OpenAI’s ChatGPT search tool is under scrutiny after a Guardian investigation revealed vulnerabilities to manipulation and malicious content. Hidden text on websites can alter AI responses, raising concerns over the tool’s reliability. The search feature, currently available to premium users, could misrepresent products or services by summarising planted positive content, even when negative reviews exist.
Cybersecurity researcher Jacob Larsen warned that the AI system in its current form might enable deceptive practices. Tests revealed how hidden prompts on webpages influence ChatGPT to deliver biased reviews. The same mechanism could be exploited to distribute malicious code, as highlighted in a recent cryptocurrency scam where the tool inadvertently shared credential-stealing instructions.
Experts emphasised that while combining search with AI models like ChatGPT offers potential, it also increases risks. Karsten Nohl, a scientist at SR Labs, likened such AI tools to a ‘co-pilot’ requiring oversight. Misjudgments by the technology could amplify risks, particularly as it lacks the ability to critically evaluate sources.
OpenAI acknowledges the possibility of errors, cautioning users to verify information. However, broader implications, such as how these vulnerabilities could impact website practices, remain unclear. Hidden text, while traditionally penalised by search engines like Google, may find new life in manipulating AI-based tools, posing challenges for OpenAI in securing the system.
If AI was the buzzword for 2023 and 2024, quantum computing looks set to claim the spotlight in the years ahead. Despite growing interest, much remains unknown about this transformative technology, even as leading companies explore its immense potential.
Quantum computing and AI stand as two revolutionary technologies, each with distinct principles and goals. Quantum systems operate on the principles of quantum mechanics, using qubits capable of existing in multiple states simultaneously due to superposition. Such systems can address problems far beyond the reach of classical computers, including molecular simulations for medical research and complex optimisation challenges.
AI and quantum computing intersect in areas like machine learning, though AI still depends on classical computing infrastructure. Significant hurdles remain for quantum technology, including qubit errors and scalability. The extreme sensitivity of qubits to external factors, such as vibrations and temperature, complicates their control.
Experts suggest quantum computers could become practical within 10 to 20 years. Classical computers are unlikely to be replaced, as quantum systems will primarily focus on solving tasks beyond classical capabilities. Leading companies are working to shorten development timelines, with advancements poised to transform the way technology is utilised.
Huge investments in quantum computing
Investments in quantum computing have reached record levels, with start-ups raising $1.5 billion across 50 funding rounds in 2024. Figure like this one nearly doubles the $785 million raised the previous year, setting a new benchmark. The growth in AI is partly driving these investments, as quantum computing promises to handle AI’s significant computational demands more efficiently.
Quantum computing offers unmatched speed and energy efficiency, with some estimates suggesting energy use could be reduced by up to 100 times compared to traditional supercomputers. As the demand for faster, more sustainable computing grows, quantum technologies are emerging as a key solution.
Microsoft and Atom Computing announce breakthrough
In November 2024, Microsoft and Atom Computing achieved a milestone in quantum computing. Their system linked 24 logical qubits using just 80 physical qubits, setting a record in efficiency. This advancement could transform industries like blockchain and cryptography by enabling faster problem-solving and enhancing security protocols.
Despite the challenges of implementing such systems, both companies are aiming to release a 1,000-qubit quantum computer by 2025. The development could accelerate the adoption of quantum technologies across various sectors, paving the way for breakthroughs in areas such as machine learning and materials science.
Overcoming traditional computing’s limitations
Start-ups like BlueQubit are transforming quantum computing into a practical tool for industries. The San Francisco-based company has raised $10 million to launch its Quantum-Software-as-a-Service platform, enabling businesses to use quantum processors and emulators that perform tasks up to 100 times faster than conventional systems.
Industries such as finance and pharmaceuticals are already leveraging quantum optimisation. Specialised algorithms are addressing challenges like financial modelling and drug discovery, showcasing quantum computing’s potential to surpass traditional systems in tackling complex problems.
Google among giants pushing quantum computing
Google has recently introduced its cutting-edge quantum chip, Willow, capable of solving a computational problem in just five minutes. Traditional supercomputers would require approximately 10 septillion years for the same task.
The achievement has sparked discussions about quantum computing’s link to multiverse theories. Hartmut Neven, head of Google’s Quantum AI team, suggested the performance might hint at parallel universes influencing quantum calculations. Willow’s success marks significant advancements in cryptography, material science, and artificial intelligence.
Commercialisation is already underway
Global collaborations are fast-tracking quantum technology’s commercialisation. SDT, a Korean firm, and Finnish start-up SemiQon have signed an agreement to integrate SemiQon’s silicon-based quantum processing units into SDT’s precision measurement systems.
SemiQon’s processors, designed to work with existing semiconductor infrastructure, lower production costs and enhance scalability. These partnerships pave the way for more stable and cost-effective quantum systems, bringing their use closer to mainstream industries.
Quantum technologies aiding mobile networks
Telefonica Germany and AWS are exploring quantum applications in mobile networks. Their pilot project aims to optimise mobile tower placement, improve network security with quantum encryption, and prepare for future 6G networks.
Telefonica’s migration of millions of 5G users to AWS cloud infrastructure demonstrates how combining quantum and cloud technologies can enhance network efficiency. The project highlights the growing impact of quantum computing on telecommunications.
Addressing emerging risks
Chinese researchers at Shanghai University have exposed the potential threats quantum computing poses to existing encryption standards. Using a D-Wave quantum computer, they breached algorithms critical to modern cryptographic systems, including AES-256, commonly used for securing cryptocurrency wallets.
Although current quantum hardware faces environmental and technical limitations, researchers stress the urgent need for quantum-resistant cryptography. New encryption methods are essential to safeguard digital systems against future quantum-based vulnerabilities.
Quantum computing promises revolutionary capabilities but must overcome significant challenges in scaling and stability. Its progress depends on interdisciplinary collaboration in physics, engineering, and economics. While AI thrives on rapid commercial investment, quantum technology requires long-term support to fulfil its transformative potential.
Google contractors improving the Gemini AI model have been tasked with comparing its responses against those of Anthropic’s Claude, according to internal documents reviewed by TechCrunch. The evaluation process involves scoring responses on criteria such as truthfulness and verbosity, with contractors given up to 30 minutes per prompt to determine which model performs better. Notably, some outputs identify themselves as Claude, sparking questions about Google’s use of its competitor’s model.
Claude’s responses, known for emphasising safety, have sometimes refused to answer prompts deemed unsafe, unlike Gemini, which has faced criticism for safety violations. One such instance involved Gemini generating responses flagged for inappropriate content. Despite Google’s significant investment in Anthropic, Claude’s terms of service prohibit its use to train or build competing AI models without prior approval.
A spokesperson for Google DeepMind stated that while the company compares model outputs for evaluation purposes, it does not train Gemini using Anthropic models. Anthropic, however, declined to comment on whether Google had obtained permission to use Claude for these tests. Recent revelations also highlight contractor concerns over Gemini producing potentially inaccurate information on sensitive topics, including healthcare.
Japanese farmers are embracing AI technology to address the challenges posed by climate change and labour shortages in agriculture. Farmers like Hiroaki Asakura in Aichi Prefecture are turning to smartphone apps that use machine learning to forecast pest outbreaks, enabling timely pesticide application. These tools help farmers optimise crop protection and reduce chemical usage, a significant step forward in modern farming.
One such app, developed by Mirai Vegetable Garden, analyses over a million pest and weather records to provide accurate predictions. For Asakura, this meant spraying pesticides earlier than usual to prevent black rot in his broccoli fields, a decision informed by the app’s warnings of rising risks. The technology, supporting crops like strawberries and tomatoes, also allows farmers to share outbreak information with neighbours for broader community protection.
These AI solutions are gaining traction nationwide. Apps developed by companies like Nihon Nohyaku Co and NTT Data CCS Corp identify over 1,100 pest species from photographs, offering farmers swift diagnosis and advice. As changing climate patterns lead to unusual pest behaviours, these innovations are vital. Japanese farmers and officials alike note that AI can bridge the gap between traditional know-how and modern challenges, ensuring sustainable crop production in the face of global warming.
Researchers have achieved a milestone in AI, teaching it to predict the complex aromas of whiskies and even identify their origins. The study, conducted in Germany, utilised AI to analyse the molecular makeup of 16 American and Scottish whiskies. It then predicted the five strongest aroma notes and distinguished between the two countries of origin with remarkable accuracy.
The AI surpassed human experts in consistency and precision, identifying aromas like menthol and citronellol for US whiskies and smoky, medicinal notes for Scotch. This innovation could ensure flavour consistency in whisky production, detect counterfeit goods, and even find applications in blending recycled materials to reduce odours.
While promising, the study was limited to a small selection of whiskies, raising questions about its performance on broader varieties or aged batches. Experts also noted that flavour perception depends on external factors, highlighting room for further exploration in this emotive domain. Nonetheless, this blend of technology and tradition signals a new step for the whisky industry.
Elon Musk’s AI company, xAI, has raised $6 billion in its latest funding round, doubling its total to $12 billion this year. The investment attracted high-profile backers such as Andreessen Horowitz, BlackRock, and Fidelity, with participation limited to existing investors. Reports suggest the company is now targeting a $50 billion valuation.
Founded last year, xAI released its flagship generative AI model, Grok, which powers features on X, formerly known as Twitter. Grok, known for its bold and unconventional responses, has integrated capabilities like image generation and news summarisation. The company has also launched APIs and a standalone app, aiming to compete with AI giants like OpenAI and Anthropic.
The company’s Memphis data centre, housing 100,000 Nvidia GPUs, is central to training the next generation of AI models. Plans are underway to double GPU capacity and secure additional power to support operations. However, these efforts have faced criticism over potential environmental impacts.
xAI envisions integrating its AI models with Musk’s other ventures, such as Tesla and SpaceX, sparking concerns among Tesla shareholders. Despite these challenges, xAI’s rapid growth positions it as a formidable contender in the expanding AI industry.
Venture funding in Europe may be headed for a flat year overall, but European AI startups are thriving, with AI companies receiving 25% of the region’s VC funding in 2024, totalling $13.7 billion. This marks a significant rise from 15% four years ago and has led to the creation of new unicorns like Poolside and Wayve. According to James Wise of Balderton Capital, breakthrough AI technology in Europe can now attract hundreds of millions, or even billions, of euros at the early stages, similar to the US.
The collective value of European AI companies has doubled in four years, reaching $508 billion, now making up nearly 15% of the region’s entire tech sector. While much of the funding still comes from outside Europe, especially the US, the local AI ecosystem is flourishing with a growing talent pool. In 2024, 349,000 people were employed by AI companies in Europe, a 168% increase since 2020, indicating a buoyant and increasingly productive sector.
Wise suggests that the rise of smaller, highly productive AI companies will be the future, with generative AI tools significantly boosting efficiency in various industries. This growing adoption of AI tools is likely to continue benefiting the European AI sector in the long run, even if the category becomes less distinct in the future.
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.
