【領導管理】10種摧毀團隊的領導方式 (10 leadership traits that will kill your company)周建良 Zhou Jian Liang
【領導管理】10種摧毀團隊的領導方式 (10 Leadership Traits That Will Kill Your Company)
領導能力不足,卻被放到需要帶人管理的職務上,可能不僅是自己的噩夢,更可能為團隊帶來災難。
優秀的團隊管理者,共通點就是具備領導能力。但對許多主管而言,常常是突然因為工作表現優異,進而被拔升到主管職務,但這不代表著就已經具備領導能力。領導能力不足,卻被放到需要帶人管理的職務上,可能不僅是自己的噩夢,更可能為團隊帶來災難。
矽谷行銷公司Adogy創辦人John Rampton就指出,他在第一次創業時,發現有10種領導方式,幾乎在幾個月的時間內,就可讓自己的企業完蛋,他將這些體會,以「10 Leadership Traits That Will Kill Your Company」(10種摧毀你公司的領導特質)寫在美國《Inc.》雜誌的網站上,建議所有管理者避免以下這10種領導方式:
1.缺乏願景(Lack of vision)
2.溝通失敗(Failure to communicate)
3.恐嚇(Intimidation
4.控制狂(Micromanagement)
5.零容忍政策(No tolerance policy)
6.無所不知( Being a know-it-all)
7.給予獎勵(Offering incentives)
8.「暗坎」有用的資訊(Withholding helpful information)
9.搶部屬的功勞(Taking credit for others' work)
10.過往走動式管理(Management by walking around the office)
【領導管理】10種摧毀團隊的領導方式 (10 leadership traits that will kill your company)周建良 Zhou Jian Liang
【領導管理】10種摧毀團隊的領導方式 (10 Leadership Traits That Will Kill Your Company)
領導能力不足,卻被放到需要帶人管理的職務上,可能不僅是自己的噩夢,更可能為團隊帶來災難。
優秀的團隊管理者,共通點就是具備領導能力。但對許多主管而言,常常是突然因為工作表現優異,進而被拔升到主管職務,但這不代表著就已經具備領導能力。領導能力不足,卻被放到需要帶人管理的職務上,可能不僅是自己的噩夢,更可能為團隊帶來災難。
矽谷行銷公司Adogy創辦人John Rampton就指出,他在第一次創業時,發現有10種領導方式,幾乎在幾個月的時間內,就可讓自己的企業完蛋,他將這些體會,以「10 Leadership Traits That Will Kill Your Company」(10種摧毀你公司的領導特質)寫在美國《Inc.》雜誌的網站上,建議所有管理者避免以下這10種領導方式:
1.缺乏願景(Lack of vision)
2.溝通失敗(Failure to communicate)
3.恐嚇(Intimidation
4.控制狂(Micromanagement)
5.零容忍政策(No tolerance policy)
6.無所不知( Being a know-it-all)
7.給予獎勵(Offering incentives)
8.「暗坎」有用的資訊(Withholding helpful information)
9.搶部屬的功勞(Taking credit for others' work)
10.過往走動式管理(Management by walking around the office)
The NFAIS Foresight Webinar - Artificial Intelligence: Weighing the Value for the Information Community, given by Bohyun Kim.
https://www.niso.org/events/2019/09/nfais-foresight-artificial-intelligence-weighing-value-information-community
The NFAIS Foresight Webinar - Artificial Intelligence: Weighing the Value for the Information Community, given by Bohyun Kim.
https://www.niso.org/events/2019/09/nfais-foresight-artificial-intelligence-weighing-value-information-community
The AI Revolution - Oregon Realtors - Aaron Stelle WFG Title Aaron Stelle
In this presentation, Aaron Stelle, VP of Marketing and Technology for WFG National Title, talks about the 4th technological revolution which is AI. This focuses on the application for the Real Estate industry and ChatGPT but touches on multiple other uses and AI companies. This presentation was done for the Oregon Realtors on 4/25/2023.
TXO & Komfo - AI: The good, the bad, and the ugly of AIKomfo
The good, the bad, and the ugly of AI - How do you meet the now frontier?
AI has already moved into many areas of business, society, and everyday life. But how do you embrace the now frontier in digital transformation and implement it in your organization? Grimur Fjeldsted, an expert in digital transformation and innovation management, is here to tackle the hard questions and give you a concrete view on the current AI landscape by uncovering all the key aspects.
In Agenda:
- Stay ahead of the AI curve with insights into the current state, future outlook, as well as the ethics of AI.
- Actionable tips into how to turn an allegedly disruptive technology into a major opportunity for business
- Insights into big data and social data integration for business
What is Artificial Intelligence? : Everything You Need to Know about AIDashTechnologiesInc
Artificial Intelligence may be a buzzword now, but it’s not a new term. It was coined in 1956 by Minsky and McCarthy. Even though their effort to bring AI into the world’s attention failed, scientists and innovators started researching and developing machines that would mimic humans. In a nutshell;
These Slides includes the basic understanding of cutting-edge technology Machine Learning and Artificial Intelligence, its misconceptions, a discrimination b/w machine and humans, and some of the major applications of AI.
The AI Revolution - Aaron Stelle - WFG TitleAaron Stelle
In this presentation, Aaron Stelle VP Marketing and Technology for WFG National Title, goes over the applications of AI in real estate. There is a focus on the good, the bad, and the scary. There will be a focus on Chat GPT, but mixing in many other platforms.
In 1980, John Searle introduced a division of the field of AI into.docxwilcockiris
In 1980, John Searle introduced a division of the field of AI into "strong" and "weak" AI. Strong AI denoted the attempt to develop a full human-like intelligence, while weak AI denoted the use of AI techniques to either better understand human reasoning or to solve more limited problems. Although there was little progress in developing a strong AI through symbolic programming methods, the attempt to program computers to carry out limited human functions has been quite successful. Much of what is currently labeled AI research follows a functional model, applying particular programming techniques, such as knowledge engineering, fuzzy logic, genetic algorithms, neural networking, heuristic searching, and machine learning via statistical methods, to practical problems. This view sees AI as advanced computing. It produces working programs that can take over certain human tasks. Such programs are used in manufacturing operations, transportation, education, financial markets, "smart" buildings, and even household appliances.
For a functional AI, there need be no quality labeled "intelligence" that is shared by humans and computers. All computers need do is perform a task that requires intelligence for a human to perform. It is also unnecessary, in functional AI, to model a program after the thought processes that humans use. If results are what matters, then it is possible to exploit the speed and storage capabilities of the digital computer while ignoring parts of human thought that are not understood or easily modeled, such as intuition. This is, in fact, what was done in designing the chess-playing program Deep Blue, which in 1997 beat the reigning world chess champion, Gary Kasparov. Deep Blue does not attempt to mimic the thought of a human chess player. Instead, it capitalizes on the strengths of the computer by examining an extremely large number of moves, more moves than any human player could possibly examine.
There are two problems with functional AI. The first is the difficulty of determining what falls into the category of AI and what is simply a normal computer application. A definition of AI that includes any program that accomplishes some function normally done by a human being would encompass virtually all computer programs. Nor is there agreement among computer scientists as to what sorts of programs should fall under the rubric of AI. Once an application is mastered, there is a tendency to no longer define that application as AI. For example, while game playing is one of the classical fields of AI, Deep Blue's design team emphatically states that Deep Blue is not artificial intelligence, since it uses standard programming and parallel processing techniques that are in no way designed to mimic human thought. The implication here is that merely programming a computer to complete a human task is not AI if the computer does not complete the task in the same way a human would.
For a functional approach to result in a full human-like in.
BENEFITS OF ARTIFICIAL INTELLIGENCE.pptxAkoloThomas1
Artificial Intelligence (AI) is one of the emerging technologies from the field of computer science that tries to simulate human reasoning in AI systems. John McCarthy invented the term AI in the year 1950. He said, ‘Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves
The training content covers:
- Basics of Artificial Intelligence
- Penetration of AI in our daily lives
- Few examples and Use cases
- A brief on how future with AI looks like
Reasoning About Machine intuition- David CollsThoughtworks
Machines are now bettering people at a range of specific intuition tasks. How are they doing this and what might be next?
What does this mean for the development and governance of intelligent products and services that we access as consumers and citizens, and what are the implications for the organisations that provide them?
David explores the impact across the product lifecycle, from customer perception, to job design, technology development, knowledge management, risk and ethics.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.