Tag: Artificial Intelligence

Artificial Intelligence, Blockchains

Implementing AI Governance: Practical Ideas for Business | KPMG Report

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question, was submitted by Rolando from Panama City, and he wants to know about artificial intelligence (AI) governance?

Rolando, you came to the right place. Plenty of people are asking the same question. Recently I came across an KPMG article titled, “The Shape of AI Governance to Come”, in which KPMG explored fundamental questions related to AI governance. KPMG is a major business consultancy firm and recognized thought leader in the field of AI. Let’s see what KPMG said, beginning with the notion of trust.

According to KPMG research, for AI to become transformative, four critical trust factors must be addressed: integrity; explainability, fairness, and resiliency. These four trust factors make sense and are not surprising. The newsworthy discovery is that 87% of IT decision makers advocate regulations to achieve trustworthy AI governance. If regulations are the solution, what is the current state of regulations? KPMG spotted several trends.

First, the most advanced AI markets are putting in place regulations to protect and promote domestic research and development. Second, governments are establishing advisory committees consisting of industry, academic, and government officials to produce guidelines, regulations, etc. For example, Austria passed an AI ethics law, Singapore adopted a 5-year AI plan, and Japan also approved an AI technology strategy. Laws and regulations are a third trend. The implications of these trends could be far reaching and hence worth an examination.

Clearly, governments hope to realize long-term benefits through increased overall operational efficiency for both the public and private sectors. Second, governments expect to create intellectual property related to AI, and thus obtain patents, copyrights, and trademarks for various AI intellectual property. Attracting and keeping talent is a third implication. People will want to work in a country leading the way in AI, thus setting the stage for tax and immigration policy debates.

KPMG acknowledges AI moves faster than politics, hence KMPG provides six AI governance ideas businesses can deploy now until politics catches up.

  2. 1. Develop AI principles, policies and design criteria, and improve controls within an environment that fosters innovation, flexibility, and trust while identifying unique risks. Inventory AI capabilities and use cases by understanding the AI footprint within the organization.
  4. 2. Design, implement, and operationalize an end-to-end AI governance and operating model across the entire AI development life cycle, including strategy, building, training, evaluating deploying, operating, and monitoring AI. Establish separate governance committees and councils to discuss the unique risks and complexities associated with AI.
  6. 3. Assess the current governance and risk framework and do a gap analysis to identify opportunities and areas that need to be addressed.
  7. 4. Design a cross-functional governance to deliver AI solutions and innovation through guidelines, templates, tooling, and accelerators to deliver AI solutions quickly, but responsibly.
  9. 5. Integrate a risk management framework to find and prioritize business-critical algorithms, and incorporate an agile risk mitigation strategy to address cybersecurity, integrity, fairness, and resiliency considerations during design and operation.

Time to go, but first a proverb from Suriname, where they say: You don’t need to see the top of the river to know which way the current is flowing.

Until Next Time,

Yogi Nelson

Artificial Intelligence, Blockchains, Productivity, Yogi Nelson

Leonardo AI: Image Generation Software Beyond Co-Pilot | Review

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Thomas from Los Angeles and he wants to know what other image generation software companies are available beyond Co-Pilot?

Thomas, you came to the right place.  As you know, I constantly compare image generation artificial intelligence software programs.  Generally, I use Co-Pilot by Microsoft.  However, your question trigger curiosity so I decided to broaden my options. During my research I discovered Leonardo AI and today, I’ll give my assessment.  However, I should start with a few words about Leonardo AI.

Leonardo AI is an Australian AI company specializing in image generation using artificial intelligence.  Although Leonardo AI is new and small, it is catching fire.  For instance, 1.2M artists have used the product to create over 1B images and more than 2.7M videos.  Holy momentum Batman!

Leonardo AI offers three products:  AI art generation; AI video generation; and transparent RPG generator.  Let’s examine each, beginning with AI art generation.  Essentially, Leonardo AI allows users to write instructions (called prompts in AI speak) to create images using artificial intelligence.  Check out the image I created using Leonardo AI.  Somewhat accurate self-portrait but I don’t have facial hair. My prompt (instruction) said:

Create an image of a 66-year-old Hispanic man with long hair in Puerto Rico using his laptop computer to learn image generation while sitting in a setting with vibrant colors. 

With Leonardo AI’s, Motion, you can turn your AI images into videos!  I tried it–loved it!  Impressive results for sure.  Basically, you can create a short video for a commercial or a special occasion.  Last, Leonardo AI offers a transparent RPG generator.  This function allows users to remove unwanted background visual elements from videos and images.  Now it’s time to discuss tasks Leonardo AI says are ideal use cases.

Topping the list, is using Leonardo AI to create a marketing campaign.  During a live presentation earlier this week, Jessie, Leonardo AI Senior Creative Creator, showed how the tools could be used to produce a campaign related to orange juice and apple juice.  The images she created were sensational, top quality, and engaging.  I did notice a troubling point.  Jessie instructed Leonardo AI to create an image of a woman drinking orange juice.  Jessie did not give any detail about what type of woman.  No race, age, appearance, etc.  Leonardo AI produced an image of a young, attractive, White woman.  Apparently, Leonardo AI was programmed with a bias.  Not good.

A second use case is AI graphic design.  For example, Jessie created lettering for the orange juice image poster that appeared to be made from orange fruit peel.  Without Leonardo AI, someone would need to physically peel the orange and shape the peel into the words orange juice, that would have been time consuming.  With Leonardo AI it took seconds to create, not hours.  Number three use case is AI for printed art.  With Leonardo AI you can constantly and affordably create printed art that is suitable for framing. 

Let’s conclude with a word about architecture and interior design.  Leonardo AI might be perfect for generating imaginative images if you are an architect or interior designer.  You can easily create endless possibilities for your clients at an affordable price point.

I conclude but not before leaving you with this proverb from Australia:  Keep your eyes on the sun, and you will not see the shadows.

Until next time,

Yogi Nelson

Uncategorized

WHAT IS THE CONNECTION BETWEEN MORGAN STATE UNIVERSITY AND XRP RIPPLE?

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Hiram from Los Angeles, and he wants to know, what is the connection between Morgan State University and XRP Ripple (XRP)?

Hiram, you came to the right place.  Not long ago, I too was wondering about the connection between universities who cater to people of color and blockchain technology.  Your question prompted me to investigate the issue.  I’ll share my discovery and the fascinating connection between Morgan State University (MSU) and XRP.  Let’s commence with a short description of Morgan State University and XRP.

The MSU campus is in Baltimore, Maryland.  MSU was founded in 1867 as a public university dedicated to higher education of African Americans during a time in American history when Black people could not enter “main-stream” universities due to the rampant and institutional racism of the USA.  Today, MSU offers bachelors, masters, and doctorate degrees across dozens of fields, including computer science.  MSU of course fits into the category of Historically Black College and University (HBCU). The MSU campus was declared a national treasure by the National Trust for Historic Preservation!  Holy precious jewel, Batman!  What about XRP, you ask?

XRP is a San Francisco based blockchain and crypto currency company specializing in efficient and low-cost international money transfers.   Unlike Bitcoin, XRP uses a proof of stake consensus and a network of validators, making transactions faster and more energy efficient. XRP established the University Research Blockchain Initiative (URBI) in 2018 with the intent of fostering innovation for the next generation of blockchain engineers at Historically Black Colleges and Universities.  A blockchain with a heart, Batman!

Partnering with universities is not new to XRP.  In fact, XRP has 50+ university partners spread across 26 different countries, has developed the curriculum for 600+ fintech courses, and sponsored over 1,500 research projects.  Essentially, the foundation arm of XRP, known as URBI as noted above, deliberately focuses on developing international software engineering talent.  This makes perfect sense given XRP is in the international money transfer business. 

Let’s now discuss the nature of their partnership. Initiated in 2018, the original collaboration between MSU and XRP bolstered technological bandwidth, set new academic excellence standards, and cultivated a culture of innovation. The partnership gave rise to innovative programming such as the student-led “Morgan-Hack-A-Thon”, which drew about 150 students from 7 HBCUs and featured an XRP Ledger workshop and Finance Challenge Statement from XRP.  Under the terms of the renewed three-year partnership, XRP will donate another $1.05M to MSU.  The additional funding will lead to the creation of opportunities for new programming and research for students and faculty, says XRP.   Holy renewal of vows, Batman! 

From the perspective of MSU, they aim to boost on-going efforts, while also engaging other HBCUs, says, Ali Emdad, MSU Business School Dean. MSU also notes the importance of combining collaborative research with real-world applied learning opportunities, particularly in the fin-tech space.  Ultimately, the goal is to nurture a pipeline of diverse, skilled, and highly trained professionals ready to excel in enterprise blockchain jobs.  The partnership envisions activities that include cutting-edge research endeavors, faculty-led projects intersecting blockchain, artificial intelligence (AI) and machine learning (ML), XRPL development workshops, and an annual national blockchain and fintech conference.  Holy practical partnership, Batman!

I end with this proverb from Belize, where they say: Monkey knows from which limb to swing!

Until next time,

Yogi Nelson

Uncategorized

ARTIFICIAL INTELLIGENCE AND AUTOMOBILES, WHAT’S THE LATEST?

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Stelios from California and he wants to know what is happening with artificial intelligence (AI) in automobiles?

Stelios, you came to the right place.  After you asked me this question, I decided to do a little research and found an informative article from PwC.  I’ll start with a word about PwC and thereafter dive into their findings. 

Who is PwC?  PwC is one of the Big 4 accounting firms in America.  PwC is also a major international firm, with offices across the globe.  In addition to accounting services, PwC offers business consulting, market intelligence, and strategic development.  Recently PwC produced a report titled, Is GenAI a game changer for the e-mobility ecosystem? 

https://www.pwc.com/m1/en/publications/is-gen-ai-a-game-changer-for-the-emobility-ecosystem.html

Okay, now it’s time to examine the fundamental premise from where PwC begins.

Anyone who has purchased a new car recently knows automobiles are increasingly more electronic and software driven than mechanical.  The trend started 10 – 15 years ago and has accelerated quickly.  PwC says, software and electric vehicles will be the largest value pool of cars by 2030!

With that as a baseline premise, PwC explored the following question “… how can Generative AI (GenAI) catalyze a transformative revolution in the electric mobility sector leveraging its novel capabilities to build predictive models, develop bespoke services, optimize processes across the value chain and much more”.  Holy forecasting insight Batman! Let’s check out the six predictions in greater detail below.   

Research and Development (R&D).  PwC asserts AI will accelerate research and development (R&D) of new materials and testing processes and thereby reduce production of prototype timelines drastically.  PwC envisions AI will analyze a wide range of potential battery chemistries, for instance.

Generative Design: PwC reports that by using GenAI to analyze the significant increase in data, more design possibilities are accessible.  GenAI enable the design process leading to innovations in and optimized vehicles. 

Demand Forecasting and Inventory Optimization:  Essentially, the argument PwC makes here is that with GenAI automobile manufacturers will be able to forecast with greater precision and thus reduce waste.  For example, the GenAI system will identify demand patterns, seasonality, and potential fluctuations.    

In-Car Personalization:  This one could be particularly cool if done right.  PwC says that with GenAI user preferences, historical user interactions, sensor data, etc., could be used to customize the driving experience to the vehicle owner. We need this for the Batmobile, Batman!

Intelligence Energy Management Systems:  Clearly an electric vehicle is energy efficient, that we know.  However, the point PwC makes goes beyond a particular car and includes the entire production chain.  Wow, I hadn’t considered that.  Good point.

Traffic Control and Optimization:  With GenAI says PwC, traffic patterns, route optimization, and of course congestion management. 

Although AI in cars has existed for 10+ years, PwC concludes with the assertion that GenAI technology will hit the mass market by 2030 and become the dominant technology.  I don’t think there is any reason to disagree. What say you, Stelios?

I conclude with a proverb from Chile, where they say: liar falls faster than a thief. 

Until next time,

Yogi Nelson

Uncategorized

SHOULD YOU BE WORRIED IF YOU LAWYER USES ARTIFICIAL INTELLIGENCE (AI)

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Esquire Nave and he wants to know if he should be worried if his lawyer uses artificial?

Attorney Nava, you came to the right place.  Great question I found an interesting article on this subject, titled AI on Trial, Legal Models Hallucinate in 1 out of 6 Queries. https://hai.stanford.edu/news/ai-trial-legal-models-hallucinate-1-out-6-queries. The report was produced by Stanford’s Human AI Center.  Let’s examine their findings and the implications for the legal profession, beginning with basic findings of fact.

Large language models such as ChatGPT, are not ready for prime time in the courthouse, is the headline.  Stanford’s HAI Center study reports error rates were as high as 69%.  Holy inadmissible evidence Batman! U.S. Supreme Court Chief Justice felt compelled to make a point regarding this problem in his 2023 Annual Report on the Judiciary https://www.supremecourt.gov/publicinfo/year-end/2023year-endreport.pdf

Is there a solution?  People working on retrieval augmented generation (RAG) say yes, and they have it.  According to RAG advocates, RAG reduces hallucinations in domain-specific contexts. RAG proponents claim they can avoid hallucinations and can guarantee legal citations.  Essentially, RAG systems promise to deliver more accurate and trustworthy legal information by integrating a language model with a database of legal documents. Can RAG advocates deliver on these bold claims?  Not yet says, Stanford’s HAI Center.

Stanford put two RAG providers to the test, Lexus-Nexus and Thomson-Reuters.  Stanford found RAG providers are less prone to hallucinations when compared to ChatGPT, etc.  However, given that non-RPG models produced an error rate of 69% that wasn’t much of an accomplishment.  According to Stanford, RAG models hallucinated 17% (1 of 6) when prompted.  Which brings us to the next question, what types of queries were asked of the RAG models.  Answer, there were four basic types.

The Stanford HAI Center says they prompted the models with the following four types of queries:

1) general research questions (questions about doctrine, case holdings, or the bar exam);

2) jurisdiction or time-specific questions (questions about circuit splits and recent changes in the law);

3) false premise questions (questions that mimic a user having a mistaken understanding of the law); and

4) factual recall questions (questions about simple, objective facts that require no legal interpretation). 

The RAG models fell into one of two types of hallucination errors.

The first was to describe the law incorrectly or make a straight factual error.  The second type of error was mis-grounded.  When mis-grounded errors were produced, the AI model describes the law correctly, but cited source which did not support its claims.  Either way, a problem; a big problem.  Mis-grounded hallucinations might be more troublesome because they undermine the point of AI in legal research; namely, AI reduces the time required to conduct research.

Stanford identified three challenges facing RAG model developers.  First, legal retrieval is difficult because the “law” is more than verifiable facts; it also consists of rulings and interpretations.  Second, the complexity of American jurisprudence means retrieval of inappropriate authoritative documents happens often. Last, sycophancy is a concern.  Sycophancy is the tendency of RAG models to agree with the user’s incorrect assumptions. 

Bottom line–RAG models are superior to ChatGPT but require greater transparency, refinement, and not yet ready for Broadway.

We end with a proverb from Estonia:  One fool can ask more questions than 10 men can answer.

Until next time,

Yogi Nelson