Released on April 17, this “thinking budget” feature responds to a growing industry challenge: advanced AI models frequently overanalyse straightforward queries, consuming unnecessary computational resources and driving up operational and environmental costs.

While not revolutionary, the development represents a practical step toward addressing efficiency concerns that have emerged as reasoning capabilities become standard in commercial AI software.

The new mechanism enables precise calibration of processing resources before generating responses, potentially changing how organisations manage financial and environmental impacts of AI deployment.

“The model overthinks,” acknowledges Tulsee Doshi, Director of Product Management at Gemini. “For simple prompts, the model does think more than it needs to.”

The admission reveals the challenge facing advanced reasoning models – the equivalent of using industrial machinery to crack a walnut.

The shift toward reasoning capabilities has created unintended consequences. Where traditional large language models primarily matched patterns from training data, newer iterations attempt to work through problems logically, step by step. While this approach yields better results for complex tasks, it introduces significant inefficiency when handling simpler queries.

Balancing cost and performance

The financial implications of unchecked AI reasoning are substantial. According to Google’s technical documentation, when full reasoning is activated, generating outputs becomes approximately six times more expensive than standard processing. The cost multiplier creates a powerful incentive for fine-tuned control.

Nathan Habib, an engineer at Hugging Face who studies reasoning models, describes the problem as endemic across the industry. “In the rush to show off smarter AI, companies are reaching for reasoning models like hammers even where there’s no nail in sight,” he explained to MIT Technology Review.

The waste isn’t merely theoretical. Habib demonstrated how a leading reasoning model, when attempting to solve an organic chemistry problem, became trapped in a recursive loop, repeating “Wait, but…” hundreds of times – essentially experiencing a computational breakdown and consuming processing resources.

Kate Olszewska, who evaluates Gemini models at DeepMind, confirmed Google’s systems sometimes experience similar issues, getting stuck in loops that drain computing power without improving response quality.

Granular control mechanism

Google’s AI reasoning control provides developers with a degree of precision. The system offers a flexible spectrum ranging from zero (minimal reasoning) to 24,576 tokens of “thinking budget” – the computational units representing the model’s internal processing. The granular approach allows for customised deployment based on specific use cases.

Jack Rae, principal research scientist at DeepMind, says that defining optimal reasoning levels remains challenging: “It’s really hard to draw a boundary on, like, what’s the perfect task right now for thinking.”

Shifting development philosophy

The introduction of AI reasoning control potentially signals a change in how artificial intelligence evolves. Since 2019, companies have pursued improvements by building larger models with more parameters and training data. Google’s approach suggests an alternative path focusing on efficiency rather than scale.

“Scaling laws are being replaced,” says Habib, indicating that future advances may emerge from optimising reasoning processes rather than continuously expanding model size.

The environmental implications are equally significant. As reasoning models proliferate, their energy consumption grows proportionally. Research indicates that inferencing – generating AI responses – now contributes more to the technology’s carbon footprint than the initial training process. Google’s reasoning control mechanism offers a potential mitigating factor for this concerning trend.

Competitive dynamics

Google isn’t operating in isolation. The “open weight” DeepSeek R1 model, which emerged earlier this year, demonstrated powerful reasoning capabilities at potentially lower costs, triggering market volatility that reportedly caused nearly a trillion-dollar stock market fluctuation.

Unlike Google’s proprietary approach, DeepSeek makes its internal settings publicly available for developers to implement locally.

Despite the competition, Google DeepMind’s chief technical officer Koray Kavukcuoglu maintains that proprietary models will maintain advantages in specialised domains requiring exceptional precision: “Coding, math, and finance are cases where there’s high expectation from the model to be very accurate, to be very precise, and to be able to understand really complex situations.”

Industry maturation signs

The development of AI reasoning control reflects an industry now confronting practical limitations beyond technical benchmarks. While companies continue to push reasoning capabilities forward, Google’s approach acknowledges a important reality: efficiency matters as much as raw performance in commercial applications.

The feature also highlights tensions between technological advancement and sustainability concerns. Leaderboards tracking reasoning model performance show that single tasks can cost upwards of $200 to complete – raising questions about scaling such capabilities in production environments.

By allowing developers to dial reasoning up or down based on actual need, Google addresses both financial and environmental aspects of AI deployment.

“Reasoning is the key capability that builds up intelligence,” states Kavukcuoglu. “The moment the model starts thinking, the agency of the model has started.” The statement reveals both the promise and the challenge of reasoning models – their autonomy creates both opportunities and resource management challenges.

For organisations deploying AI solutions, the ability to fine-tune reasoning budgets could democratise access to advanced capabilities while maintaining operational discipline.

Google claims Gemini 2.5 Flash delivers “comparable metrics to other leading models for a fraction of the cost and size” – a value proposition strengthened by the ability to optimise reasoning resources for specific applications.

Practical implications

The AI reasoning control feature has immediate practical applications. Developers building commercial applications can now make informed trade-offs between processing depth and operational costs.

For simple applications like basic customer queries, minimal reasoning settings preserve resources while still using the model’s capabilities. For complex analysis requiring deep understanding, the full reasoning capacity remains available.

Google’s reasoning ‘dial’ provides a mechanism for establishing cost certainty while maintaining performance standards.

See also: Gemini 2.5: Google cooks up its ‘most intelligent’ AI model to date

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 a lot of emerging technologies are playing a role in the evolution of the finance industry, the AI revolution is one of the most prominent. With that in mind, let us look at the ways AI is transforming financial platforms, starting with a brief overview of the role AI plays in financial services.

Understanding AI in financial services

AI is changing the landscape of the financial services sector, as in other industries. The integration of AI in the financial services industry extends to changes in how companies operate, customer interactions, agentic AI, and risk management.

The three core AI-related technologies that play an important role in the finance sector, are:

• Natural language processing (NLP): The NLP aspect of AI helps companies understand and interpret human language, and is used for sentiment analysis or customer service automation through chatbots.

• Machine learning (ML): AI can let financial systems learn from past data and improve performance with minimal human intervention. ML algorithms can analyse large data volumes and make important predictions about investment opportunities and market trends.

• Predictive analytics: Businesses can use machine learning techniques and AI algorithms to identify the likelihood of certain outcomes based on historical data. Companies can use predictive analytics for better accuracy in fraud detection or risk assessment.

It also helps that AI has already been adopted to a certain extent in the financial sector. Around 70% of financial institutions and companies currently invest in AI technologies, according to a 2024 report by Gartner. Moreover, around 58% of finance functions use AI in some capacity.

AI-integrated strategies in finance

For AI integration in the finance sector to be truly successful and unlock the untapped potential in a company, strategies using the technology must be well-defined. With robust strategies, finance companies, and service providers can ensure AI prepares them for a more profitable future.

Among other areas, three important areas of or strategies for financial services that currently use AI at a much bigger scale, are:

Risk management

While risk management is an essential business function in many companies and industries, it is especially important for financial institutions. With the help of advanced algorithms and data analytics, financial organisations can take a proactive approach to identifying, assessing, and mitigating risks. As a result, you can avoid issues like leakage of revenue or loss of important data.

AI models can help with credit risk assessment of businesses and individuals by analysing large datasets. Financial companies can also use AI-based systems to monitor transactions in real-time and identify unusual patterns pointing to fraudulent activity. Additionally, financial analysts use AI to conduct market risk analysis and predict market volatility by processing extensive market data.

Compliance and regulatory monitoring

As the financial industry faces increasing regulatory scrutiny, organisations have to invest and implement robust strategies for compliance management. AI systems can help organisations automate the checking of transactions for compliance with anti-money laundering laws, and flag down suspicious activity.

Many financial service providers are developing AI-driven risk assessment frameworks that help them identify and prevent compliance risks. Plus, they also use AI to streamline reporting processes to ensure timely submission of regulatory documents and the generation of compliance reports. Lastly, processes must align with the necessary AI regulations.

Personalisation of communication and products/services

AI can also help financial organisations provide highly-personalised services to customers by analysing their preferences and requirements. By using data analytics, banks and financial organisations can provide tailored financial products that meet their specific needs. AI-powered chatbots and virtual assistants help customers get instant support and answers to queries in real-time.

Financial companies should conduct continuous and consistent analysis of transactions and customer interactions to identify robust trends and deliver targeted and highly relevant marketing and promotional messages to customers.

AI-powered tools on financial platforms

The aforementioned strategies help financial companies provide unique and high-quality services to customers. Most financial platforms offer different kinds of AI-powered tools that add several value-adding features and abilities.

Here are some of the AI-powered financial tools to know about:

AI chatbots and virtual assistants

The quality of customer service is important to the success of any financial institution or organisation. Most financial companies use AI-powered chatbots and virtual assistants to provide excellent service to customers. Chatbots can ensure timely communication, helping companies humanise AI responses, and resolve queries for customers.

Enterprise AI agents

For larger financial organisations that offer multiple services, products or operate in many locations, an enterprise management strategy is a must. A lot of companies implement enterprise AI agent platforms that help automate repetitive actions and tasks when an event or feature is triggered.

Fraud detection system

Most financial platforms use a fraud detection system to monitor transactions in real-time and flag any suspicious instances to combat fraud. The systems also help companies monitor market conditions and user behaviour to detect any unusual patterns immediately.

Data mining tools

Most financial platforms handle large volumes of financial data that can be analysed and monitored to generate valuable insights. Data mining tools can help navigate this situation by extracting insights from large data volumes with the help of machine learning algorithms. It is possible to identify patterns and trends to inform strategic and financial decisions.

Automated trading systems

AI-powered automated trading systems help companies execute trades based on predetermined criteria. Automated trading systems aid financial organisations enhance efficiency in trades and react to market changes faster than humans.

The future of AI in financial systems and services

As the financial services industry evolves, so do the role and applications of AI in the industry. Companies should keep track of emerging trends to steer the success of financial service provision.

When integrating AI technologies into financial processes, it is important for companies to choose the right platforms to ensure smooth and efficient implementation. This brings us to a comparison of Sitecore vs. WordPress – two web platforms popular in the financial services space.

While Sitecore offers a highly personalised experience for customers, making it ideal for large financial institutions with complex needs, WordPress provides an affordable and scalable solution for smaller institutions or those just beginning their AI integration journey. Understanding the strengths and limitations of each platform can help financial organisations choose the best way to adopt AI solutions.

Some industry solutions include personalised financial services tailored to the preferences and risk appetite of customers, and decentralised finance solutions that could automate lending, borrowing and trading decisions effectively.

Many financial companies are looking to implement advanced risk management tools that use AI to assess risks and predict market disruptions proactively.

The integration of AI in financial processes may be slow but it is inexorable, making it important for companies to consider implementing the technology sooner rather than later. With effective AI integration, financial companies can enjoy better operational efficiency and enhanced customer experience in the long-term.

Conclusion

The role of AI in the financial industry has been discussed and debated for some time. While most financial applications and platforms use AI to strengthen or automate certain processes, others use it to add new functions and features to the existing platform.

(Image source: Unsplash)

The post How AI transforms financial platforms: Tools and strategies appeared first on AI News.

The Alibaba-owned company has used chips from domestic suppliers, including those tied to its parent, Alibaba, and Huawei Technologies to train large language models using the Mixture of Experts (MoE) method. The results were reportedly comparable to those produced with Nvidia’s H800 chips, sources claim. While Ant continues to use Nvidia chips for some of its AI development, one sources said the company is turning increasingly to alternatives from AMD and Chinese chip-makers for its latest models.

The development signals Ant’s deeper involvement in the growing AI race between Chinese and US tech firms, particularly as companies look for cost-effective ways to train models. The experimentation with domestic hardware reflects a broader effort among Chinese firms to work around export restrictions that block access to high-end chips like Nvidia’s H800, which, although not the most advanced, is still one of the more powerful GPUs available to Chinese organisations.

Ant has published a research paper describing its work, stating that its models, in some tests, performed better than those developed by Meta. Bloomberg News, which initially reported the matter, has not verified the company’s results independently. If the models perform as claimed, Ant’s efforts may represent a step forward in China’s attempt to lower the cost of running AI applications and reduce the reliance on foreign hardware.

MoE models divide tasks into smaller data sets handled by separate components, and have gained attention among AI researchers and data scientists. The technique has been used by Google and the Hangzhou-based startup, DeepSeek. The MoE concept is similar to having a team of specialists, each handling part of a task to make the process of producing models more efficient. Ant has declined to comment on its work with respect to its hardware sources.

Training MoE models depends on high-performance GPUs which can be too expensive for smaller companies to acquire or use. Ant’s research focused on reducing that cost barrier. The paper’s title is suffixed with a clear objective: Scaling Models “without premium GPUs.” [our quotation marks]

The direction taken by Ant and the use of MoE to reduce training costs contrast with Nvidia’s approach. CEO Officer Jensen Huang has said that demand for computing power will continue to grow, even with the introduction of more efficient models like DeepSeek’s R1. His view is that companies will seek more powerful chips to drive revenue growth, rather than aiming to cut costs with cheaper alternatives. Nvidia’s strategy remains focused on building GPUs with more cores, transistors, and memory.

According to the Ant Group paper, training one trillion tokens – the basic units of data AI models use to learn – cost about 6.35 million yuan (roughly $880,000) using conventional high-performance hardware. The company’s optimised training method reduced that cost to around 5.1 million yuan by using lower-specification chips.

Ant said it plans to apply its models produced in this way – Ling-Plus and Ling-Lite – to industrial AI use cases like healthcare and finance. Earlier this year, the company acquired Haodf.com, a Chinese online medical platform, to further Ant’s ambition to deploy AI-based solutions in healthcare. It also operates other AI services, including a virtual assistant app called Zhixiaobao and a financial advisory platform known as Maxiaocai.

“If you find one point of attack to beat the world’s best kung fu master, you can still say you beat them, which is why real-world application is important,” said Robin Yu, chief technology officer of Beijing-based AI firm, Shengshang Tech.

Ant has made its models open source. Ling-Lite has 16.8 billion parameters – settings that help determine how a model functions – while Ling-Plus has 290 billion. For comparison, estimates suggest closed-source GPT-4.5 has around 1.8 trillion parameters, according to MIT Technology Review.

Despite progress, Ant’s paper noted that training models remains challenging. Small adjustments to hardware or model structure during model training sometimes resulted in unstable performance, including spikes in error rates.

(Photo by Unsplash)

See also: DeepSeek V3-0324 tops non-reasoning AI models in open-source first

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.

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The study’s goal was to interpret AI’s role in corporate budgeting, examining how well such technology performs when making financial decisions. Ultimately, it’s an investigation into whether AI’s financial decisions align with a company’s long-term strategies and how its decisions compare to human management.

The researchers, Kristof Stouthuysen, Professor of Management Accounting and Digital Finance at Vlerick Business School, and PhD researcher, Emma Willems, studied tactical and strategic budgeting approaches.

Tactical budgeting is about quick, responsive decisions, referring to short-term, data-driven financial decisions. These are aimed at improving immediate performance, like making adjustments to spending based on market trends.

Strategic budgeting typically involves a more comprehensive approach that focuses on future planning, aligning various resources with a business’s vision.

According to the research, AI is superior when performing tactical budgeting processes like cost management and resource allocation. However, the need for human insight remains important to ensure accurate and strategic financial planning over the long term.

The controlled experiment was achieved by running a management simulation where experienced managers were asked to allocate budgets for a hypothetical automotive parts company. Stouthuysen and Willems then compared these human-made decisions to those produced by an AI algorithm using the same financial data.

The results concluded that AI was superior in optimising budgets when a company’s strategic financial planning was clearly defined. However, AI struggled to make budgeting decisions when key performance indicators (KPIs) did not align with the company’s financial goals.

Stouthuysen and Willems work on the study emphasised the importance of a collaboration between humans and AI. “As AI continues to evolve, companies that use its strengths in tactical budgeting while maintaining human oversight in strategic planning will gain a competitive edge. The key is knowing where AI should lead and where human intuition remains indispensable.”

According to the study, AI can theoretically take over from humans when it comes to tactical budgeting, providing more precise and efficient outcomes. Stouthuysen and Willems believe companies need to define their strategic priorities clearly and implement AI for tactical budget-making decisions to maximise financial performances and achieve sustainable growth.

The findings challenge the widespread misconception that AI can completely substitute the need for humans in budgeting. Instead, this research emphasises the importance of taking a balanced approach, utilising both AI and humans, assigning tasks to silicon or human processes according to their proven abilities.

(Image source: “Payday” by 401(K) 2013 is licensed under CC BY-SA 2.0.)

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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GITEX ASIA 2025 will bring together 700+ tech companies, featuring 400+ startups and digital promotion agencies, and 250+ global investors & VCs from 60+ countries.

The event will serve as a bridge between the Eastern and Western technology ecosystems and feature 180+ hours of expert insights from 220 global thought leaders.

GITEX ASIA 2025 is set to foster cross-border collaboration, investment, and innovation, connecting global tech enterprises, unicorn founders, policymakers, SMEs, and academia to shape the future of digital transformation in Asia.

GITEX ASIA 2025 will comprise of five co-located events:

• AI EVERTYTHING SINGAPORE – the AI showcase.
• NORTHSTAR ASIA – for startups and investors
• GITEX CYBER VALLEY ASIA – helping create a defence ecosystem for governments and businesses
• GITEX QUANTUM QUANTUM EXPO ASIA – Asia’s quantum frontier
• GITEX DIGI HEALTH & BIOTECH SINGAPORE – the healthcare revolution

GITEX ASIA 2025 will host a lineup of conferences and summits, exploring a range of transformative trends in technology and investment. Key themes will include AI, cloud & connectivity, cybersecurity, quantum, health tech & biotech, green tech & smart cities, startups & investors, and SMEs.

Sessions will include Asia Digital AI Economy, AI Everything: AI Adoption & Commercialisation, Cybersecurity: AI-Enabled Cybersecurity & Critical Infrastructure, Digital Health, and the Supernova Pitch Competition.

The event will bring together leading voices and ideas from different industries, including public services, retail, finance, education, health, and manufacturing.

Be part of the action at GITEX ASIA 2025 and witness the future of technology unfold in Singapore. For more information and updates on GITEX ASIA, visit www.gitexasia.com

Social media links: LinkedIn | X | Facebook | Instagram | YouTube

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Enhanced observability and monitoring of model behaviours, along with a focus on data lineage can help identify when LLMs have been compromised. These techniques are crucial in strengthening the security of an organisation’s Gen AI products. Additionally, new debugging techniques can ensure optimal performance for those products.

It’s important, then, that given the rapid pace of adoption, organisations should take a more cautious approach when developing or implementing LLMs to safeguard their investments in AI.

Establishing guardrails

The implementation of new Gen AI products significantly increases the volume of data flowing through businesses today. Organisations must be aware of the type of data they provide to the LLMs that power their AI products and, importantly, how this data will be interpreted and communicated back to customers.

Due to their non-deterministic nature, LLM applications can unpredictably “hallucinate”, generating inaccurate, irrelevant, or potentially harmful responses. To mitigate this risk, organisations should establish guardrails to prevent LLMs from absorbing and relaying illegal or dangerous information.

Monitoring for malicious intent

It’s also crucial for AI systems to recognise when they are being exploited for malicious purposes. User-facing LLMs, such as chatbots, are particularly vulnerable to attacks like jailbreaking, where an attacker issues a malicious prompt that tricks the LLM into bypassing the moderation guardrails set by its application team. This poses a significant risk of exposing sensitive information.

Monitoring model behaviours for potential security vulnerabilities or malicious attacks is essential. LLM observability plays a critical role in enhancing the security of LLM applications. By tracking access patterns, input data, and model outputs, observability tools can detect anomalies that may indicate data leaks or adversarial attacks. This allows data scientists and security teams proactively identify and mitigate security threats, protecting sensitive data, and ensuring the integrity of LLM applications.

Validation through data lineage

The nature of threats to an organisation’s security – and that of its data – continues to evolve. As a result, LLMs are at risk of being hacked and being fed false data, which can distort their responses. While it’s necessary to implement measures to prevent LLMs from being breached, it is equally important to closely monitor data sources to ensure they remain uncorrupted.

In this context, data lineage will play a vital role in tracking the origins and movement of data throughout its lifecycle. By questioning the security and authenticity of the data, as well as the validity of the data libraries and dependencies that support the LLM, teams can critically assess the LLM data and accurately determine its source. Consequently, data lineage processes and investigations will enable teams to validate all new LLM data before integrating it into their Gen AI products.

A clustering approach to debugging

Ensuring the security of AI products is a key consideration, but organisations must also maintain ongoing performance to maximise their return on investment. DevOps can use techniques such as clustering, which allows them to group events to identify trends, aiding in the debugging of AI products and services.

For instance, when analysing a chatbot’s performance to pinpoint inaccurate responses, clustering can be used to group the most commonly asked questions. This approach helps determine which questions are receiving incorrect answers. By identifying trends among sets of questions that are otherwise different and unrelated, teams can better understand the issue at hand.

A streamlined and centralised method of collecting and analysing clusters of data, the technique helps save time and resources, enabling DevOps to drill down to the root of a problem and address it effectively. As a result, this ability to fix bugs both in the lab and in real-world scenarios improves the overall performance of a company’s AI products.

Since the release of LLMs like GPT, LaMDA, LLaMA, and several others, Gen AI has quickly become more integral to aspects of business, finance, security, and research than ever before. In their rush to implement the latest Gen AI products, however, organisations must remain mindful of security and performance. A compromised or bug-ridden product could be, at best, an expensive liability and, at worst, illegal and potentially dangerous. Data lineage, observability, and debugging are vital to the successful performance of any Gen AI investment.  

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.

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A key advocate for responsible AI governance and diversity in tech, Wendy has played a crucial role in global discussions on the future of AI.

In our Q&A, we spoke to her about the gender imbalance in the AI industry, the ethical implications of emerging technologies, and how businesses can harness AI while ensuring it remains an asset to humanity.

The AI sector remains heavily male-dominated. Can you share your experience of breaking into the industry and the challenges women face in achieving greater representation in AI and technology?

It’s incredibly frustrating because I wrote my first paper about the lack of women in computing back in 1987, when we were just beginning to teach computer science degree courses at Southampton. That October, we arrived at the university and realised we had no women registered on the course — none at all.

So, those of us working in computing started discussing why that was the case. There were several reasons. One significant factor was the rise of the personal computer, which was marketed as a toy for boys, fundamentally changing the culture. Since then, in the West — though not as much in countries like India or Malaysia — computing has been seen as something nerdy, something that only ‘geeks’ do. Many young girls simply do not want to be associated with that stereotype. By the time they reach their GCSE choices, they often don’t see computing as an option, and that’s where the problem begins.

Despite many efforts, we haven’t managed to change this culture. Nearly 40 years later, the industry is still overwhelmingly male-dominated, even though women make up more than

half of the global population. Women are largely absent from the design and development of computers and software. We apply them, we use them, but we are not part of the fundamental conversations shaping future technologies.

AI is even worse in this regard. If you want to work in machine learning, you need a degree in mathematics or computer science, which means we are funnelling an already male-dominated sector into an even more male-dominated pipeline.

But AI is about more than just machine learning and programming. It’s about application, ethics, values, opportunities, and mitigating potential risks. This requires a broad diversity of voices — not just in terms of gender, but also in age, ethnicity, culture, and accessibility. People with disabilities should be part of these discussions, ensuring technology is developed for everyone.

AI’s development needs input from many disciplines — law, philosophy, psychology, business, and history, to name just a few. We need all these different voices. That’s why I believe we must see AI as a socio-technical system to truly understand its impact. We need diversity in every sense of the word.

As businesses increasingly integrate AI into their operations, what steps should they take to ensure emerging technologies are developed and deployed ethically?

Take, for example, facial recognition. We still haven’t fully established the rules and regulations for when and how this technology should be applied. Did anyone ask you whether you wanted facial recognition on your phone? It was simply offered as a system update, and you could either enable it or not.

We know facial recognition is used extensively for surveillance in China, but it is creeping into use across Europe and the US as well. Security forces are adopting it, which raises concerns about privacy. At the same time, I appreciate the presence of CCTV cameras in car parks at night — they make me feel safer.

This duality applies to all emerging technologies, including AI tools we haven’t even developed yet. Every new technology has a good and a bad side — the yin and the yang, if you will. There are always benefits and risks.

The challenge is learning how to maximise the benefits for humanity, society and business while mitigating the risks. That’s what we must focus on — ensuring AI works in service of people rather than against them.

The rapid advancement of AI is transforming everyday life. How do you envision the future of AI, and what significant changes will it bring to society and the way we work?

I see a future where AI becomes part of the decision-making process, whether in legal cases, medical diagnoses, or education.

AI is already deeply embedded in our daily lives. If you use Google on your phone, you’re using AI. If you unlock your phone with facial recognition, that’s AI. Google Translate? AI. Speech processing, video analysis, image recognition, text generation, and natural language processing — these are all AI-driven technologies.

Right now, the buzz is around generative AI, particularly ChatGPT. It’s like how ‘Hoover’ became synonymous with vacuum cleaners — ChatGPT has become shorthand for AI. In reality, it’s just a clever interface created by OpenAI to allow public access to its generative AI model.

It feels like you’re having a conversation with the system, asking questions and receiving natural language responses. It works with images and videos too, making it seem incredibly advanced. But the truth is, it’s not actually intelligent. It’s not sentient. It’s simply predicting the next word in a sequence based on training data. That’s a crucial distinction.

With generative AI becoming a powerful tool for businesses, what strategies should companies adopt to leverage its capabilities while maintaining human authenticity in their processes and decision-making?

Generative AI is nothing to be afraid of, and I believe we will all start using it more and more. Essentially, it’s software that can assist with writing, summarising, and analysing information.

I compare it to when calculators first appeared. People were outraged: ‘How can we allow calculators in schools? Can we trust the answers they provide?’ But over time, we adapted. The finance industry, for example, is now entirely run by computers, yet it employs more people than ever before. I expect we’ll see something similar with generative AI.

People will be relieved not to have to write endless essays. AI will enhance creativity and efficiency, but it must be viewed as a tool to augment human intelligence, not replace it, because it’s simply not advanced enough to take over.

Look at the legal industry. AI can summarise vast amounts of data, assess the viability of legal cases, and provide predictive analysis. In the medical field, AI could support diagnoses. In education, it could help assess struggling students.

I envision AI being integrated into decision-making teams. We will consult AI, ask it questions, and use its responses as a guide — but it’s crucial to remember that AI is not infallible.

Right now, AI models are trained on biased data. If they rely on information from the internet, much of that data is inaccurate. AI systems also ‘hallucinate’ by generating false information when they don’t have a definitive answer. That’s why we can’t fully trust AI yet.

Instead, we must treat it as a collaborative partner — one that helps us be more productive and creative while ensuring that humans remain in control. Perhaps AI will even pave the way for shorter workweeks, giving us more time for other pursuits.

Photo by Igor Omilaev on Unsplash and AI Speakers Agency.

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 Dame Wendy Hall, AI Council: Shaping AI with ethics, diversity and innovation appeared first on AI News.

Scheduled for 5 – 6 May 2025 at the JW Marriott Hotel in Riyadh, this event will bring together 300+ data and AI professionals, including CDOs, CIOs, data scientists, AI directors, C-suite executives and many others, to explore the latest advances, tackle challenges, and unlock opportunities in data and artificial intelligence.

With the Kingdom’s data analytics market projected to reach $8.8 billion by 2030, the summit comes at an important time, offering a platform for public and private sector leaders to collaborate, innovate, and approach the nation’s Vision 2030 goals.

A platform for innovation and collaboration

Building on the success of its debut last year, which was inaugurated by a leading official from the Saudi Data & AI Authority (SDAIA), the 2025 edition promises to be bigger and more impactful.

The summit will feature:

• 25+ cutting-edge solution providers showcasing the latest technologies in data and AI.

• 50+ industry experts sharing insights on emerging trends, challenges, and opportunities.

• 300+ attendees, including data engineers, architects, AI pioneers, and decision-makers from Saudi Arabia’s largest organisations.

• 1:1 meetings to foster collaboration and evaluate tailored solutions.

• CPD-accredited sessions to help professionals enhance their skills and advance their careers.

Unveiling the future of data and AI

The summit’s agenda will look at important topics shaping the future of data and AI, including:

• Navigating open data in Saudi Arabia

• AI fusion and machine learning innovations

• Data virtualisation and the power of data mesh

• Ethical data governance and cybersecurity analytics

• Unified data cloud architectures

Discussions will be led by thought leaders from the Kingdom’s top organisations, including Ministry of Hajj & Umrah, Insurance Authority, Council of Health Insurance, NEOM, AlNASSR Club Company | PIF, and Abdul Latif Jameel United Finance.

DAMA Saudi Arabia joins as supporting partner

The Data Management Association (DAMA Saudi Arabia), the Kingdom’s largest data management community, has joined the summit as a supporting partner. The partnership underscores DAMA’s commitment to fostering a robust data management ecosystem and aligns with the summit’s mission to elevate Saudi Arabia’s position as a global leader in data and AI.

Abdulaziz Almanea, Founder & Chairman of the Board, DAMA Saudi, spoke of the importance of the summit: “Artificial intelligence is only as good as the data behind it. Quality, governance, and ethics must come first to ensure trust, accuracy, and impact. As Saudi Arabia accelerates its data-driven transformation, industry events like the Smart Data & AI Summit serve as vital platforms for bringing experts together to shape the future of AI with responsible and innovative data practices.”

A legacy of excellence

The inaugural edition of the summit set a high benchmark, with attendees praising the quality of speakers, depth of discussions, and opportunities for networking and collaboration. Nayef Al-Otaibi, VP & Chief Digital Officer at Saudi Aramco, said, “The event was well-managed, the coordination was excellent, and the quality of the speakers was above expectations. It was a beautiful experience connecting with industry experts during the panel discussions and sharing our experiences. This could basically help us establish the platform and collaborate and work together in future.”

Driving Vision 2030 forward

The Smart Data & AI Summit is a strategic initiative to support Saudi Arabia’s Vision 2030 goals. By bringing together global expertise, cutting-edge technologies, and local insights, the summit aims to:

• Accelerate the Kingdom’s digital transformation.

• Foster innovation and collaboration across industries.

• Address regulatory challenges and ethical considerations in data and AI.

• Unlock new opportunities for investment and growth in the Kingdom’s data and AI sectors.

Sudhir Ranjan Jena, CEO & Co-founder of Tradepass, the organising body, spoke of the summit’s mission: “The data & AI sector is entering a transformative chapter, fuelled by technology disruptions, heightened expectations, and the unprecedented expansion of digital tools and platforms. In the upcoming edition, we will delve into Vision 2030 goals, unlock limitless opportunities, and explore emerging trends and solutions that will play an integral role in shaping the Kingdom’s post-oil economy.”

A high-impact speaker lineup

The summit will feature an impressive roster of speakers, including:

• Dr Ahmed Alzahrani – Director of Business Intelligence and Data Analytics Centre, Ministry of Hajj & Umrah

• Hajar Alolah – Data Governance and Management Office Director, Saudi Development Bank

• Abdullah AlBar – Chief Data Officer, Abdul Latif Jameel United Finance

• Usamah Algemili – Chief Data Executive, Insurance Authority

• Jawad Saleemi – Director – AI & Cloud, Telenor

• Abbasi Poonawala – Executive Director – Enterprise Architecture, Alinma Bank

• Nawaf Alghamdi – Director – Data Analytics & AI, Council of Health Insurance

These experts will share their insights on the latest trends, challenges, and opportunities in data and AI, offering attendees strategies to drive innovation and growth in their organisations.

For more information, visit: https://saudi.smartdataseries.com/

Media contact:

Shrinkhal Sharad
PR & Communication Lead
Tradepass
Email: shrinkhals@tradepassglobal.com
Phone: + (91) 80 6166 4401

The post The Kingdom’s digital transformation showcased at Smart Data & AI Summit appeared first on AI News.

What are news API data feeds?

News API data feeds are platforms that aggregate, organise, and deliver structured news data from multiple sources, like websites, blogs, forums, and online publications. They simplify the process of gathering information from different outlets and formatting it into machine-readable formats like JSON or XML. These feeds eliminate the manual effort of collecting and curating data by presenting structured content ready to be processed.

Top 7 news API data feeds

Let’s explore seven top news API data feeds leading the industry. These tools provide businesses with real-time access, historical coverage, and features tailored to various industries.

1. Webz.io

Webz.io is one of the most comprehensive news APIs, offering both real-time and archived coverage from the open and deep web, as well as the dark web. It provides highly customisable data feeds for industries like finance, risk intelligence, and cybersecurity.

Key features:

• Access to open, deep, and dark web data.

• Advanced filters for sentiment, topic, and geographic coverage.

• Support for visualisation and actionable risk monitoring.

Use case: Media monitoring, sentiment analysis, and threat intelligence for corporate security teams and financial organisations.

Why Webz.io? Its expansive source list and deep customisation options make it ideal for specialised industries like cybersecurity and financial analytics.

2. GNews API

GNews API is a simple, lightweight platform that aggregates reliable news from around the globe. It is perfect for small-scale applications or developers looking for affordable yet efficient solutions.

Key features:

• Real-time global coverage.

• Filters for topics, languages, and countries.

• Affordable pricing plans suitable for startups.

Use case: Localisation-focused news widgets or small aggregators serving specific regional or language-based audiences.

Why GNews? Its intuitive design and affordability make GNews a great entry point for developers and startups.

3. The Guardian API

The Guardian API provides direct access to high-quality journalism from the Guardian’s editorial content. It offers structured news, tags, and metadata from one of the world’s most respected news organisations.

Key features:

• High-quality editorial content.

• Filtering by topic or category.

• Media-rich datan integration, including multimedia embedding.

Use case: Apps or research projects requiring trusted editorial sources for accurate analysis or curated content.

Why The Guardian API? Focused on credible data, it works best for platforms and professionals prioritising journalistic integrity.

4. Bloomberg API

Renowned for its financial insights, Bloomberg API delivers in-depth business coverage and real-time data for institutions and professional investors. It specialises in market data, financial news, and economic reports.

Key features:

• Exclusive financial data and analysis.

• Real-time market coverage.

• Seamless integration with Bloomberg’s terminals.

Use case: Analysts and investment professionals monitoring market trends and making data-driven decisions.

Why Bloomberg? Its precise focus on finance makes it essential for institutions heavily reliant on actionable market news.

5. Financial Times API

The Financial Times API is a premium solution that supplies business and economic-focused news. It is built for professional teams that require deep insights into global markets and economic activity.

Key features:

• Premium content on global finance and markets.

• Access to detailed economic reports and analyses.

• Subscription access for gated content.

Use case: Economists, researchers, or executives tracking global economic trends and industry reports.

Why Financial Times? Its premium-quality data and economic insights provide unmatched value for businesses targeting comprehensive market analysis.

6. Opoint

Opoint specialises in news monitoring and sentiment analysis, making it particularly useful for PR, marketing, and branding teams. It supports multiple languages and global sources with cutting-edge media monitoring capabilities.

Key features:

• Real-time monitoring with sentiment tagging.

• Multilingual and multi-source coverage.

• Tailored brand monitoring and competitor tracking.

Use case: PR agencies and marketers monitoring sentiment shifts or competitive landscape changes like product launches.

Why Opoint? Its advanced monitoring features help organisations stay agile in rapidly shifting media environments.

7. Mediastack API

Mediastack combines accessibility with scalability, offering a mix of free plans for developers and paid tiers for advanced features. It aggregates news in real time from over 7,500 sources globally.

Key features:

• Free and affordable paid plans.

• Multilingual support and geo-targeted searches.

• Scalable for both startups and growing enterprises.

Use case: Developers building applications that require versatile, budget-friendly news feeds with reliable real-time updates.

Why Mediastack? Its affordability and flexibility cater to businesses of all sizes, making it a versatile option for a wide range of users.

Use cases for news API data feeds

The applications of news API data feeds are as diverse as the industries relying on them:

Financial intelligence: Investment tools use APIs to analyse market-moving news in real time.

Media monitoring: PR agencies use media insights to track brand mentions and sentiment.

Risk assessment: Governments and corporations assess geopolitical risks or public sentiment.

Content platforms: Aggregators curate articles, summaries, and headlines for apps/websites.

AI & predictive analysis: APIs provide data for machine learning models that forecast trends.

(Image source: Unsplash)

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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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ARI (Advanced Research & Insights) delivers comprehensive, accurate, and interactive reports within minutes—potentially shaking up the $250 billion management consulting industry.  

You.com claims ARI completes reports that typically require weeks of labour and cost thousands of dollars in just five minutes, at a fraction of traditional expenses.

With the ability to process over 400 sources simultaneously – a figure set to grow as the technology scales – ARI promises to deliver “verified citations and insights 3X faster than other currently available solutions.”

Bryan McCann, Co-Founder and CTO of You.com, said: “ARI’s breakthrough is its ability to maintain contextual understanding while processing hundreds of sources simultaneously.

“When combined with chain-of-thought reasoning and extended test-time compute, ARI is able to discover and incorporate adjacent research areas dynamically as analysis progresses.”

A powerful AI agent for business research

Traditional AI research tools are typically limited to processing between 30 to 40 data sources at a time. ARI stands out by handling hundreds of public and private data streams, ensuring unparalleled accuracy and scope in its analysis. The system doesn’t just stop at summarising data; it enhances user experience by producing rich, interactive graphs, charts, and visualisations for deeper insights.  

Designed to cater equally to high-level professionals and knowledge workers across industries, ARI combines advanced functionality with user-friendly accessibility. This dual-purpose design allows enterprises to deploy it as a personal assistant or as a replacement for expensive research efforts traditionally carried out by consulting firms.

At the heart of ARI is a series of capabilities:

• Simultaneous source analysis: Processes hundreds of data sources, both public and private.  
• Chain-of-Thought reasoning: Dynamically evolves research parameters as insights emerge.  
• Real-time verification: Provides direct validation for every claim and data point.  
• Interactive visualisation engine: Automatically generates and cites graphs and charts to enhance reporting.  
• Enterprise data integration: Analyses a mix of public and private datasets to deliver actionable insights.  

During its initial deployment phase, ARI has demonstrated its versatility and potential for impact across several industries:

• Consulting: By analysing market reports, competitor financials, patent filings, and social sentiment data in hours rather than weeks, ARI supports due diligence with ease.  
• Financial services: With the ability to integrate real-time data from earnings calls, SEC filings, and industry news, ARI helps support faster and more accurate investment decisions.  
• Healthcare: ARI accelerates the synthesis of clinical trials, medical journals, patient data, and treatment guidelines, providing insights that support evidence-based care.  
• Media: From audience data to trending topics and competitor activity, ARI enables the rapid identification of new story angles and anticipates emerging narratives in key markets.  

Dr Dennis Ballwieser, Managing Director and Editor at Wort & Bild Verlag, commented: “The research time has dropped from a few days to just a few hours, and the accuracy across both German and English content has been remarkable.

“What excites me most is the opportunity to democratise access to professional-grade research. With ARI’s ability to analyse hundreds of verifiable sources simultaneously while maintaining accuracy, we can now offer professional insights to organisations of all sizes at a fraction of the traditional cost.”  

Accelerating access to strategic insights  

The potential for technologies like ARI goes beyond time and cost savings. For companies such as global consultancy firm APCO Worldwide, ARI’s capabilities provide a level of quality and personalisation that aligns with the modern needs of clients.

Philip Fraser, CIO at APCO Worldwide, said: “To us, ARI represents a step-change in the quality and alignment to the needs of our clients. We are very excited about working with You.com to integrate the power of ARI into our award-winning, proprietary Margy AI platform.”  

Through such integrations, ARI has the potential to move organisations away from periodic, resource-intensive research projects towards continuous real-time intelligence that drives better decision-making across all levels.  

Richard Socher, Co-Founder and CEO of You.com, added: “When every employee has instant access to comprehensive, validated insights that previously required teams of consultants and weeks of work, it changes the speed and quality of business decision-making. ARI represents a paradigm shift in how organisations operate.”

ARI is the newest addition to You.com’s expanding AI agent ecosystem, which has already seen the development of over 50,000 custom agents since late 2024. The company has raised $99 million in funding from investors such as Salesforce Ventures, NVIDIA, and Georgian Ventures.

With ARI, You.com aims to set a new standard for an enterprise-grade AI research agent as part of broader decision-making systems.

(Photo by Jeremy Beadle)

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.

The post You.com ARI: Professional-grade AI research agent for businesses appeared first on AI News.

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.

The post Fetch.ai launches first Web3 agentic AI model appeared first on AI News.