#Quantum Computing Applications in Business

[2ribu]

Evolusi Framework AI: Alat Terbaru untuk Pengembangan Model AI di 2025

Kecerdasan buatan (AI) telah menjadi salah satu bidang yang paling berkembang pesat dalam beberapa tahun terakhir. Pada tahun 2025, teknologi AI diperkirakan akan semakin maju, terutama dengan adanya berbagai alat dan framework baru yang memungkinkan pengembang untuk menciptakan model AI yang lebih canggih dan efisien. Framework AI adalah sekumpulan pustaka perangkat lunak dan alat yang digunakan…

[gracetheacefromouterspace]

I have too many hobbies, any body want one?

[techtoio]

How to Create Stunning Graphics with Adobe Photoshop

Introduction

Adobe Photoshop is the preferred software for graphic designers, photographers, and digital artists worldwide. Its powerful tools and versatile features lead to the foundation of an essential application that one needs to create the best kind of graphics. Mastering Photoshop can improve your creative-level projects, whether you are a beginner or an experienced user. In this tutorial, we will walk you through the basics and advanced techniques so you can create stunning graphics with the help of Adobe Photoshop. Read to continue

[mostlysignssomeportents]

Humans are not perfectly vigilant

I'm on tour with my new, nationally bestselling novel The Bezzle! Catch me in BOSTON with Randall "XKCD" Munroe (Apr 11), then PROVIDENCE (Apr 12), and beyond!

Here's a fun AI story: a security researcher noticed that large companies' AI-authored source-code repeatedly referenced a nonexistent library (an AI "hallucination"), so he created a (defanged) malicious library with that name and uploaded it, and thousands of developers automatically downloaded and incorporated it as they compiled the code:

https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/

These "hallucinations" are a stubbornly persistent feature of large language models, because these models only give the illusion of understanding; in reality, they are just sophisticated forms of autocomplete, drawing on huge databases to make shrewd (but reliably fallible) guesses about which word comes next:

https://dl.acm.org/doi/10.1145/3442188.3445922

Guessing the next word without understanding the meaning of the resulting sentence makes unsupervised LLMs unsuitable for high-stakes tasks. The whole AI bubble is based on convincing investors that one or more of the following is true:

There are low-stakes, high-value tasks that will recoup the massive costs of AI training and operation;

There are high-stakes, high-value tasks that can be made cheaper by adding an AI to a human operator;

Adding more training data to an AI will make it stop hallucinating, so that it can take over high-stakes, high-value tasks without a "human in the loop."

These are dubious propositions. There's a universe of low-stakes, low-value tasks – political disinformation, spam, fraud, academic cheating, nonconsensual porn, dialog for video-game NPCs – but none of them seem likely to generate enough revenue for AI companies to justify the billions spent on models, nor the trillions in valuation attributed to AI companies:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

The proposition that increasing training data will decrease hallucinations is hotly contested among AI practitioners. I confess that I don't know enough about AI to evaluate opposing sides' claims, but even if you stipulate that adding lots of human-generated training data will make the software a better guesser, there's a serious problem. All those low-value, low-stakes applications are flooding the internet with botshit. After all, the one thing AI is unarguably very good at is producing bullshit at scale. As the web becomes an anaerobic lagoon for botshit, the quantum of human-generated "content" in any internet core sample is dwindling to homeopathic levels:

https://pluralistic.net/2024/03/14/inhuman-centipede/#enshittibottification

This means that adding another order of magnitude more training data to AI won't just add massive computational expense – the data will be many orders of magnitude more expensive to acquire, even without factoring in the additional liability arising from new legal theories about scraping:

https://pluralistic.net/2023/09/17/how-to-think-about-scraping/

That leaves us with "humans in the loop" – the idea that an AI's business model is selling software to businesses that will pair it with human operators who will closely scrutinize the code's guesses. There's a version of this that sounds plausible – the one in which the human operator is in charge, and the AI acts as an eternally vigilant "sanity check" on the human's activities.

For example, my car has a system that notices when I activate my blinker while there's another car in my blind-spot. I'm pretty consistent about checking my blind spot, but I'm also a fallible human and there've been a couple times where the alert saved me from making a potentially dangerous maneuver. As disciplined as I am, I'm also sometimes forgetful about turning off lights, or waking up in time for work, or remembering someone's phone number (or birthday). I like having an automated system that does the robotically perfect trick of never forgetting something important.

There's a name for this in automation circles: a "centaur." I'm the human head, and I've fused with a powerful robot body that supports me, doing things that humans are innately bad at.

That's the good kind of automation, and we all benefit from it. But it only takes a small twist to turn this good automation into a nightmare. I'm speaking here of the reverse-centaur: automation in which the computer is in charge, bossing a human around so it can get its job done. Think of Amazon warehouse workers, who wear haptic bracelets and are continuously observed by AI cameras as autonomous shelves shuttle in front of them and demand that they pick and pack items at a pace that destroys their bodies and drives them mad:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

Automation centaurs are great: they relieve humans of drudgework and let them focus on the creative and satisfying parts of their jobs. That's how AI-assisted coding is pitched: rather than looking up tricky syntax and other tedious programming tasks, an AI "co-pilot" is billed as freeing up its human "pilot" to focus on the creative puzzle-solving that makes coding so satisfying.

But an hallucinating AI is a terrible co-pilot. It's just good enough to get the job done much of the time, but it also sneakily inserts booby-traps that are statistically guaranteed to look as plausible as the good code (that's what a next-word-guessing program does: guesses the statistically most likely word).

This turns AI-"assisted" coders into reverse centaurs. The AI can churn out code at superhuman speed, and you, the human in the loop, must maintain perfect vigilance and attention as you review that code, spotting the cleverly disguised hooks for malicious code that the AI can't be prevented from inserting into its code. As "Lena" writes, "code review [is] difficult relative to writing new code":

https://twitter.com/qntm/status/1773779967521780169

Why is that? "Passively reading someone else's code just doesn't engage my brain in the same way. It's harder to do properly":

https://twitter.com/qntm/status/1773780355708764665

There's a name for this phenomenon: "automation blindness." Humans are just not equipped for eternal vigilance. We get good at spotting patterns that occur frequently – so good that we miss the anomalies. That's why TSA agents are so good at spotting harmless shampoo bottles on X-rays, even as they miss nearly every gun and bomb that a red team smuggles through their checkpoints:

https://pluralistic.net/2023/08/23/automation-blindness/#humans-in-the-loop

"Lena"'s thread points out that this is as true for AI-assisted driving as it is for AI-assisted coding: "self-driving cars replace the experience of driving with the experience of being a driving instructor":

https://twitter.com/qntm/status/1773841546753831283

In other words, they turn you into a reverse-centaur. Whereas my blind-spot double-checking robot allows me to make maneuvers at human speed and points out the things I've missed, a "supervised" self-driving car makes maneuvers at a computer's frantic pace, and demands that its human supervisor tirelessly and perfectly assesses each of those maneuvers. No wonder Cruise's murderous "self-driving" taxis replaced each low-waged driver with 1.5 high-waged technical robot supervisors:

https://pluralistic.net/2024/01/11/robots-stole-my-jerb/#computer-says-no

AI radiology programs are said to be able to spot cancerous masses that human radiologists miss. A centaur-based AI-assisted radiology program would keep the same number of radiologists in the field, but they would get less done: every time they assessed an X-ray, the AI would give them a second opinion. If the human and the AI disagreed, the human would go back and re-assess the X-ray. We'd get better radiology, at a higher price (the price of the AI software, plus the additional hours the radiologist would work).

But back to making the AI bubble pay off: for AI to pay off, the human in the loop has to reduce the costs of the business buying an AI. No one who invests in an AI company believes that their returns will come from business customers to agree to increase their costs. The AI can't do your job, but the AI salesman can convince your boss to fire you and replace you with an AI anyway – that pitch is the most successful form of AI disinformation in the world.

An AI that "hallucinates" bad advice to fliers can't replace human customer service reps, but airlines are firing reps and replacing them with chatbots:

https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know

An AI that "hallucinates" bad legal advice to New Yorkers can't replace city services, but Mayor Adams still tells New Yorkers to get their legal advice from his chatbots:

https://arstechnica.com/ai/2024/03/nycs-government-chatbot-is-lying-about-city-laws-and-regulations/

The only reason bosses want to buy robots is to fire humans and lower their costs. That's why "AI art" is such a pisser. There are plenty of harmless ways to automate art production with software – everything from a "healing brush" in Photoshop to deepfake tools that let a video-editor alter the eye-lines of all the extras in a scene to shift the focus. A graphic novelist who models a room in The Sims and then moves the camera around to get traceable geometry for different angles is a centaur – they are genuinely offloading some finicky drudgework onto a robot that is perfectly attentive and vigilant.

But the pitch from "AI art" companies is "fire your graphic artists and replace them with botshit." They're pitching a world where the robots get to do all the creative stuff (badly) and humans have to work at robotic pace, with robotic vigilance, in order to catch the mistakes that the robots make at superhuman speed.

Reverse centaurism is brutal. That's not news: Charlie Chaplin documented the problems of reverse centaurs nearly 100 years ago:

https://en.wikipedia.org/wiki/Modern_Times_(film)

As ever, the problem with a gadget isn't what it does: it's who it does it for and who it does it to. There are plenty of benefits from being a centaur – lots of ways that automation can help workers. But the only path to AI profitability lies in reverse centaurs, automation that turns the human in the loop into the crumple-zone for a robot:

https://estsjournal.org/index.php/ests/article/view/260

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/04/01/human-in-the-loop/#monkey-in-the-middle

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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Jorge Royan (modified) https://commons.wikimedia.org/wiki/File:Munich_-_Two_boys_playing_in_a_park_-_7328.jpg

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

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Noah Wulf (modified) https://commons.m.wikimedia.org/wiki/File:Thunderbirds_at_Attention_Next_to_Thunderbird_1_-_Aviation_Nation_2019.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

[psychicxenomorphscourge]

Unlocking the Future: How Intel is Shaping Tomorrow's Technology Landscape

Introduction

In a world that is increasingly defined by technological advancements, few companies have had as profound an impact as Intel. Founded in 1968, Intel Corporation has been at the forefront of semiconductor innovation, shaping not just computing but various facets of modern life. From personal computers to cloud computing and artificial intelligence, Intel’s influence permeates every layer of technology today. The question is—how does Intel continue to unlock the future? In this article, we will explore how Intel is shaping tomorrow's technology landscape through innovation, research, sustainability efforts, and strategic partnerships.

Unlocking the Future: How Intel is Shaping Tomorrow's Technology Landscape

At its core, unlocking the future involves leveraging cutting-edge technologies to solve current challenges while also anticipating future demands. For Intel, this means investing heavily in research and development (R&D) to remain competitive in the rapidly evolving tech arena. With products that range from microprocessors to advanced AI systems, Intel stands as a pillar of innovation.

The Evolution of Semiconductor Technology A Brief History of Semiconductor Development

To truly grasp how Intel shapes technology today, it's important to understand the evolution of semiconductors. Initially Learn more here developed in the 1950s and '60s, semiconductors revolutionized electronics by allowing devices to become smaller and more efficient. Intel’s introduction of the first microprocessor in 1971 marked a significant turning point in computing history.

Current Trends in Semiconductor Technology

Today, semiconductor technology continues to evolve at an astonishing pace. Innovations such as 3D chip designs and quantum computing are on the horizon. Companies like Intel are not just keeping up—they are leading these trends through relentless R&D.

Intel's Role in Artificial Intelligence Pioneering AI Technologies

Artificial intelligence represents one of the most promising frontiers for technological advancement today. Intel has made significant strides in developing AI technologies that enhance machine learning capabilities across various sectors—from healthcare to finance.

Real-World Applications of AI Solutions

AI solutions offered by Intel can be seen in applications ranging from predictive analytics in healthcare to autonomous vehicles. These advancements not only improve efficiency but also pave the way for new business models.

Cloud Computing: The New Frontier Intel's Cloud Strategy

As businesses migrate to cloud-based solutions, Intel plays a crucial role by providing powerful processors designed specifically for cloud environments. Their Xeon processors enable data centers to run efficiently and scale dramatically.

Benefits for Businesses Adopting Cloud Solutions

Companies adopting cloud solutions with Intel technologies benefit from improved security features and reduced operational costs. This shift allows businesses to focus on innovation rather than infrastructure management.

Sustainability Initiatives at Intel Commitment to Green Technology

[sorelynobleprophet]

Unlocking the Future: How Intel is Shaping Tomorrow's Technology Landscape

Introduction

In a world that is increasingly defined by technological advancements, few companies have had as profound an impact as Intel. Founded in 1968, Intel Corporation has been at the forefront of semiconductor innovation, shaping not just computing but various facets of modern life. From personal computers to cloud computing and artificial intelligence, Intel’s influence permeates every layer of technology today. The question is—how does Intel continue to unlock the future? In this article, we will explore how Intel is shaping tomorrow's technology landscape through innovation, research, sustainability efforts, and strategic partnerships.

Unlocking the Future: How Intel is Shaping Tomorrow's Technology Landscape

At its core, unlocking the future involves leveraging cutting-edge technologies to solve current challenges while also anticipating future demands. For Intel, this means investing heavily in research and development (R&D) to remain competitive in the rapidly evolving tech arena. With products that range from microprocessors to advanced AI systems, Intel stands as a pillar of innovation.

The Evolution of Semiconductor Technology A Brief History of Semiconductor Development

To truly grasp how Intel shapes technology today, it's important to understand the evolution of semiconductors. Initially developed in the 1950s and '60s, semiconductors revolutionized electronics by allowing devices to become smaller Hop over to this website and more efficient. Intel’s introduction of the first microprocessor in 1971 marked a significant turning point in computing history.

Current Trends in Semiconductor Technology

Today, semiconductor technology continues to evolve at an astonishing pace. Innovations such as 3D chip designs and quantum computing are on the horizon. Companies like Intel are not just keeping up—they are leading these trends through relentless R&D.

Intel's Role in Artificial Intelligence Pioneering AI Technologies

Artificial intelligence represents one of the most promising frontiers for technological advancement today. Intel has made significant strides in developing AI technologies that enhance machine learning capabilities across various sectors—from healthcare to finance.

Real-World Applications of AI Solutions

AI solutions offered by Intel can be seen in applications ranging from predictive analytics in healthcare to autonomous vehicles. These advancements not only improve efficiency but also pave the way for new business models.

Cloud Computing: The New Frontier Intel's Cloud Strategy

As businesses migrate to cloud-based solutions, Intel plays a crucial role by providing powerful processors designed specifically for cloud environments. Their Xeon processors enable data centers to run efficiently and scale dramatically.

Benefits for Businesses Adopting Cloud Solutions

Companies adopting cloud solutions with Intel technologies benefit from improved security features and reduced operational costs. This shift allows businesses to focus on innovation rather than infrastructure management.

Sustainability Initiatives at Intel Commitment to Green Technology

[thailandlaww]

1. Structural Foundations of the SMART Visa Program

1.1 Legislative Architecture

The SMART Visa operates under:

Royal Decree on SMART Visa B.E. 2561 (2018)

Thailand 4.0 Economic Policy Framework

BOI Investment Promotion Act (No. 4) B.E. 2560

1.2 Interagency Governance

Primary Authority: Board of Investment (BOI)

Implementation Partners:

Immigration Bureau (visa issuance)

Ministry of Digital Economy and Society (tech qualifications)

Ministry of Higher Education (academic validation)

2. Category-Specific Qualification Matrix

2.1 SMART-T (Specialists)

Technical Thresholds:

Salary Floor: THB 200,000/month (USD 5,800)

Experience Validation:

5+ years in qualifying field

Patent holders given priority

PhD waivers for certain disciplines

Industry Prioritization:

Biotechnology (Gene Therapy, Precision Medicine)

Advanced Manufacturing (Robotics, 3D Printing)

Digital Infrastructure (AI, Quantum Computing)

2.2 SMART-I (Investors)

Due Diligence Process:

Phase 1: BOI business plan review (45 days)

Phase 2: Anti-money laundering clearance

Phase 3: Investment tracing audit

2.3 SMART-E (Entrepreneurs)

Startup Validation Framework:

Tier 1 Incubators: DEPA, Thai Venture Capital Association

Minimum Traction Metrics:

THB 10M ARR or

50,000 MAU or

Series A funding

Capital Requirements:

Bootstrapped: THB 600,000 liquid

Funded: Minimum THB 5M valuation

3. Advanced Application Mechanics

3.1 Document Authentication Protocol

Educational Credentials:

WES or IQAS evaluation for non-Thai degrees

Notarized Thai translations

Employment History:

Social security cross-verification

Reference checks with former employers

3.2 Technical Review Process

Stage 1: Automated system screening

Stage 2: BOO specialist committee review

Stage 3: Final approval by Deputy Secretary-General

4. Privilege Structure and Limitations

4.1 Work Authorization Scope

Permitted Activities:

Primary employment with sponsor

Consulting (max 20% time allocation)

Academic collaboration

Prohibited Activities:

Local employment outside specialty

Unapproved commercial research

Political activities

4.2 Dependent Provisions

Spousal Work Rights:

General employment permitted

No industry restrictions

Child Education:

International school subsidies

University admission preferences

4.3 Mobility Advantages

Fast-Track Immigration:

Dedicated SMART lanes at 6 major airports

15-minute clearance guarantee

Re-entry Flexibility:

Unlimited exits without visa voidance

Automatic 48-hour grace period

5. Compliance and Renewal Dynamics

5.1 Continuous Eligibility Monitoring

Quarterly Reporting:

Employment verification

Investment maintenance

Research output (for academics)

Annual Review:

Salary benchmark adjustment

Contribution assessment

5.2 Renewal Process

Documentation Refresh: Updated financials, health insurance

Performance Evaluation: Economic impact assessment

Fee Structure: THB 10,000 renewal fee + THB 1,900 visa stamp

5.3 Grounds for Revocation

Material Changes: Employment termination, investment withdrawal

Compliance Failures: Missed reporting, legal violations

National Security Concerns: Classified determinations

6. Comparative Analysis with Global Competitors

6.1 Strategic Advantages

Tax Optimization: 17% flat rate option

Research Incentives: BOO matching grants

Commercialization Support: THBI co-investment

7. Emerging Policy Developments

7.1 2024 Program Enhancements

Blockchain Specialist Category (Q3 rollout)

Climate Tech Fast-Track (Carbon credit linkage)

Regional Expansion: Eastern Economic Corridor focus

7.2 Pending Legislative Changes

Dual Intent Provision: PR application without visa surrender

Skills Transfer Mandate: Local training requirements

Global Talent Pool: Reciprocal agreements in negotiation

8. Practical Application Strategies

8.1 Pre-Application Optimization

Salary Structuring: Base vs variable compensation

Patent Portfolio Development: Thai IP registration

Local Network Building: Thai professional associations

8.2 Post-Approval Planning

Tax Residence Strategy: 180-day calculations

Asset Protection: Thai holding company formation

Succession Planning: Will registration requirements

9. Critical Risk Factors

9.1 Common Rejection Reasons

Document Discrepancies: Date inconsistencies

Qualification Gaps: Unrecognized certifications

Financial Irregularities: Unverified income streams

9.2 Operational Challenges

Banking Restrictions: Foreign account limitations

Healthcare Access: Specialty treatment approvals

Cultural Integration: Workplace adaptation

10. Conclusion: Strategic Implementation Framework

For optimal SMART Visa utilization:

Pre-qualification Audit: 90-day preparation period

BOI Engagement: Pre-submission consultation

Compliance Infrastructure: Digital reporting systems

Contingency Planning: Alternative category eligibility

[xublimetech]

The Future of AI: What’s Next in Machine Learning and Deep Learning?

Artificial Intelligence (AI) has rapidly evolved over the past decade, transforming industries and redefining the way businesses operate. With machine learning and deep learning at the core of AI advancements, the future holds groundbreaking innovations that will further revolutionize technology. As machine learning and deep learning continue to advance, they will unlock new opportunities across various industries, from healthcare and finance to cybersecurity and automation. In this blog, we explore the upcoming trends and what lies ahead in the world of machine learning and deep learning.

1. Advancements in Explainable AI (XAI)

As AI models become more complex, understanding their decision-making process remains a challenge. Explainable AI (XAI) aims to make machine learning and deep learning models more transparent and interpretable. Businesses and regulators are pushing for AI systems that provide clear justifications for their outputs, ensuring ethical AI adoption across industries. The growing demand for fairness and accountability in AI-driven decisions is accelerating research into interpretable AI, helping users trust and effectively utilize AI-powered tools.

2. AI-Powered Automation in IT and Business Processes

AI-driven automation is set to revolutionize business operations by minimizing human intervention. Machine learning and deep learning algorithms can predict and automate tasks in various sectors, from IT infrastructure management to customer service and finance. This shift will increase efficiency, reduce costs, and improve decision-making. Businesses that adopt AI-powered automation will gain a competitive advantage by streamlining workflows and enhancing productivity through machine learning and deep learning capabilities.

3. Neural Network Enhancements and Next-Gen Deep Learning Models

Deep learning models are becoming more sophisticated, with innovations like transformer models (e.g., GPT-4, BERT) pushing the boundaries of natural language processing (NLP). The next wave of machine learning and deep learning will focus on improving efficiency, reducing computation costs, and enhancing real-time AI applications. Advancements in neural networks will also lead to better image and speech recognition systems, making AI more accessible and functional in everyday life.

4. AI in Edge Computing for Faster and Smarter Processing

With the rise of IoT and real-time processing needs, AI is shifting toward edge computing. This allows machine learning and deep learning models to process data locally, reducing latency and dependency on cloud services. Industries like healthcare, autonomous vehicles, and smart cities will greatly benefit from edge AI integration. The fusion of edge computing with machine learning and deep learning will enable faster decision-making and improved efficiency in critical applications like medical diagnostics and predictive maintenance.

5. Ethical AI and Bias Mitigation

AI systems are prone to biases due to data limitations and model training inefficiencies. The future of machine learning and deep learning will prioritize ethical AI frameworks to mitigate bias and ensure fairness. Companies and researchers are working towards AI models that are more inclusive and free from discriminatory outputs. Ethical AI development will involve strategies like diverse dataset curation, bias auditing, and transparent AI decision-making processes to build trust in AI-powered systems.

6. Quantum AI: The Next Frontier

Quantum computing is set to revolutionize AI by enabling faster and more powerful computations. Quantum AI will significantly accelerate machine learning and deep learning processes, optimizing complex problem-solving and large-scale simulations beyond the capabilities of classical computing. As quantum AI continues to evolve, it will open new doors for solving problems that were previously considered unsolvable due to computational constraints.

7. AI-Generated Content and Creative Applications

From AI-generated art and music to automated content creation, AI is making strides in the creative industry. Generative AI models like DALL-E and ChatGPT are paving the way for more sophisticated and human-like AI creativity. The future of machine learning and deep learning will push the boundaries of AI-driven content creation, enabling businesses to leverage AI for personalized marketing, video editing, and even storytelling.

8. AI in Cybersecurity: Real-Time Threat Detection

As cyber threats evolve, AI-powered cybersecurity solutions are becoming essential. Machine learning and deep learning models can analyze and predict security vulnerabilities, detecting threats in real time. The future of AI in cybersecurity lies in its ability to autonomously defend against sophisticated cyberattacks. AI-powered security systems will continuously learn from emerging threats, adapting and strengthening defense mechanisms to ensure data privacy and protection.

9. The Role of AI in Personalized Healthcare

One of the most impactful applications of machine learning and deep learning is in healthcare. AI-driven diagnostics, predictive analytics, and drug discovery are transforming patient care. AI models can analyze medical images, detect anomalies, and provide early disease detection, improving treatment outcomes. The integration of machine learning and deep learning in healthcare will enable personalized treatment plans and faster drug development, ultimately saving lives.

10. AI and the Future of Autonomous Systems

From self-driving cars to intelligent robotics, machine learning and deep learning are at the forefront of autonomous technology. The evolution of AI-powered autonomous systems will improve safety, efficiency, and decision-making capabilities. As AI continues to advance, we can expect self-learning robots, smarter logistics systems, and fully automated industrial processes that enhance productivity across various domains.

Conclusion

The future of AI, machine learning and deep learning is brimming with possibilities. From enhancing automation to enabling ethical and explainable AI, the next phase of AI development will drive unprecedented innovation. Businesses and tech leaders must stay ahead of these trends to leverage AI's full potential. With continued advancements in machine learning and deep learning, AI will become more intelligent, efficient, and accessible, shaping the digital world like never before.

Are you ready for the AI-driven future? Stay updated with the latest AI trends and explore how these advancements can shape your business!

[digitaldetoxworld]

Top 10 Emerging Tech Trends to Watch in 2025

 Technology is evolving at an unprecedented tempo, shaping industries, economies, and day by day lifestyles. As we method 2025, several contemporary technology are set to redefine how we engage with the sector. From synthetic intelligence to quantum computing, here are the important thing emerging tech developments to look at in 2025.

Top 10 Emerging Tech Trends In 2025

1. Artificial Intelligence (AI) Evolution

AI remains a dominant force in technological advancement. By 2025, we will see AI turning into greater sophisticated and deeply incorporated into corporations and personal programs. Key tendencies include:

Generative AI: AI fashions like ChatGPT and DALL·E will strengthen similarly, generating more human-like textual content, images, and even films.

AI-Powered Automation: Companies will more and more depend upon AI-pushed automation for customer support, content material advent, and even software development.

Explainable AI (XAI): Transparency in AI decision-making becomes a priority, ensuring AI is greater trustworthy and comprehensible.

AI in Healthcare: From diagnosing sicknesses to robot surgeries, AI will revolutionize healthcare, reducing errors and improving affected person results.

2. Quantum Computing Breakthroughs

Quantum computing is transitioning from theoretical studies to real-global packages. In 2025, we will expect:

More powerful quantum processors: Companies like Google, IBM, and startups like IonQ are making full-size strides in quantum hardware.

Quantum AI: Combining quantum computing with AI will enhance machine studying fashions, making them exponentially quicker.

Commercial Quantum Applications: Industries like logistics, prescribed drugs, and cryptography will begin leveraging quantum computing for fixing complex troubles that traditional computer systems can not manage successfully.

3. The Rise of Web3 and Decentralization

The evolution of the net continues with Web3, emphasizing decentralization, blockchain, and user possession. Key factors consist of:

Decentralized Finance (DeFi): More economic services will shift to decentralized platforms, putting off intermediaries.

Non-Fungible Tokens (NFTs) Beyond Art: NFTs will find utility in actual estate, gaming, and highbrow belongings.

Decentralized Autonomous Organizations (DAOs): These blockchain-powered organizations will revolutionize governance systems, making choice-making more obvious and democratic.

Metaverse Integration: Web3 will further integrate with the metaverse, allowing secure and decentralized digital environments.

4. Extended Reality (XR) and the Metaverse

Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) will retain to improve, making the metaverse extra immersive. Key tendencies consist of:

Lighter, More Affordable AR/VR Devices: Companies like Apple, Meta, and Microsoft are working on more accessible and cushty wearable generation.

Enterprise Use Cases: Businesses will use AR/VR for far flung paintings, education, and collaboration, lowering the want for physical office spaces.

Metaverse Economy Growth: Digital belongings, digital real estate, and immersive studies will gain traction, driven via blockchain technology.

AI-Generated Virtual Worlds: AI will play a role in developing dynamic, interactive, and ever-evolving virtual landscapes.

5. Sustainable and Green Technology

With growing concerns over weather alternate, generation will play a vital function in sustainability. Some key innovations include:

Carbon Capture and Storage (CCS): New techniques will emerge to seize and keep carbon emissions efficaciously.

Smart Grids and Renewable Energy Integration: AI-powered clever grids will optimize power distribution and consumption.

Electric Vehicle (EV) Advancements: Battery generation upgrades will cause longer-lasting, faster-charging EVs.

Biodegradable Electronics: The upward thrust of green digital additives will assist lessen e-waste.

6. Biotechnology and Personalized Medicine

Healthcare is present process a metamorphosis with biotech improvements. By 2025, we expect:

Gene Editing and CRISPR Advances: Breakthroughs in gene modifying will enable treatments for genetic disorders.

Personalized Medicine: AI and big statistics will tailor remedies based on man or woman genetic profiles.

Lab-Grown Organs and Tissues: Scientists will make in addition progress in 3D-published organs and tissue engineering.

Wearable Health Monitors: More superior wearables will music fitness metrics in actual-time, presenting early warnings for illnesses.

7. Edge Computing and 5G Expansion

The developing call for for real-time statistics processing will push aspect computing to the vanguard. In 2025, we will see:

Faster 5G Networks: Global 5G insurance will increase, enabling excessive-velocity, low-latency verbal exchange.

Edge AI Processing: AI algorithms will system information in the direction of the source, reducing the want for centralized cloud computing.

Industrial IoT (IIoT) Growth: Factories, deliver chains, and logistics will advantage from real-time facts analytics and automation.

Eight. Cybersecurity and Privacy Enhancements

With the upward thrust of AI, quantum computing, and Web3, cybersecurity will become even more essential. Expect:

AI-Driven Cybersecurity: AI will come across and prevent cyber threats extra effectively than traditional methods.

Zero Trust Security Models: Organizations will undertake stricter get right of entry to controls, assuming no entity is inherently sincere.

Quantum-Resistant Cryptography: As quantum computer systems turn out to be greater effective, encryption techniques will evolve to counter potential threats.

Biometric Authentication: More structures will rely on facial reputation, retina scans, and behavioral biometrics.

9. Robotics and Automation

Automation will hold to disrupt numerous industries. By 2025, key trends encompass:

Humanoid Robots: Companies like Tesla and Boston Dynamics are growing robots for commercial and family use.

AI-Powered Supply Chains: Robotics will streamline logistics and warehouse operations.

Autonomous Vehicles: Self-using automobiles, trucks, and drones will become greater not unusual in transportation and shipping offerings.

10. Space Exploration and Commercialization

Space era is advancing swiftly, with governments and private groups pushing the boundaries. Trends in 2025 include:

Lunar and Mars Missions: NASA, SpaceX, and other groups will development of their missions to establish lunar bases.

Space Tourism: Companies like Blue Origin and Virgin Galactic will make industrial area travel more reachable.

Asteroid Mining: Early-level research and experiments in asteroid mining will start, aiming to extract rare materials from area.

[tagbintech]

Best AI Company in Gurugram: Leading AI Innovations in 2025

Introduction

Gurugram, India's emerging tech powerhouse, is witnessing a surge in artificial intelligence (AI) innovation. As businesses across industries adopt AI-driven solutions, the city has become a hub for cutting-edge AI companies. Among them, Tagbin is recognized as a leader, driving digital transformation through AI-powered advancements. This article explores the best AI companies in Gurugram and how they are shaping the future of business in 2025.

The Rise of AI Companies in Gurugram

Gurugram's strategic location, proximity to Delhi, and thriving IT ecosystem have made it a preferred destination for AI companies. With government initiatives, private investments, and an abundance of tech talent, the city is fostering AI-driven innovations that enhance operational efficiency, automation, and customer engagement.

Key Factors Driving AI Growth in Gurugram

Government Support & Policies – AI-driven initiatives are backed by policies encouraging innovation and startups.

Access to Skilled Talent – Gurugram has top tech institutes producing AI and machine learning experts.

Booming IT & Startup Ecosystem – AI firms benefit from collaborations with IT giants and startups.

Investment in AI Research – Companies are investing in AI research to develop next-gen technologies.

Demand for AI Across Industries – Sectors like healthcare, fintech, and retail are rapidly adopting AI solutions.

Top AI Companies in Gurugram Revolutionizing Business

1. Tagbin – Leading AI Innovation in India

Tagbin is at the forefront of AI development, specializing in smart experiences, data-driven insights, and AI-powered digital transformation. The company helps businesses leverage AI for customer engagement, automation, and interactive solutions.

2. Hitech AI Solutions

A prominent AI firm in Gurugram, Hitech AI Solutions focuses on machine learning, predictive analytics, and AI-driven automation.

3. NexTech AI Labs

NexTech AI Labs pioneers AI applications in healthcare and finance, providing AI-driven chatbots, recommendation systems, and automated diagnostics.

4. AI Edge Innovations

AI Edge specializes in AI-powered cybersecurity, offering fraud detection, risk assessment, and AI-driven surveillance solutions.

5. Quantum AI Systems

This company integrates quantum computing with AI, enabling faster and more precise data processing for businesses.

AI’s Impact on Businesses in Gurugram

AI is transforming businesses across multiple sectors in Gurugram, driving efficiency and innovation.

1. AI in Customer Experience

AI-powered chatbots and virtual assistants provide 24/7 customer support.

Personalization algorithms enhance user experiences in e-commerce and retail.

2. AI in Finance & Banking

AI-driven fraud detection helps identify suspicious transactions.

Predictive analytics assist in risk assessment and investment strategies.

3. AI in Healthcare

AI algorithms assist in medical diagnosis, reducing human error.

AI-powered healthcare solutions improve patient care and operational efficiency.

4. AI in Manufacturing

Smart automation streamlines production and reduces costs.

Predictive maintenance prevents equipment failures.

Why Gurugram is the Future AI Hub of India

Gurugram is rapidly establishing itself as India’s AI innovation hub due to its thriving corporate sector, extensive tech infrastructure, and a growing network of AI startups. The city’s AI ecosystem is expected to grow exponentially in the coming years, with Tagbin and other key players leading the way.

Conclusion

The rise of AI companies in Gurugram is reshaping the way businesses operate, providing smart automation, personalized customer experiences, and cutting-edge AI solutions. With its advanced AI-driven solutions, Tagbin stands out as a key player in this transformation. As Gurugram cements itself as an AI powerhouse, businesses that adopt AI technologies will gain a competitive edge in 2025 and beyond.

[realcleverscience]

There are different definitions of "AGI" (Artificial General Intelligence). Some people focus on AI's understanding and possibly even sentience, while many focus on what it can do. Some people define it as equivalent to the abilities of the average person; others, as equivalent to the abilities of experts.

Part of the challenge is that intelligence comes in many forms. For instance, the ability to grasp objects is a form of intelligence though it's not something people generally think of as a business-related skill. And at the same time, the moravec paradox observes that computers are great at things humans are not and vice versa (e.g. computers have a hard time grasping objects but can do advanced maths in milliseconds.) So, comparing human and machine intelligence is challenging.

That said, I favor the "what it can do" approach because that has the most immediate impact in people's lives. That is, if we have AI systems that can do economically useful work just as good as the average person (or even better, the average expert), that means a few things:

People won't be needed to work. (Jobs? Economy?)

All economic output could increase several times over. For instance, AI may advance our tech. At a minimum, robots can work 24/7/365 whereas humans work a fraction of that. Imagine our ability to fabricate advanced computing chips doubling, which can then be used to make more chips, etc.

We may have begun the "singularity", where digital based knowledge and skills skyrockets. This is because we will have reached a point where the AI can improve itself. This means expanding the types of jobs it can perform, improving its performance, and likely innovating new techniques or technologies to assist with its goals.

(Of course, that could have tremendously good or tremendously bad outcomes - e.g. global retirement and healthy ecosystem vs literal doom - but that's another discussion.)

This vid argues that we've hit AGI by this definition. And I think that by some narrow definitions, this may be the case. (I still think we need more accuracy, a better "ecosystem" for it to function, more real-world modeling, etc. OTOH, this isn't preventing it from being massively useful right now.) So, this doesn't mean that the things I just listed will happen tomorrow - but it does mean that we should be expecting more enormous advances in the lab, and start to see real world applications slowly beginning. The line between AI and AGI is quickly blurring. Buckle up.

p.s. I know casual readers probably hear about AI here and there but may still have a picture in their head of AI as basically just a tool for making crappy pictures. I'm begging y'all to see that AI is both way beyond that (e.g. it's now making literal movies, and rapidly approaching market-ready results) and more importantly, that it's much more than that. AI is advancing every field of science, from fusion energy to quantum computing to curing diseases and so much more. This is no longer a curiosity. This is real and it's here.

[teqful]

How-To IT

Topic: Core areas of IT

1. Hardware

• Computers (Desktops, Laptops, Workstations)

• Servers and Data Centers

• Networking Devices (Routers, Switches, Modems)

• Storage Devices (HDDs, SSDs, NAS)

• Peripheral Devices (Printers, Scanners, Monitors)

2. Software

• Operating Systems (Windows, Linux, macOS)

• Application Software (Office Suites, ERP, CRM)

• Development Software (IDEs, Code Libraries, APIs)

• Middleware (Integration Tools)

• Security Software (Antivirus, Firewalls, SIEM)

3. Networking and Telecommunications

• LAN/WAN Infrastructure

• Wireless Networking (Wi-Fi, 5G)

• VPNs (Virtual Private Networks)

• Communication Systems (VoIP, Email Servers)

• Internet Services

4. Data Management

• Databases (SQL, NoSQL)

• Data Warehousing

• Big Data Technologies (Hadoop, Spark)

• Backup and Recovery Systems

• Data Integration Tools

5. Cybersecurity

• Network Security

• Endpoint Protection

• Identity and Access Management (IAM)

• Threat Detection and Incident Response

• Encryption and Data Privacy

6. Software Development

• Front-End Development (UI/UX Design)

• Back-End Development

• DevOps and CI/CD Pipelines

• Mobile App Development

• Cloud-Native Development

7. Cloud Computing

• Infrastructure as a Service (IaaS)

• Platform as a Service (PaaS)

• Software as a Service (SaaS)

• Serverless Computing

• Cloud Storage and Management

8. IT Support and Services

• Help Desk Support

• IT Service Management (ITSM)

• System Administration

• Hardware and Software Troubleshooting

• End-User Training

9. Artificial Intelligence and Machine Learning

• AI Algorithms and Frameworks

• Natural Language Processing (NLP)

• Computer Vision

• Robotics

• Predictive Analytics

10. Business Intelligence and Analytics

• Reporting Tools (Tableau, Power BI)

• Data Visualization

• Business Analytics Platforms

• Predictive Modeling

11. Internet of Things (IoT)

• IoT Devices and Sensors

• IoT Platforms

• Edge Computing

• Smart Systems (Homes, Cities, Vehicles)

12. Enterprise Systems

• Enterprise Resource Planning (ERP)

• Customer Relationship Management (CRM)

• Human Resource Management Systems (HRMS)

• Supply Chain Management Systems

13. IT Governance and Compliance

• ITIL (Information Technology Infrastructure Library)

• COBIT (Control Objectives for Information Technologies)

• ISO/IEC Standards

• Regulatory Compliance (GDPR, HIPAA, SOX)

14. Emerging Technologies

• Blockchain

• Quantum Computing

• Augmented Reality (AR) and Virtual Reality (VR)

• 3D Printing

• Digital Twins

15. IT Project Management

• Agile, Scrum, and Kanban

• Waterfall Methodology

• Resource Allocation

• Risk Management

16. IT Infrastructure

• Data Centers

• Virtualization (VMware, Hyper-V)

• Disaster Recovery Planning

• Load Balancing

17. IT Education and Certifications

• Vendor Certifications (Microsoft, Cisco, AWS)

• Training and Development Programs

• Online Learning Platforms

18. IT Operations and Monitoring

• Performance Monitoring (APM, Network Monitoring)

• IT Asset Management

• Event and Incident Management

19. Software Testing

• Manual Testing: Human testers evaluate software by executing test cases without using automation tools.

• Automated Testing: Use of testing tools (e.g., Selenium, JUnit) to run automated scripts and check software behavior.

• Functional Testing: Validating that the software performs its intended functions.

• Non-Functional Testing: Assessing non-functional aspects such as performance, usability, and security.

• Unit Testing: Testing individual components or units of code for correctness.

• Integration Testing: Ensuring that different modules or systems work together as expected.

• System Testing: Verifying the complete software system’s behavior against requirements.

• Acceptance Testing: Conducting tests to confirm that the software meets business requirements (including UAT - User Acceptance Testing).

• Regression Testing: Ensuring that new changes or features do not negatively affect existing functionalities.

• Performance Testing: Testing software performance under various conditions (load, stress, scalability).

• Security Testing: Identifying vulnerabilities and assessing the software’s ability to protect data.

• Compatibility Testing: Ensuring the software works on different operating systems, browsers, or devices.

• Continuous Testing: Integrating testing into the development lifecycle to provide quick feedback and minimize bugs.

• Test Automation Frameworks: Tools and structures used to automate testing processes (e.g., TestNG, Appium).

19. VoIP (Voice over IP)

VoIP Protocols & Standards

• SIP (Session Initiation Protocol)

• H.323

• RTP (Real-Time Transport Protocol)

• MGCP (Media Gateway Control Protocol)

VoIP Hardware

• IP Phones (Desk Phones, Mobile Clients)

• VoIP Gateways

• Analog Telephone Adapters (ATAs)

• VoIP Servers

• Network Switches/ Routers for VoIP

VoIP Software

• Softphones (e.g., Zoiper, X-Lite)

• PBX (Private Branch Exchange) Systems

• VoIP Management Software

• Call Center Solutions (e.g., Asterisk, 3CX)

VoIP Network Infrastructure

• Quality of Service (QoS) Configuration

• VPNs (Virtual Private Networks) for VoIP

• VoIP Traffic Shaping & Bandwidth Management

• Firewall and Security Configurations for VoIP

• Network Monitoring & Optimization Tools

VoIP Security

• Encryption (SRTP, TLS)

• Authentication and Authorization

• Firewall & Intrusion Detection Systems

• VoIP Fraud DetectionVoIP Providers

• Hosted VoIP Services (e.g., RingCentral, Vonage)

• SIP Trunking Providers

• PBX Hosting & Managed Services

VoIP Quality and Testing

• Call Quality Monitoring

• Latency, Jitter, and Packet Loss Testing

• VoIP Performance Metrics and Reporting Tools

• User Acceptance Testing (UAT) for VoIP Systems

Integration with Other Systems

• CRM Integration (e.g., Salesforce with VoIP)

• Unified Communications (UC) Solutions

• Contact Center Integration

• Email, Chat, and Video Communication Integration

[wayti-blog]

"Northwestern University engineers are the first to successfully demonstrate quantum teleportation over a fiberoptic cable already carrying internet traffic.

The discovery introduces the new possibility of combining quantum communication with existing internet cables—greatly simplifying the infrastructure required for distributed quantum sensing or computing applications.

The study is published on the arXiv preprint server and is due to appear in the journal Optica."

"Only limited by the speed of light, quantum teleportation could make communications nearly instantaneous. The process works by harnessing quantum entanglement, a technique in which two particles are linked, regardless of the distance between them. Instead of particles physically traveling to deliver information, entangled particles exchange information over great distances—without physically carrying it.

"In optical communications, all signals are converted to light," Kumar explained. "While conventional signals for classical communications typically comprise millions of particles of light, quantum information uses single photons.""

continue reading

[techtoio]

How to Optimize Your Workflow with Task Management Software

Introduction

The world has become too fast-moving; hence, the demand for organization and productivity is higher than ever. From multiple tasks at hand to meeting deadlines, the pressure is always there to make things easier. Task management software provides a powerful solution for this issue. This guide will walk you through optimizing your workflow with task management software to bring about efficiency and effectiveness in both personal and professional aspects of life. Read to continue...

[cleanarchitectures]

Tagged by my dears @metaphrasis, @vinosities, @kjw1532. Thank you for sharing these insights, truly enjoy reading from you!

Last song: "The Nearness of You" by Bill Charlap

Favourite colour/s: Dark green, dark blue, the white of snow.

Last film/ TV series: I started showing my partner Devs (2020), a short series by Alex Garland, because we were discussing AI models at a high level and the possibility of creating simulations of life (for different purposes, finding cures etc). The show touches on some of that, only with quantum computing. Anyways I often enjoy material on the application of tech for these kind of theories.

For Karolina, I also watched Phantom Thread very recently, and it's a favorite of mine as well. One to revisit a few times and sit with my thoughts on the complexity of relationships.

Sweet/spicy/savoury: All have their place, I love sweets that aren't too sweet. A nutty black sesame dessert.

Last thing/s I googled: Stays in Yosemite. February is a busy month for Firefall, but maybe we could stay a weekend.

Current obsession/s: The cold, sunny Northern California winter. Taking it in on my bike rides to my office building, a small comfort before a work day. Weekend drives south to Monterey, or north to Point Reyes. Danish design and maybe the general Nordic way of life; I loved talking with my mother-in-law about the Icelandic tradition of letting babies sleep outside in the clean air, thoroughly adorable. Cleaning a little each day, keeping a neat wardrobe (depiling, steaming, cleaning and conditioning leather). Mascarpone.

I would tag @kxowledge, @petrichorals, @detachedperfectionist, @un-peu-de-vin, @semperfeminae and anyone else. Wishing you all a warm January!