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From the beginning, Josefin was designed to be clean, simple and modern. Attention to detail was paramount throughout the design and build process. Clean typography with generous amounts of white space ensure an enjoyable reading experience, while also allowing stunning photography to shine.
Whether you use Josefin for a marketing site or to sell products, we’re confident Josefin will impress your site visitors.
Finally, a clean & modern Divi Child Theme using great typography and imagery. Class all the way.
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We believe in being
Beautiful & Simple
Features
Clean Typography
Josefin features the Josefin & Merriweather Google Fonts. The styling has been meticulously set to fit the aesthetics of the theme, providing a relaxing reading experience.
Styled Counters
We’ve taken extra care in styling the number counter feature Divi offers. We’ve included dark & light background options. Showcase your stats in style with the Josefin Child Theme.
Twitter Widget
We’ve utilized and styled the Rotating Tweets Plugin by Martin Tod. Display your tweets elegantly using this plugin and the Josefin Child Theme. We’ve included an instructional video on how to set this up.
Stunning Imagery
A photo is worth a thousand words. Parallax backgrounds combined with great use of whitespace allow the focus to be on your beautiful images. We’ve also styled a masonry layout as an option for your images.
Styled Form
We’ve also taken extra care in styling the contact form in Divi. We’ve included dark & light background options. Make your contact form stand out with the Josefin Child Theme.
Instagram Widget
We’ve utilized and styled the WP Instagram Widget by Scott Evans. Display your Instagram photos elegantly using this plugin and the Josefin Child Theme. We’ve included an instructional video on how to set this up.
Extra Animations
Divi comes with some nice animation options. We’ve added additional animation classes you can apply to your sections and modules. We’ve included an instructional video on how to use these animations.
Features Grid
Easily build a features grid with Josefin. We’ve created an instructional video with links to the CSS and a Divi Layout files. Import the layout into your site with a single click.
Superior Support
Your purchase comes with our superior support. Email us and we’ll respond to you as soon as possible. We also have video tutorials showing how to implement different features of the theme.
We believe in being
Beautiful & Simple
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Finally, a clean & modern Divi Child Theme using great typography and imagery. Josefin is class all the way.
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Carefully Crafted
Layouts & Modules
Featured Video
Video Title
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#1 Hits
Million Downloads
Ready to Rock
Josefin is sure to impress
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Photography & Images provided by Unsplash.com and DeathToTheStockPhoto.com
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We believe in being
Beautiful & Simple
Heading
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Lorem ipsum dolor sit amet, consectetur adipiscing elit.
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+ Lines of Code
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Crafted Theme
Video Tutorials
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Friendly Support
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How to install Josefin Child Theme
Click here to check out our installation page. There you will find our step-by-step installation instructions, walk-through videos and FAQ’s.
Where to find great stock photography
If you don’t want to pay the $$$ for Shutterstock or Adobe Stock, some of our favorite sites are unsplash.com and deathtothestockphoto.com. You can also find some great photos at pixabay.com and pexels.com!
Installing the Josefin Google Font
Guess what? You don’t have to, it’s already installed! Booyah.
How to get support for Josefin
Submit a ticket to us via https://besuperfly.com/contact and we’ll respond to you as soon as possible.
Blog
Ethical AI: Addressing Bias and Fairness in Machine Learning Algorithms
It’s interesting to think that AI can express bias. But it’s true; Machine Learning bias, also called Artificial Intelligence bias, can occur when the algorithm delivers biased results. AI systems that generate biased outcomes often mirror and reinforce the existing human biases present in society, which is not beneficial.
The origins of bias within the system are complex; however, there are best practices for those using AI to help avoid biased results. There are also regulations and standards to protect users, which will evolve as technology evolves. In an era where AI shapes decisions across industries, ensuring fairness and eliminating bias in machine learning algorithms is of the utmost importance. We all take responsibility for developing and using systems that perform efficiently and uphold justice and integrity, creating trust in the future of technology.
Understanding Bias in AI
To better understand bias in AI, let’s explore the origins of bias in data collection and algorithm design and its impact on decision-making processes.
Bias in data collection
AI data collection can be biased when the data used to train the AI doesn’t fully represent all kinds of people or situations. For example, if an AI is trained mostly on data from one group (like men instead of women or people from one country instead of a diverse mix), it may learn patterns that don’t work well for everyone.
Biased data can occur for various reasons, including unbalanced data, historical bias, data gaps, and labeling errors. It can also occur at various stages, including the initial training data, the algorithm itself, or the predictions generated by the model.
In an article for IBM, the author states, “When bias goes unaddressed, it hinders people’s ability to participate in the economy and society. It also reduces AI’s potential. Businesses cannot benefit from systems that produce distorted results and foster mistrust among people of color, women, people with disabilities, the LGBTQ community, or other marginalized groups of people.”
Bias in AI needs to be addressed at its core: the algorithm’s design.
Algorithm design
Algorithm design plays a critical role in whether or not AI data is biased. How the algorithm is built deeply affects how it processes and interprets data. Certain design choices can introduce or amplify bias even if the data is unbiased.
If the algorithm selects or prioritizes certain features, like race or gender, inappropriately, it may produce biased outcomes. Features’ skewed weighting or biased data interpretation can also lead to unfair results. Additionally, if the algorithm’s goals focus solely on efficiency or accuracy without considering fairness, it can reinforce existing inequities.
Impact on decision-making
AI bias can significantly impact decision-making processes.
For example, if a biased algorithm is involved in the hiring process within job sectors such as healthcare or law enforcement, it might favor certain groups while disadvantaging others based on race, gender, or socioeconomic status. Unfortunately, this can lead to unequal opportunities and important resources and services.
Strategies for Fairness
Various techniques and best practices exist to reduce bias in machine learning models.
Data pre-processing
In an article from the Journal of Engineering Research, the authors discuss data pre-processing techniques to address data bias. They share four preprocessing methods to remove discrimination in classification problems:
Suppression: Remove protected features and their proxies from the dataset.
Massaging: Change instance labels to reduce disparities and discrimination.
Reweighing: Assign weights to dataset instances to mitigate biases.
Sampling: Use under-sampling and over-sampling to adjust group representation based on protected features, using uniform and preferential sampling.
Algorithm adjustments
In addition, algorithm adjustments help keep AI data fair and unbiased by actively addressing potential sources of bias during the decision-making process.
Algorithms can produce more equitable outcomes by selecting relevant features and excluding biased ones, reweighing data points to balance representation, and incorporating fairness constraints. Regularly testing and correcting for bias ensures that AI systems don’t perpetuate harmful patterns.
Regular audits
Biased AI can lead to unethical outcomes; therefore, businesses and organizations must audit their machine-learning algorithms.
An example of machine learning auditing in the U.S. is the legislation that began in NYC on July 5th, 2023. According to an article, “This law requires employers in New York City to conduct an annual third-party AI “bias audit” of technology platforms they use for hiring or promotion decisions and to publish the audit findings on their websites.”
Although the law applies to one jurisdiction, experts believe other states will soon pass similar or broader legislation requiring AI bias audits. Future laws may mandate audits for age and disability bias, expanding beyond the current focus on gender and race in New York City’s Local Law 144.
Overall, these three strategies in combination will reduce the
The Role of Transparency
Transparency equals increased trust, and with the rise of AI and all of its potential, stakeholders must clearly understand how important decisions are made. However, transparency is more than about building trust; it also helps ensure accountability, helps businesses and organizations align with ethics and laws, and ultimately helps reduce data bias.
Explainable AI (XAI) models are processes that allow users to understand and trust the reasoning behind AI and machine learning decisions.
IBM tech experts say, “It is crucial for an organization to have a full understanding of the AI decision-making processes with model monitoring and accountability of AI and not to trust them blindly. Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep learning, and neural networks.”
A specific example of this is the XAI Decision Tree model.
How it works: Decision trees break down decision-making into a series of choices based on the input data, with each “branch” representing a potential outcome or decision. This structure is easy to follow, allowing stakeholders to trace the reasoning behind a prediction.
Since decision trees clearly show how decisions are made at each step, they are highly interpretable. Stakeholders can see exactly how each input affects the outcome.
For a visualization of this decision tree model, Toward Data Science published a much more detailed example of how this works.
Regulation and Accountability
The US still needs a comprehensive federal law governing AI. However, while AI regulation is still in its early stages, a few noteworthy ethical guidelines and proposed acts to protect users exist.
- The Algorithmic Accountability Act is a proposed U.S. law that would mandate companies to provide transparency about their algorithms and ensure they are fair and free from bias.
- The Blueprint for an AI Bill of Rights This blueprint, by the White House Office of Science and Technology Policy, outlines ethical principles to protect individuals from harmful AI, including the rights to transparency, fairness, and privacy.
In addition to those in the works, several ethical frameworks have been developed to guide AI development and use. These frameworks focus on fairness, privacy, transparency, and accountability.
- Ethical guidelines such as the Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) principles focus on mitigating discrimination and promoting equitable treatment in AI decision-making.
- Regarding accountability, AI developers, organizations, and users must ensure that there is clear responsibility when an AI system causes harm. High-risk AI systems should have human oversight so that humans can intervene if the system behaves unexpectedly.
Published just recently (April 2024) in the JMIR Med Inform journal, experts discuss how research is helping AI fairness, accountability, transparency, and ethics progress.
“Fairness, accountability, transparency, and ethics (FATE) research focuses on evaluating the fairness and transparency of AI and ML models, developing accountability metrics, and designing ethical frameworks.”
The study critically assesses methodologies and practices, identifying successes and gaps while encouraging further innovation to create more ethical and fair AI systems. Key recommendations include using diverse data sets, integrating FATE principles throughout AI development, and improving evaluation metrics to account for all stakeholders. The study has significant implications for researchers, practitioners, policymakers, and the public, advocating for AI technologies that are both ethically sound and technologically advanced, fostering trust and transparency in healthcare applications.
Diverse Data and Inclusive Design
Diverse datasets and inclusive AI design teams are crucial in creating algorithms that reflect a broader range of human experiences and perspectives.
When diverse datasets are used, and inclusive teams are involved, stakeholders and end-users are more likely to feel confident that the machine learning algorithm is fair and representative of their needs.
Inclusive practices also enhance accountability, ensuring that the design and deployment of AI systems are examined by individuals from various backgrounds who can help ensure that the technology benefits everyone.
Conclusion
Eliminating bias in machine learning algorithms is imperative for ensuring fairness and trust in technology in today’s AI-driven world. By using diverse datasets and fostering inclusive design teams, organizations can create algorithms that reflect various human experiences, promoting accountability and transparency. As AI continues influencing key industry decisions, we must prioritize ethical development to build equitable, trustworthy systems for all.
AR and VR in Education: Transforming Learning Experiences
Augmented Reality (AR) and Virtual Reality (VR) are completely revolutionizing education by creating more engaging and immersive environments for all levels of students. The chalkboard has retired, and even paper and pencil aren’t always the best way for students to learn and show mastery.
AR and VR allow students to use technology while interacting in hands-on lessons and learning in virtual worlds or enhanced real-world environments. From exploring historical landmarks to conducting virtual science experiments, these technologies foster deeper engagement and understanding. Discover how these cutting-edge tools transform the classroom and make students and educators more excited about learning.
Immersive Learning Environments
AR overlays digital elements onto real-world environments, while VR immerses users entirely in virtual worlds. These technologies create engaging, interactive spaces that enhance students’ understanding of complex subjects. Instead of opening a textbook or going onto a learning platform on a Chromebook, for example, students can immerse themselves in virtual experiences.
Instead of reading about MLK Jr.’s I Have a Dream Speech, they can watch it through a headset and be in the crowd. These augmented or virtual experiences add emotion and excitement to a lesson while increasing comprehension and memory recall, especially for those visual and kinesthetic learners.
Here are a few examples of other subject areas and how they can integrate an immersive learning environment with AR and VR:
- Math: VR tools can help visualize abstract math concepts like geometry by allowing students to manipulate 3D shapes and interact with complex equations.
- Geography: VR takes students on virtual field trips to places like the Amazon rainforest, Mount Everest, or even outer space, enhancing geographical understanding.
- Art and Design: Using AR, students can view famous artworks in 3D, manipulate objects in space, or build digital sculptures, helping them grasp artistic techniques.
- Science: Biology students can explore organs, systems, and cells in detail in a 3D human body model or perform virtual dissections.
Getting the same in-depth experience is impossible by simply reading or watching a video.
Personalized Learning Paths
Educators can leverage AR and VR to tailor educational experiences to individual student needs and learning styles. The technologies allow lessons to be geared toward specific learning styles and for academic and developmental needs.
First, students can work at their own pace. In a perfect world, every student would get their own VR headset and be able to go with the flow, dive deeper into areas of interest, or get extra support where needed.
Next, AR and VR create a more accessible environment for those with disabilities. For example, an article in zSpace shares that Ray-Ban Meta smart glasses and similar AR-powered devices offer AI-driven features like voice-activated calls, texts, and real-time translations, benefiting users with disabilities. AR glasses, such as the Arges from CELLiCO and XRAI Glass, provide tailored support for students with low vision or hearing impairments by enhancing vision or displaying real-time subtitles. These advancements can significantly improve accessibility and inclusion in education for students with disabilities.
Lastly, educators can differentiate lessons for students of different levels. These are examples of how this looks in the classroom:
- Reading Comprehension: Using AR, younger or lower-ability readers can interact with 3D pop-ups of story elements or animated characters, while older or more advanced students can dive into more challenging analysis tools that highlight themes or help visualize settings, adapting to each student’s reading level.
- Language Learning: AR apps can create immersive language experiences by overlaying translations in real-time on objects or providing interactive conversations with virtual characters. Students at different fluency levels can practice at their own speed with tailored vocabulary and sentence complexity.
This flexibility makes learning more engaging, adaptive, and meaningful for every student.
Enhancing Practical Skills
The future is now, and AR and VR simulations provide hands-on training in fields like medicine, engineering, and the arts.
Examples of ways industries and educational fields are utilizing AR and VR are:
- Medical students can perform virtual surgeries with lifelike precision.
An actual real-life example: The University of Oxford uses VR simulation for medical students and doctors working in the John Radcliffe Hospital. Rather than integrating VR in a defined simulation space, the faculty implemented mobile VR trolleys to transport their equipment wherever needed.
- Engineers can build and test complex structures in immersive environments.
The University of Massachusetts Amhurst offers an entire course called Augmented and Virtual Reality Design for the students of the College of Engineering.
- Artists explore boundless creative possibilities in virtual studios.
The Savannah College of Arts and Design (SCAD) offers students AR/VR resources, a green screen, a motion media lab, and more than 800 computers networked to reduce render times. In “The Shed,” immersive reality classrooms and labs host the latest technology for creating groundbreaking visual experiences.
These are only a few of the dozens of ways that colleges and universities use AR and VR technologies.
Breaking Down Geographical Barriers
Virtual field trips and global classroom collaborations break down barriers and connect students with distant cultures, histories, and environments like never before.
Not only can they take virtual field trips, which have been available to students for some time, but they can collaborate with peers from around the world, sharing insights, learning together, and gaining a deeper understanding of diverse perspectives.
For example, using platforms like Mozilla Hubs or AltspaceVR, students from different countries can meet in virtual environments to work on global challenges like climate change, poverty, or sustainability. For instance, students from a school in Japan might collaborate with a class in Kenya to design eco-friendly housing models in a shared virtual space, combining knowledge of local conditions with global insights.
Through platforms like Labster or CoSpaces, students worldwide can work together in virtual science labs. For example, a group of students in Germany and India could collaboratively conduct a virtual chemistry experiment, sharing results and learning from each other’s approaches to scientific inquiry.
Thankfully, there are various ways students can connect with cultures and collaborate with others to explore novel environments.
Boosting Engagement and Retention
AR and VR positively impact student motivation, memory retention, and overall engagement in the learning process.
This hands-on approach keeps students deeply engaged, helping them retain information longer and stay excited about learning. The thrill of exploring virtual worlds or manipulating digital objects in real-time captivates their curiosity and sparks a passion for knowledge.
For instance, a March 2019 report from EdTech highlights a study in which students in a mixed-reality biology class outperformed their peers in terms of scores. Additionally, AR and VR have been shown to boost memory retention—EdTech cites a recent study revealing that students learning in immersive environments like VR experienced nearly a 9 percent increase in retention.
Allowing students to explore with multiple senses increases engagement, comprehension, motivation, memory retention, and recall.
Conclusion
AR and VR technologies are unlocking new, immersive ways for students to learn, collaborate, and engage with content like never before. From personalizing learning paths to breaking down geographical barriers, AR and VR transform education by making it more interactive, inclusive, and exciting for all learners. Whether exploring new worlds, mastering complex skills, or collaborating with global peers, these tools empower students to reach their full potential in ways we could only dream of.
Celebrating ATP’s Journey – A Story of Growth, Community, and Impact
In the bustling tech landscape of 1999, amidst the aftermath of the dot-com bust, a small yet determined networking group emerged in Atlanta. This group, inspired by a similar successful organization in Denver, aimed to support the struggling tech community in Atlanta. Thus, the Atlanta Telecom Professionals (ATP) was born, thanks to the vision of Bob Speers and Kevin Nicholas, with early support from Brian Nagel and a few dedicated technology professionals.
Networking and Early Success
The early days of ATP were marked by a strong focus on networking, providing a lifeline for out-of-work tech workers in Atlanta. The inaugural event at California Pizza Kitchen in Dunwoody was a resounding success, drawing an overwhelming turnout and cementing ATP’s place in the Atlanta tech community. It quickly became evident that the events needed to offer more than just networking opportunities.
From Networking to Professional Development
The spring of 2000 saw ATP’s first event focused on telecom technology, attracting an even larger crowd and setting the stage for future growth. As the year progressed, ATP’s popularity soared, leading to the formation of an official board. The organization established two key pillars: networking and professional development. Shortly thereafter, the ATP Executive Advisory Board was created, providing strategic guidance and helping to steer the organization towards greater heights.
The Birth of the ATP’s Signature Events
However, ATP’s journey was not without its challenges. Financial difficulties almost brought the organization to a halt after its first year. The co-founders personally funded the organization for a brief period until the board reinvented itself and introduced a third pillar: the Recognition of Excellence.
This led to the birth of the ATP Awards Gala, a marquee event that gained significant backing and recognition from industry leaders like Ralph de la Vega, then CEO of Cingular Wireless and later President of AT&T. With Ralph’s support, ATP gained more exposure and took a giant leap forward.
The success of the ATP Gala was soon followed by the introduction of the CIO Round Table and the ATP Golf Event, both of which became major highlights on the ATP calendar. Monthly events continued to thrive, and the CIO Round Table marked ATP’s first partnership with the Technology Association of Georgia (TAG), under the leadership of Tino Mantella.
Giving Back and the Launch of ATP Cares
As ATP’s membership and sponsorship base grew, so did the demand for board succession. Around this time, ATP introduced ATP Cares, the philanthropic arm of the organization. ATP Cares has donated thousands of dollars to tech-focused charities, including college scholarships for tech students and support for various tech groups within the community. One of the standout beneficiaries of ATP Cares has been Inspiredu, an organization dedicated to providing technology hardware and opportunities to inner-city kids and their families.
Strategic Growth Plan
In 2023, the ATP Board embarked on a five-year strategic plan aimed at further growth and impact. Today, ATP stands as one of the largest technology non-profit groups in the southeast, boasting a robust membership, a strong sponsor base, and a significant impact on the tech community. The mission of ATP has evolved beyond just hosting events; it now includes a focus on CONNECT, LEARN, and GIVE BACK, unifying inclusivity in tech, as well as a continued commitment to the ATP founding pillars, of Networking, Professional Development, Recognition of Excellence, and ATP Cares.
A Bright Future of ATP’s Continued Impact and Mission
As we reflect on ATP’s remarkable journey, we extend our heartfelt thank you to the members, sponsors, and volunteers who have made it all possible. Your dedication and support have been the driving force behind ATP’s success, and together, we look forward to an even brighter future.
Take a trip down memory lane! Check out these photos from ATP’s early years, capturing the moments that laid the foundation for where we are today.
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