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lumenore-datalytics · 11 days ago

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How AI-Powered Analytics Is Transforming Healthcare in 2025

In healthcare, seconds save lives. Imagine AI predicting a heart attack hours before symptoms strike or detecting cancer from a routine scan. This isn’t science fiction—AI-powered analytics in healthcare is making this a reality, turning data into life-saving insights.

By analyzing vast amounts of data, AI healthcare analytics help decode hidden patterns, improving diagnoses and personalizing treatments, which were unimaginable until a few years ago. The global healthcare analytics market is projected to hit $167 billion by 2030, growing at a 21.1% CAGR, thereby proving that data is becoming the foundation of modern medicine.

From real-time analytics in healthcare to AI-driven insights, the industry is witnessing a revolution—one that enhances patient care, optimizes hospital operations, and accelerates drug discovery. The future of healthcare is smarter, faster, and data-driven.

What Is AI-Powered Analytics in Healthcare?

AI-powered analytics uses artificial intelligence and machine learning to analyze patient data, detect patterns, and predict health risks. This empowers healthcare providers to make smarter, faster, and more personalized decisions. Here’s how this data revolution is reshaping healthcare:

1. Early Diagnosis and Predictive Analytics

AI-powered analytics can analyze massive datasets to identify patterns beyond human capability. Traditional diagnostic methods often rely on visible symptoms, but AI can detect subtle warning signs long before they manifest.

For example, real-time analytics in healthcare is proving life-saving in sepsis detection. Hospitals that employ AI-driven early warning systems have reported a 20% drop in sepsis mortality rates as these systems detect irregularities in vitals and trigger timely interventions.

2. Personalized Treatment Plans

AI-powered analytics can customize plans for individual patients based on genetic data, medical history, and lifestyle. This shift towards precision medicine eliminates the conventional one-size-fits-all approach.

AI also enables real-time patient monitoring and adjusting treatments based on continuous data collection from wearable devices and electronic health records (EHRs). This level of personalization is paving the way for safer, more effective treatments.

3. Smarter Hospital Operations

Hospitals generate 2,314 exabytes of data annually, yet much of it remains underutilized. AI-powered analytics is changing that by optimizing hospital operations to reduce inefficiencies and improve patient flow management.

For instance, Mount Sinai Hospital in New York uses AI-powered analytics for patient care by predicting life-threatening complications before they escalate. A clinical deterioration algorithm analyzes patient data daily, identifying 15 high-risk patients for immediate intervention by an intensive care rapid response team. Beyond emergency care, AI also prevents falls, detects delirium, and identifies malnutrition risks, ensuring proactive treatment.

4. Drug Discovery and Development

Developing a new drug is expensive and time-consuming, often taking 10-15 years and costing over $2.6 billion. However, AI-powered analytics is significantly reducing both time and costs by analyzing millions of chemical compounds, predicting potential drug candidates, and streamlining clinical trials faster than traditional methods.

During the COVID-19 pandemic, AI played a crucial role in identifying potential antiviral treatments by rapidly analyzing millions of drug interactions – a process that would have taken human researchers years. Additionally, AI is now being used to repurpose existing drugs, optimize trial designs, and predict patient responses, making pharmaceutical development faster, more efficient, and data-driven.

5. 24/7 Patient Support with AI Chatbots and Virtual Assistants

A survey by Accenture estimates that AI applications, including chatbots, could save the U.S. healthcare system around $150 billion annually by 2026. These savings stem from improved patient access and engagement, as well as a reduction in costs linked to in-person medical visits. AI-driven healthcare analytics is making healthcare more efficient, patient-centric, and responsive to individual needs.

Challenges in AI-Driven Healthcare

Despite its potential to revolutionize healthcare, AI-powered healthcare data & analytics come with challenges that must be addressed for widespread adoption. Some of the challenges are:

Data Privacy and Security: Healthcare systems handle sensitive patient data, making them prime targets for cyberattacks. Ensuring robust encryption, strict access controls, and compliance with HIPAA and GDPR is critical to maintaining patient trust and regulatory adherence.

Bias in AI Models: If AI systems are trained on biased datasets, they can perpetuate healthcare disparities, thereby leading to misdiagnoses and unequal treatment recommendations. Developing diverse, high-quality datasets and regularly auditing AI models can help mitigate bias.

Regulatory Compliance: AI-driven healthcare solutions must align with strict regulations to ensure ethical use. Organizations must work closely with regulatory bodies to maintain transparency and uphold ethical AI practices.

What’s Next in Smart Healthcare?

AI-Powered Surgeries: Robotic assistance enhances precision and reduces risks.

Smart Wearables: Track vital signs in real-time and alert patients to anomalies.

Mental Health Tech: Predictive tools offer proactive support and personalized therapy.

Why It Matters

AI isn’t replacing doctors—it’s augmenting their decision-making with data-driven insights. Healthcare systems that adopt analytics will see:

Improved patient outcomes

Reduced costs

Streamlined operations

#data analytics #no code platforms #business intelligence #ai tools #software #predictiveinsights #predictive modeling #tableau #tableau alternative #agentic ai #textile manufacturing analytics #analytics tools

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instantedownloads · 1 month ago

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How to Use n8n and AI to Build an Automation System

Automation is changing how we work every day. It helps save time, reduce mistakes, and get more done with less effort. If you want to automate your tasks but don’t know where to start, this guide is for you. In this post, you will learn how to use n8n — a free, open-source automation tool — combined with AI to build smart workflows that do work for you. What Is n8n? n8n (pronounced…

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jcmarchi · 7 months ago

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10 Best AI Phone Platforms & Agents for Call Centers (November 2024)

New Post has been published on https://thedigitalinsider.com/10-best-ai-phone-platforms-agents-for-call-centers-november-2024/

10 Best AI Phone Platforms & Agents for Call Centers (November 2024)

Vapi is a technical platform focused on the development and deployment of voice AI applications, specifically designed with developers as the primary users. The system’s architecture handles complex voice processing tasks including turn-taking mechanisms, interruption management, and backchanneling, with voice-to-voice response times operating within a 500-800 millisecond range. This technical framework enables the creation of voice agents that can process and respond to speech inputs while maintaining natural conversation flow.

The platform’s infrastructure includes open-source software development kits (SDKs) that support multiple development environments, including Web, iOS, Flutter, React Native, and Python. These tools enable integration with external services through function calling capabilities, allowing voice agents to perform specific tasks such as data retrieval or appointment scheduling. The system’s security architecture incorporates HIPAA compliance measures, providing a framework for handling sensitive data in regulated contexts.

Key features

Voice processing architecture with sub-second response times

Open-source SDK support across multiple development platforms

Function calling system for external service integration

Conversation management system for handling complex interaction patterns

Security framework designed for HIPAA compliance standards

Visit Vapi →

Synthflow AI provides a platform for creating voice assistants through a visual interface that doesn’t require programming knowledge. The system’s architecture enables users to construct conversational flows through drag-and-drop components, which are then converted into functional voice interactions. The platform includes integration points with over 130 external services and tools, including common business applications like HubSpot, Google Suite, and Stripe, allowing for data exchange and workflow automation across different business systems.

The technical framework of Synthflow incorporates real-time voice processing capabilities and text-to-speech conversion mechanisms for handling customer interactions. The platform’s infrastructure is designed to process multiple concurrent voice sessions, with the system managing conversation flows and responses based on predefined patterns set through the visual interface. This approach to voice assistant creation emphasizes accessibility for non-technical users while maintaining the ability to create complex interaction patterns.

Key features

Visual interface system for creating conversation flows without code

Integration framework supporting connections to over 130 external services

Real-time voice processing architecture for immediate interactions

Text-to-speech conversion system for response generation

Concurrent session management for handling multiple conversations

Visit Synthflow →

Bland AI operates as a phone automation system that processes customer calls through artificial intelligence agents, focusing on creating voice interactions that closely approximate human speech patterns. The system’s architecture processes millions of concurrent calls while maintaining consistent response patterns, operating independently of traditional business hours. The platform incorporates real-time data analysis capabilities, extracting and processing information from conversations to generate operational insights.

The technical framework allows for customization of voice characteristics and integration with existing business systems, enabling organizations to maintain their established workflows while implementing automated call handling. The platform’s infrastructure supports continuous operation, managing call distribution and processing across multiple channels simultaneously. While the system aims to replicate human-like interactions, it functions within the current limitations of conversational AI technology.

Key features

Advanced speech synthesis system for natural-sounding interactions

Concurrent call processing architecture supporting millions of simultaneous conversations

Voice customization framework with multiple configuration options

Integration system compatible with standard business infrastructure

Real-time data processing and analysis capabilities for conversation insights

Visit Bland AI →

Brilo AI functions as a programmable phone system that processes calls through AI agents, with a technical architecture focused on minimizing response latency in voice interactions. The system incorporates text-to-speech technology for voice generation, while managing multiple concurrent calls through parallel processing capabilities. The platform includes functionality for transitioning conversations between AI and human agents when interactions require additional support or complexity.

The infrastructure of Brilo AI includes real-time transcription processing, converting spoken interactions into text data as conversations occur. This framework operates through an API-first design, enabling technical teams to integrate the system with various platforms and services. The architecture supports continuous operation and scales based on call volume requirements, while maintaining consistent processing speeds across different interaction types.

Key features

Low-latency voice processing system for rapid responses

Parallel call handling architecture for multiple simultaneous conversations

Call transfer mechanism for AI-to-human handoffs

Real-time speech-to-text conversion system

API framework for platform integration and customization

Visit Brilo AI →

GoVoice operates as an automated phone system that combines customer service and sales functions through AI-powered voice agents. The system’s architecture handles various interaction types, from basic customer support to complex sales conversations, while incorporating data from customer histories to inform responses. The platform includes specific modules for appointment management and technical support provision, with built-in calendar integration capabilities and structured problem-solving protocols.

The technical framework of GoVoice operates within established security standards, maintaining SOC 2 and HIPAA certifications for data protection. The infrastructure supports continuous operation for customer support functions, while simultaneously processing sales-oriented conversations that can include product recommendations and upselling sequences. The system manages these different interaction types through specialized conversation flows, though it operates within the current limitations of AI voice technology.

Key features

Continuous operation system for round-the-clock call processing

Customer data integration framework for personalized interactions

Appointment scheduling system with calendar synchronization

Technical support protocols with structured problem resolution paths

Security architecture meeting SOC 2 and HIPAA requirements

Visit GoVoice →

GetVocal functions as an automated phone interaction system that processes customer communications through AI agents trained on organization-specific information. The system’s architecture incorporates customer relationship management (CRM) integration capabilities, enabling data exchange between the voice processing system and existing customer databases. This allows for contextual information to be accessed and updated during calls, while maintaining continuous operation across different time zones.

The platform’s infrastructure includes language processing capabilities for multiple languages, enabling cross-regional communication through the same technical framework. It supports scaling of concurrent call processing, adapting to varying call volumes while maintaining consistent response patterns. While the AI agents are trained on company-specific data, they operate within the constraints of current AI technology limitations for natural language processing and voice interaction.

Key features

Company-specific AI training system for customized responses

CRM integration framework for real-time data exchange

Multi-language processing capabilities for international communication

Scalable architecture for handling varying call volumes

Continuous operation system for 24-hour availability

Visit GetVocal →

Goodcall operates as a virtual receptionist system that processes inbound calls through AI agents. The system includes lead management capabilities that automatically transfer call information to various business tools including SMS, email, Google Sheets, and CRM systems. The platform provides analytical tools for monitoring call metrics, including automation rates, call duration tracking, and caller pattern analysis, enabling organizations to assess operational performance through a centralized dashboard.

The technical framework incorporates integration capabilities with over 10,000 external tools through Zapier, creating connections between the phone system and various business applications. The infrastructure supports appointment scheduling functions and customizable call routing, with options for local number assignment or conditional forwarding from existing phone lines. The system processes call data and maintains records of interactions.

Key features

Automated lead capture system with multi-channel data distribution

Analytics framework for tracking call metrics and patterns

Integration architecture supporting thousands of external tools

Appointment management system with scheduling automation

Customizable call routing and number assignment options

Visit Goodcall →

Phonecall.bot functions as an automated phone system that processes customer interactions through AI agents equipped with voice synthesis technology. The system includes multilingual processing capabilities, enabling communication across different languages while maintaining consistent voice characteristics. The platform also incorporates integration points with scheduling software and customer relationship management systems, allowing for data exchange and automated appointment booking during calls.

The technical framework supports continuous operation of the voice processing system, with built-in protocols for transferring conversations to human operators when interactions exceed AI capabilities. The infrastructure manages call routing and knowledge base access for information retrieval during conversations, while coordinating with external business systems for data synchronization.

Key features

Voice synthesis system with multiple voice configuration options

Multi-language processing architecture for international communication

Integration framework for business software synchronization

Appointment scheduling system with real-time booking capability

Call transfer protocols for AI-to-human handoffs

Visit Phonecall.bot →

Choosing an AI Phone Agent

The diversity of AI phone agents available today reflects the growing sophistication of automated communication technology. Each system brings unique strengths to the market – from Vapi’s developer-centric approach and sub-second response times to Synthflow’s no-code interface and Bland AI’s focus on human-like interactions. What unites these platforms is their foundation in AI-powered voice processing and their emphasis on seamless integration with existing business systems, making them practical solutions for organizations of varying sizes and technical capabilities.

As this technology continues to evolve, we’re likely to see further specialization among these platforms, with some focusing on specific industry needs while others pursue broader applications. The future of business communication will likely be shaped by these systems’ ability to process natural language, handle complex conversations, and integrate with an expanding ecosystem of business tools. Organizations considering these solutions should evaluate their specific needs against each platform’s capabilities, keeping in mind that the most effective communication strategies often combine automated efficiency with strategic human interaction.

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nocodebusinessintelligence · 11 months ago

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The Cost-Effectiveness of No-Code BI Software: Analysis and Insights

Are you tired of the high costs and complexities associated with traditional BI software? Imagine a world where you can harness the power of business intelligence without the hefty price tag and intricate setup. How much more efficient could your business be if your data tools were intuitive and affordable? Welcome to the era of no-code BI software, where cost-effectiveness meets cutting-edge technology.

No-code BI solutions are transforming how businesses approach data analytics, offering a seamless and economical alternative to traditional BI systems. Companies using no-code BI tools report a major reduction in total cost of ownership compared to those relying on traditional BI software. This shift is not just about saving money; it's about gaining efficiency and agility in an increasingly data-driven world.

Consider the case of Houwzer, a modern real estate agency in Philadelphia. They revolutionized their business model with technology and a salaried team of experts, saving home sellers an average of $11,000. However, they faced significant challenges in data organization and manual reporting. Greg Phillips, Houwzer's Chief Technology Officer, sought a solution to centralize their data and streamline reporting. His search led him to Grow's no-code BI software.

Phillips found that Grow allowed him to consolidate various information sources, providing live updates and high visibility across the office. This transparency enabled their teams to track conversion rates from leads to closed deals in real-time, fostering a competitive and data-driven culture. "Implementing BI really isn’t a question," Phillips said.

"You have to look at the data for your decisions because if you go with your opinions, your decisions could be way off-base."

In this blog, we'll delve into the cost-effectiveness of no-code BI software, offering detailed analysis and insights to help you understand why this technology is the future of business intelligence. We'll compare the total cost of ownership (TCO) of traditional and no-code BI tools, highlight efficiency and productivity gains, and showcase real-world examples like Houwzer to illustrate the substantial benefits.

Stay tuned to discover how No-code Business Intelligence solutions can transform your business operations, enhance decision-making speed, and ultimately save you money.

Cost Components of Traditional BI Software

Implementing traditional BI software often involves significant costs, impacting various aspects of a business's financial and operational landscape.

Initial Setup Costs

Software Purchase or Licensing Fees

Acquiring traditional BI software typically requires a substantial initial investment. Businesses often need to purchase licenses for each user or pay for a perpetual license, which can be a considerable expense, especially for large organizations. This upfront cost can strain budgets, particularly for small to mid-sized enterprises.

Hardware and Infrastructure Investments

In most cases, solid hardware and infrastructure are required for traditional BI systems. Companies must invest in high-performance servers, data storage solutions, and networking equipment to support the software's demands. These infrastructure investments not only increase initial setup costs but also require ongoing maintenance and upgrades to keep up with technological advancements.

Maintenance and Support Costs

Ongoing IT Support and Maintenance Expenses

Maintaining a traditional BI system demands continuous IT support. Companies often need to hire or contract skilled IT professionals to manage the system, ensure its smooth operation, and troubleshoot any issues that arise. This ongoing support is a recurring cost that can accumulate over time, impacting the total cost of ownership.

Costs of Software Updates and Upgrades

BI software vendors regularly release updates and upgrades to improve functionality, security, and performance. While these updates are crucial, they come with additional costs. Businesses must allocate a budget for purchasing new versions, implementing updates, and training staff on new features. This cycle of updates can be both financially and operationally taxing.

Training and Development Costs

Training Staff to Use Complex BI Tools

Traditional BI tools often have a steep learning curve, requiring extensive training for staff. Businesses must invest in comprehensive training programs to ensure employees can effectively use the software. This training is not only time-consuming but also adds to the overall cost, as it may involve hiring external trainers or dedicating internal resources to education.

How Much Time and Money Go Into Making Tailored Solutions

In order to make BI solutions fit the demands of individual businesses, customization is typically required. Developing these custom solutions requires significant time and resources. IT teams must work closely with business users to understand requirements, design custom reports, and integrate various data sources. This process can be lengthy and expensive, diverting resources from other critical projects.

Cost Components of No-Code BI Software

Adopting no-code BI software can provide significant cost savings and operational efficiencies for businesses. Here's a detailed analysis of the cost components associated with no-code BI software aimed at helping business users, data analysts, and BI professionals understand its financial and practical benefits.

Lower Initial Investment

Subscription-Based Pricing Models

No-code BI software often operates on a subscription-based pricing model, which offers several financial advantages. Instead of a large upfront payment, businesses can spread costs over time with manageable monthly or annual fees. This model not only reduces the initial financial burden but also allows for predictable budgeting and financial planning. Furthermore, subscription plans often include ongoing support and updates, providing continuous value without unexpected expenses.

Minimal Hardware Requirements

One of the standout features of no-code BI tools is their minimal hardware requirements. Unlike traditional BI systems that demand significant investments in servers and data storage, no-code BI solutions leverage cloud infrastructure. This approach eliminates the need for expensive on-premises hardware, reducing both initial setup costs and ongoing maintenance expenses. Businesses can deploy and scale their BI capabilities without investing in physical infrastructure, making it an attractive option for companies of all sizes.

Reduced Maintenance Costs

Cloud-Based Solutions with Automated Updates

No-code BI software is predominantly cloud-based, which brings substantial maintenance cost reductions. Cloud-based solutions come with automated updates, ensuring that the software is always up-to-date with the latest features and security patches. This automation eliminates the need for manual updates and the associated downtime, allowing businesses to focus on data analysis rather than IT maintenance. Additionally, cloud providers handle the underlying infrastructure, further reducing the need for dedicated IT support.

Decreased Need for Dedicated IT Support

With traditional BI systems, businesses often require a team of IT professionals to manage, troubleshoot, and support the infrastructure. No-code BI tools drastically reduce this need. Their user-friendly nature means that even non-technical staff can handle many tasks that previously required IT intervention. This reduction in IT dependency not only lowers costs but also speeds up the resolution of issues, improving overall productivity and efficiency.

User Training and Adoption

Intuitive User Interfaces Reducing Training Time

A key advantage of no-code BI software is its intuitive user interface. Designed with the end-user in mind, these interfaces simplify the process of creating reports, visualizations, and dashboards. Employees can quickly learn to navigate and utilize the software without extensive training. This ease of use translates to reduced training costs and faster onboarding times, enabling teams to leverage BI capabilities almost immediately after implementation.

Faster Adoption Rates Leading to Quicker ROI

The simplicity and accessibility of no-code BI tools lead to higher and faster adoption rates across the organization. When employees can easily integrate BI tools into their daily workflows, the business can start reaping the benefits of data-driven decision-making sooner. This rapid adoption accelerates the return on investment (ROI), as the business can quickly capitalize on insights gained from data analysis. The quicker the adoption, the sooner the company can see improvements in efficiency, productivity, and profitability.

Comparative Analysis

Total Cost of Ownership (TCO)

When evaluating business intelligence solutions, understanding the Total Cost of Ownership (TCO) is crucial. There are typically hefty initial expenditures associated with traditional BI software, like as licensing fees, hardware investments, and maintenance costs over the long run. Conversely, no-code BI software operates on a subscription-based model, spreading costs over time and reducing initial financial burdens.

Traditional BI Software: The TCO for traditional BI systems includes high initial licensing fees, substantial hardware purchases, and ongoing IT maintenance. Companies must also consider the costs of software updates, staff training, and custom solution development. These factors collectively contribute to a higher TCO over the software's lifespan.

No-Code BI Software: The TCO for no-code BI tools is considerably lower. Subscription-based pricing models eliminate hefty upfront costs, and cloud-based infrastructure reduces the need for expensive hardware. Additionally, automated updates and reduced IT support requirements contribute to lower maintenance costs, making No-code Business Intelligence solutions more financially viable in the long term.

Case Studies or Examples Illustrating Cost Savings

Small to Mid-Sized Business: A mid-sized retail company transitioned from a traditional BI system to a no-code BI solution. Initially, they spent $150,000 on licenses and hardware for their traditional BI setup. The yearly expenditures for support and maintenance reached $50,000, with an additional $20,000 going into training new employees. By switching to a no-code BI software with a $2,000 monthly subscription, the company reduced its TCO by over 50%, with additional savings from decreased IT support needs.

Large Enterprise: A large manufacturing firm faced escalating costs with its traditional BI system, including $500,000 in initial setup costs and $200,000 annually for maintenance and support. Transitioning to a no-code BI solution at $10,000 per month led to a substantial reduction in their TCO, saving the company millions over a five-year period while also enhancing their data analysis capabilities.

Efficiency and Productivity Gains

Users can develop and manage dashboards, data visualizations, reports, and more with no-code BI software, which greatly improves workflow efficiency. The intuitive interfaces and drag-and-drop functionality allow users to quickly generate insights, reducing the dependency on IT departments and accelerating data-driven decision-making.

Streamlined Reporting: No-code BI tools simplify the reporting process. Business users can easily pull data from various sources, customize reports, and share insights with stakeholders. This reduces the time spent on manual data handling and reporting, allowing teams to focus on analysis and strategy.

Automation and Integration: Many no-code BI solutions offer automation features, such as scheduled reporting and real-time data updates. These capabilities ensure that decision-makers always have access to the latest information, enhancing responsiveness and agility in business operations.

Impact on Business Operations and Decision-Making Speed

The adoption of no-code BI tools has a profound impact on business operations and decision-making speed. By providing immediate access to data and enabling rapid analysis, these tools empower businesses to make informed decisions swiftly.

Faster Decision-Making: With traditional BI systems, generating reports and insights often involves multiple steps and considerable time. No-code BI software eliminates these delays, allowing users to access and analyze data in real-time. This speed is crucial in dynamic business environments where timely decisions can provide a competitive edge.

Operational Efficiency: By streamlining data workflows and reducing the burden on IT resources, no-code BI solutions enhance overall operational efficiency. Employees can allocate more time to strategic initiatives rather than getting bogged down by technical tasks, leading to improved productivity and business performance.

Conclusion

The analysis of the cost-effectiveness of no-code BI software demonstrates its substantial advantages over traditional BI systems. With lower initial investments, reduced maintenance costs, and simplified user training, no-code BI solutions offer a financially and operationally attractive alternative. Businesses can achieve significant cost savings while enhancing their data analysis capabilities, leading to better decision-making and overall improved performance.

Grow's no-code BI software exemplifies these benefits, providing a robust, cost-effective solution tailored to meet the dynamic needs of modern businesses. By adopting Grow’s no-code BI tools, organizations can unlock the full potential of their data without the hefty price tag and complexities of traditional BI systems.

Take the next step towards transforming your business intelligence strategy with Grow. Experience firsthand how our no-code BI solution can revolutionize your data operations. Sign up for a 14-day free demo today and discover the efficiency and cost savings Grow can bring to your organization.

Curious about what others have to say? Check out "Grow Reviews Cost & Features GetApp" to see how Grow is helping businesses like yours achieve their BI goals. Don't miss out on the opportunity to elevate your data strategy with Grow’s innovative no-code BI software.

Original Source: https://bit.ly/3SwyaN4

#no-code BI software #no-code BI tools #No-code Business Intelligence #no-code BI solution

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datavisualizationdashboard · 11 months ago

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What Happens When Marketing Teams Use Dynamic Data Visualization

What is Dynamic Data Visualization?

The practice of presenting data in formats that are both interactive and continually updated is known as dynamic business data visualization. Visualizations that are dynamic change(bit.ly/4deJLIx) in real-time to reflect the influx of fresh data, in contrast to static visualizations that only show the data at a predetermined moment. Thanks to this feature, users can delve deeper into data and find insights that static representations can miss.

Key Components of Dynamic Data Visualization

Dynamic Business Intelligence data visualization encompasses several critical elements that distinguish it from traditional methods:

Real-Time Data Updates: Real-time data feeds ensure that visualizations are always current, providing up-to-the-minute insights.

Interactivity: Users can interact with the visualizations, drilling down into details, filtering data, and changing perspectives to uncover deeper insights.

Customization: Dynamic visualization tools often offer customizable dashboards, allowing users to tailor the visuals to meet specific business needs.

Scalability: As businesses grow and their data volume increases, dynamic visualizations can scale to accommodate this growth, ensuring continuous and effective data analysis.

Why do marketing teams rely on Dynamic Data Visualization

Dynamic data visualization offers numerous benefits to marketing teams, driving better campaign performance and strategic decision-making:

Enhanced Decision-Making

Transforming raw data into interactive and real-time visual formats, businesses can unlock actionable insights and make informed decisions quickly and efficiently.

Dynamic data visualization plays a crucial role in transforming complex data sets into clear, understandable visuals that can be interacted with in real-time. This capability allows decision-makers to see the full picture, identify trends, and act on insights promptly. Unlike traditional static charts, dynamic visualizations continuously update, ensuring that the information is always current and relevant.

One of its primary benefits is its ability to provide real-time insights. This immediacy helps in our fast-paced business environment, where timely decisions can significantly impact outcomes. With real-time data, marketing teams can monitor campaigns as they unfold, adjust strategies on the fly, and respond to emerging trends without delay.

Taking an example, a marketing team using business data visualization tools can track customer engagement metrics in real-time. If a campaign is underperforming, they can quickly identify the issue, adjust their approach, and improve results. This agility is made possible by the interactive nature of dynamic visualizations, which allow users to drill down into data, filter results, and view metrics from different perspectives.

Effective decision-making often requires collaboration and communication across different departments. Dynamic Business Intelligence data visualization tools foster this by providing a shared platform where teams can view and interact with the same data. This transparency ensures that everyone is on the same page and can contribute to informed decision-making.

For instance, a marketing team can share their dynamic dashboards with the sales and product development teams. This collaboration allows sales to adjust their pitches based on current marketing performance and customer feedback, while product development can prioritize features that resonate most with customers. The result is a more cohesive and aligned strategy that drives business success.

Improved Campaign Performance

One of the most significant advantages of dynamic data visualization is the ability to monitor campaigns in real-time. Business data visualization tools allow marketing teams to track key performance indicators (KPIs) such as click-through rates, conversion rates, and customer engagement as they happen. This real-time insight enables teams to adjust their strategies on the fly, ensuring that campaigns remain effective throughout their duration.

For instance, a marketing team using BI software can set up dashboards that display real-time data from various channels, such as social media, email, and paid advertising. If a particular ad is underperforming, the team can quickly identify the issue and make necessary adjustments, such as changing the ad copy or targeting different audience segments. This agility ensures that marketing efforts are always optimized for maximum impact.

With no-code data visualization tools, marketing teams can easily create interactive dashboards that compare the performance of different ads, emails, or landing pages side by side. This comparison helps teams quickly determine which version resonates best with their audience. It’s called A/B testing.

Learn: Why Every Marketer Should Be A/B Testing Their Dashboards for Conversion Optimization

Taking an example of a team might test two versions of an email campaign to see which one generates more conversions. By visualizing the results in real-time, they can identify the winning version early in the process and allocate more resources to it, thereby maximizing the overall campaign performance.

Better Customer Understanding

Visualizing data from various sources, companies can identify distinct customer groups based on behavior, demographics, and preferences. It can lead to a manner of segmentation that’s suited for more targeted and personalized marketing efforts. Business data visualization tools allow companies to track customer interactions and behaviors as they happen, offering a current and accurate picture of customer activities.

Personalization is a critical aspect of modern marketing, and dynamic Business Intelligence data visualization provides the insights needed to achieve it. By analyzing customer data visually, businesses can identify individual preferences and tailor their communications and offerings accordingly.

Mapping the customer journey is essential for understanding how customers interact with a business across various touchpoints. Dynamic data visualization enables companies to create detailed and interactive customer journey maps that highlight key interactions and pain points.

A travel agency, for example, can use BI software to visualize the customer journey from initial inquiry to booking and post-travel feedback. By identifying where customers drop off or face challenges, the agency can make targeted improvements to the customer experience, leading to higher satisfaction and loyalty.

You can also visualize customer feedback data; businesses can identify common themes, track sentiment, and prioritize areas for improvement.

Collaborative Insights

Decision-making often involves multiple departments and stakeholders. Collaborative insights allow teams to work together more effectively, combining their expertise to interpret data and derive actionable strategies. Business data visualization tools are essential in this process, as they provide a common platform for viewing and analyzing data.

Taking dynamic data visualization in full stride, better communication within and between teams is easily within reach. Data visualizations, as opposed to raw figures or text-based reports, are frequently more conducive to discussion and comprehension. By visualizing data, teams can quickly grasp complex information, identify trends, and communicate findings more effectively.

When teams regularly use BI software to analyze and share data, it reinforces the importance of data in decision-making processes. This cultural shift leads to more objective, evidence-based strategies and reduces reliance on intuition or guesswork.

Conclusion

Dynamic data visualization transforms how marketing teams operate, making them more agile, data-driven, and effective. By providing real-time insights, enhancing decision-making, improving campaign performance, and fostering collaborative insights, dynamic data visualization empowers marketing teams to achieve superior results. The ability to interact with and customize data visualizations enables teams to uncover deep insights, optimize strategies on the fly, and better understand their customers.

For marketing teams looking to leverage the power of dynamic data visualization, Grow offers robust BI software that supports no-code data visualization, making it accessible to everyone. Grow's platform allows you to create interactive, real-time dashboards that integrate seamlessly with your existing data sources, providing a unified view of your marketing performance.

Ready to see the difference for yourself? Start your journey to better marketing insights with a 14-day free trial of Grow. Explore how Grow’s dynamic data dashboards can transform your marketing strategies and drive better business outcomes.

Want to hear what others are saying about Grow? Check out the reviews on the Grow data dashboard Trustradius and see why businesses trust Grow for their data visualization needs.

Enhance your marketing efforts with Grow and unlock the full potential of your data.

Original Source: https://bit.ly/3yiQHVY

#business data visualization #Business Intelligence data visualization #BI software #no-code data visualization

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pell-software · 1 year ago

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aashishkumar · 1 year ago

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Which Industries Will Be Affected by AI Development Services ?

AI is more than just computing. It has the ability to transform numerous industries and parts of our life.

Modern AI, particularly "narrow AI," which uses data-trained models to perform objective functions, has had a significant impact on almost every major business. It is especially true in recent years, when strong IoT connection, the proliferation of connected devices, and quicker computer processing have allowed for considerable increases in data collecting and analysis.

AI and Cybersecurity

As AI development services advances, so are cyber risks. AI can improve cybersecurity by predicting and mitigating cyberattacks in real time. Machine learning algorithms can discover anomalies and patterns in network data, allowing for early identification of potential security breaches. AI and cybersecurity will protect sensitive data and key infrastructure.

AI in Space Exploration and Astronomy

AI services will play an important role in evaluating the massive volumes of data gathered by space missions and telescopes. Machine learning algorithms can help detect celestial objects, forecast space events, and enhance mission planning. AI integration with space exploration will lead to new discoveries and expand our understanding of the universe.

Health care

AI has also had an impact on medicine, however the majority of application cases, such as patient diagnosis and drug discovery, are still limited to research labs. Of course, ChatGPT's entrance has made reality even more intriguing. Some clinicians apparently use the AI chatbot to help them write clinical summaries and come up with diagnosis ideas. We can also consider the less contentious topic of administrative AI. Administrators may now integrate AI systems into their workflows, which is greatly needed in this field. For example, the technology can shorten the projected 16-hour time required to complete the prior authorization process with a health plan provider.

#artificial intelligence #app development #web development #software development #business #coding #business software

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alirazaahmed · 2 years ago

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Crafting Engaging Learning Environments: The Power of LearnDash, LearnPress, and BuddyBoss

In today's digital age, creating an interactive and intuitive online learning platform is crucial. With the rise of e-learning, having a website that not only educates but also engages learners is paramount. Enter Ali Raza, a skilled professional on Fiverr, ready to transform your educational vision into a captivating reality.

Ali Raza's expertise shines through in his gig titled Design LearnDash, LearnPress, BuddyBoss Website & Circle Community. Let's delve into the realm of these powerful tools and explore how they can elevate your online education experience.

1. LearnDash: Empowering Educators LearnDash is a top-tier Learning Management System (LMS) for WordPress, offering robust features tailored for educators. With Ali Raza's expertise, LearnDash becomes a canvas where courses come to life. Interactive lessons, quizzes, and multimedia content seamlessly blend to create engaging learning modules.

2. LearnPress: Seamless Course Creation LearnPress, another remarkable WordPress LMS plugin, empowers instructors to build and monetize their courses effortlessly. Ali Raza leverages LearnPress to craft comprehensive course structures. From content organization to student progress tracking, every aspect is meticulously designed for optimal user experience.

3. BuddyBoss: Building Community-Centric Platforms BuddyBoss transforms ordinary websites into thriving online communities. With BuddyBoss, Ali Raza fosters a sense of belonging among learners. Discussion forums, member profiles, and social networking features enhance the collaborative aspect of education, encouraging interaction and knowledge sharing.

4. Circle Community: Facilitating Meaningful Connections Circle Community, a platform for creators, enables authentic conversations. Ali Raza utilizes Circle to create a vibrant space where learners, instructors, and enthusiasts come together. Discussions, resource sharing, and mentorship opportunities abound, fostering a supportive learning ecosystem.

Why Choose Ali Raza's Services? Ali Raza goes beyond conventional web design. He understands the nuances of effective e-learning and community building. His designs are not just visually appealing; they are strategically structured to enhance user engagement and foster a sense of community.

Conclusion: Transform Your Learning Journey Embarking on an educational journey should be exciting, immersive, and collaborative. Ali Raza's expertise in LearnDash, LearnPress, BuddyBoss, and Circle Community ensures that your online platform becomes a dynamic hub of learning and interaction. Visit Ali Raza's Fiverr profile and embark on a transformative learning experience today. Let the digital realm enhance your educational endeavors, making every lesson a step toward empowerment and enlightenment.

#books & libraries #business #artificial intelligence #programming #software engineering #html #coding #python #learndash #wordpress #woocommerce #website #webdevelopment #blogger #email

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dragonpointsoftware · 2 years ago

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Custom Software Development Jacksonville - Dragon Point Software

Discover the limitless potential of your business with Dragon Point Software's premier custom software development Jacksonville. As a leading software development company, we understand that one-size-fits-all solutions simply won't cut it. That's why we specialize in crafting personalized software applications that align perfectly with your unique requirements and goals. From intuitive user interfaces to robust backend systems, our expert team leverages the latest technologies and industry best practices to deliver unparalleled results.

#artificial intelligence #coding #custom business software #software

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dreaminginthedeepsouth · 5 months ago

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LETTERS FROM AN AMERICAN

February 4, 2025

Heather Cox Richardson

Feb 05, 2025

Shortly after 1:00 this morning, Vittoria Elliott, Dhruv Mehrotra, Leah Feiger, and Tim Marchman of Wired reported that, according to three of their sources, “[a] 25-year-old engineer named Marko Elez, who previously worked for two Elon Musk companies [SpaceX and X], has direct access to Treasury Department systems responsible for nearly all payments made by the US government.”

According to the reporters, Elez apparently has the privileges to write code on the programs at the Bureau of Fiscal Service that control more than 20% of the U.S. economy, including government payments of veterans’ benefits, Social Security benefits, and veterans’ pay. The admin privileges he has typically permit a user “to log in to servers through secure shell access, navigate the entire file system, change user permissions, and delete or modify critical files. That could allow someone to bypass the security measures of, and potentially cause irreversible changes to, the very systems they have access to.”

“If you would have asked me a week ago” if an outsider could’ve been given access to a government server, one federal IT worker told the Wired reporters, “I'd have told you that this kind of thing would never in a million years happen. But now, who the f*ck knows."

The reporters note that control of the Bureau of Fiscal Service computers could enable someone to cut off monies to specific agencies or even individuals. “Will DOGE cut funding to programs approved by Congress that Donald Trump decides he doesn’t like?” asked Senator Chuck Schumer (D-NY) yesterday. “What about cancer research? Food banks? School lunches? Veterans aid? Literacy programs? Small business loans?”

Josh Marshall of Talking Points Memo reported that his sources said that Elez and possibly others got full admin access to the Treasury computers on Friday, January 31, and that he—or they—have “already made extensive changes to the code base for the payment system.” They are leaning on existing staff in the agency for help, which those workers have provided reluctantly in hopes of keeping the entire system from crashing. Marshall reports those staffers are “freaking out.” The system is due to undergo a migration to another system this weekend; how the changes will interact with that long-planned migration is unclear.

The changes, Marshall’s sources tell him, “all seem to relate to creating new paths to block payments and possibly leave less visibility into what has been blocked.”

Both Wired and the New York Times reported yesterday that Musk’s team intends to cut government workers and to use artificial intelligence, or AI, to make budget cuts and to find waste and abuse in the federal government.

Today Jason Koebler, Joseph Cox, and Emanuel Maiberg of 404 Media reported that they had obtained the audio of a meeting held Monday by Thomas Shedd for government technology workers. Shedd is a former Musk employee at Tesla who is now leading the General Services Administration’s Technology Transformation Services (TTS), the team that is recoding the government programs.

At the meeting, Shedd told government workers that “things are going to get intense” as his team creates “AI coding agents” to write software that would, for example, change the way logging into the government systems works. Currently, that software cannot access any information about individuals; as the reporters note, login.gov currently assures users that it “does not affect or have any information related to the specific agency you are trying to access.”

But Shedd said they were working through how to change that login “to further identify individuals and detect and prevent fraud.”

When a government employee pointed out that the Privacy Act makes it illegal for agencies to share personal information without consent, Shedd appeared unfazed by the idea they were trying something illegal. “The idea would be that folks would give consent to help with the login flow, but again, that's an example of something that we have a vision, that needs [to be] worked on, and needs clarified. And if we hit a roadblock, then we hit a roadblock. But we still should push forward and see what we can do.”

A government employee told Koebler, Cox, and Maiberg that using AI coding agents is a major security risk. “Government software is concerned with things like foreign adversaries attempting to insert backdoors into government code. With code generated by AI, it seems possible that security vulnerabilities could be introduced unintentionally. Or could be introduced intentionally via an AI-related exploit that creates obfuscated code that includes vulnerabilities that might expose the data of American citizens or of national security importance.”

A blizzard of lawsuits has greeted Musk’s campaign and other Trump administration efforts to undermine Congress. Today, Senator Chuck Schumer (D-NY) and Representative Hakeem Jeffries (D-NY), the minority leaders in their respective chambers, announced they were introducing legislation to stop Musk’s unlawful actions in the Treasury’s payment systems and to protect Americans, calling it “Stop the Steal,” a play on Trump’s false claims that the 2020 presidential election was stolen.

This evening, Democratic lawmakers and hundreds of protesters rallied at the Treasury Department to take a stand against Musk’s hostile takeover of the U.S. Treasury payment system. “Nobody Elected Elon,” their signs read. “He has access to all our information, our Social Security numbers, the federal payment system,” Representative Maxwell Frost (D-FL) said. “What’s going to stop him from stealing taxpayer money?”

Tonight, the Washington Post noted that Musk’s actions “appear to violate federal law.” David Super of Georgetown Law School told journalists Jeff Stein, Dan Diamond, Faiz Siddiqui, Cat Zakrzewski, Hannah Natanson, and Jacqueline Alemany: “So many of these things are so wildly illegal that I think they’re playing a quantity game and assuming the system can’t react to all this illegality at once.”

Musk’s takeover of the U.S. government to override Congress and dictate what programs he considers worthwhile is a logical outcome of forty years of Republican rhetoric. After World War II, members of both political parties agreed that the government should regulate business, provide a basic social safety net, promote infrastructure, and protect civil rights. The idea was to use tax dollars to create national wealth. The government would hold the economic playing field level by protecting every American’s access to education, healthcare, transportation and communication, employment, and resources so that anyone could work hard and rise to prosperity.

Businessmen who opposed regulation and taxes tried to convince voters to abandon this system but had no luck. The liberal consensus—“liberal” because it used the government to protect individual freedom, and “consensus” because it enjoyed wide support—won the votes of members of both major political parties.

But those opposed to the liberal consensus gained traction after the Supreme Court’s 1954 Brown v. Board of Education of Topeka, Kansas, decision declared segregation in the public schools unconstitutional. Three years later, in 1957, President Dwight D. Eisenhower, a Republican, sent troops to help desegregate Central High School in Little Rock, Arkansas. Those trying to tear apart the liberal consensus used the crisis to warn voters that the programs in place to help all Americans build the nation as they rose to prosperity were really an attempt to redistribute cash from white taxpayers to undeserving racial minorities, especially Black Americans. Such programs were, opponents insisted, a form of socialism, or even communism.

That argument worked to undermine white support for the liberal consensus. Over the years, Republican voters increasingly abandoned the idea of using tax money to help Americans build wealth.

When majorities continued to support the liberal consensus, Republicans responded by suppressing the vote, rigging the system through gerrymandering, and flooding our political system with dark money and using right-wing media to push propaganda. Republicans came to believe that they were the only legitimate lawmakers in the nation; when Democrats won, the election must have been rigged. Even so, they were unable to destroy the post–World War II government completely because policies like the destruction of Social Security and Medicaid, or the elimination of the Department of Education, remained unpopular.

Now, MAGA Republicans in charge of the government have made it clear they intend to get rid of that government once and for all. Trump’s nominee to direct the Office of Management and Budget, Russell Vought, was a key architect of Project 2025, which called for dramatically reducing the power of Congress and the United States civil service. Vought has referred to career civil servants as “villains” and called for ending funding for most government programs. “The stark reality in America is that we are in the late stages of a complete Marxist takeover of the country,” he said recently.

In the name of combatting diversity, equity, and inclusion programs, the Trump administration is taking down websites of information paid for with tax dollars, slashing programs that advance health and science, ending investments in infrastructure, trying to end foreign aid, working to eliminate the Department of Education, and so on. Today the administration offered buyouts to all the people who work at the Central Intelligence Agency, saying that anyone who opposes Trump’s policies should leave. Today, Musk’s people entered the headquarters of the National Oceanic and Atmospheric Administration (NOAA), which provides daily weather and wind predictions; cutting NOAA and privatizing its services is listed as a priority in Project 2025.

Stunningly, Secretary of State Marco Rubio announced today that the U.S. has made a deal with El Salvador to send deportees of any nationality—including U.S. citizens, which would be wildly unconstitutional—for imprisonment in that nation’s 40,000-person Terrorism Confinement Center, for a fee that would pay for El Salvador’s prison system.

Tonight the Senate confirmed Trump loyalist Pam Bondi as attorney general. Bondi is an election denier who refuses to say that Trump lost the 2020 presidential election. As Matt Cohen of Democracy Docket noted, a coalition of more than 300 civil rights groups urged senators to vote against her confirmation because of her opposition to LGBTQ rights, immigrants’ rights, and reproductive rights, and her record of anti-voting activities. The vote was along party lines except for Senator John Fetterman (D-PA), who crossed over to vote in favor.

Musk’s so-called Department of Government Efficiency is the logical outcome of the mentality that the government should not enable Americans to create wealth but rather should put cash in the pockets of a few elites. Far from representing a majority, Musk is unelected, and he is slashing through the government programs he opposes. With full control of both chambers of Congress, Republicans could cut those parts themselves, but such cuts would be too unpopular ever to pass. So, instead, Musk is single-handedly slashing through the government Americans have built over the past 90 years.

Now, MAGA voters are about to discover that the wide-ranging cuts he claims to be making to end diversity, equity, and inclusion (DEI) programs skewer them as well as their neighbors. Attracting white voters with racism was always a tool to end the liberal consensus that worked for everyone, and if Musk’s cuts stand, the U.S. is about to learn that lesson the hard way.

In yet another bombshell, after meeting with Israeli prime minister Benjamin Netanyahu, Trump told reporters tonight that the U.S. “will take over the Gaza Strip,” and suggested sending troops to make that happen. “We’ll own it,” he said. “We’re going to take over that piece, develop it and create thousands and thousands of jobs, and it will be something the entire Middle East can be proud of.” It could become “the Riviera of the Middle East,” he said.

Reaction has been swift and incredulous. Senator Tim Kaine (D-VA), who sits on the Foreign Relations Committee, called the plan “deranged” and “nuts.” Another Foreign Relations Committee member, Senator Chris Coons (D-DE), said he was “speechless,” adding: “That’s insane.” While MAGA representative Nancy Mace (R-SC) posted in support, “Let’s turn Gaza into Mar-a-Lago,” Senator Thom Tillis (R-NC) told NBC News reporters Frank Thorp V and Raquel Coronell Uribe that there were “a few kinks in that slinky,” a reference to a spring toy that fails if it gets bent.

Senator Chris Murphy (D-CT) suggested that Trump was trying to distract people from “the real story—the billionaires seizing government to steal from regular people.”

LETTERS FROM AN AMERICAN

HEATHER COX RICHARDSON

#Heather Cox Richardson #Letters From An American #Right Wing Coup #Musk #TFG #Gaza #history #American History #the US Treasury #treasury department #MAGA #here we go folks

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mariacallous · 3 months ago

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The damage the Trump administration has done to science in a few short months is both well documented and incalculable, but in recent days that assault has taken an alarming twist. Their latest project is not firing researchers or pulling funds—although there’s still plenty of that going on. It’s the inversion of science itself.

Here’s how it works. Three “dire wolves” are born in an undisclosed location in the continental United States, and the media goes wild. This is big news for Game of Thrones fans and anyone interested in “de-extinction,” the promise of bringing back long-vanished species.

There’s a lot to unpack here: Are these dire wolves really dire wolves? (They’re technically grey wolves with edited genes, so not everyone’s convinced.) Is this a publicity stunt or a watershed moment of discovery? If we’re staying in the Song of Ice and Fire universe, can we do ice dragons next?

All more or less reasonable reactions. And then there’s secretary of the interior Doug Burgum, a former software executive and investor now charged with managing public lands in the US. “The marvel of ‘de-extinction’ technology can help forge a future where populations are never at risk,” Burgum wrote in a post on X this week. “The revival of the Dire Wolf heralds the advent of a thrilling new era of scientific wonder, showcasing how the concept of ‘de-extinction’ can serve as a bedrock for modern species conservation.”

What Burgum is suggesting here is that the answer to 18,000 threatened species—as classified and tallied by the nonprofit International Union for Conservation of Nature—is that scientists can simply slice and dice their genes back together. It’s like playing Contra with the infinite lives code, but for the global ecosystem.

This logic is wrong, the argument is bad. More to the point, though, it’s the kind of upside-down takeaway that will be used not to advance conservation efforts but to repeal them. Oh, fracking may kill off the California condor? Here’s a mutant vulture as a make-good.

“Developing genetic technology cannot be viewed as the solution to human-caused extinction, especially not when this administration is seeking to actively destroy the habitats and legal protections imperiled species need,” said Mike Senatore, senior vice president of conservation programs at the nonprofit Defenders of Wildlife, in a statement. “What we are seeing is anti-wildlife, pro-business politicians vilify the Endangered Species Act and claim we can Frankenstein our way to the future.”

On Tuesday, Donald Trump put on a show of signing an executive order that promotes coal production in the United States. The EO explicitly cites the need to power data centers for artificial intelligence. Yes, AI is energy-intensive. They’ve got that right. Appropriate responses to that fact might include “can we make AI more energy-efficient?” or “Can we push AI companies to draw on renewable resources.” Instead, the Trump administration has decided that the linchpin technology of the future should be driven by the energy source of the past. You might as well push UPS to deliver exclusively by Clydesdale. Everything is twisted and nothing makes sense.

The nonsense jujitsu is absurd, but is it sincere? In some cases, it’s hard to say. In others it seems more likely that scientific illiteracy serves a cover for retribution. This week, the Commerce Department canceled federal support for three Princeton University initiatives focused on climate research. The stated reason, for one of those programs: “This cooperative agreement promotes exaggerated and implausible climate threats, contributing to a phenomenon known as ‘climate anxiety,’ which has increased significantly among America’s youth.”

Commerce Department, you’re so close! Climate anxiety among young people is definitely something to look out for. Telling them to close their eyes and stick their fingers in their ears while the world burns is probably not the best way to address it. If you think their climate stress is bad now, just wait until half of Miami is underwater.

There are two important pieces of broader context here. First is that Donald Trump does not believe in climate change, and therefore his administration proceeds as though it does not exist. Second is that Princeton University president Christopher Eisengruber had the audacity to suggest that the federal government not routinely shake down academic institutions under the guise of stopping antisemitism. Two weeks later, the Trump administration suspended dozens of research grants to Princeton totaling hundreds of millions of dollars. And now, “climate anxiety.”

This is all against the backdrop of a government whose leading health officials are Robert F. Kennedy Jr. and Mehmet Oz, two men who, to varying degrees, have built their careers peddling unscientific malarky. The Trump administration has made clear that it will not stop at the destruction and degradation of scientific research in the United States. It will also misrepresent, misinterpret, and bastardize it to achieve distinctly unscientific ends.

Those dire wolves aren’t going to solve anything; they’re not going to be reintroduced to the wild, they’re not going to help thin out deer and elk populations.

But buried in the announcement was something that could make a difference. It turns out Colossal also cloned a number of red wolves—a species that is critically endangered but very much not extinct—with the goal of increasing genetic diversity among the population. It doesn’t resurrect a species that humanity has wiped out. It helps one survive.

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misfitwashere · 5 months ago

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HEATHER COX RICHARDSON

FEB 5

“If you would have asked me a week ago” if an outsider could’ve been given access to a government server, one federal IT worker told the Wiredreporters, “I'd have told you that this kind of thing would never in a million years happen. But now, who the f*ck knows."

The changes, Marshall’s sources tell him, “all seem to relate to creating new paths to block payments and possibly leave less visibility into what has been blocked.”

Today Jason Koebler, Joseph Cox, and Emanuel Maiberg of 404 Mediareported that they had obtained the audio of a meeting held Monday by Thomas Shedd for government technology workers. Shedd is a former Musk employee at Tesla who is now leading the General Services Administration’s Technology Transformation Services (TTS), the team that is recoding the government programs.

But Shedd said they were working through how to change that login “to further identify individuals and detect and prevent fraud.”

That argument worked to undermine white support for the liberal consensus. Over the years, Republican voters increasingly abandoned the idea of using tax money to help Americans build wealth.

Senator Chris Murphy (D-CT) suggested that Trump was trying to distract people from “the real story—the billionaires seizing government to steal from regular people.”

—

#heather cox richardson #letters from an american...

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jcmarchi · 9 months ago

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How AI is Redefining Team Dynamics in Collaborative Software Development

New Post has been published on https://thedigitalinsider.com/how-ai-is-redefining-team-dynamics-in-collaborative-software-development/

How AI is Redefining Team Dynamics in Collaborative Software Development

While artificial intelligence is transforming various industries worldwide, its impact on software development is especially significant. AI-powered tools are enhancing code quality and efficiency and redefining how teams work together in collaborative environments. As AI continues to evolve, it’s becoming a key player in reconfiguring team dynamics, enhancing productivity, and streamlining communication. This article explores how AI redefines team dynamics in collaborative software development, unlocking new ways of working and shaping the industry’s future.

The Shift to AI-Augmented Development

In the past, software development relied heavily on human expertise at every stage, from design and coding to testing and deployment. While this traditional approach has driven significant progress, it faces bottlenecks, including inefficiencies, communication barriers, and human errors. Recent advancements in AI, however, are offering intelligent solutions that effectively address these challenges, transforming how development teams operate.

AI-augmented development redefines team collaboration by automating routine tasks such as bug detection, code reviews, and version control. By handling these repetitive tasks, AI allows developers to focus on more complex, higher-order problems, improving their productivity and efficiency. This automation also promotes effective collaboration by minimizing bottlenecks and reducing the need for constant manual intervention.

In addition, AI-powered tools like GitHub Copilot and DeepCode are helping developers to write code cleaner and faster. These tools provide real-time suggestions, enabling teams to maintain consistent coding standards across multiple developers. This reduces team friction and creates a more harmonious work environment, enabling junior developers to work independently while following best practices.

Enhancing Cross-Functional Collaboration

AI’s impact goes beyond just coding; it’s becoming vital for enhancing collaboration among teams, especially in agile development environments. Software development relies heavily on teamwork, shifting responsibilities between developers, testers, product managers, and business users. These teams must interact and communicate effectively to achieve their shared goals. AI tools are helping to break down the traditional silos that often get in the way of effective communication.

For instance, AI-driven project management platforms like Asana and Jira optimize task allocation by analyzing team performance and identifying skill gaps. These platforms predict potential roadblocks and suggest workflows that ensure tasks are assigned to the most appropriate team members, improving project outcomes. AI also assists in forecasting timelines, reducing project delays, and providing data-driven insights that help team leaders make more informed decisions.

Furthermore, AI’s natural language processing (NLP) capabilities enable more effective communication between technical and non-technical team members. AI-powered chatbots and virtual assistants can now interpret technical jargon and translate it into language that product managers or clients can understand. This communication mechanism creates a more inclusive team environment where everyone is on the same page, regardless of their technical expertise.

Boosting Remote and Distributed Teams

In today’s globalized world, remote work has become the norm for many software development teams. Distributed teams often face challenges related to communication, coordination, and maintaining productivity across time zones. AI is crucial in bridging these gaps and ensuring that remote teams remain as effective as co-located ones.

AI-powered collaboration tools like Slack and Microsoft Teams incorporate features that help manage distributed workforces. These platforms utilize AI to analyze communication patterns, flag potential miscommunications, and suggest the best meeting times based on team members’ availability across different time zones.

Additionally, AI is transforming code review processes for remote teams. Tools like Codacy and CodeClimate use machine learning algorithms to automate code reviews, ensuring that teams follow best practices even when senior developers are not immediately available for oversight. This mechanism accelerates the review process and maintains consistency in code quality merged into the project.

AI also helps maintain team bonding in a remote setting. AI-powered sentiment analysis tools can monitor communication channels, identifying signs of burnout or disengagement among team members. These insights allow managers to intervene early and provide support, ensuring remote teams remain motivated and productive.

AI and Continuous Integration/Continuous Delivery (CI/CD)

One of the most significant shifts AI drives in team dynamics is in continuous integration and continuous delivery (CI/CD). AI-powered tools enhance CI/CD pipelines by automating various aspects of the software development lifecycle, from testing to deployment.

Traditionally, teams invested significant manual effort in managing CI/CD pipelines to ensure they tested, integrated, and deployed code changes without disrupting the system. However, AI automates these processes, allowing teams to implement changes more frequently and confidently. Tools like CircleCI and Jenkins now integrate AI algorithms that predict the success of builds, identify failure points, and optimize deployment strategies.

AI-driven CI/CD fosters better collaboration among developers and operations teams (DevOps). By automating routine deployment tasks, AI allows DevOps teams to focus on strategic improvements and infrastructure scalability instead of constantly dealing with deployment issues. This enhances the synergy between development and operations teams, creating a more cohesive workflow that aligns with the project’s broader goals.

Democratizing Software Development

As AI increasingly integrates into collaborative development environments, software development becomes more accessible to everyone. AI-powered low-code and no-code platforms are allowing non-developers to contribute to software projects in ways that were previously impossible.

Platforms like OutSystems and Appian use AI to guide users through the software development process, enabling business analysts, project managers, and clients to create functional applications without extensive coding expertise. This democratization shifts the traditional dynamic of software teams, where developers are the sole gatekeepers of technical knowledge. Now, diverse teams can actively participate in the development process, contributing to innovation and bringing new perspectives.

These developments have also led to the rise of “citizen developers,” who can quickly prototype ideas, test them, and iterate without relying on professional developers for every process step. This evolution speeds up the innovation cycle and allows software development teams to focus on refining and scaling ideas rather than being bogged down by the initial stages of development.

AI as a Team Member: The Rise of AI Pair Programming

One of the most fascinating developments in AI-assisted software development is the concept of AI as a virtual team member. AI pair programming, where a human developer collaborates with an AI tool to write and review code, is gaining traction. GitHub Copilot, for example, uses OpenAI’s Codex model to assist developers by suggesting code completions, functions, and entire blocks of code based on context.

AI pair programming tools are not just passive assistants; they actively participate in the development process by learning from past codebases and user interactions to provide increasingly accurate suggestions. This evolution fundamentally changes how developers interact with their work, reducing cognitive load and allowing them to focus on more complex, creative tasks.

AI is changing traditional team dynamics by being a constant collaborator. It is reducing the need for junior developers to rely heavily on senior colleagues for guidance. AI tools can now provide that guidance in real time, helping to level the playing field and accelerate the onboarding process for new team members.

The Bottom Line

AI is not just a tool for improving efficiency; it fundamentally reshapes how teams collaborate and innovate in software development. By automating routine tasks, enhancing cross-functional communication, and enabling more inclusive and democratized development processes, AI is setting the stage for a new era of teamwork.

As AI continues to advance, the future of collaborative software development looks promising. Human creativity and AI-driven automation will work together to unlock new levels of productivity and innovation. Teams will be able to confidently tackle increasingly complex projects, knowing that AI is there to support them at every turn.

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mostlysignssomeportents · 2 years ago

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"Open" "AI" isn’t

Tomorrow (19 Aug), I'm appearing at the San Diego Union-Tribune Festival of Books. I'm on a 2:30PM panel called "Return From Retirement," followed by a signing:

https://www.sandiegouniontribune.com/festivalofbooks

The crybabies who freak out about The Communist Manifesto appearing on university curriculum clearly never read it – chapter one is basically a long hymn to capitalism's flexibility and inventiveness, its ability to change form and adapt itself to everything the world throws at it and come out on top:

https://www.marxists.org/archive/marx/works/1848/communist-manifesto/ch01.htm#007

Today, leftists signal this protean capacity of capital with the -washing suffix: greenwashing, genderwashing, queerwashing, wokewashing – all the ways capital cloaks itself in liberatory, progressive values, while still serving as a force for extraction, exploitation, and political corruption.

A smart capitalist is someone who, sensing the outrage at a world run by 150 old white guys in boardrooms, proposes replacing half of them with women, queers, and people of color. This is a superficial maneuver, sure, but it's an incredibly effective one.

In "Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI," a new working paper, Meredith Whittaker, David Gray Widder and Sarah B Myers document a new kind of -washing: openwashing:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4543807

Openwashing is the trick that large "AI" companies use to evade regulation and neutralizing critics, by casting themselves as forces of ethical capitalism, committed to the virtue of openness. No one should be surprised to learn that the products of the "open" wing of an industry whose products are neither "artificial," nor "intelligent," are also not "open." Every word AI huxters say is a lie; including "and," and "the."

So what work does the "open" in "open AI" do? "Open" here is supposed to invoke the "open" in "open source," a movement that emphasizes a software development methodology that promotes code transparency, reusability and extensibility, which are three important virtues.

But "open source" itself is an offshoot of a more foundational movement, the Free Software movement, whose goal is to promote freedom, and whose method is openness. The point of software freedom was technological self-determination, the right of technology users to decide not just what their technology does, but who it does it to and who it does it for:

https://locusmag.com/2022/01/cory-doctorow-science-fiction-is-a-luddite-literature/

The open source split from free software was ostensibly driven by the need to reassure investors and businesspeople so they would join the movement. The "free" in free software is (deliberately) ambiguous, a bit of wordplay that sometimes misleads people into thinking it means "Free as in Beer" when really it means "Free as in Speech" (in Romance languages, these distinctions are captured by translating "free" as "libre" rather than "gratis").

The idea behind open source was to rebrand free software in a less ambiguous – and more instrumental – package that stressed cost-savings and software quality, as well as "ecosystem benefits" from a co-operative form of development that recruited tinkerers, independents, and rivals to contribute to a robust infrastructural commons.

But "open" doesn't merely resolve the linguistic ambiguity of libre vs gratis – it does so by removing the "liberty" from "libre," the "freedom" from "free." "Open" changes the pole-star that movement participants follow as they set their course. Rather than asking "Which course of action makes us more free?" they ask, "Which course of action makes our software better?"

Thus, by dribs and drabs, the freedom leeches out of openness. Today's tech giants have mobilized "open" to create a two-tier system: the largest tech firms enjoy broad freedom themselves – they alone get to decide how their software stack is configured. But for all of us who rely on that (increasingly unavoidable) software stack, all we have is "open": the ability to peer inside that software and see how it works, and perhaps suggest improvements to it:

https://www.youtube.com/watch?v=vBknF2yUZZ8

In the Big Tech internet, it's freedom for them, openness for us. "Openness" – transparency, reusability and extensibility – is valuable, but it shouldn't be mistaken for technological self-determination. As the tech sector becomes ever-more concentrated, the limits of openness become more apparent.

But even by those standards, the openness of "open AI" is thin gruel indeed (that goes triple for the company that calls itself "OpenAI," which is a particularly egregious openwasher).

The paper's authors start by suggesting that the "open" in "open AI" is meant to imply that an "open AI" can be scratch-built by competitors (or even hobbyists), but that this isn't true. Not only is the material that "open AI" companies publish insufficient for reproducing their products, even if those gaps were plugged, the resource burden required to do so is so intense that only the largest companies could do so.

Beyond this, the "open" parts of "open AI" are insufficient for achieving the other claimed benefits of "open AI": they don't promote auditing, or safety, or competition. Indeed, they often cut against these goals.

"Open AI" is a wordgame that exploits the malleability of "open," but also the ambiguity of the term "AI": "a grab bag of approaches, not… a technical term of art, but more … marketing and a signifier of aspirations." Hitching this vague term to "open" creates all kinds of bait-and-switch opportunities.

That's how you get Meta claiming that LLaMa2 is "open source," despite being licensed in a way that is absolutely incompatible with any widely accepted definition of the term:

https://blog.opensource.org/metas-llama-2-license-is-not-open-source/

LLaMa-2 is a particularly egregious openwashing example, but there are plenty of other ways that "open" is misleadingly applied to AI: sometimes it means you can see the source code, sometimes that you can see the training data, and sometimes that you can tune a model, all to different degrees, alone and in combination.

But even the most "open" systems can't be independently replicated, due to raw computing requirements. This isn't the fault of the AI industry – the computational intensity is a fact, not a choice – but when the AI industry claims that "open" will "democratize" AI, they are hiding the ball. People who hear these "democratization" claims (especially policymakers) are thinking about entrepreneurial kids in garages, but unless these kids have access to multi-billion-dollar data centers, they can't be "disruptors" who topple tech giants with cool new ideas. At best, they can hope to pay rent to those giants for access to their compute grids, in order to create products and services at the margin that rely on existing products, rather than displacing them.

The "open" story, with its claims of democratization, is an especially important one in the context of regulation. In Europe, where a variety of AI regulations have been proposed, the AI industry has co-opted the open source movement's hard-won narrative battles about the harms of ill-considered regulation.

For open source (and free software) advocates, many tech regulations aimed at taming large, abusive companies – such as requirements to surveil and control users to extinguish toxic behavior – wreak collateral damage on the free, open, user-centric systems that we see as superior alternatives to Big Tech. This leads to the paradoxical effect of passing regulation to "punish" Big Tech that end up simply shaving an infinitesimal percentage off the giants' profits, while destroying the small co-ops, nonprofits and startups before they can grow to be a viable alternative.

The years-long fight to get regulators to understand this risk has been waged by principled actors working for subsistence nonprofit wages or for free, and now the AI industry is capitalizing on lawmakers' hard-won consideration for collateral damage by claiming to be "open AI" and thus vulnerable to overbroad regulation.

But the "open" projects that lawmakers have been coached to value are precious because they deliver a level playing field, competition, innovation and democratization – all things that "open AI" fails to deliver. The regulations the AI industry is fighting also don't necessarily implicate the speech implications that are core to protecting free software:

https://www.eff.org/deeplinks/2015/04/remembering-case-established-code-speech

Just think about LLaMa-2. You can download it for free, along with the model weights it relies on – but not detailed specs for the data that was used in its training. And the source-code is licensed under a homebrewed license cooked up by Meta's lawyers, a license that only glancingly resembles anything from the Open Source Definition:

https://opensource.org/osd/

Core to Big Tech companies' "open AI" offerings are tools, like Meta's PyTorch and Google's TensorFlow. These tools are indeed "open source," licensed under real OSS terms. But they are designed and maintained by the companies that sponsor them, and optimize for the proprietary back-ends each company offers in its own cloud. When programmers train themselves to develop in these environments, they are gaining expertise in adding value to a monopolist's ecosystem, locking themselves in with their own expertise. This a classic example of software freedom for tech giants and open source for the rest of us.

One way to understand how "open" can produce a lock-in that "free" might prevent is to think of Android: Android is an open platform in the sense that its sourcecode is freely licensed, but the existence of Android doesn't make it any easier to challenge the mobile OS duopoly with a new mobile OS; nor does it make it easier to switch from Android to iOS and vice versa.

Another example: MongoDB, a free/open database tool that was adopted by Amazon, which subsequently forked the codebase and tuning it to work on their proprietary cloud infrastructure.

The value of open tooling as a stickytrap for creating a pool of developers who end up as sharecroppers who are glued to a specific company's closed infrastructure is well-understood and openly acknowledged by "open AI" companies. Zuckerberg boasts about how PyTorch ropes developers into Meta's stack, "when there are opportunities to make integrations with products, [so] it’s much easier to make sure that developers and other folks are compatible with the things that we need in the way that our systems work."

Tooling is a relatively obscure issue, primarily debated by developers. A much broader debate has raged over training data – how it is acquired, labeled, sorted and used. Many of the biggest "open AI" companies are totally opaque when it comes to training data. Google and OpenAI won't even say how many pieces of data went into their models' training – let alone which data they used.

Other "open AI" companies use publicly available datasets like the Pile and CommonCrawl. But you can't replicate their models by shoveling these datasets into an algorithm. Each one has to be groomed – labeled, sorted, de-duplicated, and otherwise filtered. Many "open" models merge these datasets with other, proprietary sets, in varying (and secret) proportions.

Quality filtering and labeling for training data is incredibly expensive and labor-intensive, and involves some of the most exploitative and traumatizing clickwork in the world, as poorly paid workers in the Global South make pennies for reviewing data that includes graphic violence, rape, and gore.

Not only is the product of this "data pipeline" kept a secret by "open" companies, the very nature of the pipeline is likewise cloaked in mystery, in order to obscure the exploitative labor relations it embodies (the joke that "AI" stands for "absent Indians" comes out of the South Asian clickwork industry).

The most common "open" in "open AI" is a model that arrives built and trained, which is "open" in the sense that end-users can "fine-tune" it – usually while running it on the manufacturer's own proprietary cloud hardware, under that company's supervision and surveillance. These tunable models are undocumented blobs, not the rigorously peer-reviewed transparent tools celebrated by the open source movement.

If "open" was a way to transform "free software" from an ethical proposition to an efficient methodology for developing high-quality software; then "open AI" is a way to transform "open source" into a rent-extracting black box.

Some "open AI" has slipped out of the corporate silo. Meta's LLaMa was leaked by early testers, republished on 4chan, and is now in the wild. Some exciting stuff has emerged from this, but despite this work happening outside of Meta's control, it is not without benefits to Meta. As an infamous leaked Google memo explains:

Paradoxically, the one clear winner in all of this is Meta. Because the leaked model was theirs, they have effectively garnered an entire planet's worth of free labor. Since most open source innovation is happening on top of their architecture, there is nothing stopping them from directly incorporating it into their products.

https://www.searchenginejournal.com/leaked-google-memo-admits-defeat-by-open-source-ai/486290/

Thus, "open AI" is best understood as "as free product development" for large, well-capitalized AI companies, conducted by tinkerers who will not be able to escape these giants' proprietary compute silos and opaque training corpuses, and whose work product is guaranteed to be compatible with the giants' own systems.

The instrumental story about the virtues of "open" often invoke auditability: the fact that anyone can look at the source code makes it easier for bugs to be identified. But as open source projects have learned the hard way, the fact that anyone can audit your widely used, high-stakes code doesn't mean that anyone will.

The Heartbleed vulnerability in OpenSSL was a wake-up call for the open source movement – a bug that endangered every secure webserver connection in the world, which had hidden in plain sight for years. The result was an admirable and successful effort to build institutions whose job it is to actually make use of open source transparency to conduct regular, deep, systemic audits.

In other words, "open" is a necessary, but insufficient, precondition for auditing. But when the "open AI" movement touts its "safety" thanks to its "auditability," it fails to describe any steps it is taking to replicate these auditing institutions – how they'll be constituted, funded and directed. The story starts and ends with "transparency" and then makes the unjustifiable leap to "safety," without any intermediate steps about how the one will turn into the other.

It's a Magic Underpants Gnome story, in other words:

Step One: Transparency

Step Two: ??

Step Three: Safety

https://www.youtube.com/watch?v=a5ih_TQWqCA

Meanwhile, OpenAI itself has gone on record as objecting to "burdensome mechanisms like licenses or audits" as an impediment to "innovation" – all the while arguing that these "burdensome mechanisms" should be mandatory for rival offerings that are more advanced than its own. To call this a "transparent ruse" is to do violence to good, hardworking transparent ruses all the world over:

https://openai.com/blog/governance-of-superintelligence

Some "open AI" is much more open than the industry dominating offerings. There's EleutherAI, a donor-supported nonprofit whose model comes with documentation and code, licensed Apache 2.0. There are also some smaller academic offerings: Vicuna (UCSD/CMU/Berkeley); Koala (Berkeley) and Alpaca (Stanford).

These are indeed more open (though Alpaca – which ran on a laptop – had to be withdrawn because it "hallucinated" so profusely). But to the extent that the "open AI" movement invokes (or cares about) these projects, it is in order to brandish them before hostile policymakers and say, "Won't someone please think of the academics?" These are the poster children for proposals like exempting AI from antitrust enforcement, but they're not significant players in the "open AI" industry, nor are they likely to be for so long as the largest companies are running the show:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4493900

I'm kickstarting the audiobook for "The Internet Con: How To Seize the Means of Computation," a Big Tech disassembly manual to disenshittify the web and make a new, good internet to succeed the old, good internet. It's a DRM-free book, which means Audible won't carry it, so this crowdfunder is essential. Back now to get the audio, Verso hardcover and ebook:

http://seizethemeansofcomputation.org

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

#pluralistic #llama-2 #meta #openwashing #floss #free software #open ai #open source #osi #open source initiative #osd #open source definition #code is speech

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river-taxbird · 6 months ago

Text

The Four Horsemen of the Digital Apocalypse

Blockchain. Artificial Intelligence. Internet of Things. Big Data.

Do these terms sound familiar? You have probably been hearing some or all of them non stop for years. "They are the future. You don't want to be left behind, do you?"

While these topics, particularly crypto and AI, have been the subject of tech hype bubbles and inescapable on social media, there is actually something deeper and weirder going on if you scratch below the surface.

I am getting ready to apply for my PhD in financial technology, and in the academic business studies literature (Which is barely a science, but sometimes in academia you need to wade into the trash can.) any discussion of digital transformation or the process by which companies adopt IT seem to have a very specific idea about the future of technology, and it's always the same list, that list being, blockchain, AI, IoT, and Big Data. Sometimes the list changes with additions and substitutions, like the metaverse, advanced robotics, or gene editing, but there is this pervasive idea that the future of technology is fixed, and the list includes tech that goes from questionable to outright fraudulent, so where is this pervasive idea in the academic literature that has been bleeding into the wider culture coming from? What the hell is going on?

The answer is, it all comes from one guy. That guy is Klaus Schwab, the head of the World Economic Forum. Now there are a lot of conspiracies about the WEF and I don't really care about them, but the basic facts are it is a think tank that lobbies for sustainable capitalist agendas, and they famously hold a meeting every year where billionaires get together and talk about how bad they feel that they are destroying the planet and promise to do better. I am not here to pass judgement on the WEF. I don't buy into any of the conspiracies, there are plenty of real reasons to criticize them, and I am not going into that.

Basically, Schwab wrote a book titled the Fourth Industrial Revolution. In his model, the first three so-called industrial revolutions are:

1. The industrial revolution we all know about. Factories and mass production basically didn't exist before this. Using steam and water power allowed the transition from hand production to mass production, and accelerated the shift towards capitalism.

2. Electrification, allowing for light and machines for more efficient production lines. Phones for instant long distance communication. It allowed for much faster transfer of information and speed of production in factories.

3. Computing. The Space Age. Computing was introduced for industrial applications in the 50s, meaning previously problems that needed a specific machine engineered to solve them could now be solved in software by writing code, and certain problems would have been too big to solve without computing. Legend has it, Turing convinced the UK government to fund the building of the first computer by promising it could run chemical simulations to improve plastic production. Later, the introduction of home computing and the internet drastically affecting people's lives and their ability to access information.

That's fine, I will give him that. To me, they all represent changes in the means of production and the flow of information, but the Fourth Industrial revolution, Schwab argues, is how the technology of the 21st century is going to revolutionize business and capitalism, the way the first three did before. The technology in question being AI, Blockchain, IoT, and Big Data analytics. Buzzword, Buzzword, Buzzword.

The kicker though? Schwab based the Fourth Industrial revolution on a series of meetings he had, and did not construct it with any academic rigor or evidence. The meetings were with "numerous conversations I have had with business, government and civil society leaders, as well as technology pioneers and young people." (P.10 of the book) Despite apparently having two phds so presumably being capable of research, it seems like he just had a bunch of meetings where the techbros of the mid 2010s fed him a bunch of buzzwords, and got overly excited and wrote a book about it. And now, a generation of academics and researchers have uncritically taken that book as read, filled the business studies academic literature with the idea that these technologies are inevitably the future, and now that is permeating into the wider business ecosystem.

There are plenty of criticisms out there about the fourth industrial revolution as an idea, but I will just give the simplest one that I thought immediately as soon as I heard about the idea. How are any of the technologies listed in the fourth industrial revolution categorically different from computing? Are they actually changing the means of production and flow of information to a comparable degree to the previous revolutions, to such an extent as to be considered a new revolution entirely? The previous so called industrial revolutions were all huge paradigm shifts, and I do not see how a few new weird, questionable, and unreliable applications of computing count as a new paradigm shift.

What benefits will these new technologies actually bring? Who will they benefit? Do the researchers know? Does Schwab know? Does anyone know? I certainly don't, and despite reading a bunch of papers that are treating it as the inevitable future, I have not seen them offering any explanation.

There are plenty of other criticisms, and I found a nice summary from ICT Works here, it is a revolutionary view of history, an elite view of history, is based in great man theory, and most importantly, the fourth industrial revolution is a self fulfilling prophecy. One rich asshole wrote a book about some tech he got excited about, and now a generation are trying to build the world around it. The future is not fixed, we do not need to accept these technologies, and I have to believe a better technological world is possible instead of this capitalist infinite growth tech economy as big tech reckons with its midlife crisis, and how to make the internet sustainable as Apple, Google, Microsoft, Amazon, and Facebook, the most monopolistic and despotic tech companies in the world, are running out of new innovations and new markets to monopolize. The reason the big five are jumping on the fourth industrial revolution buzzwords as hard as they are is because they have run out of real, tangible innovations, and therefore run out of potential to grow.

#ai #artificial intelligence #blockchain #cryptocurrency #fourth industrial revolution #tech #technology #enshittification #anti ai #ai bullshit #world economic forum

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kittygawa--jr · 2 months ago

Note

Any katagawa hcs?

Random Katagawa Jr. Headcanons | 500-ish Words

content warning for substance abuse and vague allusions to self harm

does a little spin. hi anon. i have a physical written collection of katagawa headcanons. i have dedicated an entire notebook to this which is purely katagawa (at the moment) headcanons and notes i wish to keep track of. do not get me going.

23 at death

Just about shorter than Zer0.

Vegetarian, sometimes indulges in fish.

Actually cut his pinkies off to fit into the Zer0 suit

Covered in moles :3

Has impacted canines, still has braces as an adult, was in headgear as a child.

Prefers clothes made to be prudish, that are then altered to be revealing. (Cropped/sleeveless turtlenecks, pants with slits/holes/tears for example, idk, he dresses like a slutty steve jobs)

Very small appetite, prefers to snack throughout the day rather than eat a few big meals.

Prosthetic eye and port are self developed, modelling off Atlas prosthetic software/hardware. Implanted them first at 18.

Bar code tattoo was given at 16, when starting a proper corporate mentorship and actually beginning to contribute to Maliwan.

His combat training was mandatory from 13 onward; he struggled to find a field to go into, so becoming useful as a weapon was the compromise until he could contribute another way.

He stuck with it after finding his place within Maliwan because the regular exercise and rigorous routine kept him busy and gave him an out for his frustrations at home.

He's extremely insecure about his height, always feeling awkward over never fitting into spaces well.

Remedies the previous problem by training himself to be extremely flexible.

Struggles with substance abuse. Towards his later teens he started drinking with encouragement from his brothers, while he only began really messing with drugs after meeting Troy and being introduced to Eridium based offerings.

Addressing prior point, for a few of his BL3 appearances he's likely coming down or currently high. He was most definitely actively using during his fight, however.

He takes great pride in getting his hair styled and taking care of it once it's out of a pompadour. I like to headcanon him as having a fancier pomp with am elaborate back and a slight ducktail/mullet in the back too, but what do I know.

The Zanara is his only space that is truly his own that he has to himself, away from his family. He wasn't JUST being a big baby about not being able to take what he dishes out okay. Rhys blew up something very very important to his sanity.

He drools in his sleep more than Rhys does.

He has a couple tattoos under his suit and tie: a replica of Rhys' neck tattoo on his inner right forearm, matching katana tattoos with his brothers over his left pectoral, and a work in progress of a cat prowling down his right thigh. He died before he even got the colors started.

Most intelligence he actually possesses in any real capacity mostly surrounds technology. He loves to tinker and customize and mod, though it was rarely what was asked of him at home or at Maliwan, since he already had other siblings that were also good with tech. It was almost entirely a personal hobby inspired by Rhys.

His family nickname is Junior. Do not ever call him Junior his face will get so red it'll be so funny. uh. I mean Don't do that.

He holds so much tension at all times that you would think his muscles were his bones from how stiff and strained they always are.

#borderlands #borderlands 3 #katagawa jr #headcanons #writing requests

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