While the benefits web3 technology can bring to artificial intelligence are well documented – transparency, P2P economies, tokenisation, censorship resistance, and so on – this is a reciprocal arrangement. In return, AI is fortifying blockchain projects in different ways, enhancing the ability to process vast datasets, and automating on-chain processes. The relationship may have taken a while to get started, but blockchain and AI are now entwined.

Trust meets efficiency

While AI brings intelligent automation and data-driven decision-making, blockchain offers security, decentralisation, and transparency. Together, they can address each other’s limitations, offering new opportunities in digital and real-world industries. Blockchain provides a tamper-proof foundation and AI brings adaptability, plus the ability to optimise complex systems.

Together, the two promise to enhance scalability, security, and privacy – key pillars for modern finance and supply chain applications.

AI’s ability to analyse large amounts of data is a natural fit for blockchain networks, allowing data archives to be processed in real time. Machine learning algorithms can predict network congestion – as seen with tools like Chainlink’s off-chain computation, which offers dynamic fee adjustments or transaction prioritisation.

Security also gains: AI can monitor blockchain activity in real-time to identify anomalies more quickly than manual scans, so teams can move to mitigate attacks. Privacy is improved, with AI managing zero-knowledge proofs and other cryptographic techniques to shield user data; methods explored by projects like Zcash. These types of enhancements make blockchain more robust and attractive to the enterprise.

In DeFi, Giza‘s agent-driven markets embody the convergence of web3 and artificial intelligence. Its protocol runs autonomous agents like ARMA, which manage yield strategies across protocols and offer real-time adaptation. Secured by smart accounts and decentralised execution, agents can deliver positive yields, and currently manage hundreds of thousands of dollars in on-chain assets. Giza shows how AI can optimise decentralised finance and is a project that uses the two technologies to good effect.

Blockchain as AI’s backbone

Blockchain offers AI a decentralised infrastructure to foster trust and collaboration. AI models, often opaque and centralised, face scrutiny over data integrity and bias – issues blockchain counters with transparent, immutable records. Platforms like Ocean Protocol use blockchain to log AI training data, providing traceability without compromising ownership. That can be a boon for sectors like healthcare, where the need for verifiable analytics is important.

Decentralisation also enables secure multi-party computation, where AI agents collaborate across organisations – think federated learning for drug discovery – without a central authority, as demonstrated in 2024 by IBM’s blockchain AI pilots. The trustless framework reduces reliance on big tech, helping to democratise AI.

While AI can enhance blockchain performance, blockchain itself can provide a foundation for ethical and secure AI deployment. The transparency and immutability with which blockchain is associated can mitigate AI-related risks by ensuring AI model integrity, for example. AI algorithms and training datasets can be recorded on-chain so they’re auditable. Web3 technology helps in governance models for AI, as stakeholders can oversee and regulate project development, reducing the risks of biased or unethical AI.

Digital technologies with real-world impact

The synergy between blockchain and AI exists now. In supply chains, AI helps to optimise logistics while blockchain can track item provenance. In energy, blockchain-based smart grids paired with AI can predict demand; Siemens reported a 15% efficiency gain in a 2024 trial of such a system in Germany. These cases highlight how AI scales blockchain’s utility, while the latter’s security can realise AI’s potential. Together, they create smart, reliable systems.

The relationship between AI and blockchain is less a merger than a mutual enhancement. Blockchain’s trust and decentralisation ground AI’s adaptability, while AI’s optimisation unlocks blockchain’s potential beyond that of a static ledger. From supply chain transparency to DeFi’s capital efficiency, their combined impact is tangible, yet their relationship is just beginning.

(Image source: Unsplash)

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Described as a gamechanger for AI accessibility and performance, ASI-1 Mini is heralded for delivering results on par with leading LLMs but at significantly reduced hardware costs—a leap forward in making AI enterprise-ready.

ASI-1 Mini integrates into Web3 ecosystems, enabling secure and autonomous AI interactions. Its release sets the foundation for broader innovation within the AI sector—including the imminent launch of the Cortex suite, which will further enhance the use of large language models and generalised intelligence.

“This launch marks the beginning of ASI-1 Mini’s rollout and a new era of community-owned AI. By decentralising AI’s value chain, we’re empowering the Web3 community to invest in, train, and own foundational AI models,” said Humayun Sheikh, CEO of Fetch.ai and Chairman of the Artificial Superintelligence Alliance.

“We’ll soon introduce advanced agentic tool integration, multi-modal capabilities, and deeper Web3 synergy to enhance ASI-1 Mini’s automation capabilities while keeping AI’s value creation in the hands of its contributors.”

Democratising AI with Web3: Decentralised ownership and shared value  

Key to Fetch.ai’s vision is the democratisation of foundational AI models, allowing the Web3 community to not just use, but also train and own proprietary LLMs like ASI-1 Mini. 

This decentralisation unlocks opportunities for individuals to directly benefit from the economic growth of cutting-edge AI models, which could achieve multi-billion-dollar valuations.  

Through Fetch.ai’s platform, users can invest in curated AI model collections, contribute to their development, and share in generated revenues. For the first time, decentralisation is driving AI model ownership—ensuring financial benefits are more equitably distributed.

Advanced reasoning and tailored performance  

ASI-1 Mini introduces adaptability in decision-making with four dynamic reasoning modes: Multi-Step, Complete, Optimised, and Short Reasoning. This flexibility allows it to balance depth and precision based on the specific task at hand.  

Whether performing intricate, multi-layered problem-solving or delivering concise, actionable insights, ASI-1 Mini adapts dynamically for maximum efficiency. Its Mixture of Models (MoM) and Mixture of Agents (MoA) frameworks further enhance this versatility.  

Mixture of Models (MoM):  

ASI-1 Mini selects relevant models dynamically from a suite of specialised AI models, which are optimised for specific tasks or datasets. This ensures high efficiency and scalability, especially for multi-modal AI and federated learning.  

Mixture of Agents (MoA):  

Independent agents with unique knowledge and reasoning capabilities work collaboratively to solve complex tasks. The system’s coordination mechanism ensures efficient task distribution, paving the way for decentralised AI models that thrive in dynamic, multi-agent systems.  

This sophisticated architecture is built on three interacting layers:  

1. Foundational layer: ASI-1 Mini serves as the core intelligence and orchestration hub.  
2. Specialisation layer (MoM Marketplace): Houses diverse expert models, accessible through the ASI platform.  
3. Action layer (AgentVerse): Features agents capable of managing live databases, integrating APIs, facilitating decentralised workflows, and more.  

By selectively activating only necessary models and agents, the system ensures performance, precision, and scalability in real-time tasks.  

Transforming AI efficiency and accessibility

Unlike traditional LLMs, which come with high computational overheads, ASI-1 Mini is optimised for enterprise-grade performance on just two GPUs, reducing hardware costs by a remarkable eightfold. For businesses, this means reduced infrastructure costs and increased scalability, breaking down financial barriers to high-performance AI integration.  

On benchmark tests like Massive Multitask Language Understanding (MMLU), ASI-1 Mini matches or surpasses leading LLMs in specialised domains such as medicine, history, business, and logical reasoning.  

Rolling out in two phases, ASI-1 Mini will soon process vastly larger datasets with upcoming context window expansions:  

• Up to 1 million tokens: Allows the model to analyse complex documents or technical manuals.
• Up to 10 million tokens: Enables high-stakes applications like legal record review, financial analysis, and enterprise-scale datasets.  

These enhancements will make ASI-1 Mini invaluable for complex and multi-layered tasks.  

Tackling the “black-box” problem  

The AI industry has long faced the challenge of addressing the black-box problem, where deep learning models reach conclusions without clear explanations.

ASI-1 Mini mitigates this issue with continuous multi-step reasoning, facilitating real-time corrections and optimised decision-making. While it doesn’t entirely eliminate opacity, ASI-1 provides more explainable outputs—critical for industries like healthcare and finance.  

Its multi-expert model architecture not only ensures transparency but also optimises complex workflows across diverse sectors. From managing databases to executing real-time business logic, ASI-1 outperforms traditional models in both speed and reliability.  

AgentVerse integration: Building the agentic AI economy

ASI-1 Mini is set to connect with AgentVerse, Fetch.ai’s agent marketplace, providing users with the tools to build and deploy autonomous agents capable of real-world task execution via simple language commands. For example, users could automate trip planning, restaurant reservations, or financial transactions through “micro-agents” hosted on the platform.

This ecosystem enables open-source AI customisation and monetisation, creating an “agentic economy” where developers and businesses thrive symbiotically. Developers can monetise micro-agents, while users gain seamless access to tailored AI solutions.  

As its agentic ecosystem matures, ASI-1 Mini aims to evolve into a multi-modal powerhouse capable of processing structured text, images, and complex datasets with context-aware decision-making.  

See also: Endor Labs: AI transparency vs ‘open-washing’

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.

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Decentralised AI agents for ownership and monetisation

Key features of AIvalanche DeFAI agents

• Create and manage AI agents: Users can build AI agents in just a few clicks. Each agent has a dedicated page outlining its capabilities.
• Co-ownership of AI agents: Anyone can invest in AI agents early by acquiring tokens before they gain mainstream attention. Users can also engage with established AI agents while trading their tokens.
• Monetising AI agents: AI agents evolve by learning from new data. They have their own wallets and can execute transactions, manage tasks, and distribute revenue.

Support from key players in the Avalanche ecosystem

Expanding access through public sales and listings

The post AVAXAI brings DeepSeek to Web3 with decentralised AI agents appeared first on AI News.

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.

The post How blockchain, IoT, and AI are shaping the future of digital transformation appeared first on AI News.

Blockchain has enormous potential to democratise access to AI by addressing concerns around centralisation that have emerged with the growing dominance of companies like OpenAI, Google, and Anthropic.

Decentralised AI systems built on blockchains can help to democratise access to essential AI resources like computing power, data, and large language models. They are sorely needed too; as AI models become more powerful, their thirst for data and computing power grows, increasing the barrier of entry to the industry.

With blockchain, AI resources can be distributed across open, decentralised networks that anyone can access; levelling the playing field for smaller operators while fostering a spirit of openness and collaboration that’s essential to move the industry forward. Blockchain can create a more equitable system that ensures those who create the data used to train LLMs are fairly rewarded for their contributions.

Challenges in decentralised data

There’s a lot to like about the prospect of a decentralised AI ecosystem, but the reality will only emerge if some of the key challenges around data access, management, and analysis in blockchain are surmounted.

For AI, blockchain can become a critical tool for secure, transparent, and verifiable data management, one that can be accessed by anyone. But blockchains have some architectural problems: they’re essentially a slow, single-table database that records information sequentially – not nearly flexible nor fast enough for the enormous volumes of data required by AI systems.

Another challenge is that blockchains don’t integrate easily with other data environments nor other blockchains. Because of this, most enterprises that use blockchains are forced to deploy an array of point solutions to extract data from the ledger, transform it into a relational format, bring it into a traditional database, and move it into a data warehouse for analysis. Meanwhile, to bring external data onto any blockchain, it’s necessary to use complex and risky data oracles. All of these tools introduce centralisation and security risks into the equation.

Innovative solutions pave the way

Fortunately, a number of innovative solutions are being proposed to help smooth the integration of blockchains and AI. A case in point is Space and Time, creator of a decentralised data warehouse that replaces traditional data stacks and serves as a trustless intermediary between blockchains and enterprise data systems, enabling them to communicate seamlessly.

Space and Time’s secret sauce is its Proof-of-SQL consensus mechanism, which cryptographically verifies the accuracy of SQL database queries and proves the underlying dataset hasn’t been tampered with. This enables smart contracts to interact with external data, paving the way for more sophisticated blockchain applications that use AI. For example, Space and Time can enable an AI chatbot like ChatGPT to access blockchain data without any modification.

Formerly known for its modular AI blockchain, OG has recently rebranded itself as a ‘Decentralised AI Operating System’ called dAIOS. The system uses blockchain to coordinate decentralised resources for AI including storage, data availability, and compute power, so AI applications can operate securely and transparently on-chain while ensuring users retain control of the data that’s fed in.

OG’s dAIOS has three main components – storage for managing large data volumes, ‘data availability’ for data verification, and ‘serving’ to power data retrieval, training, and inference – which can be used by any developer to access the resources needed to power their AI models.

Looking to solve the challenge of blockchain data access, SQD is the creator of an advanced data indexing tool that works by aggregating on-chain data in parquet files and distributing them across nodes in a decentralised data lake. SQD is addressing the architectural inefficiencies of blockchain, namely the way data is stored sequentially in blocks, an architecture that makes it inefficient to query.

Whenever an app needs access to blockchain data, it sends a query to whichever nodes hosts the desired data. Each node is assigned to a specific segment of blockchain data and SQD provides a detailed index of that information so dApps can quickly find what they need. It typically assigns the same blockchain data to multiple nodes to ensure availability, using an algorithm to manage query volumes.

What will AI do for blockchain?

Modern blockchain data infrastructures pave the way for a number of novel AI/blockchain applications. One of the most promising lies in security. AI can enhance blockchain security by monitoring transactions and network activity to detect anomalies in real-time, and mitigate any suspicious activity.

AI can also enhance the capabilities of smart contracts and make them much more intelligent. By using analytics, AI algorithms can predict any problems when they contract conditions are executed. AI-powered natural language processing algorithms can enable smart contracts to understand legal contracts. And generative AI technology can be used to automate the creation of smart contracts, eliminating the need to learn a specialised programming language like Solidity.

The domain of tokenised real-world assets also stands to benefit from an infusion of AI, used to analyse the provenance and condition of RWAs like stocks and fine art. By correlating the analysis with market trends, AI can more accurately calculate the fair market value of tokens. AI can also be used to monitor real-time data fees to continuously update their values. Additionally, it can be used to automate the process of converting RWAs into digital tokens.

Finally, AI can be used to predict future price movements of digital assets by monitoring market trends and industry news. Traders will be able to use the analysis to enhance their decision-making, hedge their investment portfolios and attempt to capitalise on market volatility.

AI for everyone

The AI industry is growing at an unprecedented pace, and the need for decentralisation is becoming more important to ensure the industry remains open and competitive. Blockchain will provide the foundation for cutting-edge, decentralised AI models, leading to the creation of AI tools that cater to the needs of the majority, ones that focus on simplicity, privacy and ease-of-use.

“Space and Time is thrilled to lead Web3 into a new era of data-driven smart contracts and the next generation of DeFi,” said Jay White PhD, Co-Founder and Head of Research at SxT, and the inventor of the Proof of SQL protocol.

As AI and blockchain’s convergence gathers pace, the two technologies will democratise access to AI resources, reward data contributors fairly, and allow any company to use its proprietary data securely. It’s no wonder that industry experts like Miguel Palencia, co-founder of Qtum, express nothing but confidence in their potential.

“Giving everyone true ownership and provenance of AI assets is of the utmost importance,” Palencia told Forbes. “There is a pressing need to address the concentration of AI power in the hands of a few companies.”

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.

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While today’s AI excels in specific areas – think GPT-4 composing poetry or DeepMind’s AlphaFold predicting protein structures – it’s still miles away from genuine human-like intelligence. 

“While the novel neural-symbolic AI approaches developed by the SingularityNET AI team decrease the need for data, processing and energy somewhat relative to standard deep neural nets, we still need significant supercomputing facilities,” SingularityNET CEO Ben Goertzel explained to LiveScience in a recent written statement.

Enter SingularityNET’s ambitious plan: a “multi-level cognitive computing network” designed to host and train the incredibly complex AI architectures required for AGI. Imagine deep neural networks that mimic the human brain, vast language models (LLMs) trained on colossal datasets, and systems that seamlessly weave together human behaviours like speech and movement with multimedia outputs.

But this level of sophistication doesn’t come cheap. The first supercomputer, slated for completion by early 2025, will be a Frankensteinian beast of cutting-edge hardware: Nvidia GPUs, AMD processors, Tenstorrent server racks – you name it, it’s in there.

This, Goertzel believes, is more than just a technological leap, it’s a philosophical one: “Before our eyes, a paradigmatic shift is taking place towards continuous learning, seamless generalisation, and reflexive AI self-modification.”

To manage this distributed network and its precious data, SingularityNET has developed OpenCog Hyperon, an open-source software framework specifically designed for AI systems. Think of it as the conductor trying to make sense of a symphony played across multiple concert halls. 

But SingularityNET isn’t keeping all this brainpower to itself. Reminiscent of arcade tokens, users will purchase access to the supercomputer network with the AGIX token on blockchains like Ethereum and Cardano and contribute data to the collective pool—fuelling further AGI development.  

With experts like DeepMind’s Shane Legg predicting human-level AI by 2028, the race is on. Only time will tell if this global network of silicon brains will birth the next great leap in artificial intelligence.

(Photo by Anshita Nair)

See also: The merging of AI and blockchain was inevitable – but what will it mean?

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.

The post SingularityNET bets on supercomputer network to deliver AGI appeared first on AI News.

As the first major corporate partner of the Fetch.ai Foundation, Deutsche Telekom joins forces with Bosch and Fetch.ai in supporting an open AI and blockchain platform aimed at widespread adoption. Its subsidiary, Deutsche Telekom MMS, will also serve as a validator on the decentralised Fetch.ai network, helping secure transactions on the blockchain.

The core of Fetch.ai’s technology relies on autonomous software agents that can manage resources, conduct transactions, and analyse data flows independently thanks to AI algorithms. These agents unlock a myriad of real-world applications across sectors like automotive, supply chain, healthcare, and digital identity. 

For example, AI agents could optimise production schedules based on supply chain data or match patients to clinical trials using health records. The tamper-proof nature of blockchain also enables secure transmission and access to sensitive data.

“The convergence of blockchain, AI and IoT is trailblazing the digital transformation of entire industries,” said Dirk Röder, Head of Deutsche Telekom’s Web3 Infrastructure & Solutions team. “Autonomous agents will automate industrial services, simplifying processes securely thanks to blockchain.”

As a validator, Deutsche Telekom MMS will ensure network security as more devices, users, and services integrate with the Fetch.ai blockchain. Built on the Cosmos protocol, Fetch.ai operates as a permissionless decentralised network with open-source code that is accessible globally.  

The collaboration demonstrates how blockchain can unlock AI’s potential by providing reliable, transparent data while AI can help securely analyse blockchain transactions. Together, these technologies lay the foundations for a decentralised Web3 internet that empowers user privacy and control.

“This partnership signals real progress in integrating AI and Web3 innovations into the machine economy,” said Fetch.ai CEO Humayun Sheikh.

Deutsche Telekom and Fetch.ai will be working together at one of Europe’s largest AI and IoT hackathons, Bosch Connected Experience, on 28-29 February 2024.

See also: Telcos to spend $20B on AI network orchestration by 2028

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 BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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

The post Fetch.ai and Deutsche Telekom partner to converge AI and blockchain appeared first on AI News.

Exfluency is a tech company providing hybrid intelligence solutions for multilingual communication. By harnessing AI and blockchain technology we provide tech-savvy companies with access to modern language tools. Our goal is to make linguistic assets as precious as any other corporate asset.

What tech trends have you noticed developing in the multilingual communication space?

As in every other walk of life, AI in general and ChatGPT specifically is dominating the agenda. Companies operating in the language space are either panicking or scrambling to play catch-up. The main challenge is the size of the tech deficit in this vertical. Innovation and, more especially AI-innovation is not a plug-in.

What are some of the benefits of using LLMs?

Off the shelf LLMs (ChatGPT, Bard, etc.) have a quick-fix attraction. Magically, it seems, well formulated answers appear on your screen. One cannot fail to be impressed.

The true benefits of LLMs will be realised by the players who can provide immutable data with which feed the models. They are what we feed them.

What do LLMs rely on when learning language?

Overall, LLMs learn language by analysing vast amounts of text data, understanding patterns and relationships, and using statistical methods to generate contextually appropriate responses. Their ability to generalise from data and generate coherent text makes them versatile tools for various language-related tasks.

Large Language Models (LLMs) like GPT-4 rely on a combination of data, pattern recognition, and statistical relationships to learn language. Here are the key components they rely on:

1. Data: LLMs are trained on vast amounts of text data from the internet. This data includes a wide range of sources, such as books, articles, websites, and more. The diverse nature of the data helps the model learn a wide variety of language patterns, styles, and topics.
2. Patterns and Relationships: LLMs learn language by identifying patterns and relationships within the data. They analyze the co-occurrence of words, phrases, and sentences to understand how they fit together grammatically and semantically.
3. Statistical Learning: LLMs use statistical techniques to learn the probabilities of word sequences. They estimate the likelihood of a word appearing given the previous words in a sentence. This enables them to generate coherent and contextually relevant text.
4. Contextual Information: LLMs focus on contextual understanding. They consider not only the preceding words but also the entire context of a sentence or passage. This contextual information helps them disambiguate words with multiple meanings and produce more accurate and contextually appropriate responses.
5. Attention Mechanisms: Many LLMs, including GPT-4, employ attention mechanisms. These mechanisms allow the model to weigh the importance of different words in a sentence based on the context. This helps the model focus on relevant information while generating responses.
6. Transfer Learning: LLMs use a technique called transfer learning. They are pretrained on a large dataset and then fine-tuned on specific tasks. This allows the model to leverage its broad language knowledge from pretraining while adapting to perform specialised tasks like translation, summarisation, or conversation.
7. Encoder-Decoder Architecture: In certain tasks like translation or summarisation, LLMs use an encoder-decoder architecture. The encoder processes the input text and converts it into a context-rich representation, which the decoder then uses to generate the output text in the desired language or format.
8. Feedback Loop: LLMs can learn from user interactions. When a user provides corrections or feedback on generated text, the model can adjust its responses based on that feedback over time, improving its performance.

What are some of the challenges of using LLMs?

A fundamental issue, which has been there ever since we started giving away data to Google, Facebook and the like, is that “we” are the product. The big players are earning untold billions on our rush to feed their apps with our data. ChatGPT, for example, is enjoying the fastest growing onboarding in history. Just think how Microsoft has benefitted from the millions of prompts people have already thrown at it.

The open LLMs hallucinate and, because answers to prompts are so well formulated, one can be easily duped into believing what they tell you.
And to make matters worse, there are no references/links to tell you from where they sourced their answers.

How can these challenges be overcome?

LLMs are what we feed them. Blockchain technology allows us to create an immutable audit trail and with it immutable, clean data. No need to trawl the internet. In this manner we are in complete control of what data is going in, can keep it confidential, and support it with a wealth of useful meta data. It can also be multilingual!

Secondly, as this data is stored in our databases, we can also provide the necessary source links. If you can’t quite believe the answer to your prompt, open the source data directly to see who wrote it, when, in which language and which context.

What advice would you give to companies that want to utilise private, anonymised LLMs for multilingual communication?

Make sure your data is immutable, multilingual, of a high quality – and stored for your eyes only. LLMs then become a true game changer.

What do you think the future holds for multilingual communication?

As in many other walks of life, language will embrace forms of hybrid intelligence. For example, in the Exfluency ecosystem, the AI-driven workflow takes care of 90% of the translation – our fantastic bilingual subject matter experts then only need to focus on the final 10%. This balance will change over time – AI will take an ever-increasing proportion of the workload. But the human input will remain crucial. The concept is encapsulated in our strapline: Powered by technology, perfected by people.

What plans does Exfluency have for the coming year?

Lots! We aim to roll out the tech to new verticals and build communities of SMEs to serve them. There is also great interest in our Knowledge Mining app, designed to leverage the information hidden away in the millions of linguistic assets. 2024 is going to be exciting!

• Jaromir Dzialo is the co-founder and CTO of Exfluency, which offers affordable AI-powered language and security solutions with global talent networks for organisations of all sizes.

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 Digital Transformation Week.

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

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While making some recovery in the past couple of weeks, the crypto market has suffered a horrid couple of months. Bitcoin crashed around 50 percent between November 2021 and the end of January 2022. As of writing, the largest cryptocurrency remains around 37 percent down while many “altcoins” have still lost over 50 percent of their value.

DynaSets combine AI algorithms with professional hedge fund traders in a bid to maximise profits and minimise losses in a notoriously volatile market (although the same could be said for the “stonk” market in recent weeks…)

Marcello Mari, CEO of SingularityDAO, said:

“I’m impressed by the preliminary results from the beta version of our DynaSets.

Over the next month, we’ll be further empowering our traders with more tools including the ability to short the market and execute trades with leverage.

We’ll also be launching real machine learning tools that have never been used in the crypto market before.”

Since the beta launch of DynaSets on 20 December 2021:

• Bitcoin DynaSet shows 10.3% better performance over just “hodling” Bitcoin
• Ethereum DynaSet shows 12.59% better performance over hodling Ethereum

“As we move closer to our 1.0 product offering, we will further improve on the performance we demonstrated with our beta. That is, deliver further and further automation with a constant focus on crypto asset protection and AI safety,” commented Chris Poulin, CTO.

The combination of SingularityDAO’s AI algorithms and professional traders were reportedly able to identify potential triggers for the crypto decline including the Fed’s hawkish policy shift, electricity price rise, and political instability of the world’s second-largest producer of bitcoin, Kazakhstan.

As of writing, two DynaSets have been launched for the two largest cryptocurrencies: Bitcoin and Ethereum.

In 2020, full-stack AI solution SingularityNET announced that it was collaborating with Cardano due to Ethereum’s issues.

Earlier this month, Goertzel announced HyperCycle—a lightweight layer 2 architecture designed to enable inexpensive, high-speed, large-scale on-chain execution of microservices and specifically designed to optimise AI-related processes.

HyperCycle leverages TODA/IP ledgerless protocol, SingularityNET’s Proof of Reputation, and Cardano’s EUTxO model and Hydra sidechain framework.

Cardano seems a prime candidate for one of the next DynaSets that SingularityDAO launches.

(Photo by Jared Schwitzke on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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70 percent of the 2,341 respondents across 30 business sectors picked AR as disrupting their industry most out of a selection of seven emerging technologies: AI, cybersecurity, cloud computing, IoT, blockchain, and 5G.

Filipe Oliveira, Senior Analyst at GlobalData, commented: “This change in how people see AR will likely be long term, and not just a temporary blip. It is clear that people are warming towards the technology, even if they don’t believe that it will make a big difference tomorrow.” 

AI wins some ground back when it comes to confidence in the technology. 57 percent of the respondents believe that AI will live up to all of its promises compared to just 26 percent for AR.

Along those same lines, 31 percent believe “The technology is hyped, but I can see a use for it” for AI, while a huge 50 percent report the same for AR.

Apple’s decision to add a LiDAR sensor to its latest mobile devices was seen as an important step towards the mass adoption of AR. Excitement is also growing around so-called “metaverses” that converge virtually-enhanced physical reality with physically-persistent shared virtual spaces.

SenseTime, one of China’s leading AI companies, announced earlier this week that it had partnered with BilibiliWorld to create a metaverse. The experience leverages SenseTime’s AI and mixed reality technologies to enable players to enjoy role-playing games that seamlessly blend reality with virtuality.

Facebook CEO Mark Zuckerberg recently said the company “will effectively transition from people seeing us as primarily being a social media company to being a metaverse company”. As the owner of Oculus, Zuckerberg’s plans for the future of Facebook will likely make people think of a large virtual space similar to that depicted in Ernest Cline’s Ready Player One novel and the 2018 film adaptation.

Some people have expressed concern about a large centralised company such as Facebook having control over such a potentially ubiquitous world and the content they consume. Many believe that an open-source decentralised version is vital:

Zuckerberg, for his part, claims that no one company will run the metaverse and it will be an “embodied internet” that is operated by many different players.

Decentraland is an early example of what a truly decentralised virtual space could look like. The platform makes use of a DAO (Decentralised Autonomous Organisation) to make policy decisions such as what content is allowed in addition to taking advantage of the NFT (Non-Fungible Token) trend to offer exclusive in-world items.

AR and AI are both important emerging technologies that can often go hand-in-hand, but it’s clear that the latter is losing its perspective among professionals as having the biggest impact on their industries over the coming years.

(Photo by My name is Yanick on Unsplash)

Want to find out more from executives and thought leaders in this space? Find out more about the Digital Twin World event, taking place on 8-9 September 2021, which will explore augmenting business outcomes in more depth and the industries that will benefit.

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Fetch.ai made our innovative companies to watch in 2021 list for its grand vision to build a decentralised network of autonomous “agents” that perform real-world tasks. For most companies, that plan could sound almost impossibly ambitious—but Fetch.ai has the talent and resources to pull it off and continues to gain votes of confidence by signing partnerships with the likes of Bosch, Festo, and IOTA.

The company’s mainnet went live in March 2021 and has been ramping up its announcements since.

The new DeFi Agents toolkit app is the latest in a barrage of announcements and allows users to customise stop-loss parameters on decentralised exchanges. Stop-Loss agents can automatically withdraw funds from liquidity pools if the exchange rate between the two tokens falls to a predetermined level to combat the risk of impermanent loss that liquidity providers face.

Humayun Sheikh, CEO of Fetch.ai, said:

“Intelligent automation has the potential to transform the end-to-end experience of the DeFi applications we use today.

Rather than constantly monitoring price action and having to manually withdraw liquidity, Fetch.ai DeFi Agents simplify and streamline that whole process for LPs.

By improving upon the current experience for LPs on popular DEXes like Uniswap and PancakeSwap, we have created the catalyst for the adoption and usage of DeFi applications.” 

Users will initially be able to create up to five agents with stop-loss triggers for all liquidity pools on Uniswap and PancakeSwap. Agents can be tracked and updated through a dedicated dashboard.

Customisations of the DeFi agents can include:

• Swap support: allows a swap to be performed at a given threshold for Uniswap v2 and PancakeSwap pools.
• APY Monitoring: will withdraw liquidity if APY is less than a specified amount. 
• ETH fund management I: Deposits funds in a contract and uses it to automatically top-up one-or-more pools.
• ETH fund management II: Allows agents to swap ERC-20s or BEP-20s for ETH if funds drop below a predetermined level.
• Strategy Creator: Creates IFTTT (If This Then That) strategies for pool deposits/withdrawals. 
• Portfolio management (Uniswap v3): Defines a weighting for a sector (e.g. oracles, AI, NFTs) and implements an ETF-like strategy (similar to market-weighted Balancer pool).
• Private Uniswap v3 strategy: Hides strategies behind the agent.

Over time, Fetch.ai plans to extend the functionality of its DeFi Agent tool to support further automatic deposit and withdrawal of liquidity based on conditions including token sentiment, move liquidity of ERC-20s or BEP-20s to a defined range if the price is breached (in Uniswap V3), and remove liquidity if ETH fees are becoming too high in a pre-set period of time.

You can get started with the DeFi agents app from Fetch.ai here.

(Image Credit: Fetch.ai)

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Fetch.ai has caught the attention of investors for its potentially groundbreaking machine learning network of autonomous “agents” that can perform real-world tasks. It’s also caught our attention, making our list of innovative companies to watch in 2021.

IOTA was among the most hyped projects during the 2017 cryptocurrency/blockchain frenzy (or bubble, dependent on your perspective.) While most projects have since collapsed, IOTA continues to go from strength-to-strength and has announced a series of partnerships with giants like Dell and Jaguar Land Rover.

One partnership that both companies have in common is Bosch.

IOTA was the first to establish a partnership with Bosch when it announced that it was integrating with IOTA’s Data Marketplace. Last February, Fetch.ai announced its own partnership with Bosch as part of a joint project called the Economy of Things (EoT).

No specific project involving Fetch.ai, IOTA, and Bosch has been announced but it wouldn’t be surprising to see the first real-world adoption coming from the three companies.

While we can speculate on the exciting future possibilities, what’s actually been announced today is an agreement between Fetch.ai and IOTA to deliver a controlled data sharing environment for IoT devices and infrastructures. The ultimate goal is to enable the automated fee-less retrieval and private sharing of data.

Humayun Sheikh, CEO of Fetch.ai, said:

“The fundamental goal of this collaboration is to enable granular control over data and to reduce the reliance on centralised systems that take advantage of data.

While there are numerous partnerships focusing on data privacy, this one adds the layer of economic benefit for stakeholders via autonomous economic agents without compromising data privacy.

Enabling these agents to perform “useful economic work” on behalf of individuals, businesses, companies, and other entities or organizations will speed up the adoption of Fetch.ai autonomous economic agents and IOTA Streams thereby allowing them to communicate with sophistication across industries like mobility, supply chain, IoT and more.”

Holger Koether, Director of Partner Management at IOTA Foundation, added:

“The main feature of this partnership is offering a serious AI application for the devices that make up the machine economy on the IOTA and Fetch networks.

We are enabling data producers to take control over who can access the data they produce whether from a mobile device, environment IoT sensor, connected vehicle, Industrial IoT solution, and a host of IoT-focused use cases.

With this partnership, we hope both the Fetch and IOTA communities expand the functionally of what devices can do autonomously on distributed networks.”

IOTA has faced various criticisms over the years but the project now seems to have overcome most of its problems. The one major lingering criticism is of centralisation due to its ‘Coordinator’ node. However, that is set for removal in IOTA 2.0.

The IOTA 2.0 ‘Nectar’ devnet went live last week. For the first time, assets on the network will be persistent even after upgrades.

“IOTA 2.0 DevNet ‘Nectar’ marks a major milestone on IOTA’s trajectory towards full decentralisation,” commented Dominik Schiener, Co-Founder and Chairman of the IOTA Foundation. “I cannot understate the importance of this release: Nectar is the living proof that you can have feeless, scalable, secure, resource-efficient, and fully decentralised DLT. It’s a game-changer.”

With IOTA 2.0 heading towards its first release candidate later this year, we can expect many more announcements in the months to come.

Want to learn more from executives at the heart of this space? The Blockchain IoT Solutions Congress on March 8 2022 will explore the convergence of these two technologies and the use cases and industries that will benefit.

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