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The evolution of the professional education industry has been quite dramatic over the past decade, and I can truly testify to its considerable shift since its early days. Over these years, I have had the privilege to gain invaluable insight into the sector, and have had the chance to derive enriching experiences from serving Fortune 500 companies, government sector clients and organizations across the globe.

Succinctly put, there have been two major shifts in EdTech. The first being the evolution of the different formats of professional education, from mainly being classroom-based, to now being blended with online and classroom-based learning, and enhanced with experiential and simulation-based set-ups. The second being, the increased acceptance of these new formats by organizations and employees. Now, organizations are looking to do a lot more learning online or in blended formats, and have even questioned the need to employ standalone classroom-based learning. About 8-10 years ago, there were MOOCs (Massive Open Online Courses) that came about – these were mostly an initial attempt at online learning by most schools or organizations in a self-paced format. The world of online learning has significantly evolved more recently, there has been a shift to SPOC (Small Private Online Courses) learning, focused on blending live learning, recorded modules, application based assignments, simulations and smaller cohorts resulting in a deeper impact with higher completion (85% or higher) rates and a better return on investment. According to a recent research report by Emeritus, majority of professionals (over 80%) – ranging from age 21-65 across the globe believe that online learning adoption will increase in the near term. Over 77% of respondents across nine countries stated they would consider a fully online or hybrid approach to learning.

The draw of EdTech

The education industry has always held a significant amount of attraction and interest for me, and I think my four years at the Eruditus group has only further strengthened this view. I also largely attribute my choosing of the Eruditus group based on the goal of making higher-education accessible and affordable across the globe. The emergence of these two organizations was of great consequence, as before this, if you had to undertake higher-education, it would entail taking a break from your career for a year or two, and going headlong into a full-time program at a higher-education institution. However, through our university partners, and the programs developed, both in the blended formats and the online formats, we provide participants with unprecedented access, and an alternative that’s free of compromises. In our State of Executive Education 2023 survey conducted, over 74% senior executives reported having seen a positive impact as a result of executive education.  

The way forward for EdTech

The hastened changes, as necessitated by COVID-19, have ensured that the world has had to resort to online or remote learning in the primary, secondary, tertiary and professional education sectors. Previously, while there was a hesitancy to fully pivot to online-learning, there is now an increased need to re-think and re-imagine the curriculum to suit this medium across all levels of education. In a recent article, Ashley Chiampo and I shared our views on how organizations can determine ways of moving their face to face workforce learning online.

The next few months are going to be crucial as well, as organizations grapple with the new normal and the implications thereof. I think this is where we could step in and help organizations address their talent transformation needs. Provide them insights, and introduce them to a series of knowledge interventions from leading universities & educators across topics of innovation, digitization and data-analytics, as well as design programs that best equip their teams to bring about a smoother transition to the new normal of their choosing. 

Compliments and challenges

A good day at work for me would be one where I hear from our corporate clients on the impact we’ve made on their lives and careers, and the tangible benefit to their organizations. Typically, a good day is also one where I get to spend time with clients, listening to them, understanding their particular needs and thereby helping them design and develop effective programs. On the other hand, a challenging day at work is characterized by a client approaching us with something unusual or ‘not standard’. This is what we enjoy & what keeps us driven & motivated – designing solutions to transform their teams and deliver impact across their organization.

The impetus to innovate

There has hardly been a better time to be innovative in EdTech than the present, as the pandemic has prompted us to push the innovation button more than ever. In keeping with this, we developed and launched a series of short online learning sessions with our educator network that involved renowned and expert speakers from academic and practitioner backgrounds from across the globe. These sessions, named Emeritus Knowledge Bytes, delivered informative and valuable discussions and debates on interesting topics that enabled us to keep our employees and clients motivated and engaged over the last few months.

Vision for 2030

While it’s difficult to predict the future, if I may take a stab at it, I see our companies being the largest education enablers and the largest education platform in the world by 2030, as we continue to make professional education universally accessible. As such, there has never been a better time to be a part of the Eruditus family, and the future looks bright indeed!

This article first appeared on LinkedIn Pulse

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What Role Does Machine Learning Play In Biotechnology?

ML is changing biological research. This has led to new discoveries in biotechnology and healthcare.

Machine Learning and Artificial Intelligence are changing the way that people live and work. These fields have been praised and criticized. AI and ML, or as they are commonly known, have many applications and benefits across a wide variety of industries. They are changing biological research and resulting in new discoveries in biotechnology and healthcare.

What are the Applications of Machine Learning in Biotechnology?

Here are some use cases of ML in biotech:

Identifying Gene Coding Regions

Next-generation sequencing is a fast and efficient way to study genomics. The machine-learning approach to discovering gene coding regions in a genome is now being used. These machine-learning-based gene prediction techniques are more sensitive than traditional sequence analysis based on homology.

Structure Prediction

PPI has been mentioned in the context of proteomics before. However, ML has improved structure prediction accuracy by more than 70% to over 80%. Text mining has great potential. Training sets can be used to identify new or unusual pharmacological targets using many journals articles and secondary databases.

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Neural Networks

Deep learning, an extension of neural networks, is a relatively recent topic in ML. Deep learning refers to the number of layers that data can be changed. Deep learning is therefore analogous to a multilayer neural structure. Multi-layer nodes simulate the brain’s workings to help solve problems. ML already uses neural networks. Neural network-based ML algorithms need to be able to analyze the raw data. It is becoming more difficult to analyze significant data due to the increasing amount of information generated by genome sequencing. Multiple layers of neural networks filter information and interact with one another, which allows for refined output.

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AI in Healthcare

Final Thoughts

Every business sector and industry has been affected by digitization. These effects aren’t limited to the biotech, healthcare, and biology industries. Companies are looking for a way to combine their operations and allow them to exchange and transmit data more efficiently, faster, and in a more efficient manner. Bioinformatics and biomedicine have struggled for years with processing biological data.

The Role Of Java Logging In App Development

Java Logging is crucial to app development because it ensures Java applications work as intended. Logging involves recording events and messages an application generates during its runtime. These messages are used to spot errors, monitor performance, maintain compliance, and debug issues with Java-based applications. 

The Java logging framework has different sets of APIs to log messages at various levels of severity, namely INFO, WARNING, and SEVERE. These log messages can be written to different targets like a remote server, a file, or a console. 

App developers can use the logging framework to configure the logging behavior of Java-based applications, such as the log level and the format and destination of the log messages. They can also filter and format log messages according to different criteria like their source or severity level. To gain in-depth knowledge on this concept, read this Java logging series of guides.

Importance of Java Logging

Developers often rely on Java logging when building and testing their Java-based applications. Here are some benefits of the process:

Identifying errors and troubleshooting

Java logging is essential for identifying errors that prevent a Java-based application from running as intended. Whenever such an application develops an error or exhibits strange behavior, developers can examine the log to determine what went wrong and diagnose the problem.

Compliance and audit trails

The process is crucial for audits and compliance because log messages detail all the actions that occur during an application’s runtime. This allows senior developers to audit the application and ensure it is programmed to function according to the development plan. 

Performance monitoring

Java logging is invaluable for app performance monitoring because developers can use it to measure how long some actions take. This provides a basis for app optimization and allows developers to improve their applications.

Security overview

Logging helps security efforts by recording failed login attempts, security breaches, and user activity within an application. Cybersecurity professionals can analyze this data and use their findings to develop more robust app security measures to prevent the recurrence of previous security incidents.

Java Logging Framework

This framework provides a standardized way to record and manage log messages in Java-based applications. Java logging frameworks consist of numerous components that work in tandem to facilitate the logging process. Here are some of the important ones:

Logger

As the central component of a Java logging framework, loggers are responsible for receiving log messages and forwarding them to the appropriate handler to be processed. They are defined by a hierarchical naming convention reflective of the structure of the application in question. Developers use this to control the granularity of the logging output by making adjustments to the log levels at different levels of the hierarchy. 

There are different types of Java loggers available, and these are the most widely used:

Java Logging (java.util.logging)

This is the default logging framework that accompanies the Java Development Kit and performs the basic logging functions.

Log4j

Logback

Logback has similar functionality to Log4j but is faster, more efficient, and has additional features.

Handler

Handlers process the log messages from the logger and send them to their appropriate output destination. The destination can be a console output, database, file, or socket. Several logging frameworks have built-in handlers, while others permit developers to create custom ones.

Formatter

This component formats log messages before being forwarded to the handler. Formatters format the time and date stamps, class names, message texts, and log levels of log messages so handlers can process them appropriately. 

Filter

Filters determine the log messages that the handler processes. They filter log messages based on criteria like class name, log level, and keywords. 

Tips for Efficient Java Logging

Developers should consider the following tips to ensure effective Java logging in their applications:

Define log levels

Defining log levels will ensure messages are logged at the right severity level. The widely used levels are DEBUG, ERROR, INFO, FATAL, and WARN. 

Use descriptive log messages

Log messages should be descriptive enough for developers to understand. They should contain relevant information like class, method, time stamp, level of severity, and text.

Do not log sensitive information

Logging sensitive information like credit card numbers, social security numbers, and passwords can compromise an app’s security. As a resultevelopers should avoid logging them.

Use contextual information

Contextual information like request IDs, user IDs, and session IDs are helpful to developers when tracing the sequence of events that led up to an error they are investigating, so they should use it.

Implement log rotation

Log rotation is used to prevent log files from growing too large. Large log files make it difficult to investigate application errors, so configuring their maximum size and age simplifies the process.

Endnote

Role Of Media In Influencing Culture And Society

It is impossible to overstate social media’s influence on society. The mass media has impacted cultural shifts in our society and has come to define the roles of men and women. Communication across cultures and borders was impacted as a result. The impact of culture on individual behavior is something that has attracted the attention of researchers all around the world.

What are the Cultural Functions of Media Use?

The media has a significant impact on society. Media is the term used in communication to describe the medium employed to disseminate information to a broad, diversified, and sometimes unknowing audience. Media representations depict different cultural groups in the media, while media effects are studies of the media’s impact on its viewers. The core tenet of social construction is the denial of any absolute reality. In contrast, proponents of this foundation emphasize that all knowledge is culturally and historically contingent. The media, a powerful social system, heavily influence an individual’s perception of the world. It is important to remember that even people who limit their time in front of the TV still feel the consequences of media exposure.

As “Dumbing Down of Society”

According to “The Crisis in Culture,” market-driven media will eventually bring all cultures under the control of the entertainment business. Susan Sontag claims that the entertainment sector is the source of the most “intelligible, seductive ideas.” Therefore, debates about “the tepid, the glib, and the senselessly cruel” are the norm. Some observers assert that interest in celebrity culture is on the rise. People complain that high-quality drama has been replaced by gardening shows, cooking demonstrations, and other “lifestyle” programs on television and that newspapers that once included foreign news now feature celebrity gossip and photos of scantily-clad young women.

One critic argued that great art and genuine folk culture had been replaced by “tasteless industrialized artifacts,” or mass-produced items designed to appeal to the broadest possible audience. They argue that the media industry’s meteoric rise to prominence after WWII led to its eventual consolidation into a handful of multinational conglomerates. Sensationalism and titillation have replaced serious reporting in the mainstream media, which feeds people’s “fears, prejudices, scapegoating processes, paranoia, and aggression.”

Public Participation

Participation from the public is a common by-product of media research, especially social media research. The issue of disaster aid provides a good illustration of this point. The unfortunate occurrence of natural catastrophes like hurricanes, floods, and tsunamis generates demand in many locations; many citizens want their government’s help with food supply, housing, and medical attention. There are occasions when disasters are so extensive that governments do not have the resources to fix everything that has been broken. People worldwide can contribute by donating money thanks to the widespread dissemination of information about recent natural catastrophes on social media platforms. Through their websites, you can donate to organizations like the Red Cross, World Relief, Hands, and World Vision. The media is being used proactively, making it simple for anyone to contribute financially.

Books, periodicals, newspapers, and other print media are useful for disseminating disaster-related information. However, they need to be more suited to delivering this material quickly and on a massive scale. Furthermore, there is no opportunity for engagement in these textual formats. The media’s presentation of information is effective because of the speed with which it can be disseminated and the incentives it offers its audience to participate.

Media – Catalyst for Preserving and Promoting Local Culture

The media is a mirror of societal values and beliefs. The spread of knowledge, education, and consciousness throughout a country is facilitated by the media and can contribute to a cultural revolution. There is a positive and welcoming dynamic between culture and the media. The media in Malaysia and Singapore, for instance, frequently covers the celebrations of the various communities, as well as the religious observances and other customs of those communities. In an unusual phenomenon, Malaysians of many ethnic and religious origins have celebrated the Chinese New Year’s Yee Sang ceremony together. This trend has been popularised by television reports and media reports on the intermixing of different communities in Malaysia.

Individual Accountability and Popular Culture

Ultimately, media literacy teaches that it is the individual’s responsibility to evaluate and comprehend the messages and images they encounter. Everyone in a packed theatre of a million people is still an individual, no matter how often they have seen the same mass media work. There are many right ways to interpret what we see in the media; rather, a wide range of reasonable inferences can be made based on factors including background knowledge and personal experience. We live in a media-rich world, but we can make the most of it by reading, understanding, and critically evaluating the various forms of communication we encounter.

Persuasion and Cultural Values

Conclusion

Corporate Sustainability – Meaning, Examples, And Importance

blog / Business Management Corporate Sustainability – Meaning, Examples, and Importance

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This article was originally published by the Network for Business Sustainability. It was written by Tima Bansal and Devika Agarwal.

Most people still find the concept of corporate sustainability unclear. We explain what it means and why it’s important.

In the last two years, there has been a tidal wave of companies committing to “sustainability.” They might set net zero carbon goals, diversify their workforce, or move into new, cleaner lines of business. And, this is just the front edge of the wave. The interest in sustainability is likely to grow even more over the next decade, as businesses feel pressure from social movements and environmental challenges.

But corporate sustainability is still confusing to many people. People often ask me: “So, what do you mean by sustainability?” I’m a researcher who has studied this topic for over 20 years and I work closely with companies. Here, I describe what corporate (or business) sustainability means, why it matters, and how to make it part of your business.

What are the principles of sustainability?

Corporate sustainability comes from the concept of “sustainable development.” The World Commission on Environment and Development, a United Nations initiative, defined that concept in 1987. Sustainable development means actions that “meet the needs of present generations without compromising the needs of future generations.”

To contribute to sustainable development, businesses should create wealth to reduce poverty, but do so without harming the natural environment. In this way, businesses help our world today and ensure that future generations can also thrive.

In practice, this means that business must consider three key things in their operations:

Human rights and social justice. Sustainability requires businesses to recognize their impact on the people they employ and the communities around them. This recognition means committing to fair wages, just and ethical treatment, and a clean and safe environment.

Natural resource extraction and waste. Businesses often rely on natural resources such as land, water and energy. While many natural resources can renew or “regenerate,” this takes time. Businesses need to respect these cycles, by using natural resources at the speed at which they regenerate.

Short- and long-term thinking. Businesses face intense pressure for immediate profits, but sustainability requires investing in technologies and people for the future, even though financial benefits show up much later. Companies are used to longer-term thinking for capital investments, but a sustainability orientation applies this logic to investments in people and society.

For example: Some fossil fuel companies have reimagined themselves as energy companies, even though major investments in renewable energies are less profitable in the short run than their oil, gas or coal operations. They recognize that climate change requires them to build new capabilities and sources of energy.

How does corporate sustainability differ from corporate social responsibility?

Many terms exist to describe companies’ social and environmental initiatives. Corporate social responsibility (CSR) is the most common; others include environmental, social, and governance (ESG), shared value, the triple bottom line, and managing environmental impacts.

I see ‘sustainability’ as the most complete and powerful of these related concepts. That’s because sustainability asks managers to take a “systems view.” A systems outlook recognizes that companies are part of a larger social and environmental system, that systems change, and that today’s actions must consider the future.

CSR emphasizes a company’s ethical responsibilities. However, what is ethical for one person or company may not be seen ethical by another. For example, some people see a minimum wage as being responsible, whereas others see a higher “living wage” as the ethical choice. Corporate sustainability emphasizes science-based principles for corporate action. A corporate sustainability lens would set a wage in which people could meet their basic needs, which will vary from place to place.  

Additionally, CSR generally does not speak to fairness across generations; it focuses more on the present.

But don’t get too lost in the definitions. Ultimately, all of these terms ask businesses to think about the broader world in which they operate, and not just on short-term self-interest.

Why is corporate sustainability important?

Business is a powerful actor in society, with some businesses being larger than some governments. For example, Amazon’s revenues in 2023 were $US281bn: larger than Pakistan’s GDP.[1] Businesses now have so much power that executives can choose to create a better life for all or just a few.

Society is also pushing companies to invest in sustainability. Many governments, citizens, and other stakeholders want to see companies showing concern for their communities. Failing to do so can mean losing the social license to operate, which is society’s trust in a company.

Additionally, companies can benefit in the long term from being green and good. Evidence shows that financial benefits come in many forms. For example:

Reducing waste, e.g. through energy efficiency investments, often produces savings.

Investors increasingly look for companies that have higher “ESG” (environmental, social and governance) ratings, as a way of managing risks.

Creative and committed individuals seek out employers committed to sustainability and are even willing to take a lower salary if such a commitment is sincere.

But, let’s be honest. Sustainability is not just about making money. It is also a vision of what executives running powerful businesses want to see in the world they create. They imagine a world in which everyone can flourish, living on a planet that is resilient and rich with biodiversity. They don’t want to inhabit a world in which only a few live well, whereas others live with disease and waste.

How do you build a corporate sustainability strategy?

Companies can move step by step toward sustainability, gradually increasing and expanding their actions. Often companies begin by putting their own houses in order, looking internally at decision-making, operations, culture, and other areas. They may move on to partnering with suppliers, vendors and other companies can help organizations learn and share best practices. Eventually, companies need to engage with society, from community stakeholders to NGOs.

Ultimately, no single company can create sustainable development: it must be a collective effort. That’s because many sustainability issues, such as climate change and poverty, are so huge that they require action by many citizens and organizations. And for any single company to create zero emissions, it needs suppliers to innovate cleaner products and regulators and customers willing to support their efforts. Sustainability requires new forms of collaboration and new thinking about the economy.

Corporate sustainability may not be simple, but it is necessary. Those companies that embrace the full complexity of sustainability ideas sooner than later will contribute to a better world and experience higher long-term profits. Why wouldn’t we all want to work towards that vision?

About the Series

The Network for Business Sustainability “Basics” series provides essential knowledge about core business sustainability topics for business leaders thinking ahead. “The Basics” provides essential knowledge about core business sustainability topics. The Network for Business Sustainability builds these articles for business leaders thinking ahead.

About the Authors

Devika Agarwal is an MBA/MS student at the University of Michigan Erb Institute for Global Sustainable Enterprise. Devika hails from strategic sourcing in the retail industry and now works to influence sustainability and innovation in the Global Supply Chain.

[1] Based on corporate revenues ranked against national GDP. Amazon’s total would rank it 41st in the list of world economies based on GDP, just above Pakistan.

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Hyperparameters In Machine Learning Explained

To improve the learning model of machine learning, there are various concepts given in machine learning. Hyperparameters are one of such important concepts that are used to improve the learning model. They are generally classified as model hyperparameters that are not included while setting or fitting the machine to the training set because they refer to the model selection task. In deep learning and machine learning, hyperparameters are the variables that you need to apply or set before the application of a learning algorithm to a dataset.

What are Hyperparameters?

Hyperparameters are those parameters that are specifically defined by the user to improve the learning model and control the process of training the machine. They are explicitly used in machine learning so that their values are set before applying the learning process of the model. This simply means that the values cannot be changed during the training of machine learning. Hyperparameters make it easy for the learning process to control the overfitting of the training set. Hyperparameters provide the best or optimal way to control the learning process.

Hyperparameters are externally applied to the training process and their values cannot be changed during the process. Most of the time, people get confused between parameters and hyperparameters used in the learning process. But parameters and hyperparameters are different in various aspects. Let us have a brief look over the differences between parameters and hyperparameters in the below section.

Parameters Vs Hyperparameters

These are generally misunderstood terms by users. But hyperparameters and parameters are very different from each other. You will get to know these differences as below −

Model parameters are the variables that are learned from the training data by the model itself. On the other hand, hyperparameters are set by the user before training the model.

The values of model parameters are learned during the process whereas, the values of hyperparameters cannot be learned or changed during the learning process.

Model parameters, as the name suggests, have a fixed number of parameters, and hyperparameters are not part of the trained model so the values of hyperparameters are not saved.

Classification of Hyperparameters

Hyperparameters are broadly classified into two categories. They are explained below −

Hyperparameter for Optimization

The hyperparameters that are used for the enhancement of the learning model are known as hyperparameters for optimization. The most important optimization hyperparameters are given below −

Learning Rate − The learning rate hyperparameter decides how it overrides the previously available data in the dataset. If the learning rate hyperparameter has a high value of optimization, then the learning model will be unable to optimize properly and this will lead to the possibility that the hyperparameter will skip over minima. Alternatively, if the learning rate hyperparameter has a very low value of optimization, then the convergence will also be very slow which may raise problems in determining the cross-checking of the learning model.

Batch Size − The optimization of a learning model depends upon different hyperparameters. Batch size is one of those hyperparameters. The speed of the learning process can be enhanced using the batch method. This method involves speeding up the learning process of the dataset by dividing the hyperparameters into different batches. To adjust the values of all the hyperparameters, the batch method is acquired. In this method, the training model follows the procedure of making small batches, training them, and evaluating to adjust the different values of all the hyperparameters. Batch size affects many factors like memory, time, etc. If you increase the size of the batch, then more learning time will be needed and more memory will also be required to process the calculation. In the same manner, the smaller size of the batch will lower the performance of hyperparameters and it will lead to more noise in the error calculation.

Number of Epochs − An epoch in machine learning is a type of hyperparameter that specifies one complete cycle of training data. The epoch number is a major hyperparameter for the training of the data. An epoch number is always an integer value that is represented after every cycle. An epoch plays a major role in the learning process where repetition of trial and error procedure is required. Validation errors can be controlled by increasing the number of epochs. Epoch is also named as an early stopping hyperparameter.

Hyperparameter for Specific Models

Number of Hidden Units − There are various neural networks hidden in deep learning models. These neural networks must be defined to know the learning capacity of the model. The hyperparameter used to find the number of these neural networks is known as the number of hidden units. The number of hidden units is defined for critical functions and it should not overfit the learning model.

Number of Layers − Hyperparameters that use more layers can give better performance than that of less number of layers. It helps in performance enhancement as it makes the training model more reliable and error-free.

Conclusion

Hyperparameters are those parameters that are externally defined by machine learning engineers to improve the learning model.

Hyperparameters control the process of training the machine.

Parameters and hyperparameters are terms that sound similar but they differ in nature and performance completely.

Parameters are the variables that can be changed during the learning process but hyperparameters are externally applied to the training process and their values cannot be changed during the process.

There are various methods categorized in different types of hyperparameters that enhance the performance of the learning model and also make error-free learning models.

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