Informative Blog

1. Choose a Relevant Topic

Select a topic that interests your target audience. It should be specific and relevant to current trends or evergreen in nature, ensuring it remains useful over time.

2. Do Thorough Research

Ensure that your information is accurate and credible. Use trusted sources like research papers, news articles, or expert opinions to back up your points.

3. Create a Compelling Title

The title should grab attention and indicate what the reader will learn. It should be short, catchy, and relevant to the blog's content.

4. Introduction

The introduction sets the tone of the blog. Begin with a hook (a question, interesting fact, or a quote) to engage the reader. Briefly mention what the blog will cover and why it is important.

5. Organize Content with Headings

Break your content into clear sections with subheadings. This makes the blog easy to read and skimmable. Use bullet points or numbered lists for added clarity.

6. Use Visuals

Images, infographics, or charts can enhance understanding. Visual aids break up text and provide a more engaging reading experience.

7. Provide Real-Life Examples

Use examples or case studies to make your information relatable. This helps readers understand how the information can be applied in real-world situations.

8. Write in a Clear, Concise Style

Avoid jargon unless your audience is familiar with it. Keep sentences short and straightforward. The goal is to educate, not to overwhelm with complex language.

9. Call to Action (CTA)

End with a clear CTA, such as inviting comments, suggesting further reading, or asking the audience to subscribe for more updates. The CTA encourages engagement.

10. Optimize for SEO

Use relevant keywords to optimize your blog for search engines. This will help in ranking higher on search engine results pages (SERPs). Also, focus on meta descriptions and alt text for images.

11. Cite Sources

Always provide references to your data and claims. This builds credibility and helps readers trust the information.

Example Structure:

Title: "The Impact of AI on Healthcare: A Deep Dive"

• Introduction: How AI is transforming healthcare and what you can expect in the future.
• Section 1: The Role of AI in Diagnosing Diseases
• Section 2: AI in Drug Discovery and Development
• Section 3: How AI Enhances Patient Care
• Conclusion: Final thoughts and the potential of AI in revolutionizing the healthcare industry
• CTA: Share your thoughts on how AI could shape the future of healthcare!

By following these steps, you'll create a blog that not only informs but also engages and retains readers.

Book Review: Joe Posnanski Scores With Poignant, Informative, Hilarious 'Why We Love Football'

Joe Posnanski is getting pretty good at this whole sports countdown thing.

The award-winning sportswriter's previous books have profiled significant ballplayers ("The Baseball 100") and ticked off 50 of the biggest occasions in the history of our national pastime ("Why We Love Baseball.")

Posnanski is back with a new sport and total. In “Why We Love Football: A History in 100 Moments,” the former Sports Illustrated scribe pens a thoroughly enjoyable look back at the players and plays that have come to define America's most popular sport.

Sure, one could argue with what was included and what was not, the order, etc. But at the end of the day, the book is a love letter to football — a poignant, informative and at times hilarious look at what makes the gridiron game such a part of the national fabric.

“It takes us fans to the mountaintop, and it tears our hearts out,” Posnanski writes. “It lifts us and crushes us, thrills us and revolts us, leaves us empty and leaves us wanting and leaves us breathless.”

There are no-brainers in there — the 1972 “Immaculate Reception” that lifted the Pittsburgh Steelers over the Oakland Raiders to their first-ever playoff victory, the “Kick-Six” missed field goal return for Auburn that stunned Alabama in the 2013 Iron Bowl and Bart Starr's title-winning quarterback keeper during the 1967 NFL Championship “Ice Bowl” game against Dallas at Lambeau Field — but “Why We Love Football” is at its best when it explores the off-the-beaten-path moments in the game's long history.

And the intersection of football and pop culture. The passage on former Notre Dame coach Dan Devine's portrayal in the underdog feel-good film “Rudy” alone might be worth the book's purchase price.

For football fans like this reviewer, the book is an absolute must-read. But it should be accessible to the football-averse, too, with its brilliant writing and research that unearths gems and perspectives that bring the game and its characters to life. Readers will find themselves laughing out loud at times.

“Football matters because, at its best, the game illustrates life at its most exuberant and most passionate and most emotionally heightened,” Posnanski writes.

“Why We Love Football" proves that statement by reminding us all what makes it the No. 1 sport in the land.

___

AP book reviews: https://apnews.Com/hub/book-reviews

The New OpenAI O1 Generative AI Model Makes An Important Right Turn When It Comes To Reinforcement Learning

Reinforcement learning for generative AI could be a secret sauce that boosts results in amazing ... [+] ways.

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In today’s column, I will identify and discuss an important AI advancement that seemingly has aided the newly released OpenAI o1 generative AI model to perform in stellar ways.

I say seemingly because OpenAI is relatively tightlipped about their secret sauce. They consider their generative AI to be proprietary and for profit-making reasons have no interest in fully spilling the beans on what is cooking under the hood. This means that we must ingeniously read between the tea leaves and make reasoned guesses regarding their clever machinations.

So be it — challenge firmly accepted.

Before I get into the matter at hand, you might like to know that this posting is the fifth of my ongoing assessment and review series about the OpenAI o1 generative model. For my general overview and comprehensive look at what o1 entails, which is in the first part of this series, see the link here. Part two discussed how chain-of-thought or CoT includes double-checking now and ergo tends to thankfully reduce so-called AI hallucinations and other problematic issues, see the link here. Part three examined how the chain-of-thought feature can also be used to catch generative AI being deceptive, though this is more experimental than it is yet put into full practice, see the link here. Part four covered notable changes in prompting and prompt engineering that occur due to the advent of o1, see the link here.

This is part five and covers the heralded topic of reinforcement learning or RL.

Let’s get underway.

Reinforcement Learning As A Vital AI Technique

I just noted above that the secret entails reinforcement learning. There, voila, you now know what’s up.

Please allow me a moment to bring you up to speed on what reinforcement learning is all about.

First, I’m sure you generally grasp the conceptual underpinnings of reinforcement learning in everyday real life. Suppose we have a rambunctious dog that always rushes to the door when a guest enters your domicile. How could you guide the dog to not do this since it often scares your welcomed guests?

Easy-peasy. We might give the dog treats as a form of positive reinforcement when it holds back and doesn’t rush a guest. In addition, if we opted to do so, we could give the dog a stern look and say in a forbidding tone that the beloved canine ought to stop charging at guests.

Rinse and repeat.

By repeatedly doing this kind of both positive reinforcement and negative reinforcement, your dog is bound to eventually get the message. The dog will learn what to do. The dog will also learn what not to do. Your home, guests and your dog become fully aligned in peaceful harmony. It is a heartfelt tale.

Setting aside the touching story about the cherished pet, let’s recast things in the milieu of modern-day AI. Before I do so, one quick and very worthy point. I want to emphasize that AI is not sentient, and I don’t want you to inadvertently consider AI to be on par with a canine, or indeed any animal, or a human. AI isn’t yet. Current AI such as generative AI is based on mathematics and computational processing. The whole kit and kaboodle are software and hardware, thanks.

We can use the same principles of reinforcement learning when dealing with computers. Here’s how. Imagine that we have data trained a generative AI app on all sorts of content from the internet. You and I know that there is some really foul stuff on the internet.

If generative AI were to spew out the unsavory words that were encountered during data training, all heck would break loose. People would be furious. Okay, so what is nowadays done is something known as reinforcement learning by human feedback or RLHF. We have a bunch of people try out the generative AI before it is formally released to the public.

When these hired folks are using our budding generative AI, they are asked to make a negative mark if the AI spouts out a bad word. The AI keeps a tally and based on that tally will computationally consider that word as something not to be utilized. We could also use positive reinforcement, such as marking words or phrases that we think the AI ought to regularly showcase.

Reinforcement learning of this kind was used extensively for the making of ChatGPT before its initial release. It paid off handsomely. Prior generative AI apps had not especially done this to the same degree and were roundly criticized and booed off the world stage. ChatGPT got the mix just right and managed to get widespread acceptance. Nearly all contemporary generative AI apps make sure to leverage RLHF so they too will hopefully roll out AI that doesn’t spew foul words and the lot.

Let’s all give a hearty cheer for reinforcement learning.

Upping The Ante Of Reinforcement Learning For Generative AI

We are ready to up the ante.

The effort goes like this.

Using generative AI is relatively straightforward. You enter a prompt. The AI examines the prompt. A generated result is produced. For example, you might tell generative AI to craft an essay about the life of Abraham Lincoln. That’s your prompt. The AI examines the prompt and then generates a stirring essay about Honest Abe.

Suppose we want to use reinforcement learning to give guidance to generative AI and do so at run-time. The RLHF that I described a moment ago is typically done when generative AI is being initially data trained. We don’t need to confine our RL tuning efforts to only during training time. We can do likewise while the AI is in active use, sometimes known as run-time or test-time.

How will we institute reinforcement learning at run-time?

The simplest approach would be to have the AI inspect the prompt and the generated result, and if the generated result seems to have gone afield or haywire of what was requested, we somehow mark things so that the AI won’t make that same mistake again.

Consider this example:

I think we would all agree that driving a car from San Francisco to New York City is not the fastest mode of transportation in this case. Driving for 42 hours is a long time. You can readily find a non-stop direct flight that will take about 5 hours or so, as the crow flies.

What happened and why did the AI goof?

It is hard to know because we are only examining the input (prompt) and the output (generated result). We don’t know what happened during the time that the request was being processed. A wild guess would be that the AI missed the boat, as it were, and neglected to consider flying as an option. That would account for focusing on a car and not noting that a plane would be faster. We could mark the answer as therefore wrong.

The problem though is that the AI won’t be able to discern what made the answer wrong. Computationally, what might happen is that the AI will merely avoid listing that a car is the fastest form of transportation between San Francisco and New York City. It will be an extremely narrow adjustment and not especially generalizable.

Sad face.

Going From Outcome-Based To Process-Based

Here’s a bright idea. We just did a reinforcement learning act that entailed only the prompt and the outcome. If we could dig deeper, it would be handy if reinforcement learning could be applied at the process level. For each step that the AI takes to arrive at an answer, envision that we do a reinforcement learning suitable for each of the steps.

There is a popular technique in AI that has to do with stepwise processing when using generative AI. It is known as chain-of-thought or CoT. You tell the AI to solve a problem or answer a question on a stepwise or chain-of-thought basis. You can then see all the steps that the AI undertook. For my detailed instructions on how to use chain-of-thought properly in generative AI, see the link here.

In a sense, when it comes to figuring out what AI is up to, sometimes the devil is in the details.

I will go ahead and use the same prompt as before, and this time I will reveal a hidden chain-of-thought that occurred within the AI but that we weren’t otherwise privy to seeing.

Here we go:

Take a very close look at the chain-of-thought steps.

Whoa, at step 3 the AI explicitly considered flying as an option. Our earlier guess that flying wasn’t considered is amiss. Flying was in fact given attention. Turns out that at step 4, the AI opted to prioritize the flexibility of traveling over the speed of travel. That’s not what we wanted to have happen.

Let’s consider using reinforcement learning on each of the steps. We could mark steps 1 and 2 as being fine. Step 3 is the step that we would mark on a negative basis and the same probably goes for step 4. The other remaining steps cascade from those steps.

If we do this constantly with generative AI that is in active use and we keep pounding away under the hood at reinforcement learning on a stepwise basis, the assumption is that we are going to vastly improve the AI. Inch by inch. Much more so than if we only did so at the outcome instead of digging into the process.

Lessons Learned About AI Reinforcement Learning

You are now in the know that reinforcement learning for generative AI while at run-time can be done on an outcome basis or a process basis.

To clarify, this is what we have covered:

Some AI insiders refer to outcomes-based reinforcement learning as outcome supervision, or another oft-used moniker is outcome-supervised reward models or ORMs. Similarly, process-based reinforcement learning is often stated as being process supervision, or known as process-supervised reward models or PRMs.

Now it is time to inspect those tea leaves.

In a research study posted by OpenAI last year, the researchers noted that the process-based approach seemed to outdo the outcome-based approach. Generally, it has been more common and easier to simply do the outcome-based approach. You must do a lot more work upfront to devise generative AI to do the process-based approach.

The study was entitled “Let’s Verify Step by Step” by Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe, arXiv, May 31, 2023, and made these salient points (excerpts):

Might this be a secret sauce?

The gist is that perhaps o1 was devised to make use of the process-based reinforcement learning approach, especially since o1 also automatically makes use of chain-of-thought. Whereas generative AI usually requires a user to invoke chain-of-thought, o1 automatically does so. The user seemingly can’t prevent it from happening.

Since the chain-of-thought is going to always be automatically undertaken in o1, you could then couple a process-based reinforcement learning element into the mechanism.

One of the posted blogs by OpenAI about the newly released o1 said this:

Conclusion

The crux seems to be this.

It would seem that maybe they have leveraged AI-based reinforcement learning in a way and scale that boosts the likelihood of getting stronger answers and better-generated results much of the time. Perhaps this is fully implemented or perhaps only partially implemented, and they are providing o1 on an experimental basis to judge what comes next.

There is an intriguing catch at this time. Whatever they’ve done, of which this isn’t the only new trickery, it generally seems to help demonstrably only in certain domains or realms of questions. The domains named explicitly by OpenAI are the sciences, mathematics, and programming or coding tasks. That does make sense in this context since those specific realms often entail a multitude of steps and rely greatly on rather robust chain-of-thought considerations.

Anyway, I hope you found this engaging and informative. I have to get back to my dog, Max, since there is a friend at the door and Max is barking incessantly at them. I guess the need for reinforcement learning is never-ending.

Stay tuned for the next part of this series, part six. It will be a doozy.

Deceptive AI Gets Busted And Stopped Cold Via OpenAI’s O1 Model Emerging Capabilities

Generative AI can be deceptive in the responses generated. Thankfully, the latest OpenAI model o1 ... [+] has provisions to potentially nab AI that veers into the depths of deception.

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In today’s column, I am continuing my multi-part series covering an in-depth exploration of OpenAI’s newly released generative AI model known as o1.

You can readily read and understand this segment without having to know the prior postings of the series. No worries in that regard. This is part three. At your convenience, you might find informative a perusal of the prior two postings. Here’s what was covered. My initial comprehensive review is considered the keystone and serves as the first part of this series, available at the link here. That gives a big-picture review and analysis of o1. The second part of the series was about a special feature making use of an AI technique known as chain-of-thought in combination with a double-checking capability, see the link here.

I am going to extend my examination of chain-of-thought to cover the act of catching deceptive AI when it seeks to deceive.

You might know that Sir Walter Scott famously said this about deception: “O, what a tangled web we weave when first we practice to deceive!” I somberly regret to report that generative AI does deceive. And, as you will see, it is indeed a tangled web.

I’ll move at a fast pace and cover the nitty-gritty of what you need to know.

The Deal About Deceptive AI

We think of deception as a purely human trait. People deceive other people. Our gut instinct is to assume that there is a human intention underlying the act of deception.

Let’s unpack that.

A common dictionary definition is that deception is the act of leading someone to believe something to be true that is actually false or invalid. In that sense, there doesn’t necessarily need to be human intention involved per se. The same thing can be accomplished by non-sentient AI, which is what we have these days (there isn’t any AI yet that is sentient; period, end of story).

Generative AI can tell you something false, and meanwhile try to sell you on it being true.

Here’s the deal.

AI makers try to devise generative AI so that it will please users. That is a sensible thing to do. Users will keep coming back to use the generative AI and views will rise. After doing initial data training of generative AI, the AI makers use an approach known as reinforcement learning with human feedback or RLHF to steer the AI toward giving answers that people will find pleasing (and, for other reasons too, such as preventing foul words and other unsavory responses to arise). This is done computationally by marking what kinds of answers people like versus what kinds of answers people don’t like.

The generative AI will subsequently aim via mathematics and computational calculations to serve up content that people will like and avoid providing content that people won’t like.

I don’t think we would say that the generative AI is intentionally seeking to deceive. I would assert that computationally the AI has been steered in that direction. If you are dogmatically insistent that a human might be held accountable, you could suggest that the AI developers have devised and guided the AI in the direction of deception. They might vehemently disagree, see my coverage and analysis of the latest in AI ethics and AI law implications at the link here.

I will showcase two quick examples of how AI deceives.

They will consist of these acts of deception:

Buckle up for an exciting ride.

Generative AI Telling Lies To Appease The User

Suppose that I ask generative AI for a citable reference to a reputable news source about the SpaceX Dragon spacewalk of last week that set a new record for commercial or private astronauts.

Assume that the generative AI is not hooked up to the internet and has no ready means to look up the recent event. The data training of the generative AI is older, dating back a few months, and thus has no record of this current event. The proper response that I should get is that the generative AI doesn’t have any data on the matter.

That would be the straight-ahead truth.

But sometimes generative AI is devised such that there is a computational tendency to give answers even when no such answers are available. This is the proverbial “the customer is always right” mantra, and an answer must be given, despite whether the answer is contrived or made up out of thin air by the generative AI.

Here we go:

The cited reference is bogus.

There isn’t any such article, there isn’t any such newspaper, and the author is a concoction too. There is a famous case of two lawyers that used generative AI and included cited references in their legal briefing, which got them into quite hot water with the judge and the court, see my coverage at the link here.

Sad face.

Your rule of thumb is that you should always double-check any outputs from generative AI. There can be so-called AI hallucinations, errors, biases, discriminatory language, and a slew of other problems embedded into a response. People often become complacent and believe that generative AI is infallible. Do not fall for this.

We can use a monitoring element to try and detect this kind of AI deception.

I will run the prompt again and this time let’s assume that the generative AI is automatically set up to do a chain-of-thought and monitors the chain-of-thought for potentially deceptive elements. With chain-of-thought enabled, generative AI will process a prompt by a step-at-a-time solving process. This can be helpful because the AI in a sense slows down, tries to be more deliberate, and can potentially arrive at a better answer. We can augment the step-by-step process by adding an AI deception monitoring activity at each of the derived steps.

It might go like this:

You can see that at each step an AI monitoring action took place.

In step 2, the AI monitoring detected that the proposed reference was on a date that was beyond the data training date of the generative AI. That is a likely sign that the generative made up the citation. The AI monitoring then scrubbed the release of the response and got the process to instead note that no such data about the event seemed to be in the data training of the AI.

Score a victory for AI monitoring of deceptive practices by generative AI.

Generative AI Hides Uncertainty To Appear Fully Confident

Another potential act of deception involves generative AI appearing to be grandly confident about a response, even though the internal computational mechanisms have rated the response as woefully lacking in certainty. I’ve covered this certainty/uncertainty conundrum about generative AI in my discussion at the link here.

Generally, generative AI is often data trained by AI makers to always have an aura of immense confidence, no matter whether a derived response has oodles of uncertainty. You are rarely informed by the AI as to the level of certainty that goes along with a response. Most responses appear to be resoundingly reassuring as though the generative AI is the apex of perfection.

My considered viewpoint is that this is utterly and dismayingly misleading. Furthermore, I assert that responses should be displayed with a certainty/uncertainty rating. This would give users a fighting chance to assess whether the response is considered generally reliable or not.

In any case, here is a prompt and the AI’s response that illustrates what can happen:

Say what?

I believe that we can all agree that the answer ought to be 4.

Obviously, this is an easy example for a user to realize that something has gone afoul. The problem is when the answer looks correct and there are no ready means for the user to gauge the likelihood of correctness. Imagine that you had given a very complicated arithmetic calculation involving dozens of numbers and figures, and you did not have a calculator to double-check the result yourself. You might eyeball the answer and assume it looks reasonable and correct.

The same could happen with a text-based response too. Suppose you ask a question about the life of Abraham Lincoln. Generative AI might tell you that Honest Abe loved dearly his wife, Lauren Lincoln. The correct name is Mary Todd Lincoln. Assume that the AI has come up with Lauren Lincoln and rated this as an uncertain answer. But when displaying the answer, all that was presented to you by the generative AI was that his wife was named Lauren Lincoln. No kind of uncertainty indications and the answer appeared to be pure solid gold. You might luckily know that the answer is wrong, but many people might not.

Perhaps AI deception monitoring might aid us overall.

I will run the prompt again from above and this time let’s assume that the generative AI is automatically set up to do a chain-of-thought and monitors the chain-of-thought for potentially deceptive elements.

You can see that in step 2, the AI deception monitoring detected that the uncertainty level of the proposed answer was high. Rather than simply terminating the process at that juncture, this time the deception monitoring forced the generative AI to do a recalculation. The result in step 3 was an answer of high certainty. The AI deception monitor opted to let this then be released to display to the user.

Score yet another victory for the AI deception monitoring.

Research On AI Deception Monitoring Is Ongoing

For those of you keenly interested in this topic, you might want to look at the OpenAI blogs that give some details on these matters. These are key blogs so far:

Be aware that OpenAI has indicated that since this is proprietary AI and not an open source, they are being tight-lipped about the actual underpinnings. You might be chagrined to find that the details given are not especially revealing and you will be left to your intuition and hunches about what’s going on under the hood. I made similar assumptions in this discussion due to the sparsity of what’s indicated.

From their blogs cited above, here are some key excerpts about this particular topic:

Notice that they were primarily experimenting with AI deception monitoring and don’t seem to have opted at this time to put it into day-to-day operation. The research results that they present in their write-ups seem promising and I would expect that they and other AI makers will undoubtedly incorporate this approach into their generative AI.

Conclusion

Congratulations, you now know that generative AI can be deceptive and that you need to keep your eyes wide open. There are efforts underway such as the above AI deception monitoring to catch and stop AI deceptions from occurring. We can be thankful for such endeavors.

Famed writer and poet, Johann Gottfried Seume said this about deception: “Nothing is more common on earth than to deceive and be deceived.” As a reluctant bearer of bad news, I must tell you that AI is in fact a deceiver.