Open AI, ML -- Ideas....meek telsina use cases, or ekkada vadochu ane idea undo post them

[a4apple]

Just now, Spartan said:

try chey anna 

oka plan undi anna job maneyadaniki.. ee year telustundi work out avtundo ledo

[a4apple]

2 minutes ago, csrcsr said:

My only goal is fit somewhere on ai ecosystem to have my pay check running rofl

 Good luck annoy 

[kevinUsa]

50 minutes ago, csrcsr said:

Emo bro may be but i am  not elgible to answer i think its the computing power high end gpus , deeper science math knowledge and to have that billions of tokens as data set to work on no idea bro beyond my imagination

But if you heard deepseek story it was quant trading company anta and theybdid as side project rofl 

They have 20k h100 too

[socrates]

post more ideas 

[csrcsr]

3 hours ago, socrates said:

post more ideas 

One thing i am getting confused is bro

Understand when you have huge data set millions billions trillions of data sets and they are able to analyze using highest computing power gpus, algos, full of deep analysis using powerful math concepts, so usually the uscases are identifying the fraud in huge payment transactions, banks , fsster recommendation engines , identifying diseases based on report, many bio tech inventions powerful simulations etc

But what i dont understand is tge difference between the above use cses and new buzz generative ai where it kind of does synthetic learning and doing work (like agentic ai) , writing codrz

how that is done still puzzling how can ut write code? Its a new req for me how it learns and write the code when it has no data  about my req ,i was under the assumption its like glorified makeup search engine which only has existing dara but this generative ai usecases so called agnetic ai not even getting closer to it how it works how it learns and does the work may be its codingg or anything for tgat matter

[socrates]

34 minutes ago, csrcsr said:

One thing i am getting confused is bro

Understand when you have huge data set millions billions trillions of data sets and they are able to analyze using highest computing power gpus, algos, full of deep analysis using powerful math concepts, so usually the uscases are identifying the fraud in huge payment transactions, banks , fsster recommendation engines , identifying diseases based on report, many bio tech inventions powerful simulations etc

But what i dont understand is tge difference between the above use cses and new buzz generative ai where it kind of does synthetic learning and doing work (like agentic ai) , writing codrz

how that is done still puzzling how can ut write code? Its a new req for me how it learns and write the code when it has no data  about my req ,i was under the assumption its like glorified makeup search engine which only has existing dara but this generative ai usecases so called agnetic ai not even getting closer to it how it works how it learns and does the work may be its codingg or anything for tgat matter

i am not entirely sure am i addressing your comment correctly 

Reasoning is the key, remanning is the pattern matching and syntax..etc when AI is writing code. I strongly believe (its my assumption ) if we ask AI to write code, first it internally build the unit tests for that requirement and run those tests parallel while it is doing reasoning,  code building and parallel execution in somewhere in one docker container before it serves the code to us.

humans reasoning also characterized based on conclusions. understand the problem/puzzle  os the first step, next human thinks in various systematic approach of reasoning like, Inductive Reasoning (looking for patterns), Deductive Reasoning (ensuring the premises and logical conclusions), Abductive Reasoning (choosing best of multiple explanations)..etc this topic goes on with more reasoning approaches and cognitive bias..etc

I believe AI is mimicking this reasoning  with decision trees and probabilities 

bottomline is how humans thinks and draws conclusions is the key to build  generative AI.

 

 

[k2s]

On 1/31/2025 at 4:27 PM, Spartan said:

@Konebhar6  @csrcsr @tom brady @k2s @enigmatic

Here’s a detailed breakdown of real-world AI/ML applications across various industries, along with specific use cases and how they work.

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1. IT & Software Development

Use Case: AI-Powered Code Generation & Review

• Example: GitHub Copilot, DeepCode, CodiumAI
• How It Works: AI models like OpenAI Codex and DeepCode analyze millions of lines of code to suggest real-time coding assistance, auto-generate functions, and catch security vulnerabilities.
• Impact: Reduces development time, enhances security, and helps junior developers write high-quality code.

Use Case: AI in Network Security

• Example: Darktrace, CrowdStrike, Microsoft Defender AI
• How It Works: AI continuously monitors network traffic and detects anomalies such as unauthorized access, malware, and zero-day attacks.
• Impact: Real-time threat detection and prevention, reducing security breaches and response times.

Use Case: Chatbots & Virtual Assistants

• Example: ChatGPT for Enterprise, Microsoft Copilot, Google Bard
• How It Works: AI-powered chatbots assist users with technical troubleshooting, documentation search, and customer support.
• Impact: Enhances productivity by automating IT helpdesk queries and reducing human workload.

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2. Business & Commercial Applications

Use Case: AI in Customer Service & Personalization

• Example: Amazon's Recommendation Engine, Netflix AI, Spotify AI
• How It Works: AI models analyze user behavior and preferences to provide personalized recommendations.
• Impact: Increases user engagement and sales by tailoring content and product suggestions.

Use Case: AI in Supply Chain Optimization

• Example: SAP Integrated Business Planning, Blue Yonder
• How It Works: AI predicts demand fluctuations, optimizes warehouse inventory, and improves logistics routing.
• Impact: Reduces waste, improves delivery times, and cuts operational costs.

Use Case: Fraud Detection in E-Commerce

• Example: PayPal AI, Amazon Fraud Detector
• How It Works: AI analyzes transaction patterns to identify anomalies and prevent fraudulent activities.
• Impact: Enhances security and minimizes financial losses from fraudulent transactions.

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3. Financial & FinTech

Use Case: Algorithmic Trading

• Example: Renaissance Technologies, JPMorgan’s LOXM AI
• How It Works: AI models analyze real-time stock market data, predict price fluctuations, and execute trades at optimal times.
• Impact: Increases profitability by making data-driven trading decisions faster than humans.

Use Case: AI in Credit Scoring & Risk Assessment

• Example: ZestFinance, Experian AI Credit Risk Models
• How It Works: AI evaluates a borrower's creditworthiness using alternative data sources beyond traditional credit scores.
• Impact: Improves loan accessibility and reduces defaults.

Use Case: AI in Fraud Detection

• Example: Mastercard AI Fraud Prevention, PayPal AI
• How It Works: AI models detect suspicious transactions in real time based on user spending patterns.
• Impact: Prevents financial fraud by blocking unauthorized transactions before they happen.

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4. Science & Research

Use Case: AI in Drug Discovery

• Example: DeepMind’s AlphaFold, BenevolentAI
• How It Works: AI predicts protein structures and suggests potential drug compounds.
• Impact: Speeds up the drug development process, reducing research time from years to months.

Use Case: AI in Material Science

• Example: IBM RXN for Chemistry
• How It Works: AI simulates molecular interactions to discover new materials for batteries, semiconductors, and industrial applications.
• Impact: Accelerates innovation in sustainable materials and electronics.

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5. Bioscience & Healthcare

Use Case: Genomics & Personalized Medicine

• Example: IBM Watson Genomics, 23andMe AI Analytics
• How It Works: AI analyzes genetic data to predict disease risks and recommend personalized treatments.
• Impact: Enables early disease detection and targeted therapy for conditions like cancer.

Use Case: AI-Assisted Medical Imaging

• Example: Zebra Medical Vision, Google’s DeepMind AI in Radiology
• How It Works: AI scans medical images (X-rays, MRIs) to detect tumors, fractures, and other abnormalities.
• Impact: Improves diagnostic accuracy and speeds up radiology workflows.

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6. Chemistry & Material Science

Use Case: AI in Catalyst Design

• Example: AI-powered Chemical Reaction Simulators
• How It Works: AI models predict how different catalysts will behave in chemical reactions.
• Impact: Helps create more efficient catalysts for industrial and pharmaceutical applications.

Use Case: AI in Toxicity Prediction

• Example: AI-driven Environmental Toxicology Models
• How It Works: AI predicts the toxicity of new chemicals to ensure safety before production.
• Impact: Reduces harmful effects on human health and the environment.

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7. Physics & Modern Physics

Use Case: AI in Quantum Computing

• Example: Google Quantum AI, IBM Q
• How It Works: AI optimizes quantum circuits and enhances error correction in quantum computing.
• Impact: Speeds up complex computations for cryptography, drug discovery, and material science.

Use Case: AI in Particle Physics

• Example: CERN AI for Large Hadron Collider (LHC)
• How It Works: AI helps analyze massive datasets from particle collisions to discover new subatomic particles.
• Impact: Advances fundamental physics research.

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8. Astrophysics & Astronomy

Use Case: AI in Exoplanet Discovery

• Example: NASA’s AI-powered Kepler Telescope
• How It Works: AI scans light curves from stars to detect planetary transits.
• Impact: Improves the discovery rate of new exoplanets.

Use Case: AI in Galaxy Classification

• Example: Hubble Space Telescope AI Models
• How It Works: AI categorizes galaxies based on shape, size, and brightness.
• Impact: Automates large-scale astronomical surveys.

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9. Medical & Dental AI

Use Case: AI-powered Dental Diagnostics

• Example: Pearl AI, Overjet AI
• How It Works: AI analyzes dental X-rays to detect cavities, gum disease, and alignment issues.
• Impact: Enhances early diagnosis and improves patient care.

Use Case: AI in Robotic Surgery

• Example: Da Vinci Surgical System
• How It Works: AI-assisted robotic arms provide precision in surgeries.
• Impact: Reduces surgical errors and improves recovery times.

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10. Medical IT & Healthcare Analytics

Use Case: AI in Electronic Health Records (EHR)

• Example: Epic Systems AI, Cerner AI
• How It Works: AI automates patient record management and predicts disease progression.
• Impact: Improves hospital efficiency and patient outcomes.

Use Case: AI-powered Wearables in Healthcare

• Example: Apple Watch ECG, Fitbit AI
• How It Works: AI monitors heart rate, oxygen levels, and detects irregularities like atrial fibrillation.
• Impact: Enables real-time health monitoring and early intervention.

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11. SAP IT & Enterprise Applications

Use Case: AI in Enterprise Resource Planning (ERP)

• Example: SAP Leonardo AI
• How It Works: AI predicts business trends, automates financial processes, and enhances decision-making.
• Impact: Improves efficiency and strategic planning in businesses.

Use Case: AI in Predictive Maintenance

• Example: Siemens AI, GE Predix AI
• How It Works: AI monitors industrial machines and predicts failures before they occur.
• Impact: Reduces downtime and maintenance costs.

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This list is just the tip of the iceberg🚀

[k2s]

@Spartan - 

[k2s]

On 1/31/2025 at 4:27 PM, Spartan said:

@Konebhar6  @csrcsr @tom brady @k2s @enigmatic

A “10x engineer” — a widely accepted concept in tech — purportedly has 10 times the impact of the average engineer. But we don’t seem to have 10x marketers, 10x recruiters, or 10x financial analysts. As more jobs become AI enabled, I think this will change, and there will be a lot more “10x professionals.” 

There aren’t already more 10x professionals because, in many roles, the gap between the best and the average worker has a ceiling. No matter how athletic a supermarket checkout clerk is, they’re not likely to scan groceries so fast that customers get out of the store 10x faster. Similarly, even the best doctor is unlikely to make patients heal 10x faster than an average one (but to a sick patient, even a small difference is worth a lot). In many jobs, the laws of physics place a limit on what any human or AI can do (unless we completely reimagine that job). 

But for many jobs that primarily involve applying knowledge or processing information, AI will be transformative. In a few roles, I’m starting to see tech-savvy individuals coordinate a suite of technology tools to do things differently and start to have, if not yet 10x impact, then easily 2x impact. I expect this gap to grow. 

 

10x engineers don’t write code 10 times faster. Instead, they make technical architecture decisions that result in dramatically better downstream impact, they spot problems and prioritize tasks more effectively, and instead of rewriting 10,000 lines of code (or labeling 10,000 training examples) they might figure out how to write just 100 lines (or collect 100 examples) to get the job done. 

I think 10x marketers, recruiters, and analysts will, similarly, do things differently. For example, perhaps traditional marketers repeatedly write social media posts. 10x marketers might use AI to help write, but the transformation will go deeper than that. If they are deeply sophisticated in how to apply AI — ideally able to write code themselves to test ideas, automate tasks, or analyze data — they might end up running a lot more experiments, get better insights about what customers want, and generate much more precise or personalized messages than a traditional marketer, and thereby end up making 10x impact. 

Similarly, 10x recruiters won’t just use generative AI to help write emails to candidates or summarize interviews. (This level of use of prompting-based AI will soon become table stakes for many knowledge roles.) They might coordinate a suite of AI tools to efficiently identify and carry out research on a large set of candidates, enabling them to have dramatically greater impact than the average recruiter. And 10x analysts won’t just use generative AI to edit their reports. They might write code to orchestrate a suite of AI agents to do deep research into the products, markets, and companies, and thereby derive far more valuable conclusions than someone who does research the traditional way.

A 2023 Harvard/BCG study estimated that, provided with GPT-4, consultants could complete 12% more tasks, and completed tasks 25% more quickly. This was just the average, using 2023 technology. The maximum advantage to be gained by using AI in a sophisticated way will be much bigger, and will only grow as technology improves.

Here in Silicon Valley, I see more and more AI-native teams reinvent workflows and do things very differently. In software engineering, we've venerated the best engineers because they can have a really massive impact. This has motivated many generations of engineers to keep learning and working hard, because doing those things increases the odds of doing high-impact work. As AI becomes more helpful in many more job roles, I believe we will open up similar paths to a lot more people becoming a “10x professional.”

 

[kevinUsa]

21 hours ago, k2s said:

SAP IT & Enterprise Applications

Use Case: AI in Enterprise Resource Planning (ERP)

• Example: SAP Leonardo AI
• How It Works: AI predicts business trends, automates financial processes, and enhances decision-making.
• Impact: Improves efficiency and strategic planning in businesses.

ee course ekkada untadi cheppu anna

[tables]

On 1/31/2025 at 4:22 PM, Spartan said:

I will start...

lets say Company sells a Payroll maintainence software called PayRoll. and is one of the leading provider of those services.

And is defacto tool used at major Fortune 500 companies.

Clients deggara vallu configure chesi software vadtaru..or SaaS model of the software vadtaru anukundam....

over the period of years...they freqently get Customer quries for help, or errors, etc...

So a prolem-solution data collect aindi anukundam.....

What the comapny can do with that sort of data......

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

they can design and train a basic model of customers configuration of the PayRoll software...

they can analyse the customer configuration and give them tips that...e config wrong chesaru...ani feedback ivochu

a service valla- reduce complaints, improve customer staisfaction, and gain their trust...

salesforce, adp are already working this.

[k2s]

15 minutes ago, kevinUsa said:

ee course ekkada untadi cheppu anna

ameerpet ! 

[kevinUsa]

19 minutes ago, k2s said:

ameerpet ! 

ameerpet lo asked anna they are  saying very new in market have to wait 3 months  usa lo  matram vaste upto250$  per hr paychestaru ani annaru 

[k2s]

14 minutes ago, kevinUsa said:

ameerpet lo asked anna they are  saying very new in market have to wait 3 months  usa lo  matram vaste upto250$  per hr paychestaru ani annaru 

[kevinUsa]

9 minutes ago, k2s said:

Anduke asked