Best Practices - Revision history

BPeat at 09:48, 30 April 2024

2024-04-30T09:48:12Z

BPeat: /* Artificial Intelligence Center of Excellence (AI CoE) */

2024-04-30T09:35:21Z

‎Artificial Intelligence Center of Excellence (AI CoE)

BPeat at 09:26, 30 April 2024

2024-04-30T09:26:54Z

BPeat: /* Pareto */

2024-04-30T09:26:36Z

‎Pareto

BPeat: /* Artificial Intelligence Center of Excellence (AI CoE) */

2024-04-30T09:25:43Z

‎Artificial Intelligence Center of Excellence (AI CoE)

BPeat: /* Pareto */

2024-04-30T09:18:19Z

‎Pareto

BPeat at 10:51, 5 July 2023

2023-07-05T10:51:54Z

BPeat at 10:51, 5 July 2023

2023-07-05T10:51:36Z

BPeat at 10:42, 5 July 2023

2023-07-05T10:42:33Z

BPeat at 16:29, 30 June 2023

2023-06-30T16:29:10Z

← Older revision		Revision as of 09:48, 30 April 2024
Line 111:		Line 111:
	\|}<!-- B -->		\|}<!-- B -->

−	~~= Artificial Intelligence Center of Excellence (AI CoE) =~~
−	* [https://hbr.org/2019/01/how-to-set-up-an-ai-center-of-excellence How to Set Up an AI Center of Excellence - Harvard Business Review]
−	* [https://pureai.com/articles/2019/01/17/ai-coe.aspx?m=1 How To Create an Enterprise 'AI Center of Excellence' \| Pure AI]
−	* [https://sites.udel.edu/ai/ Artificial Intelligence Center of Excellence (AICoE) - WordPress \| UD]
−	* [https://www.forbes.com/sites/forbestechcouncil/2022/12/09/four-steps-for-building-an-ai-center-of-excellence/ Four Steps For Building An AI Center Of Excellence \| Forbes]
−
−	Establishing an AI Center of Excellence is a strategic move that can significantly enhance your organization's AI capabilities. Remember that AI adoption is an ongoing journey. Continuously monitor progress, iterate on your strategy, and adapt to changing technology and business landscapes. By following this plan, your organization can establish a robust AI CoE that drives innovation, efficiency, and value across the board. Here's an outline to guide your organization in adopting AI effectively and maximizing its value:
−
−	* <b>Create the AI Vision:</b>
−	** Define a clear and compelling vision for AI adoption within your organization. Understand how AI aligns with your business goals, mission, and long-term strategy.
−	** Engage key stakeholders, including executives, business leaders, and technical experts, to ensure buy-in and alignment.
−	* <b>Identify Use Cases:</b>
−	** Conduct a thorough assessment of your organization’s processes, pain points, and opportunities.
−	** Identify specific use cases where AI can add value. Prioritize use cases based on their potential impact, feasibility, and alignment with strategic objectives.
−	* <b>Determine Ambition Level:</b>
−	** Define the level of ambition for your AI initiatives. Consider factors such as budget, resources, and risk tolerance.
−	** Decide whether you want to start with small-scale pilots or aim for broader, organization-wide AI adoption.
−	* <b>Create a Data Architecture:</b>
−	** Establish a robust data infrastructure to support AI initiatives.
−	** Ensure data quality, security, and accessibility. Consider data governance, privacy, and compliance requirements.
−	** Explore cloud-based solutions for scalability and flexibility.
−	* <b>Manage External Partnerships:</b>
−	** Collaborate with external partners, such as AI vendors, research institutions, and industry experts.
−	** Leverage their expertise, tools, and technologies to accelerate AI development.
−	** Establish clear communication channels and expectations.
−	* <b>Identify AI Champions:</b>
−	** Identify individuals within your organization who are passionate about AI and can drive its adoption.
−	** These champions can be from various departments—data science, IT, business units, etc.
−	** Empower them to lead AI initiatives, advocate for AI adoption, and share best practices.
−	* <b>Use AI Technology:</b>
−	** Explore AI tools, frameworks, and platforms
−	** Choose technologies that align with your organization’s needs and goals
−	** Start with a small-scale project or proof of concept
−	** Validate AI concepts and demonstrate feasibility
−	* <b>Share Success Stories:</b>
−	** Communicate wins and achievements related to AI projects.
−	** Showcase how AI has improved processes, efficiency, customer experiences, or revenue.
−	** Use success stories to build momentum and encourage broader adoption.
−	* <b>Plan for Change Management:</b>
−	** Communicate the AI roadmap to stakeholders.
−	** Address concerns, manage expectations, and ensure smooth implementation.
−
−	~~<youtube>2ZpvLW7S33k</youtube>~~
−	~~<youtube>Go2mtZ_mjUU</youtube>~~

	= Heuristics =		= Heuristics =

@@ Line 140: / Line 140: @@
 ** These champions can be from various departments—data science, IT, business units, etc.
 ** Empower them to lead AI initiatives, advocate for AI adoption, and share best practices.
 * <b>Share Success Stories:</b>
 ** Communicate wins and achievements related to AI projects.
 ** Showcase how AI has improved processes, efficiency, customer experiences, or revenue.
 ** Use success stories to build momentum and encourage broader adoption.
+* <b>Plan for Change Management:</b>
 <youtube>2ZpvLW7S33k</youtube>

@@ Line 183: / Line 183: @@
 <b>The Heuristics Revolution – Gerd Gigerenzer at Summer Institute 2018
 </b><br>Decision problems come in two kinds: risk and uncertainty. Risk applies to situations that are well structured and stable, such as gambles, lotteries, and cancer screening, and has been tamed by the probabilistic revolution in the sciences since the beginning of the 17th century. Uncertainty applies to situations that are ill structured and instable, such as human interaction, investment, and business. Although this is an old distinction, most economists and psychologists have ignored it and used the probability theory as the sole tool for dealing with both. Part of the problem was that no theory of decision-making under uncertainty existed until recently. Based on Herbert Simon’s work, we are now developing such a theory of intelligent heuristics that are able to deal with situations of uncertainty. The science of heuristics addresses three questions. The first is descriptive: What are the heuristics in the adaptive toolbox of a species, an organization, or an individual, and how do people choose between heuristics? The second is normative: In which situations is a heuristic better than a complex strategy? This question is known as the study of the ecological rationality of heuristics, which proceeds using analysis and simulation. The third question is one of intuitive design: How can systems be designed that help experts and laypeople make better decisions, be it in developing simple rules for safer financial regulation or improving medical diagnosis? The methodological tools are threefold: formal models of heuristics (instead of vague labels such as “System 1”), competitive testing of heuristics against complex strategies (instead of null hypothesis testing), and tests of the predictive power of heuristics (instead of data fitting). The heuristic revolution complements the probabilistic revolution and overcomes the earlier misconception of heuristics as biases.
 |}
 |}<!-- B -->

@@ Line 108: / Line 108: @@
 </b><br>In this webinar you will learn techniques and practices in Model-Based Design to verify and validate software designs and embedded code using MathWorks tools. We will address requirements driven [[development]], model coverage testing, and static code analysis of embedded software.  About the Presenters: Nishaat Vasi is a product marketing manager at MathWorks. Since joining MathWorks in 2007, Nishaat has partnered with customers involved in high-integrity applications to promote the adoption of MathWorks tools for embedded software verification. He holds an M.S. in electrical engineering from University of Massachusetts and a B.E. in electronics engineering from University of Mumbai. Jay Abraham is a product marketing manager at MathWorks. His area of expertise is in software tools for the verification of critical embedded applications. He has over 20 years of software and hardware design experience. Jay has an M.S. in computer engineering from Syracuse University and a B.S. in electrical engineering from Boston University.  See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1
 Download a trial: https://goo.gl/PSa78r
 |}
 |}<!-- B -->

@@ Line 154: / Line 154: @@
 = Artificial Intelligence Center of Excellence (AI CoE) =
-(1) How to Set Up an AI Center of Excellence - Harvard Business Review. https://hbr.org/2019/01/how-to-set-up-an-ai-center-of-excellence.
+* [https://hbr.org/2019/01/how-to-set-up-an-ai-center-of-excellence How to Set Up an AI Center of Excellence - Harvard Business Review]
-(2) How To Create an Enterprise 'AI Center of Excellence' -- Pure AI. https://pureai.com/articles/2019/01/17/ai-coe.aspx?m=1.
+* [https://pureai.com/articles/2019/01/17/ai-coe.aspx?m=1 How To Create an Enterprise 'AI Center of Excellence' | Pure AI]
-(3) Artificial Intelligence Center of Excellence (AICoE) - WordPress at UD. https://sites.udel.edu/ai/.
+* [https://sites.udel.edu/ai/ Artificial Intelligence Center of Excellence (AICoE) - WordPress | UD]
-(4) Four Steps For Building An AI Center Of Excellence. https://www.forbes.com/sites/forbestechcouncil/2022/12/09/four-steps-for-building-an-ai-center-of-excellence/.
+* [https://www.forbes.com/sites/forbestechcouncil/2022/12/09/four-steps-for-building-an-ai-center-of-excellence/ Four Steps For Building An AI Center Of Excellence | Forbes]
-Establishing an AI Center of Excellence is a strategic move that can significantly enhance your organization's AI capabilities. Remember that AI adoption is an ongoing journey. Continuously monitor progress, iterate on your strategy, and adapt to changing technology and business landscapes. By following this plan, your organization can establish a robust AI CoE that drives innovation, efficiency, and value across the board. Here's a plan to guide your organization in adopting AI effectively and maximizing its value:
+Establishing an AI Center of Excellence is a strategic move that can significantly enhance your organization's AI capabilities. Remember that AI adoption is an ongoing journey. Continuously monitor progress, iterate on your strategy, and adapt to changing technology and business landscapes. By following this plan, your organization can establish a robust AI CoE that drives innovation, efficiency, and value across the board. Here's an outline to guide your organization in adopting AI effectively and maximizing its value:
 * <b>Create the AI Vision:</b>
@@ Line 176: / Line 176: @@
 * <b>Manage External Partnerships:</b>
 ** Collaborate with external partners, such as AI vendors, research institutions, and industry experts.
-Leverage their expertise, tools, and technologies to accelerate AI development.
+** Leverage their expertise, tools, and technologies to accelerate AI development.
-Establish clear communication channels and expectations.
+** Establish clear communication channels and expectations.
 * <b>Identify AI Champions:</b>
 ** Identify individuals within your organization who are passionate about AI and can drive its adoption.
@@ Line 188: / Line 188: @@
-<youtube>pkJkHB_c3nA</youtube>
+<youtube>2ZpvLW7S33k</youtube>
-<youtube>pkJkHB_c3nA</youtube>
+<youtube>Go2mtZ_mjUU</youtube>
 = Heuristics =

@@ Line 19: / Line 19: @@
 [https://news.google.com/search?q=ai+Best+Practice ...Google News]
 [https://www.bing.com/news/search?q=ai+Best+Practice&qft=interval%3d%228%22 ...Bing News]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=ai+Best+Practice&qft=interval%3d%228%22 ...Bing News]
 * [[Leadership]]
 * [[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]]

@@ Line 22: / Line 22: @@
 * [[Leadership]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
-* [[AI Governance]]
+* [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
-* [[Data Governance]]
+* [[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]]
 * [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning: Best Practices for ML Engineering | Martin Zinkevich - ][[Google]]
 * [https://cloud.google.com/solutions/machine-learning/best-practices-for-ml-performance-cost Best practices for performance and cost optimization for machine learning |] [[Google]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, Facebook
+|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
 }}
 [https://www.youtube.com/results?search_query=ai+Best+Practice YouTube search...]
@@ Line 10: / Line 19: @@
 [https://news.google.com/search?q=ai+Best+Practice ...Google News]
 [https://www.bing.com/news/search?q=ai+Best+Practice&qft=interval%3d%228%22 ...Bing News]
 * [[Leadership]]