dub’). Apple Among other things, dub-dub is best known as being the birthing ground of almost all of Apple’s suite of ubiquitous consumer technology. The original iPhone, iPad, Apple Watch, and Apple TV and subsequent upgrades have all been launched at the event. This year, this stable of consumer devices is joined by an entirely new product for the first time since 2015. That new product is the VisionPro. There is nothing I can tell you about the VisionPro or Apple’s vision for the future of extended reality that a quick browse of , or the internet at large cannot. The point of this article is instead to try and observe: Twitter LinkedIn how we got here; why people are building computers for your face and the many divergent paths this may lead us on. Content Overview XR: A Brief History Headsets Today: Code New Worlds The Next Frontier for Spatial Computers Requests for Startups XR: A Brief History For the uninitiated, XR refers to - a catch-all blend of (AR, e.g Pokemon Go), virtual reality (VR, e.g. Oculus Rift), and ‘capital R’ Reality (e.g. Google Street View). Mixed reality is another term that exists largely on the same dimension as AR and thus will mostly be ignored here. extended reality augmented reality The modern vision for XR begins around 1935. This was the year in which published . In this short story, protagonist Dan Burke is disillusioned with the reality in which he finds himself. To remedy this, he puts on a pair of glasses that transport him to a world of eternal youth and happiness. The only catch is that he obeys its rules (_T&C_s, in modern parlance). Stanley Weinbaum Pygmalion’s Spectacles Fast forward a few decades and we begin to see the first builds of XR in real environments. This begins with Morton Heilig’s ‘ ’ (excluded from the timeline below), a sort of 4D cinema experience that provided the viewer with smells and a vibrating chair in addition to the sounds & sight of the film. Sensorama Also excluded from the timeline below is the first military application of XR. In 1961, became the first headset with motion-tracking technology. Philco Headsight Though it has since been discontinued, the Oculus Rift was the first headset to gain real commercial appeal. Following Oculus, many of big tech’s household names joined the action. Curiously, most had different visions for who the primary customer base for these headsets would be. The dominant expected use case for most was in immersive entertainment applications, more specifically in gaming. This was the path followed by Oculus, Sony through their , and with the Meta Quest line of products. PlayStation VR Meta Others chose to focus predominantly on enterprise. Early pioneers in this segment included and These early movers focused their aims on things like enterprise collaboration platforms (much like some of the metaverse plays described below), emergency response training, and medical education. HTC Vive Magic Leap. Then, the hype cycle. Corporations the world over began to envision what it would look like if we took our lived environment but made it *virtual*. The vision was essentially , but this time with less porn and more corporate work stuff. metaverse SecondLife In the wake of COVID-19, the rise of remote work created a supposed need for collaborative digital environments. joined the party with its own industrial metaverse. Accenture launched a ‘metaverse services’ division. Facebook changed its name to Meta and began spending $1bn per month on this vision, culminating in this brilliant . Microsoft presentation I have had fun panning the metaverse, but the hype cycle around it was an important moment in raising awareness for the arrival of what Apple would refer to this week as spatial computing. As this space evolves, there is every chance that many of these ideas come back into vogue. Other use cases for commercial XR varied from the practical to the benign. Some of them are beginning to become commonplace in popular web back-ends. You take your pick as to which fits which description from the below: Virtual tourism Virtual home inspections Virtual clothing fitting (If you’re going to watch any of these videos, do yourself a favour and make it this one) Corporate training & education Surgical simulations Product design & prototyping Disney-fied sporting telecasts. Most of the existing devices listed above tried to achieve some sort of balance with the use cases they were targeting. The most successful to date by sales, the Meta Quest, did so by positioning hard as a gaming device. Normally, this would be a lesson. However, if anyone is to buck this trend it would be Apple. Apple has a broad suite of already ubiquitous products (1.6bn active iPhones, to name one) that can and will serve as secondary endpoints for VisionPro applications. While Apple’s inaugural demos for the VisionPro were all hosted within working environments, it is more than likely that it will end up being a general-purpose VR. I foresee its role as being a form of spatial augmentation for Apple’s existing ecosystem of products (e.g Maps, FaceTime, Siri et al) as its vision for computers shifts modalities. More on other potential applications later in this piece. Headsets Today: Code New Worlds Spatial Computing Rather than ride the wave of today’s tech buzzwords, Apple used its informational market-making power to stamp authority on a new one at WWDC. https://twitter.com/tim_cook/status/1665806600261763072?embedable=true While it sounds intuitive enough on the surface, let’s delve a little bit deeper into what this term may actually convey going forward. Besides the obvious branding benefits of coining a buzz phrase, the use of the term ‘computing’ feels extremely deliberate. It represents a logical next step from previous eras of ‘desktop computing’ and ‘mobile computing’. Just as Apple has done with the previous generations of personal computers, it will aim to make this concept and terminology ubiquitous. To quote Tim Cook from WWDC: “In the future, you’ll wonder how you led your life without augmented reality” Besides the change in hardware, how is spatial computing functionally different from its predecessors? Arguably the most important immediate shift will be from working in 2D to working in 3D. Tasks such as visualization, workspace navigation (i.e. clicking and moving things), and collaboration will all be enabled in ways not seen under previous modalities. In the longer term, and are more likely to prove the groundbreaking features of spatial computing. contextual awareness interconnectivity refers to the ability of the computer to; Contextual awareness i) adapt existing recommendations and notifications to a user’s sensory context (i.e recommendations based on location and movement patterns, automatic changes to screen presentations based on user habits, etc) and ii) provide customized (possibly agentic) assistance to users as they complete tasks in extended reality. refers to the user’s ability to extend extended reality beyond the device. Beyond just working and collaborating on holographic interfaces, users can control and interact with other devices in their environment. To take a boring example, users can change smart home settings from within the device. Interconnectivity In the longer run, interconnectivity may see Siri’s role as a virtual assistant evolve from voice commanded search aggregator to a living assistant that can help complete tasks from within the device based on the user’s previous habits and preferences. As the baseline capabilities of autonomous agents advance, the combination of Apple’s distribution network and the familiarity of Siri may make it the killer technology for bringing personal agents to the world en masse. Information Presentation As mentioned above, Apple’s demos of the VisionPro focused almost exclusively on working environments. Make no mistake, this first generation is very much geared toward information workers. How will they use it? Firstly, there is obvious and trodden ground. Apple will re-do and re-design all the enterprise XR solutions we have seen before. They will provide resources for institutions to train their employees, students, and leaders. They will provide a bevy of try-on solutions, gaming applications, and cloud TV solutions that we have seen before. One relatively novel example that was showcased as part of the launch was the ‘Minority Report’ style computer interfaces. Instead of having to boot up a physical device every time you want to work on a file, access it from anywhere in the cloud in an instant. But how does this make the core experience of personal computing itself different? Some early ideas: at any given point in time Unlimited monitors with information work (speech, physical movement etc) Multimodal forms of interacting Intelligent browser navigation through sensory (e.g eye) tracking Entirely automatically tailored to user preference custom 3D screen presentations (boring but important) 3D data visualization Each of these will present its own universe of custom ideas that will take the place of extensions and app stores before it. AppleLM This tweet from founder Sulaiman Omar feels prescient. Apple does indeed have a history of sacrificing first-mover advantage for the sake of developing a better product over a longer timeframe. The VisionPro is a perfect example of this, being released 13 years after the first commercially available Oculus Rift. Cognosys AI Integrating language models into its headsets will open up a number of possibilities for Apple that aren’t available to incumbents without their own proprietary LLMs (note: Meta’s LlaMa may put them in the same boat - Balaji Srinivasan has published a highlighting their potential role in the spatial computing race). great thread https://twitter.com/balajis/status/1666506719084843009?embedable=true&thread=true Among the possibilities that this may usher in: trained on users’ real-life habits. Virtual gaming experiences Immediate through the facial ‘browser’. queries built into the headset and trained on user habits. Agentic virtual assistants that recommend actions at a point in time. Spatial recommendation engines Real-time translation. allowing users to re-arrange objects in their space. Environmental augmentation that plays out immediately as the user speaks it. Generative content creation But could it be done more elegantly… The Next Frontier for Spatial Computers Many a commentator proclaimed the death of Google Glass upon seeing it. Who would want to wear a computer on the face? Why would you opt into looking a bit like a dork? Many have noted that not a single Apple executive even tried a VisionPro on as part of the launch. There has to be a better reason for strapping one on than “because everyone else is”. The key question in the long run then becomes: Why would anyone want to strap an XR computer to their face when you can have one inside your brain? The classic counter to this would be the idea of ‘ ’. When everyone is wearing an XR mask, we can reproject photons to make it look as though no one is. Because reprojection involves creating entirely new photons altogether, people can even make themselves look entirely different from what they actually do without the mask. This would become a killer app for catfishing. reprojection The techno-optimist’s concept of living in augmented reality is akin to living in a perpetual lucid dream. Sleep mask when you go to bed, extended reality mask when you get out of it. Examine exhibit A below: The current paradigm of XR relies on these exoskeletal aids for users to navigate alternative realities. The reality is that we are already so close to no longer needing to depend on these aids. Enter the brain-machine interface. BMIs In the long run, the envisioned reality is likely to trump any form of mixed reality that exists today. I use the term envisioned reality here to describe any form of environmental alteration that is; a) completely customizable to the user’s requirements or demands and b) requires no wearable aids. The most obvious conduit for envisioned reality today is (BMIs). For a longer primer, I cannot recommend Tim Urban’s piece on ‘ ’ from as far back as 2017. brain-machine interfaces Wizard hats for the brain BMIs offer many of the same ‘spatial computation’ benefits as tools like the VisionPro but without the need for strapping a weighty device to the face. existing prototypes for the first commercial BMIs involve invasive implants that, understandably, make many uncomfortable (even if the reward is superhuman intelligence and memory). What about when these become less invasive, more accessible, and thus more palatable to the human public? Neuralink’s Extended reality abounds. Requests for Startups Mentions of batteries were oddly absent from Apple’s inaugural launch of the VisionPro. It is less obvious for ‘in the moment’ demos and usage than other design features like interfaces, compute, and weight (as referenced in ) but equally important when used day-in-day-out. Better battery systems for today’s XR devices. this thread by Kyle Samani Not since first dates moved from cafés to online chat rooms has the core wiring of how humans interact with one another been so fundamentally rocked. How can behavior be moderated in a world where people can make themselves appear to be anything? (note: Apple already seems to be working on some form of protocol which may protect against this). What kind of content becomes more pervasive when the range of presentation methods expands so broadly? How do people come together online? Social infrastructure for an XR world. proof-of-identity SecondLife, Minecraft, and Roblox all created huger-than-expected businesses off the back of secondary marketplaces for digital assets. These marketplaces were enabled by creative tools baked into the very fabric of the platforms themselves. Arm the Rebels: Creative Tools for XR. While there are many developers today already working on designs for new experiences etc for headsets and metaverse-style projects, how can we make this process more accessible for the layman (more specifically the 8-year Roblocker) to create worlds of their own imagination? What will be the second coming of Minecraft for the augmented reality or ‘envisioned reality’ paradigm? A large part of the appeal of virtual/augmented/envisioned reality is the idea of living a different life from the one you have now. Much like Dan Burke in anyone will now be able to optimize their lived experiences within virtual worlds. Designs for Lived Experiences (i.e Qualia). Pygmalion’s Spectacles, In the event that such technologies do become ubiquitous, this creates a giant universal market for synthetic instances of subjective experience. The design scope is theoretically infinite. How many ways can you experience pure ecstasy? Nostalgia? The pool of rewards for those who can bring these kinds of experiences to the world through some form of extended reality are enormous. qualia - Also underappreciated may be the development of tools for users to capture or develop these qualia themselves. Is there a market for the ‘recording’ of one’s own lived experiences to share with others? How can people engineer new experiences to take to market and compose atop other open-source qualia? How can we provide guarantees of privacy for personal qualia if they begin to be used in recommendation engines? For the curious, check out the to learn more. Qualia Research Institute One thing that XR still has in common with other computing paradigms before it is its top-down nature. Users are at the whims of Apple for how they want to interact with their phones. If they want to protest this, they can select another one of the depressingly finite options on the market. To date, composability and customisability in hardware have been extremely limited. Open-Source Hardware & XR Software Marketplaces. But what if we were to open-source the development of headsets themselves through decentralized labs? Active participants could work to modify and iterate upon different hardware specifications to their preference. Modular designs could allow for customization at the layman's level. Just as importantly, these open-source labs would have no incentive to create closed-loop systems for software development. Developers could experiment with, ship, and deploy code that could be available to all headsets at the rate that they can build it. Such open-source development would represent a step change in the way people interact with both software and hardware. For some inspiration, check out the great work the team at is already doing. Auki Labs Linked to the point above, the top-down software ecosystem of today means that incentives built into software tend to be warped. Rather than going all out on user utility, market incentives corner developers into building sub-optimal applications that aim to maximize metrics like ‘average session time’ or ‘clickthrough rates’. Private User Agents. The opportunity that open-source development studios would have to outcompete legacy tech companies by building in private, natural language user agents that act as functional assistants to their users is immense. Also published here. The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "Augmented reality headsets"

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