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OpenAI has added its voice to the growing chorus of tech leaders and politicians opposing a controversial AI safety bill in California. The company argues that the legislation, SB 1047 , would stifle innovation and that regulation should be handled at a federal level. In a letter sent to California State Senator Scott Wiener’s office, OpenAI expressed concerns that the bill could have “broad and significant” implications for US competitiveness and national security. The company argued that SB 1047 would threaten California’s position as a global leader in AI, prompting talent to seek “greater opportunity elsewhere.”  Introduced by Senator Wiener, the bill aims to enact “common sense safety standards” for companies developing large AI models exceeding specific size and cost thresholds. These standards would require companies to implement shut-down mechanisms, take “reasonable care” to prevent catastrophic outcomes, and submit compliance statements to the California attorney general. Failure to comply could result in lawsuits and civil penalties. Lieutenant General John (Jack) Shanahan, who served in the US Air Force and was the inaugural director of the US Department of Defense’s Joint Artificial Intelligence Center (JAIC), believes the bill “thoughtfully navigates the serious risks that AI poses to both civil society and national security” and provides “pragmatic solutions”. Hon. Andrew C. Weber – former Assistant Secretary of Defense for Nuclear, Chemical, and Biological Defense Programs – echoed the national security concerns. “The theft of a powerful AI system from a leading lab by our adversaries would impose considerable risks on us all,” said Weber. “Developers of the most advanced AI systems need to take significant cybersecurity precautions given the potential risks involved in their work. I’m glad to see that SB 1047 helps establish the necessary protective measures.” SB 1047 has sparked fierce opposition from major tech companies, startups, and venture capitalists who argue that it overreaches for a nascent technology, potentially stifling innovation and driving businesses from the state. These concerns are echoed by OpenAI, with sources revealing that the company has paused plans to expand its San Francisco offices due to the uncertain regulatory landscape. Senator Wiener defended the bill, stating that OpenAI’s letter fails to “criticise a single provision.” He dismissed concerns about talent exodus as “nonsensical,” stating that the law would apply to any company conducting business in California, regardless of their physical location. Wiener highlighted the bill’s “highly reasonable” requirement for large AI labs to test their models for catastrophic safety risks, a practice many have already committed to. Critics, however, counter that mandating the submission of model details to the government will hinder innovation. They also fear that the threat of lawsuits will deter smaller, open-source developers from establishing startups.  In response to the backlash, Senator Wiener recently amended the bill to eliminate criminal liability for non-compliant companies, safeguard smaller developers, and remove the proposed “Frontier Model Division.” OpenAI maintains that a clear federal framework, rather than state-level regulation, is essential for preserving public safety while maintaining  US competitiveness against rivals like China. The company highlighted the suitability of federal agencies, such as the White House Office of Science and Technology Policy and the Department of Commerce, to govern AI risks. Senator Wiener acknowledged the ideal of congressional action but expressed scepticism about its likelihood. He drew parallels with California’s data privacy law, passed in the absence of federal action, suggesting that inaction from Congress shouldn’t preclude California from taking a leading role. The California state assembly is set to vote on SB 1047 this month. If passed, the bill will land on the desk of Governor Gavin Newsom, whose stance on the legislation remains unclear. However, Newsom has publicly recognised the need to balance AI innovation with risk mitigation.

Discover how AI in digital marketing is revolutionising with success stories and key strategies. Learn about personalisation, predictive analytics, content creation, and more. The rapid evolution of AI is revolutionising digital marketing, offering unprecedented opportunities for personalisation, efficiency, and customer engagement. By leveraging advanced algorithms and machine learning techniques, AI is transforming how marketers interact with their audiences, predict customer behaviour, and optimise their strategies for better results. This article delves into the multifaceted impact of AI on digital marketing, highlighting success stories and key strategies that are shaping the future of the industry. AI’s influence on digital marketing careers and education AI is reshaping digital marketing careers, requiring new skills and knowledge. As AI continues to integrate into marketing practices, professionals must adapt by acquiring expertise in data analysis, machine learning, and AI tools. Personalisation and customer insights AI helps in creating highly personalised marketing campaigns by analysing vast amounts of data to derive customer insights. Machine learning algorithms can identify patterns and preferences, allowing marketers to tailor their messages to individual customers. For instance, Netflix and Amazon use AI to recommend products and content based on user behaviour, resulting in higher engagement and satisfaction. Predictive analytics and decision-making AI-driven predictive analytics enable marketers to anticipate customer behaviour and make informed decisions. By analysing historical data, AI can forecast future trends, helping businesses to plan their strategies effectively. For example, retailers use predictive analytics to optimise inventory levels and marketing efforts, reducing costs and improving customer satisfaction. AI-driven content creation and curation AI tools are revolutionising content creation and curation, allowing marketers to produce high-quality content efficiently. Tools like GPT-4 are capable of generating high-quality text content, from blog posts to social media updates. These tools can create content that is engaging and relevant, saving time and resources for marketers. For example, The Washington Post uses AI to write news articles, freeing up journalists to focus on in-depth reporting. Content optimisation and SEO AI helps optimise content for search engines by analysing keywords, recommending improvements, and tracking performance. AI-driven SEO tools can identify the most effective keywords, suggest content structure, and monitor rankings. A table comparing traditional vs. AI-driven SEO strategies highlights the efficiency and accuracy of AI in optimising content. Traditional SEO Strategies AI-Driven SEO Strategies Manual keyword research Automated keyword analysis Basic performance tracking Advanced performance insights Static optimisation methods Dynamic content recommendations AI in customer engagement and support AI significantly improves customer engagement and support through advanced technologies like chatbots and virtual assistants. AI-powered chatbots AI-powered chatbots provide 24/7 customer support, offering personalised responses and handling multiple queries simultaneously. Companies like H&M and Sephora use chatbots to assist customers with product recommendations, order tracking, and more, enhancing the overall customer experience. Virtual assistants Virtual assistants streamline customer interactions by providing seamless and personalised services. Technologies like Google Assistant and Amazon Alexa are examples of AI-driven virtual assistants that help businesses engage with customers through voice commands and smart interactions. AI in advertising and campaign management AI is transforming advertising by enabling precise targeting, real-time bidding, and campaign optimisation. Programmatic advertising Programmatic advertising uses AI to automate the buying and selling of ad space in real time. This method ensures that ads are shown to the right audience at the right time, maximising ROI. Case studies show that businesses using programmatic advertising see significant improvements in ad performance and cost-efficiency. Audience targeting and segmentation AI helps in segmenting audiences based on behaviour, demographics, and preferences. AI tools like Google Ads and Facebook Ads Manager allow marketers to target ads more effectively, resulting in higher engagement rates. A list of top AI tools for audience targeting includes platforms like AdRoll, Quantcast, and Smartly.io . Ethical considerations and challenges in AI marketing Despite its benefits, AI in marketing also raises ethical concerns and challenges that need to be addressed. Data privacy concerns AI’s reliance on data poses significant privacy concerns. Companies must ensure compliance with data protection regulations like GDPR to protect customer information. Best practices for data privacy include data anonymisation, secure data storage, and transparent data usage policies. Addressing algorithmic bias Algorithmic bias can lead to unfair and discriminatory outcomes in AI-driven marketing tools. Identifying and mitigating bias is crucial to ensure ethical AI usage. Examples of biased algorithms and corrective steps include regular audits, diverse data sets, and inclusive algorithm design. Trends and future of AI in digital marketing The future of AI in digital marketing is promising, with emerging trends set to further revolutionise the industry. AI and augmented reality (AR) AI is being integrated with AR to create immersive marketing experiences. Brands like IKEA and L’Oreal use AR to allow customers to visualise products in their own environment, enhancing engagement and purchase decisions. Voice search and AI The rise of voice search is changing how content is optimised for voice-based queries. AI tools optimise content for voice search by focusing on natural language processing and conversational keywords. Statistics show that voice search is becoming increasingly popular, with tips for voice search optimisation including the use of long-tail keywords and local SEO. AI in Digital Marketing is the future AI is undeniably transforming digital marketing, offering innovative solutions for personalisation, efficiency, and customer engagement. As AI continues to evolve, staying updated with the latest trends and technologies is essential for businesses to remain competitive. Embrace the power of AI to drive your marketing strategies and achieve unparalleled success in the digital landscape

Since the 1990s, the most hardcore gamers have traditionally opted for PCs and laptops. Not only did this allow them to dive deeper into their favourite games, even creating things like mods, but PC gaming also offered the ability to tinker with hardware. Many PC gamers enjoy improving their hardware by adding new parts. They also enjoy playing a diverse range of games. Some of the world’s most competitive eSports focus on PC hits, from FPSs like Counter-Strike: Global Offensive to MOBAs like Dota 2 . Though console gamers are also present, most gamers associate these titles with PCs. Beyond the scope of hyper-visible eSports hits, PC gamers also have access to other titles. Casino games, for example, are often played via browser. Slots are a popular choice, offering dozens of formats and hundreds of themes for them to choose from. Straight from a laptop or PC, players can spin the reel. The same is true for DCCGs. These types of card games have taken off over the last decade, including new hits like Hearthstone and Marvel Snap. Though traditionally played in person, PCs are now the preferred format for many DCCG competitors. One of the most unique developments in the world of PC gaming is the rise of AI-driven laptops and PCs. These powerful devices are designed to handle even more complex processing challenges. Both their hardware and software are advanced, and designed to handle machine-learning tasks. If you’ve been considering an AI-driven laptop to improve your gaming experience, here are some of the best ai-driven laptop and pcs you should keep an eye on. The AI-driven metrics The best AI-driven laptops and PCs have a performance-focused processor. Some of the best for AI-driven tasks are Intel Core i7 and i9—but you’ll have other options, too. Along with the processor, focus on the device’s graphics card—the higher, the better. But keep a look out for storage, as the same is true for RAM. Better storage relates to faster loading times and general responsiveness—which are hugely important for gamers who need to avoid even a millisecond of lag. The NVMe SSDs are considered the best in the industry in 2024. AI-driven laptop and pcs - Alienware Aurora R14 In terms of actual products, the Alienware Aurora R14 is one of the best choices for AI-driven gaming . That’s because it has all of the most advanced hardware features, including a graphics card from NVIDIA GeForce, an Intel Core i9 processor, and 32GB of RAM. That’s fast, accurate, and advanced gaming at its best. AI-driven laptop and pcs - MSI Trident X This PC is a little bit different in that it focuses on being more compact. Oftentimes, the more advanced the PC, the bulkier the hardware becomes. That’s not the case with the MSI Trident X. Despite its compact size, it has the Intel Core i9 processor, NVIDIA GeForce Graphics Card, and 32GB of storage—all the same as the Alienware Aurora, just in a slightly more manageable package. AI-driven laptop and pcs - HP Omen Obelisk With the HP Omen Obelisk, the focus is on customization. The AI-driven PC includes all the same features as the other two products—even down to the brands used. However, it also offers tool-free access to the interior, which makes both maintenance and upgrading a breeze. Unsurprisingly, it also has a glass side window that makes observing the hardware enjoyable. AI-driven laptop and pcs - HP Spectre x360 14 If you need to get more out of your AI-driven PC than gaming sessions, then the HP Spectre is a great option. (Although it’s a laptop—not a PC.) Its robust features will take your gaming sessions to the next level while also handling other types of demanding tasks, from graphic design to video editing. The sheer range of options makes this one popular for anyone who also needs a professional device. AI-driven laptop and pcs - ASUS ROG Zephyrus G14 This option is slightly more affordable than others on the list—though you’ll notice the more limited features. Specifically, it has less storage than the other AI-driven PCs, along with a slightly less powerful processor. But it still packs a huge punch into its space-saving laptop hardware, which makes it a solid option for most gamers.

Microsoft and Apple have decided against taking up board seats at OpenAI. The decision comes as regulatory bodies intensify their scrutiny of big tech’s involvement in AI development and deployment. According to a Bloomberg report on July 10, citing an anonymous source familiar with the matter, Microsoft has officially communicated its withdrawal from the OpenAI board. This move comes approximately a year after the Redmond-based company made a substantial $13 billion investment in OpenAI in April 2023. In a memo addressed to OpenAI, Microsoft stated: “Over the past eight months we have witnessed significant progress from the newly formed board and are confident in the company’s direction.” The tech giant added, “We no longer believe our limited role as an observer is necessary.” Contrary to recent reports suggesting that Apple would secure an observer role on OpenAI’s board as part of a landmark agreement announced in June, it appears that OpenAI will now have no board observers following Microsoft’s departure. Responding to these developments, OpenAI expressed gratitude towards Microsoft, stating, “We’re grateful to Microsoft for voicing confidence in the board and the direction of the company, and we look forward to continuing our successful partnership.” This retreat from board involvement by major tech players occurs against a backdrop of mounting regulatory pressure. Concerns about the potential impact of big tech on AI development and industry dominance have prompted increased scrutiny from regulatory bodies worldwide. In June, European Union regulators announced that OpenAI could face an EU antitrust investigation over its partnership with Microsoft. EU competition chief Margrethe Vestager also revealed plans for local regulators to seek additional third-party views and survey firms such as Microsoft, Google, Meta, and ByteDance’s TikTok regarding their AI partnerships. Subscribe to TheGen's AI newsletter for more such Gen AI news.

The success of Chinese AI education applications like Question.AI and Gauth in the US market comes at a time of fierce competition within China, where over 200 large language models—critical for generative AI services like ChatGPT—have been developed. As of March, more than half of these received approval from Chinese authorities for public release. Faced with a saturated domestic market , more Chinese app developers are now setting their sights on Western markets, including the US. The  South China Morning Post  reported that Chinese AI apps have swiftly gained traction in the US, particularly in the education sector. Applications like Question.AI , owned by Beijing-based educational technology startup Zuoyebang and ByteDance’s Gauth, are revolutionising how American students tackle their homework by providing instant solutions and explanations through advanced AI algorithms.  For context, Question.AI and Gauth are popular educational apps that use generative AI to help US students in various subjects. Users can photograph homework problems to receive solutions with step-by-step explanations. Question.AI launched in mid-2023, while Gauth (originally Gauthmath) started in 2020 as a math solver before expanding. Both offer free essential use with paid additional features. As of recent rankings, Gauth is the second most popular educational app globally, with Question.AI at seventh. This convenience has resonated with students and parents, offering a seamless blend of technology and education that complements the increasingly digital learning environment. Initially designed for China’s vast and competitive market, these apps began bringing cutting-edge AI capabilities to American classrooms. After all, with its high digital adoption rates and openness to educational innovation, the US market presents a lucrative opportunity for Chinese developers looking to expand their user base beyond domestic borders. According to mobile app intelligence service AppMagic, Question.AI and Gauth, generative AI-driven homework helpers, were ranked among the top three free educational apps in the US on Apple’s iOS store and Google Play from February to May. AI in education: Domestic pressure driving global expansion  In China, the development of large language models has been prolific. With over 200 such models created, the competition among AI developers is intense. This high-stakes environment has driven many companies to seek growth opportunities abroad. The approval of these models for public release by Chinese authorities signifies the maturity and readiness of these technologies for broader application, encouraging developers to explore international markets. This push for global expansion is not just about finding new revenue streams but also about gaining a competitive edge and establishing a global presence. For Chinese AI companies, breaking into Western markets, particularly the US, symbolises commercial success and technological leadership on a global scale. The adoption of Chinese AI apps in the US education sector also illustrates some strategic advantages these tools possess. The sophisticated AI technology in Question.AI and Gauth delivers individual-learnt experiences. In the US, educators appreciate such granularity as they are committed to personalised instruction for students with various learning styles. Moreover, the flexibility and accessibility of these AI tools align well with the digital transformation sweeping through American education. Given that the pandemic has expedited online learning, AI-powered educational apps stand to bridge this gap in traditional teaching methodologies by providing timely help and improving their delivery methods. Navigating challenges: Data privacy and cultural integration Even with their technological prowess, Chinese AI apps will be met by data privacy and security concerns when entering US markets. There will be increased oversight on how these apps manage user data, especially in light of the geopolitical tensions between the US and China. Ensuring compliance with stringent US data privacy regulations is crucial for gaining user trust and widespread acceptance. Additionally, cultural integration poses another hurdle. Chinese educational philosophies often emphasise rote learning and discipline, which may contrast with American education’s focus on creativity and critical thinking. Successfully blending these approaches to create a holistic learning experience will be essential to the sustained success of these apps in the US. Ultimately, the success of Chinese AI apps like Question.AI and Gauth in the US clearly demonstrates the advanced technological capabilities that have been developed through intense domestic competition. As these companies continue to navigate the complexities of entering the Western market, their impact on the future of education is expected to expand.

Professionals in the news industry are beginning to wonder how generative AI will change the way they do their jobs. Some fear their work may disappear because of AI, while others believe it won’t help them at all. As a student journalist at Northwestern University, but also as someone double majoring in computer science, I don’t fear AI completely replacing me (for now), but I do believe that leveraging the right tools can help me work more efficiently. It always seems like I don’t have enough time to write and publish all the story ideas I have. So I wondered: How could generative AI speed up my work? Open AI recently launched the GPT store , which contains specialized versions of ChatGPT built by both OpenAI and other users to help with specific tasks. Some journalists have started developing these custom GPTs to support specific tasks they perform. In hopes of finding more of these kinds of tools to help me research, write, report, and illustrate my work more efficiently, this post walks through how I systematically explored what’s on offer there to support journalists. I first describe how I collected a broad set of custom GPTs that may be relevant to journalism, and then I expand on how I rated and ranked them for journalistic tasks. Collecting GPTs Since the GPT store doesn’t have a complete list of all GPTs available to the public, I decided to acquire them through the platform’s search functionality. To do this programmatically, I executed search requests of the GPT store using hundreds of keywords relating to a wide range of journalism tasks and activities. To create a starting point for these keywords, I prompted ChatGPT (see footnote [1]). For the prompt, I plugged in the 30 tasks and 19 work activities found on ONet under the “News Analysts, Reporters, and Journalists” occupation page. ONet, which was developed for the U.S. Department of Labor, provides a comprehensive and detailed database of occupational information, ensuring reliable and current data on job roles and responsibilities. This prompt produced 40 keywords, but to find an even more comprehensive list of GPTs, I wanted to further expand my base of search terms. To do this, I again used ChatGPT to perform a keyword expansion on each of my first 40 keywords (see footnote [2]). After this keyword expansion step, I had 586 journalism-related keywords and keyphrases. I did some quick trimming to eliminate terms that were obviously not journalistic and wouldn’t produce the types of GPTs that I sought. After this filtering, I had 552 terms left . To collect relevant GPTs, I ran a search request using cURL (a command line utility) with each keyword (for a template of the request see footnote [3]). Each request returned a JSON object that contained information about the first 10 GPTs matching the keyword (it seems to be a limitation of the search functionality that it can only return up to 10 items per search). From these requests, I was able to collect the name, description and unique ID of the GPT, as well as example conversation starters (these are meant to help users understand how they could use the GPT), and the number of conversations (an indicator of GPT use). After performing these requests for each keyword and filtering out duplicates based on ID number, I had a corpus of 3,749 GPTs. For efficiency in downstream analysis, I decided to filter out GPTs that had fewer than 100 conversations, focusing the corpus on those GPTs that had more traction and usage. After doing this, I was left with 693 GPTs . Rating and Ranking GPTs With a long list of journalism-related GPTs, I needed a system for deciding which GPTs could be the most useful. Ideally, we would test these all manually and write reviews on them, but this wasn’t a feasible starting point for almost 700 models. So I decided to use GPT-4 via the OpenAI API to rank the GPTs I had collected and then manually test the highest-ranked models. To rate them on a scale from 1 (not useful) to 4 (very useful) I used the same 30 journalistic tasks from ONet as above to assess the utility of each GPT for each of the tasks (see footnote [4]). With these rankings, I calculated some average statistics about each GPT to have a better idea of what the results meant. I averaged each GPT’s ratings across tasks and also tallied the distribution of 4s, 3s, 2s, and 1s for each GPT. I thought this would give me an idea of which GPTs would be the most useful for my purposes because I could filter by the highest-ranked GPT and by GPTs with a lot of high rankings. However, I realized this may not tell the entire story. Instead of only judging the GPTs based on their general scores, I also decided to see if certain GPTs were highly ranked in certain specialties or clusters of tasks, but potentially ranked lower overall. To do this I manually divided the 30 tasks into four subgroups (Writing and Editing, Reporting and Investigating, Broadcast and Multimedia, and Communication and Engagement). Then, using my already generated rankings, I calculated averages for each of the subgroups. This allowed me to find GPTs that were more specific to a more focused set of tasks in journalism, instead of only GPTs that were helpful across the entire set of tasks. Testing GPTs Based on the ratings and rankings I created, the next step was to manually test some of the highest-ranked GPTs. I wanted to see if they were actually useful in the story creation process. To create my test set I took the four overall highest-ranked GPTs, and the two highest-ranked from each task subgroup, resulting in 12 GPTs . Then, to test the selected GPTs, I decided to use them in the way I would as a journalist and record my observations. While not entirely systematic, as a first cut at assessment, this was the most flexible way to see how the GPTs might be useful in my workflow. I spent 45 minutes experimenting with each GPT. For the four GPTs that got the highest overall scores, I tried performing tasks that could benefit me throughout all aspects of my work. For the more specialized models, I focused on aspects of journalism that were more targeted and pertinent to the described subset of tasks they could assist with. Takeaways The most obvious takeaway from this experience was the difference between using the high overall ranking GPTs for a broad range of tasks versus using specialized GPTs for a refined range of tasks. For example, Journalist Pro , a GPT that was supposed to be able to assist with a wide range of tasks, gave very bland and, in many cases, unhelpful results. On the other hand, Fact-checking , a GPT specifically designed to identify incorrect statements and opinions vs. facts in writing, was successfully able to verify my writing was based in fact and spot sentences to either double check or change. This trend continued across all of the GPTs I tested: the general GPTs gave disappointing and basic results, while the specialized GPTs were useful in solving their smaller tasks. Even when I attempted to have the more general GPTs perform the smaller tasks that the specialized GPTs excelled in, they let me down. Legal Eye , the 2nd highest overall ranked model, is a GPT that is described as helping with research, sourcing, and writing. However, I found it ineffective for finding pertinent articles for my ideas, suggested interview subjects that didn’t make sense for my angle, and when provided with information, wrote a subpar story that lacked the legal and political analysis it claimed it would give. Improve my Writing , my highest ranked writing and editing GPT did a fantastic job of finding grammatical errors and correcting sentence structure. A feature I particularly enjoyed was how it could alter an entire piece of writing by converting the tone or writing style to a completely different one. For example, I gave the model a piece I wrote that was informative and serious. I had the GPT change the story to something more catered to a younger audience, and it did an impressive job of this. This model, while smaller in scope of tasks than Legal Eye, did a more thorough job of the tasks I had it perform. Video Maker by Lucas AI , my highest-ranked broadcast and multimedia GPT, did a great job of script creation and making informative videos with interesting visuals. It converted written pieces into reasonable videos. However, these videos were not production level, with the narrator being monotone and the images sometimes being mismatched to the script. It was a good tool to create a baseline video, but additional editing would be needed to publish it. Conclusion This project gave me some new ideas that I think can be useful to other journalists thinking about using custom GPTs. My advice would be to find the biggest pain points in your process and then do research into specialized GPTs that could solve this issue. For example, if you find yourself spinning your wheels on identifying useful interviewees for your story, dig around for GPTs that specialize in finding people or doing article research. Don’t assume a general GPT aimed towards journalism can help you. Research GPT , a custom-built GPT that is designed to help with complex research issues by providing experts to speak to and articles to read, may be of use in this situation. If it isn’t, doing a simple search for “source finder” and adjacent keywords in the GPT store will generate GPTs built specifically for your task at hand. From there, play around with each one, as they will have small discrepancies that could influence your work. Also, looking at the GPT’s ratings and number of conversations can be an indicator of which models are better developed and more tested. In the future it could also make sense to develop a more rigorous set of evaluation criteria for each — would you want in-depth reviews of custom GPTs for specific use cases and tasks you have? Let me know!

Gen AI news - Samsung’s latest flagship smartphones and wearables have been made lighter and slimmer, while incorporating enhanced AI features to appeal to high-end consumers. Samsung, which pioneered the foldable smartphone segment in 2019, faces increasing competition in this niche market. Data from Canalys shows that Samsung’s share of foldable phone shipments dropped from 81% in 2022 to 63% in 2023, highlighting the importance of this latest launch. Responding to market pressures, Samsung has made significant improvements to its foldable lineup: The Galaxy Z Fold 6, featuring a wide screen, is now the lightest and slimmest version in its series, aimed at attracting new customers to the form factor. The clamshell Galaxy Z Flip 6 boasts longer battery life, a higher resolution camera, and a new vapour chamber for improved cooling. These enhancements address key issues identified through customer feedback. Despite rising material costs and after maintaining stable prices for three years, Samsung has implemented a modest price increase. The Z Flip 6 is priced at $1,099.99, while the Z Fold 6 starts at $1,899.99, representing a $100 increase over last year’s models. Samsung has introduced several new AI-powered features, including: A “listening mode” that provides simultaneous voice interpretation when paired with Galaxy Buds earphones. Collaboration with Google to develop new AI search functions, such as displaying step-by-step solutions to math problems when circled on the screen. The company has also significantly enhanced its Galaxy Watch products: A new 3-nanometer chip triples application booting and processing efficiencies compared to last year’s model. The watch has received US FDA approval as a monitoring device for sleep apnea. New features include measurement of functional threshold power (FTP) for cycling enthusiasts and advanced glycation end-products (AGEs) related to diabetes. Samsung’s commitment to health monitoring is further exemplified by the introduction of the Galaxy Ring . Priced at $399.99, this smart ring comes in gold, silver, and black, featuring a titanium frame with 10ATM water resistance and an IP68 rating. At 7mm wide and 2.6mm thick, it’s designed to be slim and lightweight, weighing between 2.3 and 3g depending on the size. The Galaxy Ring primarily functions as a health tracker, equipped with an accelerometer, optical heart rate sensor, and skin temperature sensor. It can monitor sleep, heart rate, and activity, while introducing new Galaxy AI-powered metrics such as Energy Score and Wellness Tips. The ring offers 6-7 days of battery life and comes with a unique, transparent charging case that holds 1.5 times the charge. Industry analyst Jack Leathem from Canalys emphasises the importance of AI-powered health and fitness features in wearables, noting that they are “core to attracting brand switchers in the premium segment” and crucial for differentiating Samsung from other smartwatch vendors. The Galaxy Ring is only compatible with Android phones running the Samsung Health app, with some features exclusive to Galaxy phones. A standout feature for Galaxy Z Fold 6 and Z Flip 6 users (soon to be available on the S24) is the ability to control the phone’s camera or dismiss alarms using a double pinch gesture on the ring. While the Galaxy Ring shows promise in hardware design and ecosystem integration, its success will ultimately depend on tracking accuracy and consistent battery performance. Samsung’s expansion into the smart ring market, coupled with its enhancements to foldable phones and smartwatches, demonstrates the company’s commitment to innovating across the wearable and smartphone sectors. The new lineup of products – including the foldable phones, watches, and ring – will be available starting July 24 in South Korea, North America, and Europe, marking a significant step in Samsung’s strategy to innovate and compete in the high-end smartphone and wearable markets.

The global PC market is showing solid signs of recovery, with Apple leading the charge among significant manufacturers. According to the  latest data  from International Data Corporation (IDC), the traditional PC market experienced a 3% year-over-year (YoY) growth in the second quarter of 2024, marking its second consecutive quarter of expansion after a prolonged decline. The report reveals that worldwide PC shipments reached 64.9 million units in Q2 2024, with Apple as the top performer among major brands. The Cupertino-based tech giant saw an impressive 20.8% increase in Mac shipments compared to last year, significantly outpacing its competitors and strengthening its position in the global PC market. This resurgence comes as a welcome development for an industry grappling with challenges in recent years. The PC market had previously experienced seven consecutive quarters of decline, making this turnaround particularly noteworthy. While the overall market benefited from favourable comparisons to 2023, the growth was uneven across all regions.  Notably, weak results in China continued to hold back the market’s full potential. Excluding China, the global PC market showed even more robust growth, with shipments increasing by more than 5% YoY. This disparity highlights the uneven nature of the recovery and the ongoing challenges faced in specific key markets. Apple’s exceptional performance can be attributed to several factors, including the growing popularity of its M-series chips, which have garnered praise for their power efficiency and performance. The company’s focus on integrating its hardware and software ecosystems has also likely increased consumer interest in Mac products. While Apple led the pack in terms of growth rate, other major manufacturers also saw positive trends. Lenovo maintained its position as the market leader with a 3.7% shipment increase, capturing 22.7% of the market share. HP Inc. followed closely with a a 21.1% market share and a 1.8% shipment growth. Acer Group also performed well, with a 13.7% increase in shipments. Interestingly, Dell Technologies was the only top-five vendor to experience a decline, with a 2.4% decrease in shipments compared to Q2 2023. However, the company still maintained a significant 15.5% market share. The stage is set for the AI PC revolution Industry analysts attribute the overall market recovery to several factors, including a commercial refresh cycle and increasing interest in AI-capable PCs. Ryan Reith, group vice president with IDC’s Worldwide Device Trackers, noted that while the PC market faces challenges due to maturity and economic headwinds, the combination of two consecutive quarters of growth, market hype around AI PCs, and an ongoing commercial refresh cycle has injected new life into the mature market. The buzz surrounding AI-enhanced PCs is expected to drive further growth in the coming months, with significant players in the industry laying out their initial strategies for AI integration. While the commercial market is seen as having the most significant short-term upside for AI in the PC industry, there is growing anticipation for developments in the consumer segment. IDC also reckons all eyes are on Apple to potentially drive the consumer AI narrative later this year with anticipated product launches. However, “it shouldn’t be overlooked that Qualcomm, Intel, and AMD are all likely to make noise around both consumer and commercial AI PCs,” the report reads. Beyond Apple and AI: What’s next in the global PC market? Beyond the AI factor, the market has also benefited from promotional activities from consumer-oriented brands and channels, Jitesh Ubrani, research manager with IDC’s Worldwide Mobile Device Trackers, shared. He believes the industry has moved past the rock-bottom pricing brought about by excess inventory last year, leading to growth in average selling prices due to richer configurations and reduced discounting. As the PC market continues recovering, it faces opportunities and challenges. The ongoing commercial refresh cycle and the emerging AI PC segment present significant growth potential. However, regional disparities, particularly the weakness in the Chinese market, remain a concern for overall market performance. The industry will be closely watching how manufacturers capitalize on the AI trend and whether they can sustain the current growth momentum. Apple’s strong performance sets a high bar for competitors and may prompt increased innovation and marketing efforts. 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 .

Gen AI News - SoftBank has announced its acquisition of Graphcore , a leading British AI chipmaker. The deal will see Graphcore becoming a wholly-owned subsidiary of SoftBank. This acquisition, reportedly valued at about $600 million, is not SoftBank’s first foray into the UK tech scene. In 2016, SoftBank controversially acquired British chip designer Arm in a much larger deal. However, the Graphcore purchase comes at a lower valuation than the total funding the company is said to have raised, which was around $700 million. Graphcore will continue to operate under its own name and maintain its headquarters in Bristol, UK. The company also retains its offices in Cambridge, London, Gdansk, and Hsinchu, signalling SoftBank’s commitment to preserving Graphcore’s established presence and operations. Nigel Toon, co-founder and CEO of Graphcore, said: “This is a tremendous endorsement of our team and their ability to build truly transformative AI technologies at scale, as well as a great outcome for our company.” Toon went on to emphasise the ongoing demand for AI compute and the work that remains to be done in improving efficiency, resilience, and computational power to fully realise AI’s potential. Graphcore’s key offering is a range of “Intelligence Processing Units” – accelerators designed specifically for AI workloads – along with a software stack that allows developers to utilise its hardware effectively. The company’s technology has often impressed. In 2020, a Graphcore device outperformed an Nvidia A100 GPU , and in another instance, its hardware halved the time required to handle a GPU-based drug discovery workload. Despite these technological successes, Graphcore has struggled to generate significant revenue and achieve profitability. In 2022, the company reported revenue of just $2.7 million – a 46 percent year-on-year decrease – while operating expenses reached $206.8 million. Vikas J. Parekh, Managing Partner at SoftBank Investment Advisers, commented: “Society is embracing the opportunities offered by foundation models, generative AI applications, and new approaches to scientific discovery. “Next generation semiconductors and compute systems are essential in the AGI journey, we’re pleased to collaborate with Graphcore in this mission.” The mention of AGI (Artificial General Intelligence) in Parekh’s statement suggests that SoftBank sees Graphcore’s technology as a key component in the pursuit of more advanced AI systems that can match or exceed human-level intelligence across a wide range of tasks. Graphcore has built a reputation as a leading employer in the UK’s high-tech economy, and the company has committed to continuing its investment in creating high-skilled jobs across various disciplines. The acquisition of Graphcore by SoftBank is likely to provide the AI chipmaker with significant resources and opportunities for expansion. It also reflects the increasing competition in the AI chip market, where companies like NVIDIA , Intel , and AMD have been vying for dominance. As AI continues to permeate various sectors of the economy and society, the demand for specialised AI hardware is expected to grow. Graphcore’s integration into SoftBank’s portfolio positions both companies to capitalise on this trend.

Ever found yourself limited by existing AI models? Closed Source AI might be stifling your innovation. In this article, we explore the realm of Generative AI, where science meets art, to drive progress and creativity in AI research. Breaking Free: Can Generative AI Unleash Unbounded Innovation? Is the traditional approach to AI research holding back true innovation? Generative AI has emerged as a game-changer, offering fresh perspectives and empowering researchers to explore uncharted territories. Let's delve into the fusion of science and art that defines Generative AI. Advancements with Generative AI: Diverse Output: Generate a wide array of outputs, from images to texts. Innovative Possibilities: Encourage creativity in AI research and development. Enhanced Flexibility: Tailor AI models to specific requirements. A survey reported a 45% increase in innovation within AI research after adopting Generative AI methodologies . Understanding Generative AI: A Symphony of Algorithms and Creativity How does Generative AI work, and what sets it apart from traditional AI models? Generative AI involves algorithms that use probability and patterns to create new content. It's a unique blend of science, mathematics, and creativity, enabling machines to generate content similar to human creativity. Key Aspects of Generative AI: Algorithmic Foundations: Utilizing complex algorithms for content generation. Learning from Data: Training models based on vast datasets for diverse outputs. Creative Output: Generating content that resembles human creativity. A comparison study highlighted a 50% increase in content diversity using Generative AI over Closed Source AI . Applications and Impact: How Generative AI Reshapes Industries In what ways is Generative AI making its mark across various industries? Generative AI's impact stretches across domains, revolutionizing how industries approach content creation, design, and innovation. Art and Design: Creating unique artworks and designs with AI assistance. Content Creation: Enhancing efficiency and creativity in content development. Fashion and Style: Assisting in trend prediction and design. A real-world implementation showcased a 30% increase in design efficiency using Generative AI . Overcoming Constraints: Generative AI vs. Closed Source AI Can Generative AI effectively address the limitations imposed by Closed Source AI? Generative AI offers a fresh perspective by providing flexibility, creativity, and adaptability, countering the constraints of Closed Source AI. Advantages of Generative AI: Flexibility: Adapting to specific requirements with ease. Cost-Effectiveness: Proving to be cost-effective in the long run. Enhanced Creativity: Encouraging creativity within AI research. Industry Insights: A case study demonstrated a 25% reduction in costs through the integration of Generative AI. Future Horizons: The Path Ahead for Generative AI Research What does the future hold for Generative AI and its role in AI research? Generative AI is poised to steer AI research into uncharted territories, driving innovation, and redefining how we perceive artificial intelligence. Future Prospects: AI-Generated Innovations: Pioneering groundbreaking solutions through Generative AI. Collaborative AI: Fostering collective creativity and knowledge sharing. Ethical Considerations: Addressing ethical implications and biases in AI generation. Industry Insights: A forecast predicted a 40% increase in AI-generated innovations within the next five years. Generative AI And Creativity Generative AI is not just about algorithms; it's about unleashing creativity and innovation. By embracing Generative AI, we can overcome the limitations imposed by Closed Source AI and pave the way for a more creative and adaptive future in AI research. Integrate Generative AI methodologies into your AI research to enhance creativity and drive innovation. Pondering Question: How can your organization leverage Generative AI to drive groundbreaking innovations in AI research? Follow TheGen.ai for more on GenAI news, trends, and more. FAQs on Generative AI Q1: What sets Generative AI apart from traditional AI models? Generative AI utilizes algorithms and creativity to generate diverse outputs, while traditional AI models often follow predefined patterns. Q2: How can Generative AI enhance content creation and design in the fashion industry? Generative AI can assist in predicting trends and generating unique design concepts, enhancing efficiency and creativity in the fashion industry. Q3: Are there cost advantages associated with using Generative AI over Closed Source AI? Yes, Generative AI can prove to be cost-effective in the long run due to its adaptability and efficiency in generating content. Q4: How is Generative AI expected to impact the future of AI research? Generative AI is expected to drive innovations, foster collaboration, and address ethical considerations, shaping the future of AI research. Q5: Can Generative AI be integrated into existing AI research methodologies effectively? Yes, Generative AI can be seamlessly integrated into existing AI research methodologies, enhancing creativity and innovation in the process.

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Microsoft has announced the Phi-3 family of open small language models (SLMs), touting them as the most capable and cost-effective of their size available. The innovative training approach developed by Microsoft researchers has allowed the Phi-3 models to outperform larger models on language, coding, and math benchmarks. “What we’re going to start to see is not a shift from large to small, but a shift from a singular category of models to a portfolio of models where customers get the ability to make a decision on what is the best model for their scenario,” said Sonali Yadav, Principal Product Manager for Generative AI at Microsoft. The first Phi-3 model, Phi-3-mini at 3.8 billion parameters, is now publicly available in Azure AI Model Catalog , Hugging Face , Ollama , and as an NVIDIA NIM microservice. Despite its compact size, Phi-3-mini outperforms models twice its size. Additional Phi-3 models like Phi-3-small (7B parameters) and Phi-3-medium (14B parameters) will follow soon. “Some customers may only need small models, some will need big models and many are going to want to combine both in a variety of ways,” said Luis Vargas, Microsoft VP of AI. The key advantage of SLMs is their smaller size enabling on-device deployment for low-latency AI experiences without network connectivity. Potential use cases include smart sensors, cameras, farming equipment, and more. Privacy is another benefit by keeping data on the device. (Credit: Microsoft) Large language models (LLMs) excel at complex reasoning over vast datasets—strengths suited to applications like drug discovery by understanding interactions across scientific literature. However, SLMs offer a compelling alternative for simpler query answering, summarisation, content generation, and the like. “Rather than chasing ever-larger models, Microsoft is developing tools with more carefully curated data and specialised training,” commented Victor Botev, CTO and Co-Founder of Iris.ai . “This allows for improved performance and reasoning abilities without the massive computational costs of models with trillions of parameters. Fulfilling this promise would mean tearing down a huge adoption barrier for businesses looking for AI solutions.” Breakthrough training technique What enabled Microsoft’s SLM quality leap was an innovative data filtering and generation approach inspired by bedtime story books. “Instead of training on just raw web data, why don’t you look for data which is of extremely high quality?” asked Sebastien Bubeck, Microsoft VP leading SLM research.   Ronen Eldan’s nightly reading routine with his daughter sparked the idea to generate a ‘TinyStories’ dataset of millions of simple narratives created by prompting a large model with combinations of words a 4-year-old would know. Remarkably, a 10M parameter model trained on TinyStories could generate fluent stories with perfect grammar. Building on that early success, the team procured high-quality web data vetted for educational value to create the ‘CodeTextbook’ dataset. This was synthesised through rounds of prompting, generation, and filtering by both humans and large AI models. “A lot of care goes into producing these synthetic data,” Bubeck said. “We don’t take everything that we produce.” The high-quality training data proved transformative. “Because it’s reading from textbook-like material…you make the task of the language model to read and understand this material much easier,” Bubeck explained. Mitigating AI safety risks Despite the thoughtful data curation, Microsoft emphasises applying additional safety practices to the Phi-3 release mirroring its standard processes for all generative AI models. “As with all generative AI model releases, Microsoft’s product and responsible AI teams used a multi-layered approach to manage and mitigate risks in developing Phi-3 models,” a blog post stated.   This included further training examples to reinforce expected behaviours, assessments to identify vulnerabilities through red-teaming, and offering Azure AI tools for customers to build trustworthy applications atop Phi-3.