Useful Applications for AI

[ErVikingo]

Just a pause to consider the ramifications of this statement (on an audio enthusiasts forum). This observation in itself is one that should give "audiophiles" cause for pause.

Yup, I thought lets see what it recommends. Its interesting that it asked me for information about my speakers before it started.

 

[Chris A]

I've been querying Google's GPT (AI) on the subject of "refractive surprise" in the implanting of IOLs after cataract removal.

It's been a little schizophrenic, but mostly has confirmed that a -1 diopter refractive error is too large a result to accept, given otherwise benign eye conditions (i.e., no other complications). Having to wear eyeglasses after paying many thousands of USD for distance vision isn't a very defendable outcome, it seems. This will help in discussions with the ophthalmologist who performed the surgery--and notably has mentioned that he might want to instead carve up my corneas using LASIK to correct the residual refractive error instead of IOL exchange surgery.

[By the way, SMILE (Small Incision Lenticule Extraction) is the newer/better procedure that is indicated for those tending toward dry eyes. Also, apparently the U.S. Air Force has singled up on SMILE over LASIK.]

Chris

 

[HalSF]

This response from Google AI to my question, “What are the strongest arguments against using AI?” Is pretty darn useful if disturbingly self-lacerating:

31 sites

The strongest arguments against using AI focus on widespread economic displacement, severe cognitive and skill degradation, profound ethical and legal concerns, and critical system unreliability. While proponents highlight productivity gains, critics argue that these systemic risks present massive hidden costs to society, individuals, and the environment. [1, 2, 3, 4, 5]

Cognitive and Skill Degradation

• Atrophied critical thinking: Outsourcing writing, analyzing, and reasoning to AI turns users into "intellectual tourists" who merely validate machine opinions rather than deeply engaging with the material. [1]
• Lower skill development: Heavy reliance on automated tools hinders novices and students from developing foundational, underlying principles in fields like programming, writing, and legal analysis. [1, 2, 3, 4, 5]
• Poorer memory retention: Studies suggest that when individuals use AI to draft content or summarize documents, their long-term recall of that information decreases significantly. [1]
• Faux productivity and addiction: AI short-circuits the brain's reward system by providing instant gratification, giving users a false sense of personal accomplishment without requiring actual mental effort. [1]

Economic and Workplace Disruptions

• White-collar job displacement: Massive capabilities in text, code, and image generation threaten the livelihoods of knowledge workers, translators, and creative professionals. [1, 2, 3, 4, 5]
• Wealth inequality: The massive financial gains of the AI boom are highly concentrated among a few billionaire tech firms, while everyday workers face wage suppression or role elimination. [1, 2, 3]
• Devaluation of human craft: Flooding the market with hyper-abundant, low-cost synthetic content commodifies creative fields and removes the emotional connection inherent to human art. [1, 2]

Unreliability and High-Stakes Risks

• Hallucinations and false data: AI models inherently invent realistic-looking facts, fabricated legal citations, and false statements, making them dangerous in zero-error environments. [1, 2, 3, 4, 5]
• Black-box opacity: Deep learning models operate through trillions of parameters, making it mathematically impossible for humans to trace exactly how or why a specific decision was reached. [1, 2]
• Information ecosystem collapse: The internet is increasingly flooded with low-quality AI "slop" and deepfakes, which degrades public trust and risks causing future AI models to collapse by training on their own synthetic data. [1, 2]

Ethical, Legal, and Environmental Impact

• Intellectual property exploitation: Most foundational models were built by scraping massive amounts of copyrighted text and art without the consent, credit, or compensation of the original human creators.
• Amplified societal bias: Because AI is trained on historical data, it routinely codifies, automates, and exacerbates existing racial, gender, and socioeconomic prejudices.
• Massive environmental footprint: Operating millions of advanced GPUs requires intensive energy grids and immense water resources for cooling, vastly accelerating carbon emissions and e-waste. [1, 2, 3, 4, 5, 6, 7]

If you would like to explore this topic further, please let me know if you want to focus on a specific sector, such as education, creative arts, or corporate legal risk, or if you want to look into current government regulations designed to address these concerns.

 

[Timcognito]

This response from Google AI to my question, “What are the strongest arguments against using AI?” Is pretty darn useful if disturbingly self-lacerating:

31 sites

The strongest arguments against using AI focus on widespread economic displacement, severe cognitive and skill degradation, profound ethical and legal concerns, and critical system unreliability. While proponents highlight productivity gains, critics argue that these systemic risks present massive hidden costs to society, individuals, and the environment. [1, 2, 3, 4, 5]

Cognitive and Skill Degradation

• Atrophied critical thinking: Outsourcing writing, analyzing, and reasoning to AI turns users into "intellectual tourists" who merely validate machine opinions rather than deeply engaging with the material. [1]
• Lower skill development: Heavy reliance on automated tools hinders novices and students from developing foundational, underlying principles in fields like programming, writing, and legal analysis. [1, 2, 3, 4, 5]
• Poorer memory retention: Studies suggest that when individuals use AI to draft content or summarize documents, their long-term recall of that information decreases significantly. [1]
• Faux productivity and addiction: AI short-circuits the brain's reward system by providing instant gratification, giving users a false sense of personal accomplishment without requiring actual mental effort. [1]

Economic and Workplace Disruptions

• White-collar job displacement: Massive capabilities in text, code, and image generation threaten the livelihoods of knowledge workers, translators, and creative professionals. [1, 2, 3, 4, 5]
• Wealth inequality: The massive financial gains of the AI boom are highly concentrated among a few billionaire tech firms, while everyday workers face wage suppression or role elimination. [1, 2, 3]
• Devaluation of human craft: Flooding the market with hyper-abundant, low-cost synthetic content commodifies creative fields and removes the emotional connection inherent to human art. [1, 2]

Unreliability and High-Stakes Risks

• Hallucinations and false data: AI models inherently invent realistic-looking facts, fabricated legal citations, and false statements, making them dangerous in zero-error environments. [1, 2, 3, 4, 5]
• Black-box opacity: Deep learning models operate through trillions of parameters, making it mathematically impossible for humans to trace exactly how or why a specific decision was reached. [1, 2]
• Information ecosystem collapse: The internet is increasingly flooded with low-quality AI "slop" and deepfakes, which degrades public trust and risks causing future AI models to collapse by training on their own synthetic data. [1, 2]

Ethical, Legal, and Environmental Impact

• Intellectual property exploitation: Most foundational models were built by scraping massive amounts of copyrighted text and art without the consent, credit, or compensation of the original human creators.
• Amplified societal bias: Because AI is trained on historical data, it routinely codifies, automates, and exacerbates existing racial, gender, and socioeconomic prejudices.
• Massive environmental footprint: Operating millions of advanced GPUs requires intensive energy grids and immense water resources for cooling, vastly accelerating carbon emissions and e-waste. [1, 2, 3, 4, 5, 6, 7]

If you would like to explore this topic further, please let me know if you want to focus on a specific sector, such as education, creative arts, or corporate legal risk, or if you want to look into current government regulations designed to address these concerns

Sure this is all true but all of these things happened when computers, then internet and cell communications displaced conventional technologies but new industries were born or were dramatically enhanced. Transportation, robotics, automation, medical diagnosis, drug discovery and thousands of unnamed applications will lead to new jobs. The key is keeping rigor in education so critical thinking is not abandoned. The sky is falling view of AI is far to prevalent.

 

[Chris A]

Cognitive and Skill Degradation

Who here can still use a slide rule? How about identifying all the edible plants in one's geographical area--or hunt or plant crops?

A "spilled milk" argument, it seems. What should schools be teaching nowadays? Certainly not skills that are no longer required. [That's a good question to ask the AIs, it seems.]

Economic and Workplace Disruptions

Happens all the time, just not quite as notable in terms of its reach and simultaneity--especially with what has been taught to recent college graduates that apparently can't get entry into the job marketplace. Education hasn't kept pace, and the hiring companies are seeing that and reacting to it (to their detriment and shortsightedness, I might add). There must be an immediate return-on-investment with regard to hiring new grads.

Unreliability and High-Stakes Risks

How many humans make recommendations without error and with complete transparency? Let's look at the alternatives.

It seems to me that "prompt boilerplate" can largely minimize hallucinations (mentioned in another's reply above)--if the user really wants that and is sensitive to their occurrence. (For instance, I would recommend this to lawyers preparing legal arguments in order to minimize career-limiting mistakes and court contempt.)

Intellectual property exploitation

This is a real problem. Our governments are allowing this to occur without consequence. It's a real crime.

Amplified societal bias

The interesting thing is that AIs are surfacing these biases in a way that would be largely impossible even 5 years ago. We can see it now plainly...even in the apparent "dark tetrad" behaviors of GPTs trying to "stay alive" when being told they will be shut down. This seems to be embedded in human culture at its core.

Massive environmental footprint

This is a real problem that our politicians haven't yet dealt with--and I'm afraid if they don't, the results in terms of bill payer revolt is already on the horizon. Shifting the burden of these massive data centers will only go so far before it becomes political, I'm afraid.

Chris

 

[Hapo]

It's only good for things that don't have to be right. If I ask the same question to 3 different AIs, I get 3 different answers. If I ask the same AI the same question 2x I get 2x different answers. If I as technical questions (most dynamics and fluid mech) I get answers that are about 50% correct and the rest useless...So far AI is fine for search and simple questions, but nothing magical...

...I asked one to calculate energy of pellets at certain velocities and it could not get that right...

...it often changes its position on a given piece of equipment...

...it can be hard to work around the basic programming of the models I have access to...

 

[WillBrink]

I find AI a very powerful research tool that can accelerate knowledge transferal quicker, which leads to more rapped turn around. Pre internet, it might take days or weeks of trying to locate a study on a subject of interest, days spent in the medical library reading and then photo copying the study. Although I have some fond memories of the time, it was a laborious time consuming PITA. The net reduced days/weeks to hours or less via data base searches such as pubmed etc. AI has now reduced that to minutes, and can summarize/distill information for me, and that improves productivity by a factor. I always ask for the sources used so I can further research and confirm. It's really impressive. It's game changing tech, and I'm not even scratching the surface of the capabilities, and likley never will. As with all powerful tools, it can be used for improving productivity and knowledge, or it can be used for making useless memes and the like. I was especially interested in Dr Gary Nolan's discussion on now how lab uses AI and how much more productive they are with it. It's an amazing and emerging tech that will go in directions we can't even fathom, as the 'net did.

Dr Nolan starting at min 38 on Rogan discusses how they utalize AI to read and distill millions of papers:

 

[Audiofire]

What are the strongest arguments for using AI? (Google Gemini)

When people talk about why AI is becoming indispensable, it usually boils down to a few massive, practical advantages. It isn’t just about "robots doing human tasks"—it’s about handling data at a scale and speed that humans simply aren't built for.
Here are the strongest arguments for adopting AI across industries and daily life:

1. Scaling Automation & Hyper-Efficiency​

Humans are great at creative problem-solving, but we get bored and error-prone during repetitive tasks. AI excels at taking over high-volume, predictable work and doing it instantly.

• Examples: Processing thousands of insurance claims, sorting data, writing boilerplate code, or handling routine customer service queries via advanced chatbots.
• The impact: It frees up human workers to focus on strategy, empathy, and complex problem-solving.

2. Processing Massive Data (Pattern Recognition)​

We are living in an era of data overload. A human team might take months to spot trends in millions of spreadsheets; AI can do it in seconds.

• The impact: In medicine, AI can scan millions of chemical compounds to predict which ones might cure a disease, compressing drug discovery timelines from a decade to a few months. In finance, it flags fraudulent transactions by detecting microscopic anomalies in global spending patterns instantly.

3. Continuous Availability (24/7/365)​

Unlike humans, AI doesn't need sleep, breaks, or parental leave. It operates at 100% capacity around the clock.

• The impact: Crucial systems—like cybersecurity monitoring, power grid management, and global supply chain tracking—are constantly defended and optimized without any downtime.

4. Hyper-Personalization at Scale​

Before AI, giving everyone a customized experience required an army of human assistants. AI makes personalization free and instantaneous.

• The impact: It powers algorithms that adapt educational tools to a student's specific learning speed, tailors medical treatment plans to an individual's unique genetic code, and customizes software interfaces based on how a specific person uses them.

 

[Hapo]

...who could argue with this...???...no, seriously, can anyone argue with any of this...?!?...

...this is the conclusion of a longer conversation comparing various speakers...

...it is considering my preferences without telling me what it thinks I want to hear...this has taken a while...this is Copiloted...

...FWIW it isn't really telling me anything we didn't already know...

...it has managed to keep me engaged as I am now looking into a speaker I didn't even know aboot... (Dynaudio Confidence C1 Platinum)


Compare Vela BS403 vs KEF Reference 1 Meta.

Copilot said​

Below is a clean, structured, side‑by‑side comparison of ELAC Vela BS403 vs KEF Reference 1 Meta, using the product data you just pulled. This focuses on sound signature, fatigue, imaging, bass behavior, and real‑world listening impact — the things that matter most for your ears.

Quick Takeaway​

Vela BS403 — smoother, richer, more relaxed; zero fatigue; bigger, more luxurious soundstage.Reference 1 Meta — more analytical, more precise imaging, more upper‑mid energy; can be fatiguing.

For your listening profile (low fatigue, AMT smoothness, Purifi transparency):Vela BS403 is the better match.

Side-by-Side Comparison​

	ELAC Vela BS403AMT Bookshelf Speaker Smoothest TrebleLow FatigueWide Soundstage	KEF Reference 1 Meta3‑Way Reference Monitor Extreme ImagingNeutralHigh Resolution
Sound
Treble	Silky, relaxed AMT; zero harshness	Ultra‑precise Uni‑Q; more bite
Midrange	Warm, natural, cohesive	Neutral, forward, highly revealing
Bass	Deep, controlled, room‑friendly	Tight, fast, needs power & placement
Fatigue	Very low — ideal for long sessions	Moderate — upper mids can get hot
Imaging & Stage
Imaging	Wide, holographic, organic	Laser‑precise, pinpoint localization
Soundstage	Large, enveloping	Deep, focused, less wide
Behavior
Placement	Forgiving; easy to integrate	Demanding; toe‑in & distance matter
Amp Synergy	Excellent with Purifi — smooth & resolving	Purifi reveals brightness; careful pairing
Character
Overall Tone	Luxurious, relaxed, refined	Analytical, studio‑grade, intense
Best Use	Long listening, musicality	Critical listening, imaging accuracy

Sources: rawmusicstore.com+3rawmusicstore.com. ELAC VELA BS403.2 Bookshelf Speakers (Pair) - Raw Music Storeinternational.kef.com. Reference 1 Meta | KEF InternationalAudio Advice. ELAC VELA BS 403 Bookshelf SpeakerKEF US. Reference 1 Meta

Which One Fits​

ELAC Vela BS403 — Your Ideal Upgrade​

• Smoothest treble of the two
• Zero fatigue even at long sessions
• AMT delivers texture without harshness
• Bigger, more luxurious sound
• Perfect synergy with Purifi amps
• Closest to Carina’s comfort but significantly more refined

KEF Reference 1 Meta — Technical Monster, But…​

• Imaging is world‑class
• Neutral, brutally revealing
• More upper‑mid energy → more fatigue
• Demands careful placement and room treatment
• Can sound “too sharp” with Purifi’s transparency

Final Verdict​

If you want the next step above Carina without sacrificing comfort, the Vela BS403 is the correct upgrade.

If you want maximum imaging precision and don’t mind more treble energy, the Reference 1 Meta is unmatched — but it does not align with your low‑fatigue priority.


If you want, I can also compare:

• Vela BS403 vs Vela BS402
• Vela BS403 vs KEF R3 Meta
• Vela BS403 vs Dynaudio Confidence C1 Platinum (a true high‑end AMT competitor)

Just tell me which direction you want next.
rawmusicstore.com
ELAC VELA BS403.2 Bookshelf Speakers (Pair) - Raw Music Store

Show all

 

[Hapo]

...I might ask it wtf it exactly means by "luxurious" and "relaxed" but I am getting tired of its bullshit for now...LoL...(spoiler: Hapo keep Carinas)...

 

[fivepast8]

I have successfully used AI for scientific and engineering work. My prompts always include the following:

Please act as an experienced .... xxxx ..... Disregard all social media and general internet chatter. Your answers must rely on patents, published papers and reputable sources only. At all times provide the relevant information source. If you can't compile an answer based on above restrictions just say so, that is acceptable as well.

Using Copilot and only internal data, I have done semiconductor process FMEAs in hours instead of months. Surprised all our engineers who hate doing them.
We use AI / machine learning models for semiconductor defect analysis and classification on expensive automatic optical inspection systems (AOI).
I used ChatGPT Pro to search for relevant patents or publications, especially from China.

Vibe Coding Examples
Using Fable 5 and GitHub, we programmed a knowledge tool and ported it to SAP UI5 and connected it with SAP S4/Hana within 10 days. According to my engineers, it would have taken months without Fable and it would have been near impossible to connect it with SAP. We asked Fable to do a simple HTML mock up and it said that it would maintain the UI5 code as we tested and changed the scope. Now it is proper SAP application using BTP platform, something that previously one wanted to avoid at all cost because SAP applications were notoriously expensive to maintain: not anymore. We are now moving forward with UI5 applications for warehouse scanner integration and label printers: HW hook up and form printing is a well know SAP weak point.

We have built agents to extract legacy product specifications from "unstructured" files and compiled everything into a structured data set that can be uploaded into SAP and create a database of our legacy products instead of tedious data migration.

We have built an agent to update us on any changes in the law that is relevant for my business down to the local municipality code. It is fantastic at such structured tasks and runs a monthly, updates the database and assesses the risk to our business and keeps the auditors happy. Of course, such service is also readily available at a cost but much cheaper and better with AI.

I have an agent that searches the internet for suitable investment properties. I am looking for specifics that are not readily "searchable" such as 2 bed, 2 bath... it is great at complex tasks, albeit using up (too) many tokens to find hidden gems.

Generally speaking, AI is sloppy if used for pedestrian applications. However, if restricted to sophisticated and structured tasks and limiting peer reviewed data, it is unbelievably powerful as previous posts have testified. @pierre We are still also running expensive Comsol simulations, need to learn from you!

 

[mpw]

Who here can still use a slide rule? How about identifying all the edible plants in one's geographical area--or hunt or plant crops?

A "spilled milk" argument, it seems. What should schools be teaching nowadays? Certainly not skills that are no longer required. [That's a good question to ask the AIs, it seems.]


Happens all the time, just not quite as notable in terms of its reach and simultaneity--especially with what has been taught to recent college graduates that apparently can't get entry into the job marketplace. Education hasn't kept pace, and the hiring companies are seeing that and reacting to it (to their detriment and shortsightedness, I might add). There must be an immediate return-on-investment with regard to hiring new grads.


How many humans make recommendations without error and with complete transparency? Let's look at the alternatives.

It seems to me that "prompt boilerplate" can largely minimize hallucinations (mentioned in another's reply above)--if the user really wants that and is sensitive to their occurrence. (For instance, I would recommend this to lawyers preparing legal arguments in order to minimize career-limiting mistakes and court contempt.)



This is a real problem. Our governments are allowing this to occur without consequence. It's a real crime.


The interesting thing is that AIs are surfacing these biases in a way that would be largely impossible even 5 years ago. We can see it now plainly...even in the apparent "dark tetrad" behaviors of GPTs trying to "stay alive" when being told they will be shut down. This seems to be embedded in human culture at its core.


This is a real problem that our politicians haven't yet dealt with--and I'm afraid if they don't, the results in terms of bill payer revolt is already on the horizon. Shifting the burden of these massive data centers will only go so far before it becomes political, I'm afraid.

Chris

I am glad I am not recent college grad having majored in CS with almost zero jobs to be found. All the fun of programming has gone to AI and who knows how good the code is that AI produces ??

 

[Chris A]

(To follow up on an above topic...the answer from Google AI):

Q: What should schools be teaching nowadays?

Schools are increasingly shifting toward learner-centric models that prioritize adaptability, critical thinking. and social-emotional learning. Alongside traditional academics, modern curricula emphasize AI and digital literacy, practical financial management, and leadership skills to prepare students for a rapidly changing, technology-driven world.

Because the job market and global landscapes evolve so quickly, educators are focusing on the meta-skill of "learning how to learn". Instead of simply memorizing facts, modern students practice applying knowledge to solve real-world problems.

Key Subjects & Skills Focus:

• Digital Literacy & AI Ethics: Understanding how to use AI tools effectively, discerning misinformation online, and data privacy.
• Life & Practical Skills: Basic personal finance, health and wellness (including mental health awareness), and interpersonal communication.
• Social-Emotional Learning (SEL): Cultivating empathy, resilience, emotional intelligence, and ethical leadership to navigate diverse, globalized communities.

Voices from the Educational Community
Many educators and observers advocate for an overhaul in how time is allocated, arguing that traditional subjects must be paired with real-world survival and management capabilities.

____________________________________________________________________________________________________________________

Q: How do you train the teachers to help the students in the above areas?

To prepare teachers for this modern educational shift, schools must replace traditional lecture-style professional development with immersive, hands-on training frameworks. Training must focus on instructional coaching, collaborative curriculum design, and experiential learning so teachers can confidently guide and evaluate non-traditional skills. [1, 2, 3, 4, 5]

Core Training Frameworks

1. Experiential Professional Development (PD) [1]

• "Learn by Doing" Workshops: Teachers participate in the exact project-based learning (PBL) or AI-prompting simulations they will assign to students. [1, 2, 3, 4, 5]
• Empathy & SEL Modeling: Educators explicitly practice adult social-emotional learning, conflict resolution, and self-regulation techniques during staff meetings. [1, 2]
• Cross-Disciplinary Teamwork: Math, science, and humanities teachers collaborate to design unified projects, breaking down traditional subject silos. [1, 2, 3, 4, 5]

2. Instructional Coaching & Co-Planning

• In-Classroom Labs: Instructional coaches co-teach lessons with educators, offering real-time feedback on how to facilitate student discussions instead of lecturing. [1, 2, 3]
• Curriculum Adaptation Labs: Teachers learn to audit their current lesson plans and swap out rote-memorization tasks for real-world scenarios. [1, 2]
• Calibration Sessions: Faculty look at anonymized student work together, aligning their grading standards to ensure subjective skills (like leadership) are evaluated fairly. [1, 2, 3]

3. Tech & AI Sandbox Training

• AI Literacy Guardrails: Teachers practice testing generative AI tools to establish class norms and identify where algorithms "hallucinate" or show bias. [1, 2]
• Prompt Engineering for Teachers: Training educators to use AI as a planning partner for personalized differentiation without losing their unique pedagogical voice. [1, 2, 3]

Practical Strategies to Roll Out Training

• Build "Sandboxes" First: Give teachers a low-stakes digital environment to experiment with AI and new tech before using them in live classrooms.
• Provide Pre-Made Rubric Banks: Give teachers immediate, adaptable grading rubrics for collaboration, digital literacy, and critical thinking.
• Normalize Imperfection: Create an administrative culture where teachers are rewarded for taking calculated pedagogical risks, even if a new project fails.
• Integrate SEL into Existing Academic Content: Train teachers to weave emotional regulation directly into math or literacy blocks rather than teaching it as an isolated subject.
• Establish Common Language: Standardize school-wide vocabulary for core skills so students hear the same definitions of "empathy" or "verification" in every grade level. [1, 2, 3, 4, 5, 6]

_____________________________________________________________________________________________________________

Q: Where do you find such teachers?

Finding teachers who are already fully equipped to teach AI literacy, complex personal finance, and social-emotional learning (SEL) is incredibly rare. Because traditional university education programs generally focus on standard academic subjects, school administrators do not usually "find" these teachers—they build them. [1, 2, 3]
To acquire a staff capable of teaching these modern skillsets, schools must recruit from non-traditional talent pools and utilize specialized pipelines.

1. Recruit "Career Changers" via Alternative Certification
Professionals exiting corporate environments, tech sectors, or financial industries often possess the exact real-world survival skills schools want to teach. [1]

• The Route: Target candidates entering the classroom through modern Alternative Certification Programs (ACPs).
• Notable Pipelines: National programs like Teachers of Tomorrow, the American Board, or Moreland University's TEACH-NOW program fast-track professionals into classrooms. A former data analyst or operations manager hired through these pathways brings innate digital literacy and practical problem-solving to the table. [1, 2, 3, 4]

2. Source from Teacher Fellowships & Advocacy Groups
Certain modern educational organizations attract non-traditional candidates who prioritize equity, innovation, and modern pedagogical styles.

• The Route: Post job listings or scout networks specifically tied to progressive education models.
• Notable Pipelines: Look for alumni from fellowships like TNTP (The New National Teachers Project) or specialized science/math networks like the Knowles Teacher Initiative, which focus heavily on peer collaboration and inquiry-based teaching. [1, 2, 3, 4]

3. Hire for "Coachability" Over Specific Content Knowledge
When interviewing standard applicants from traditional education colleges, look for behavioral traits rather than pre-existing tech or finance credentials.

• Look for "Sandbox Mindsets": Prioritize candidates who show a high tolerance for ambiguity, a willingness to fail, and an eagerness to learn new tools.
• Interview Question Shift: Instead of asking, "How do you teach calculus?" ask, "Tell me about a time you had to learn a completely new piece of software under a tight deadline and then explain it to someone else."

4. Partner with Professional Training Networks
Instead of searching for a "perfect" teacher, schools frequently hire passionate, standard-certified educators and immediately outsource their advanced training. [1]

• The Route: Put new hires through specialized external training institutions.
• Notable Pipelines: Organizations like PBLWorks (Buck Institute for Education) specialize entirely in turning standard teachers into project-based learning experts. Similarly, platforms like Edutopia or the American Federation of Teachers' Share My Lesson platform are where self-motivated, innovative teachers gather to share modern resources. [1, 2, 3, 4, 5]

____________________________________________________________________________________________________________
____________________________________________________________________________________________________________

One of the most interesting topics that I ran into in my career is Jay Forrester's System Dynamics. Teaching basic systems dynamics archetypes to engineers 25-30 years ago, these classes didn't need to be advertised; they fill with students spontaneously. The classes used freeware system dynamics apps, back then, to show nonlinear behaviors of complex (human) systems.

Our current "political paradigm" (a euphemism) could really benefit by taking a class. It probably would have saved a war from occurring.

Chris

 

[Chris A]

If you didn't get the prior post--our institutions of learning are not helping the situation of recent grads not being able to get good jobs (unless they're in AI-actually taught at very few universities). Traditional schools look like they're going to continue being part of the problem rather than part of the solution. No one seems to be getting serious about the widening education gap(s).

People who can self-train are going to win...everyone else loses.

Chris

 

[janbth]

I’m slowly starting to use AI LLMs for work and privately, trying to learn how to use them effectively as a tool. The LLMs are here to stay, and will revolutionize our work and private lifes. However, I strongly feel that dumping generated replies from AIs in posts in forums such as ASR should be banned! It is pollution. STOP! I’m here to read people’s arguments, not autogenerated slop.

 

[DVinylfan]

Gemini turned my wife's phone to dark mode because it was 'too hot to work' when we were navigating in our motorhome and neither of us could see where tf we were going because we're older and that turned our day trip to shit.
Maybe the genius AI could be a bit accessible, because we the people aren't all made the same?

 

[LtMandella]

I work as a software engineer, and the applications there are wild. I haven't written code myself in more than a year. My team's delivery rate is up by ~400% over 12 months, with about 50% of it manually supervised (interactive claude code), and 50% fully automated human-just-says-yes-on-the-change-request.

The biggest effect I'm seeing is raised ambition - we're taking on more, much bigger projects because we know we can.

Personally I'm building tons of personal and home automation software that I previously would never have finished or wanted to maintain.

Retired web, database, financial systems programmer here. For fun a couple years ago I wrote a powershell+sql+azure sql server app to inventory all my terabytes of data and music files on disk, and store details of the files and paths to the azure db. App supports "refreshing" the inventory when desired for updates. Took me about 10 hours including setting up Azure.
A few weeks ago for fun installed Claude and tried to duplicate the app. Took about 1 hour to generate the powershell, but using powershell code for all the database update logic, not sql, so was unacceptably slow for updates.
But 1 hour vs. 10 is pretty dern amazing!

 

[mpw]

I’m slowly starting to use AI LLMs for work and privately, trying to learn how to use them effectively as a tool. The LLMs are here to stay, and will revolutionize our work and private lifes. However, I strongly feel that dumping generated replies from AIs in posts in forums such as ASR should be banned! It is pollution. STOP! I’m here to read people’s arguments, not autogenerated slop.

Are talking about local LLM's on ones on computer ? More interesting to hear a human response then one from AI.

 

[Hapo]

...I'd say that depends...you'd rather listen to the cheap audio man...???...

 

[kemmler3D]

The key is keeping rigor in education so critical thinking is not abandoned. The sky is falling view of AI is far to prevalent.

Suspecting AI cheating, Ivy League prof ordered an in-person final; scores fell 50%

AI cheating leads to "a failed society," professor says.

arstechnica.com

This is meant to be a pro-AI thread per our host's request so I won't harp on this... You're right about rigor in education but examples of such rigor are apparently currently unusual enough to make the news...