|Human Resource Machine|
|Release||Microsoft Windows, OS XWii U|
For Human Resource Machine on the PC, Guide and Walkthrough by The Sound Defense. Human Resource Machine is a puzzle game. In each level, your boss gives you a job. Automate it by programming your little office worker! If you succeed, you'll be promoted up to the next level for another year of work in the vast office building. Play Human Resource Machine on PC and Mac with BlueStacks and embark in this nerdy adventure by programming your workers to do the best they can. If you succeed, you will get promoted to the next level for another year, guaranteeing more wages to come in the next months. Keep doing that for as long as you can, as much as possible.
Human Resource Machine is an unusual specimen, a puzzle game more intricate than most puzzle games, with a very demanding level of difficulty and which requires a certain mindset to be enjoyed. That being said, its puzzles are very challenging and its control system works perfectly, whether on handheld mode or with the Nintendo Switch on its Dock.
Human Resource Machine is a visual programming-based puzzlevideo game developed by Tomorrow Corporation. The game was released for Microsoft Windows, OS X and Wii U in October 2015, being additionally released for Linux in March 2016, for iOS in June 2016, for Android in December 2016 and for the Nintendo Switch in March 2017.Human Resource Machine uses the concept of a corporate office worker assigned to perform tasks that involve moving objects between an inbox, an outbox, and to and from storage areas as a metaphor for assembly language concepts. The player works through some forty puzzles in constructing a program to complete a specific task.
A sequel, 7 Billion Humans, was released on August 23, 2018.
The game includes approximately 40 programming puzzles, each considered one 'year' of the player's avatar tenure in a corporate structure. In each puzzle, the player creates a list of instructions from rudimentary commands to control the movements of their avatar on an overhead view of an office; the office includes two conveyor belts, one an inbox that sends in either an integer or a single alphabetic character represented as a small box, the other an outbox to receive these. The office floor typically also includes a number of marked number spaces that can hold one box each. For each puzzle, the player is told of a specific task, such as adding two numbers as they come in on the inbox, or sorting a zero-terminated string of characters, delivering these results in the proper order to the outbox.
The player uses simple commands to create a list of instructions to perform the given task. Such commands include picking up the first item at the inbox, placing the item the avatar is currently carrying at the outbox, copying the carried item to a marked square, performing addition or subtraction of the carried item with the item at the marked square, and making decisions based on the value of the carried item such as if it is zero or negative. As such, these mimic the elements of assembly language: the simple instructions equivalent to opcodes, the ability of the avatar to hold an item mirroring a processor register, and the spaces on the office floor representing main memory. Later, the player gains the ability to use the concept of memory addresses, in which they can direct instructions to operate on a specific floor space that is labeled with the number of a different floor space. The visual approach to the language also allows the player to place simple handdrawn notes as labels in both the list of instructions or to label floor spaces for clarity. The loops and jump commands are also marked with arrows to help the player identify the logic flow. Once they have created the program, they can run it through, increasing the speed for longer programs, or pause and move step by step for debugging purposes. If the outbox received any boxes it is not expected for that program, the program will immediately terminate and the player will need to figure out how to correct it. Though the player will be only be shown their list of instructions operation on one set of input and the expected output, the game will also test the list against other randomized sets of input and output, and will alert the player if any of these also fail. The player can receive a reminder of their puzzle task and an example of what type of output it should produce from a supervisor character that watches their avatar work, and the player can also gain hints on how to solve some programs.
Once the player has completed a puzzle, they are shown how many instructions it took and how long it took to process that program on average. Most puzzles have two challenges based on reaching or beating these two metrics; meeting both challenges may be mutually exclusive, but the player has the ability to return to any puzzle once solved to optimize it.
The game has a number of cutscenes shown after certain puzzles that show that the city that the player's avatar is working for is coming under attack by robots, who later gradually replace other workers with robots.
Human Resource Machine was developed by Tomorrow Corporation, a development company founded by Kyle Gabler, Allan Blomquist and Kyle Gray. The game is considered by Gabler as an extension of earlier titles where they have applied gamification to other principles; World of Goo (developed by Gabler and Ron Carmel under 2D Boy) applied the game idea to the concept of Hooke's law, while Little Inferno used the game nature to explore the value of time. The development team saw that the same principles could be applied to computers and used that as the basis of Human Resource Machine. In contrast to Little Inferno, which Gabler stated was difficult to talk to the press without revealing a major revelation of the second half of the game, the concept of Human Resource Machine was very simple to grasp and without any secrets to keep hidden. They decided on using the office environment as it made it easy to create real-life analogs for assembly language concepts that players could grasp, and making it easier for the player to build up the list of instructions. The game was fleshed out by developing the on-screen dialog of the supervisor explaining the task in language that was clear to understand but still has 'a little bit of sassiness' to it, and that such language was used consistently across the whole game.
Reviews for Human Resource Machine were generally positive, crediting the game for being able to distill the complexities of assembly programming into an easily understood visual metaphor. Angus Morrison of PC Gamer rated the game 75 out of 100, considered the puzzle progression to be strong and taught important programming concepts though would have appreciated more instruction on some of the advanced approaches; Morrison also felt the setting and story of the game was under utilized.Laura Kate Dale of Destructoid, giving the game a 6 out of 10, also found the lack of instructional material for advanced concepts in the second half of the game to be troublesome, and felt that the game didn't fully commit to either being a good instructional game for new programmers or a difficult challenge for advanced ones. Laura Hudson of Boing Boing believed the game's approach to programming and its visual style, matching that of Little Inferno, helps to avoid scaring off would-be players, and that some of the puzzles were designed to help make the player feel smart after completing them.
In January 2018, Tomorrow Corporation announced a sequel to Human Resource Machine, titled 7 Billion Humans, which was released on August 23, 2018. The game is based on the same visual programming principles as Human Resource Machine, but now sees the player controlling multiple human agents with the same program to complete tasks.
- ^'Androids! It's time to download… Human Resource Machine'. Tomorrow Corporation. Retrieved December 6, 2016.
- ^'World Of Goo, Little Inferno, Human Resource Machine Heading To Switch'. Game Informer. Retrieved January 24, 2017.
- ^'Tomorrow Corporation: 7 Billion Humans'. Tomorrow Corporation. Retrieved January 29, 2018.
- ^ abcde'Why a World of Goo dev made a puzzle game about programming humans'. Gamasutra. Retrieved November 6, 2015.
- ^'Human Resource Machine for PC Reviews'. Metacritic. CBS Interactive. Retrieved December 6, 2018.
- ^'Human Resource Machine for Wii U Reviews'. Metacritic. CBS Interactive. Retrieved December 6, 2018.
- ^'Human Resource Machine for iPhone/iPad Reviews'. Metacritic. CBS Interactive. Retrieved December 6, 2018.
- ^'Human Resource Machine for Switch Reviews'. Metacritic. CBS Interactive. Retrieved December 6, 2018.
- ^Funnell, Rob (June 9, 2016). ''Human Resource Machine' Review – Sine of Greatness'. TouchArcade. Retrieved December 6, 2018.
- ^'Human Resource Machine'. PC Gamer. Retrieved November 6, 2015.
- ^'Human Resource Machine'. Destructoid. Retrieved November 6, 2015.
- ^'Human Resource Machine will teach you to program and it will be adorable'. Boing Boing. Retrieved November 6, 2015.
- ^Madnani, Mikhail (January 24, 2018). 'Tomorrow Corporation Announces '7 Billion Humans', a Follow up to 'Human Resource Machine' and It Isn't Coming to iOS for Now'. TouchArcade. Retrieved January 24, 2018.
- ^Tarason, Dominic (August 15, 2018). '7 Billion Humans puts a cheerily dystopian face on programming next week'. Rock Paper Shotgun. Retrieved August 15, 2018.
- ^O'Conner, Alice (August 23, 2018). 'Tomorrow Corporation's 7 Billion Humans is out now'. Rock Paper Shotgun. Retrieved August 23, 2018.
Human resources has been slower to come to the table with machine learning and artificial intelligence than other fields—marketing, communications, even health care. But the value of machine learning in human resources can now be measured, thanks to advances in algorithms that can predict employee attrition, for example, or deep learning neural networks that are edging toward more transparent reasoning in showing why a particular result or conclusion was made.
The value beyond numbers for CEOs and managers is the power in understanding what’s actually happening within a company i.e. with their people. As Glint’s Justin Black articulated in a webinar for the Human Capital Institute (HCI), executives and leaders need information that helps them point people in the right direction; information—sales data, KPIs, etc.—change over time, and machine learning can react faster than people in helping draw out the insights and inferences that might otherwise take reams of manpower or not be uncovered at all.
Though not an exhaustive list, below is an outline of solid examples of machine learning and artificial intelligence applications at work in human resources today, along with developing and near-future applications.
Current Machine Learning Human Resources Applications
Applicant Tracking & Assessment
Applicant tracking and assessment has topped the list in early machine learning applications, especially for companies and roles that receive high volumes of applicants. Glint is not an AI company, but they use AI tools to help companies save money and provide a better work experience. Machine learning tools help HR and management personnel hire new team members by tracking a candidate’s journey throughout the interview process and helping speed up the process of getting streamlined feedback to applicants.
Peoplise is another solution for helping companies calculate fit score for new talent, combining tools like digital screening and online interview results to help hiring managers arrive at decisions.
While competition for the best people has driven many HR departments to use algorithmic-based assessments, a CEB article on using machine learning to eliminate bias cautions that human oversight is still of paramount importance. It’s not enough to act directly on data insights, but to use this information in tandem with driving question such as: 1) how I can link applicant traits to business outcomes; 2) which outcomes should be our focus when hiring; and 3) can predictions (hiring and otherwise) be made in an unbiased way.
Attracting talent before hiring has also seen an upswing in machine-learning based applications in the past few years. Black, who is Glint’s senior director of Organizational Development, named LinkedIn as an example of a company using one of the most common versions of basic machine learning—recommending jobs. Other job-finding sites, including Indeed, Glassdoor, and Seek use similar algorithms to build interaction maps based on users’ data from previous searches, connections, posts, and clicks.
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PhenomPeople is one example of a suite of machine learning-based tools that helps lead potential talent to a company’s career site through multiple social media and job search channels. Black notes that this is really just one step past a keyword search, albeit a big step computationally, as there’s a lot more to do.
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Understanding people and why they decide to stay at or leave a job is arguably one of the most important questions for HR to answer. Identifying attrition risk calls for advanced pattern recognition in surveying an array of variables.
In the earlier mentioned HCI webinar, Black describes a hypothetical situation of identifying specific risk factors based on scores to an employee survey. If a human were to try and detect attrition risk among female engineers in Palo Alto with less than 2 years of tenure, the variance analyses to reach that conclusion are innumerable, like finding a needle in haystack, but machine learning allows us to connect these dots in seconds, freeing HR representatives to spend time supporting teams instead of analyzing data.
Glint’s employee engagement platform
Advances in NLP have included the ability to process large amounts of unstructured data, and algorithms can also do things like identify emotional activity in comments and tease out prescriptive comments, or actionable suggestions. Black describes “prototypicality” algorithms that can pull out individual comments that represent the sum of what everyone’s saying, allowing companies to get a broadly inclusive but digestible pulse on company processes and specific issues.
JPMorgan is apparently one of several financial institutions that has also put into place algorithms that can survey employee behavior and identify “rogue employees” before any criminal activity takes place, an obviously more insidious form of attrition with dire consequences—watch the interview with Bloomberg Reporter Hugh Son as he discusses these new safeguards with Bloomberg Technology.
Individual Skills Management/Performance Development
Machine learning is showing its potential in boosting individual skill management and development. While there is definitely room for growth in this arena, platforms that can give calibrated guidance without human coaches save time and provide the opportunity for more people to grow in their careers and stay engaged. Workday is just one example of a company building personalized training recommendations for employees based on a company’s needs, market trends, and employee specifics.
Black elaborates that these types of performance development assessments are useful when actually read, which is why this type of machine-based feedback has been successful for individuals. But this becomes more difficult at the level of the organization, where it’s almost impossible to make sense of enormous amounts of varying data; this is an area where machine learning is evolving, with an increased focus on the overall performance of the corporate lattice.
Future Machine Learning Human Resources Applications
As alluded to in the last example, enterprise management and engagement based on machine learning insights is already here in early forms but has yet to be taken to scale. KPMG promotes its customized “Intelligent Enterprise Approach”, leveraging predictive analytics and big data management to help companies make business decisions that optimize key KPIs and other metrics. re:Work, which provides best workplace practices and ideas from Google and other leading organizations (including KPMG), is an excellent resource for staying up-to-date on new tools and case studies in this space.
Google’s People Analytics department has been a pioneer in building performance-management engines at the enterprise level. From an early stage, the team (led by Prasad Setty) posed existing questions—for example, what’s the ideal size for a given team or department—but focused on finding new ways to use data in order to help answer these questions. In turn, People Analytics has helped pave the way for solving fundamental business problems related to the employee life cycle, with a focus on improving “Googlers'” production and overall wellness. As outline by Chris Derose for The Atlantic, over the last half of a decade, the team has produced insights that have led to improvements in company-wide actions, such as:
- Limiting the number of interviews required for an applicant (more than four didn’t lead to higher quality)
- Revealing optimal organizational size and department size
- Better managing of maternity leave (Google saw a 50 percent decrease in defections)
- Creating on-boarding agenda for an employee’s first four days of work, with increased productivity levels of up to 15 percent
Post-Hire Outcome Algorithms
CEB notes that the ideal hiring algorithm would predict a post-hire outcome (for example, reducing time taking customer service calls while keeping customer satisfaction high) rather than just matching job requirements with items on an employee’s resume or pre-hire assessment results.
The article goes on to note that it’s sometimes the counterintuitive aspects that predict job performance, information that a machine is better at finding through analysis than human inference. For example, CEB describes a model created for a call center representative role that linked call center experience to resulting poor performance. While a link to the source or actual model would be helpful, the idea is interesting and reflects machine learning’s strengths in “invisible” pattern recognition
When Talent Analytics Chief Scientist Pasha Roberts discussed the role of predictive analytics in human resource management with Emerj in 2016, he brought up the internal movement of employees within a company as an issue unique to HR and analytics. “You can use agent-based modeling to simulate and look at how people can move within a company…and be better able to hire a person at the entry-level that will be likely to move through corporate ladder,” said Roberts. While there are early systems in place, more data over time should lead to a more robust and scalable model for internal management over the next five years.
Increased Behavior Tracking and Data-Based Decision Making
Ben Waber, president and CEO of Humanyze and also a past guest on Emerj, talked about the increasing use of IoT wearable data in the workplace. These types of gadgets are more common at the enterprise level—bluetooth headphones and smart ID badges, for example—and companies are continuing to add sensor technology to the workplace in order to collect data. This is an area that Waber researched while serving as a visiting scientist at the MIT Media Lab, using data collected from smart badges to look at things like employee dialogue, interaction, networks within a company, where people spent their time, etc. It would seem that privacy might be a concern, but technologies like smart badges are starting to proliferate quickly (with vendors like Atmel, in the below video, introducing new and updated apps for Android phones). This type of data, says Waber, allows us to pose and answer crucial business-driving questions that we couldn’t ask before, such as ‘how much does my sales team talk to my engineering team?’
Things to Keep in Mind: Machine Learning in Human Resources
Google People Analytics Lead, Ian O’Keefe, told a story at the People Analytics & Future of Work conference in January 2016 about his team’s efforts to quantify things like efficiency, effectiveness and employee experience by looking at hiring decisions, team climate, and personal development. In the end, his team found that people armed with better data make better decisions than algorithms alone can do.
Well-designed AI applications, says Black, have three main cross functions: main expertise, data science expertise, and design/user experience expertise. At present, very few providers do all three of these well. The best solutions today and in the near future don’t replace humans, but emphasize scaling better decision making with the use of machines as a tool and collaborator.
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Our survey of machine learning in human resources illuminates the development of a more people-centric approach, paving the way for more more valuable programs and less wasted time; reduced bias in programs; less administration and more individual development; and the ability to act proactively rather than reactively, moving seamlessly from the level of the individual to the organization and back again.
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