Get GenAI guide

Access HaxiTAG GenAI research content, trends and predictions.

Showing posts with label generative AI. Show all posts

Wednesday, September 18, 2024

BadSpot: Using GenAI for Mole Inspection

September 18, 2024

The service process of BadSpot is simple and efficient. Users only need to send pictures of their moles, and the system will analyze the potential risks. This intelligent analysis system not only saves time but also reduces the potential human errors in traditional medical examinations. However, this process requires a high level of expertise and technical support.

Intelligence Pipeline Requiring Decades of Education and Experience

The success of BadSpot relies on its complex intelligence pipeline, which is similar to military intelligence systems. Unlike low-risk applications (such as CutePup for pet identification and ClaimRight for insurance claims), BadSpot deals with major issues concerning human health. Therefore, the people operating these intelligent tasks must be highly intelligent, well-trained, and experienced.

High-Risk Analysis and Expertise

In BadSpot's intelligence pipeline, participants must be professional doctors (MDs). This means that they have not only completed medical school and residency but also accumulated rich experience in medical practice. Such a professional background enables them to keenly identify potential dangerous moles, just like the doctors in the TV show "House," conducting in-depth medical analysis with their wisdom and creativity.

Advanced Intelligent Analysis and Medical Monitoring

The analysis process of BadSpot involves multiple complex steps, including:

Image Analysis: The system identifies and extracts the characteristics of moles through high-precision image processing technology.
Data Comparison: The characteristics of the mole are compared with known dangerous moles in the database to determine its risk level.
Risk Assessment: Based on the analysis results, a detailed risk assessment report is generated for the user.

The Role of GenAI in Medical Testing Workflows

The successful case of BadSpot showcases the broad application prospects of GenAI in the medical field. By introducing GenAI technology, medical testing workflows become more efficient and accurate, significantly improving the quality of medical monitoring and sample analysis. This not only helps in the early detection and prevention of diseases but also provides more personalized and precise medical services for patients.

Conclusion

The application of GenAI in the medical field not only improves the efficiency and accuracy of medical testing but also shows great potential in medical monitoring reviews and sample analysis. BadSpot, as a representative in this field, has successfully applied GenAI technology to mole risk assessment through its advanced intelligence pipeline and professional medical analysis, providing valuable experience and reference for the medical community. In the future, with the continuous development of GenAI technology, we have reason to expect more innovations and breakthroughs in the medical field.

The Future Impact of Globalization and Generative AI

September 18, 2024

At the 2024 Shanghai Bund Summit, Kevin Kelly shared his insights on the future impact of AI. As technology rapidly advances, the process of globalization and the rise of generative artificial intelligence are providing unprecedented opportunities and challenges for the future of human society. Kevin Kelly’s speech at the 2024 Shanghai Bund Summit delved into the formation of the global technological superorganism, the acceleration of innovation, and the potential of generative AI. These developments will deeply influence the global economy, culture, and labor market. Against this backdrop, understanding and grasping these trends is crucial for individuals, businesses, and society.

Globalization: The Rise of the Technological Superorganism
Kelly emphasized that globalization is no longer merely the convergence of physical boundaries, but more importantly, the integration of technology. As smartphones, computers, and servers across the globe gradually connect into a vast network system, we are witnessing the birth of a "technological superorganism." Each device, every terminal, functions like a neuron in this system, collectively driving the operation of the global technology platform. This superorganism is not only a convergence of technology but also a deep fusion of the global economy and culture.

This technological platform of globalization provides strong support for the development of artificial intelligence, particularly generative AI. Generative AI, through real-time cross-language translation and global virtual collaboration, breaks down national, linguistic, and cultural barriers, promoting greater flexibility and interconnectivity in the global labor market. This means that the global flow of talent will no longer be constrained by language; anyone can leverage AI tools to contribute their skills and value globally.

Acceleration: The Rapid Advancement of Innovation and Learning
The formation of the global technological superorganism not only alters the landscape of globalization but also greatly accelerates the pace of innovation. Kelly pointed out that the development of technology has enabled information to be disseminated and shared more rapidly than ever before. Emerging technological tools like augmented reality (AR), virtual reality (VR), and generative AI allow people to learn and innovate in entirely new ways.

Generative AI is redefining the way we learn. With intelligent assistants like ChatGPT, the threshold for learning has significantly lowered, enabling young people to access knowledge and resources from around the globe at any time. As AI technology continues to advance, answers are no longer scarce; the real challenge and value lie in asking the right questions and developing a unique mindset. This shift in thinking will be critical for future success, especially in a rapidly evolving job market where career forms are constantly changing.

Kelly’s insights suggest that future job opportunities will largely depend on technologies and tools that have yet to be invented. This means that traditional educational models may not fully keep pace with the times. Learning how to learn, how to quickly adapt and innovate, will be the core competencies for navigating future changes.

Generative AI: Creating New Tasks and Opportunities
Generative AI not only takes over traditional repetitive tasks but also begins to engage in and create entirely new forms of work. In his speech, Kelly cited precision agriculture as an example, demonstrating AI’s potential in tasks that humans cannot complete. By applying precise amounts of water and fertilizer to each plant, generative AI can significantly improve agricultural efficiency and reduce resource waste.

However, the true value of generative AI lies in its ability to create entirely new tasks. This means that AI is not merely a simple tool, but can collaborate with humans to generate solutions or innovative products that we had never thought of before. This unique non-human way of thinking is the driving force behind future innovation and wealth creation.

In the long run, generative AI will profoundly change economic structures, offering more opportunities to all social strata, particularly those who perform poorly in traditional economic systems. By empowering them with AI, they will be able to create more efficient and valuable work results, opening up new possibilities for social equity and inclusion.

Conclusion: The Symbiotic Future of Globalization, Acceleration, and Generation
Globalization, technological acceleration, and the rise of generative AI are the core driving forces of the future society envisioned by Kevin Kelly. The formation of a global technological superorganism will encourage closer cooperation among nations, while the acceleration of innovation and the potential of generative AI will continuously generate new opportunities and challenges. Success in the future will not only depend on technological advancements but also on how we utilize these technologies to create more value for global society.

As Kelly noted, imagining the future is the first step to making it a reality. By deeply understanding and applying generative AI, we have the opportunity to shape a more innovative, inclusive, and sustainable global society. This is not just a technological transformation but a profound shift in culture, economy, and human thinking.

Software Usage Skills and AI Programming Assistance for University Students: Current Status and Future Development

September 13, 2024

In modern education and professional environments, software usage skills and AI programming assistance tools are becoming increasingly important. This article will explore the current state of university students' software usage skills and the potential applications of AI programming assistance tools in education and the workplace.

Current State of University Students' Software Usage Skills

Deficiencies in Office Software

Many university students show significant deficiencies in using Office software, particularly Excel. This not only affects their learning efficiency during their studies but may also present challenges in their future careers. Excel, as a powerful data processing tool, is widely used in various fields such as business analysis, data management, and financial reporting. A lack of skills in this area can place students at a disadvantage in job searches and professional settings.

Reduced Dependence on Microsoft Products

University students' dependence on Microsoft products has decreased, possibly due to their increased use of alternative software in their studies and daily lives. For example, Canva, a design tool known for its ease of use and powerful features, is widely used for creating posters, presentations, and reports. Canva allows users to easily create and edit design content, and even export multi-page reports as PDFs for printing.

Software Applications in the Workplace

Application of Office Software

In the work environment, Office software remains the primary tool for handling government documents and formal paperwork. Instant messaging tools such as Line are used for daily communication and information exchange, ensuring timely and convenient information transmission. The diverse use of these tools reflects the advantages of different software in various scenarios.

Workplace Application of Canva

Canva is also becoming increasingly popular in the workplace, especially in roles requiring creative design. Its intuitive user interface and extensive template library enable non-design professionals to quickly get started and produce high-quality design work.

Application of AI Programming Assistance Tools

Innovation of SheetLLM

Microsoft recently released SheetLLM, an innovative spreadsheet language model that can automatically analyze data and generate insights through voice commands. The application of such AI tools significantly reduces the skill requirements for users, allowing non-technical personnel to efficiently handle complex data tasks.

Cultivating Data Thinking

Although AI can simplify operational processes, cultivating and training data thinking remains a crucial focus. Mastering basic data analysis concepts and logic is essential for effectively utilizing AI tools.

Using Canva for Assignments and Reports

University students using Canva for assignments and reports not only improve their completion efficiency but also enhance the aesthetic and professional quality of their content. Canva provides a wealth of templates and design elements, allowing users to create documents that meet requirements in a short time. The widespread use of such tools further reduces dependence on traditional Office software and promotes the diversification of digital learning tools.

Conclusion

The deficiencies in university students' software usage skills and the rise of AI programming assistance tools reflect the changing technological demands in education and the workplace. By strengthening skills training and promoting the use of intelligent tools, university students can better adapt to future professional challenges. Meanwhile, the application of AI technology will play a significant role in improving work efficiency and simplifying operational processes. As technology continues to advance and become more widespread, mastering a variety of software usage skills and data analysis capabilities will become a crucial component of professional competitiveness.

How Generative AI Tools Like GitHub Copilot Are Transforming Software Development and Reshaping the Labor Market

September 11, 2024

In today's era of technological change, generative AI is gradually demonstrating its potential to enhance the productivity of high-skilled knowledge workers, particularly in the field of software development. Research in this area has shown that generative AI tools, such as GitHub Copilot, not only assist developers with coding but also significantly increase their productivity. Through an analysis of experimental data covering 4,867 developers, researchers found that developers using Copilot completed 26.08% more tasks on average, with junior developers benefiting the most. This finding suggests that generative AI is reshaping the way software development is conducted and may have profound implications for the labor market.

The study involved 4,867 software developers from Microsoft, Accenture, and an anonymous Fortune 100 electronics manufacturing company. A subset of developers was randomly selected and given access to GitHub Copilot. Across three experimental results, developers using AI tools completed 26.08% more tasks (standard error: 10.3%). Junior developers showed a higher adoption rate and a more significant increase in productivity.

GitHub Copilot is an AI programming assistant co-developed by GitHub and OpenAI. During the study, large language models like ChatGPT rapidly gained popularity, which may have influenced the experimental outcomes.

The rigor of the experimental design and data analysis This study employed a large-scale randomized controlled trial (RCT), encompassing software developers from companies such as Microsoft and Accenture, providing strong external validity to the experimental process. By randomly assigning access to AI tools, the researchers effectively addressed endogeneity concerns. Additionally, the experiment tracked developers' output over time and consolidated multiple experimental results to ensure the reliability of the conclusions. Various output metrics (such as pull requests, commits, and build success rates) not only measured developers' productivity but also analyzed code quality, offering a comprehensive evaluation of the actual impact of generative AI tools.

Heterogeneous effects: Developers with different levels of experience benefit differently The study specifically pointed out that generative AI tools had varying impacts on developers with different levels of experience. Junior and less skilled developers gained more from GitHub Copilot, a phenomenon that supports the theory of skill-biased technological change. AI tools not only helped these developers complete tasks faster but also provided an opportunity to bridge the skill gap. This effect indicates that the widespread adoption of AI technology could redefine the skill requirements of companies in the future, thereby accelerating the diffusion of technology among employees with varying skill levels.

Impacts and implications of AI tools on the labor market The implications of this study for the labor market are significant. First, generative AI tools like GitHub Copilot not only enhance the productivity of high-skilled workers but may also have far-reaching effects on the supply and demand of labor. As AI technology continues to evolve, companies may need to pay more attention to managing and training employees with different skill levels when deploying AI tools. Additionally, policymakers should monitor the speed and impact of AI technology adoption to address the challenges of technological unemployment and skill retraining.

doc share :
https://drive.google.com/file/d/1wv3uxVPV5ahSa7TFghGvYeTVVMutV64c/view?usp=sharing

AI Impact on Content Creation and Distribution: Innovations and Challenges in Community Media Platforms
Optimizing Product Feedback with HaxiTAG Studio: A Powerful Analysis Framework
Navigating the Competitive Landscape: How AI-Driven Digital Strategies Revolutionized SEO for a Financial Software Solutions Leader
Mastering Market Entry: A Comprehensive Guide to Understanding and Navigating New Business Landscapes in Global Markets
Strategic Evolution of SEO and SEM in the AI Era: Revolutionizing Digital Marketing with AI
The Integration and Innovation of Generative AI in Online Marketing
A Comprehensive Guide to Understanding the Commercial Climate of a Target Market Through Integrated Research Steps and Practical Insights

The Impact of OpenAI's ChatGPT Enterprise, Team, and Edu Products on Business Productivity

September 09, 2024

Since the launch of GPT 4o mini by OpenAI, API usage has doubled, indicating a strong market interest in smaller language models. OpenAI further demonstrated the significant role of its products in enhancing business productivity through the introduction of ChatGPT Enterprise, Team, and Edu. This article will delve into the core features, applications, practical experiences, and constraints of these products to help readers fully understand their value and growth potential.

Key Insights

Research and surveys from OpenAI show that the ChatGPT Enterprise, Team, and Edu products have achieved remarkable results in improving business productivity. Specific data reveals:

92% of respondents reported a significant increase in productivity.
88% of respondents indicated that these tools helped save time.
75% of respondents believed the tools enhanced creativity and innovation.

These products are primarily used for research collection, content drafting, and editing tasks, reflecting the practical application and effectiveness of generative AI in business operations.

Solutions and Core Methods

OpenAI’s solutions involve the following steps and strategies:

Product Launches:
- GPT 4o Mini: A cost-effective small model suited for handling specific tasks.
- ChatGPT Enterprise: Provides the latest model (GPT 4o), longer context windows, data analysis, and customization features to enhance business productivity and efficiency.
- ChatGPT Team: Designed for small teams and small to medium-sized enterprises, offering similar features to Enterprise.
- ChatGPT Edu: Supports educational institutions with similar functionalities as Enterprise.
Feature Highlights:
- Enhanced Productivity: Optimizes workflows with efficient generative AI tools.
- Time Savings: Reduces manual tasks, improving efficiency.
- Creativity Boost: Supports creative and innovative processes through intelligent content generation and editing.
Business Applications:
- Content Generation and Editing: Efficiently handles research collection, content drafting, and editing.
- IT Process Automation: Enhances employee productivity and reduces manual intervention.

Practical Experience Guidelines

For new users, here are some practical recommendations:

Choose the Appropriate Model: Select the suitable model version (e.g., GPT 4o mini) based on business needs to ensure it meets specific task requirements.
Utilize Productivity Tools: Leverage ChatGPT Enterprise, Team, or Edu to improve work efficiency, particularly in content creation and editing.
Optimize Configuration: Adjust the model with customization features to best fit specific business needs.

Constraints and Limitations

Cost Issues: Although GPT 4o mini offers a cost-effective solution, the total cost, including subscription fees and application development, must be considered.
Data Privacy: Businesses need to ensure compliance with data privacy and security requirements when using these models.
Context Limits: While ChatGPT offers long context windows, there are limitations in handling very complex tasks.

Conclusion

OpenAI’s ChatGPT Enterprise, Team, and Edu products significantly enhance productivity in content generation and editing through advanced generative AI tools. The successful application of these tools not only improves work efficiency and saves time but also fosters creativity and innovation. Effective use of these products requires careful selection and configuration, with attention to cost and data security constraints. As the demand for generative AI in businesses and educational institutions continues to grow, these tools demonstrate significant market potential and application value.

from VB

IBM's Text-to-SQL Generator: How Generative AI is Revolutionizing Enterprise Data Insights and Queries

August 25, 2024

IBM recently launched a text-to-SQL generator that has made significant strides in handling complex database queries, ranking first on the BIRD benchmark. This solution, based on IBM's Granite code model, is part of IBM's broader effort to integrate generative AI into data services to help enterprises extract fresh insights from large databases.

As the volume of enterprise data surges—from website clicks to sales reports—companies are collecting and storing more data than ever before. However, the tools for searching across databases, data warehouses, and data lakehouses, and transforming this information into useful insights, have not kept pace with the data's growth. Many companies fail to fully utilize their data because employees either can't find the information they need or can't translate their questions into the code required to unlock the answers.

Generative AI is poised to simplify this process. Large language models (LLMs) are removing key barriers that currently make it difficult to search, retrieve, and transform tabular data. SQL is the dominant language for interacting with databases, yet within any given enterprise, only a limited number of individuals understand how large databases are structured and can query them in SQL. This effectively restricts who can access the data to uncover insights that could improve business operations.

To make enterprise data more accessible to a broader range of users, IBM and other tech companies have focused on teaching LLMs to write SQL. In a recent milestone, IBM's Granite code model topped the BIRD leaderboard, which measures how well LLMs can parse a natural language question and translate it into SQL to run on real data and answer the question.

IBM's text-to-SQL generator still has a long way to go. Despite being the top performer on BIRD, it answered only 68% of questions correctly, compared to the 93% accuracy achieved by engineers who participated in the test. However, considering the rapid progress LLMs have made in other programming tasks, such as refactoring COBOL code into Java, the gap between AI and human-generated SQL may soon narrow.

In BIRD's benchmark for code execution speed—measuring the computational resources required to run the AI-generated SQL against the database—BIRD evaluators scored IBM's solution at 80, just below the 90 scored by volunteer engineers, while other AI systems scored 65.

IBM's SQL code generator is just one of several technologies that IBM researchers are developing to help enterprises find, retrieve, transform, and visualize their data. IBM has already rolled out other LLM-powered components that enrich structured data with descriptions and business terminology, making database tables and columns easier to locate. These technologies were recently integrated into IBM's Knowledge Catalog and watsonx.data products.

“We're on a mission to drive AI into the entire data services pipeline,” said Lisa Amini, a research director at IBM who led the team developing the data enrichment technologies and SQL generator. “The features we're developing can help data stewards and engineers be more productive, and enable data and business analysts to reach insights faster.”

IBM researchers have designed a conversational graphical user interface (CGUI) that allows data engineers, stewards, and analysts to interact with their data through conversation. The CGUI combines the personal touch of an AI chat interface with the intuitive nature of a web-based GUI, helping users more easily interact with structured data and explore results.

In conclusion, IBM's text-to-SQL generator and its underlying Granite code model bring innovation to enterprise data services, enabling companies to more effectively extract valuable insights from vast amounts of data. This not only enhances data analysis efficiency but also opens up new avenues for non-technical users to access data. With IBM's continued innovation in generative AI and LLMs, we can expect even more powerful tools for data interaction and analysis, further driving transformation in enterprise data utilization.

Zhipu AI's All Tools: A Case Study of Spring Festival Travel Data Analysis

August 08, 2024

With the rapid development of artificial intelligence technology, AI large models are increasingly becoming key tools for driving innovation and enhancing productivity. Zhipu AI's All Tools platform showcases its exceptional performance in data analysis, text-to-image generation, code interpretation, and web browsing by integrating various large model capabilities. This article delves into how the All Tools platform leverages GLM-4 to automatically invoke multiple model capabilities based on user intent, using a case study of Spring Festival travel data analysis to demonstrate its immense potential in practical applications.

Core Functions of All Tools

The All Tools platform by Zhipu AI integrates multiple functionalities, including the CogView2 text-to-image model, code interpreter, web browsing, and Function Call. It intelligently invokes the required models to complete complex tasks based on user natural language instructions. Below is a brief introduction to its main functions:

Continuous Text and Image Creation: Leveraging CogView2, All Tools can interact continuously with users within the context, generating high-quality text and image content.
Web Browsing: The model autonomously plans search tasks, selects information sources, interacts with them, and accurately retrieves the required information.
Code Interpreter: Supports complex calculations, file processing, data analysis, and chart generation tasks.
Function Call: Automatically selects the necessary functions based on user-provided descriptions, generates parameters, and responds according to the function's return values.

Case Study: Generating a Spring Festival Travel Data Line Chart

In practical applications, All Tools has demonstrated its efficiency and intelligence. The following are the specific steps to complete the Spring Festival travel data analysis using the All Tools platform:

Data Acquisition: The user issues a natural language instruction such as "Find the Spring Festival travel data for the past three years and draw a line chart." The All Tools platform invokes the web browsing capability to automatically search and extract data from authoritative sources like the Chinese government website.
Data Processing: The extracted data is compiled into an Excel sheet, where the code interpreter is used to organize and process the data.
Chart Generation: Finally, through the chart generation function of the code interpreter, a clear line chart of the Spring Festival travel data is produced.

This integrated operation greatly simplifies the data analysis process, enhancing both efficiency and accuracy.

Future Prospects

The All Tools platform by Zhipu AI not only demonstrates strong advantages in data analysis but also has broad application prospects in text-to-image generation, language understanding, and image creation. In the future, with the continuous advancement of AI technology, All Tools is expected to further expand its functionalities, supporting more application areas.

Through the case study of Spring Festival travel data analysis, we can see how Zhipu AI's All Tools platform utilizes the GLM-4 large model to intelligently invoke multiple model capabilities and efficiently complete complex tasks. As the operating system (OS) of the AI era, All Tools showcases its immense potential in practical applications, providing robust support for the intelligent transformation of various industries.

Insights 2024: Analysis of Global Researchers' and Clinicians' Attitudes and Expectations Toward AI

July 30, 2024

Based on the document "Insights 2024: Attitudes Toward AI" that you provided, I will conduct an in-depth analysis and present its themes, viewpoints, factual evidence, data records, sources, and personal insights in English.

Themes

The "Insights 2024: Attitudes Toward AI" report primarily explores the attitudes, perceptions, usage, and future expectations of researchers and clinicians worldwide regarding artificial intelligence (AI), especially generative AI (GenAI).

Viewpoints

Institutional Perspective: As the publisher of the report, Elsevier emphasizes the potential of AI in research, education, and healthcare while addressing ethical, transparency, and accuracy issues that accompany technological development. Personal Perspective: The surveyed researchers and clinicians hold complex attitudes toward AI. They recognize its potential while also expressing concerns about possible issues.

Factual Evidence

High Awareness: 96% of respondents have heard of AI, with 89% familiar with ChatGPT. Usage: 54% of respondents have used AI, with 31% using it for work purposes. The proportion of AI usage at work is higher in China than in the US and India. Time and Resource Constraints: 49% of non-users cited a lack of time as the main reason for not using AI.

Data Records and Sources

Survey Period: December 2023 to February 2024. Sample Size: 2,999 researchers and clinicians from 123 countries. Data Weighting: Based on OECD/Pharma Factbook demographic data to ensure representativeness in research and healthcare sectors.

Personal Insights

Balancing Technology and Ethics: The rapid development of AI technology brings significant potential but also ethical, transparency, and accuracy challenges. The high awareness and limited routine use of AI indicated in the report suggest that while people expect convenience from AI, they also seek to ensure its safety and reliability. Cultural and Regional Differences: Attitudes toward AI vary by region, with respondents in the Asia-Pacific region showing a more positive attitude toward AI, which may be related to regional culture, education, and economic development levels. Future Outlook: The report's expectations, such as AI accelerating knowledge discovery, increasing research volume, and reducing costs, indicate AI's important role in future research and healthcare. However, concerns about misleading information, critical errors, and societal disruption highlight the need for caution among technology developers and institutions when promoting AI applications.

Structure and Logic

The report is well-structured, first presenting the current state of AI, including awareness, attitudes, and practical applications. It then explores the potential impacts, benefits, and drawbacks of AI from a future perspective. Finally, it discusses pathways to building an AI-driven future, including user concerns, factors influencing trust in AI, and actionable recommendations for technology developers and institutions.

Overall Evaluation

The "Insights 2024: Attitudes Toward AI" report provides a comprehensive perspective to understand the complex views of professionals worldwide on AI. The report's data and analysis not only reveal the current state and future trends of AI technology but also highlight the ethical and social issues to consider in its development. This report helps us better understand the global acceptance of AI technology and provides guidance for future technological development and applications.

Join us to read more industry research, technical analyses, and papers and reports.

https://www.haxitag.ai/p/haxitag-bot.html

Get GenAI guide

Wednesday, September 18, 2024

Intelligence Pipeline Requiring Decades of Education and Experience

High-Risk Analysis and Expertise

Advanced Intelligent Analysis and Medical Monitoring

The Role of GenAI in Medical Testing Workflows

Conclusion

Related topic:

Related Topic

Friday, September 13, 2024

Current State of University Students' Software Usage Skills

Deficiencies in Office Software

Reduced Dependence on Microsoft Products

Software Applications in the Workplace

Application of Office Software

Workplace Application of Canva

Application of AI Programming Assistance Tools

Innovation of SheetLLM

Cultivating Data Thinking

Using Canva for Assignments and Reports

Conclusion

Related topic:

Wednesday, September 11, 2024

Related article

Monday, September 9, 2024

Related topic:

Sunday, August 25, 2024

Related Articles

Thursday, August 8, 2024

Core Functions of All Tools

Case Study: Generating a Spring Festival Travel Data Line Chart

Future Prospects

Related topic

Tuesday, July 30, 2024

Related topic:

Views

Product

Labels