Tag: ai

Artificial Intelligence, Blockchains, Patents

How are DARPA, Explainable Artificial Intelligence and Nvidia Connected? 

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, comes from Susan in Irvine and she ask how are DARPA, Explainable Artificial Intelligence (XAI) and Nvidia all connected?

Susan, you came to the right place.  As a wealth manager I know you have a fiduciary duty to maintain current on investable technology on behalf of your clients.  Around 1990 a new technology was on the horizon–the internet–and it created trillions in new wealth!  Artificial intelligence (AI) is on a similar trajectory.  However, AI is not well understood–yet.  Let’s take a moment to acquaint ourselves with AI from the perspective of DARPA. In hindsight, DARPAs XAI program could have been used to foresee a tremendous investment opportunity—Nvidia the advanced computer chip maker essential to the AI business.

DARPA is an acronym that stands for Defense Advanced Research Projects Agency.  DARPA is part of the Department of Defense (DoD) and its mission is development of emerging technologies for military use.  DARPA created the internet in the late 1960s to facilitate control and command of military communications.  Under DARPA leadership, the US military maintains technological superiority over all other nations. 

In 2015 DARPA released an article titled, Explainable Artificial Intelligence (XAI).  https://www.researchgate.net/publication/356781652_DARPA_’s_explainable_AI_XAI_program_A_retrospective     Remember the XAI project was undertaken in 2015–years before the public had access to ChatGPT and other similar products.   Let’s examine the research findings.

According to DARPA in 2015, “…dramatic success in machine learning will lead to numerous AI applications.  It appears AI will eventually produce autonomous systems that will perceive, learn, decide, and act on their own”, DARPA predicted.  However, DARPA was concerned the effectiveness of AI systems would be stymied unless and until machines can creditably explain their decisions and actions to human users. Holy transparency, Batman! Therefore, the XAI program was intended to create a suite of machine learning techniques that would:

  • Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and
  • Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

XAI program was focused on the development of multiple systems by addressing challenges in two areas: 

  • machine learning problems to classify events based on multimedia data  
  • machine learning problems to construct decision policies for an autonomous system to perform a variety of simulated missions

DARPA selected these two areas because both represented major operational challenges.  The first relates to classification and reinforcement learning and the second is centered on intelligence analysis and autonomous systems.  In other words, DARPA was working on building AI machines that understand the context and environment in which they operate, and over time allow them to characterize real world phenomena precisely and in real time.  Basically, DAPRA was developing AI-Gen 2 before the public had access to AI-Gen 1! Holy Explainable AI, Batman! 

DARPA released XAI seven public findings of the XAI program and kept an unknown number of classified secret findings! LOL. Holy keep it on the down-low, Batman! LOL!

  1. Users prefer systems that include explanations rather than just answers (no surprise)
  2. To improve end results the task must be sufficiently difficult that the XAI explanation helps (makes sense)
  3. User cognitive load to interpret explanations can hinder user performance (in other words there are times when too much explanation is a negative)
  4. XAI is more helpful in edge cases
  5. Explanation effectiveness can change over time
  6. XAI combined with an advisor helps (no kidding, LOL)
  7. XAI is effective for aligning mental models

What’s are the two takeaways?  Obviously, DARPA did not leak military secrets on the internet and only disclosed the minimum. However, DARPA did leave an interesting information trail in 2015 that had we followed it, could have been a monster opportunity.  The XAI 2015 article was a glimpse into the future and we might have forecasted the rise of Nvidia.  Holy unicorn, Batman!  Nvidia stock has since soared, and we could have, should have…

I end with a Russian proverb: trust but verify!

Until next time,

Yogi Nelson

Artificial Intelligence, Blockchains, Sports AI

IS THERE A ROLE FOR ARTIFICIAL INTELLIGENCE (AI) IN SPORTS?

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your technology questions are answered, mostly correct!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Riezl in Las Vegas and she wants to know is there a role for AI in sports?

Riezl, you came to the right place.  You pose a fascinating question worthy of a thorough answer. The answer is yes!  I will discuss how AI may impact sports betting, sport analytics, and sports medicine.  Let’s start with sports betting, but first a disclosure. I do not endorse gambling, nor any company mentioned in this article.   

SPORTS BETTING

MySports.AI is an application that uses advanced machine learning algorithms, to predict outcomes. A unique feature of Mysports.AI, is its ability to customize different betting strategies for each player, catering to various funds and betting cycles. The platform provides back-tested records, allowing players to analyze the profitability of various events under each betting strategy. Mysports.AI backers say the model has undergone tens of thousands of machine learning training sessions.

SPORTS ANALYTICS

Sport analytics was popularized by the movie, Money Ball.  Sports analytics involves collecting and analyzing relevant statistics to facilitate decision making.  Acceptance of analytics in sports is commonplace and soon AI will join the club.  New York University offers a sports analytics certificate https://www.sps.nyu.edu/professional-pathways/certificates/sports-management/sports and a required course is artificial intelligence and machine learning!

Sports are competitions set within a multi-billion-dollar business milieu.  Not surprisingly there are on-field and off-field analytics. On-field refers to improving the performance of teams and players.  For example, in the NBA the analytics indicate superior three-point shooting is essential.  No team can expect to hoist a championship trophy if they are dearth of three-point shooters. Holy Stephon Curry, Batman!  AI algorithms are being used in Formula 1 to improve racing tactics and to automated tire replacement strategies. Piccinotti (2021).  Even tennis racquets are going high-tech with the advent of rackets based on Real HawkEye computer vision system data! There are several on-field analytics AI firms including  GeminiSports.com.

Last, certain sports require considerable human judgment to determine the winner, i.e., gymnastics and ice skating. Recently, Fujitsu created an AI to analyze the performance of a gymnast’s movements using 3D laser sensors (Atiković et al., 2020).

Off-field analytics is concerned with the business of sports. Teams have multiple revenue sources, including ticket sales, merchandise, parking, television broadcast, etc. and expense centers. AI powered sports business analytics can discover ways to maximize revenues, reduce costs, improve fan acquisition, marketing strategies, and perhaps the financial impact of a new sponsorship.  As shown by Chmait et al. (2020a), the relationship between the success of athletes and their salaries is an important value proposition to explore. GWI.Com and others are focused on AI in sports business.

Sports Medicine Analytics and AI

If we enter the time machine and travel to 1995, we discover researchers such as Bartlett discussing potential applications of artificial intelligence in sports performance! In 2006, Bartlett published Artificial Intelligence in Sports Biomechanics: New Dawn or False Hope, wherein he correctly predicted multi-layer artificial neural networks (aka Deep Learning) will take center stage in sports technique analysis.  Other researchers such as Brockett, are using AI to detect patterns that lead to injuries. Fister et al. (2015) has researched whether nature-inspired AI algorithms can investigate safe and effective training programs since 2015.

I end with a proverb from Rwanda:  All proverbs have a good reason for existing.

Until next time,

Yogi Nelson

Artificial Intelligence, Yogi Nelson

IS BULGARIA A LEADER IN ARTIFICIAL INTELLIGENCE EDUCATION?

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your technology questions are answered, mostly correct! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question, comes from Fernie, in Las Vegas, and he wants to know if Bulgaria is a leader in artificial intelligence education and research?

Fernie, you came to the right place. Rather than answer directly, I will share information about a certain institute located in Sofia, Bulgaria, and let you decide. Let’s jump right into it by shouting out the Institute for Computer Science and Artificial Intelligence Technology, INSAIT.
https://insait.ai/

INSAIT is a mere toddler; it was founded in April 2022. INSAIT is the first university in Eastern Europe dedicated exclusively to computer science and AI technology education. INSAIT is part of Sofia University, but functions autonomously. Holy independence, Batman!

INSAIT is backed by technology elites, including two of Switzerland’s most prestigious universities and global technology behemoths, i.e., Google, DeepMind, and VMware, etc. INSAIT says its sole focus is on scientific excellence, conducting world-class research, attracting outstanding international scientist, and training the next generation of technology leaders. Let’s peruse their course offerings, beginning with machine learning.

Machine Learning. INSAIT defines machine learning as the area of computer science that focuses on extracting knowledge from large data sets and using this knowledge for making predictions and decision making. INSAIT believes the combination of colossal data sets and increasing access to powerful computation means there are exciting opportunities for data-driven algorithms.

Computer Vision. According to INSAIT, computer vision is an interdisciplinary field that studies algorithms through which computers can comprehend images at a high level. INSAIT says, and with good reason, there are numerous applications for computer vision, including autonomous driving, robotics, object recognition, gesture analysis, tracking, scene understanding, medical images, and embodied AI. Will AI be peeking through my window? LOL.

Quantum Computing (QC). Quantum computing is hyper computing. QC can leverage the properties of quantum states. Look out!!! QC is revolutionary and will make what is now impossible or impractical, possible in seconds. Holy no speed limits, Batman! INSAIT is focused on the software side of QC, including programming languages, optimization, verification, and algorithms.

Cyber Security. INSAIT intends to become a cyber security tour-de-force. INSAIT says it will focus on cryptography and developing new security protocols, including communication systems. Holy bat shield, Batman!

Automated Reasoning. Reasoning is what makes human intelligence and allows us to draw conclusions based on logic, says INSAIT. Not surprisingly, INSAIT has a focus on automated reasoning related to fundamental math and logic questions. Their automated reasoning program examines decision making under uncertain conditions. Fabulous! All of life works under uncertain conditions! LOL!

Natural Language Processing. Speech perception is central to human language. According INSAIT, natural language process is the study of computational techniques for automated processing and analysis of speech and text. INSAIT aims to develop principled algorithms for processing massive amounts of text and speech data and insights based on linguistics. INSAIT keep in mind, the spoken word is only half of all communication!

INSAIT also supports graduate studies in data management, algorithms, and computer programming languages. A complete buffet of AI graduate education for the amazing price of $36,000 per year, including room and board. If I were 22 again I would … but I’m closer to 122 LOL.

Time to end with a proverb from Bulgaria, where they say, “A slip of the foot is not nearly as dangerous as a slip of the tongue.

Until next time,

Yogi Nelson

Artificial Intelligence, Yogi Nelson

IS IT POSSIBLE TO EARN $10M PROVING WEAK AI CAN SUPERVISE STRONG AI?

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your technology questions are answered, mostly correct! Here no question is too mundane. As a bonus, a proverb is included. Today’s question is from Victor, in Altadena, CA. He wants to know if he can earn $10M by developing a Weak artificial intelligence (AI) system capable of supervising a Strong AI.

Victor, you came to the right place. On December 14th, Open AI released a research paper titled, Weak-to-Strong Generalization (system) in which they explored the very question you asked!

https://openai.com/research/weak-to-strong-generalization

Open AI starts with a premise; the premise is Strong AI systems will be vastly more intelligent than humans in 10 years. Holy left behind Batman! Open AI says our current method of controlling AI relies on human supervision. However, future AI systems (Strong AI) will be capable of extremely complex and creative behaviors making it difficult for humans to reliably supervise them. Sounds like a sci-fi horror movie! Based on that premise, OpenAI has the following hypothesis–humans can control weak AI systems and weak AI systems can be designed, built, and used to control much stronger AI systems. Therefore, controlling Strong AI systems in the future should be possible. OpenAI says their hypothesis has been proven correct in a lab setting. Let’s unpack their research and findings.

I’ll quote from the report. “The research suggest there remains important disanalogies between our current empirical setup and the ultimate problem of aligning superhuman models.” In other words, more work is needed.

Open AI says, it may be easier for future models to imitate weak human errors than for current strong models to imitate current weak model errors, which could make generalization (control) harder in the future. Nevertheless, we believe our setup captures some key difficulties of aligning future superhuman models, enabling us to start making empirical progress on this problem today.” Said succinctly, we know managing superhuman models is likely to become a major issue, but we are working on it! Are you reassured?

Let’s examine the three findings:

Finding 1: When ChatGPT2 was used to supervise GPT-4, the resulting model typically performs somewhere between GPT-3 and GPT-3.5 and Open AI was able to recover much of GPT-4’s capabilities employing weaker supervision.

Finding 2: Naive human supervision, such as reinforcement learning from human feedback, could scale poorly to superhuman models without further work.

Finding 3: It’s feasible to substantially improve weak-to-strong generalization.

After discovering that Weak AI in the lab was able to control Strong AI, Open AI says it is committed to undertake two actions.

To start, AI has committed to releasing open source code to make it easy to get started with weak-to-strong generalization experiments today. In other words, the computer codes used in the research will be publicly available to researchers thus putting more brain power to work. Fantastic! https://openai.com/blog/superalignment-fast-grants

Second, OpenAI will launch a $10 million grants program for graduate students, academics, and other researchers to work on superhuman AI alignment broadly. https://openai.com/blog/superalignment-fast-grants. Outstanding!

In summary, the Open AI report said, 1) managing future superhuman AI models is likely to be a problem; 2) preliminary research suggest Weak AI could manage Strong AI models; 3) more research is needed; and 4) $10M in grants is available to any one capable of designing a Weak AI system capable of controlling a Strong AI system.

We end with a proverb from Mali, where they say: When an old woman dies, a library burns to the ground.

Until next time,

Yogi Nelson

Artificial Intelligence, Yogi Nelson

WHAT IS THE STATE OF AFFAIRS IN ARTIFICIAL INTELLIGENCE SYSTEMS?

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your technology questions are answered, mostly correct! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question, comes from Richard, in Los Angeles, and he wants to know what is the state of affairs in artificial intelligence systems?

Richard, you came to the right place. There has been an explosion in recent months of companies announcing large language model (LLMs) AI projects. For this article I’ll refer to LLMs as AI systems. Let’s examine the situation by asking various AI systems to introduce themselves by answering this question:

   Can you please describe yourself and your capabilities in 50 words or less?

Below I cut and paste their respective responses, where available, verbatim.

ChatGPT by Open AI. OpenAI is the California based software company behind ChatGPT.

“I am ChatGPT, a sophisticated AI language model created by OpenAI. With extensive training on diverse topics, I excel in generating human-like text, answering queries, and providing information. My abilities encompass a wide range of subjects, making me a versatile tool for communication and knowledge sharing.”

Bard by Google. Bard is a product of Google, a California company that needs no introduction.

“I’m Bard, a brainy AI from Google. I can chat, write, translate, and more, using knowledge from the real world to fuel your words. Ask me anything! (49 words)”

Notice Bard used 27 words but claims it used 49! Apparently, Bard cannot count! Lol.

Co-Pilot by Microsoft. Microsoft calls its AI system Co-Pilot or Bing which is the name of Microsoft’s search engine.

“Certainly! I am Bing, an AI language model. My capabilities include generating creative content, answering questions, providing summaries, and more. I can write poems, stories, code, and engage in interesting conversations. Feel free to ask, and I’ll do my best to assist!”

Mistral: Mistral is an AI company from Paris. Their website says the project is under construction. I tried to log on, and instead received an email indicating access was by invitation only! Ouch! How rude! Or as they say in France, Impoli! Lol! I don’t want to be a member of a club that would have me as a member! Lol!

Olympus by Amazon. According to Reuters, Amazon is investing millions in training an ambitious large language model, expecting to surpass ChatGPT and Bard. As with Mistral, Amazon is late. Perhaps Jeff needs to unretire and get back to work! Lol!

Gauss by Samsung. Samsung is tardy. When ready, Samsung says its LLM will have the capabilities to generate text, computer code, and images based on machine learning technology. It is not just one model, but a family of models, Samsung Chairman Lee explained to Business Korea on December 18, 2023.

Grok by X. Grok is in beta testing. Why? Maybe Elon is busy on his rocket ship or pumping garbage crypto meme coins. Lol! On November 2, Musk tweeted “Grok is intended to answer almost anything and suggest what questions to ask.” Musk claims Grok will possess a sense of humor, hopefully not his. Lol! What makes Grok superior according to Musk is “… Grok has real-time knowledge of the world via the X platform and will answer ‘spicy questions’ that are rejected by most other AI systems.” Knowledge on X? I guess that’s the humor! Lol!

I will close with a proverb from Togo: A man that does not lie shall never marry. Brother, you got that right for sure! Lol, lol, lol!
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Until next time,

Yogi Nelson