“Adriano Pedrosa’s Venice Biennale: ‘Stranieri Ovunque

“Adriano Pedrosa’s Venice Biennale: ‘Stranieri Ovunque

Adriano Pedrosa's Venice Biennale: 'Stranieri Ovunque

Potential Future Trends in the Art Industry

The art industry is constantly evolving, reflecting the changing trends and interests of society. As we look into the future, there are several key themes that are likely to shape the future of art and the way it is consumed and experienced by the public. These themes include globalization, diversity, technology, and sustainability.


Globalization has already had a significant impact on the art world, with artists and artworks becoming more accessible and visible around the world. This trend is likely to continue, as advancements in technology facilitate the global dissemination of art. With the rise of online platforms and social media, artists have the opportunity to reach a wider audience than ever before. This increased global exposure not only provides artists with new opportunities for recognition and sales but also allows for cross-cultural exchange and dialogue.

One potential future trend related to globalization is the increased collaboration between artists from different countries and regions. As barriers continue to be broken down, artists are likely to seek inspiration and collaboration beyond their own borders, resulting in new and exciting artistic expressions. We can expect to see more international art exhibitions and collaborations that celebrate diversity and cultural exchange.


The art world is becoming more diverse, both in terms of the artists themselves and the themes and perspectives they bring to the table. This trend is driven by a growing recognition of the importance of diverse voices and experiences in shaping our understanding of the world. As we move forward, it is likely that the art industry will continue to embrace diversity and give marginalized artists and voices a platform to be heard.

A potential future trend related to diversity is the increased representation of underrepresented groups in art. We can expect to see more exhibitions and collections that highlight the work of female artists, artists of color, LGBTQ+ artists, and artists from marginalized communities. This shift in representation will not only bring a fresh perspective to the art world but also challenge traditional notions of art and beauty.


Technology has already had a significant impact on the art industry, and this trend is likely to continue in the future. Advancements in virtual reality, augmented reality, and artificial intelligence have the potential to revolutionize the way art is created, exhibited, and experienced.

One potential future trend related to technology is the integration of digital art and traditional art forms. We can expect to see more artists experimenting with new technologies to create innovative and interactive artworks. Virtual reality exhibitions and augmented reality experiences may become more common, allowing art enthusiasts to immerse themselves in a virtual art world.


Sustainability has become a pressing issue in many industries, including the art world. As awareness of climate change and environmental degradation grows, artists and institutions are likely to prioritize sustainability in their practices.

A potential future trend related to sustainability is the emergence of eco-friendly art materials and practices. Artists may turn to organic and recycled materials, as well as renewable energy sources, to create their artworks. Art institutions may also adopt sustainable practices, such as reducing waste and carbon emissions.

Predictions and Recommendations for the Industry

Based on the key themes discussed, several predictions and recommendations can be made for the future of the art industry:

  1. Embrace globalization: Art institutions should continue to support and promote international collaborations and cross-cultural exchange. This can be done through hosting international exhibitions, artist residencies, and exchange programs.
  2. Prioritize diversity: Art institutions should actively seek to diversify their collections, exhibitions, and staff. This includes investing in the work of underrepresented artists and creating opportunities for marginalized communities to engage with art.
  3. Embrace technology: Artists and institutions should embrace new technologies and find creative ways to integrate them into their practices. This can include experimenting with virtual reality, augmented reality, and other digital mediums.
  4. Promote sustainability: Artists and institutions should prioritize sustainability in their practices. This can involve using eco-friendly materials, adopting renewable energy sources, and implementing waste reduction strategies.

The future of the art industry is exciting and full of possibilities. By embracing globalization, diversity, technology, and sustainability, the art world can continue to evolve and thrive. Through meaningful collaborations, representation, technological innovation, and sustainability practices, the industry can create a more inclusive and sustainable future for artists and art enthusiasts alike.

– Article by Adriano Pedrosa in the April 2024 issue of Apollo
– “Globalization and the Art World” by Edward Winkleman
– “Diversity in the Art World: Why It Matters” by Cori Sherman North

“Efficient Training Acceleration for Large-Scale Deep Learning Models”

“Efficient Training Acceleration for Large-Scale Deep Learning Models”

Expert Commentary: Accelerating Training of Large-scale Deep Learning Models

The article highlights the increasing demand for computing power and the associated energy costs and carbon emissions when training large-scale deep learning models such as BERT, GPT, and ViT. These models have revolutionized various domains, including natural language processing (NLP) and computer vision (CV). However, the computational requirements for training these models are exponentially growing, making it imperative to develop efficient training solutions.

The authors propose a multi-level framework for training acceleration, based on key observations of inter- and intra-layer similarities among feature maps and attentions. The framework utilizes three basic operators: Coalescing, De-coalescing, and Interpolation, which can be combined to build a V-cycle training process. This process progressively down- and up-scales the model size and transfers parameters between adjacent levels through coalescing and de-coalescing. The goal is to leverage a smaller, quickly trainable model to provide high-quality intermediate solutions for the next level’s larger network.

An important aspect of the framework is the interpolation operator, which is designed to overcome the symmetry of neurons caused by de-coalescing. This helps improve convergence performance. The experiments conducted on transformer-based language models such as BERT, GPT, and a vision model called DeiT demonstrate the effectiveness of the proposed framework. It achieves a reduction in computational cost by approximately 20% for training BERT/GPT-Base models and up to 51.6% for training the BERT-Large model, while maintaining performance.

This research addresses a crucial challenge in the field of deep learning, namely the high computational requirements for training large-scale models. By leveraging the inherent similarities within feature maps and attentions, the proposed framework significantly reduces training costs without sacrificing model performance. This has profound implications for both researchers and practitioners, as it allows for faster experimentation and deployment of state-of-the-art models, ultimately accelerating the pace of innovation in NLP, CV, and other domains.

Furthermore, the framework presents an interesting approach to managing computational resources in deep learning. By utilizing multi-level training and parameter transfer, it maximizes the efficiency of training processes. This aligns with the growing need for sustainable and energy-efficient AI systems, as reducing energy consumption and carbon emissions is critical for mitigating the environmental impact of deep learning.

In terms of future developments, it would be valuable to explore the applicability of the proposed framework to other types of deep learning models and domains. Additionally, investigating the potential for further reducing computational costs while maintaining or even improving performance would be an exciting avenue of research. As deep learning models continue to grow in size and complexity, finding efficient training strategies will remain a crucial area of investigation.

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