What are the best smart thermostats in 2024?


How did open-source AI model growth change since 2022?

Since 2022, there has been a resurgence of open-source models owing to their lower costs, growing capabilities, and broader accessibility for developers and enterprises alike[1]. These models are freely available for anyone to use, modify, and build upon[1].

China is leading the open-source race, with three large-scale models released in 2025 – DeepSeek-R1, Alibaba Qwen-32B, and Baidu Ernie 4.5[1]. Open-source AI is fueling sovereign AI initiatives, local language models, and community-led innovation[1].

Space: Trends In Artificial Intelligence 2025 By Mary Meeker et. Al

What are the latest developments in renewable energy policy?

Transcript

Recent developments in renewable energy policy include the UK's Energy Act 2023, which aims to enhance energy security and support net zero ambitions while making long-term household bills more affordable through increased competition and smart appliances. Globally, the IEA reports that governments are now earmarking nearly USD 2 trillion for clean energy investments, with a focus on policies that promote renewable deployment, including carbon pricing and eliminating fossil fuel subsidies. Additionally, the COP28 pledge emphasizes the urgent need to triple global renewable capacity by 2030 and improve energy efficiency.

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What's the friendliest nomad location?

title: 'Best Cities for Digital Nomads In 2025: The Ultimate Guide'

Based on the sources, Chiang Mai stands out as a friendly nomad location due to its warm culture, affordable living costs, and reliable internet speeds, making it a favorite among remote workers looking for a peaceful lifestyle[2]. The city is known for its community of digital nomads who enjoy a mix of nature and city life, along with a plethora of cafes and coworking spaces that foster connections[1].

Medellín also receives praise for its welcoming digital nomad communities and mild climate, making it an attractive destination for those looking for both innovation and a laid-back atmosphere[2]. These characteristics contribute to making these cities particularly friendly environments for digital nomads.

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Quality frames for art display


Will we ever find el Dorado?

Transcript

The text states, 'There was a desperate search for El Dorado. They never found it because it never existed.' This indicates that the legend of El Dorado was built on distortions of rituals and myths surrounding gold in Colombia. Therefore, based on the information provided, it is clear that we will not find El Dorado, as it is a myth rather than a tangible place.

[1] standard.co.uk Favicon standard.co.uk
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Current Challenges in Cybersecurity

'a walkway between tall buildings at night'
title: 'Cybersecurity trends and challenges in 2024: the top game changers' and caption: 'a walkway between tall buildings at night'

The cybersecurity landscape is fraught with challenges that organizations must navigate to protect their assets and data effectively. Among these challenges are the emergence of advanced threats, the increasing complexity and sophistication of attacks, and a significant skills gap in the workforce.

Evolving Threat Landscape

Top Ransomware Trends for 2024-2025 Security Teams Can't Ignore
title: 'Top Ransomware Trends for 2024-2025 Security Teams Can't Ignore' and caption: 'a man standing in front of a screen'

Cybercriminals are diversifying their tactics, making the threat landscape more volatile than ever. The projected costs of cybercrime are expected to skyrocket, reaching $10.5 trillion by 2025, underscoring the urgent need for organizations to bolster their security measures to counteract these escalating risks[11]. One of the most prominent threats comes from ransomware, which continues to plague organizations across various sectors. In 2024, healthcare, government, and critical infrastructure sectors remain particularly vulnerable, as these industries are prime targets for ransomware attacks that maximize potential returns for cybercriminals[4][5].

The sophistication of attacks has also increased, with adversaries employing methods such as identity theft and phishing that exploit vulnerabilities within cloud systems[3]. Moreover, generative AI is anticipated to further empower cybercriminals, allowing for more convincing phishing attempts and automated malware, which could give attackers a significant advantage over defenders in the coming years[10][11].

Regulatory and Compliance Pressures

The NIS2 Essential Insights Every Organization Should Know
title: 'The NIS2 Essential Insights Every Organization Should Know' and caption: 'a woman wearing glasses looking at a computer screen'

As cyber threats evolve, so do the regulatory environments that govern how organizations must protect sensitive information. The introduction of stringent regulations, such as the EU's Network and Information Systems Directive 2 (NIS2), emphasizes the need for compliance with specific cybersecurity measures[2]. Organizations face ever-tightening legal requirements aiming to enhance cybersecurity resilience, which necessitates a proactive approach to incident reporting and compliance adherence.

The fragmented regulatory landscape also presents challenges, with 34% of leaders reporting that too many conflicting regulations create difficulties in compliance[11]. This complexity can result in inconsistencies in cybersecurity practices across different jurisdictions and industries, further complicating organizations' efforts to maintain security.

Skills and Workforce Shortages

'a white paper with blue and white graphics'
title: 'Source: https://gaper.io/cybersecurity-skill-shortage/' and caption: 'a white paper with blue and white graphics'

A significant and ongoing challenge is the shortage of skilled cybersecurity professionals. Reports indicate that the cybersecurity skills gap has worsened, with 54% of cybersecurity professionals stating that the skills shortage has negatively impacted their organizations[6][4]. As operations increasingly shift to digital and cloud environments, the demand for cybersecurity expertise will continue to rise, leaving organizations scrambling to fill critical roles.

The high turnover rate in the cybersecurity workforce, particularly in the European Union where it's reported to exceed 20%, compounds these challenges. Organizations are finding it essential to not only attract new talent but also retain existing employees through upskilling initiatives and making cybersecurity roles more appealing[2][4]. Upskilling current employees is becoming a prevalent strategy since many organizations report a willingness to invest in cybersecurity training and certifications to equip employees with the necessary skills[11].

Financial Implications of Cyber Threats

'a group of glass triangles'
title: 'Top 7 Cybersecurity Threats for 2024' and caption: 'a group of glass triangles'

The financial implications of cyber threats are enormous and growing. The cost of data breaches has risen, with the average cost of a breach reaching $4.45 million in 2023, marking a 15% increase compared to previous years[11]. Additionally, remote work environment vulnerabilities have led to breaches costing an average of $173,074 more than in strictly on-site operations[11]. This financial burden is worsened by rising cyber insurance premiums, which surged by 50% in 2022, making it difficult for small to medium enterprises to secure adequate coverage without significant investment[11].

As ransomware attacks continue to evolve, the use of extortion tactics, including double and triple extortion, further complicates recovery efforts and increases costs[5][8]. The average ransomware payout has jumped significantly, illustrating how organizations must navigate both direct ransom payments and the higher costs associated with recovery from attacks[11].

Supply Chain and Ecosystem Challenges

'a graph of a number of blue bars'
title: 'image png sep pm' and caption: 'a graph of a number of blue bars'

The interconnected nature of today's digital ecosystem introduces additional vulnerability. Supply chain risks have become a focal point for many cybersecurity discussions, with 41% of organizations acknowledging that material incidents in the past year originated from third-party providers[10]. This signifies the necessity for organizations to not only assess their cybersecurity posture but also that of their supply chain partners. However, many report insufficient visibility into supply chain vulnerabilities, hindering their capacity to manage risks effectively[6][10].

Organizations face pressure to enhance their overall ecosystem resilience while addressing individual weaknesses within their supply chains. This effort requires collaboration between organizations, regulatory bodies, and industry partners to collectively bolster security measures and infrastructure against systemic threats.

Conclusion

Cybersecurity today is marked by an evolving landscape filled with sophisticated threats, regulatory pressures, and a critical shortage of skilled professionals. The financial implications of these challenges are substantial, and the interconnected nature of digital infrastructures necessitates a comprehensive approach to mitigate risks. As we advance further into 2024, organizations must adopt proactive strategies, invest in talent development, and enhance collaboration across every facet of their operations to secure their assets effectively and respond to the relentless tide of cyber threats.


How are special effects created?

title: 'Special effect - Wikipedia'

Special effects in film are created using two main categories: practical effects and digital effects. Practical effects involve physical techniques that are captured on camera, including makeup, prosthetics, animatronics, miniatures, and pyrotechnics. These methods have been used historically to create illusions like explosions and transformations, adding tangible realism to scenes[1][2][3].

Digital effects, also known as visual effects (VFX), utilize computer-generated imagery (CGI), motion capture technology, and digital compositing. These techniques allow filmmakers to create stunning visuals that would be hard or impossible to achieve practically. Advances in technology have made digital effects a significant component of modern filmmaking, allowing for a seamless blend with live-action footage[2][3][4].

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What is the most used browser in Android?

According to the article from Tom’s Guide, the most used browser on Android[1] is Google Chrome[1]. The article states that Chrome is the best Android browser for most people[1] and is considered the most reliable option. Furthermore, Chrome is the dominant browser on the market[1], and every web developer considers[1] Google[1]'s browser when building a website[1]. The article also mentions that Chrome offers extensive additional features[1] and the ability to sync with its desktop counterpart.

[1] tomsguide.com Favicon tomsguide.com

Advancements in Speech Recognition: An Overview of Deep Speech 2

Deep Speech 2 represents a significant leap in automatic speech recognition (ASR) technologies, addressing challenges in recognizing speech in both English and Mandarin. Developed by Baidu Research, this paper outlines how end-to-end deep learning can revolutionize speech recognition by leveraging a single powerful system with substantial improvements over previous models.

The Core Concept of Deep Speech 2

Deep Speech 2 is designed as an end-to-end learning approach for speech recognition, differing from traditional methods that often rely on elaborate pipelines of processing elements. By using a unified architecture composed of deep neural networks, the system aims to effectively process diverse speech inputs, including variations in accents, dialects, and noisy environments. The research emphasizes that this method can achieve impressive performance even when trained on limited data, as it adopts a more efficient way of learning across languages.

Specifically, the paper claims, 'we show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech—two vastly different languages.' This flexibility is one of the key highlights, as it allows for a broader application of the technology across different languages without significant restructuring[1].

Training and Model Architecture

The training process for Deep Speech 2 involves the use of various datasets comprising hours of labeled speech data. This enables the model to learn various aspects of language recognition effectively. The architecture employs recurrent neural networks (RNNs) to process the sequences of speech data, making it adept at handling temporal dependencies within the audio input.

Table 6: Comparison of WER for English and CER for Mandarin with and without a language model. These are simple RNN models with only one layer of 1D invariant convolution.
Table 6: Comparison of WER for English and CER for Mandarin with and without a language model. These are simple RNN models with only one layer of 1D invariant convolution.

The researchers observed that models performing end-to-end learning can bypass the traditional requirement of hand-engineering for various components of speech recognition systems, significantly reducing development time and resource investment. In addition, they implemented techniques like Batch Normalization and SortaGrad, which substantially improved training efficiency and accuracy[1].

For instance, using deep neural networks with multiple layers, 'we integrated a language model into the system, significantly enhancing accuracy.' This highlights how integrating auxiliary models can further refine the primary speech recognition capabilities of Deep Speech 2[1].

Performance Advantages

One of the standout features of Deep Speech 2 is its performance metrics, particularly in terms of word error rate (WER). The research presents substantial improvements in WER compared to previous models, making it clear that this approach has implications for real-world applications. For example, the paper states a reduction in WER by over 40% when using massive datasets and optimizing model configurations.

Table 10: Comparison of English WER for Regular and Noisy development sets on increasing training dataset size. The architecture is a 9-layer model with 2 layers of 2D-invariant convolution and 7 recurrent layers with 68M parameters.
Table 10: Comparison of English WER for Regular and Noisy development sets on increasing training dataset size. The architecture is a 9-layer model with 2 layers of 2D-invariant convolution and 7 recurrent layers with 68M parameters.

In experiments carried out, the results demonstrated that 'we can train up to 20 epochs on the full dataset, reporting a word error rate that is competitive with human transcribers.' This suggests that the model can perform at levels comparable to human accuracy under certain conditions, a critical benchmark for any ASR technology[1].

Real-World Applications and Efficiency

The deployment of Deep Speech 2 focuses on reducing latency and improving throughput, key aspects for real-world applications. With the growing demand for efficient speech recognition systems in interactive environments, the paper emphasizes that 'deployment requires a speech system to transcribe in real time or with relatively low latency'[1].

To achieve this, substantial improvements in computational efficiency were noted. The architecture leverages the capabilities of modern GPUs, permitting the processing of multiple streams simultaneously. This is particularly beneficial in applications that necessitate quick responses, like customer service bots or transcription tools in various business contexts.

Conclusion and Future Directions

The implications of Deep Speech 2 extend beyond technical achievements; they suggest a promising pathway for future developments in speech recognition technologies. The integration of enhanced neural architectures and learning strategies paves the way for improvements in accuracy and efficiency, enabling broader adoption in diverse contexts, from academic research to commercial applications.

Table 16: Comparison of the improvements in DeepSpeech with architectural improvements. The development and test sets are Baidu internal corpora. All the models in the table have about 80 million parameters each
Table 16: Comparison of the improvements in DeepSpeech with architectural improvements. The development and test sets are Baidu internal corpora. All the models in the table have about 80 million parameters each

Overall, the advancements showcased in the Deep Speech 2 paper illustrate the potential for deep learning to reshape how we approach complex tasks in speech recognition. The convergence of model sophistication and practical deployment capabilities signifies a forward momentum in the evolution of ASR systems, highlighting the ongoing relevance of research in this dynamic field[1].