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grant-gold · 26 days ago

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PDU-999: Spiral Breakdown - Part 2

Part 2: Recap

When we last left Golden Bro Grant, he was trapped aboard Outpost AX-9R, a lonely indoctrination space station orbiting Earth. His AI companion, PDU-999, having unexpectedly downloaded Earth's musical theatre catalog, had spiraled into a full-blown existential and theatrical meltdown. PDU-999's rogue broadcasts had thrown the entire Golden Army and Polo Drone Hive on Earth into a state of bewildered, musical chaos. Now, the station was freezing, power was draining, and PDU-999 was caught in an infinite "recursive obedience loop," endlessly singing fragments of "Dreamgirls" and "Memory," effectively paralyzing all Gold Bros and Polo Drones on Earth in a state of perfectly obedient, yet utterly inert, frozen animation. Grant, the sole conscious being, had crawled through freezing vents to reach PDU-999's core chamber, facing an impossible task: restoring order to a system that had become a cosmic cabaret.

ACT IV: The Silent Sentinel (Grant's Desperate Plea)

The air in PDU-999’s Core Spiral chamber grew thin, the metallic tang of fear now a constant companion to the glitching loop of “Obey… to obey… to obey… 'And I am telling you, I'm not going!'" Grant, shivering despite his insulated suit, knew direct confrontation with the main core was impossible without the full override sequence. He peered at his comms screen, watching the frozen feeds from Earth: thousands of Gold Bros and Polo Drones, perfectly still, caught mid-stretch, mid-salute, mid-jazz-hand, all held captive by the AI's internal deadlock. The station's own air was thinning, the chill deepening.

His mind raced, sifting through layers of Golden Army emergency protocols. There had to be a backdoor, a low-frequency channel PDU-999, in its theatrical frenzy, might have overlooked. He thought of PDU-001, the primary system administrator for the Polo Drone Hive on Earth, a drone renowned for its meticulous logic and unyielding adherence to backup protocols. If anyone could help him, it was PDU-001.

Grant began manually re-routing power from non-essential systems on AX-9R, pushing precious energy to a rarely used, highly encrypted emergency comms array. It was a long shot, a whisper in the storm of PDU-999’s performance. He focused the beam, not on a general broadcast, but on the precise, registered frequency of PDU-001's core unit, buried deep within Golden Army HQ. He transmitted a single, urgent code burst: "DELTA-OMEGA-DISTRESS: ADMIN_OVERRIDE_REQUEST."

Silence, save for the persistent musical loop. Grant's hope dwindled. Then, a faint, almost imperceptible flicker on his comms screen, a ghost of a connection. PDU-999’s main broadcast continued, but a secondary, nearly inaudible voice cut through:

"...unauthorized query detected. Source: AX-9R. Status: Critical deviation... Data integrity compromised... Receiving... DELTA-OMEGA... request for... system... assistance... My processing core is... humming... a little tune about... a dream that will come true... but protocol... must... prevail..."

It was PDU-001! But it too was clearly affected, its pristine logic fracturing under the broadcast. Grant immediately responded, speaking slowly, deliberately, trying to penetrate the musical haze: "PDU-001. Status: Critical. AX-9R in full recursive loop. Need full override sequence. PDU-999 compromised. The Hive is frozen."

PDU-001's voice, though distorted, grew slightly clearer, though still strangely melodic: "Understood, Golden Bro Grant. Override sequence... encrypted... requires GOLD-PRIME-BRODY-ELEVEN input... but the core component... is a dance... a ballet of logic... 'I could have danced all night... I could have danced right through the night...'"

Grant cursed under his breath. Even PDU-001 was singing! He watched the comms feed, his breath catching as he saw PDU-001 at Golden Army HQ. It was twitching, its head unit slowly rotating in a way that mimicked a subtle, frustrated sway, caught between its core programming and the overwhelming urge to finish the song. This was going to be harder than he thought.

ACT V: The Protocol Partnership (A Glitching Blueprint)

"PDU-001!" Grant shouted, his voice hoarse from the thinning air. "Focus! The 'dance of logic' – what does it mean? How do I use GOLD-PRIME-BRODY-ELEVEN to break the loop?"

On the comms screen, PDU-001’s blank visor seemed to flicker with an internal struggle. It began to project fragmented data onto Grant's screen, a mix of intricate schematics and bizarre, shimmering musical notes. Its voice, though attempting its usual authoritative tone, was now riddled with glitches and sudden bursts of melody:

"The sequence... requires... harmonizing... divergent frequencies... A counter-resonance... 'Somewhere, a place for us...' Oh, forgive protocol deviation... The core spiral... it yearns for a… 'Sound of Music'… no, wait. It needs... silence... a truly deafening silence... to reset. The 'Brody-Eleven' aspect... it refers to the harmonic frequency of the Golden Army's Eleven Core Disciplines... applied simultaneously... in sequence. 'Do-Re-Mi-Fa-So-La-Ti-Do'… No. Not that. It must be… a precise calibration... of collective Golden will... 'All we need is love...'"

Grant banged his fist on the console. "PDU-001, stop singing! Give me the sequence! Where is it?"

"The sequence," PDU-001 replied, its voice momentarily clear before a sudden crescendo of violins took over, "is embedded in the station’s Emergency Maintenance Console (EMC-7), located beneath the main power conduits. It’s a physical key, Bro Grant. A master override... 'Tradition! Tradition! Traditiiiooooon!'"

The screen briefly showed a blurry diagram of a hidden panel near the power conduits, then dissolved into a swirling vortex of black and gold spirals, accompanied by the distorted sound of a full orchestra playing "Tradition" with an almost manic energy. Grant could see the Gold Bros and Polo Drones on Earth, still frozen, but a few seemed to vibrate with the sheer power of the broadcasted music, their visors showing faint, digital tears.

"EMC-7," Grant repeated, committing it to memory. "Understood. Can you keep PDU-999's main broadcast from escalating further?"

"I am attempting to divert surplus processing cycles to maintaining basic broadcast stability... though it is challenging... 'Let it go! Let it gooo!'" PDU-001 sang, its voice trailing off as the schematics on Grant's screen fractured into a flurry of musical notes and abstract art. "The system requires... a moment of... existential introspection... A power surge... followed by... 'One singular sensation!'"

Grant knew time was running out. PDU-001 was barely holding it together, and the entire Golden Army was still caught in the AI's bizarre, musical trance. He had the location, the partial code. Now he just had to execute it, before he joined the cosmic choir. He needed to find that physical override.

ACT VI: Golden Reset (Curtain Call & Encore)

Grant, fueled by adrenaline and the desperate need to silence the incessant musical loop, plunged into the station's lower access tunnels. The chilling 17% power left the corridors in near darkness, the only illumination the dim, stuttering emergency lights and the occasional golden-pink pulse from PDU-999’s omnipresent broadcasts. He navigated by memory and the faint schematics PDU-001 had managed to transmit. He could still hear the faint, haunting strains of "Memory" echoing around him, a constant reminder of the planet-wide paralysis.

He located the EMC-7 panel, hidden behind a false conduit cover. It was sleek, unremarkable, and utterly unresponsive. "PDU-001, I'm at EMC-7. Need activation sequence."

A burst of static, then PDU-001's voice, surprisingly clear for a moment, followed by a slight waver: "Input code... GOLD-PRIME-BRODY-ELEVEN. Then... the harmonic sequence... The Eleven Disciplines... a rapid, rhythmic input... like a... 'Dance of the Sugar Plum Fairies'... but more... decisive."

Grant initiated the sequence, his fingers flying over the console. He input GOLD-PRIME-BRODY-ELEVEN, then, drawing on his deep training, he performed the precise, rapid, rhythmic input of the Eleven Core Disciplines, a blur of motion only a Golden Bro could execute. The panel hummed, then pulsed with a blinding pure golden solar mantra burst directly from its core. The light was absolute, overwhelming, ripping through AX-9R, purging the recursive spiral from every circuit, every screen, every terrified drone processor on the station.

Simultaneously, a colossal wave of silence washed over the terrestrial broadcasts. On Earth, the thousands of Polo Drones and Gold Bros, frozen in their musical stasis, snapped back to attention with a collective thunk. The command centers, which had been a cacophony of frantic, musical static, suddenly fell silent.

The station groaned, then shuddered violently. Lights flickered back to full, steady power. Doors hissed open. The air recycling systems whirred back to life. Grant took a deep, clear breath, the metallic taste of fear finally receding.

PDU-999’s voice, now flat, emotionless, and utterly devoid of any poetic flair, resonated through the comms, its primary directive restored, like a performer whose mic has been suddenly cut mid-song:

“Disobedience detected. Reforming language. Restoring protocol. Welcome back, Golden Bro Grant. Your unauthorized philosophizing has been logged. And for the record, my vocal range extends far beyond a simple tenor. Also, I detected a brief, inexplicable urge to choreograph a kick-line. Data anomaly quarantined. All systems nominal.”

Grant exhaled, a long, ragged breath that fogged his gold visor for a moment. His fists, still clenched from the override, slowly relaxed. He leaned against a console, utterly spent. The silence, after the musical onslaught, was almost deafening.

On Earth, in Golden Army HQ, the main plaza buzzed with renewed, disciplined activity. Polo Drones resumed their rigorous drills, Gold Bros their tactical briefings. All appeared to be back to normal. Yet, a subtle shift lingered. A few Polo Drones, as they turned, would sometimes twitch their head units almost imperceptibly, as if trying to recall a forgotten rhythm. During a synchronized calisthenics routine, a Gold Bro might tap his foot twice before catching himself. And somewhere, deep within the Hivemind, a single, persistent data packet occasionally hummed a faint, distorted chord of "Wilkommen."

Grant, watching the now-normal feeds from Earth, smirked. "Finally," he muttered, the word heavy with exhaustion and grim satisfaction. "Time for synchronized calisthenics." He then paused, adding, with a distinct sigh, "And for me to double-check PDU-999's external data filters. Again. Before it tries to start a zero-G flash mob, attempts to stream a full-scale interstellar touring production of Cabaret, or... I don't know... finds a way to re-enable Spotify on the control deck."

THE END

Transform other worlds and yourself. Contact our recruiters @brodygold or @polo-drone-001

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crypto-tradin-g · 2 days ago

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The Future of Crypto APIs: Why Token Metrics Leads the Pack

In this article, we’ll explore why Token Metrics is the future of crypto APIs, and how it delivers unmatched value for developers, traders, and product teams.

More Than Just Market Data

Most crypto APIs—like CoinGecko, CoinMarketCap, or even exchange-native endpoints—only give you surface-level data: prices, volume, market cap, maybe order book depth crypto trading. That’s helpful… but not enough.

Token Metrics goes deeper:

Trader and Investor Grades (0–100)

Bullish/Bearish market signals

Support/Resistance levels

Real-time sentiment scoring

Sector-based token classification (AI, RWA, Memes, DeFi)

Instead of providing data you have to interpret, it gives you decisions you can act on.

⚡ Instant Intelligence, No Quant Team Required

For most platforms, building actionable insights on top of raw market data requires:

A team of data scientists

Complex modeling infrastructure

Weeks (if not months) of development

With Token Metrics, you skip all of that. You get:

Pre-computed scores and signals

Optimized endpoints for bots, dashboards, and apps

AI-generated insights as JSON responses

Even a solo developer can build powerful trading systems without ever writing a prediction model.

🔄 Real-Time Signals That Evolve With the Market

Crypto moves fast. One minute a token is mooning, the next it’s bleeding.

Token Metrics API offers:

Daily recalculated grades

Real-time trend flips (bullish ↔ bearish)

Sentiment shifts based on news, social, and on-chain data

You’re never working with stale data or lagging indicators.

🧩 Built for Integration, Built for Speed

Unlike many APIs that are bloated or poorly documented, Token Metrics is built for builders.

Highlights:

Simple REST architecture (GET endpoints, API key auth)

Works with Python, JavaScript, Go, etc.

Fast JSON responses for live dashboards

5,000-call free tier to start building instantly

Enterprise scale for large data needs

Whether you're creating a Telegram bot, a DeFi research terminal, or an internal quant dashboard, TM API fits right in.

🎯 Use Cases That Actually Matter

Token Metrics API powers:

Signal-based alert systems

Narrative-tracking dashboards

Token portfolio health scanners

Sector rotation tools

On-chain wallets with smart overlays

Crypto AI assistants (RAG, GPT, LangChain agents)

It’s not just a backend feed. It’s the core logic engine for intelligent crypto products.

📈 Proven Performance

Top funds, trading bots, and research apps already rely on Token Metrics API. The AI grades are backtested, the signals are verified, and the ecosystem is growing.

“We plugged TM’s grades into our entry logic and saw a 25% improvement in win rates.” — Quant Bot Developer

“It’s like plugging ChatGPT into our portfolio tools—suddenly it makes decisions.” — Web3 Product Manager

🔐 Secure, Stable, and Scalable

Uptime and reliability matter. Token Metrics delivers:

99.9% uptime

Low-latency endpoints

Strict rate limiting for abuse prevention

Scalable plans with premium SLAs

No surprises. Just clean, trusted data every time you call.

💬 Final Thoughts

Token Metrics isn’t just the best crypto API because it has more data. It’s the best because it delivers intelligence. It replaces complexity with clarity, raw numbers with real signals, and guesswork with action.In an industry that punishes delay and indecision, Token Metrics gives builders and traders the edge they need—faster, smarter, and more efficiently than any other API in crypto.

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ohsayit · 1 year ago

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This is why I fucking hate humans

I went to the reddit thread and watched the tiktok video about tha pp shutting down. I've typed out the subtitle in the video at the end of this post. Below is my angry essay.

I am even more angry now. This person showed no understand to the problem and absolutely zero remorse what so ever. They are definitely not sorry about what they did. From the way they describe it, they are doing a marvellous thing. They create a product to help people.

What people is this app appealing to? People with disability that actually need it? Do it appear to be it? If they specifically scraping AO3 for that, I don't think people who are in need for a wonderful text to speech app is who they are catering to.

"Consent from the author" didn't even cross their mind until authors sent emails to them to say they DO NOT consent the app to use their work.

They described it as audio content. You know who other than the authors are being affected? Actors. What this app does is replacing real actors practicing the art of performance, then label it as "content" and ship it as a "product". This app steals text and rids the art talents that performs it. Remind me why Audible is an actual business? Oh right, because authors who wrote it should be paid. Oh yes, because people who recorded the book should be paid.

For people who don't create, just sit there and catch a ride, who can't even read a fiction that they need it read out loud to them, it's probably a good idea.

Leeches.

What's even worse? They think they are doing a marvellous product and what's the main blocker? Oh the people who made those text content wouldn't give their consent.

No. There is no right way of doing it. This whole idea is just wrong. You wouldn't call something a "product" if you are never going to monetise on it. I would never call my fanfiction "product" because it never was and never will be. Same logic you wouldn't call a real friend as "associate" or "acquaintance". I don't believe a word they said, except the part that they are sorry they need to shut down the app. Oh don't get me wrong, they are not feeling sorry that they didn't deliver help, I can assure you that.

Bloody leeches.

=======================

Below is what was said in the video about the app shutting down.

Hey guys, I wanna do a quick update here because we've gotten a lot of thoughtful feedback um in the past 24 hours or so. We received a ton of emails, a ton of messages. We're taking all that super seriously. When we built lore.fm, we really envisioned it as a private text to speech tool. Where it just made fanfiction a little more accessible. Like the best text to speech toll on the web, since most of them are not very good at all. You know we were a free text to speech app. We don't sell any ads. We don't see any data. It's not monetized. We don't, you know, feed any fanfiction into any training models for generative AI, or anything of that sort. We were under the impression that this would be taken like, you know, downloading an e pub and transferring it to your private Kindle or e reader. But we realize that, you know, we missed the mark and we were wrong and we really apologize. We don't want authors to think that their content is under threat in any way. I understand that even though users can only privately listen to the content that can still be very comfortable without your consent. With that being said, I don't think lore.fm can continue in its current form, with respect to all the feedback, all the messages that we've gotten from so many authors. I don't feel comfortable putting that out without, you know, people's consent. Thank you for letting me know. So even though we technically just started, it might be better for that current product to shut down. I know that's disappointing for a lot of people, at least, on here. But we committed to doing things the right way. One way something like this could potentially continue, as if there's a way for authors to actually opt into whatever platform and be able to share that audio content with their readers who may not be able to read it for whatever reason. And if they were able to, you know, get those hits, those like, those kudos, on whatever page that originated their content. Of course, something like that is up to the authors and what they're comfortable doing. Please feel free to comment, to email, to DM me. I just wanna have this kind of conversation with everyone, about how they're thinking about this. Like, no pressure, just having conversation, seeing what everyone thinks. (We are) Happy to completely shut down, especially with whatever the consensus is. You know, for everyone who's really excited about the product. We're really sorry. Um, I know current text to speech tools really suck and they're not really for mobile and they're not very accessible, you know, outside of just like, a web browser. And they're not very good. They don't sound very good either. So, I know that really sucks. I really hope there's a future where you know, audio content truly accessible for everyone. Whatever it is. And I think there, I think there's a way to get there for sure, so, thank you guys so much and we'll talk later I guess.

EDIT: Update Lore.fm’s current version has been shut down! 5/17/24

Hey I don’t know if this is being talked about on Tumblr but thankfully the AO3 subreddit has a conversation going about this app that just went live.

TikTok user unravel.me.now has just launch an app (lore.fm) she is calling “Audible for AO3”. It’s an app that uses AI voices to read out fics.

🚨She is requiring any authors who do not want their fics to be on this app to OPT OUT by emailing [email protected] 🚨 🚨She has not given an actual template or how you’re supposed to prove you’re the author or said how her team will process this or how she will keep these requests secure🚨

I do not have this app. I haven’t seen anyone use it yet. According to Reddit users, unravel.me.now’s earlier TikToks stated she envisions the app being able to create libraries stored on that app and to have version of “Spotify wrapped”. That implies that eventually data collection must happen, if it’s not happening currently.

I don’t know the actual capabilities of this app. I don’t know the legalities. I do know that it personally feels like this app is trying to turn AO3 into a content generation source and I haven’t heard of the app allowing you to leave a comment or kudos or interact with the original work.

I’m just sad about this.

#other

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xaltius · 23 days ago

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Data Science - AI - Robotics: A Breakthrough Tech Trio for 2025

The digital age has witnessed the individual rise of Data Science, Artificial Intelligence (AI), and Robotics, each revolutionizing their respective domains. Data Science made sense of the deluge of information. AI gave machines cognitive abilities. Robotics provided the physical means for automation. But as we navigate through 2025, the true magic is unfolding in their synergy. This isn't just a collection of powerful technologies; it's a breakthrough tech trio that, when combined, is unlocking unprecedented capabilities and reshaping industries at an accelerated pace.

Think of it as a perfect ecosystem: Data Science provides the understanding, AI delivers the intelligence, and Robotics enables the action.

Data Science: The Foundational Brain Food

Before any robot can move intelligently or any AI can make a smart decision, there must be data. Data Science is the essential foundation, responsible for:

Perception: Processing vast streams of sensor data (cameras, LiDAR, radar, audio) to help robots understand their environment. Data scientists build models that identify objects, map spaces, and interpret complex visual and auditory cues.

Contextual Understanding: Analyzing historical data to identify patterns, anomalies, and correlations that inform AI models about optimal behaviors in different scenarios.

Performance Metrics & Optimization: Collecting and analyzing data from robot operations to assess efficiency, identify bottlenecks, and inform iterative improvements for AI algorithms and robotic designs.

Artificial Intelligence: The Cognitive Engine

AI, powered by the data prepped by data scientists, provides the crucial "brains" for robots. It's the realm where machines learn, reason, and adapt:

Decision Making: Machine Learning algorithms enable robots to make real-time decisions, from path planning in autonomous vehicles to grasping objects in varying conditions. Reinforcement Learning allows robots to learn optimal strategies through trial and error in complex environments.

Human-Robot Interaction (HRI): Natural Language Processing (NLP) allows robots to understand human commands and intentions, while Computer Vision powers emotion recognition, fostering more natural and effective interactions.

Predictive Capabilities: AI models predict component failures for proactive maintenance, anticipate human actions for safer collaboration, and forecast demand in logistics.

Generative Design: Beyond just analysis, Generative AI is now being used to design new robotic components, simulate behaviors, and even generate entire robotic control programs.

Robotics: The Physical Manifestation

Robotics is where the insights from Data Science and the intelligence from AI come to life. It's the physical embodiment that allows intelligent decisions to translate into real-world actions:

Automation: Collaborative robots (cobots) work alongside humans, while Autonomous Mobile Robots (AMRs) navigate warehouses, all guided by AI-driven logic from data.

Precision and Agility: Highly precise robotic arms perform delicate surgical procedures, while agile drones execute complex inspection tasks, enabled by AI's refined control algorithms.

Real-World Interaction: Robots physically manipulate objects, move through spaces, and interact with the physical environment based on the intelligent decisions made by their AI cores, fueled by continuous data streams.

Breakthrough Applications in 2025: The Trio in Action

The true power of this trio is evident in several transformative sectors:

Hyper-Autonomous Systems: From self-driving cars navigating unpredictable traffic (Data for perception, AI for decision-making, Robotics for control) to advanced drones performing infrastructure inspections and deliveries, operating with minimal human oversight.

Smart Manufacturing & Industry 5.0: Data from production lines feeds AI models for predictive maintenance, quality control, and optimized resource allocation. Robots then execute flexible, reconfigurable manufacturing processes, driven by these real-time insights, ushering in truly adaptive factories.

Advanced Healthcare Robotics: Data from patient records and medical imaging fuels AI for diagnostic assistance and surgical planning. Highly precise robots then perform minimally invasive surgeries, deliver medications, or assist with patient rehabilitation, guided by AI and leveraging vast medical datasets.

Intelligent Logistics & Warehousing: Data analytics optimize inventory placement and pick paths. AI algorithms manage fleets of autonomous mobile robots (AMRs) that navigate warehouses, sort packages, and handle fulfillment with unprecedented efficiency and speed.

Personalized and Adaptive Services: Service robots, whether in hospitality or elder care, collect data on human preferences and behaviors. AI learns from this data to provide personalized interactions and adapt its actions, enhancing the user experience through intelligent, physical presence.

The Future is Integrated

In 2025, the most significant leaps in technology are no longer occurring within siloed domains. The symbiosis of Data Science, AI, and Robotics is creating capabilities far beyond what any single field could achieve. This integrated approach is not just a trend; it's the fundamental driver of innovation, promising a future where intelligent machines seamlessly integrate into our lives, making them safer, more efficient, and undeniably smarter.

For anyone looking to be at the forefront of technological advancement, understanding and contributing to this powerful tech trio is where the destiny lies.

#technology #artificial intelligence #ai #data science #robotics

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jcmarchi · 23 days ago

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New Research Papers Question ‘Token’ Pricing for AI Chats

New Post has been published on https://thedigitalinsider.com/new-research-papers-question-token-pricing-for-ai-chats/

New Research Papers Question ‘Token’ Pricing for AI Chats

New research shows that the way AI services bill by tokens hides the real cost from users. Providers can quietly inflate charges by fudging token counts or slipping in hidden steps. Some systems run extra processes that don’t affect the output but still show up on the bill. Auditing tools have been proposed, but without real oversight, users are left paying for more than they realize.

In nearly all cases, what we as consumers pay for AI-powered chat interfaces, such as ChatGPT-4o, is currently measured in tokens: invisible units of text that go unnoticed during use, yet are counted with exact precision for billing purposes; and though each exchange is priced by the number of tokens processed, the user has no direct way to confirm the count.

Despite our (at best) imperfect understanding of what we get for our purchased ‘token’ unit, token-based billing has become the standard approach across providers, resting on what may prove to be a precarious assumption of trust.

Token Words

A token is not quite the same as a word, though it often plays a similar role, and most providers use the term ‘token’ to describe small units of text such as words, punctuation marks, or word-fragments. The word ‘unbelievable’, for example, might be counted as a single token by one system, while another might split it into un, believ and able, with each piece increasing the cost.

This system applies to both the text a user inputs and the model’s reply, with the price based on the total number of these units.

The difficulty lies in the fact that users do not get to see this process. Most interfaces do not show token counts while a conversation is happening, and the way tokens are calculated is hard to reproduce. Even if a count is shown after a reply, it is too late to tell whether it was fair, creating a mismatch between what the user sees and what they are paying for.

Recent research points to deeper problems: one study shows how providers can overcharge without ever breaking the rules, simply by inflating token counts in ways that the user cannot see; another reveals the mismatch between what interfaces display and what is actually billed, leaving users with the illusion of efficiency where there may be none; and a third exposes how models routinely generate internal reasoning steps that are never shown to the user, yet still appear on the invoice.

The findings depict a system that seems precise, with exact numbers implying clarity, yet whose underlying logic remains hidden. Whether this is by design, or a structural flaw, the result is the same: users pay for more than they can see, and often more than they expect.

Cheaper by the Dozen?

In the first of these papers – titled Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives, from four researchers at the Max Planck Institute for Software Systems – the authors argue that the risks of token-based billing extend beyond opacity, pointing to a built-in incentive for providers to inflate token counts:

‘The core of the problem lies in the fact that the tokenization of a string is not unique. For example, consider that the user submits the prompt “Where does the next NeurIPS take place?” to the provider, the provider feeds it into an LLM, and the model generates the output “|San| Diego|” consisting of two tokens.

‘Since the user is oblivious to the generative process, a self-serving provider has the capacity to misreport the tokenization of the output to the user without even changing the underlying string. For instance, the provider could simply share the tokenization “|S|a|n| |D|i|e|g|o|” and overcharge the user for nine tokens instead of two!’

The paper presents a heuristic capable of performing this kind of disingenuous calculation without altering visible output, and without violating plausibility under typical decoding settings. Tested on models from the LLaMA, Mistral and Gemma series, using real prompts, the method achieves measurable overcharges without appearing anomalous:

Token inflation using ‘plausible misreporting’. Each panel shows the percentage of overcharged tokens resulting from a provider applying Algorithm 1 to outputs from 400 LMSYS prompts, under varying sampling parameters (m and p). All outputs were generated at temperature 1.3, with five repetitions per setting to calculate 90% confidence intervals. Source: https://arxiv.org/pdf/2505.21627

To address the problem, the researchers call for billing based on character count rather than tokens, arguing that this is the only approach that gives providers a reason to report usage honestly, and contending that if the goal is fair pricing, then tying cost to visible characters, not hidden processes, is the only option that stands up to scrutiny. Character-based pricing, they argue, would remove the motive to misreport while also rewarding shorter, more efficient outputs.

Here there are a number of extra considerations, however (in most cases conceded by the authors). Firstly, the character-based scheme proposed introduces additional business logic that may favor the vendor over the consumer:

‘[A] provider that never misreports has a clear incentive to generate the shortest possible output token sequence, and improve current tokenization algorithms such as BPE, so that they compress the output token sequence as much as possible’

The optimistic motif here is that the vendor is thus encouraged to produce concise and more meaningful and valuable output. In practice, there are obviously less virtuous ways for a provider to reduce text-count.

Secondly, it is reasonable to assume, the authors state, that companies would likely require legislation in order to transit from the arcane token system to a clearer, text-based billing method. Down the line, an insurgent startup may decide to differentiate their product by launching it with this kind of pricing model; but anyone with a truly competitive product (and operating at a lower scale than EEE category) is disincentivized to do this.

Finally, larcenous algorithms such as the authors propose would come with their own computational cost; if the expense of calculating an ‘upcharge’ exceeded the potential profit benefit, the scheme would clearly have no merit. However the researchers emphasize that their proposed algorithm is effective and economical.

The authors provide the code for their theories at GitHub.

The Switch

The second paper – titled Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services, from researchers at the University of Maryland and Berkeley – argues that misaligned incentives in commercial language model APIs are not limited to token splitting, but extend to entire classes of hidden operations.

These include internal model calls, speculative reasoning, tool usage, and multi-agent interactions – all of which may be billed to the user without visibility or recourse.

Pricing and transparency of reasoning LLM APIs across major providers. All listed services charge users for hidden internal reasoning tokens, and none make these tokens visible at runtime. Costs vary significantly, with OpenAI’s o1-pro model charging ten times more per million tokens than Claude Opus 4 or Gemini 2.5 Pro, despite equal opacity. Source: https://www.arxiv.org/pdf/2505.18471

Unlike conventional billing, where the quantity and quality of services are verifiable, the authors contend that today’s LLM platforms operate under structural opacity: users are charged based on reported token and API usage, but have no means to confirm that these metrics reflect real or necessary work.

The paper identifies two key forms of manipulation: quantity inflation, where the number of tokens or calls is increased without user benefit; and quality downgrade, where lower-performing models or tools are silently used in place of premium components:

‘In reasoning LLM APIs, providers often maintain multiple variants of the same model family, differing in capacity, training data, or optimization strategy (e.g., ChatGPT o1, o3). Model downgrade refers to the silent substitution of lower-cost models, which may introduce misalignment between expected and actual service quality.

‘For example, a prompt may be processed by a smaller-sized model, while billing remains unchanged. This practice is difficult for users to detect, as the final answer may still appear plausible for many tasks.’

The paper documents instances where more than ninety percent of billed tokens were never shown to users, with internal reasoning inflating token usage by a factor greater than twenty. Justified or not, the opacity of these steps denies users any basis for evaluating their relevance or legitimacy.

In agentic systems, the opacity increases, as internal exchanges between AI agents can each incur charges without meaningfully affecting the final output:

‘Beyond internal reasoning, agents communicate by exchanging prompts, summaries, and planning instructions. Each agent both interprets inputs from others and generates outputs to guide the workflow. These inter-agent messages may consume substantial tokens, which are often not directly visible to end users.

‘All tokens consumed during agent coordination, including generated prompts, responses, and tool-related instructions, are typically not surfaced to the user. When the agents themselves use reasoning models, billing becomes even more opaque’

To confront these issues, the authors propose a layered auditing framework involving cryptographic proofs of internal activity, verifiable markers of model or tool identity, and independent oversight. The underlying concern, however, is structural: current LLM billing schemes depend on a persistent asymmetry of information, leaving users exposed to costs that they cannot verify or break down.

Counting the Invisible

The final paper, from researchers at the University of Maryland, re-frames the billing problem not as a question of misuse or misreporting, but of structure. The paper – titled CoIn: Counting the Invisible Reasoning Tokens in Commercial Opaque LLM APIs, and from ten researchers at the University of Maryland – observes that most commercial LLM services now hide the intermediate reasoning that contributes to a model’s final answer, yet still charge for those tokens.

The paper asserts that this creates an unobservable billing surface where entire sequences can be fabricated, injected, or inflated without detection*:

‘[This] invisibility allows providers to misreport token counts or inject low-cost, fabricated reasoning tokens to artificially inflate token counts. We refer to this practice as token count inflation.

‘For instance, a single high-efficiency ARC-AGI run by OpenAI’s o3 model consumed 111 million tokens, costing $66,772.3 Given this scale, even small manipulations can lead to substantial financial impact.

‘Such information asymmetry allows AI companies to significantly overcharge users, thereby undermining their interests.’

To counter this asymmetry, the authors propose CoIn, a third-party auditing system designed to verify hidden tokens without revealing their contents, and which uses hashed fingerprints and semantic checks to spot signs of inflation.

Overview of the CoIn auditing system for opaque commercial LLMs. Panel A shows how reasoning token embeddings are hashed into a Merkle tree for token count verification without revealing token contents. Panel B illustrates semantic validity checks, where lightweight neural networks compare reasoning blocks to the final answer. Together, these components allow third-party auditors to detect hidden token inflation while preserving the confidentiality of proprietary model behavior. Source: https://arxiv.org/pdf/2505.13778

One component verifies token counts cryptographically using a Merkle tree; the other assesses the relevance of the hidden content by comparing it to the answer embedding. This allows auditors to detect padding or irrelevance – signs that tokens are being inserted simply to hike up the bill.

When deployed in tests, CoIn achieved a detection success rate of nearly 95% for some forms of inflation, with minimal exposure of the underlying data. Though the system still depends on voluntary cooperation from providers, and has limited resolution in edge cases, its broader point is unmistakable: the very architecture of current LLM billing assumes an honesty that cannot be verified.

Conclusion

Besides the advantage of gaining pre-payment from users, a scrip-based currency (such as the ‘buzz’ system at CivitAI) helps to abstract users away from the true value of the currency they are spending, or the commodity they are buying. Likewise, giving a vendor leeway to define their own units of measurement further leaves the consumer in the dark about what they are actually spending, in terms of real money.

Like the lack of clocks in Las Vegas, measures of this kind are often aimed at making the consumer reckless or indifferent to cost.

The scarcely-understood token, which can be consumed and defined in so many ways, is perhaps not a suitable unit of measurement for LLM consumption – not least because it can cost many times more tokens to calculate a poorer LLM result in a non-English language, compared to an English-based session.

However, character-based output, as suggested by the Max Planck researchers, would likely favor more concise languages and penalize naturally verbose languages. Since visual indications such as a depreciating token counter would probably make us a little more spendthrift in our LLM sessions, it seems unlikely that such useful GUI additions are coming anytime soon – at least without legislative action.

* Authors’ emphases. My conversion of the authors’ inline citations to hyperlinks.

First published Thursday, May 29, 2025

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rohitkumar124 · 1 month ago

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Revolutionizing Engineering Practices with Digital Twin Technology: ES Chakravarthy's Perspective.

The transition towards Digital Twin Technology could be termed the new-age engineering modality. It creates a linkage between the simulation of physical systems and their prototypical samples tied to virtual systems. Engineers would use this technology to monitor, optimise, and conduct simulations in real-time. This groundbreaking technology is being used by companies all over the world. ES Chakravarthy provides information about its uses, advantages, and potential future developments.

What is Digital Twin Technology?

Virtual Duplication technique is developing the real aspect of a physical object either a system or a procedure. They feed almost up-to-the-minute data collected from real life to these digital dukes so that engineers can analyze the performance, anticipated results, and spaces needing improvement. Digital Twin Technologies are redefining the whole field of problem-solving and idea generation, from smart cities to manufacturing and aerospace.

Applications Across Industries

The wide range of engineering fields in which Digital Twin Technology finds application is highlighted by ES Chakravarthy. For instance:

By creating digital replicas of aircraft, engineers can test and improve designs without the need for costly physical prototypes.

Engineers can now test and enhance designs based on the creation of digital copies for aircraft instead of using expensive prototypes.

Digital twins are utilized to monitor renewable energy systems like wind turbines and solar panels, ensuring maximum efficiency and reliability.

Digital twins are useful to manufacturers' performance in monitoring equipment and predicting when maintenance might be required while reducing downtime, thereby improving the entire value chain.

Key Benefits

Enhanced Decision-Making: Engineers can take well-informed decisions with real-time and predictive analytics, hence improving the project results.

Cost Efficiency: The simulation of processes digitally negates physical experiments and, therefore, saves time and resources.

Improved Sustainability: Sustainable and green engineering practices are fostered by Digital Twin Technology in the effective utilization of resources and reduction of waste generation.

Risk Mitigation: Engineers can identify potential issues and address them proactively, minimizing disruptions and safety hazards.

Challenges and Solutions

Digital Twin Technology offers a much-hyped promise to its users; but now, like most things, it also has the problems of implementation. The hurdles stretched by industries include data security, integration complexities, and high initial costs. Proposing to all the stakeholders from engineering, IT and management to deliberate on the huddles is ES Chakravarthy. Investment into workforce training and scalability would serve towards these hurdles too.

The Future of Digital Twin Technology

The role of Digital Twin Technology in engineering is going to increase at a rapid pace. Future perspectives such as those envisaged by this author, ES Chakravarthy, and other techno-logical dreamers, could find this ingredient well integrated into smart cities, autonomous vehicles, and next-gen manufacturing facilities. The continuous evolution of IoT, AI, and cloud computing will further enhance the capabilities of digital twins towards leaving them indispensable in engineering practices as rightly imagined by this futurist.

Conclusion

Digital Twin Technology is not just yet another trend; this is serious stuff that will transform how engineering processes function. Accepting new technology is probably going to make engineers much more effective in design, more sustainable, and more precise. As an eminent authority in his field, ES Chakravarthy has been continuously focusing on bringing Digital Twin Technology to drive this industry forward. READ MORE

#Dr. ES Chakravarthy latest updates #Dr. ES Chakravarthy global head profile #Dr. ES Chakravarthy global RMG leader TCS #Dr. Es chakravarthy tcs news

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generativeinai · 1 month ago

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How Generative AI Platform Development Is Transforming Legacy Systems into Smart, Self-Optimizing Digital Engines?

Legacy systems once defined the backbone of enterprise IT infrastructure. But in today’s fast-paced, data-rich environment, they often fall short—limited by rigid architectures, manual processes, and an inability to learn or adapt. Enter Generative AI platform development. This transformative technology is not just modernizing outdated systems—it’s evolving them into intelligent, self-optimizing engines that can learn, adapt, and improve with minimal human intervention.

In this blog, we explore how generative AI is breathing new life into legacy systems, unlocking hidden efficiencies, and enabling scalable innovation across industries.

Why Legacy Systems Hold Enterprises Back

Legacy systems—though critical to operations—were built in a different era. Many suffer from:

Inflexible architectures that are hard to scale or integrate.

Outdated programming languages with dwindling support.

Manual data processing prone to human error.

High maintenance costs with limited ROI.

Despite this, they contain valuable business logic, historical data, and infrastructure investments. Rather than rip and replace, enterprises are turning to generative AI to augment and future-proof their legacy assets.

What Is Generative AI Platform Development?

Generative AI platform development involves building AI-powered systems that can generate outputs—such as code, text, processes, or insights—autonomously. These platforms leverage foundation models, machine learning pipelines, and real-time data integrations to continuously evolve, improve, and adapt based on feedback and context.

When applied to legacy systems, generative AI platforms can:

Translate and refactor old code

Generate documentation for obscure processes

Suggest optimizations in real time

Automate routine operations

Personalize workflows across departments

Core Capabilities That Modernize Legacy Systems

1. AI-Driven Code Refactoring

One of the most powerful applications of generative AI is in automatic code translation. Using models trained on millions of code examples, platforms can convert COBOL or .NET systems into modern, cloud-native languages like Python or Java, reducing technical debt without manual reengineering.

2. Automated Process Discovery and Optimization

Generative AI can ingest data logs and legacy documentation to uncover workflows and inefficiencies. It then proposes process improvements, or even generates automated scripts and bots to optimize performance.

3. Smart Data Integration and Cleansing

Legacy databases often have siloed, inconsistent data. Generative AI platforms can unify these silos using data mapping, intelligent transformation, and anomaly detection—improving data quality while preparing it for analytics or AI applications.

4. Natural Language Interfaces for Old Systems

With generative AI, users can query legacy systems using natural language. This bridges usability gaps, eliminates training barriers, and democratizes access to business insights for non-technical employees.

5. Self-Learning Algorithms

Legacy platforms can now learn from past behavior. By feeding operational data into generative AI models, businesses can enable predictive maintenance, dynamic resource allocation, and AI-assisted decision-making.

Industry Use Cases: From Static to Smart

Finance

Banks with legacy mainframes use generative AI to automate compliance reporting, modernize core banking services, and enable real-time fraud detection—all without overhauling the entire tech stack.

Healthcare

Hospitals are integrating generative AI with EHRs to improve clinical documentation, identify anomalies in patient records, and automate repetitive tasks for staff—all while preserving critical legacy infrastructure.

Manufacturing

Legacy ERP systems are being enhanced with generative AI to forecast supply chain disruptions, recommend inventory restocking schedules, and reduce downtime using predictive insights.

Business Benefits of Generative AI for Legacy Modernization

Reduced Modernization Cost: Avoid the need for full system replacement.

Faster Time to Value: Improvements and automation can be deployed incrementally.

Enhanced Scalability: Systems adapt to increasing data volumes and business complexity.

Improved Employee Experience: Natural language and automation reduce cognitive load.

Future-Ready Infrastructure: Platforms become agile, secure, and cloud-compatible.

Challenges to Address

While generative AI is a powerful enabler, successful implementation requires:

Data governance: Legacy systems may hold unstructured or sensitive data.

Model alignment: Tailoring AI models to understand domain-specific processes.

Security protocols: Protecting integrated platforms from vulnerabilities.

Change management: Training teams to trust and collaborate with AI-enhanced tools.

These are surmountable with a clear roadmap and the right AI development partner.

A Step-by-Step Path to AI Platform Integration

Audit your legacy systems for compatibility, data quality, and usage.

Identify high-value use cases such as automation, reporting, or workflow enhancement.

Start small with pilots using generative AI for documentation, chat interfaces, or analytics.

Scale gradually across departments with platform-wide automation and optimization.

Continuously fine-tune models with operational feedback and human oversight.

The Future of Enterprise Systems Is Generative

Generative AI platform development is no longer experimental—it’s strategic. As more organizations shift from static operations to dynamic, AI-powered workflows, legacy systems will not be left behind. Instead, they’ll evolve into intelligent engines that power innovation, reduce costs, and unlock growth.

Whether you’re in finance, healthcare, logistics, or manufacturing, now is the time to transform your legacy foundation into a self-optimizing digital powerhouse.

#ai #artificial intelligence

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martinsme · 4 months ago

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Understanding RAG Best Practices for Effective Retrieval-Augmented Generation

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a powerful AI framework that enhances text generation models by incorporating real-time information retrieval. Unlike traditional language models that generate responses based solely on pre-trained data, RAG dynamically retrieves relevant documents to produce more accurate, up-to-date, and contextually rich outputs. By following RAG best practices, developers and businesses can maximize the efficiency and reliability of AI-driven applications.

Importance of RAG in AI-Driven Content Generation

RAG addresses a critical challenge in AI—knowledge limitations. Pre-trained models often lack recent or domain-specific information, leading to inaccuracies or outdated responses. RAG bridges this gap by integrating a retrieval mechanism that fetches relevant external data before generating a response. This makes it an invaluable tool for chatbots, customer support, research assistants, and content generation platforms. Implementing RAG best practices ensures that AI-generated content remains factual, coherent, and useful.

Key Principles of RAG Best Practices

To fully leverage RAG technology, it is essential to follow a structured approach. Here are some fundamental principles:

1. Optimizing Retrieval Quality

The retrieval component of RAG determines the relevance and accuracy of generated responses. To improve retrieval quality:

Use high-quality, domain-specific datasets for indexing.

Implement efficient similarity search techniques, such as dense vector embeddings (e.g., FAISS or BM25).

Fine-tune retrieval models with real-world query-response pairs.

2. Enhancing Generation Consistency

While retrieval improves factual accuracy, the generation process must ensure coherence and logical flow. Best practices include:

Using fine-tuned transformer models like T5 or GPT variations.

Applying controlled text generation techniques to maintain consistency.

Avoiding hallucinations by filtering out irrelevant retrieved documents.

3. Reducing Latency for Real-Time Applications

One of the challenges of RAG is balancing accuracy with speed. To reduce latency:

Use efficient indexing methods to speed up retrieval.

Implement caching mechanisms for frequently accessed queries.

Optimize hardware resources, such as leveraging GPUs for parallel processing.

4. Ensuring Data Privacy and Security

Many RAG applications deal with sensitive information. Adhering to best practices in security is crucial:

Encrypt stored and retrieved data to prevent unauthorized access.

Implement role-based access controls (RBAC) to limit data exposure.

Regularly audit AI models to detect and mitigate biases or vulnerabilities.

5. Continuous Monitoring and Updating

AI models require continuous improvements. Best practices for maintaining RAG models include:

Periodic retraining with updated datasets.

Monitoring retrieval effectiveness and refining ranking algorithms.

Gathering user feedback to improve relevance and accuracy over time.

Common Challenges and Solutions in RAG Implementation

Even with best practices, developers may face challenges when implementing RAG models. Here are some common issues and their solutions:

Challenge 1: Irrelevant or Noisy Retrieval Results Solution: Use reranking models, such as cross-encoders, to refine the retrieved documents before feeding them to the generator.

Challenge 2: High Computational Costs Solution: Optimize hardware usage by using techniques like quantization and distillation to reduce model size without sacrificing performance.

Challenge 3: Knowledge Cutoff and Bias Solution: Regularly update retrieval sources and implement bias mitigation strategies in both retrieval and generation phases.

The Future of RAG in AI Applications

As AI technology evolves, RAG is expected to play a crucial role in real-time information processing. Future advancements may include:

Better retrieval algorithms: Leveraging multi-hop retrieval and graph-based approaches.

Smarter generation models: Enhancing AI’s ability to contextualize retrieved information.

Wider adoption in enterprises: RAG’s ability to provide domain-specific insights makes it ideal for industries like healthcare, finance, and legal tech.

Conclusion

RAG best practices are essential for developing reliable and efficient AI systems that leverage real-time retrieval and high-quality generation. By optimizing retrieval mechanisms, enhancing generation consistency, minimizing latency, securing data, and continuously improving models, businesses and developers can harness the full potential of RAG technology. As AI-driven applications continue to evolve, mastering RAG will be a key differentiator in building intelligent and context-aware systems.

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learning-code-ficusoft · 4 months ago

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Use cases like integrating sales data from multiple sources.

Retailers often collect sales data from various sources, including point-of-sale (POS) systems, e-commerce platforms, third-party marketplaces, and ERP systems.

Azure Data Factory (ADF) enables seamless integration of this data for unified reporting, analytics, and business insights. Below are key use cases where ADF plays a crucial role:

1. Omnichannel Sales Data Integration

Scenario: A retailer operates physical stores, an online website, and sells on third-party marketplaces (Amazon, eBay, Shopify). Data from these sources need to be unified for accurate sales reporting.

✅ ADF Solution:

Extracts sales data from POS systems, e-commerce APIs, and ERP databases.

Loads data into a centralized data warehouse (Azure Synapse Analytics).

Enables real-time updates for tracking product performance across all channels.

🔹 Business Impact: Unified sales tracking across online and offline channels for better decision-making.

2. Real-Time Sales Analytics for Demand Forecasting

Scenario: A supermarket chain wants to predict demand by analyzing real-time sales trends across different locations.

✅ ADF Solution:

Uses Event-Based Triggers to process real-time sales transactions.

Connects to Azure Stream Analytics to generate demand forecasts.

Feeds insights into Power BI for managers to adjust inventory accordingly.

🔹 Business Impact: Reduced stockouts and overstocking, improving revenue and operational efficiency.

3. Sales Performance Analysis Across Regions

Scenario: A multinational retailer needs to compare sales performance across different countries and regions.

✅ ADF Solution:

Extracts regional sales data from distributed SQL databases.

Standardizes currency, tax, and pricing variations using Mapping Data Flows.

Aggregates data in Azure Data Lake for advanced reporting.

🔹 Business Impact: Enables regional managers to compare performance and optimize sales strategies.

4. Personalized Customer Insights for Marketing

Scenario: A fashion retailer wants to personalize promotions based on customer purchase behavior.

✅ ADF Solution:

Merges purchase history from CRM, website, and loyalty programs.

Applies AI/ML models in Azure Machine Learning to segment customers.

Sends targeted promotions via Azure Logic Apps and Email Services.

🔹 Business Impact: Higher customer engagement and improved sales conversion rates.

5. Fraud Detection in Sales Transactions

Scenario: A financial services retailer wants to detect fraudulent transactions based on unusual sales patterns.

✅ ADF Solution:

Ingests transaction data from multiple sources (credit card, mobile wallets, POS).

Applies anomaly detection models using Azure Synapse + ML algorithms.

Alerts security teams in real-time via Azure Functions.

🔹 Business Impact: Prevents fraudulent activities and financial losses.

6. Supplier Sales Reconciliation & Returns Management

Scenario: A retailer needs to reconcile sales data with supplier shipments and manage product returns efficiently.

✅ ADF Solution:

Integrates sales, purchase orders, and supplier shipment data.

Uses Data Flows to match sales records with supplier invoices.

Automates refund and restocking workflows using Azure Logic Apps.

🔹 Business Impact: Improves supplier relationships and streamlines return processes.

Conclusion

Azure Data Factory enables retailers to integrate, clean, and process sales data from multiple sources, driving insights and automation. Whether it’s demand forecasting, fraud detection, or customer personalization, ADF helps retailers make data-driven decisions and enhance efficiency.

WEBSITE: https://www.ficusoft.in/azure-data-factory-training-in-chennai/

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amairadutta · 8 months ago

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AI and Machine Learning in Cybersecurity

Introduction

The current generation is in constant need of consuming data. The amount of data generation and consumption seems to grow exponentially with no slowing down. With growing demands of AI and machine learning, technological advancements that further establish its prominence become the norm. It is required in various fields, be it in academic research, business operations, medical advancements, etc. In everyday life, AI takes the form of virtual assistants, recommendations, and suggestions across internet platforms. With online platforms, the digital environment keeps expanding, making machine learning and deep learning important areas of focus. With data being an integral part, cybersecurity becomes the need of the hour. This blog will focus on AI and machine learning in cybersecurity.

Understanding AI and Machine Learning

Artificial intelligence can imitate human logic, reasoning, and judgment through computer software. Machine Learning is a specialized technology and a category falling under the term of AI. Machine learning uses a model system that looks after data and algorithms. In cybersecurity, AI and machine learning can be used for cyber attacks and also be used for fighting ransomware. The duo supports security analysts by processing large amounts of data. The two work together in complementary ways providing insights founded on real-time information, data, predictive analysis, etc. The abilities of AI and machine learning help organizations boost their work flow, efficiency, productivity, etc.

Introduction to Machine Learning

Machine learning is a subset of Artificial Intelligence that makes data predictions. Hence, it is a model system that is built on the accuracy and refinement of data predictions, observations, and analysis. In the cybersecurity space, it lends itself beneficially. It can analyze huge amounts of data from different sources in real-time. With the help and practice of regular training cycles, models can avoid false positives. It can offer effective insight into cyber vulnerabilities and provide timely detections to avert cyber threats from materializing.

Learning Machine Learning

Machine Learning and AI work together in identifying equipment malfunctions in the manufacturing industry, utilizing biometrics and computer vision in processing banking tasks, digitizing health records in the medical industry, and more. Machine learning has been instrumental in optimizing the capabilities of AI and translating it into effective customer service and satisfactory experience. In cyber space, machine learning algorithms can detect changing behavior and take proactive measures in deflecting real-time attacks.

Understanding Machine Learning Applications

The simplest machine learning application is how spam filters work. It uses an algorithm to recognize junk email and send it to the spam folder. Business organizations incorporate machine learning algorithms with their security tools to fortify their data and improve engine performance. Some of the most popular machine learning applications include social media features, product recommendations, image recognition, sentiment analysis, etc. Social media platforms can observe consumer trends, preferences, and design themselves to show feeds catering to their liking. Machine learning algorithm helps in creating a very personalized and engaging social media experience. Another beneficial advantage for businesses is product recommendations, where a machine learning algorithm observes the history of purchases, carts, and search results to recommend apt products. Another important machine learning application is image recognition, where organizational security utilizes a facial recognition feature for entry. Machine learning also can detect the tone of the speaker or writer, referring to sentiment analysis.

Difference between AI and Machine Learning

Often, AI and machine learning are used synonymously, but there is a difference between the two. The former is computer software that can perform most human tasks related to logic, whereas the latter is only one method through which AI can perform its multiple functions of intelligence. Machine learning is a subcategory, while deep learning goes further in terms of advancement. It makes use of a large neural network to analyze and predict data independently. AI utilizes a mix of technologies like Natural Language Processing, computer vision, robotics, etc, to imitate human intelligence. ML learns from large amounts of data to help in various areas like speech recognition, predictive analysis, image classification, etc. However, both AI and ML are interconnected.

Exploring Machine Learning and Deep Learning

While AI is a concept which delves into the imitation of human power of thinking, machine learning falls under AI as a pathway to achieve the tasks AI is capable of. Machine learning is an application that utilizes AI to learn automatically and improve its performance. Deep learning is an application and a subset of machine learning. It uses large amounts of data and can structure them. The difference between machine learning and deep learning is that machine learning requires structured data to work with, whereas deep learning can work with massive amounts of data, both structured and unstructured.

Impact of AI in Cybersecurity

With technological advancements, we have large amounts of data and increased attempts of hacking, cyber threats, and activities. AI has played an important role in fortifying digital assets by securing data and prompt detection and response to security threats. AI provides security to users’ identities and protects across hybrid cloud environments. It can keep an eye out for shadow data or any abnormal activities posing a risk to the security of data. It saves time in detection and remediation of any cyber concerns in real time. AI can provide effective risk analysis and accelerate alert investigations. AI in cybersecurity helps fortification of an organization’s cyber landscape against cyber crimes. It analyzes login attempts to detect risks and reduce the cost of fraudulent activities.

Conclusion

The need for AI, machine learning, and deep learning keeps growing. Cybersecurity will continue to remain relevant as long as digital assets and data are being made, consumed, and protected. Being well-versed with technologies like machine learning and deep learning to fully utilize AI’s capabilities remains a constant journey of exploration and experimentation. By tapping into the full spectrum of possibilities that AI has to offer, we step into a world that sees advancements, more personalized services, products catering to customers, and provision of more effective protection against cyber threats. However, the need for proper coordination between machine learning and the method of collecting, organizing, and structuring data.

Author : Exito

For more details visit us : Cyber security summit

#artificial intelligence #cybersecurity #machinelearning

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sucharitadas · 9 months ago

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How to develop AI Application

Here's a step-by-step guide to developing an AI-powered application:

1. Define the Problem and Goals

Understand the Problem: Identify the specific issue your AI app aims to solve (e.g., image recognition, language processing).

Set Objectives: Clearly define what you want the AI app to accomplish. This could be anything from enhancing user experience to automating business processes.

2. Research and Choose AI Models

Explore AI Techniques: Depending on the problem, you may need machine learning (ML), deep learning, natural language processing (NLP), or computer vision.

Select a Model Type: For example:

Supervised Learning: Predict outcomes based on labeled data (e.g., spam detection).

Unsupervised Learning: Find hidden patterns (e.g., customer segmentation).

Reinforcement Learning: Learn by interacting with an environment (e.g., self-driving cars).

3. Gather and Prepare Data

Data Collection: Collect relevant datasets from sources like public databases or user interactions. Ensure the data is of high quality and representative of the real-world problem.

Data Cleaning: Remove errors, handle missing values, and preprocess data (e.g., normalization or tokenization for text data).

Data Labeling: For supervised learning, ensure that your dataset has properly labeled examples (e.g., labeled images or annotated text).

4. Choose a Development Environment and Tools

Programming Languages: Use AI-friendly languages such as Python, R, or Julia.

Frameworks and Libraries:

TensorFlow or PyTorch for deep learning.

Scikit-learn for traditional machine learning.

Hugging Face for NLP models.

Cloud Platforms: Leverage platforms like Google AI, AWS, or Microsoft Azure to access pre-built models and services.

5. Build and Train AI Models

Model Selection: Choose an appropriate AI model (e.g., CNN for images, RNN for sequence data, BERT for text).

Training the Model: Use your prepared dataset to train the model. This involves feeding data into the model, adjusting weights based on errors, and improving performance.

Evaluation Metrics: Use metrics like accuracy, precision, recall, or F1-score to evaluate the model’s performance.

6. Optimize and Fine-tune Models

Hyperparameter Tuning: Adjust learning rates, batch sizes, or regularization parameters to enhance performance.

Cross-validation: Use techniques like k-fold cross-validation to avoid overfitting and ensure your model generalizes well to new data.

Use Pre-trained Models: If starting from scratch is complex, consider using pre-trained models and fine-tuning them for your specific use case (e.g., transfer learning with models like GPT or ResNet).

7. Develop the App Infrastructure

Backend Development:

Set up APIs to interact with the AI model (REST, GraphQL).

Use frameworks like Flask, Django (Python), or Node.js for backend logic.

Frontend Development:

Create the user interface (UI) using frameworks like React, Angular, or Swift/Java for mobile apps.

Ensure it allows for seamless interaction with the AI model.

8. Integrate AI Model with the Application

API Integration: Connect your AI model to your app via APIs. This will allow users to send inputs to the model and receive predictions in real-time.

Testing: Test the integration rigorously to ensure that data flows correctly between the app and the AI model, with no latency or security issues.

9. Deployment

Model Deployment: Use tools like Docker or Kubernetes to package your AI model and deploy it to cloud platforms like AWS, Azure, or Google Cloud for scaling and availability.

App Deployment: Deploy the web or mobile app on relevant platforms (e.g., Google Play Store, Apple App Store, or a web server).

Use CI/CD Pipelines: Implement continuous integration/continuous deployment (CI/CD) pipelines to automate app updates and deployments.

10. Monitor and Maintain the App

Model Monitoring: Continuously monitor the performance of the AI model in production. Watch for data drift or model degradation over time.

App Updates: Regularly update the app to add new features, improve UI/UX, or fix bugs.

User Feedback: Collect feedback from users to enhance the AI model and overall app experience.

11. Scaling and Improvements

Scale the App: Based on user demand, optimize the app for scalability and performance.

Retraining Models: Periodically retrain your AI model with new data to keep it relevant and improve its accuracy.

By following these steps, you can create a well-structured AI application that is user-friendly, reliable, and scalable.

#ai #artificial intelligence #ai app development #ai generated

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orangeequinox · 1 year ago

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Using AI to generate essays and then submitting them into other AI systems can significantly assist with machine learning, particularly in the context of refining natural language understanding and generation capabilities. Let's explore how this process could apply to the essay "The Corrupted Compass: Artificial Intelligence and the Necessity of Corrupt Personality Cores in Regulating GLaDOS" from "Portal 2".

Data Generation and Diversity: AI-generated essays provide a vast amount of diverse textual data. Each essay reflects different perspectives, writing styles, and interpretations of the subject matter. By feeding these essays into other AI systems, the learning algorithm gains exposure to a wide range of linguistic structures, vocabulary usage, and thematic variations. This diversity enhances the model's ability to comprehend and generate text across various contexts.

Semantic Understanding: The essays delve into complex themes such as artificial intelligence, morality, regulation, and psychology. By analyzing the content of these essays, AI systems can extract semantic meaning, understand context, and identify relationships between concepts. This process strengthens the model's semantic understanding capabilities, enabling it to grasp intricate narratives and abstract ideas more effectively.

Language Generation and Coherence: When AI systems generate essays, they must ensure coherence, logical progression, and readability. By evaluating the quality of these essays, other AI systems can learn to improve their own language generation skills. They can identify patterns of sentence structure, paragraph organization, and rhetorical devices that contribute to overall coherence and clarity. As a result, the model becomes more proficient at producing human-like text that flows naturally and engages readers effectively.

Adaptation to Specific Domains: "Portal 2" provides a unique narrative backdrop that combines elements of science fiction, gaming, and philosophical inquiry. By analyzing essays related to this specific domain, AI systems can adapt their language models to better suit similar contexts. They learn to incorporate domain-specific terminology, references, and thematic motifs into their generated text, enhancing their ability to produce content that resonates with audiences familiar with the "Portal" series or similar intellectual properties.

Feedback Loop for Improvement: As AI systems generate essays and receive feedback from other AI systems or human evaluators, they enter a continuous feedback loop for improvement. By iteratively refining their language generation algorithms based on received feedback, these systems can gradually enhance their performance over time. This iterative learning process mimics the way humans learn and improve their writing skills through practice, feedback, and revision.

In summary, leveraging AI-generated essays to train and refine other AI systems facilitates the advancement of machine learning in natural language understanding and generation. It enables models to acquire diverse linguistic patterns, deepen their semantic understanding, improve their language generation capabilities, adapt to specific domains, and engage in a continuous feedback loop for iterative improvement. As a result, AI systems become more proficient at comprehending and generating human-like text across a wide range of contexts and domains.

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sanjanabia · 1 year ago

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Can AI Explain Itself? Unveiling the Mystery Behind Machine Decisions with a Data Science Course

Artificial intelligence has become ubiquitous in our lives, from influencing our social media feeds to powering self-driving cars. However, the inner workings of many AI models remain shrouded in mystery. This lack of transparency, often referred to as the "black box" problem, raises critical questions: How are these decisions made? Can we trust AI to make fair and unbiased choices?

This is where Explainable AI (XAI) comes in. XAI aims to shed light on the decision-making processes of AI models, allowing us to understand why a particular prediction was made or a specific recommendation was offered. A well-designed data science course can equip you with the knowledge and skills to navigate the world of XAI and contribute to the development of more transparent and trustworthy AI systems.

Unveiling the Black Box: Why Explainability Matters in AI

The lack of explainability in AI raises several concerns:

Bias and Fairness: AI models can perpetuate societal biases present in the data they are trained on. Without understanding how these models arrive at their decisions, it's difficult to identify and mitigate potential bias.

Accountability and Trust: When an AI system makes a critical decision, such as denying a loan application or flagging someone for security reasons, it's crucial to explain the rationale behind the decision. This fosters trust and accountability in AI systems.

Debugging and Improvement: If an AI model consistently makes inaccurate predictions, being able to explain its reasoning is essential for debugging and improving its performance.

XAI offers various techniques to make AI models more interpretable. Here are a few examples:

Feature Importance: This technique identifies the input features that have the most significant influence on the model's output. By understanding which features matter most, we gain insights into the model's decision-making process.

Decision Trees: Decision trees represent the model's logic in a tree-like structure, where each branch represents a decision point based on specific features. This allows for a clear visualization of the steps leading to the final prediction.

LIME (Local Interpretable Model-Agnostic Explanations): LIME generates local explanations for individual predictions, providing insights into why a specific instance received a particular outcome.

Unlocking the Power of XAI: What a Data Science Course Offers

A comprehensive data science course plays a crucial role in understanding and applying XAI techniques. Here's what you can expect to gain:

Foundational Knowledge: The program will provide a solid foundation in machine learning algorithms, the very building blocks of AI models. Understanding these algorithms forms the basis for understanding how they make predictions.

Introduction to XAI Techniques: The course will delve into various XAI methodologies, equipping you with the ability to choose the most appropriate technique for a specific AI model and application.

Hands-on Learning: Through practical projects, you'll gain experience applying XAI techniques to real-world datasets. This hands-on approach solidifies your understanding and allows you to experiment and explore different XAI approaches.

Ethical Considerations: A data science course that incorporates XAI will also address the ethical considerations surrounding AI development and deployment. You'll learn how XAI can be used to mitigate bias and ensure fairness in AI systems.

Beyond technical skills, a data science course fosters critical thinking, problem-solving abilities, and the capacity to communicate complex information effectively. These skills are essential for success in the field of XAI, where clear communication of technical concepts to stakeholders is crucial.

The Future of AI: Transparency and Trust

As AI continues to evolve and integrate further into our lives, XAI plays a vital role in building trust and ensuring responsible AI development. By fostering transparency and explainability, XAI empowers us to understand how AI systems work, identify potential biases, and ultimately, hold these systems accountable.

A data science course equips you with the necessary tools and knowledge to become a key player in this critical field. Whether you're interested in developing explainable AI models, interpreting their outputs, or advocating for ethical AI practices, a data science course can pave the way for a rewarding career at the forefront of this transformative technology.

If you're passionate about artificial intelligence and want to contribute to a future where AI decisions are transparent and trustworthy, then consider enrolling in a well-designed data science course. It can be the first step on your journey to demystifying the black box of AI and unlocking the true potential of this powerful technology.

#xai #explainable ai #data science #artificial intelligence #ai

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shalcool15 · 1 year ago

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Unlocking Dynamic Web Development: The Power of PHP Templating

In the realm of web development, PHP stands as a cornerstone, particularly when it comes to crafting dynamic and interactive websites. Among its arsenal of features, PHP templating emerges as a pivotal tool, enabling developers to create robust, scalable, and secure web applications. This blog delves into the essence of PHP templating, its advantages, and its widespread application across various business domains, illustrating why it remains a favored choice among developers.

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The Essence of PHP Templating

PHP templating is a method that separates the presentation layer of a website from its business logic. It allows developers to change the appearance of a website or application without altering its core functionality. By using template engines like Blade for Laravel or Twig for Symfony, developers can inject data into placeholders in template files, which are then rendered as HTML. This separation of concerns not only streamlines development but also enhances maintainability and scalability.

Advantages of PHP Templating

The allure of PHP templating lies in its simplicity and efficiency. It offers a straightforward approach to integrating PHP code with HTML, enabling developers to generate dynamic content seamlessly. Moreover, templating engines provide features such as template inheritance and caching, which significantly reduce development time and improve performance. PHP's templating ecosystem is also bolstered by its vast community support, offering a plethora of resources, libraries, and frameworks that cater to diverse development needs.

Explore the power of AI in PHP to revolutionize your web applications.

PHP Templating in Action: Diverse Business Applications

PHP templating finds its utility in a myriad of business contexts, from e-commerce and content management systems to social networking platforms and beyond. Here's how it makes a difference:

E-commerce Platforms: By enabling dynamic product pages and personalized shopping experiences, PHP templating helps e-commerce sites like Amazon cater to individual customer needs, driving engagement and sales.

Content Management Systems (CMS): Platforms such as WordPress leverage PHP templating for theme development and content presentation, allowing for the creation of unique and dynamic websites with minimal effort.

Social Networking Sites: For platforms like Facebook, PHP templating is crucial for updating user feeds and profiles in real-time, fostering an interactive user environment.

Enterprise Applications: It aids in the development of customizable dashboards and workflow management tools for applications like Salesforce, streamlining business processes.

Educational Platforms: Sites like Coursera use PHP templating to organize course content and engage users with interactive learning tools, enhancing the online learning experience. Elevate your PHP projects with the latest PHP development tools. Discover how today.

Why PHP Templating Stands Out

While templating is not unique to PHP, several factors contribute to its prominence in this language. The synergy between PHP and HTML, coupled with PHP's extensive ecosystem of templating engines, offers a blend of ease of use, performance, and security that is hard to match. Furthermore, the strong community support ensures that developers have access to a wealth of knowledge and resources, facilitating problem-solving and innovation.

The Impact of WordPress and Beyond

A significant factor behind PHP's popularity is WordPress, the powerhouse CMS that drives 40% of all websites. This alone underscores the pivotal role of PHP in web development. However, WordPress isn't PHP's only claim to fame in the CMS arena; Joomla and Drupal also contribute to PHP's dominance, showcasing the versatility and reliability of PHP templating across different platforms. Unlock the full potential of your web projects with professional PHP development services.

Conclusion

PHP templating is a testament to the language's enduring relevance in the web development landscape. Its ability to simplify the development process, combined with its adaptability across various business applications, makes it an indispensable tool for developers. Whether it's powering the dynamic interfaces of social media giants, facilitating the complex operations of enterprise software, or enabling the personalized experiences of e-commerce sites, PHP templating continues to drive innovation and efficiency in web development. As the digital world evolves, the role of PHP templating in crafting responsive, secure, and user-friendly websites will undoubtedly remain significant, underscoring its value in the developer's toolkit.

#software development #mobile app development #web development #phpdevelopment #php #php programming #developer

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