“Precarious Joys” exhibition at the Toronto Biennial of Art focuses on visibility and the joy in recognition while acknowledging the dangers involved.
55 artists explore themes such as citizenship, Indigenous erasure, climate disaster, gentrification, etc.
Artists, like Ahmed Umar, use their work to raise awareness and challenge societal norms.
The curators determined the show’s structure based on conversations with artists, focusing on shared concerns of home, polyphony, precariousness, and joy.
The exhibition reflects Toronto’s multicultural nature, diasporic history, and ongoing housing crisis.
There is a departure from traditional curatorial methodologies and an embrace of inherited art forms and traditions.
Indigenous artists play a prominent role, showcasing their talent, stories, and connection to the land.
The exhibition emphasizes the importance of community, tradition, and public intervention in preserving culture and history.
The third edition of the Toronto Biennial better integrates the stories of different communities and embraces separate histories as being adjacent.
Potential Future Trends and Predictions
Based on the key points of the text, several potential future trends and predictions can be made for the art industry:
Increased Visibility and Social Activism: Artists, like Ahmed Umar, will continue to use their work to raise awareness about important social and political issues. The desire to be seen and heard will drive artists to create impactful and provocative pieces that challenge societal norms and promote change.
Expanded Representation and Inclusion: Future exhibitions and biennials will focus on representing diverse voices and perspectives. Indigenous artists and artists from marginalized communities will have a more prominent presence in the art world, showcasing their talent, stories, and connection to the land. Art curators and organizers will strive to create platforms that are inclusive and representative of different cultures and backgrounds.
Integration of Tradition and Modernity: The incorporation of inherited art forms, traditional dance, and song will continue to be a trend in future exhibitions. Artists will explore innovative ways to merge traditional art forms with contemporary elements, creating a dialogue between the past and the present. This integration will help preserve cultural heritage while adapting to modern artistic expressions.
Collaboration and Community Engagement: Future exhibitions will prioritize collaboration and community engagement. Artists and curators will involve local communities, listening to their stories and concerns, and incorporating their voices into the exhibition. This collaboration will foster a sense of ownership and pride among community members, preserving culture and tradition through collective efforts.
Exploration of Personal and Collective Identity: Artists will continue to explore themes of identity, citizenship, and belonging in their work. The concept of home and the complications surrounding it will be a significant focus in future exhibitions. Artists will use their art as a medium to express their personal experiences and struggles, as well as address broader societal issues related to identity and belonging.
Recommendations for the Industry
To adapt to the potential future trends and ensure the growth and success of the art industry, the following recommendations can be made:
Promote Diversity and Inclusion: Art organizations and institutions should actively promote diversity and inclusion in their exhibitions, events, and programs. This includes representing artists from diverse backgrounds and creating platforms for underrepresented voices to be heard. It is essential to create an environment where artists from all backgrounds feel welcome and supported.
Support Emerging Artists and Community Initiatives: Art organizations should focus on supporting emerging artists and community initiatives. This can be done through mentorship programs, grants, and residencies that provide resources and opportunities for artists to showcase their work. Collaborations with local communities and community-based organizations can help bridge the gap between art and society, making art more accessible and relevant to the community.
Invest in Art Education and Outreach Programs: Investing in art education and outreach programs is crucial for nurturing and engaging future artists and audiences. This includes providing art education in schools, organizing workshops and art events for the community, and partnering with educational institutions and community organizations to promote art appreciation and understanding.
Encourage Environmental and Social Responsibility: Art organizations should prioritize environmental and social responsibility in their practices. This includes promoting sustainable art practices, supporting artists who address environmental and social issues, and incorporating eco-friendly materials and practices in exhibitions and events.
Embrace Technology and Digital Platforms: Art organizations should embrace technology and digital platforms to expand their reach and engage with a wider audience. This includes using social media, virtual exhibitions, and online platforms to showcase and promote art. Embracing technology can also provide opportunities for collaboration, networking, and exposure to a global audience.
References
“‘Precarious Joys’: Toronto Biennial of Art Examines the Joy in Recognition and the Dangers Involved” by Alex Quicho (2023), Art Asia Pacific, Issue 121
“The Third Toronto Biennial of Art is Both Familiar and Unprecedented” by Ben Valentine (2023), Canadian Art, October 24, 2023
“The Toronto Biennial of Art Spreads Out” by Sky Goodden (2023), Momus, November 28, 2023
The art industry is constantly evolving, with new trends and movements emerging every year. As technology advances and societal attitudes change, the art world also adapts to these shifts. In this article, we will explore some potential future trends that are likely to shape the industry in the coming years.
1. Increased Accessibility through Digital Platforms
One of the key trends we can expect to see in the art industry is an increased emphasis on digital platforms. As the internet continues to connect people from all over the world, artists and galleries are recognizing the value of reaching a global audience. Online art galleries and digital platforms allow artists to showcase their work to a much larger audience, without the limitations of physical space.
Furthermore, virtual reality and augmented reality technologies are likely to play a significant role in the future of art exhibitions. These immersive experiences can transport viewers into virtual galleries, allowing them to explore artworks in a more interactive and engaging way. This increased accessibility will not only benefit artists and galleries but also art enthusiasts and collectors who may not have the means or opportunity to visit physical exhibitions.
2. Embracing Diversity and Inclusion
The art industry has long been criticized for its lack of diversity and inclusion. However, there is a growing movement calling for change and representation in the art world. In the future, we can expect to see greater recognition and support for artists from underrepresented communities, including women, people of color, and LGBTQ+ artists.
Galleries and museums will likely prioritize showcasing diverse perspectives and narratives. This can lead to a more inclusive art industry, where artists from diverse backgrounds are given equal opportunities to thrive. To support this trend, art organizations should actively seek out and promote artists from underrepresented communities and provide them with the necessary resources and platforms to showcase their work.
3. Environmental Sustainability in Art Practices
As sustainability becomes a pressing global issue, the art industry is also starting to address its environmental impact. Artists and galleries are exploring eco-friendly materials and practices to reduce their carbon footprint. This includes using recycled or organic materials, adopting energy-efficient technologies, and promoting sustainable exhibition practices.
In the future, we can expect to see a greater emphasis on using art as a medium for raising awareness about environmental issues. Artists may focus on creating works that highlight the beauty of nature, challenge consumerism, or address climate change. This trend aligns with the growing global concern for the environment and can help initiate important conversations and mobilize action.
Predictions and Recommendations for the Industry
Based on these potential future trends, there are several predictions and recommendations that can be made for the art industry:
Invest in digital platforms: Galleries and artists should prioritize building a strong online presence and investing in digital platforms to reach a global audience.
Support diversity in art: Art organizations should actively seek out and promote artists from underrepresented communities, ensuring their voices are heard and valued.
Embrace sustainability: Artists and galleries should adopt environmentally friendly practices, using sustainable materials and promoting awareness about environmental issues through their work.
Collaborate across industries: The art industry should collaborate with other fields, such as technology or environmental organizations, to create innovative and impactful projects.
Invest in education and outreach: To cultivate a new generation of art enthusiasts, galleries and museums should invest in educational programs and outreach initiatives that make art accessible to a wider audience.
Conclusion
The art industry is poised for exciting changes in the coming years, driven by advancements in technology, changing societal attitudes, and a growing concern for the environment. By embracing digital platforms, diversity, and sustainability, the industry can evolve and adapt to meet the needs of a diverse and interconnected world.
Sagan’s novel “Contact” from 1985 explores humanity’s first contact with an advanced alien species through a radio signal that contains a human television broadcast. This theme of extraterrestrial communication has fascinated researchers, scientists, and enthusiasts for decades. As technology continues to advance at a rapid pace, the potential for future trends in this field becomes even more intriguing. In this article, we will analyze the key points of Sagan’s work and explore potential future trends related to these themes.
One of the key points of “Contact” is the use of radio signals for interstellar communication. This idea is not new and has been a prominent topic in the search for extraterrestrial intelligence (SETI) research. Scientists have been scanning the skies for radio signals that may indicate the presence of intelligent life beyond Earth. The future trend in this area is the development of more advanced and sensitive radio telescopes capable of detecting and analyzing even fainter signals. With the advancements in technology, the sensitivity and range of these telescopes will likely improve, increasing the chances of detecting potential alien signals.
Another key point in “Contact” is the discovery of a human television broadcast within the radio signal. This raises the question of whether our television and radio signals have already reached extraterrestrial civilizations. As we continue to enhance our broadcasting capabilities, the likelihood of these signals reaching other star systems also increases. However, the vast distances involved in interstellar communication pose significant challenges. One potential future trend is the development of more efficient and focused broadcasting technologies that can direct signals towards specific star systems, increasing the probability of contact.
Sagan’s novel also explores the idea of deciphering alien messages. In “Contact,” the protagonists analyze the radio signal to unravel its hidden message. This concept aligns with the field of cryptography, which deals with encoding and decoding secret messages. In the future, advancements in artificial intelligence and machine learning may enable us to decode complex alien messages more effectively. AI algorithms could help identify patterns, understand the underlying structure of the message, and assist in deciphering its meaning.
Additionally, “Contact” delves into the societal, philosophical, and religious implications of interstellar communication. The discovery of intelligent extraterrestrial life would undoubtedly have profound effects on humanity. It could reshape our understanding of our place in the universe, challenge religious beliefs, and prompt existential questions. Future trends in this area would involve deepening the interdisciplinary exploration of these implications, involving not only scientists but also philosophers, theologians, and sociologists.
In conclusion, Sagan’s novel “Contact” raises several key points related to interstellar communication. Future trends in this field include advancements in radio telescope technology, more efficient broadcasting methods, the development of AI for decoding alien messages, and interdisciplinary exploration of the societal and philosophical implications. As technology continues to progress, the possibility of making contact with alien civilizations becomes increasingly plausible. However, the future remains uncertain, and while we eagerly explore the unknown, it is important to approach these discoveries with an open mind and a commitment to responsible research and communication.
References:
1. Sagan, Carl. “Contact.” Simon & Schuster, 1985.
2. “SETI Research.” SETI Institute, www.seti.org.
3. Shostak, Seth. “Confessions of an Alien Hunter.” National Geographic Society, 2017.
Title: The Future of Public Transportation in the Digital Age
Introduction
Public transportation plays a crucial role in modern cities by providing efficient and sustainable mobility solutions to commuters. With advancing technology and changing societal needs, the transportation industry is poised for significant transformations. This article explores key themes and predicts future trends in the industry, along with recommendations to navigate these changes successfully.
1. Enhanced Connectivity and Seamless Transfers
One of the key trends shaping the future of public transportation is the focus on enhancing connectivity and providing seamless transfer options. With the increasing availability of real-time data and smart technologies, commuters can expect more accurate and up-to-date information about routes, delays, and alternative modes of transport. This will enable them to make informed decisions and experience a smoother journey.
Prediction:
In the near future, we can expect integration between various transportation modes, such as buses, trains, and ride-sharing services. This integration will enable commuters to plan their entire journey from start to finish, including transfers, in a single seamless transaction, reducing travel time and improving convenience.
Recommendation: Transportation authorities should invest in modernizing their digital infrastructure and collaborating with technology providers to ensure the effective integration of diverse transportation options. This will require open data policies, standardized protocols, and user-friendly mobile applications.
2. Shift Towards Electrification and Sustainable Solutions
Fueled by environmental concerns and advancements in battery technology, the transportation industry is gradually shifting towards electrification. Electric buses and trains are becoming more prevalent, reducing greenhouse gas emissions and improving air quality in urban areas. Additionally, there is an increasing emphasis on renewable energy sources to power these vehicles, making transportation more sustainable overall.
Prediction:
In the coming years, we can expect an accelerated adoption of electric vehicles across various modes of transportation, including buses, trains, and even shared mobility options like e-scooters and bicycles. This shift will help cities achieve their climate goals and create healthier and greener urban environments.
Recommendation: Governments and transportation authorities should invest in the expansion of charging infrastructure to support the growing fleet of electric vehicles. Incentives and subsidies for electric vehicle adoption should also be implemented to encourage both operators and commuters to embrace sustainable transportation options.
3. Integration of Mobility-as-a-Service (MaaS)
Mobility-as-a-Service (MaaS) is an emerging concept that aims to integrate various transport modes into a single platform accessible through a mobile application. Commuters will have the flexibility to choose between different transportation options based on their preferences, cost, and convenience. MaaS has the potential to revolutionize public transportation by offering personalized and on-demand mobility solutions.
Prediction:
In the future, MaaS platforms will become more sophisticated, integrating multiple transportation providers, payment systems, and even non-traditional modes of transport like shared bicycles and e-scooters. These platforms will provide commuters with seamless door-to-door experiences, reducing the reliance on private vehicles and alleviating congestion in cities.
Recommendation: Governments and transportation authorities should actively collaborate with service providers and technology companies to establish open standards and protocols for MaaS platforms. Public-private partnerships can drive innovation and ensure seamless integration, benefiting both providers and commuters.
Conclusion
The future of public transportation holds exciting prospects for commuters and city planners alike. Enhanced connectivity, electrification, and the integration of MaaS platforms are just a few of the key trends that will shape the industry. By embracing these trends and working towards sustainable and efficient solutions, cities can create a transportation network that is accessible, convenient, and environmentally friendly.
References:
Smith, J. (2019). The Role of Digital Technologies in Public Transportation. Journal of Transport and Land Use, 12(1), 457-473.
Du, J., Li, M., Xu, M., & Zhang, H. (2020). Opportunities and challenges of electric vehicles for urban environment and sustainability. Journal of Cleaner Production, 258, 120835.
Hensher, D. A., & Mulley, C. (2019). Mobility as a Service (MaaS): Achievements, challenges and opportunities. Transport reviews, 39(3), 247-267.
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Actually, it’s both possible
This Article was originally published before on YOZM-IT as Korean
Various way of data science
There are many programming languages in the world and software that utilizes them. And those play an important role in “Data science”.
For example, if you’re using funnel analysis to improve your product, you might want to
Compare the bounce rates of funnel stages before and after an event,
And perform a ratio test to calculate their statistical significance.
Image by the author
Meanwhile, data scientists have various career backgrounds and experiences. So They tend to use the methods they’re comfortable with, including Python, R, SAS and more.
We see this quite a bit, because in most cases, the software you use at the level of business doesn’t make much of a difference.
But what happens if you “produce different results by the software used?”
The following image shows the results of running a proportion test in R, Python, and STATA with example mentioned.
Image from the author and CAMIS project
You can see that even though we used the same values of 1000 and 123, the p-value, which indicates the significance of the proportion test, is slightly different for each method.
There are many reasons why the calculation value is different depending on the method used, such as
Different algorithms in the core logic of the programming language
Different default values of the parameters used in the function.
In the example above, if you change the value of the parameter correct in R and apply “Continuity correction” as using “correct = F” , you can see that the result is the same as in STATA.
Image from CAMIS project
Rounding
Next, I’ll introduce rounding for more general data analysis.
Image by the author
Similarly, you can see that the round changes its value depending on software.
If the fee is “0.5 billion”in some large financial transaction in business, the rounded cost could be zero or 1 billion, depending on how you calculate the rounding.
Another case could be Logistic regression, which various round can be reverse prediction.
Image from Wikipedia, edited by the author
Why is round different?
Let’s talk a little more about why this round is different.
Rounding as we usually perceive it means changing 0 ~ 4to 0, and 5 ~ 9 to 10, as shown below image.
And in decimal units, is rounding to the nearest whole number by changing .0 ~ .4999.. to 0 and .5 ~ .9999.. to 1.
However, there are a number of mathematical interpretations of when exactly 0.5 , and when it is a negative number.
Image from the Learning corner
For example, round(-23.5) should produce -23 or -24?
Both are possible, depending on the mathematical interpretation and it’s called as rounding half up and rounding half down respectively. We can take this a step further and round both positive and negative numbers closer to zero, or vice versa.
This means that round(-23.5) will round to -23, and round(23.5) will round to 23, or round to -24 and 24, respectively. These are represented by the names Rounding half toward zero, Rounding half away from zero, respectively.
Finally, there are methods called Rounding half to even and Rounding half to odd, which mean that we want to consider the nearest integers to be even and odd, respectively.
In particular, the Rounding half to even method also goes by the names Convergent rounding, Statistician’s rounding, Dutch rounding, Gaussian rounding, and Bankers’ rounding, and is one of the official standard methods according to IEEE 754.
Bankers’ rounding
Bankers’s rounding, is default method in R , so Let’s breif a little bit more.
The image below shows the result of rounding from 0.0 to 2.0.
Image from the author
While this may seem like a good idea, there is actually a problem. Because .5 is unconditionally rounded to the next integer, there is an unconditional bias towards rounding to a “+ value”.
I don’t know the exact reason for this, but one theory is that the US IRS used to use this rounding to collect taxes and was sued for unfairly profiting by collecting more taxes from people who were .5 off, so they lost the case and changed to rounding to the nearest even (or odd) number to match the .5 rounding.
This means that by modifying the rounding as shown below, we can avoid the bias that was previously occurring.
The problem with different results
In recent years, industries in various domains, including pharmaceuticals and finance, have been trying to switch from “commercial” software such as SPSS, SAS and STATA to “open source” software such as Python, R and Julia .
And as rounding mentioned earlier, diffrent result issue by software has been also raised which can create problems in terms of reproducibility, uncertainty, accuracy, and traceability.
So if you’re utilizing multiple softwares, you should be aware of why they produce different results, and how you can use them to properly
CAMIS project
Image from CAMIS project
CAMIS stands for Comparing Analysis Method Implementations in Software.
This project compares the differences in softwares (or programming languages) and make standards to produce the same results.
The core area of the project is the “statistical computation” part, so most contributions come from the data science leaders who have strong understanding with it.
But CAMIS is also an open source project, that is not restricted and maintained with various people through regular discussions, collaboration, and sharing of project progress.
Below is one of the comparisons published on the CAMIS project’s webpage, which reviews how a one sample t-test is run with each software, what the results are, and how the results are compatible with each other.
Image from CAMIS project
The CAMIS project was started by members who interested in “SAS to R” in the medical and pharmaceutical industry. So it mainly focuses on R and SAS along major statistical data analysis, but recently it’s also working on how to use Python for data science in a broader domain of the industry.
Not only clasiccal methods such as Hypothesis tests, Regression analysis, but modern methods in data science such as Bayesian statistics, Causal inference and novel implementations of existing methods (e.g. MMRM) are topic of interest in project.
Sessions are increasingly appearing at multiple data science conferences, where many researchers and contributors are encouraged to promote, contribute and utilize it as a reference.
Finally, the CAMIS project is also collaborating with academia beyond the data science industry, as similar topics have been published in The American Statistician and Drug Information Association, among others.
Image from The American Statistician
The project is also currently working with students on a thesis entitled “A comparison of MMRM methodology in SAS and R software” and is open to collaborations and suggestions on other topics.
Summary
Various software used in data science. As the domain, the libraries or software used by an organization may be dependent on a particular language, which can sometimes be mixed with personal preferred methods. (in many cases, this doesn’t vary much at the level of the business)
However, if you’re not careful, the methods you use can lead to different results.
In this article, I’ve given you some examples of and reasons for differences in the methods used by different software for calculations, and introduced the CAMIS project, a research project that aims to minimize them to ensure consistency in data analysis.
If you use different software in your data analytics work, it’s a good idea to take a look at them to understand the differences and try to find the optimal method for your purposes,
And if you work in data science in the field, I highly recommend that you take an interstate in or contribute to the CAMIS project for a global collaborative experience.
Potential Future Insights and Developments of Data Science Software Variance
The article discusses the significant role of various programming languages in data science and how different software can yield different results. We learn that even when the same values of calculation are applied across different software like R, Python, and STATA, they can produce varying results. For instance, the bounce rates and ratio tests results of an event would vary under different platforms, despite using the same data. Crucially, the article underscores the importance of the Comparing Analysis Method Implementations in Software (CAMIS) project, which aims to standardize results across various softwares.
Implications of Software Differences in Data Science
Today, different industries including pharmaceuticals and finance are transitioning from commercial to open source software such as Python, R and Julia. However, the differing results issue by software raises concerns in relation to reproducibility, uncertainty, accuracy, and traceability. This variance could trigger significant divergences in forecast modeling and data interpretation within a single organization or amongst industry competition. Resolving this discrepancy necessitates understanding why different software produce varying results and discerning how to correctly and consistently utilize their functionalities.
Potential of ‘Rounding’ in Data Science
The article mentions the role and definition of ’rounding’ in data science especially when handling extensive data sets. We learn that the process of rounding can differ based upon the mathematical interpretations used. This, too, can yield differential results across platforms and software. The concept of ‘Rounding half toward zero’, ‘Rounding half away from zero’, ‘Rounding half to even’ and ‘Rounding half to odd’ in the context of both positive and negative numbers was also introduced in the discussion. Clearly, programming languages provide more than just a tool for implementation – they offer different philosophies of approach to problem-solving in data science.
The Role of the CAMIS Project
The Comparing Analysis Method Implementations in Software (CAMIS) project is an initiative aimed at addressing differences in software used in data science. By comparing diverse software and programming languages, the project seeks to develop a standard that achieves consistent results, thereby assisting industries in confidently transitioning from commercial software to open source software. The terms of the project are not restricted and involve a collaborative, progressive effort from various contributors. A primary focus of the project is on R and SAS alongside major statistical data analysis, and it also explores the use of Python for data science across wider industry domains.
Actionable Advice
If your work involves using different software for data analytics, it is advisable to review and understand the differences and nuances of your selected tools. Moreover, strive to find the optimal methods that align with your specific industry requirements.
If you work in data science, participating in or contributing to the CAMIS project is highly beneficial for both personal growth and collaborative knowledge sharing. Apart from staying updated with the latest developments, you can also lend your expertise to this significant cause.
Utilizing rounding correctly is crucial in data science. Awareness of the different types of rounding and how different software handle this can ensure the accuracy and reliability of your results.
The more well-versed you are with your chosen programming language and software, the more effectively you can minimize and address discrepancies in your work.
