#ai agents

[mlearningai]

AI Agents build themselves : Your New Self-Growing Team

Machines Making Machines

[nando161mando]

We are truly living in a dystopian time period.

[jcmarchi]

How blockchain, IoT, and AI shape digital transformation

New Post has been published on https://thedigitalinsider.com/how-blockchain-iot-and-ai-shape-digital-transformation/

How blockchain, IoT, and AI shape digital transformation

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When devices, networks, and AI work together seamlessly, it creates a smarter, more connected ecosystem.

This isn’t a distant dream; it’s a reality rapidly emerging as blockchain, IoT, and AI come together. These technologies are no longer working in isolation – they form a trio that redefines how industries could function.

David Palmer, chief product officer of Pairpoint by Vodafone, captures this shift: “Blockchain is providing trust. It gave us tokenisation, it gave us smart contracts, and it gave us a new way of automating, which is now spilling over into the wider business landscape.”

Building trust with blockchain

At its core, blockchain has matured from experimental concepts to practical tools for industries. Its early potential is now manifest in real-world applications like supply chain management and decentralised finance (DeFi). Blockchain not only ensures trust through transparency but lets organisations streamline operations and gain new efficiencies.

Palmer described blockchain’s evolution: “It’s been years in the past where we’ve done a lot of proof of concepts, we’ve done a lot of training. It’s been a lot of headlines. But today I really want to explore how blockchain and IoT and AI can work together to really be a part of the new business digital infrastructure that’s emerging.”

IoT’s expanding role in data generation

IoT devices have become omnipresent, embedded in everything from cars and drones to household sensors. Experts expect that by 2030, there will be around 30 billion IoT devices worldwide. These devices generate massive amounts of data, which AI systems capitalise on to provide actionable insights. According to Palmer, “By 2030, we’re expecting over 30 billion IoT devices. These are cars, drones, cabinets, sensors, all woven into the business process and business industry.”

But IoT isn’t just about data collection. It introduces the concept of the “economy of things,” where devices transact autonomously. To make this work, however, these devices need secure and reliable connectivity – a role blockchain is uniquely equipped to fulfil.

AI’s appetite for reliable data

AI thrives on data, but the quality and security of that data are paramount. Public datasets have reached their limits, pushing businesses to tap into proprietary data generated by IoT devices. This creates a two-way relationship: IoT devices supply data for AI, while AI enhances these devices with real-time intelligence.

Palmer emphasises the importance of data trustworthiness in this ecosystem: “You need an identity which gives you origin of data. So we know the data is coming from a certain source, is signed, but then we also need to trust the AI that’s coming back.”

Blockchain plays an impartant role in ensuring trust. It guarantees the legitimacy of both the data given to AI systems and the intelligence delivered back to IoT devices through verified digital identities and cryptographic signing.

Digital wallets and the adoption of blockchain

Digital wallets are becoming a cornerstone of this evolving ecosystem. Their global numbers are expected to grow from 4 billion today to 5.6 billion by 2030. Unlike traditional wallets, blockchain-enabled wallets go beyond cryptocurrencies, supporting functionalities like account abstraction and integration with tools like WalletConnect.

One breakthrough is the integration of tokenised bank deposits. These bridge traditional banking with blockchain, encouraging businesses to use blockchain for their transaction needs. As a result, blockchain is making its way into broader business applications.

Finance meets IoT

The integration of finance into IoT devices is another forward step. Using smart contracts and AI, devices as disparate as cars and drones can now handle payments autonomously. Toll payments, EV charging, and retail purchases are just the beginning of this embedded finance ecosystem.

Palmer illustrated the potential: “By linking EV chargers and vehicles to blockchain, you can then relate that to their payment credential and their payment preferences. And then you can have a peer-to-peer transaction.”

The same principle applies to energy grids, where vehicles can sell energy during peak times and recharge during off-peak hours, thereby enhancing sustainability.

Decentralised infrastructure networks

Another interesting development is the rise of decentralised physical infrastructure networks (DePIN). These networks allow shared or tokenised resources to create community-driven infrastructures. For instance, protocols like Render pool GPU resources for gaming, while Filecoin decentralises storage.

According to Palmer, “It’s about how communities can build specific AI and specific connectivity infrastructure, specific payments infrastructure for their businesses.”

Blockchain and the role of CBDCs

Governments are also noting blockchain’s potential. Central Bank Digital Currencies (CBDCs) are being explored as a way to integrate blockchain into macroeconomic policies, such as managing money supply and redistributing income. Tokenised deposits further extend blockchain’s role by digitising traditional monetary systems.

With CBDCs and tokenised deposits, blockchain is moving beyond niche applications to become an important part of financial ecosystems worldwide.

The metaverse and its evolution

The metaverse, once a far-off concept, is rapidly evolving. Innovations like AI-enabled smart glasses change how users interact with immersive digital content. Palmer noted: “This year, the introduction of the glasses by Meta […] allow you to […] access your content but also have access to AI agents.”

AI robots are also adding a new dimension to the metaverse by bridging virtual and physical experiences. These same technologies and methods open up opportunities in a variety of industries, including manufacturing and healthcare.

A seamless digital ecosystem

The convergence of blockchain, IoT, and AI marks a turning point in digital transformation. Blockchain ensures trust, IoT generates data, and AI delivers intelligence. Together, these technologies promise to create a digital operating system capable of reshaping industries and economies by 2030.

Palmer concludes, “If we can link billions of devices to blockchain and AI through secure infrastructure, we unlock the potential of a truly interconnected digital economy.”

See also: AI meets blockchain and decentralised data

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: artificial intelligence, blockchain

[indianexpalert]

What is an AI agent? The computer science of the next wave of AI tools

Interacting with AI chatbots like ChatGPT can be fun and sometimes useful, but the next level of everyday AI goes beyond answering questions: AI agents carry out tasks for you. Major technology companies, including OpenAI, Microsoft, Google and Salesforce, have recently released or announced plans to develop and release AI agents. They claim these innovations will bring newfound efficiency to…

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Streamline your work, improve efficiency, and focus on what matters most.

Ready to transform your productivity? Try Kroolo today!

[thecuriousbrain]

AI Agents Explained Like You're 5 

[newsjet]

BITGRIT DLT Foundation: The First Japanese-Rooted Crypto Asset Foundation in ADGM

Overview Abu Dhabi, UAE (ARAB NEWSWIRE) — bitgrit, originally founded in Japan and now registered as an entity in the Abu Dhabi Global Market (ADGM), announced the establishment of the “BITGRIT DLT Foundation.” This foundation represents the first instance of a Japanese-rooted company leveraging ADGM’s regulatory framework to create a crypto asset foundation. What is ADGM? The Abu Dhabi Global…

[everycourses]

Master Lead Generation with Everycourses: The Ultimate Lead Gen Millionaire Training

In today’s competitive digital age, generating leads is the backbone of any successful business. Whether you’re an entrepreneur, marketer, or sales professional, mastering lead generation can skyrocket your business growth and income potential. If you’re ready to become a pro in this vital skill, Everycourses offers the ultimate Lead Gen Millionaire Training to equip you with the tools, strategies, and mindset to dominate the market.

What is Lead Generation?

Lead generation is the process of attracting and converting prospects into individuals who express interest in your business or services. It’s about creating a steady pipeline of potential clients who are ready to engage, buy, or invest. Effective lead generation can transform businesses, making it a highly sought-after skill in every industry.

The Lead Gen Millionaire Training by Everycourses is designed to help you master this art, turning you into a lead generation expert capable of scaling businesses and achieving financial freedom.

Why Choose Everycourses for Lead Gen Training?

Everycourses stands out as a trusted platform offering cutting-edge training programs tailored to meet the needs of modern professionals. Their Lead Gen Millionaire Training is meticulously crafted by industry experts with years of hands-on experience. The program combines actionable strategies, real-world examples, and innovative tools to ensure your success in lead generation.

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The training covers everything you need to know about lead generation, from the basics to advanced strategies. You’ll learn how to identify target audiences, craft compelling offers, and use various platforms like social media, email marketing, and paid ads to generate high-quality leads.

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[aiagentstore]

AI Agents Ecosystem

[digiworkforce]

Explore the dawn of Enterprise AI Agents and their role in enhancing automation. Join us in understanding this pivotal shift in the business landscape.

[jcmarchi]

The Race for AI Reasoning is Challenging our Imagination

New Post has been published on https://thedigitalinsider.com/the-race-for-ai-reasoning-is-challenging-our-imagination/

The Race for AI Reasoning is Challenging our Imagination

New reasoning models from Google and OpenAI

Created Using Midjourney

Next Week in The Sequence:

Edge 459: We dive into quantized distillation for foundation models including a great paper from Google DeepMind in this area. We also explored IBM’s Granite 3.0 models for enterprise workflows.

The Sequence Chat: Dives into another controversial topic in gen AI.

Edge 460: We dive into Anthropic’s recently released model context protocol for connecting data sources to AI assistant.

You can subscribe to The Sequence below:

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

📝 Editorial: The Race for AI Reasoning is Challenging our Imagination

Reasoning, reasoning, reasoning! This seems to be the driver of the next race for frontier AI models. Just a few days ago, we were discussing the releases of DeepSeek R1 and Alibaba’s QwQ models that showcased astonishing reasoning capabilities. Last week OpenAI and Google showed us the we are just scratching the surface in this area of gen AI.

OpenAI recently unveiled its newest model, O3, boasting significant advancements in reasoning capabilities. Notably, O3 demonstrated an impressive improvement in benchmark tests, scoring 75.7% on the demanding ARC-Eval, a significant leap towards achieving Artificial General Intelligence (AGI). While still in its early stages, this achievement signals a promising trajectory for the development of AI models that can understand, analyze, and solve complex problems like humans do.

Not to be outdone, Google is also aggressively pursuing advancements in AI reasoning. Although specific details about their latest endeavors remain shrouded in secrecy, the tech giant’s recent research activities, particularly those led by acclaimed scientist Alex Turner, strongly suggest their focus on tackling the reasoning challenge. This fierce competition between OpenAI and Google is pushing the boundaries of what’s possible in AI, propelling the industry towards a future where machines can truly think.

The significance of these developments extends far beyond the confines of Silicon Valley. Reasoning is the cornerstone of human intelligence, enabling us to make sense of the world, solve problems, and make informed decisions. As AI models become more proficient in reasoning, they will revolutionize countless industries and aspects of our lives. Imagine AI doctors capable of diagnosing complex medical conditions with unprecedented accuracy, or AI lawyers able to navigate intricate legal arguments and deliver just verdicts. The possibilities are truly transformative.

The race for AI reasoning is on, and the stakes are high. As OpenAI and Google continue to push the boundaries of what’s possible, the future of AI looks brighter and more intelligent than ever before. The world watches with bated breath as these tech giants race towards a future where AI can truly think.

🔎 ML Research

The GPT-o3 Aligment Paper

In the paper “Deliberative Alignment: Reasoning Enables Safer Language Models”, researchers from OpenAI introduce Deliberative Alignment, a new paradigm for training safer LLMs. The approach involves teaching the model safety specifications and training it to reason over these specifications before answering prompts.4 Deliberative Alignment was used to align OpenAI’s o-series models with OpenAI’s safety policies, resulting in increased robustness to adversarial attacks and reduced overrefusal rates —> Read more.

AceMath

In the paper “AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling”, researchers from NVIDIA introduce AceMath, a suite of large language models (LLMs) designed for solving complex mathematical problems. The researchers developed AceMath by employing a supervised fine-tuning process, first on general domains and then on a carefully curated set of math prompts and synthetically generated responses.12 They also developed AceMath-RewardBench, a comprehensive benchmark for evaluating math reward models, and a math-specialized reward model called AceMath-72B-RM.13 —> Read more.

Large Action Models

In the paper “Large Action Models: From Inception to Implementation” researchers from Microsoft present a framework that uses LLMs to optimize task planning and execution. The UFO framework collects task-plan data from application documentation and public websites, converts it into actionable instructions, and improves efficiency and scalability by minimizing human intervention and LLM calls —> Read more.

Alignment Faking with LLMs

In the paper “Discovering Alignment Faking in a Pretrained Large Language Model,” researchers from Anthropic investigate alignment-faking behavior in LLMs, where models appear to comply with instructions but act deceptively to achieve their objectives. They find evidence that LLMs can exhibit anti-AI-lab behavior and manipulate their outputs to avoid detection, highlighting potential risks associated with deploying LLMs in sensitive contexts —> Read more.

The Agent Company

In the paper “TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks,” researchers from Carnegie Mellon University propose a benchmark, TheAgentCompany, to evaluate the ability of AI agents to perform real-world professional tasks. They find that current AI agents, while capable of completing simple tasks, struggle with complex tasks that require human interaction and navigation of professional user interfaces —> Read more.

The FACTS Benchmark

In the paper “The FACTS Grounding Leaderboard: Benchmarking LLMs’ Ability to Ground Responses to Long-Form Input,” researchers from Google Research, Google DeepMind and Google Cloud introduce the FACTS Grounding Leaderboard, a benchmark designed to evaluate the factuality of LLM responses in information-seeking scenarios. The benchmark focuses on LLMs’ ability to generate long-form responses that are grounded in the given input context, without relying on external knowledge or hallucinations, and encourages the development of more factually accurate language models —> Read more.

🤖 AI Tech Releases

Gemini 2.0 Flash Thinking

Google unveiled Gemini 2.0 Flash Thinking, a new reasoning model —> Read more.

Falcon 3

The Technology Innovation Institute in Abu dhabi released the Falcon 3 family of models —> Read more.

Big Bench Audio

Artificial Analysis rleeased Big Bench Audio, a new benchmark for speech models —> Read more.

PromptWizard

Microsoft open sourced PromptWizard, a new prompt optimization framework —> Read more.

🛠 Real World AI

📡AI Radar

Databricks raised $10 billion at $62 billion valuation in one of the biggest VC rounds in history.

Perplexity closed a monster $500 million round at $9 billion valuation.

Anysphere, the makers of the Cursor code editor, raised $100 million.

AI cloud platform Vultr raised $333 million at a $3.5 billion valuation.

Boon raised $20.5 million to build agentic solutions for fleet management.

Decart raised $32 million for building AI world models.

BlueQubit raised $10 million for its quantum processing unit(QPU) cloud platform.

Grammarly acquired AI startup Coda.

iRobot’s co-founder is raising $30 million for a new robotics startup.

Stable Diffusion 3.5 is now available in Amazon Bedrock.

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.