Quantization, a technique widely used to improve AI efficiency, may have reached its limits, according to recent research. This method reduces the precision of data in AI models, making them faster and cheaper to operate. However, studies suggest that as models grow larger and are trained on vast datasets, quantization can degrade their performance.
Researchers from leading institutions found that highly trained models suffer more when quantised. For AI companies relying on massive models to enhance quality, this finding raises concerns about the long-term viability of cost-saving approaches. Quantization already impacts models like Meta’s Llama 3, which reportedly shows reduced performance compared to other AI systems.
Efforts to lower AI model costs continue as inference—using models to generate responses—remains the most significant expense for AI labs. Techniques like training in low precision and hardware supporting ultra-low bit precision are being explored. Yet, such strategies face diminishing returns and risks of quality loss if precision drops too far.
Experts believe a shift towards better data curation and filtering, alongside new architectures optimised for low-precision training, may offer solutions. These advancements could help balance efficiency and performance as AI evolves beyond traditional scaling methods.
Apple is closing in on a historic $4 trillion market valuation, driven by investor enthusiasm over its advancements in artificial intelligence and hopes for a surge in iPhone upgrades. Shares have surged 16% since November, adding $500 billion to its market cap, and positioning Apple ahead of rivals Nvidia and Microsoft in the race to this milestone. Analysts attribute the rally to expectations of a new “supercycle” in iPhone sales fueled by AI enhancements, despite modest revenue growth projections for the holiday season.
Apple’s integration of AI tools like OpenAI’s ChatGPT across its devices and apps marks a strategic pivot in a market long dominated by Microsoft, Alphabet, and Meta. Although iPhone demand remains muted, analysts forecast a rebound in 2025, as AI-powered features and broader availability drive renewed interest. Meanwhile, Apple’s premium valuation—its price-to-earnings ratio recently hit a three-year high of 33.5—has sparked mixed reactions among investors, with Warren Buffett’s Berkshire Hathaway scaling back its holdings.
Despite challenges such as geopolitical risks and fluctuating market conditions, Apple’s approach to this milestone underscores its enduring dominance in the tech sector. Analysts and investors remain optimistic about the company’s ability to navigate near-term hurdles and leverage AI innovation to maintain its leadership in a competitive landscape.
Microsoft is taking steps to diversify the AI powering its flagship product, Microsoft 365 Copilot. While OpenAI’s GPT-4 model has been a cornerstone of the AI assistant since its launch in March 2023, Microsoft is now integrating internal and third-party AI models, including its proprietary Phi-4, to reduce costs and improve efficiency. This move reflects Microsoft’s broader strategy to lessen reliance on OpenAI, its long-time partner, as it looks to offer faster, more cost-effective solutions to enterprise customers.
The shift is driven by concerns over the high costs and slower speeds associated with OpenAI’s technology for enterprise users. A company spokesperson confirmed that OpenAI remains a partner for advanced models but emphasised that Microsoft customises and incorporates a range of AI models depending on the product. Beyond its collaboration with OpenAI, Microsoft is also customising open-weight models to make its services more accessible and affordable, with potential cost savings for customers.
Microsoft’s approach mirrors similar changes in its other business units. For example, GitHub, acquired by Microsoft in 2018, has started incorporating AI models from Anthropic and Google as alternatives to OpenAI’s offerings. These efforts align with Microsoft’s goal of demonstrating the return on investment for its AI tools, particularly as some enterprises remain cautious about adopting 365 Copilot due to concerns over pricing and utility.
Despite these challenges, Microsoft reports growing adoption of 365 Copilot. The company states that 70% of Fortune 500 companies are using the AI assistant, and analysts predict that more than 10 million users will adopt it this year. As Microsoft continues refining its AI technology, leaders like CEO Satya Nadella are keeping a close watch, underscoring the company’s commitment to innovation in enterprise AI.
Google has introduced Gemini 2.0 Flash Thinking Experimental, an AI model designed for advanced reasoning, now available on its AI Studio platform. Billed as effective for multimodal understanding, coding, and complex problem-solving, it aims to enhance AI’s reasoning capabilities.
Unlike typical AI, reasoning models like Gemini fact-check themselves during response generation, improving accuracy but requiring more processing time. However, early testing shows mixed results, suggesting room for refinement in practical applications.
The rise of reasoning models reflects the industry’s search for new methods to optimise AI performance. While promising, challenges such as high computational costs and uncertain scalability remain points of debate.
Elon Musk’s AI venture, xAI, has unveiled a standalone iOS app for its chatbot, Grok, marking its first major expansion beyond the X platform. The app, currently in beta testing across Australia and a few other regions, offers users an array of generative AI features, including real-time web access, text rewriting, summarisation, and even image generation from text prompts.
Grok, described as a ‘maximally truthful and curious’ assistant, is designed to provide accurate answers, create photorealistic images, and analyse uploaded pictures. While previously restricted to paying X subscribers, a free version of the chatbot was launched in November and has recently been made accessible to all users.
The app also serves as a precursor to a dedicated web platform, Grok.com, which is in the works. xAI has touted the chatbot’s ability to produce detailed and unrestricted image content, even allowing creations involving public figures and copyrighted material. This open approach sets Grok apart from other AI tools with stricter content policies.
As the beta rollout progresses, Grok is poised to become a versatile tool for users seeking generative AI capabilities in a dynamic and user-friendly interface.
Data centres in the United States could consume up to 12% of the country’s electricity by 2028 due to the rapid growth of AI, according to a new report. The Department of Energy-backed study predicts energy usage from data centres will rise from 4% to between 6.7% and 12%, depending on GPU availability and demand.
The shift to AI-driven infrastructure is driving the surge, with GPU-accelerated servers and cooling systems responsible for doubling power use in recent years. Researchers are calling for annual reports and strategies to track trends and enhance efficiency.
The findings highlight concerns about the impact of AI on power grids, energy bills, and climate change. Researchers also suggest increased transparency in data centre energy use, aiming to encourage efficiency and sustainable growth within the industry.
NETSCOUT SYSTEMS announced significant updates to its Arbor Edge Defense (AED) and Arbor Enterprise Manager (AEM) products as part of its Adaptive DDoS Protection solution. These enhancements are designed to address the growing threats of AI-enabled DDoS attacks, which have surged in sophistication and frequency.
Application-layer and volumetric attacks have increased by 43% and 30%, respectively, with DDoS-for-hire services making attacks easier to execute. To combat these evolving threats, NETSCOUT leverages AI and machine learning (ML) within its ATLAS Threat Intelligence system, which monitors over 550 Tbps of real-time internet traffic across 500 ISPs and 2,000 enterprise sites worldwide.
The AI/ML-powered solution enables dynamic threat identification and mitigation, creating a scalable, proactive defence mechanism. The updated AED and AEM products automate a closed-loop DDoS attack detection and mitigation process, providing real-time protection by adapting to changing attack vectors and applying mitigation recommendations automatically.
NETSCOUT’s solution also offers comprehensive protection across hybrid IT environments, including on-premise infrastructure, private data centres, and public cloud platforms like AWS and Microsoft Azure, with enhancements such as 200 Gbps mitigation capacity, high-performance decryption, and visibility into non-DDoS threats.
By minimising downtime and safeguarding business-critical services, NETSCOUT’s Adaptive DDoS Protection reduces business risks and protects productivity and reputation. As the threat landscape continues to evolve, organisations can rely on NETSCOUT’s innovative technology to stay ahead of attackers and maintain IT resilience. Industry experts and agencies like the Cybersecurity & Infrastructure Security Agency (CISA) highlight the need for adaptive cybersecurity measures. NETSCOUT’s AI/ML-driven solutions meet these demands by offering robust, future-proof protection for critical IT infrastructure.
Generative AI, has begun to mimic an intriguing aspect of human perception, the blending of sensory experiences. Research shows that humans naturally associate colours, shapes, and even sounds with flavours a phenomenon known as cross-modal correspondence. For instance, red hues often evoke sweetness, while sharp shapes suggest bitterness. AI systems, trained on human data, appear to be trained to replicate these associations, offering new perspectives on how deeply such connections are embedded in our perception.
This revelation emerged through studies where AI was tasked with answering prompts about the relationships between sensory elements, such as the sweetness of certain shapes or colours. The results closely mirrored human responses, particularly when using advanced models like ChatGPT-4. Researchers believe this reflects the biases in the data the AI was trained on, highlighting how common and universal these sensory links might be.
The potential applications of this technology are vast. Marketing, for example, could use AI to design products and packaging that enhance sensory appeal. However, experts warn that AI’s insights should complement, not replace, human creativity. While AI offers inspiration, the nuances of human perception remain essential for creating designs that resonate deeply with people.
By understanding how AI interprets sensory input, researchers hope to not only enhance technology but also unlock more about the mysteries of the human brain. As AI continues to explore the sensory dimensions, it might pave the way for innovative approaches to art, marketing, and even neuroscience.
OpenAI has announced internal testing of its latest reasoning models, o3 and o3 mini, which aim to tackle complex problems more effectively than their predecessors. The o3 mini model is expected to launch by January, with the full o3 model to follow. These developments signal increased competition with rivals like Google, which recently released its second-generation Gemini AI model.
OpenAI’s advancements build on its earlier o1 models, released in September, which demonstrated improved reasoning in science, coding, and mathematics. The company is inviting external researchers to test the new o3 models before public release.
The announcement follows OpenAI’s $6.6 billion funding round in October, highlighting its growing influence in the generative AI market. As competition intensifies, both OpenAI and Google aim to push the boundaries of AI technology.
Tetsuwan Scientific, a startup founded by Cristian Ponce and Théo Schäfer, is developing robotic AI scientists designed to automate lab experiments. Inspired by the rapid evolution of AI models like GPT-4, these robots aim to address the repetitive and labour-intensive aspects of research. They combine low-cost robotic hardware with advanced software that interprets and executes scientific tasks autonomously.
The breakthrough came when Ponce tested AI’s ability to diagnose scientific data and offer solutions. However, existing lab robots lacked the ability to physically act on these insights. Tetsuwan’s solution integrates AI to give robots the context and flexibility to perform tasks like pipetting and analysing results without constant programming.
Currently working with La Jolla Labs in RNA therapeutic drug development, Tetsuwan has secured $2.7 million in funding to advance its technology. The ultimate goal is to create self-reliant AI scientists capable of automating the entire scientific process, from hypothesis to reproducible results, potentially accelerating innovation at an unprecedented pace.
