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Category Archives: Machine Learning
With school out for the year and many taking their summer break, many families will be looking for something fun to do over the next few weeks.
Google's latest machine-learning game may be one way to pass the time, thanks to Blob Opera.
Four actual opera singers Christian Joel (tenor), Frederick Tong (bass), Joanna Gamble (mezzosoprano), and Olivia Doutney (soprano) recorded 16 hours of singing and their voices were used to train amachine learning model to create an algorithm for whatopera sounds like mathematically.
The algorithm was then combined with for very cute blob characters which represent the different opera voice typesand you can move them around to make them sing different notes. The algorithm then does it's magic and calculates how the other 3 blobs should sing to perfectly harmonise with your blob allowing you to compose opera of your own without having to sing a note!
Michelle Dickinson joined Francesca Rudkin to explain what this means.
According to research conducted by Glassdoor, on average, the entire interview process conducted by companies in the United Stated usually takes about 22.9 days and the same in Germany, France and the UK takes 4-9 days longer . Another research by the Society for Human Resources that studied data from more than 275,000 members in 160 countries found that the average time taken to fill a position is 42 days . Clearly, hiring is a time-consuming and tedious process. Groundbreaking technologies like cloud computing, big data, augmented reality, virtual reality, blockchain technology and the Internet of Things can play a key role in making this process move faster. Machine learning in human resources is one such technology that has made the recruitment process not just faster but more effective.
Machine learning (ML) is treated as a subset of artificial intelligence (AI). AI is a branch of computer science which deals with building smart machines that are capable of performing certain tasks that typically require human intelligence. Machine learning, by definition, is the study of algorithms that enhance itself automatically over time with more data and experience. It is the science of getting machines (computers) to learn how to think and act like humans. To improve the learnings of a machine learning algorithm, data is fed into it over time in the form of observations and real-world interactions.The algorithms of ML are built on models based on sample or training data to make predictions and decisions without being explicitly programmed to do so.
Machine learning in itself is not a new technology but its integration with the HR function of organizations has been gradual and only recently started to have an impact. In this blog, we talk about how machine learning has contributed in making HR processes easier, how it works and what are its real-world applications. Let us begin by learning about this concept in brief.
The HR departments responsibilities with regards to recruitment used to be gathering and screening resumes, reaching out to candidates that fit the job description, lining up interviews and sending offer letters. It also includes managing a new employees on-boarding process and taking care of the exit process of an employee that decides to leave. Today, the human resource department is about all of this and much more. The department is now also expected to be able to predict employee attrition and candidate success, and this is possible through AI and machine learning in HR.
The objective behind integrating machine learning in human resource processes is the identification and automation of repetitive, time consuming tasks to free up the HR staff. By automating these processes, they can devote more time and resources to other imperative strategic projects and actual human interactions with prospective employees. ML is capable of efficiently handling the following HR roles, tasks and functions:
SEE ALSO:The Role of AI and Machine Learning in Affiliate Marketing
An HR professional keeps track of who saw the job posting and the job portal on which the applicant saw the posting. They collect the CVs and resumes of all the applicants and come up with a way to categorize the data in those documents. Additionally, they schedule, standardize and streamline the entire interview process. Moreover, they keep track of the social media activities of applicants along with other relevant data. All of this data collected by the HR professional is fed into a machine learning HR software from the first day itself. Soon enough, HR analytics in machine learning begins analyzing the data fed to discover and display insights and patterns.
The opportunities of learning through insights provided by machine learning HR are endless. The software helps HR professionals discover things like which interviewer is better at identifying the right candidate and which job portal or job posting attracts more or quality applicants.
With HR analytics and machine learning, fine-tuning and personalization of training is possible which makes the training experience more relevant to the freshly hired employee. It helps in identifying knowledge gaps or loopholes in training early on. It can also become a useful resource for company-related FAQs and information like company policies, code of conduct, benefits and conflict resolution.
The best way to better understand how machine learning has made HR processes more efficient is by getting acquainted with the real world applications of this technology. Let us have a look at some applications below.
SEE ALSO:The Importance of Human Resources Analytics
Scheduling is generally a time-demanding task. It includes coordinating with candidates and scheduling interviews, enhancing the onboarding experience, calling the candidates for follow-ups, performance reviews, training, testing and answering the common HR queries. Automating these tedious processes is one of the first applications of machine learning in human resource. ML takes away the burden of these cumbersome tasks from the HR staff by streamlining and automating it which frees up their time to focus on bigger issues at hand.A few of the best recruitment scheduling software are Beamery, Yello and Avature.
Once an HR professional is informed about the kind of talent that is needed to be hired in a company, one challenge is letting this information out and attracting the right set of candidates that might be fit for the role. Huge amount of companies trust ML for this task. Renowned job search platforms like LinkedIn and Glassdoor use machine learning and intelligent algorithms to help HR professionals filter and find out the best suitable candidates for the job.
Machine learning in human resources is also used to track new and potential applicants as they come into the system. A study was conducted by Capterra to look at how the use of recruitment software or applicant tracking software helped recruiters. It found 75% of the recruiters they contacted used some form of recruitment or applicant tracking software with 94% agreeing that it improved their hiring process. It further found that just 5% of recruiters thought that using applicant tracking software had a negative impact on their company .
Using such software also gives the HR professional access to predictive analytics which helps them analyze if the person would be best suitable for the job and a good fit for the company. Some of the best applicant tracking software that are available in the market are Pinpoint, Greenhouse and ClearCompany.
If hiring an employee is difficult, retaining an employee is even more challenging. There are factors in a company that make an employee stay or move to their next job. A study which was conducted by Gallup asked employees from different organizations if theyd leave or stay if certain perks were provided to them. The study found that 37% would quit their present job and take up a new job thatll allow them to work remotely part-time. 54% would switch for monetary bonuses, 51% for flexible working hours and 51% for employers offering retirement plans with pensions . Though employee retention depends on various factors, it is imperative for an HR professional to understand, manage and predict employee attrition.
Machine learning HR tools provide valuable data and insights into the above mentioned factors and help HR professionals make decisions regarding employing someone (or not) more efficiently. By understanding this data about employee turnover, they are in a better position to take corrective measures well in advance to eliminate or minimize the issues.
An engaged employee is one who is involved in, committed to and enthusiastic about their work and workplace. The State of the Global Workforce report by Gallop found that 85% of the employees in the workplace are disengaged. Translation: Majority of the workforce views their workplace negatively or only does the bare minimum to get through the day, with little to no attachment to their work or workplace. The study further addresses why employee engagement is necessary. It found that offices with more engaged employees result in 10% higher customer metrics, 17% higher productivity, 20% more sales and 21% more profitability. Moreover, it found that highly engaged workplaces saw 41% less absenteeism .
Machine learning HR software helps the human resource department in making the employees more engaged. The insights provided by HR analytics by machine learning software help the HR team significantly in increasing employee productivity and reducing employee turnover rates. Software from Workometry and Glint aids immeasurable in measuring, analyzing and reporting on employee engagement and the general feeling towards their work.
The applications of machine learning in human resources we read above are already in use by HR professionals across the globe. Though the human element from human resources wont completely disappear, machine learning can guide and assist HR professionals substantially in ensuring the various functions of this department are well aligned and the strategic decisions made on a day-to-day basis are more accurate.
These are definitely exciting times for the HR industry and it is crucial that those working in this department are aware of the existing cutting-edge solutions available and the new trends that continue to develop.
The automation of HR functions like hiring & recruitment, training, development and retention has already made a profound positive effect on companies. Companies that refuse to or are slow to adapt and adopt machine learning and other new technologies will find themselves at a competitive disadvantage while those embrace them happily will flourish.
SEE ALSO:Future of Human Resource Management: HR Tech Trends of 2019
For more updates and latest tech news, keep reading iTMunch
 Glassdoor (2015) Why is Hiring Taking Longer, New Insights from Glassdoor Data [Online] Available from: https://www.glassdoor.com/research/app/uploads/sites/2/2015/06/GD_Report_3-2.pdf [Accessed December 2020]
 [Society for Human Resource Management (2016) 2016 Human Capital Benchmarking Report [Online] Available from: https://www.ebiinc.com/wp-content/uploads/attachments/2016-Human-Capital-Report.pdf [Accessed December 2020]
 Capterra (2015) Recruiting Software Impact Report [Online] Available from: https://www.capterra.com/recruiting-software/impact-of-recruiting-software-on-businesses [Accessed December 2020]
 Gallup (2017) State of the American Workplace Report [Online] Available from: https://www.gallup.com/workplace/238085/state-american-workplace-report-2017.aspx [Accessed December 2020]
 Gallup (2017) State of the Global Workplace [Online] Available from: https://www.gallup.com/workplace/238079/state-global-workplace-2017.aspx#formheader [Accessed December 2020]
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Supporting Content Decision Makers With Machine Learning Machine Learning Times – The Predictive Analytics Times
By: Melody Dye, Chaitanya Ekanadham, Avneesh Saluja, Ashish RastogiOriginally published in The Netflix Tech Blog, Dec 10, 2020.
Netflix is pioneering content creation at an unprecedented scale. Our catalog of thousands of films and series caters to 195M+ members in over 190 countries who span a broad and diverse range of tastes. Content, marketing, and studio production executives make the key decisions that aspire to maximize each series or films potential to bring joy to our subscribers as it progresses from pitch to play on our service. Our job is to support them.
The commissioning of a series or film, which we refer to as a title, is a creative decision. Executives consider many factors including narrative quality, relation to the current societal context or zeitgeist, creative talent relationships, and audience composition and size, to name a few. The stakes are high (content is expensive!) as is the uncertainty of the outcome (it is difficult to predict which shows or films will become hits). To mitigate this uncertainty, executives throughout the entertainment industry have always consulted historical data to help characterize the potential audience of a title using comparable titles, if they exist. Two key questions in this endeavor are:
The increasing vastness and diversity of what our members are watching make answering these questions particularly challenging using conventional methods, which draw on a limited set of comparable titles and their respective performance metrics (e.g., box office, Nielsen ratings). This challenge is also an opportunity. In this post we explore how machine learning and statistical modeling can aid creative decision makers in tackling these questions at a global scale. The key advantage of these techniques is twofold. First, they draw on a much wider range of historical titles (spanning global as well as niche audiences). Second, they leverage each historical title more effectively by isolating the components (e.g., thematic elements) that are relevant for the title in question.
To continue reading this article, click here.
Lomit Patel, VP of growth at IMVU and best-selling author of Lean AI, shares lessons learned and practical advice for app marketers to unlock open budgets and sustainable growth with machine learning.
The first step in the automation journey is to identify where you and your team stand. In his book Lean AI: How Innovative Startups Use Artificial Intelligence to Grow, Lomit introduces the Lean AI Autonomy Scale, which ranks companies from 0 to 5 based on their level of AI & automation adoption.
A lot of companies arent fully relying on AI and automation to power their growth strategies. In fact, on a Lean AI Autonomy Scale from 0 to 5, most companies are at stage 2 or 3, where they rely on the AI of some of their partners without fully garnering the potential of these tools.
Heres how app marketers can start working their way up to level 5:
Put your performance strategy to the test by setting the right indicators. Marketers KPIs should be geared towards measuring growth. Identify the metrics that show whats driving more user quality conversions and revenue, such as:
Analyzing data is a critical step towards measuring success through the right KPIs. When getting data ready to be automated and processed with AI, marketers should make sure:
The better the data, the more effective decisions it will allow you to take. By aggregating data, marketers gain a comprehensive view of their efforts, which in turn leads to a better understanding of success metrics.
Youve got to make sure that youre giving them [partners] the right data so that their algorithms can optimize towards your outcomes and clearly define what success is. Lomit Patel.
The role of AI is not to replace jobs or people, but to replace tasks that people do, letting them focus on the things they are good at.
With Lean AI, the machine does a lot of the heavy lifting, allowing marketers to process data and surface insights in a way that wasnt possible beforeand with more data, the accuracy rate continues to go up.
It can be used to:
With our AI machine, were constantly testing different audiences, creatives, bids, budgets, and moving all of those different dials. On average, were generally running about ten thousand experiments at scale. A majority of those are based on creatives, its become a much bigger lever for us. Lomit Patel.
Theres a reason why growth partners have been around for a long time. For a lot of companies, the hassle of taking all marketing operations in-house doesnt make sense. At first, building a huge in-house data science team might seem like a great way to start leveraging AIbut:
Performance partners bring experience from working with multiple players across a number of verticals, making it easier to identify and implement the most effective automation strategy for each marketer. Their knowledge about industry benchmarks and best practices goes a long way in helping marketers outscore their competitors.
Last but not least, once you find the right partners, set them up for success by sharing the right data.
These recommendations are the takeaways from the first episode of App Marketers Unplugged. Created by Jampp, this video podcast series connects industry leaders and influencers to discuss challenges and trends with their peers.
Watch the full App Marketers Unplugged session with Lomit Patel to learn more about how Lean AI can help you gain users insights more efficiently and what marketers need to sail through the automation journey.
Machine learning is a fast-growing and successful branch of artificial intelligence. In essence, machine learning is the process of allowing a computer system to teach itself how to perform complex tasks by analyzing large sets of data, rather than being explicitly programmed with a particular algorithm or solution.
In this way, machine learning enables a computer to learn how to perform a task on its own and to continue to optimize its approach over time, without direct human input.
In other words, it's the computer that is creating the algorithm, not the programmers, and often these algorithms are sufficiently complicated that programmers can't explain how the computer is solving the problem. Humans can't trace the computer's logic from beginning to end; they can only determine if it's finding the right solution to the assigned problem, which is output as a "prediction."
There are several different approaches to training expert systems that rely on machine learning, specifically "deep" learning that functions through the processing of computational nodes. Here are the most common forms:
Supervised learning is a model in which computers are given data that has already been structured by humans. For example, computers can learn from databases and spreadsheets in which the data has already been organized, such as financial data or geographic observations recorded by satellites.
Unsupervised learning uses databases that are mostly or entirely unstructured. This is common in situations where the data is collected in a way that humans can't easily organize or structure it. A common example of unstructured learning is spam detection, in which a computer is given access to enormous quantities of emails and it learns on its own to distinguish between wanted and unwanted mail.
Reinforcement learning is when humans monitor the output of the computer system and help guide it toward the optimal solution through trial and error. One way to visualize reinforcement learning is to view the algorithm as being "rewarded" for achieving the best outcome, which helps it determine how to interpret its data more accurately.
The field of machine learning is very active right now, with many common applications in business, academia, and industry. Here are a few representative examples:
Recommendation engines use machine learning to learn from previous choices people have made. For example, machine learning is commonly used in software like video streaming services to suggest movies or TV shows that users might want to watch based on previous viewing choices, as well as "you might also like" recommendations on retail sites.
Banks and insurance companies rely on machine learning to detect and prevent fraud through subtle signals of strange behavior and unexpected transactions. Traditional methods for flagging suspicious activity are usually very rigid and rules-based, which can miss new and unexpected patterns, while also overwhelming investigators with false positives. Machine learning algorithms can be trained with real-world fraud data, allowing the system to classify suspicious fraud cases far more accurately.
Inventory optimization a part of the retail workflow is increasingly performed by systems trained with machine learning. Machine learning systems can analyze vast quantities of sales and inventory data to find patterns that elude human inventory planners. These computer systems can make more accurate probability forecasting for customer demand.
Machine automation increasingly relies on machine learning. For example, self-driving car technology is deeply indebted to machine learning algorithms for the ability to detect objects on the road, classify those objects, and make accurate predictions about their potential movement and behavior.
U.S. Special Operations Command Employs AI and Machine Learning to Improve Operations – BroadbandBreakfast.com
December 11, 2020 In todays digital environment, winning wars requires more than boots on the ground. It also requires computer algorithms and artificial intelligence.
The United States Special Operations Command is currently playing a critical role advancing the employment of AI and machine learning in the fight against the countrys current and future advisories, through Project Maven.
To discuss the initiatives taking place as part of the project, General Richard Clarke, who currently serves as the Commander of USSOCOM, and Richard Shultz, who has served as a security consultant to various U.S. government agencies since the mid-1980s, joined the Hudson Institute for a virtual discussion on Monday.
Among other objectives, Project Maven aims to develop and integrate computer-vision algorithms needed to help military and civilian analysts encumbered by the sheer volume of full-motion video data that the Department of Defense collects every day in support of counterinsurgency and counter terrorism operation, according to Clarke.
When troops carry out militarized site exploration, or military raids, they bring back copious amounts of computers, papers, and hard drives, filled with potential evidence. In order to manage enormous quantities of information in real time to achieve strategic objectives, the Algorithmic Warfare Cross-Function task force, launched in April 2017, began utilizing AI to help.
We had to find a way to put all of this data into a common database, said Clarke. Over the last few years, humans were tasked with sorting through this content watching every video, and reading every detainee report. A human cannot sort and shift through this data quickly and deeply enough, he said.
AI and machine learning have demonstrated that algorithmic warfare can aid military operations.
Project Maven initiatives helped increase the frequency of raid operations from 20 raids a month to 300 raids a month, said Schultz. AI technology increases both the number of decisions that can be made, and the scale. Faster more effective decisions on your part, are going to give enemies more issues.
Project Maven initiatives have increased the accuracy of bomb targeting. Instead of hundreds of people working on these initiatives, today it is tens of people, said Clarke.
AI has also been used to rival adversary propaganda. I now spend over 70 percent of my time in the information environment. If we dont influence a population first, ISIS will get information out more quickly, said Clarke.
AI and machine learning tools, enable USSOCOM to understand what an enemy is sending and receiving, what are false narratives, what are bots, and more, the detection of which allows decision makers to make faster, and more accurate, calls.
Military use of machine learning for precision raids and bomb strikes naturally raises concerns. In 2018, more than 3,000 Google employees signed a petition in protest against the companys involvement with Project Maven.
In an open letter addressed to CEO Sundar Pichai, Google employees expressed concern that the U.S. military could weaponize AI and apply the technology towards refining drone strikes and other kinds of lethal attacks. We believe that Google should not be in the business of war, the letter read.