Difference between revisions of "Leadership"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
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| − | [ | + | [https://www.youtube.com/results?search_query=Leadership+artificial+intelligence+Deep+Machine+Learning YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Leadership+artificial+intelligence+Deep+Machine+Learning ...Google search] |
| − | * [[ | + | * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]] |
| − | + | * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]] | |
| − | + | * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]] | |
| − | + | * [[History of Artificial Intelligence (AI)]] ... [[Creatives]] | |
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| − | <youtube> | + | <youtube>nFm9GsTqL2k</youtube> |
| − | <b> | + | <b>The New HR: New Leadership Skills, Distributed Teams and AI |
| − | </b><br> | + | </b><br>We are in the midst of some very significant changes to our workforce, jobs and management. This panel will help you answer: Is “virtual” the new workplace? Why should we be having conversations about race and social justice in the workplace and how do you do that? As HR becomes more complex, what role can automation and AI play in making it more efficient, effective and equitable? This panel event brings together top practitioners talking about how to respond to pressing concerns and co-create not just a new normal, but a better normal. Learn more about our online HR Expert Seminar series! https://bit.ly/3ggFmrt |
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| − | <youtube> | + | <youtube>DqkFCIT4Hno</youtube> |
| − | <b> | + | <b>What does AI mean to leadership | Milo Jones | TEDxIEMadrid |
| − | </b><br>AI | + | </b><br>With good humor and fantastic stories, Milo Jones take us through the history and virtues of AI, connecting it with leadership and presenting an optimistic future. Don´t miss! Dr. Milo Jones has been a Visiting Professor at IE since 2008. In the past, he has worked for Morgan Stanley in New York, Accenture in London, and served as an officer in the United States Marine Corps. At IE Business School, he teaches "Geopolitics" and "Intelligence Tools for the Business Professional" in the MBA and MIAF programmes, and “Cyberintelligence” in the Masters in Cybersecurity programme. He is currently research the geopolitical impact of advances in AI and automation. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx |
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| − | <youtube> | + | <youtube>uBxM0RTHd28</youtube> |
| − | <b> | + | <b>Who Makes AI Projects Successful |
| − | </b><br> | + | </b><br>Business leaders often have high expectations of AI/ML projects, and are sorely disappointed when things don't work out. AI implementations are more than just solving the technology problem. There are many other aspects to consider, and you'll need someone who has strong knowledge and background in business, technology (especially AI/ML), and data to guide the business on projects to take on, strategic direction, updates, and many other aspects. In this video, I call out the need for such a role because the underlying paradigm of software [[development]] is shifting. Here's what I can do to help you. I speak on the topics of architecture and AI, help you integrate AI into your organization, educate your team on what AI can or cannot do, and make things simple enough that you can take action from your new knowledge. I work with your organization to understand the nuances and challenges that you face, and together we can understand, frame, analyze, and address challenges in a systematic way so you see improvement in your overall business, is aligned with your strategy, and most importantly, you and your organization can incrementally change to transform and thrive in the future. If any of this sounds like something you might need, please reach out to me at dr.raj.ramesh@topsigma.com, and we'll get back in touch within a day. Thanks for watching my videos and for subscribing. www.topsigma.com www.linkedin.com/in/rajramesh |
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| − | <youtube> | + | <youtube>PhQYmbNOut8</youtube> |
| − | <b> | + | <b>Lecture 2.7 Working with an AI team — AI For Everyone | [[Creatives#Andrew Ng|Andrew Ng]] |
| − | </b><br> | + | </b><br>AI For Everyone lectures by Andrew Ng and our own Learning Notes. |
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| − | <youtube> | + | <youtube>mzTlqNTHTmc</youtube> |
| − | <b> | + | <b>Building a Data Science Team with Open Source Tools |
| − | </b><br> | + | </b><br>[[Anaconda]]Con 2018. Katrina Riehl. Open source data science technologies have changed the face of building and operating a data science organization. In this talk, Katrina will explore how and why open source technologies are necessary for the success of businesses hoping to use data science and machine learning to power innovation. She will discuss how HomeAway.com is using tools like [[Anaconda]], conda, and other [[Python]]-powered open source libraries to change how they look at their market and stay competitive. She will also discuss her journey in making [[Python]] a first-class citizen in a traditionally Java-based organization while growing a data science team from the ground up. |
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| − | <youtube> | + | <youtube>C5JElgliTeE</youtube> |
| − | <b> | + | <b>Leading Data Science Teams: A Framework To Help Guide Data Science Project Managers - Jeffrey Saltz |
| − | </b><br> | + | </b><br>Open Data Science Data science managers (and senior leaders managing data science teams) need to think through many questions relating to how to best execute their data science efforts. For example, what is the most effective way to lead a data science project? How to make sure my data science team does not expose my organization to issues relating to the misuse of data and/or algorithms? How do I validate the results provided by the data science team? This video will provide a framework managers can use to help ensure a successful data science project. The focus of this framework is not on which specific algorithm a team should use, but rather, how to ensure that the data science effort is progressing effectively and efficiently. Key aspects of the framework, that will be discussed, include: |
| − | + | 1. Forming Data Science Teams 2. Establishing Processes for Developing Analytical Solutions 3. Risk Management You can visit our website and choose the nearest ODSC Event to attend and experience all our Trainings and Workshops: odsc.com/california odsc.com/london Don't forget to Check our AI learning platform out as well | |
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Latest revision as of 06:36, 5 July 2023
YouTube search... ...Google search
- Strategy & Tactics ... Project Management ... Best Practices ... Checklists ... Project Check-in ... Evaluation ... Measures
- Risk, Compliance and Regulation ... Ethics ... Privacy ... Law ... AI Governance ... AI Verification and Validation
- Data Science ... Governance ... Preprocessing ... Exploration ... Interoperability ... Master Data Management (MDM) ... Bias and Variances ... Benchmarks ... Datasets
- History of Artificial Intelligence (AI) ... Creatives
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