Tag: ai

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

Uncategorized

WHAT TOOLS ARE BEING CREATED TO ENSURE ARTIFICIAL INTELLIGENCE SECURITY?

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 Ish and he wants to know what tools are being created to ensure artificial intelligence (AI) security?

Ish, you came to the right place.  I did a little research and discovered an excellent report from the RAND Corporation.  Let’s review what they say, but first a word about the Rand Corporation.

The RAND Corporation is a non-profit policy analysis organization.  RAND was established in 1948.  Their mission is to advise governments and large corporation on matters of national importance.  RAND was contracted by the United Kingdom to examine the range of tools designed for the development, deployment and use of trustworthy AI in the United Kingdom and the United States.  Wow!  No easy task, Batman!  What did RAND conclude?

Let’s begin with tools for trustworthy AI.  RAND acknowledges the rather obvious point—there is not a universally accept definition for the term “tools for trustworthy AI”.  However, governments and large institutions have consistently articulated certain principles that would enhance trustworthiness in AI.  Those principles include fairness, transparency, accountability, privacy, safety and explainability.  Notwithstanding the lack of consensus, RAND made the attempt and offered the following definition:

Tools for trustworthy AI are specific approaches or methods to help make AI more trustworthy and can help to bridge the gap between the high-level AI principles and characteristics, on the one hand, and the practical implementation of trustworthy AI, on the other.

Based on their definition, RAND identified far more tools for developing trustworthy AI than what I expected—233 to be precise.  RAND divides the tool set into three categories:  technical, procedural, and educational.  You may be surprised to learn the US depends more on technical professionals and academia to develop AI trust tools when compared to the UK.  Additionally, large US corporation develop more trustworthy AI tools compared to their counterparts in the UK.  The UK prefers procedural methosds and relies less on academia. 

As expected, the RAND study offers a series of recommendations.  RAND offers the following guiding principle:

AI tools need to be complemented by a collaborative and inclusive approach that involves multiple perspectives and actors, such as governments, businesses, civil society, academia and international organizations.  In other words, be willing to listen to voices from across the spectrum.  RAND also suggested governments use the following methodology:

  1.  Link up with relevant stakeholders to proactively track and analyze the landscape of tools for trustworthy AI in the UK, the US and beyond.
  2. Systematically capture experiences and lessons learnt on tools for trustworthy AI, share those insights with stakeholders and use them to anticipate potential future directions.
  3. Promote the consistent use of a common vocabulary for trustworthy AI among stakeholders in the UK and the US.
  4. Encourage the inclusion of assessment processes in the development and use of tools for trustworthy AI to gain a better understanding of their effectiveness.
  5. Continue to partner and build diverse coalitions with international organizations and initiatives, and to promote interoperable tools for trustworthy AI.
  6. Join forces to provide resources such as data and computing power to support and democratize the development of tools for trustworthy AI.

Time to end with a proverb from the UK where they say: Every cloud has its silver lining.

Until next time,

Yogi Nelson

Artificial Intelligence, Blockchains, France, Yogi Nelson

A GLIMPSE AT MISTRAL—THE FRENCH LEADER IN THE AI REVOLUTION

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 Luis from Malibu and he wants to know is Mistral leading the French AI revolution?

Luis, you came to the right place. Your question has perfect timing. I just returned from France and am feeling the French vibe! Rather than covering the entire waterfront of AI developments in France, I’ll limit myself to a French company named Mistral.  Let’s start with a bit of background on Mistral, including its name which is French to the core–c’est la vie!

The word mistral has multiple uses in French.  Not only does Mistral mean a cold northwesterly wind, but it’s also not uncommon for companies in industries related to wind energy, sailing, or the environment to use it in their name. The French say it evokes a sense of power, speed, and a connection to the natural world. It can also mean masterly.  While traveling in France, I noticed Mistral as a last name as we say in the USA or as they say in France, the family name.  Holy multi-tasking word, Batman!  Now a word about the Mistral team and company mission.

With fewer than two dozen members, the Mistral team is small.  According to Mistral, their mission is to make frontier AI ubiquitous, and to provide tailor-made AI to all developers.  Mistral says, their mission requires fierce independence, strong commitment to open, portable, and custom solutions, and an extreme focus on shipping the most advanced technology in limited time.  How I discovered Mistral comes next, followed by what Mistral does, and what it offers.

Approximately three months ago I discovered Mistral while researching AI projects for this blog.  I attempted access but was denied.  Instead, Mistral put me on a waiting list of interested users and recently granted me access with a full disclosure that their large language model (LLM) is in beta testing status. 

Today, I put Mistral’s LLM, Le Chat, to the test against, two American companies—Open AI’s ChatGPT and Microsoft’s Co-Pilot– by asking all three identical questions.  All three-offer free access to their LLM services.  I won’t detail the contest; it’s not necessary.  Essentially, Mistral is lacking behind the competition.  Why Mistral trails, is a matter of speculation.  Perhaps its funding related?  Or maybe Mistral team size means it’s too small to compete with the titans? Could be they started later?  I don’t know, but I do know the competition is ahead.  Okay, now we go beyond their LLM to a preview of Mistrals AI products. 

On their website, Mistral has a click option labeled La Plataforme (the Platform).  This is where you’ll find the heart of their offerings and services.  Basically, La Plataforme is a subscription service.  For a fee, Mistral offers access to their AI developer tools. Mistral claims the tools permits users to develop AI agents and other related products.  The subscription grants access to the latest Mistral models and to pay based on what you use.  That’s a good feature.  Moreover, users can set monthly spending limits and if there are multiple users with an enterprise, Mistral will centralize billing.  The subscription grants access to the corresponding documentation and of course users can create API keys to access Mistral AI.

Time to say “au revoir” (goodbye) but not before sharing this French proverb:  Only imbeciles don’t change their opinions.”   Well said, my French friends!

Sincerely,

Yogi Nelson

Artificial Intelligence, Blockchains, China, international aid

IS CHINA USING ITS INTERNATIONAL AID TO PROMOTE ITS ARTIFICIAL INTELLIGENCE TECH AND GAIN MARKET DOMINANCE IN DEVELOPING COUNTRIES?

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Marcia in Palmdale, CA and she wants to know is China using its international aid to advance use of its artificial intelligence technology and gain market dominance in developing countries?

Marcia, you came to the right place.  You ask a tough question, fortunately there is an organization headquartered in Santa Monica, CA that researches the subject and their name is RAND.  RAND stands for Research and Development Corporation. Let’s understand RAND then answer your specific question.  

RAND is a non-profit public policy research organization.  RAND was established in 1948 with the intent of providing non-partisan policy research and analysis.  Almost 100% of RAND’s 1,800 research staff members have either a Ph.D. or master’s degree.   With seven offices spread across the globe in the USA, UK, and Australia, RAND has a worldwide audience.  Last year RAND launched 800+ new research projects funded by more than 350 clients.  RAND conducts policy research across 10 subjects, including national security.  Bottom line–RAND is an impressive research organization consisting of brainy people.  Holy nerds, Batman!  Now let’s turn to the question asked by Marcia.

According to RAND, at $85B per year, China is the world leader in development finance.  USA development finance falls a far second at only $45B per year.  RAND says, Chinese tech companies can use this large government funding support to deploy state-of-the-art artificial intelligence (AI) tools in development contracts in recipient countries. And therefore, the AI exports facilitated by these arrangements are likely to bolster China’s growing global AI technology-related supply chains, trade flows, technology standards, and regulatory systems.  In other words, when China provides development finance (aid) to Pakistan, for example, the contract with Pakistan, will include conditions that require Pakistan to use Chinese AI technology.  Holy foot in the door, Batman! 

Given the situation, RAND’s national security team was contracted (they don’t say by whom, but you can safety assume a branch of the U.S. government) to explore two questions:

1.  How can China’s AI exports be systematically tracked and analyzed using reliable data sources?

2.  What would promote a better understanding of the global landscape of China’s AI exports?

China does not participate in the International Aid Transparency Initiative.  Hence, researchers tracked Chinese development finance by receiving countries. Researchers used OCED (Organization for Economic Cooperation and Development) data in their research. The dataset captured all official financial and in-kind commitments from China from 2000–2021 to developing countries.

First, let’s look at the published findings of the 33-page report.   https://www.rand.org/pubs/research_reports/RRA2696-1.html  followed by a few words regarding unpublished findings, as perhaps those too are interesting. 

Reported Findings:

  • Researchers discovered 155 AI applications and AI infrastructure projects that were exported to 64 countries
  • The researchers used GIS to map their findings.  Although Pakistan was the largest beneficiary, the map clearly shows most Chinese aid was dedicated to African nations.   

Unreported Findings:

The report has six blank pages and no explanation for the blank pages is offered. The blank pages are not labeled, “Intentionally Left Blank”.  The pages are simply blank with no explanation whatsoever.  And no mention of what who paid for the report!  Holy mystery, Batman!

I end with a proverb from Pakistan:  The friend appears in hard times, not at big dinners.

Until next time,

Yogi Nelson