It’s been reported that following the recent announcement that ChatGPT is to be integrated into Apple’s devices and its new “Apple Intelligence” technology is to be across its suite of apps, Apple is to take up an observer role on OpenAI’s board.

Bloomberg reported that with effect from later this year, Apple ‘Fellow’ and head of Apple’s App Store, Phil Schiller, will be attending OpenAI board meetings as an observer (without voting rights).

OpenAI announced in March that it would be appointing new directors to its board, including company OpenAI CEO Sam Altman, Sue Desmond-Hellmann, a former CEO of the Bill and Melinda Gates Foundation, Nicole Seligman, formerly a president of Sony Entertainment, and Fidji Simo, CEO of Instacart. Adding an Apple member to OpenAI’s board will help Apple to keep up with the other main players in the AI race.

A grieving family from Berkshire have reported how online fraudsters used a photo of their recently deceased son on social media to make mourners click on bogus link for a streamed funeral service with the goal of exploiting their grief to get data and cash.

Alex Chadwick’s photograph was used by the fraudsters and although the funeral service was not filmed (despite the fraudsters using a bogus streaming-link), the family have expressed their shock at the criminals’ tactics and have called for legislation to stop it happening to others.

Alex Chadwick’s father Gary has been reported (BBC) as saying that he believed the family had been targeted because his son was young and had a lot of followers on social media.

A new fast-charging battery technology from Nyobolt that can charge an EV battery from 10 to 80 per cent in just under five minutes has just successfully completed its first demo road test.

Live Demo 

Founded in 2019, Cambridge-based EV battery company Nyobolt has just conducted its first live road test demo of the new battery in Bedford, in front of an invited audience of industry professionals. The new battery, which was fitted to a sports car and tested over two days, achieved a range of 120 miles after four minutes. The company says this was achievable because the first four minutes are at a constant current of 500A.

Still A Success Despite Challenges On The Day 

In lab conditions, the fast-charging battery can charge from 0 per cent to 100 per cent in six minutes but on the day, factors like the hot weather, issues with the car’s cooling system, plus having to use an on-site charger (not made by Nyobolt) meant that it only charged from 10 per cent to 80 per cent in four minutes and 37 seconds. However, that is still a very impressive result considering that a Tesla supercharger takes 15-20 minutes to charge a car battery to 80 per cent. Using a 350kW DC charger, Nyobolt says its batteries can charge at twice the speed of the fastest-charging vehicles on the road without the degradation typically associated with lithium-ion batteries.

Fast Charge And Retention  

Nyobolt also points out that independent testing of its technology by a leading global OEM has confirmed that its longer-lasting (and more sustainable batteries) can achieve over 4,000 fast charge cycles, or 600,000 miles, maintaining over 80 per cent battery capacity retention. This is considerably higher than the warranties of much larger EV batteries on the road today and highlights longer-lasting performance benefit of Nyobolt’s battery technology.

Benefits 

The company says its ultra-fast charging battery eliminates slow and inconvenient recharge stops, i.e. it saves time and combats ‘range anxiety’.

In Talks With Other OEMs 

Nyobolt doesn’t intend to make its own EVs but says it is now in talks with eight OEMs about using its technology in high performance EVs.

Lighter EVs 

Nyobolt also says that the fact that the 35kWh battery pack, as tested in the EV prototype, is compact could also benefit car makers and motorists, enabling energy-efficient electric vehicles that are cheaper to buy and run, and use fewer resources to manufacture.

Nyobolt’s co-founder and CEO, Dr Sai Shivareddy says: “Our Nyobolt EV demonstrates the efficiency gains facilitated by our fast-charging, longer-life battery technology, enabling capacity to be right-sized while still delivering the required performance,” and adds “Nyobolt is removing the obstacle of slow and inconvenient charging, making electrification appealing and accessible to those who don’t have the time for lengthy charging times or space for a home charger.” 

What Does This Mean For Your Organisation? 

Nyobolt’s groundbreaking fast-charging battery technology could be transformative for various stakeholders within the EV ecosystem. For Nyobolt itself, this development not only validates their technological innovations but may well also position them at the forefront of the EV battery market. The successful demonstration in Bedford, despite the challenges faced, highlights their capability to deliver a product that can significantly reduce charging times while maintaining high performance and longevity. This achievement is likely to attract further interest from OEMs and investors (8 are interested already), accelerating Nyobolt’s growth and market penetration.

For other EV manufacturers, the introduction of Nyobolt’s technology presents both an opportunity and a challenge. The ability to charge an EV battery to 80 per cent in under five minutes sets a new benchmark in the industry and is likely to compel other manufacturers to either adopt this technology or innovate rapidly to remain competitive. This could lead to a surge in partnerships and collaborations as manufacturers try to integrate these advanced batteries into their next-generation vehicles. Also, the focus on sustainability and longer battery life aligns with the broader industry goals of reducing environmental impact and improving the overall efficiency of EVs.

The EV market as a whole stands to benefit significantly from this technological leap. The reduction in charging times addresses one of the primary concerns of potential EV buyers – range anxiety. Faster charging infrastructure will likely catalyse broader adoption of EVs, as it makes the transition from traditional petrol and diesel vehicles more seamless. The compact nature of Nyobolt’s battery packs means vehicles can be lighter and more energy-efficient, potentially lowering the cost of EVs and making them more accessible to a wider audience. This could lead to a more rapid shift towards electric mobility, reducing the carbon footprint of the transportation sector.

For EV buyers, Nyobolt’s technology promises a more convenient and user-friendly experience, i.e. the ability to recharge quickly and efficiently means less time spent at charging stations and more time on the road. This may be particularly appealing to those with busy lifestyles or limited access to home charging setups. Also, the extended battery life and capacity retention may translate to lower long-term costs and enhanced vehicle reliability. As a result, consumers can expect a more cost-effective and sustainable ownership experience, which could drive higher satisfaction and loyalty within the EV market.

Nyobolt’s fast-charging battery technology, therefore, could herald a new era in the EV industry (which needs a boost about now), offering substantial benefits across the board. From improving Nyobolt’s market position and challenging other manufacturers to elevate their offerings, to making EVs more appealing and accessible to consumers, this innovation could reshape the landscape of electric mobility in the UK and beyond. Organisations within the EV sector will, no doubt, be closely monitoring these developments and considering how to integrate or respond to this technology to stay ahead in a rapidly evolving market.

If you ever find yourself locked-out of LinkedIn, this video is for you …

The Action Centre in Windows 10/11 provides quick access to common settings and notifications. You can customise the quick actions to include the settings you use most frequently. Here’s how:

To open Action Centre:

– Click on the Action Centre icon in the taskbar (or press Win + A).

To customise Quick Actions:

– Click on Expand to see all quick actions.

– Right-click on any quick action and select Edit quick actions.

– Drag and drop icons to rearrange them or click on Add to include new actions.

To save changes:

– Click ‘Done’ to save your customised quick actions.

Two new studies show that Google’s Gemini AI models may not live up to the hype in terms of answering questions about large datasets correctly.

Google Gemini 

Google Gemini is an advanced AI language model developed by Google to enhance various applications with sophisticated natural language understanding and generation capabilities. It features multimodal capabilities, enabling it to process and integrate information from text, images, and possibly audio for more comprehensive and context-aware responses. The model also boasts a deep contextual understanding, allowing it to generate relevant and accurate answers in complex conversations or tasks.

Google has highlighted Gemini’s scalability and adaptability as being its strong points, and how its highly scalable architecture can help with handling large-scale data efficiently and fine-tuning for specific tasks or industries.

Also, Gemini is thought to deliver superior performance in speed and accuracy due to advancements in machine learning techniques and infrastructure.

Studies 

However, the results of two studies appear to go against Google’s narrative that Gemini is particularly good at analysing large amounts of data.

For example, the Cornell University “One Thousand and One Pairs: A ‘novel’ challenge for long-context language models” study, co-authored by Marzena Karpinska, a postdoc at UMass Amherst, tested how well long-context Large Language Models (LLMs) can retrieve, synthesise, and reason over information across book-length inputs.

The study involved using a dataset called ‘NoCha’, which consisted of 1,001 pairs of true and false claims about 67 recently published English fiction books. The claims required global reasoning over the entire book to verify, posing a significant challenge for the models.

Unfortunately, the research revealed that no open-weight model performed above random chance, and even the best-performing model, GPT-4o, achieved only 55.8 per cent accuracy. Also, the study found that the models struggled with global reasoning tasks, particularly with speculative fiction that involves extensive world-building.

The models were found to frequently fail to answer questions correctly about large datasets, with accuracy rates between 40-50 per cent in document-based tests.

The research results suggest that while models can technically process long contexts, they often fail to truly understand the content. Also, the results may highlight the limitations of current long-context language models such as Google Gemini (Gemini 1.5 Pro and 1.5 Flash).

The Second Study 

The second study, co-authored by researchers at UC Santa Barbara, focused on the Gemini models’ performance in video analysis and their ability to ‘reason’ over the videos when being asked questions about them. However, the results also proved to be poor, highlighting difficulties with transcribing and recognising objects in images, thereby perhaps indicating significant limitations in the models’ data analysis capabilities.

Discrepancies Between Claims And Performance? 

Both studies appear to highlight possible discrepancies between Google’s claims and the actual performance of the Gemini models, thereby raising questions about their efficacy and shedding light on the broader challenges faced by generative AI technology.

Posted On X 

Marzena Karpinska, also noted (on X/Twitter) other interesting points about LLMs from the research, including:

– Even when models output correct labels, their explanations are often inaccurate.

– On average, all LLMs perform much better on pairs requiring sentence-level retrieval than global reasoning (59.8 per cent vs 41.6 per cent), but still their accuracy on these pairs is much lower than on the “needle-in-a-haystack” task.

– Models perform substantially worse on books with extensive world-building (fantasy and sci-fi) than contemporary and historical novels (romance or mystery).

What Does Google Say? 

Google has not directly commented on the specific studies that critique the performance of its Gemini models. However, Google has highlighted the advancements and capabilities of the Gemini models in their official communications. For example, Sundar Pichai, CEO of Google and Alphabet, has emphasised that Gemini models are designed to be highly capable and general, featuring state-of-the-art performance across multiple benchmarks. Google asserts that Gemini’s long context understanding, and multimodal capabilities significantly enhance its ability to process and reason about vast amounts of information, including text, images, audio, and video.

Google has tried to highlight its focus on the continuous improvement and rigorous testing of Gemini models, showcasing their performance on a wide variety of tasks, from natural image understanding to complex reasoning. The company has also been actively working on increasing the models’ efficiency and context window capacity, allowing them to process up to 1 million tokens (the basic units of text that the model processes). Google hopes these improvements will enable more sophisticated and context-aware AI applications.

What Does This Mean For Your Business? 

The findings from these studies may have significant implications for businesses relying on AI for data analysis and decision-making. The apparent underperformance of Google’s Gemini models in handling large datasets suggests that businesses might not be able to fully leverage these AI tools for complex data analysis tasks just yet. This could impact sectors like finance, healthcare, and any industry requiring detailed and accurate data interpretation, where businesses may need to reassess their dependence on such models for critical operations.

For Google, these studies may highlight a gap between their promotional claims and the actual capabilities of their AI models. This could prompt Google to accelerate its research and development efforts to address these shortcomings and enhance the practical utility of their models. It also places pressure on Google to maintain transparency about the limitations of their technologies while continuing to push the boundaries of AI performance.

Other AI companies might view these findings as both a caution and an opportunity. On one hand, the discrepancies in performance underline the inherent challenges in developing robust AI models. On the other hand, they provide a competitive edge for companies that can deliver more reliable and accurate AI solutions. This competitive landscape could drive innovation and lead to the emergence of more capable AI models that better meet the complex needs of businesses.

In summary then, while the current limitations of AI models like Google Gemini pose challenges, they also highlight areas ripe for innovation and improvement. Businesses should stay informed about these developments and be prepared to adapt their strategies to harness the full potential of evolving AI technologies.