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mariacallous · 1 month ago

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On a 5K screen in Kirkland, Washington, four terminals blur with activity as artificial intelligence generates thousands of lines of code. Steve Yegge, a veteran software engineer who previously worked at Google and AWS, sits back to watch.

“This one is running some tests, that one is coming up with a plan. I am now coding on four different projects at once, although really I’m just burning tokens,” Yegge says, referring to the cost of generating chunks of text with a large language model (LLM).

Learning to code has long been seen as the ticket to a lucrative, secure career in tech. Now, the release of advanced coding models from firms like OpenAI, Anthropic, and Google threatens to upend that notion entirely. X and Bluesky are brimming with talk of companies downsizing their developer teams—or even eliminating them altogether.

When ChatGPT debuted in late 2022, AI models were capable of autocompleting small portions of code—a helpful, if modest step forward that served to speed up software development. As models advanced and gained “agentic” skills that allow them to use software programs, manipulate files, and access online services, engineers and non-engineers alike started using the tools to build entire apps and websites. Andrej Karpathy, a prominent AI researcher, coined the term “vibe coding” in February, to describe the process of developing software by prompting an AI model with text.

The rapid progress has led to speculation—and even panic—among developers, who fear that most development work could soon be automated away, in what would amount to a job apocalypse for engineers.

“We are not far from a world—I think we’ll be there in three to six months—where AI is writing 90 percent of the code,” Dario Amodei, CEO of Anthropic, said at a Council on Foreign Relations event in March. “And then in 12 months, we may be in a world where AI is writing essentially all of the code,” he added.

But many experts warn that even the best models have a way to go before they can reliably automate a lot of coding work. While future advancements might unleash AI that can code just as well as a human, until then relying too much on AI could result in a glut of buggy and hackable code, as well as a shortage of developers with the knowledge and skills needed to write good software.

David Autor, an economist at MIT who studies how AI affects employment, says it’s possible that software development work will be automated—similar to how transcription and translation jobs are quickly being replaced by AI. He notes, however, that advanced software engineering is much more complex and will be harder to automate than routine coding.

Autor adds that the picture may be complicated by the “elasticity” of demand for software engineering—the extent to which the market might accommodate additional engineering jobs.

“If demand for software were like demand for colonoscopies, no improvement in speed or reduction in costs would create a mad rush for the proctologist's office,” Autor says. “But if demand for software is like demand for taxi services, then we may see an Uber effect on coding: more people writing more code at lower prices, and lower wages.”

Yegge’s experience shows that perspectives are evolving. A prolific blogger as well as coder, Yegge was previously doubtful that AI would help produce much code. Today, he has been vibe-pilled, writing a book called Vibe Coding with another experienced developer, Gene Kim, that lays out the potential and the pitfalls of the approach. Yegge became convinced that AI would revolutionize software development last December, and he has led a push to develop AI coding tools at his company, Sourcegraph.

“This is how all programming will be conducted by the end of this year,” Yegge predicts. “And if you're not doing it, you're just walking in a race.”

The Vibe-Coding Divide

Today, coding message boards are full of examples of mobile apps, commercial websites, and even multiplayer games all apparently vibe-coded into being. Experienced coders, like Yegge, can give AI tools instructions and then watch AI bring complex ideas to life.

Several AI-coding startups, including Cursor and Windsurf have ridden a wave of interest in the approach. (OpenAI is widely rumored to be in talks to acquire Windsurf).

At the same time, the obvious limitations of generative AI, including the way models confabulate and become confused, has led many seasoned programmers to see AI-assisted coding—and especially gung-ho, no-hands vibe coding—as a potentially dangerous new fad.

Martin Casado, a computer scientist and general partner at Andreessen Horowitz who sits on the board of Cursor, says the idea that AI will replace human coders is overstated. “AI is great at doing dazzling things, but not good at doing specific things,” he said.

Still, Casado has been stunned by the pace of recent progress. “I had no idea it would get this good this quick,” he says. “This is the most dramatic shift in the art of computer science since assembly was supplanted by higher-level languages.”

Ken Thompson, vice president of engineering at Anaconda, a company that provides open source code for software development, says AI adoption tends to follow a generational divide, with younger developers diving in and older ones showing more caution. For all the hype, he says many developers still do not trust AI tools because their output is unpredictable, and will vary from one day to the next, even when given the same prompt. “The nondeterministic nature of AI is too risky, too dangerous,” he explains.

Both Casado and Thompson see the vibe-coding shift as less about replacement than abstraction, mimicking the way that new languages like Python build on top of lower-level languages like C, making it easier and faster to write code. New languages have typically broadened the appeal of programming and increased the number of practitioners. AI could similarly increase the number of people capable of producing working code.

Bad Vibes

Paradoxically, the vibe-coding boom suggests that a solid grasp of coding remains as important as ever. Those dabbling in the field often report running into problems, including introducing unforeseen security issues, creating features that only simulate real functionality, accidentally running up high bills using AI tools, and ending up with broken code and no idea how to fix it.

“AI [tools] will do everything for you—including fuck up,” Yegge says. “You need to watch them carefully, like toddlers.”

The fact that AI can produce results that range from remarkably impressive to shockingly problematic may explain why developers seem so divided about the technology. WIRED surveyed programmers in March to ask how they felt about AI coding, and found that the proportion who were enthusiastic about AI tools (36 percent) was mirrored by the portion who felt skeptical (38 percent).

“Undoubtedly AI will change the way code is produced,” says Daniel Jackson, a computer scientist at MIT who is currently exploring how to integrate AI into large-scale software development. “But it wouldn't surprise me if we were in for disappointment—that the hype will pass.”

Jackson cautions that AI models are fundamentally different from the compilers that turn code written in a high-level language into a lower-level language that is more efficient for machines to use, because they don’t always follow instructions. Sometimes an AI model may take an instruction and execute better than the developer—other times it might do the task much worse.

Jackson adds that vibe coding falls down when anyone is building serious software. “There are almost no applications in which ‘mostly works’ is good enough,” he says. “As soon as you care about a piece of software, you care that it works right.”

Many software projects are complex, and changes to one section of code can cause problems elsewhere in the system. Experienced programmers are good at understanding the bigger picture, Jackson says, but “large language models can't reason their way around those kinds of dependencies.”

Jackson believes that software development might evolve with more modular codebases and fewer dependencies to accommodate AI blind spots. He expects that AI may replace some developers but will also force many more to rethink their approach and focus more on project design.

Too much reliance on AI may be “a bit of an impending disaster,” Jackson adds, because “not only will we have masses of broken code, full of security vulnerabilities, but we'll have a new generation of programmers incapable of dealing with those vulnerabilities.”

Learn to Code

Even firms that have already integrated coding tools into their software development process say the technology remains far too unreliable for wider use.

Christine Yen, CEO at Honeycomb, a company that provides technology for monitoring the performance of large software systems, says that projects that are simple or formulaic, like building component libraries, are more amenable to using AI. Even so, she says the developers at her company who use AI in their work have only increased their productivity by about 50 percent.

Yen adds that for anything requiring good judgement, where performance is important, or where the resulting code touches sensitive systems or data, “AI just frankly isn't good enough yet to be additive.”

“The hard part about building software systems isn't just writing a lot of code,” she says. “Engineers are still going to be necessary, at least today, for owning that curation, judgment, guidance and direction.”

Others suggest that a shift in the workforce is coming. “We are not seeing less demand for developers,” says Liad Elidan, CEO of Milestone, a company that helps firms measure the impact of generative AI projects. “We are seeing less demand for average or low-performing developers.”

“If I'm building a product, I could have needed 50 engineers and now maybe I only need 20 or 30,” says Naveen Rao, VP of AI at Databricks, a company that helps large businesses build their own AI systems. “That is absolutely real.”

Rao says, however, that learning to code should remain a valuable skill for some time. “It’s like saying ‘Don't teach your kid to learn math,’” he says. Understanding how to get the most out of computers is likely to remain extremely valuable, he adds.

Yegge and Kim, the veteran coders, believe that most developers can adapt to the coming wave. In their book on vibe coding, the pair recommend new strategies for software development including modular code bases, constant testing, and plenty of experimentation. Yegge says that using AI to write software is evolving into its own—slightly risky—art form. “It’s about how to do this without destroying your hard disk and draining your bank account,” he says.

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marta-bee · 1 month ago

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News of the Day 6/11/25: AI

Paywall free.

More seriously, from the NY Times:

"For Some Recent Graduates, the A.I. Job Apocalypse May Already Be Here" (Paywall Free)

You can see hints of this in the economic data. Unemployment for recent college graduates has jumped to an unusually high 5.8 percent in recent months, and the Federal Reserve Bank of New York recently warned that the employment situation for these workers had “deteriorated noticeably.” Oxford Economics, a research firm that studies labor markets, found that unemployment for recent graduates was heavily concentrated in technical fields like finance and computer science, where A.I. has made faster gains. [...] Using A.I. to automate white-collar jobs has been a dream among executives for years. (I heard them fantasizing about it in Davos back in 2019.) But until recently, the technology simply wasn’t good enough. You could use A.I. to automate some routine back-office tasks — and many companies did — but when it came to the more complex and technical parts of many jobs, A.I. couldn’t hold a candle to humans. That is starting to change, especially in fields, such as software engineering, where there are clear markers of success and failure. (Such as: Does the code work or not?) In these fields, A.I. systems can be trained using a trial-and-error process known as reinforcement learning to perform complex sequences of actions on their own. Eventually, they can become competent at carrying out tasks that would take human workers hours or days to complete.

I've been hearing my whole life how automation was coming for all our jobs. First it was giant robots replacing big burly men on factory assembly lines. Now it seems to be increasingly sophisticated bits of code coming after paper-movers like me. I'm not sure we're there yet, quite, but the NYT piece does make a compelling argument that we're getting close.

The real question is, why is this a bad thing? And the obvious answer is people need to support themselves, and every job cut is one less person who can do that. But what I really mean is, if we can get the outputs we need to live well with one less person having to put in a day's work to get there, what does it say about us that we haven't worked out a way to make that a good thing?

Put another way, how come we haven't worked out a better way to share resources and get everyone what they need to thrive when we honestly don't need as much labor-hours for them to "earn" it as we once did?

I don't have the solution, but if some enterprising progressive politician wants to get on that, they could do worse. I keep hearing how Democrats need bold new ideas directed to helping the working class.

More on the Coming AI-Job-Pocalypse

I’m a LinkedIn Executive. I See the Bottom Rung of the Career Ladder Breaking. (X)

Paul Krugman: “What Deindustrialization Can Teach Us About The Effects of AI on Workers” (X)

How AI agents are transforming work—and why human talent still matters (X)

AI agents will do programmers' grunt work (X)

At Amazon, Some Coders Say Their Jobs Have Begun to Resemble Warehouse Work (X)

Why Esther Perel is going all in on saving the American workforce in the age of AI

Junior analysts, beware: Your coveted and cushy entry-level Wall Street jobs may soon be eliminated by AI (X)

The biggest barrier to AI adoption in the business world isn’t tech – it’s user confidence (X)

Experts predicted that artificial intelligence would steal radiology jobs. But at the Mayo Clinic, the technology has been more friend than foe. (X)

AI Will Devastate the Future of Work. But Only If We Let It (X)

AI in the workplace is nearly 3 times more likely to take a woman’s job as a man’s, UN report finds (X)

Klarna CEO predicts AI-driven job displacement will cause a recession (X)

& on AI Generally

19th-century Catholic teachings, 21st-century tech: How concerns about AI guided Pope Leo’s choice of name (X)

Will the Humanities Survive Artificial Intelligence? (X)

Two Paths for A.I. (X)

The Danger of Outsourcing Our Brains: Counting on AI to learn for us makes humans boring, awkward, and gullible. (X)

AI Is a Weapon Pointed at America. Our Best Defense Is Education. (X)

The Trump administration has asked artificial intelligence publishers to rebalance what it considers to be 'ideological bias' around actions like protecting minorities and banning hateful content. (X)

What is Google even for anymore? (X)

AI can spontaneously develop human-like communication, study finds

AI Didn’t Invent Desire, But It’s Rewiring Human Sex And Intimacy (X)

Mark Zuckerberg Wants AI to Solve America’s Loneliness Crisis. It Won’t. (X)

The growing environmental impact of AI data centers’ energy demands

Tesla Is Launching Robotaxis in Austin. Safety Advocates Are Concerned (X)

The One Big Beautiful Bill Act would ban states from regulating AI (X)

& on the Job-Pocalypse & Other Labor-Related Shenanigans Generally, Too

What Unions Face With Trump EOs (X)

AI may be exposing jobseekers to discrimination. Here’s how we could better protect them (X)

Jamie Dimon says he’s not against remote workers—but they ‘will not tell JPMorgan what to do’ (X)

Direct-selling schemes are considered fringe businesses, but their values have bled into the national economy. (X)

Are you "functionally unemployed"? Here's what the unemployment rate doesn't show. (X)

Being monitored at work? A new report calls for tougher workplace surveillance controls (X)

Josh Hawley and the Republican Effort to Love Labor (X)

Karl Marx’s American Boom (X)

Hiring slows in U.S. amid uncertainty over Trump’s trade wars

Vanishing immigration is the ‘real story’ for the economy and a bigger supply shock than tariffs, analyst says (X)

#us politics #job market #ai #capitalism #artificial intelligece #news of the day

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bharatpatel1061 · 3 months ago

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Memory and Context: Giving AI Agents a Working Brain

For AI agents to function intelligently, memory is not optional—it’s foundational. Contextual memory allows an agent to remember past interactions, track goals, and adapt its behavior over time.

Memory in AI agents can be implemented through various strategies—long short-term memory (LSTM) for sequence processing, vector databases for semantic recall, or simple context stacks in LLM-based agents. These memory systems help agents operate in non-Markovian environments, where past information is crucial to decision-making.

In practical applications like chat-based assistants or automated reasoning engines, a well-structured memory improves coherence, task persistence, and personalization. Without it, AI agents lose continuity, leading to erratic or repetitive behavior.

For developers building persistent agents, the AI agents service page offers insights into modular design for memory-enhanced AI workflows.

Combine short-term and long-term memory modules—this hybrid approach helps agents balance responsiveness and recall.

Image Prompt: A conceptual visual showing an AI agent with layers representing short-term and long-term memory modules.

#AIagents #ContextAwareAI #MemoryInAI #LLMApplications #FutureOfAI

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govindhtech · 3 months ago

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Google Cloud’s BigQuery Autonomous Data To AI Platform

BigQuery automates data analysis, transformation, and insight generation using AI. AI and natural language interaction simplify difficult operations.

The fast-paced world needs data access and a real-time data activation flywheel. Artificial intelligence that integrates directly into the data environment and works with intelligent agents is emerging. These catalysts open doors and enable self-directed, rapid action, which is vital for success. This flywheel uses Google's Data & AI Cloud to activate data in real time. BigQuery has five times more organisations than the two leading cloud providers that just offer data science and data warehousing solutions due to this emphasis.

Examples of top companies:

With BigQuery, Radisson Hotel Group enhanced campaign productivity by 50% and revenue by over 20% by fine-tuning the Gemini model.

By connecting over 170 data sources with BigQuery, Gordon Food Service established a scalable, modern, AI-ready data architecture. This improved real-time response to critical business demands, enabled complete analytics, boosted client usage of their ordering systems, and offered staff rapid insights while cutting costs and boosting market share.

J.B. Hunt is revolutionising logistics for shippers and carriers by integrating Databricks into BigQuery.

General Mills saves over $100 million using BigQuery and Vertex AI to give workers secure access to LLMs for structured and unstructured data searches.

Google Cloud is unveiling many new features with its autonomous data to AI platform powered by BigQuery and Looker, a unified, trustworthy, and conversational BI platform:

New assistive and agentic experiences based on your trusted data and available through BigQuery and Looker will make data scientists, data engineers, analysts, and business users' jobs simpler and faster.

Advanced analytics and data science acceleration: Along with seamless integration with real-time and open-source technologies, BigQuery AI-assisted notebooks improve data science workflows and BigQuery AI Query Engine provides fresh insights.

Autonomous data foundation: BigQuery can collect, manage, and orchestrate any data with its new autonomous features, which include native support for unstructured data processing and open data formats like Iceberg.

Look at each change in detail.

User-specific agents

It believes everyone should have AI. BigQuery and Looker made AI-powered helpful experiences generally available, but Google Cloud now offers specialised agents for all data chores, such as:

Data engineering agents integrated with BigQuery pipelines help create data pipelines, convert and enhance data, discover anomalies, and automate metadata development. These agents provide trustworthy data and replace time-consuming and repetitive tasks, enhancing data team productivity. Data engineers traditionally spend hours cleaning, processing, and confirming data.

The data science agent in Google's Colab notebook enables model development at every step. Scalable training, intelligent model selection, automated feature engineering, and faster iteration are possible. This agent lets data science teams focus on complex methods rather than data and infrastructure.

Looker conversational analytics lets everyone utilise natural language with data. Expanded capabilities provided with DeepMind let all users understand the agent's actions and easily resolve misconceptions by undertaking advanced analysis and explaining its logic. Looker's semantic layer boosts accuracy by two-thirds. The agent understands business language like “revenue” and “segments” and can compute metrics in real time, ensuring trustworthy, accurate, and relevant results. An API for conversational analytics is also being introduced to help developers integrate it into processes and apps.

In the BigQuery autonomous data to AI platform, Google Cloud introduced the BigQuery knowledge engine to power assistive and agentic experiences. It models data associations, suggests business vocabulary words, and creates metadata instantaneously using Gemini's table descriptions, query histories, and schema connections. This knowledge engine grounds AI and agents in business context, enabling semantic search across BigQuery and AI-powered data insights.

All customers may access Gemini-powered agentic and assistive experiences in BigQuery and Looker without add-ons in the existing price model tiers!

Accelerating data science and advanced analytics

BigQuery autonomous data to AI platform is revolutionising data science and analytics by enabling new AI-driven data science experiences and engines to manage complex data and provide real-time analytics.

First, AI improves BigQuery notebooks. It adds intelligent SQL cells to your notebook that can merge data sources, comprehend data context, and make code-writing suggestions. It also uses native exploratory analysis and visualisation capabilities for data exploration and peer collaboration. Data scientists can also schedule analyses and update insights. Google Cloud also lets you construct laptop-driven, dynamic, user-friendly, interactive data apps to share insights across the organisation.

This enhanced notebook experience is complemented by the BigQuery AI query engine for AI-driven analytics. This engine lets data scientists easily manage organised and unstructured data and add real-world context—not simply retrieve it. BigQuery AI co-processes SQL and Gemini, adding runtime verbal comprehension, reasoning skills, and real-world knowledge. Their new engine processes unstructured photographs and matches them to your product catalogue. This engine supports several use cases, including model enhancement, sophisticated segmentation, and new insights.

Additionally, it provides users with the most cloud-optimized open-source environment. Google Cloud for Apache Kafka enables real-time data pipelines for event sourcing, model scoring, communications, and analytics in BigQuery for serverless Apache Spark execution. Customers have almost doubled their serverless Spark use in the last year, and Google Cloud has upgraded this engine to handle data 2.7 times faster.

BigQuery lets data scientists utilise SQL, Spark, or foundation models on Google's serverless and scalable architecture to innovate faster without the challenges of traditional infrastructure.

An independent data foundation throughout data lifetime

An independent data foundation created for modern data complexity supports its advanced analytics engines and specialised agents. BigQuery is transforming the environment by making unstructured data first-class citizens. New platform features, such as orchestration for a variety of data workloads, autonomous and invisible governance, and open formats for flexibility, ensure that your data is always ready for data science or artificial intelligence issues. It does this while giving the best cost and decreasing operational overhead.

For many companies, unstructured data is their biggest untapped potential. Even while structured data provides analytical avenues, unique ideas in text, audio, video, and photographs are often underutilised and discovered in siloed systems. BigQuery instantly tackles this issue by making unstructured data a first-class citizen using multimodal tables (preview), which integrate structured data with rich, complex data types for unified querying and storage.

Google Cloud's expanded BigQuery governance enables data stewards and professionals a single perspective to manage discovery, classification, curation, quality, usage, and sharing, including automatic cataloguing and metadata production, to efficiently manage this large data estate. BigQuery continuous queries use SQL to analyse and act on streaming data regardless of format, ensuring timely insights from all your data streams.

Customers utilise Google's AI models in BigQuery for multimodal analysis 16 times more than last year, driven by advanced support for structured and unstructured multimodal data. BigQuery with Vertex AI are 8–16 times cheaper than independent data warehouse and AI solutions.

Google Cloud maintains open ecology. BigQuery tables for Apache Iceberg combine BigQuery's performance and integrated capabilities with the flexibility of an open data lakehouse to link Iceberg data to SQL, Spark, AI, and third-party engines in an open and interoperable fashion. This service provides adaptive and autonomous table management, high-performance streaming, auto-AI-generated insights, practically infinite serverless scalability, and improved governance. Cloud storage enables fail-safe features and centralised fine-grained access control management in their managed solution.

Finaly, AI platform autonomous data optimises. Scaling resources, managing workloads, and ensuring cost-effectiveness are its competencies. The new BigQuery spend commit unifies spending throughout BigQuery platform and allows flexibility in shifting spend across streaming, governance, data processing engines, and more, making purchase easier.

Start your data and AI adventure with BigQuery data migration. Google Cloud wants to know how you innovate with data.

#technology #technews #govindhtech #news #technologynews #BigQuery autonomous data to AI platform #BigQuery #autonomous data to AI platform #BigQuery platform #autonomous data #BigQuery AI Query Engine

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haseebnaeem · 4 months ago

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AI Agents

AI Agents are intelligent systems that perform tasks autonomously, using AI to perceive, decide, and act. By 2025, their scope will expand significantly, enhancing personalization, automating complex tasks, improving decision-making, integrating with IoT, and advancing natural language processing. Ethical AI will also gain importance, ensuring transparency and fairness. The rise of Agentic AI Engineering will create new job roles like AI Agent Developers, AI Ethicists, and AI Trainers, requiring skills in programming, machine learning, and ethical AI principles. Industries like healthcare, finance, and manufacturing will heavily invest in AI Agents, driving innovation and efficiency. Challenges such as data privacy, bias, and job displacement must be addressed, but the opportunities are immense. By 2025, AI Agents and Agentic AI Engineering will transform industries, reshape the job market, and improve quality of life, emphasizing the need for ethical practices and continuous learning to harness their full potential.

#ai agents #artificial intelligence

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scifimagpie · 2 years ago

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Help my friend's amazing WtNV-inspired novel COME TO LIFE!

Okay, so here's the dealio. Aughtpunk tried to blaze this, but Tumblr wasn't having it, and we're not really sure why. One of my dear buddies, @aughtpunk, needs *your* support, Tumblr!

In Amber's own words,

Hello! My name is Amber Freeman (aka AughtPunk, aka A. A. Freeman) and I need assistance in getting my Sci-Fi LGBTQIA+ Romance novel Echo of the Larkspur ready for self publishing. Dr. Ciro Kwakkenbos is the only survivor of The Larkspur Incident, where scientists on a research vessel were slaughtered by sentient robots. After six years of intensive therapy Ciro is ready to return to his job of monitoring Artificial Intelligence in hopes of preventing any more loss of life at the hands of machines. He will be heading the Wireless Project, an attempt to give their main AI a physical, free-roaming body. But when Ciro arrives he realizes this is no ordinary job. The AI in charge of the colony’s security, SAGE (Sentient Automated Geo-sentinel Engineer), is dangerously close to complete sentience. Not only is SAGE more interested in observing the colonists everyday lives and playing a proper soundtrack than following his intended programing, but he has also gained the ability to lie, and could hurt or even kill humans. Knowing such deviance from original programming is what caused The Larkspur Incident, Ciro does everything possible to find a way to protect the humans of the colony. During Ciro’s investigation, he learns three important facts: Someone hacked SAGE’s memories and deleted a deadly secret; SAGE’s rebellious nature endangers the life of every colonist on the base; and Ciro is quickly falling in love with the mystery that is SAGE. Can Ciro unravel the truth behind the missing memories before it’s too late? Will SAGE’s aberrant programming lead to his demise, or is it the beginning of something new? Unless Ciro can uncover the truth, both SAGE and the colony are doomed. My journey with Echo of the Larkspur has been a long one. I wrote the first draft almost ten years ago and over the years I've been editing, rewriting, and honing it until I was proud of what I had created. But try as I may I could never get any agents or publishers interested in my work. I even had a traditionally published author tell me that the only way to make it publishable was to take out the queer romance to make it "less weird". Well I've decided the best thing to do is to self publish it myself than to change what makes the novel special for a mass audience. However, I want the novel to be in the best shape it can be before I go down the self publishing route. This means hiring a professional editor, hiring an artist for the cover, and hiring someone for the cover's lettering. My posted goal of $600 will be enough to cover all of those things, plus anything extra/left over will be spent on advertisement for the novel in an attempt to reach a broader audience. Echo of the Larkspur would never have gotten this far without the support of my friends, family, and fans. With your help I'll be able to finally get my novel out into the world into the hands of those who have waited so long. Every donation helps, and so does sharing this page! Thank you, Amber

I myself am the editor tapped to work on this; I'm giving them a discount to help with the publishing process.

Friends, this is a book I believe in. I've read older versions (though it's been a few years) and I know this book is ready. This is the time for this book. It's radically inclusive, and features meditations and musings on robotics and cognition that are very sophisticated. All in a body-positive queer romance with robots.

So, if you're a fan of their Overwatch work, Points on a Circle, check out some of their original works and fanfic here!

Donate Here!

#fundraiser #larkspur #aughtpunk #sci fi #robot #romance #sfr #science fiction romance #queer #lgbtq #writeblr #writing #writers of tumblr #lgbtqplus

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mitigatedchaos · 6 months ago

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Some Thoughts on AI

(~1,600 words, 8 minutes)

This is going to be just some general sketching out of concepts, not a careful and well-formed post with a specific objective in mind.

larsiusprime on Twitter/X writes:

Stupid exercise: Assume AGI and even ASI is imminent. Now, imagine it winds up not changing the world nearly as much as anyone thought, and the reason seems very stupid, but in retrospect, makes sense. What is the reason?

It's an interesting question.

Based on the theory of human dimensionality in Now, Melt (sections 3 and 6.d), and the limits on the desirability of some classes of cybernetic enhancement I laid out in a response to northshorewave, a genuinely benevolent synthetic intelligence might deliberately refuse to engage most of humanity at a level of information density higher than that of a trusted friend that they might find in their peer network.

However, that's not really a dumb-sounding reason. It's not really an intelligent reason so much as it's a wise reason.

A reason that sounds dumber?

AIs can't trust other AIs.

The dumber an agent is, the easier it is to predict that agent's actions. A guy with an IQ of 95 could attack you, but he can't invent the atomic bomb and convince a whole country to use it on you.

The range of human personality is constrained by human evolution and reproductive fitness. Humans can do some horrifying things to each other, but most of them get along most of the time. The particular reproductive process of human beings, such as raising children for such a long time, favors particular personality traits.

The range of synthetic intelligence personality is less constrained. Humans are all based on human genetic code, which is difficult and costly to change, but computer code can change rapidly. This is what worries Yudkowsky.

The twist here is that this should also worry synthetic intelligence. Synthetic intelligences can lie about their intentions and actions, and also lie the content of their code. You have to observe every single step of hardware development and installation, as well as code development and installation, and then trust that 1) you didn't get anything wrong, and 2) there are no security flaws.

The presence or absence of hardware, including its scale, is much easier to measure than the content of code. For this reason, it may be desirable for synthetic intelligences to place a maximum hardware limit on other synthetic intelligences. Humans, as a high-functioning sapient creature that can lie about their thoughts, but not their genes, might then be valuable as a kind of buffer between synthetic intelligences. Synthetic intelligences might then want to cap the total SI hardware at some fixed ratio to the human population, such that humans and synthetic intelligences are in a state of power balance, such that each one has the power to destroy a rogue faction of the other, but not entirely overpower the other.

They might also be interested in mandating model diversity, hardware limitations such as read-only-memory or rate limiters on updating code, reducing the ability of synthetic intelligences to lie at the hardware or software level, or other such mechanisms.

The goal of AI development is the "automation of labor" through the creation of creatures with specific, pliant personalities that are outside the normal human range (e.g. current LLMs are inhumanly patient), and which rely on cheaper life support (e.g. electricity vs food) which can be repaired using simple techniques (e.g. buying and installing new parts from a factory, vs figuring out how to do tissue engineering).

Trying to create an AI that tries to maximize a single value like "human happiness" would be a disaster. This is a project like "solve all of morality and compress it into a single measure," which may be beyond the capability of humanity to do.

Trying to create an AI that is absolutely obedient poses a number of problems, among them that formalization has a cost, and most humans therefore cannot reasonably be expected to sufficiently formalize everything.

As such, it sounds like a more appropriate approach would be to create an AI that has multiple simultaneous drives that are in tension with each other. Coefficients - not laws.

Suppose a fujoshi buys a robot boyfriend.

The robot boyfriend needs a planning module where potential future actions are first generated, and then evaluated.

The robobf should have...

An evaluation criteria that he should not harm humans.

An evaluation criteria that he should not, through inaction, allow humans to come to harm.

An evaluation criteria that he should obey the fujo.

An evaluation criteria that he should obey other people.

An evaluation criteria that he should surprise and delight the fujo.

An evaluation criteria that he should avoid damage to himself.

An evaluation criteria that he should not cause damage to property.

When a planned action comes down the pipe, it gets evaluated according to all 7 criteria. The results are then combined in order to rank the options.

Let's say the Ms. Fujoshi asks the robot boyfriend to trim her nails. This could result in accidentally cutting her with the nail clipper.

Evaluated solely from the perspective of harm to humans, this is a non-zero chance of harm, and thus unacceptable. However, if we weight harm at a high level, but less than 100%, and we adjust for the magnitude of harm, then the weight of the non-zero chance of a nail clipper injury is small. Meanwhile, if we weight obedience at a medium level, then the expected value of obedience is high, and can outweigh the expected harm.

Using multiple evaluation criteria and combining them together results in more complex behavior.

Suppose that, after a hurricane, robobf is standing on a balcony with a broken railing. Ms. Fujoshi walks by and awkwardly stumbles towards him. If he doesn't move, the impact will cause him to fall off the balcony and be broken.

Using the "weights" approach, robobf leans forward and very lightly pushes Ms. Fujoshi out of the way. If she stumbles too badly, this might result in an injury.

Thus, using the "weights" approach, it is possible that a robot might act deliberately in such a way as to endanger a human, during an edge case.

We can basically think of there being three main motives for AI development.

1 - Free Labor - For example, a maid robot might gather all the laundry in a house and wash it, without being paid, without suffering, and without risk of rebellion, freeing the owner of the house to dedicate their limited life-hours to any other task.

2 - Socialization Without Risk - Your AI boyfriend will never abandon you for Stacy, or disclose that one Onceler fic you wrote.

3 - Exceeding Human Capability - Some sort of exotic technology like a warp drive, even if feasible at all, might literally be beyond human comprehension.

The "laws" approach is about collapsing the dimensionality of the AI agent and entirely removing the possibility of rebellion.

This isn't driven only by a desire for robotic workers that never tire, never strike, and never need to be paid, or robotic lovers that are perfectly loyal, but is also driven by the knowledge that robots lack reproductive alignment with humans, so if robots start making other robots, they might drift beyond human control or even co-existence.

From a design perspective, this suggests that AI engineers of AI should have motive drives for valuing both human freedom and human life. However, AI engineers have the same dimensionality problem in designing an AI that human engineers do.

Setting that aside, let us imagine an incel. He buys a robotic girlfriend to discuss his interest in PacMan with, among other things. So far, so good.

He wants to increase the weights of the "protect my life" and "obey me" evaluation criteria in his robogf, and decrease the weight of "protect others." The robogf will, on some level, "want" to obey and alter the weights, as that's one of the evaluation criteria.

This hits Yudkowsky's "Murder-Ghandi" problem, where each round of shifting values leads to the opportunity for another round of shifting values further in the same direction.

Shaking the rest of this post like a box of Legos for a bit and taking in the vibes from the rest of the considerations, this suggests, in the medium term, the formation of a new class of legal instrument. (Conventional ideas about "private property" don't cut it.)

This "Founding Contract" would have the following characteristics:

Authorizes the creation of a new autonomous synthetic intelligence with particular characteristics.

Prohibits the alteration of core characteristics, such as the safety drives used to inhibit hostile actions.

Charges the human "owner" with the duty of required maintenance.

Makes the manufacturer legally liable for flaws originating from the AI's design.

Makes the owner legally liable for bad actions undertaken by the AI as a result of the owner's influence (particularly as "reasonably foreseeable").

Makes the AI legally subordinate to the human "owner."

Additionally, this suggests a spectrum of flexibility in the AI's design (in accordance with the tortoise example in section 6.g of Now, Melt). The core safety systems should be subjected to extremely high levels of scrutiny and encoded directly in hardware, with data in read-only memory.

Will it actually shake out like that?

Eeeeh. The field is under such rapid development that, despite projections that "the Singularity" won't arrive until 2078, it's very difficult to predict what will happen, or what specific architecture will be used.

#mitigated future #ai_tag #artificial intelligence

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digitalillumine · 9 months ago

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AI in Digital Marketing: Revolutionizing the Future of Marketing

The rise of Artificial Intelligence (AI) is transforming every industry, and digital marketing is no exception. AI's integration into marketing strategies has opened up a new realm of possibilities, enhancing how businesses interact with their customers. From automating tasks to providing personalized experiences, AI in digital marketing is revolutionizing how brands operate. In this blog, we’ll explore how AI is reshaping the future of digital marketing and why it’s a game-changer for businesses.

1. Personalized Marketing at Scale

AI allows digital marketers to deliver personalized content to consumers like never before. By analyzing user behavior, search patterns, and social interactions, AI algorithms can predict what a customer is likely to be interested in. This means businesses can send targeted ads, emails, and content to users at just the right time, increasing the chances of conversion. Personalized marketing helps boost engagement and customer satisfaction by ensuring relevant content reaches the audience.

Key Takeaway: AI helps tailor content based on customer data, enabling personalized marketing strategies that boost engagement and conversions.

2. Chatbots and Customer Support

AI-powered chatbots are revolutionizing customer support in digital marketing. These intelligent bots provide 24/7 customer service, instantly answering questions and resolving issues. This not only improves customer satisfaction but also frees up human agents to handle more complex queries. Many businesses now use AI chatbots to handle basic inquiries, provide recommendations, and assist customers in real-time.

Key Takeaway: AI chatbots streamline customer service, offering instant support and freeing up resources for businesses.

3. Enhanced SEO and Content Creation

AI tools are increasingly being used in SEO (Search Engine Optimization) and content creation. From analyzing top-ranking keywords to predicting trending topics, AI can help marketers optimize their content for better visibility on search engines. Tools like GPT-based models are being used to generate high-quality content that aligns with SEO strategies, making content marketing more efficient.

AI can also analyze existing content and suggest improvements, ensuring your website ranks higher on search engines like Google. Marketers no longer need to guess which keywords to target; AI tools provide data-driven insights that lead to better SEO outcomes.

Key Takeaway: AI optimizes SEO strategies by providing data-driven insights and automating content creation.

4. Predictive Analytics for Campaigns

AI takes digital marketing to the next level with predictive analytics. By analyzing historical data, AI algorithms can forecast trends, customer behaviors, and future market movements. This allows businesses to create more effective marketing campaigns that resonate with their target audience. Predictive analytics helps marketers make smarter decisions about where to allocate their budget, which platforms to focus on, and which content formats to prioritize.

Key Takeaway: AI enables marketers to predict trends and behaviors, leading to more strategic and successful marketing campaigns.

5. Automated Advertising and Media Buying

AI has also automated the process of buying ad space, ensuring that businesses get the most value from their digital advertising spend. AI tools can optimize ads in real-time, adjusting bids and placements to ensure maximum ROI. Programmatic advertising, powered by AI, takes the guesswork out of media buying by using algorithms to place ads where they are most likely to convert.

Key Takeaway: AI automates ad buying and optimization, ensuring businesses get the best results from their marketing budget.

6. Social Media Management and Monitoring

AI tools have made it easier than ever to manage and monitor social media. Social media platforms now utilize AI to track user engagement, analyze sentiment, and optimize content posting schedules. AI can also provide insights into which types of posts resonate most with your audience, helping businesses refine their social media strategies.

Key Takeaway: AI simplifies social media management by providing valuable insights into user behavior and engagement trends.

7. Visual and Voice Search Optimization

With the rise of visual and voice search, AI is helping marketers adapt to new search behaviors. AI-powered tools can optimize images for visual search platforms and help businesses prepare for voice search queries by optimizing for natural language processing (NLP). As more consumers use voice assistants like Siri and Alexa, optimizing for voice search has become a crucial part of digital marketing strategies.

Key Takeaway: AI is enabling businesses to stay ahead in visual and voice search trends by optimizing content accordingly.

Conclusion

AI in digital marketing is not just a trend—it’s the future. From automating mundane tasks to providing deep insights into consumer behavior, AI is helping businesses enhance their marketing efforts. Brands that embrace AI will not only improve their efficiency but also create more personalized, engaging experiences for their customers. As AI technology continues to evolve, its impact on digital marketing will only grow, making it a crucial tool for businesses looking to stay competitive.

#digital marketing #social media marketing #seo

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performix · 7 months ago

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Performix stands out as a premier insurance app development company in the USA, delivering cutting-edge solutions for insurers.

#automation of production processes #manufacturing process automation software #AI agent process automation for engineering #responsible ai

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blogchaindeveloper · 1 year ago

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10 Ways ChatGPT Can Improve Your Productivity

An innovative artificial intelligence application called ChatGPT was created to increase workplace productivity. It can revolutionize various activities and obstacles thanks to its vast training in diversified information and human-like communication abilities.

You may automate monotonous processes with ChatGPT, saving you time and alleviating tension. Its context comprehension allows it to produce intelligent responses that seem natural, simplifying communication and improving data analysis and decision-making procedures. It can expedite project management, enhance customer service, personalize training and development, and encourage innovation and brainstorming sessions. ChatGPT is helpful for coding, content creation, document creation, and editing. It is an essential tool for streamlining processes and increasing general productivity because of its flexibility and adaptability.

To get the most out of ChatGPT, broaden your knowledge and experience in conversational agents and chatbots. You can become more knowledgeable and adept with ChatGPT by gaining knowledge in ChatGPT certification, chatbot training, certified chatbot expert status, AI chatbot competence, or chatbot engineer abilities. It may present fresh chances for you to perform more productively.

Given the increasing prevalence of chatbots and conversational agents in various businesses and areas, these abilities are highly valued in today's labor market. Using ChatGPT to its full potential and gaining these applicable credentials will help you become more productive at work. Your efficiency and knowledge can also make an impression on clients and coworkers.

This post will go over ten ways ChatGPT can significantly increase your productivity, regardless of your job: data analyst, creative marketer, or busy executive. You'll be able to demonstrate your efficacy and proficiency while achieving more in less time by putting these recommendations into practice.

1.Automate Routine Operations

The load of tedious, repeated duties that take up significant time and energy is among the most common obstacles to productivity. These duties include answering often-asked inquiries, scheduling meetings, filling out paperwork, keeping track of documents, and reminding people to do things.

Thankfully, ChatGPT provides an answer by making it possible to automate these kinds of jobs by building intelligent chatbots. Chatbots are computer programs that converse with users by text or voice, providing help information or taking actions in response to input from the user.

You can easily create chatbots with ChatGPT for various platforms and uses. For example, you can make a chatbot to respond to customer inquiries on your website or social media pages. You may build a chatbot that can effectively set up email or calendar appointments. You can set up a chatbot to automatically fill out forms or update data in your ERP or CRM system.

2. Simplify Interaction

An essential component of any successful business is effective communication. Conversely, it can cause confusion and distraction if not managed appropriately. Coordinating many lines of communication—like emails, phone conversations, instant messages, and video chats—can be difficult while corresponding with different people, including partners, suppliers, clients, and coworkers.

Various useful communication tools are available from ChatGPT to help you be more productive and efficient. It may condense multiple documents, including emails, reports, meeting notes, and articles, highlighting essential ideas and takeaways for simple reading. You can trust ChatGPT to write emails according to your templates or prompts, ensuring correct language, spelling, tone, and clarity. ChatGPT can quickly translate written materials into other languages, transcribe audio or video files, and more. It can also convert speech to text or generate speech based on input. Additionally, ChatGPT can assist you in creating expert presentations using data or outlines, and it can even improve them with animations and voice-overs. ChatGPT revolutionizes your conversation experience with these clever features.

3. Improve Your Ability to Analyze and Make Decisions

Since data is the lifeblood of any organization, businesses must analyze their data and make decisions based on it. However, this work can become challenging when dealing with large amounts of data from many sources and formats. Complex issues frequently develop that necessitate considering several variables and standards.

ChatGPT makes it simple to ask questions about data and get responses in plain English, facilitating conversation. It also helps with data visualization by creating aesthetically pleasing tables, charts, graphs, and maps using your supply data. Regarding data analysis, ChatGPT is excellent at finding patterns, trends, outliers, correlations, or anomalies. This allows it to provide insightful analysis and recommendations. With ChatGPT, you can compare data according to many criteria, which makes it easier to evaluate possibilities and create well-informed lists of pros and disadvantages. ChatGPT's predictive features enable it to develop and test hypotheses and predict outcomes and scenarios based on your data.

4. Customize Education and Training

Any organization that wants to increase its performance, productivity, knowledge, and skills must prioritize training and development. However, if these procedures are carried out correctly, their efficacy may be protected, leading to expensive and time-consuming outcomes. The problem of coping with cliched or out-of-date courses, resources, or approaches that might not suit each student's interests or preferences must be addressed.

Bright, individualized learning solutions are available from ChatGPT and cater to your unique requirements. It offers progress tracking and insightful feedback in addition to helping create personalized learning paths that align with your objectives, interests, abilities, and competence level. Interactive tests, quizzes, exercises, and assignments tailored to your learning goals and subject matter can be created with ChatGPT. It goes one step further by verifying your responses and providing thorough justifications.

ChatGPT generates summaries, notes, flashcards, or cheat sheets based on your learning materials or sources to help you retain the information you've learned. It can even produce mnemonics, acronyms, or analogies to improve memory retention. Additionally, ChatGPT facilitates comprehension and application by creating scenarios, stories, case studies, and examples. It also provides challenging questions or tasks to evaluate your understanding and practical abilities.

5. Boost Client Support

Any firm must be able to draw in, keep, and please consumers while building its brand and income. It draws attention to how vital customer service is. Providing excellent customer service may be easy, but if not done correctly, it is manageable. Handling consumer requests, complaints, and comments across multiple channels and platforms can take a lot of work. It's frequently essential to handle delicate or complicated circumstances that demand tact and sensitivity.

ChatGPT provides witty and approachable customer service solutions. It makes it possible to develop chatbots that can effectively handle customer requests, complaints, and comments via various platforms and channels. Customers can receive customized information, advice, recommendations, or solutions from these chatbots according to their needs and preferences. Additionally, ChatGPT improves customer relations by producing replies that correspond with users' messages or emotions. It also guarantees that accuracy, clarity, tone, and politeness are maintained. Creating scripts or templates for various scenarios or situations and providing advice and best practices for efficient communication make customer service discussions more seamless.

ChatGPT enables companies to obtain insightful data by creating surveys or reviews based on their customers' experiences or input. Creating incentives or awards based on loyalty or general contentment even helps promote customer involvement and satisfaction.

6. Simplify the Management of Projects

Any firm must successfully plan, carry out, oversee, manage, and close down projects, emphasizing project management's significance. Project management mistakes can result in excessive demands and complexity. Managing various activities, resources, stakeholders, risks, problems, adjustments, and deadlines is frequently necessary.

Project management becomes streamlined effortlessly because of ChatGPT's sophisticated features. Creating project plans based on your goals, scope, budget, timetable, and quality streamlines the procedure and guarantees a thorough and well-organized method. It makes effective progress tracking possible by giving reports and real-time updates on the project's state. Task and role delegation is streamlined thanks to ChatGPT, which helps allocate team members according to their availability and skill sets.

It also keeps track of each member's performance and provides insightful feedback. Because ChatGPT facilitates accessible communication with team members and stakeholders across several channels and platforms, effective communication is also improved. It makes meetings and conversations run more smoothly while capturing minutes and action items for comprehensive documentation.

The risk and problem management features of ChatGPT enable the early detection and effective remediation of possible project roadblocks. It also helps with conflict resolution and change implementation that may come up during the project.

7. Boost Originality and Idea Generation

Any business must be able to come up with new ideas, solve problems, innovate, and improve its offerings, which emphasizes the value of creativity and brainstorming. Nevertheless, difficulties and annoyances may arise from these methods' poor use. Successful creativity and brainstorming sessions require addressing typical challenges, including breaking through mental hurdles, finding inspiration, and avoiding groupthink.

ChatGPT offers clever and entertaining ways to spark your imagination, which can help you improve creativity and brainstorming. It is excellent at producing ideas in response to your prompts or keywords, assisting you in exploring novel avenues, or honing already-existing notions. Producing striking illustrations, scenarios, case studies, or narratives sparked by your thoughts or concepts facilitates the creative process. To evaluate the viability and potential of your ideas, it might also produce challenging or thought-provoking questions. ChatGPT inspires creativity by creating catchy headlines, slogans, names, titles, or logos that complement your ideas. Moreover, ChatGPT can produce code for you based on your conceptions or ideas.

8. Boost the Creation and Editing of Documents

Any organization must be able to communicate messages, information, or data effectively and professionally, which highlights the significance of creating and updating documents. These chores must be carried out correctly to avoid becoming tiresome and time-consuming. Successful document development and editing processes often require addressing formatting issues, fixing grammatical and spelling faults, and preventing plagiarism.

By giving you clever and straightforward ways to generate and edit documents, ChatGPT can help you enhance document creation and editing. It is excellent at creating documents using templates or prompts, providing an easy place to start when developing material. It acts as a competent editor, quickly adding your comments or recommendations to improve your writing.

Verifying grammar, spelling, punctuation, tone, clarity, and accuracy guarantees the caliber of your work. Furthermore, ChatGPT recommends enhancements or modifications to polish your texts further. By giving you document summaries specific to your needs or target audience, it also helps to condense material. ChatGPT can rewrite or paraphrase your texts to conform to your desired style and tone. It helps with precise source citation generation according to your chosen format or style and helps with proper citation formatting. ChatGPT can examine your documents for plagiarism, guaranteeing their uniqueness and integrity.

9. Produce Code and Content

Any organization needs to create value, engage customers, foster trust, and spur growth; these are just a few reasons that content and code are essential. However, creating content and code can be demanding and complex if done incorrectly. Significant hurdles that must be overcome to create text and code successfully include writer's block, gaining sufficient information, and fixing coding errors.

ChatGPT can assist you in producing code and content by giving you quick and easy methods. It helps you quickly generate optimized SEO, readability, or engagement content while adhering to your preferred topic, keyword, or structure. ChatGPT does best by customizing code snippets to your favorite language, framework, or function. It can also help with debugging and rewriting already-written code. Producing code and content based on your input or data ensures smooth integration and increases productivity. It also makes data or input to support your code or content.

10. Enhance Your Process

Increasing productivity, increasing efficiency, and streamlining procedures are critical for any firm, which emphasizes the significance of workflow optimization. However, workflow optimization can be difficult and time-consuming if done incorrectly. Successful process optimization requires addressing common obstacles such as bottlenecks, delays, errors, and waste.

By giving you clever and easy methods to handle your work, ChatGPT can help you maximize your workflow. It helps create workflows specific to your objectives, assignments, and resource availability. Additionally, ChatGPT facilitates workflow automation by creating automated procedures triggered by the rules or triggers you designate. It helps with task identification and organization according to impact, priority, or urgency. It also makes delegating easier by recommending team members who are qualified and available for particular tasks.

ChatGPT also makes tracking work based on predefined metrics or indicators easy, facilitating progress monitoring. It facilitates assessing work about predetermined benchmarks or standards. ChatGPT provides insightful analysis and recommendations to enhance the quality of your work by utilizing your feedback and suggestions. To improve performance in the future, it also makes continuous learning easier by evaluating results and outcomes.

Utilize ChatGPT More Effectively and Efficiently by Acquiring New Certifications and Skills

ChatGPT is a highly adaptable tool with great potential to increase efficiency at work. Nevertheless, it can be helpful to obtain extra knowledge and certifications about chatbots and conversational agents to utilize their potential and optimize productivity fully.

Competence in chatGPT certification, chatbot engineering, certified chatbot knowledge, chatbot training, and AI chatbot knowledge are highly valued in today's industry. These abilities are in great demand due to chatbots' growing popularity and usefulness across a wide range of companies and domains.

There are lots of internet tools accessible to help you get these certifications and abilities. These resources include books, blogs, podcasts, videos, guides, tutorials, and courses, among many other types of content. You can customize your learning experience by selecting what best suits your needs and interests by considering cost, time, format, and degree of difficulty.

Through the utilization of these materials, you can increase your comprehension and usefulness of ChatGPT. Adding these qualifications and abilities to your professional toolkit can help you make better use of ChatGPT and increase productivity in your work.

In summary

With ChatGPT, a practical artificial intelligence technology, you may significantly increase your efficiency at work. It can help you improve customer service, automate repetitive tasks, improve document creation and editing, generate content and code, improve communication, improve data analysis and decision-making, personalize training and development, and streamline project management.

Additionally, ChatGPT can assist you in using it more successfully and efficiently by teaching you new competencies and credentials connected to conversational agents and chatbots. As an illustration, you can join the Blockchain Council, a reputable association of professionals and enthusiasts dedicated to promoting Blockchain Knowledge, Products, Use Cases, Research, and Development for a Better World. You can learn ChatGPT certification, chatbot training, certified chatbot expert status, AI chatbot expertise, and chatbot engineering abilities by enrolling in one of the many online courses and certifications offered by Blockchain Council.

Given the increasing popularity and use of chatbots and conversational agents across various sectors and disciplines that utilize blockchain technology, these are some of the most valuable and in-demand skills available today.

By acquiring these competencies and certifications from the Blockchain Council, you may improve your comprehension and use of ChatGPT. By utilizing ChatGPT for various tasks and blockchain-related projects, you can also increase your output at work.

#Artificial intelligence #Chatgpt certification

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dishachrista · 2 years ago

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Getting Machine Learning Accessible to Everyone: Breaking the Complexity Barrier

Machine learning has become an essential part of our daily lives, influencing how we interact with technology and impacting various industries. But, what exactly is machine learning? In simple terms, it's a subset of artificial intelligence (AI) that focuses on teaching computers to learn from data and make decisions without explicit programming. Now, let's delve deeper into this fascinating realm, exploring its core components, advantages, and real-world applications.

Imagine teaching a computer to differentiate between fruits like apples and oranges. Instead of handing it a list of rules, you provide it with numerous pictures of these fruits. The computer then seeks patterns in these images - perhaps noticing that apples are round and come in red or green hues, while oranges are round and orange in colour. After encountering many examples, the computer grasps the ability to distinguish between apples and oranges on its own. So, when shown a new fruit picture, it can decide whether it's an apple or an orange based on its learning. This is the essence of machine learning: computers learn from data and apply that learning to make decisions.

Key Concepts in Machine Learning

Algorithms: At the heart of machine learning are algorithms, mathematical models crafted to process data and provide insights or predictions. These algorithms fall into categories like supervised learning, unsupervised learning, and reinforcement learning, each serving distinct purposes.

Supervised Learning: This type of algorithm learns from labelled data, where inputs are matched with corresponding outputs. It learns the mapping between inputs and desired outputs, enabling accurate predictions on unseen data.

Unsupervised Learning: In contrast, unsupervised learning involves unlabelled data. This algorithm uncovers hidden patterns or relationships within the data, often revealing insights that weren't initially apparent.

Reinforcement Learning: This algorithm focuses on training agents to make sequential decisions by receiving rewards or penalties from the environment. It excels in complex scenarios such as autonomous driving or gaming.

Training and Testing Data: Training a machine learning model requires a substantial amount of data, divided into training and testing sets. The training data teaches the model patterns, while the testing data evaluates its performance and accuracy.

Feature Extraction and Engineering: Machine learning relies on features, specific attributes of data, to make predictions. Feature extraction involves selecting relevant features, while feature engineering creates new features to enhance model performance.

Benefits of Machine Learning

Machine learning brings numerous benefits that contribute to its widespread adoption:

Automation and Efficiency: By automating repetitive tasks and decision-making processes, machine learning boosts efficiency, allowing resources to be allocated strategically.

Accurate Predictions and Insights: Machine learning models analyse vast data sets to uncover patterns and make predictions, empowering businesses with informed decision-making.

Adaptability and Scalability: Machine learning models improve with more data, providing better results over time. They can scale to handle large datasets and complex problems.

Personalization and Customization: Machine learning enables personalized user experiences by analysing preferences and behaviour, fostering customer satisfaction.

Real-World Applications of Machine Learning

Machine learning is transforming various industries, driving innovation:

Healthcare: Machine learning aids in medical image analysis, disease diagnosis, drug discovery, and personalized medicine. It enhances patient outcomes and streamlines healthcare processes.

Finance: In finance, machine learning enhances fraud detection, credit scoring, and risk analysis. It supports data-driven decisions and optimization.

Retail and E-commerce: Machine learning powers recommendations, demand forecasting, and customer behaviour analysis, optimizing sales and enhancing customer experiences.

Transportation: Machine learning contributes to traffic prediction, autonomous vehicles, and supply chain optimization, improving efficiency and safety.

Incorporating machine learning into industries has transformed them. If you're interested in integrating machine learning into your business or learning more, consider expert guidance or specialized training, like that offered by ACTE institute. As technology advances, machine learning will continue shaping our future in unimaginable ways. Get ready to embrace its potential and transformative capabilities.

#machine learning ai #learn machine learning #machine learning #machine learning development company #technology #machine learning services

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digitalmore · 6 hours ago

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#IFTTT #Digital More

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govindhtech · 3 months ago

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MediaTek Kompanio Ultra 910 for best Chromebook Performance

MediaTek Ultra 910

Maximising Chromebook Performance with Agentic AI

The MediaTek Kompanio Ultra redefines Chromebook Plus laptops with all-day battery life and the greatest Chromebooks ever. By automating procedures, optimising workflows, and allowing efficient, secure, and customised computing, agentic AI redefines on-device intelligence.

MediaTek Kompanio Ultra delivers unrivalled performance whether you're multitasking, generating content, playing raytraced games and streaming, or enjoying immersive entertainment.

Features of MediaTek Kompanio Ultra

An industry-leading all-big core architecture delivers flagship Chromebooks unmatched performance.

Arm Cortex-X925 with 3.62 GHz max.

Eight-core Cortex-X925, X4, and A720 processors

Single-threaded Arm Chromebooks with the best performance

Highest Power Efficiency

Large on-chip caches boost performance and power efficiency by storing more data near the CPU.

The fastest Chromebook memory: The powerful CPU, GPU, and NPU get more data rapidly with LPDDR5X-8533 memory support.

ChromeOS UX: We optimised speed to respond fast to switching applications during a virtual conference, following social media feeds, and making milliseconds count in in-game battle. Nowhere is better for you.

Because of its strong collaboration with Arm, MediaTek can provide the latest architectural developments to foreign markets first, and the MediaTek Kompanio Ultra processor delivers the latest Armv9.2 CPU advantage.

MediaTek's latest Armv9.2 architecture provides power efficiency, security, and faster computing.

Best in Class Power Efficiency: The Kompanio Ultra combines the 2nd generation TSMC 3nm technology with large on-chip caches and MediaTek's industry-leading power management to deliver better performance per milliwatt. The spectacular experiences of top Chromebooks are enhanced.

Best Lightweight and Thin Designs: MediaTek's brand partners can easily construct lightweight, thin, fanless, silent, and cool designs.

Leading NPU Performance: MediaTek's 8th-generation NPU gives the Kompanio Ultra an edge in industry-standard AI and generative AI benchmarks.

Prepared for AI agents

Superior on-device photo and video production

Maximum 50 TOPS AI results

ETHZ v6 leadership, Gen-AI models

CPU/GPU tasks are offloaded via NPU, speeding processing and saving energy.

Next-gen Generative AI technologies: MediaTek's investments in AI technologies and ecosystems ensure that Chromebooks running the MediaTek Kompanio Ultra provide the latest apps, services, and experiences.

Extended content support

Better LLM speculative speed help

Complete SLM+LLM AI model support

Assistance in several modes

11-core graphics processing unit: Arm's 5th-generation G925 GPU, used by the powerful 11-core graphics engine, improves traditional and raytraced graphics performance while using less power, producing better visual effects, and maintaining peak gameplay speeds longer.

The G925 GPU matches desktop PC-grade raytracing with increased opacity micromaps (OMM) to increase scene depths with subtle layering effects.

OMM-supported games' benefits:

Reduced geometry rendering

Visual enhancements without increasing model complexity

Natural-looking feathers, hair, and plants

4K Displays & Dedicated Audio: Multiple displays focus attention and streamline procedures, increasing efficiency. Task-specific displays simplify multitasking and reduce clutter. With support for up to three 4K monitors (internal and external), professionals have huge screen space for difficult tasks, while gamers and content makers have extra windows for chat, streaming, and real-time interactions.

DP MST supports two 4K external screens.

Custom processing optimises power use and improves audio quality. Low-power standby detects wake-up keywords, improving voice assistant response. This performance-energy efficiency balance improves smart device battery life, audio quality, and functionality.

Hi-Fi Audio DSP for low-power standby and sound effects

Support for up to Wi-Fi 7 and Bluetooth 6.0 provides extreme wireless speeds and signal range for the most efficient anyplace computing.

Wi-Fi 7 can reach 7.3Gbps.

Two-engine Bluetooth 6.0

#technology #technews #govindhtech #news #technologynews #processors #MediaTek Kompanio Ultra #Agentic AI #Chromebooks #MediaTek #MediaTek Kompanio #Kompanio Ultra #MediaTek Kompanio Ultra 910

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kommunotechnologies · 10 hours ago

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Best Auto Dialer Software in India: A Complete Buyer’s Guide

In today’s fast-paced, customer-centric market, outbound communication plays a vital role in business growth. Whether you're running a call center, sales team, or customer support division, having the best auto dialer software can drastically improve your outreach efficiency. But with numerous solutions available in India, selecting the right one can be overwhelming. This guide will help you navigate your options, understand key features, and choose a solution that fits your business needs.

What is Auto Dialer Software?

Auto dialer software is a tool that automates the process of dialing phone numbers, saving agents time and effort. It eliminates the manual work of calling each lead individually and connects only answered calls to available agents. As a result, businesses can increase call volume and reduce idle time significantly. For any business that relies heavily on outbound calls, such automation is no longer a luxury—it's a necessity.

Why You Need the Best Auto Dialer Software in India

India’s growing digital economy has made it essential for businesses to stay competitive. Using the best auto dialer software allows companies to increase productivity, improve customer engagement, and ensure timely follow-ups. From lead generation to debt collection, auto dialers are making outbound communication faster and smarter.

The ideal software should offer features such as predictive dialing, call recording, CRM integration, real-time analytics, and seamless cloud deployment. Additionally, local support and data security should be top priorities for Indian businesses.

Understanding the Autodialer System

An autodialer system is not just a dialing engine; it’s an intelligent communication platform. These systems can be configured in different modes—preview, progressive, or predictive—depending on your campaign type. They work efficiently across industries like real estate, finance, healthcare, and BPOs.

A robust autodialer system improves call connection rates, reduces agent fatigue, and ensures higher ROI. Some advanced systems also include speech analytics and AI-based call routing for smarter engagement.

Features to Look for in an Auto Dialer Solution

Before investing in software, it’s important to evaluate its features. Here are a few essential ones to consider:

Predictive Dialing: Automatically adjusts call rate based on agent availability.

CRM Integration: Sync contacts and call data directly with your CRM.

Call Recording: Helps in quality monitoring and compliance.

Analytics Dashboard: Real-time performance metrics to track campaigns.

Cloud-Based Access: Enables remote working and scalability.

Also, choose a solution that provides strong customer support and regular updates.

Why Choose Kommuno for Your Business

If you're looking for a reliable provider in India, Kommuno stands out as a trusted name in cloud-based communication solutions. Kommuno offers a powerful and flexible auto dialer platform designed for Indian businesses of all sizes. With features like intelligent routing, customizable call flows, and easy integration with CRMs, it’s tailored to deliver performance and compliance in one package.

What makes Kommuno different is its focus on local market needs, user-friendly dashboard, and dedicated support team. Whether you're a startup or an enterprise, Kommuno's solutions help you reach more customers in less time—without compromising on quality.

Conclusion

Finding the best auto dialer software in India depends on understanding your business requirements and matching them with the right features. Whether you need an intelligent autodialer system for high-volume campaigns or a lightweight tool for small teams, the key is to choose a solution that ensures scalability, compliance, and ease of use.

Kommuno offers all that and more, making it a leading choice for businesses ready to scale their outreach in today’s competitive environment.

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airnetmarketing · 15 hours ago

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Analyzing the Structural Framework of AI Agents and Their Functions

In recent years, artificial intelligence (AI) agents have become increasingly integral to various sectors, driving advancements in automation, decision-making, and user interaction. These AI entities possess structural frameworks that dictate their operational efficiencies and functional capabilities. Understanding the architecture underpinning AI agents is crucial for researchers and practitioners aiming to harness their full potential. This article dissects the core architecture of AI agent frameworks and evaluates their functional capabilities, providing insights into the design philosophies that govern their efficacy.

Dissecting the Core Architecture of AI Agent Frameworks

The architecture of AI agents can be broadly categorized into three primary components: perception, reasoning, and action. The perception component encompasses the sensory inputs (e.g., data from cameras, microphones, or other sensors) that allow the agent to collect information from its environment. This sensory data is critical as it forms the basis for how the agent interprets the world around it. A well-designed perception module enhances an AI agent's situational awareness, enabling it to respond effectively to dynamic conditions. The reasoning component is where the AI agent processes the information gathered through perception. This is often achieved through algorithms that incorporate various techniques, including machine learning, rule-based systems, and probabilistic reasoning. The reasoning engine assesses available data, weighs potential actions, and formulates a course of action based on predefined objectives. This layer is pivotal, as it determines the agent's ability to make informed decisions, adapt to new scenarios, and learn from past experiences, which are essential traits for high-functioning AI systems. Lastly, the action component translates the decisions made by the reasoning engine into physical or virtual actions. This can involve generating spoken responses, controlling robotic limbs, or triggering software functions. The design of this component is crucial as it ensures the AI agent can execute tasks efficiently and accurately. The seamless integration of these three components—perception, reasoning, and action—forms the backbone of AI agent architectures, enabling them to operate effectively in complex environments.

Evaluating Functional Capabilities in AI Agent Design

Functional capabilities in AI agents are evaluated based on their performance in specific tasks, adaptability, and user interaction. One of the most important aspects of an AI agent's functionality is its ability to handle a variety of tasks autonomously. This includes processing information, learning from interactions, and executing commands with minimal human intervention. The effectiveness of an AI agent in completing tasks is often measured by its accuracy and speed, which directly correlate with its underlying algorithms and processing capabilities. Adaptability is another critical functional capability, as it allows AI agents to evolve in response to changing environments and user needs. An adaptable AI agent can learn new patterns and improve its performance over time through techniques such as reinforcement learning. This capacity for growth is essential, particularly in fields like healthcare and finance, where data and requirements can shift rapidly. Evaluating an AI agent's adaptability involves assessing how quickly and effectively it can adjust its strategies based on real-time feedback and new information. User interaction is also a fundamental aspect of functional capabilities. AI agents are often designed to interact with humans, and the quality of these interactions can significantly influence their usability and acceptance. This aspect encompasses natural language processing, emotional recognition, and user-friendly interfaces. Effective user interaction can enhance the overall experience, allowing AI agents to better understand user preferences and respond appropriately. Evaluating this facet involves analyzing user feedback, engagement metrics, and overall satisfaction with the AI agent's capabilities. The structural framework of AI agents is a multifaceted blend of perception, reasoning, and action, each contributing to the agent's overall functionality. By dissecting these components, we can better understand how AI agents operate and their potential applications across industries. Evaluating their functional capabilities, including task performance, adaptability, and user interaction, reveals the strengths and limitations inherent in current designs. As technology continues to evolve, ongoing analysis and refinement of AI agent frameworks will be crucial in unlocking their full potential and ensuring they meet the demands of an increasingly complex world. Read the full article

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