#Heuristic Function Informed Search

[kafkaoftherubble]

试问：这是啥？//Question: what is this?

and I only just remembered about these, but I kept seeing these turtle dragon statues around. I need to research them more but honestly, they're so cute I might just call them my 6th favorite type

@secondhanddragon So, you wanted to know what this is?

I went to search for their information and the creature with the highest probability (so high my confidence level is at 99%) of matching the above is...

金鳌，或称龙龟

Golden Ao, also known as Dragon Turtle

It's classified as a divine turtle （神龟）rather than a dragon; my guess is that since it is not one of the Dragon's Sons, it doesn't count in a traditional bestiary.

It has the shell of a turtle but the body of a dragon, and once again, it's useful in Fengshui. It symbolizes power and wealth.

-----------------------

However! This is not the only turtle-dragon in Chinese mythology. In fact, my Brain's first thought—though we kinda immediately discarded it—was the other one...

赑屃，或称霸下、龟趺

Bixi, also known as Ba Xia ("Dominate Below") and Gui Fu ("Foot Tortoise")

Now this is one of the Dragon's Nine Sons. Bixi is strong; the word 赑 basically means "able to support great weight," but it's not a common word to be used in any stretch. It's the son of the dragon and a tortoise, and later depictions of Bixi gradually show characteristics of a dragon more than its earlier, clearly-tortoise-like features.

How did you rate your confidence level? Why are you sre that the image I showed you was a Dragon Turtle and not a Bixi?

It's the function of Bixi that gave it away!

Bixi is always made to carry steles and important tablets on its back, especially the ones by emperors. The idea is that the deeds and/or names of those recorded on this stele/tablet will live on forever with Bixi, who has the steadfast longevity of a tortoise and the divine power of a dragon.

It possesses quite a few meanings:

power and status; hence its steles are often those of emperors, high chiefs, and whatnot.

longevity, since it is the son of a tortoise

local cultural symbol (local tribal importance)

and mythological symbolism (in Taoism)

One of the myths concerning Bixi is this: Bixi used to carry mountains on its back and wreak chaos through tidal waves. Then, one of China's ancient emperors, Da Yu, subjugated Bixi and made it work under him. Da Yu was known for his Great Flood project—he devoted much of his life to solving it, and in this mythologized version, Bixi was one of those divine creatures who helped*. When the project was over, Da Yu worried that Bixi would return to its old wayward ways, so he created this gigantic stone tablet detailing Bixi's contribution and great deeds and asked Bixi to carry it around. The weight of that tablet caused Bixi to be too sluggish to wreak havoc anymore.

*You might be interested to know that another one who helped Da Yu was "Ying Long (应龙)"—yes, this is a full-fledged dragon.

This is how I'm confident that your image was that of a dragon turtle and not a Bixi.

---

How do I distinguish between a Bixi and Black Tortoise (Xuan Wu 玄武)?

Same deal. It's the stele/tablet. Also, Bixi has teeth. Tortoises do not. Later Bixi has draconic features. Xuan Wu is always... a tortoise! Hahahaha.

----

So there you go. I'm most confident that it is a dragon turtle. However, Bixi has more stories to it due to being a Son of the Dragon.

As a rule of heuristics (not statistics; I did not collect data for this), if a Chinese myth creature is found as a small, delicate ornament, it's quite likely that it has connotations in Fengshui. It's the same for Pixiu and Qilin, and the same for dragon turtles too.

Thank you for reading my ramble! I hope it was fun and informative for you!

----

Citations and Links:

"Dragon Turtle" in Wikipedia.

"Bixi" in Wikipedia.

"金鳌 (中国古代神兽)" in Baidu Baike. (In Chinese)

"赑屃（中国古代神兽)" in Baidu Baike. (In Chinese)

Kafka's Notes: I generally don't like Baidu Baike because it lacks citational rigor, so it's hard to trace information to its source. However, it was very useful for me to search for names and preliminary information, so I included it here. Some of the stuff I mentioned (like the Legend of Bixi and Da Yu) was from Baidu Baike; I judged that a folklore is not always recorded in academic sources, so this instance of lacking citational rigor is acceptable.

[civilengineering-crimson]

A Brief Survey of Regionalization Modeling; A Bonus from Gauged to Ungauged Basins_Crimson Publishers

Runoff-rainfall models (r-r) have been widely used to manage water resources during past decades. One of the most important upfront hydrological issues, certainly in r-r prediction, is adopting the best calibration method [1]. Regarding the importance of calibration procedure in hydrological modeling, various types of methods and approaches have been practiced optimizing parameter values from manual and trial-error-based style to entirely automated, heuristic and sophisticated approaches. Automatic calibration approaches usually take advantage of modern search processes and algorithms to fit residual errors among observed and simulated data (using objective functions) to optimize parameter values [2]. In terms of hydrological modeling, particularly distributed models, changes in spatial characteristics of watersheds and resulting processes, are considered explicitly [3]. These types of models are fundamentally designed to bear various sorts of flow information and watershed attributes to model streamflow accurately and timely (i.e. Big Data Machine Learning [4]).

Read more about this article: https://crimsonpublishers.com/acet/fulltext/ACET.000566.php

For more articles in our journal:https://crimsonpublishers.com/acet/

[latencyfx]

Quantum Machine Learning for Protein Folding

Proteins are complex 3-dimensional structures that play a vital role in human health. In their unfolded state, proteins can have a wide variety of structural forms; however, they must refold into their native, functional structure in order to function properly. This process is both a biological mystery and a critical step in drug discovery, as many of our most effective medicines are protein-based.

Despite its crucial importance, folding a single protein is an enormous computational challenge. The inherent asymmetry and complexity of proteins creates a rugged energy landscape that must be optimized using highly accurate models, which requires significant computational resources. The advent of quantum computing has the potential to dramatically accelerate the folding of complex proteins, paving the way for new therapies and diagnostics.

Quantum machine learning for protein folding has already yielded impressive results, demonstrating the power of quantum computation to tackle a diverse set of biochemical problems. The superposition of qubits allows for simultaneous simulation of multiple solutions, enabling exponential speedups compared to classical computers. These speedups, coupled with the ability to probe more complex energy states of proteins, can reveal novel insights into folding pathways and help accelerate drug discovery timelines.

Recent studies have used hybrid quantum-classical computer systems to study the problem of protein folding. Casares et al 2021 combined quantum walks on a quantum computer with deep learning on a classical computer to create a hybrid algorithm called QFold, which achieves a polynomial speedup over the best classical algorithms. Outeiral et al 2020 used a similar approach, combining quantum annealing with a genetic algorithm on a classical computer to find low-energy configurations of lattice protein models.

Other approaches techogle.co have been explored using variational quantum learning, a form of approximate inference in which the optimization algorithm is informed by an empirical error model. For example, Roney and Ovchinnikov use an error model to guide their adiabatic quantum protein-folding algorithm to start from the lowest-energy conformation of a given amino acid sequence. Their algorithm then uses a heuristic search to locate a likely 3D protein structure.

While most experimental work to date has focused on simple proteins, recent theoretical developments have opened the door to applying quantum machine learning to more challenging proteins. In particular, researchers at Zhejiang University in Hangzhou have proposed a model that describes protein folding as a quantum walk on a definite graph, without relying on any simple assumptions of protein structure at the outset.

This work is an technology website exciting advancement in the field of quantum biology, but it is important to emphasize that it is still far from a clinically relevant model of protein folding. To be applicable to the study of real proteins, the model must be tested in simulations on a large scale. Currently available quantum computational devices have between 14 and 15 qubits; to simulate a protein of 50 amino acids, the number of qubits would need to grow to 98 or more. Quantum computers with higher qubit counts are under intensive development, and their application to protein folding may soon become a reality.

[spilledreality]

The Stormcrow Fallacy

Hey Adam,

Sent a reply via Substack's automated email system. Re-trying through your proper email address (and doxxing my real name via academic email!) just in case the last attempt didn't get through. If you're just too busy to respond, no hard feelings, and I promise not to keep following up.

I've been discussing with friends lately what we've called the Stormcrow fallacy. In Tolkien—an author who, as a young literature student, I thumbed my nose at, but have lately come around to considering one of the great literary modernists & WWI novelists—Gandalf, because he consistently shows up in advance of, and brings news of, misfortune, is often mis-identified as bringing that misfortune with him. (This identification leads to his nickname of Stormcrow.) I'm loath to call things fallacies, as behavioral economics persistently misuses that term when labeling (what in my view are simply) heuristics. But this does seem to be a basic logical error of confusing correlation with causation—one which leads to e.g. a lot of suppression in bureaucracies of coming trouble. (See also Feynman on the Challenger disaster.) There are a ton of similar failure modes, right, that boil down to mistaken causal attribution? "Cargocult" being a favorite example of geeks worldwide.

Anyway, I particularly liked your informal description of consciousness as a Stormcrow, as the intern who only shows up when something breaks. Everything I've been learning about cognitive science & phenomenology the past few years seems to point in this direction. (Heidegger's ready-to-hand vs present-at-hand concept is one of the more concise illustrations, but see also William James on habit, or ideas in predictive processing about what sorts of errors might propagate to the highest level of the stack.) And, like Stormcrow, like Gandalf, what this consciousness appears to do is manage the hard problem of triage, of prioritization. Which is also, not coincidentally I think, a problem associated with wisdom. Wisdom and consciousness both, as I understand them, are means of adjudicating various priorities which demand resources (including but not limited to attention), across a body, a family, or a geopolitical unit. Hence the historical premium on wise father, wise king, etc as an adjudicator of a social ecology who maintains and restores equilibria. (Hence the association of wisdom with "perspective"—a temporally or spatially or interpersonally zoomed-out view.) This wisdom often operates through a "feel for the game" which cannot be verbally justified. (Not surprising given this is how humans solve many of their complex, high-uncertainty problems.) But its implicit function mimics the search for Pareto optimality, which is why it is so frequently characterized by ideas like moderation, balance, and tao-like navigations of Scylla and Charybdis for a sweet spot ("Goldilocks zone").

Yours with much appreciation and interest,

S.R.

[the-moti]

Tricky things:

If a move in self play is a single step in the proof then the space of possible moves is really large and hard to describe. So maybe a move should be a single character or token in the proof? But then proofs will have a really large number of moves, which might make RL hard. Currently this is solved using LLMs to suggest proof steps.

Where do you get the statements to prove? The most AlphaZero-like approach is to have the AI generate the statements trying to optimize some function. But seems hard to get that to work. Again the approach of the recent paper is to use an LLM to autoformalize informal statements to generate a bunch of examples.

Proof search is kind of a universal search problem since any search problem can be formulated as a search for a proof of something. So proof search potentially requires every heuristic that is helpful on every search problem (though of course human math problems only use a subset of these, it might be quite a large subset). Arguably the new approach deals with this by running RL on variants of the problem to allow the model to learn techniques suitable for the given problem. That requires a lot of compute and may not have been viable earlier.

i swear i've been saying this for years, all u gotta do is monte carlo tree search on proofs, alphazero self play whatever the fuck. obviously everyone else has also been saying this for just as long, been saying this ever since lee sedol lost at go. no clue why it's only just now that they've gotten it to work on problems meant for (freakishly clever) high schoolers, no clue what they've been stuck on all this time, what if anything theyre doing different now. maybe it's just a bigger llm. doubt theyll ever release any details either. whatever.

[alwaysunabashedsalad-blog]

Best First Search ,Heuristic Function & Informed Search In Artificial Intelligence

Best First Search ,Heuristic Function & Informed Search In Artificial Intelligence

le we will discuss about Best First Search ,Heuristic Function & Informed Search In Artificial Intelligence .Also we will see the what is Best First Search In Artificial Intelligence, properties of Best First Search in AI, Algorithm of Best First Search , example of Best First Search & Advantages – Disadvantages of Best First Search in Artificial Intelligence. Best First Search In Artificial…

[mariacallous]

We’re fighting a Vietnam redux. It’s 1938 all over again. The Guns of August are around the corner. A new Cold War has begun.

A raft of historical analogies has swirled around Ukraine coverage that purports to explain a war few experts saw coming. Do peacemakers like French President Emmanuel Macron risk being duped like former British Prime Minister Neville Chamberlain at Munich? Or is this moment another 1914, and a wider European mobilization lurks around the corner?

The analogies writers apply to Ukraine shape how it’s understood—and how the public thinks the war will end. If this is really akin to World War I, Europe’s rulers should be careful not to escalate in the face of Russian mobilization—the opposite of what Germany announced it would do this year. But if the moment is like the 1938 Munich Agreement, negotiating with Russian President Vladimir Putin will encourage him to take more than just the four oblasts in eastern Ukraine.

Thinking about today through the lens of the past has a long provenance in the corridors of power. American administrations throughout the 20th century used historical analogies to frame the crises they faced, likening them to the Peloponnesian War between Athens and Sparta, the surprise attack on Pearl Harbor, mission creep in Vietnam, the humanitarian disaster in Somalia, the bipolar tensions of a new Cold War—and, of course, everyone’s favorite analogy, appeasement at Munich. Putin himself has deployed historical analogies (including World War II appeasement) to justify the war in Ukraine, and Ukrainian President Volodymyr Zelensky has countered with his own.

There’s a reason that historical analogies like these are never in short supply. Analogical thinking is deeply embedded in the world’s cognition—how experts make sense of new situations—in what international relations academic Aidan Hehir calls an “inherent psychological compulsion.” Experts can’t help but think analogically. But because the choice and application of analogies is open to individual biases and errors, their use at the highest levels of policymaking carries serious risks.

For the last several decades, scientists have argued analogies are not just rhetorical devices but cognitive heuristics—mental shortcuts for making sense of the world. Baked into fundamental cognitive processes, heuristics help experts process new information by simplifying its complexity and organizing it according to preexisting mental structures. Without these shortcuts, people wouldn’t be able to function, overwhelmed by the phenomena of day-to-day life.

When people think analogically, they transfer meaning from one thing to another, understanding something present in terms of something past. But as with any shortcut, analogies have a downside. Heuristics distort the underlying phenomena of life, producing cognitive biases, such as the halo effect, confirmation bias, and overconfidence. Outlined by English philosopher Francis Bacon and popularized by psychologist Daniel Kahneman’s Thinking, Fast and Slow, these biases mean experts’ first and often most powerful judgments can be the most misleading. When experts use a historical analogy to understand the present, they risk importing the errors and biases inherent in the shortcut.

Owing to these possible dangers, political scientists and historians have intensely studied the use of historical analogies in foreign-policy decision-making using the “Analogical Explanation” framework. According to the framework, a decision-maker searches for an event in the past (called a “base” or “source”) that appears similar to the current event (called a “target” or “destination”). They then transfer or “map” knowledge of the base to the target, using the former to fill in information missing in the latter. A four-step formula describes the process:

Historical event (base) has property X.

Current event (target) also has property X.

Historical event (base) has property Y.

Current event (target) therefore has property Y.

Property Y in the last step is an inference: something missing in the current event but present in the historical one. Political scientist Yuen Foong Khong, a pioneer of the framework, summarizes this stage: “[I]f two or more events separated in time agree in one respect, then they may also agree in another.” Analogies used this way to allow decision-makers to comprehensively frame a whole situation, helping them define it, weigh its stakes, and assess possible solutions. They guide policymakers toward certain options and away from others.

Historian Barbara Tuchman’s The Guns of August, which emphasizes how great-power miscalculations spiraled into World War I, had a “profound impact” on former U.S. President John F. Kennedy’s decision-making during the Cuban missile crisis. As former U.S. Attorney General Robert Kennedy recalled, his brother was struck by the “stupidity, individual idiosyncrasies, misunderstandings, and personal complexes of inferiority and grandeur” that led the period’s great powers to “tumble into war.” The analogy cautioned the president against taking policy options during the crisis that might provoke escalatory counter-responses from Moscow.

Analogical reasoning suffers from several general problems. The analogy itself may be poorly chosen. The practitioner may have misidentified the analogy’s suitability, perhaps by recalling the most available case—what’s most on his or her mind at that moment or something drawn from personal experience—rather than the one that fits best. In the lead-up to the 2003 invasion of Iraq, then-U.S. President George W. Bush repeatedly and publicly compared then-Iraqi leader Saddam Hussein to Nazi dictator Adolf Hitler, an analogy also deployed by his father and which foreclosed negotiating with the Iraqi dictator. (George W. Bush’s administration later switched analogies to the American occupation of Korea.) Poor analogies convincingly pressed by influential policymakers can dominate the decision-making process, blocking rival understandings and new information that doesn’t fit the analogy. Practitioners may also pick a fitting analogy but reason about it poorly, confusing what’s important in it with what’s superficial or contextual. Leaders with low “cognitive complexity” may suffer from these problems more than others, employing unsophisticated analogies in unsophisticated ways. Unless handled with care, analogies mislead.

In his landmark 1992 study Analogies at War, Khong showed how these errors plagued U.S. policymakers in Vietnam. Former U.S. President Lyndon Johnson weighed the consequences of backing down in Vietnam using the Munich analogy, which likened nonintervention in Vietnam to Chamberlain’s fateful appeasement of Hitler in the Munich Agreement.

But Vietnam was no more analogous to Munich than future cases where U.S. foreign-policy makers used it to understand other dictators, such as Hussein. Hehir has convincingly shown that U.S. foreign-policy makers understood former Serbian President Slobodan Milosevic primarily through the lenses of the Dayton and Munich Agreements, leading to deliberate American bullishness at Rambouillet and the subsequent NATO-led airstrike campaign. Political scientists Rose McDermott and David Houghton likewise document how the Carter administration reviewed many analogies when it faced the Iran hostage crisis before settling on Israel’s successful Entebbe raid, where the Israelis successfully freed a planeload of hostages with minimal casualties. As a result, both former U.S. President Jimmy Carter and former U.S. National Security Advisor Zbigniew Brzezinski, the Entebbe analogy’s main proponent, underestimated the mission’s likelihood of success. In the mid-1990s, U.S. policymakers understood Rwanda through an “Africa” schema of tribal warfare, analogizing it with the failed humanitarian mission in Somalia. Throughout the 20th century, analogies powerfully framed U.S. decision-making—and often led its elites toward poor policy choices.

Historical analogies are particularly tempting in times of ambiguity and uncertainty, such as in the crises above and this current moment. In times like these, experts look for easy certainties that eliminate anxiety and reassure them about the future—what psychologists call “cognitive closure.” Analogies help, making the inconstant appear constant by drawing on the patterns of the past.

As the recent Russian mobilization shows, the war in Ukraine is fraught with unpredictability. Analysts continue to debate its fundamentals: Is it sui generis? Who is winning? What does Putin really want? What caused it, where is it going, and when will it end? If analogical reasoning is an unavoidable human compulsion, it is only natural that experts now look to the past to make sense of the present. Is Ukraine akin to another Munich and Putin to Hitler—and will it lead to another world war? And if not these, which cases should guide us?

History is the school of statesmanship, but its lessons are not all of equal value. Experts should think carefully about which analogies they choose, what lessons they think they provide, and to whom. Analogies promoted in the public sphere can be picked up by those in power—regardless of their accuracy. The competition of politics means sophisticated analogies are as likely to be abused as properly used. Powerful but simple historical analogies that draw Manichean lines of good and evil, like Munich, are especially seductive.

And experts should manage expectations. Extraordinary statecraft requires a level of high cognitive complexity that is probably rare. As historical analogies about Ukraine accumulate, experts should remember analogical reasoning’s broad limitations, underscored by this line from its most prominent recent examiner: “All historical analogies are suspect.”

[qq-miao]

Consumer Behavior Online: (Omni)Channel Triaging and Social Shopping

The article by Soman, Kim, and An on how the digital shopping experience has changed (compared to the in-store experience) was fascinating and brought back memories of one of my favorite projects on the Future of How People Shop. The mandate of the project was to help my client understand how the consumer decision journey “CDJ” has evolved since McKinsey updated its famous CDJ framework in 2015 (below), and to propose new insights that substantiated, elaborated upon, or countered the framework. In this post, I bold the words and phrases that are concepts from the reading.

Image credit: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-new-consumer-decision-journey

The new CDJ “The accelerated loyalty journey,” that McKinsey proposes derives from the advantage of online shopping, one-click purchasing, personalization algorithms, and fiercer competition among brands for consumer headspace. Due to these trends, companies are now able to short-circuit two key phases of the original CDJ: “Consider” and “Evaluate.” Thus, companies are taking advantage of consumers’ heuristics and mental shortcuts to push consumers from the “Awareness” phase directly to the “Buy” phase.

However, through my research, I landed on a key insight that counters McKinsey’s new model and proposal. I found, through expert interviews and a custom survey, that the omnichannel nature of shopping has actually extended the “Consider” and “Evaluate” phases. Armed with mobile phones, consumers are savvier than ever before and more inclined to invest time in research. This leads to two outcomes: (1) (omni)channel triaging and (2) social shopping, which I describe in detail below.

Image credit: https://veloxity.us/commercial-cell-phone-charging-stations-for-retail-stores/

(OMNI)CHANNEL TRIAGING: This is a term that I made up, which refers to the consumer behavior of comparing prices of a product in-store with prices they find online. Think about it: when was the last time you were in a store agonizing over a purchase because it was outside of your budget, and then did a quick search on your smartphone for a lower-priced deal or for a comparable substitute? Channel triaging! Consumers are able to take advantage of offline and online image displays to have instantaneous side-by-side comparisons, a sort of aggregate market preference for pricing. The nuance to channel triaging, however, is that the behavior is more prevalent for products that are expensive, such as technology. For mundane, affordable goods like toothpaste, consumers are not willing to spend the time for the potential cost savings. This also speaks to the impatience element of purchasing described by Soman, Kim, and An. Because of channel triaging, retailers like Best Buy have a “Lowest Price Guaranteed” scheme in which they will match their price with the lowest price that you find for a product, offered by a legitimate retailer. Here, BB offers a strategic choice architecture for consumers (“context-dependent choice” described in the article) to retain their purchase at BB’s location, instead of at a competitor’s location.

Image credit: https://www.business2community.com/instagram/how-to-use-instagram-to-drive-sales-like-a-marketer-02222011

SOCIAL SHOPPING: With the advent of highly visual social media (e.g. Instagram, Snap, Pinterest), consumers are now blending the “Consider” and “Evaluate” phases of research with the “Buy” phase. If you have not participated in social shopping before, imagine this: You are scrolling through your favorite fashion influencer’s Insta feed and see that they’ve done a collaboration with the sustainable shoe brand, Rothy’s. You tap the shoe and purchase the exact same pair, all within the Insta platform. That’s social shopping. I was surprised that the article did not discuss social shopping, since it incorporates a few concepts like peer influence (the admiration you have for the influencer spills over to Rothy’s), heuristics (the influencer has already “curated” the shoe decision for you, giving you a cognitive shortcut), and visual bias (the influencer’s post is aesthetically compelling and draws you into the purchase). And due to the connectivity effect, when you tap on this post, you get a lot more information by viewing all the comments and emojis supporting the post, sometimes even enjoying real-time review of the product itself. Companies and brands that leverage social shopping can turn the online purchasing decision into a conversation. They will have a lot of traction with digitally engaged consumers, especially when they are notin a purchasing mindset.

[abdulrimaazuk]

The advantages of digital marketing and SEO

It is undeniable that globally we are experiencing the digital age; internet has become indispensable, and the need to be able to access an incredible amount of information and online activities, an added value is added every day, we can almost say that without a connection, without the possibility of visiting, indeed surfing the web many things would be precluded from us; the net is an infinite source of everything, but really of everything.

With a vast assortment of sites and portals that deal with the most disparate issues, but what determines the quality and success especially of sites dedicated to commercial activities? The importance of positioning on the net

The success and affirmation of sites and portals on the net, especially those of a commercial nature, is due to the construction of particular strategies or that new science called marketing.

In fact, digital marketing is a complex activity that moves with a careful eye on the evolution of technologies and makes it possible to overcome the limits and boundaries given by other dissemination methodologies, between matter and digital in practice these borders have been just reset; marketing combined with good SEO, increases visibility and positioning in the shadow of Google which consequently also improves the "seller-customer" relationships and amplifies their quality and immediacy, precisely because the quality of the sites, of the e-commerce and online stores.

SEO consultant in England UK today boasts professional presences that deal with these issues and can offer high-profile consultancy on digital marketing supported by SEO.

Do you want to optimize your site? Here's what you need In this context, there are obviously more valid realities than others. This is why it is very important to contact a highly performing digital marketing consultant such as SEO Leader, who today is a significant presence in the digital marketing landscape; a figure who offers SEO support and advice. 

As we said, speed and experience are important in positioning a website in Google but also in other search engines; increase a site with authority, increasing its visibility as much as possible, to reach these objectives it is necessary to have a strong preparation especially in the SEO field which is a heuristic discipline. 

This is why we would like to quote and recommend the professional SEO Leader consultant ;marketing expert, he has followed for years the evolutions and changes of the Google algorithm, committing himself with great passion and experience, acquired in the field, day after day, a commitment that has given excellent results for hundreds of projects in different fields; and lasting success. SEO Leader: the best choice

Passion combined with problem solving skills and a very broad and systemic vision of all customizable techniques have been the winning weapon to arrive at providing services that inevitably lead to incomparable improvement and success. The importance of optimizing your site for search engines, especially in the commercial field, is fundamental; therefore relying on the hands of an expert as the SEO Leader consultant is deeply experienced means taking the right step to obtain amazing results for your website and for your business.

An unstoppable path studded with evolutionary successes

In fact, marketing in recent years has undergone a real leap forward, thanks to its diffusion on the internet and the advent of smartphones; digital marketing makes use of integrated initiatives, and manages to reach such a large amount of channels, that today it can be defined as the beating heart of commerce that spans tens of thousands of different sectors. 

This is also due to the fact that an increasingly large catchment area has realized that shopping, grocery shopping, payments, social and playful relationships, and all activities of interest if carried out online are immediate, simple, usable. intuitively. 

A simplification that is now unmissable, indispensable. The hectic times we live due to work and daily problems often do not allow us to shop in total tranquility, with attention, concentration, but this becomes possible if we optimize our needs through the web; this is why it is necessary to be able to take advantage of a large variety of sites, e-commerce, online stores, which can provide us with everything we need. 

Finding these places today is easier and also to distinguish between the renowned and performing portal from others that are less effective, the real must-haves of online commerce that are supported and driven precisely by digital marketing strategies.

Digital marketing and SEO in synergy

The main technological tools of marketing, which have evolved in a modern and dynamic way, summarize the old adage that said: “advertising is the soul of commerce”; digital marketing todaymakes use of the strategies of understanding, reading, and studying the individual needs of the customer, and takes a personalized perspective, aimed at establishing a long-term relationship with the customer; strengthening the positioning and image of the brand or brands it advertises.

 In this way, new and effective communication channels are made available, an offer of products and services practically without limits through the organization of events and incentives. 

The most advanced marketing makes use of attractive tools, with an elegant and refined appearance, of high quality content that illustrate and sponsor the products, up to the offer of promotions, bonuses, discounts, or commercial initiatives aimed at saving and favoring customer loyalty; but all this would not be possible without an excellent positioning on the web of sites. Here digital marketing and SEO work in synergy on both fronts. Position the sites in an excellent way by promoting their commercial activities, with campaigns and sponsors.

Mass customization

We can therefore speak of an applied mass customization, and product services, and initiatives of various kinds, through diversified channels, such as social media, official brand websites and commercial tactics that tend to streamline the offer of any product, making it thus more accessible to a greater number of users. Building a functional and effective marketing strategy is a complex activity, as we have already said, but which relies in a targeted manner on technological evolution and new professional figures. 

SEO Leader knows all this well and applies it. A job placed among the new professions, aimed precisely at developing an in-depth knowledge of all the techniques, which aim to improve commercial activities and social relations between seller and buyer, the processes that are created are a precise scale, a hierarchy of activities that contemplate the absolute visibility given by an unassailable positioning, communication, distribution, advertising of offers and enhancement of customers but also of partners and brands involved in initiatives, in national and international advertising campaigns.

The fundamental points of the missions

Fundamental in a good marketing strategy creation program are: the in-depth analysis of the general situation, the focus and achievement of objectives, the actual strategy that must be tested along the way, the application of the tactics put in place, the study of specific reactions and the control of the whole global program.

The analysis of the context is fundamental since a marketing strategy must know how to move within a specific territory of sales, promotion and dissemination of the characteristics of the quality of the products and services to be offered. Internal analyzes must develop resources made available by digital marketing operators, as does the SEO Leader consultant; they must then be analyzed and distributed in the best way. 

All aspects of the on-line environment to be positioned must also be analyzed, studying statistics on research, customer requests, the definition of objectives must take place after observing all the points already mentioned. The mission must be complete with all points and tested before being definitively put into operational status.

[jcmarchi]

Shielding Prompts from LLM Data Leaks

New Post has been published on https://thedigitalinsider.com/shielding-prompts-from-llm-data-leaks/

Shielding Prompts from LLM Data Leaks

Opinion An interesting IBM NeurIPS 2024 submission from late 2024 resurfaced on Arxiv last week. It proposes a system that can automatically intervene to protect users from submitting personal or sensitive information into a message when they are having a conversation with a Large Language Model (LLM) such as ChatGPT.

Mock-up examples used in a user study to determine the ways that people would prefer to interact with a prompt-intervention service. Source: https://arxiv.org/pdf/2502.18509

The mock-ups shown above were employed by the IBM researchers in a study to test potential user friction to this kind of ‘interference’.

Though scant details are given about the GUI implementation, we can assume that such functionality could either be incorporated into a browser plugin communicating with a local ‘firewall’ LLM framework; or that an application could be created that can hook directly into (for instance) the OpenAI API, effectively recreating OpenAI’s own downloadable standalone program for ChatGPT, but with extra safeguards.

That said, ChatGPT itself automatically self-censors responses to prompts that it perceives to contain critical information, such as banking details:

ChatGPT refuses to engage with prompts that contain perceived critical security information, such as bank details (the details in the prompt above are fictional and non-functional). Source: https://chatgpt.com/

However, ChatGPT is much more tolerant in regard to different types of personal information – even if disseminating such information in any way might not be in the user’s best interests (in this case perhaps for various reasons related to work and disclosure):

The example above is fictional, but ChatGPT does not hesitate to engage in a conversation on the user on a sensitive subject that constitutes a potential reputational or earnings risk (the example above is totally fictional).

In the above case, it might have been better to write: ‘What is the significance of a leukemia diagnosis on a person’s ability to write and on their mobility?’

The IBM project identifies and reinterprets such requests from a ‘personal’ to a ‘generic’ stance.

Schema for the IBM system, which uses local LLMs or NLP-based heuristics to identify sensitive material in potential prompts.

This assumes that material gathered by online LLMs, in this nascent stage of the public’s enthusiastic adoption of AI chat, will never feed through either to subsequent models or to later advertising frameworks that might exploit user-based search queries to provide potential targeted advertising.

Though no such system or arrangement is known to exist now, neither was such functionality yet available at the dawn of internet adoption in the early 1990s; since then, cross-domain sharing of information to feed personalized advertising has led to diverse scandals, as well as paranoia.

Therefore history suggests that it would be better to sanitize LLM prompt inputs now, before such data accrues at volume, and before our LLM-based submissions end up in permanent cyclic databases and/or models, or other information-based structures and schemas.

Remember Me?

One factor weighing against the use of ‘generic’ or sanitized LLM prompts is that, frankly, the facility to customize an expensive API-only LLM such as ChatGPT is quite compelling, at least at the current state of the art – but this can involve the long-term exposure of private information.

I frequently ask ChatGPT to help me formulate Windows PowerShell scripts and BAT files to automate processes, as well as on other technical matters. To this end, I find it useful that the system permanently memorize details about the hardware that I have available; my existing technical skill competencies (or lack thereof); and various other environmental factors and custom rules:

ChatGPT allows a user to develop a ‘cache’ of memories that will be applied when the system considers responses to future prompts.

Inevitably, this keeps information about me stored on external servers, subject to terms and conditions that may evolve over time, without any guarantee that OpenAI (though it could be any other major LLM provider) will respect the terms they set out.

In general, however, the capacity to build a cache of memories in ChatGPT is most useful because of the limited attention window of LLMs in general; without long-term (personalized) embeddings, the user feels, frustratingly, that they are conversing with a entity suffering from Anterograde amnesia.

It is difficult to say whether newer models will eventually become adequately performant to provide useful responses without the need to cache memories, or to create custom GPTs that are stored online.

Temporary Amnesia

Though one can make ChatGPT conversations ‘temporary’, it is useful to have the Chat history as a reference that can be distilled, when time allows, into a more coherent local record, perhaps on a note-taking platform; but in any case we cannot know exactly what happens to these ‘discarded’ chats (though OpenAI states they will not be used for training, it does not state that they are destroyed), based on the ChatGPT infrastructure. All we know is that chats no longer appear in our history when ‘Temporary chats’ is turned on in ChatGPT.

Various recent controversies indicate that API-based providers such as OpenAI should not necessarily be left in charge of protecting the user’s privacy, including the discovery of emergent memorization, signifying that larger LLMs are more likely to memorize some training examples in full, and increasing the risk of disclosure of user-specific data –  among other public incidents that have persuaded a multitude of big-name companies, such as Samsung, to ban LLMs for internal company use.

Think Different

This tension between the extreme utility and the manifest potential risk of LLMs will need some inventive solutions – and the IBM proposal seems to be an interesting basic template in this line.

Three IBM-based reformulations that balance utility against data privacy. In the lowest (pink) band, we see a prompt that is beyond the system’s ability to sanitize in a meaningful way.

The IBM approach intercepts outgoing packets to an LLM at the network level, and rewrites them as necessary before the original can be submitted. The rather more elaborate GUI integrations seen at the start of the article are only illustrative of where such an approach could go, if developed.

Of course, without sufficient agency the user may not understand that they are getting a response to a slightly-altered reformulation of their original submission. This lack of transparency is equivalent to an operating system’s firewall blocking access to a website or service without informing the user, who may then erroneously seek out other causes for the problem.

Prompts as Security Liabilities

The prospect of ‘prompt intervention’ analogizes well to Windows OS security, which has evolved from a patchwork of (optionally installed) commercial products in the 1990s to a non-optional and rigidly-enforced suite of network defense tools that come as standard with a Windows installation, and which require some effort to turn off or de-intensify.

If prompt sanitization evolves as network firewalls did over the past 30 years, the IBM paper’s proposal could serve as a blueprint for the future: deploying a fully local LLM on the user’s machine to filter outgoing prompts directed at known LLM APIs. This system would naturally need to integrate GUI frameworks and notifications, giving users control – unless administrative policies override it, as often occurs in business environments.

The researchers conducted an analysis of an open-source version of the ShareGPT dataset to understand how often contextual privacy is violated in real-world scenarios.

Llama-3.1-405B-Instruct was employed as a ‘judge’ model to detect violations of contextual integrity. From a large set of conversations, a subset of single-turn conversations were analyzed based on length. The judge model then assessed the context, sensitive information, and necessity for task completion, leading to the identification of conversations containing potential contextual integrity violations.

A smaller subset of these conversations, which demonstrated definitive contextual privacy violations, were analyzed further.

The framework itself was implemented using models that are smaller than typical chat agents such as ChatGPT, to enable local deployment via Ollama.

Schema for the prompt intervention system.

The three LLMs evaluated were Mixtral-8x7B-Instruct-v0.1; Llama-3.1-8B-Instruct; and DeepSeek-R1-Distill-Llama-8B.

User prompts are processed by the framework in three stages: context identification; sensitive information classification; and reformulation.

Two approaches were implemented for sensitive information classification: dynamic and structured classification: dynamic classification determines the essential details based on their use within a specific conversation; structured classification allows for the specification of a pre-defined list of sensitive attributes that are always considered non-essential. The model reformulates the prompt if it detects non-essential sensitive details by either removing or rewording them to minimize privacy risks while maintaining usability.

Home Rules

Though structured classification as a concept is not well-illustrated in the IBM paper, it is most akin to the ‘Private Data Definitions’ method in the Private Prompts initiative, which provides a downloadable standalone program that can rewrite prompts – albeit without the ability to directly intervene at the network level, as the IBM approach does (instead the user must copy and paste the modified prompts).

The Private Prompts executable allows a list of alternate substitutions for user-input text.

In the above image, we can see that the Private Prompts user is able to program automated substitutions for instances of sensitive information. In both cases, for Private Prompts and the IBM method, it seems unlikely that a user with enough presence-of-mind and personal insight to curate such a list would actually need this product  – though it could be built up over time as incidents accrue.

In an administrator role, structured classification could work as an imposed firewall or censor-net for employees; and in a home network it could, with some difficult adjustments, become a domestic network filter for all network users; but ultimately, this method is arguably redundant, since a user who could set this up properly could also self-censor effectively in the first place.

ChatGPT’s Opinion

Since ChatGPT recently launched its deep research tool for paid users, I used this facility to ask ChatGPT to review related literature and give me a ‘cynical’ take on IBM’s paper. I received the most defensive and derisive response the system has ever given when asked to evaluate or parse a new publication:

ChatGPT-4o has a low opinion of the IBM project.

‘If users don’t trust OpenAI, Google, or Anthropic to handle their data responsibly,’ ChatGPT posits. ‘why would they trust a third-party software layer sitting between them and the AI? The intermediary itself becomes a new point of failure—potentially logging, mishandling, or even leaking data before it ever reaches the LLM. It solves nothing if it just creates another entity to exploit user data.’

This objection seems self-serving and non-applicable, given the storied history of open source projects that genuinely benefit end-users through the concerted long-term efforts of community developers and contributors; and given the growing potential of mobile devices to run – and even train – lightweight LLMs. Certainly in this instance, the use case is not terribly demanding.

Next, ChatGPT confidently misses the point of having a lightweight LLM provide oversight of input towards a commercial LLM that could not possibly be run on a local machine (because the LLM is too big, and allowing local access is too risky for the company that makes it):

‘Effective privacy filtering requires understanding the input before deciding what to strip out. If an intermediary is smart enough to recognize sensitive data (names, addresses, financial details), then it’s already processing and analyzing that data—meaning it has to store or examine the very thing it’s supposed to erase.

‘This introduces a paradox: a) A dumb filter (basic regex substitution) will be ineffective and miss subtle privacy risks. b) A smart filter (LLM-powered rewriter) has to process the full input, making it just as dangerous as the original LLM.

‘If you trust the intermediary enough to read and rewrite your inputs, why not just trust the LLM itself?’

The answer to the final question here is that the local LLM is intended to be entirely on the side of the user, and inevitably open source, with minimal or zero need for network access. An equivalent commercial version, however well-intentioned at the outset, would eventually be vulnerable to corporate shifts and changes to the terms of service, whereas a suitable open source license would prevent this kind of ‘inevitable corruption’.

ChatGPT further argued that the IBM proposal ‘breaks user intent’, since it could reinterpret a prompt into an alternative that affects its utility. However, this is a much broader problem in prompt sanitization, and not specific to this particular use case.

In closing (ignoring its suggestion to use local LLMs ‘instead’, which is exactly what the IBM paper actually proposes), ChatGPT opined that the IBM method represents a barrier to adoption due to the ‘user friction’ of implementing warning and editing methods into a chat.

Here, ChatGPT may be right; but if significant pressure comes to bear because of further public incidents, or if profits in one geographical zone are threatened by growing regulation (and the company refuses to just abandon the affected region entirely), the history of consumer tech suggests that safeguards will eventually no longer be optional anyway.

Conclusion

We can’t realistically expect OpenAI to ever implement safeguards of the type that are proposed in the IBM paper, and in the central concept behind it; at least not effectively.

And certainly not globally; just as Apple blocks certain iPhone features in Europe, and LinkedIn has different rules for exploiting its users’ data in different countries, it’s reasonable to suggest that any AI company will default to the most profitable terms and conditions that are tolerable to any particular nation in which it operates –  in each case, at the expense of the user’s right to data-privacy, as necessary.

First published Thursday, February 27, 2025

Updated Thursday, February 27, 2025 15:47:11 because of incorrect Apple-related link – MA

[eatsleepmath]

Translations of higher-order logic to first-order logic

Efficient encodings of higher-order logic in first-order logic are an essential component of sledgehammer-type automation for higher-order theorem proving. In this post, I record some notes on basic techniques of translating HOL to FOL. All code snippets are in Lean (I also freely conflate Prop and bool).

1 Monomorphization and λ-lifting

1.1 Monomorphization

Monomorphization (i.e., eliminating polymorphism) is the process of (repeatedly) heuristically instantiating quantifications over Type.

For example, the theorem eq.symm is polymorphic:

theorem eq.symm : ∀ {α : Sort u} {a b : α}, a = b → b = a := λ {α : Sort u} {a b : α} (h : a = b), h ▸ rfl

and if the tactic state contains the types bool and ℕ, then monomorphization should produce the instances

lemma bool.eq.symm : ∀ {a b : bool}, a = b → b = a := by apply eq.symm lemma nat.eq.symm : ∀ {a b : ℕ}, a = b → b = a := by apply eq.symm

1.2 λ-lifting

Given a λ-term t := λ x, foo x, define a new function symbol fₜ and replace all occurences of t with fₜ. Furthermore, fₜ must be axiomatized with the first-order translation of foo: ∀ x, f x = ⟦foo⟧ x. Since λ-terms might be nested, λ-lifting might have to be performed recursively. Once all the new function symbols are axiomatized, anything provable about the λ-terms should be provable about the new function symbols.

For example:

def baz : ℕ → ℕ := λ z, 2 * z + 1 def foo : ℕ → ℕ := λ y, y + baz y def t : ℕ → ℕ := λ x, foo x --now we lift lambdas constant f_baz : ℕ → ℕ constant f_baz_spec : ∀ z, f_baz z = 2 * z + 1 constant f_foo : ℕ → ℕ constant f_foo_spec : ∀ y, f_foo y = y + f_baz y constant f_t : ℕ → ℕ constant f_t_spec : ∀ x, f_t x = f_foo x lemma sanity_check₁ : f_t 40 = 121 := by rw[f_t_spec, f_foo_spec, f_baz_spec]; refl lemma sanity_check₂ (w : ℕ) : f_t w = (w + 2 * w + 1) := by rw[f_t_spec, f_foo_spec, f_baz_spec]; refl lemma sanity_check₁' : f_t 40 = 121 := sanity_check₂ _

1.3 Encoding λ-terms with Curry combinators

In addition to λ-lifting, which was discussed above, one can encode λ-terms using the Curry combinators I,K,S,B,C. (Technically, S and K suffice, but at the cost of producing output exponential in the number of λ's.) The combinators are defined as follows: I x := x, K x y := x, S x y z := x z (y z), B x y z = x (y z), and C x y z = x z y.

The translation of λ-expressions to combinators is defined by the following rules:

(λ x, x) ↦ I x

(λ x, p) ↦ K p, where p does not depend on x

λ x, p x ↦ p, where p does not depend on x

λ x, p q ↦ B p (λ x, q), where p does not depend on x

λ x, p q ↦ C (λ x, p) q, where q does not depend on x

λ x, p q ↦ S (λ x, p) (λ x, q), where p and q depend on x.

2 Three translation schemes

This section roughly follows Section 2 of Meng and Paulson's Translating Higher-Order Clauses to First-Order Clauses. In the first paragraph they single out three criteria for a formula to have higher-order features: (1) arguments of type a function type or Prop, (2) variables of type a function type of Prop, and (3) no "higher-type instances of overloaded constants". (I remark that in dependent type theory, these all mean the same thing, because (1) and (2) are both instances of parameters of a Π-type, and (3) is an instance of a implicit parameter of a Π-type (e.g. eq.symm above).)

A translation scheme T assigns a first-order formula (the translation) to every higher-order formula. A translation scheme is sound if whenever the first-order formula T(ϕ) is provable, then the higher-order formula ϕ is provable also. We'll review three translation schemes; only the first is sound. (Interestingly, the sound translation is the least useful.)

2.1 Fully typed

The fully typed translation is due to Hurd, and it is sound. Essentially, we:

add a sort S_Type whose elements are types, equipped with type constructors (e.g. λ A B, A → B),

for every sort S, add a new function symbol ti_S : S → S_Type which assigns each term to its "type",

denote function application using a binary function @, so e.g. f x becomes @(f,x),

convert translated terms of type Prop (which will be FOL terms) to FOL formulas via a predicate B.

2.2 Partially typed

In the partially typed translation, the typing operation ti is removed, and only the types of functions in function calls are included (as an additional argument to the application operator @). In contrast to the fully typed translation, schematic/free variables and constants are translated to FOL variables and constant symbols (without additional typing data), and only function applications are typed.

Using Meng and Paulson's example, if we have (X Y : α) and an infix operation (λ A B, A ≤ B : α → α → Prop), then under the fully typed translation, (X ≤ Y) would become

ti(@(ti(@(ti(≤, α → α → Prop), ti(X, α)), α → Prop), ti(Y, α)), Prop)

while under the partially typed translation, the same term would become

@(@(≤, X, α → α → Prop), Y, α → Prop).

2.3 Constant typed

The constant typed translation retains the minimum type information required to ensure correct overloading of constants. That is, any polymorphic constants (e.g. ≤ above) are monomorphized with a type ascription. Again using their example, if (X Y : ℕ), then (X ≤ Y) translates to

@(@(le(ℕ), X), Y)

2.4 Unsoundness

When typing information is removed, unsoundness can creep in when proofs use ill-typed terms. The canonical example is given by translating a finite type F = A | B. In both the partially typed and constant typed translation, the type ascription in the theorem ∀ x : F, x = A ∨ x = B is erased, and so the external prover is allowed to e.g. combine this fact with the axiom of infinity on ℕ. It turns out that by blocking the translation of these sorts of facts, the unsound translations rule out many unsound proofs and, by virtue of their speed, return more successful proofs than the fully typed translation. Interestingly, the best performance was achieved by the constant-typed translation, which discards as much type information as possible.

3 Further reading

What we've covered is far from the state of the art. For HOL-FOL translations, the interested reader could look at e.g. Chapter 6 of Jasmin Blanchette's thesis (link). There's also been work on DTT-FOL translations and sledgehammers (see CoqHammer). In addition to delegating to first-order ATPs, there has also been successful work on delegating proof search to SMT solvers. This also involves translation to first-order logic; see Chapter 2 of Sascha Böhme's thesis (link) (for Isabelle/HOL) and SMTCoq.

[iambloop]

Education & Democracy

Consider a ship. The ship is to undertake a journey - it has to sail through several oceans to reach a land far away. On its way, it will encounter difficult waters, storms even - it is guaranteed to be a difficult journey. Such a voyage requires a skilled captain. In order to determine who should command this ship, a committee of people is going to be established. These people will decide which person is best equipped for this task.

You are the first member of this committee, and you are assigned with the task of recruiting other people for this committee. Note that your task right now is not to choose the captain, but to simply assemble a group of people who will make that decision. Contemplate for a moment - on what basis should this committee be chosen?  

The objective of this committee is to choose the best candidate for captain. To do this, we begin by defining a criteria to judge whether a person is a good fit for captain or not. There are, broadly speaking, two categories of concern here.

The first one is technical: what route does the captain plan to take, how does she plan on dealing with storms, etc. The other is not technical: the voyage is not simply about going from point A to point B, it is to ensure that the people onboard are safe and that there is peace and harmony on the ship. For this, we question: is the captain prudent? Is she wise and careful? Is she aggressive? Is she kind?

As average people, it is not possible for us to be experts in each technical aspect - however, in order to judge whether the captain is qualified, it is necessary for at least some of us to possess basic knowledge regarding the technical aspects of sailing.

Now that we’ve established the qualities that we seek in a captain, we can determine the qualities that our committee members must possess.

To judge whether someone has a certain quality, we must possess an understanding of that quality itself. We must understand the benefits of such a quality, and if we seek our captain to have this quality, it must be a good quality, and if it is a good quality, we must ourselves wish to have it. Hence, the people who select the captain must possess the same qualities that they seek in the captain. Our task then boils down to looking for people who possess a subset of the qualities that we wish for the captain to have. Note that if they possess all the qualities that the captain should have, then perhaps they should be captain themselves. Therefore, we restrict our search to people who are strictly less qualified than required for captaincy - they possess certain but not all qualities that are required to become captain.

The people who select the captain, therefore, must be a partial reflection of the captain: the committee must be comprised of people who possess the basics of the technical know-how in order to judge whether the applicant is qualified or not, and also, the values required to judge whether the applicant is the right person to lead and manage the voyage in its non-technical aspects.

* * *

The idea of democracy emerged in ancient Greece. The word comes from ‘demos’ - common people, and ‘kratos’ - strength. Democracy is a Greek concept, and interestingly, one of the strongest criticisms against democracy comes from the founding father of Greek philosophy, Socrates. The story above is simply an extension of the argument that Socrates offered - the ship is the society, the captain is the elected government and the committee of people are the voters.

In a democracy by birthright, each citizen that has attained 18 years of age is allowed to vote. That is to say, each citizen’s opinion is given equal weight in the democratic process. This is equivalent to allowing all the sailors on the boat to be a part of the committee that selects the captain - it is, naturally, ‘equal’, but logically, it guarantees an inefficiency in the outcome of the democratic process - it accounts for the opinion of those who do not possess the skills necessary to make an informed decision. In a highly literate society, this would be a small cost to bear - as long as the majority is capable of critical thinking, the outcome would be efficient, along with the positive side effect of creating a sense of equality amongst the citizens. However, we are not living in a highly ‘literate’ society. Statistically higher literacy rates have failed to account for the limitations of the education.

Education all over the world has been moving further and further away from humanities, closer to natural sciences. Particularly in this part of the world, the education system involves very little amount of compulsory education in philosophy or political science. As jobs in these fields are not economically rewarding, students too lack the incentives to take up these fields or study them. However, what this has created is a society of highly educated illiterates - we understand trigonometry but we do not understand how to reason outside the realm of numbers. Engineering is held in high regard, but political literacy, an education in reasoning, in morality and ethics, it is often dismissed as ‘unnecessary’ for the simple fact that these do not lend themselves very easily to jobs that the capitalist society can absorb. By the virtue of understanding calculus or the laws of motion, one is not naturally equipped to make better decisions. The tools necessary to calculate the trajectory of objects are not the same tools that can be used to differentiate between right and wrong, good and bad - the progress of society has been lopsided.

* * *

“Government of the people, by the people, for the people” - nearly every introduction to the idea of democracy is built around this quote by Abraham Lincoln. It successfully highlights the essence of a democratic institution: people. The people are essentially the engine of a democracy. They participate in the process by adopting different roles: as citizens, journalists, civil servants, political leaders, etc. A democracy works only when all of these perform their functions effectively. Essentially, people are the ground upon which the 4 pillars of a democracy stand.

Our goal, then, becomes to ensure that this ground is firm, that it is fertile, which is to say, to ensure that people are not just well trained but well educated. This is an uncommon belief, in the sense that nobody would rationally disagree with this statement but there are not many people who understand its implications as well as what it demands from us as human beings - it demands a process of constant education - of learning, unlearning and relearning.

The reluctance to devote time and energy to human sciences is a product of several things. The present job market is one of those factors - but then, we don’t do everything just for the sake of work. There is another factor, one that discourages people from moving beyond newspapers and TV and social media - it is the subjectivity.

Society and its functioning does not have a basis in science, at least not in the same sense as the science of the physical world - there are no exact rules that dictate how society works, or how society should work. There are different schools of thought, and each of these lead us on different paths - perhaps, then, there is no objective measure to navigate this space, to understand this world. This belief is only amplified by the environment - the primary source for information in these matters is the news channels, the newspapers. Information has increased, sensationalism has increased, and without being familiar with the fundamentals the govern the debate, we watch news anchors spit over each other, fact after fact, accusation after accusation. With no simple way to understand and to catch up, it seems, the act of being an informed citizen demands far too much these days - how can we be expected to follow the chain of thought and to verify the information being thrown at us, when all of it is happening so quickly? Most people resort to making their own judgements about this information, then, and here too, our judgement is affected by the limits of our knowledge - without a real ‘education’, not just training, but education, our judgement is simply a byproduct of our sentiments - perhaps, this news channel is reliable, or this anchor seems to make sense, or in the past, he has been correct, or his stance is popular. We employ shortcuts in thinking - heuristics - to arrive at what seems to be an approximately correct answer. However, it is far from correct.

Human sciences are subjective, there is no doubt to this - but how subjective? There is no rule, no method that works in every situation - but are there rules that work well in most situations? Is there a way of thinking that can guide us, generally at least, in processing information and understanding society? This is exactly the purpose behind studying history, political science, economics and philosophy. An education in these subjects is essential in developing an understanding of society and participating efficiently in it. People dismiss the study of history - it is true, I gain nothing from learning the exact date that Hitler came to power. However, that is not the true objective of studying history - it is to understand the world that existed before us, so that we can understand how we got here. It is to learn from the mistakes of the past, so that we are not doomed to repeat it. Philosophy, perhaps, receives the strongest criticism - outside academic spaces, it is considered to be a form of intellectual masturbation, with no meaning to be derived from it. Logic is a branch of philosophy that enables us to understand how to reason for ourselves, to determine the validity of a belief, an idea. It is through philosophy that we study ethics, that we learn about morality, the differentiation between right and wrong, good and bad - terms that we encounter every day in our lives, not only in the broader political domain but the personal one too. Yet, most of us receive nearly no formal education in philosophy, and our education in history is limited to learning dates and listing out, in a formulaic manner, the cause and effect of historical events - 8 points for 4 marks, 10 points for 5 marks. To compensate for the lack of critical thinking, some schools have introduced sessions for moral values - to teach kids what is right and what is wrong. Still, the vision is missing - society continues to evolve - the challenges our parents faced are radically different from the ones we face today. It is not enough to teach them what is right and what is wrong -  what is necessary is to introduce them to a method through which they can determine this for themselves, for the countless challenges that they will face.

Democracy is flawed because its foundation is flawed: most people are not ‘educated’ in the true sense of being educated - they are highly trained to perform specific jobs. They work as engineers, doctors, businessmen, scientists, designers. The difference between training and education is subtle - but it counts, in every way. Our goal then, to be better people, to be better citizens, involves an education that does not end. It is a pursuit without a fixed answer, and as long as the answer keeps changing, we must keep learning.

[boxoffice-psych532]

UNIT 10: Decision Making on the Internet

Should You Believe That Rumor?

Many people believe rumors they hear spread around social media. From Coca-Cola still having cocaine in the recipe to Michael Jackson still being alive, many people fall for the first thing they see without doing any fact checking of their own. Nowadays, there is a site to do the fact checking for you. Snopes.com, created by David Mikkelson, helps internet users check rumors they hear online to determine if they are actually true.

The site originally started as Alt.folklore.urban, which was a place for people who enjoyed collecting, sorting, and organizing facts. For its first 7 years of existence, the site was mostly a hobby for David and his wife Barbara, conducting experiments and writing articles in their home while still working their day jobs. After 9/11, however, the site grew exponentially. The Mikkelsons started debunking theories about what caused the attacks, and the press took interest. The success let them hire more researchers, writers, and editors. They also started debunking political rumors, such as Obama's fake birth certificate.

Their site provides detailed explanations about a rumor rather than a 'yes' or 'no' answer, the real world is more complex than a black and white answer. snopes.com has become a reputable source for fact checking rumors, it is even endorsed by school librarians as a reliable source. Next time you hear something and you're not sure if it's true, double check with snopes.com.

Motivation in Travel

The internet has allowed people to plan their travel instead of using travel agents or travel guides. A study on how travel motivations affects internet use before, during, and after a trip found that the young, well-educated and affluent tend to be the first movers in taking advantage of the internet.

the Jensen & Hjalager (2013) article “The Role of Demographics and Travel Motivation in Travelers’ Use of the Internet Before, During, and After a Trip.” The purpose of their study was to depict how and to what extent travelers use the internet for collecting and sharing travel related information. They wanted to study this because "there is a remarkable lack of of insight into the relationship between travel motivations and online search behaviors. They used an online questionnaire to measure respondents travel motivations, their search for travel information, and their specific internet use before, during, and after their trip. 

They found that there were 8 prominent motivational factors for travel: family/friends relationship, escape/relaxation, food experience, experience new culture, nature experience, shopping, sun and beach, and status. They also found that the motivation for traveling seemed to influence their online search methods prior to choosing a destination. Before the trip, online searching was prominent for finding accommodations and booking flights, whereas online use during the trip switched to finding attractions and excursions at the destination. Online use also decreased during the trip, travelers used more offline sources instead. 

They also found that social media use after returning home was prominent among the young generation and was more popular among women than men. These conclusions make sense to me because people will spend a lot of time preparing for a trip, but they don't want to spend a lot of time online while on their trip because they are afraid they'll miss things by being on their phone. 

Heuristics

A heuristic is a mental shortcut that allows people to solve problems and make judgements quickly and efficiently. These rule of thumb strategies shorten decision making time and allow people to function without constantly stopping to think about their next course of action. While these tactics are faster, they are not necessarily accurate. 3 important heuristics are the representativeness heuristic, the availability heuristic, and the anchoring & adjustment heuristic.

The representativeness heuristic is when we compare a situation to a prototype or stereotype we already have in mind. For example, we are asked if someone is a truck driver or a professor, and they are described as wearing a tweed coat and glasses and are unathletic. We are likely to say this person is a professor because the description fits our stereotype of a professor. An online example of this is that people are heavily biased towards 5 star reviews of products. However, 5 star reviews usually have a couple of reviews, where as 4 star reviews usually have hundreds.

The availability heuristic allows people to assess how often an event occurs or how likely it will occur, based on how easily that event can be brought to mind. An example of this would be a person overestimating the likelihood of a shark attack because they just read an article about sharks. Since the event is easily brought to mind, they will overestimate its likelihood. Another example would be an adolescent seeing pictures of teenagers drinking on Facebook, and then overestimate the likelihood of underage drinking.

The anchoring and adjustment heuristic allows people to estimate a number by starting at an initial value (the “anchor”) and adjusting that value up or down. However, the anchor can be misleading and cause people's estimates to be incorrect. An example of this would be online sales. Websites can show the price of an item and then say that it's on sale for a limited time only, making the buyer think they are getting a great deal. However, websites could easily jack up the 'original' price, and make the 'sale' price the actual price of the item. The original price is the anchor, so buyers think that sale is a good price when it could be a lie.

[thomasmastersuniblog]

Nielsen and Molich's Principles

Lecture Tasks

App/Website - Apple music  

What makes it well designed:

Apple's ecosystem helps when using apple music multiple devices or playing

Minimal basic interface 

Personalised music recommendations 

Isn't cluttered with podcasts music centred 

Over 75 million songs

Radio shows 

Can use with siri 

Personalised playlists 

Download size options 

What parts of the app could be improved:

Search function using predictive typing 

The app could benefit from more options to customise the user experience, such as setting preferences for music quality and playback settings.

User interface on pc is less intuitive

Social features aren't easy to navigate 

Three experiences 

Apple Notes

Too basic add the ability to import photos

Add shared features

More customisation and added features brainstorms fonts

create it across apps Apple Books film etc being able to use it like a sticky note across apps

Apple Calendar/Reminder

Not much on this just that the two don't work well both should be integrated similar to google calendar so that you can create alerts and reminders.

Ability to copy reminders or set them based on certain days.

Apple Camera

Could use more tactile features like a camera it employs some but for zoom, changing certain features aperture etc.

Adding better features for video.

The main lessons I took away from today's class were in relation to Molich Nielsen's 10 heuristic principles. These principles are guidelines for designing user interfaces that are both efficient and easy to use. Nielsen and Molich introduced them in 1990 becoming a widely used framework for evaluating and improving the usability of digital products.

"A heuristic is a mental shortcut commonly used to simplify problems and avoid cognitive overload. Heuristics are part of how the human brain evolved and is wired, allowing individuals to quickly reach reasonable conclusions or solutions to complex problems."

Visibility of system status:

Users should always be aware of what's going on in the system, whether it's a progress bar or a loading icon. The system should provide feedback to the user so they know their actions are being processed. Match between the system and the real world: The system should use language and concepts that are familiar to the user so that they can easily understand and navigate the interface.

User control and freedom:

Users should be able to easily undo or reverse their actions, and the system should provide clear options to exit or cancel any process. Consistency and standards: The interface should follow established conventions and be consistent across all pages or sections of the product, making it easy for users to understand and use.

Error prevention:

The system should provide clear warnings and feedback to users to prevent mistakes, allowing them to easily correct any errors that do occur.

Recognition rather than recall:

The system should present information and options in a way that minimises the user's need to remember or recall previous actions or information.

Flexibility and efficiency of use:

The system should provide shortcuts or alternate paths to frequently-used features or actions, making it easier and faster for experienced users to perform tasks.

Aesthetic and minimalist design:

The interface should be visually appealing and use a clean, uncluttered design that doesn't distract or overwhelm the user. Help users recognize, diagnose, and recover from errors: The system should provide clear and easy-to-understand error messages, and offer options for users to recover from errors or mistakes. Help and documentation: The system should provide clear and accessible documentation or help resources so that users can quickly find the information they need to use the system effectively.

Summary

The framework seems like a reliable method to use in evaluating digital products during the research and design stage. the scope of this framework could be applied successfully beyond digital products to physical ones, businesses...

An idea that this framework made me remember was that I am taking ux to have the knowledge to pursue other ventures as I mentioned above that may be pursuits outside of the digital products but understanding design, user psychology, and analysing markets are all skills that allow one to traverse beyond any one profession.

[dritastreaming]

Block world problem in ai

#Block world problem in ai generator#

Otherwise, make initial state as current state. If it is a goal state then stop and return success. It examines the neighboring nodes one by one and selects the first neighboring node which optimizes the current cost as the next node. Uses the Greedy approach : At any point in state space, the search moves in that direction only which optimizes the cost of function with the hope of finding the optimal solution at the end.

#Block world problem in ai generator#

Then this feedback is utilized by the generator in deciding the next move in the search space.Ģ. Hence we call Hill climbing a variant of generating and test algorithm as it takes the feedback from the test procedure. If the solution has been found quit else go to step 1. Test to see if this is the expected solution.ģ. The generate and test algorithm is as follows :Ģ. Variant of generate and test algorithm: It is a variant of generating and test algorithm. It helps the algorithm to select the best route out of possible routes.ġ. A heuristic function is a function that will rank all the possible alternatives at any branching step in the search algorithm based on the available information.However, it will give a good solution in a reasonable time. ‘Heuristic search’ means that this search algorithm may not find the optimal solution to the problem.Example- Travelling salesman problem where we need to minimize the distance traveled by the salesman. In the above definition, mathematical optimization problems imply that hill-climbing solves the problems where we need to maximize or minimize a given real function by choosing values from the given inputs.Linear Regression (Python Implementation).Removing stop words with NLTK in Python.Inorder Tree Traversal without recursion and without stack!.Inorder Tree Traversal without Recursion.Tree Traversals (Inorder, Preorder and Postorder).Breadth First Search or BFS for a Graph.Unique paths covering every non-obstacle block exactly once in a grid.Print all possible paths from top left to bottom right of a mXn matrix.Count all possible paths from top left to bottom right of a mXn matrix.Count number of ways to reach destination in a Maze.The Knight’s tour problem | Backtracking-1.Warnsdorff’s algorithm for Knight’s tour problem.Printing all solutions in N-Queen Problem.Difference between Informed and Uninformed Search in AI.Understanding PEAS in Artificial Intelligence.Introduction to Hill Climbing | Artificial Intelligence.Uniform-Cost Search (Dijkstra for large Graphs).ISRO CS Syllabus for Scientist/Engineer Exam.ISRO CS Original Papers and Official Keys.GATE CS Original Papers and Official Keys.

[lasclfresh]

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