The Nintendo Switch 2 is finally a thing you can buy in the US — or at least, a thing you can try to buy. The $450 console officially went up for sale at midnight ET on June 5, with multiple retailers offering the device both online and in-store. Pre

Dublin AI startup Solidroad raises $6.5M from First Round Capital to transform customer service training with AI that coaches human agents and improves satisfaction scores.

Here are some hints and the answers for Connections for June 5, #725.

The US Department of Commerce has renamed its AI Safety Institute to the Center for AI Standards and Innovation (CAISI), shifting its focus from overall safety to combating national security risks and preventing “burdensome and unnecessary regulation” abroad. Secretary of Commerce Howard Lutnick announced the change on June 3rd, calling the agency’s overhaul a way to “evaluate and enhance US innovation” and “ensure US dominance of international AI standards.” The AI Safety Institute was announced in 2023 under former President Joe Biden, part of a global effort to create best practices for governments mitigating AI system risk. It signed memorandums of understanding with major US AI companies, including OpenAI and Anthropic, to get access to new models and suggest improvements before release. Near the end of Biden’s term in early 2025, it released draft guidelines for managing AI risks that included using systems to create biological weapons or other clear threats to national security, but also more common categories of harmful content like child sexual abuse material (CSAM). Lutnick’s statement says that the new institute will “focus on demonstrable risks, such as cybersecurity, biosecurity, and chemical weapons” in its evaluations. It will also investigate “malign foreign influence arising from use of adversaries’ AI systems,” a category that likely includes DeepSeek, a Chinese large language model that shook up the American AI industry earlier this year. The move is part of a larger Trump administration effort to accelerate the expansion of American AI companies. On his first day in office Trump rescinded a Biden executive order that ordered new safety standards for large AI systems and a report evaluating the potential risks for US consumers and the labor market. His own executive orders have encouraged increasing generative AI adoption in fields like education and promoting coal as a source of power for energy-hungry AI data centers. And the current Republican budget bill includes a 10-year moratorium on state-level AI regulations — a provision even some in Trump’s party have come to oppose.

Reading Time: 3 minutes In a recent webinar hosted by MoEngage, QSR marketing experts from Radar and Bottle Rocket came together to unpack the findings of the 2025 State of Cross-Channel Marketing for QSRs report.  With more than 800 total survey responses, including 70 from QSR marketers, this report revealed where quick-service restaurants are focusing their energy, what is holding them back, and the emerging strategies reshaping the guest experience. Here is a recap of the key takeaways, expert insights, and actionable advice shared by our panelists.   Where QSRs Are Focused in 2025: Loyalty, Personalization & Speed The webinar kicked off with a deep dive into shifting priorities. Customer engagement and loyalty emerged as the top focus for QSR marketers in 2025, with 80% of respondents increasing investment in customer experience technology. Mobile-first experiences and real-time personalization are no longer optional; they are essential for effective QSR marketing. Nick Patrick, CEO of Radar, put it simply: “Mobile has become the primary interface between QSRs and their customers. Real-time context is what makes that interface intelligent.” Challenges: Disconnected Data, Tech Silos, and Execution Speed While QSRs have the vision, many struggle with execution.  The report found that 60% of QSR leaders still struggle with personalization, and more than a quarter cited siloed data as a top challenge. The panelists echoed these findings, pointing to fragmented systems and misaligned teams as major hurdles. Brendan shared: “The POS system was designed for speed and accuracy, not personalization. But when you can use even a basic signal, like a loyalty status, to prompt a more human, high-touch experience, it makes a real difference.” Strategies That Work: Start Small, Focus Deep, Earn Trust Panelists emphasized the value of starting small with high-impact initiatives like curbside pickup or loyalty nudges. Cross-functional alignment and choosing scalable tech partners were key themes. “Don’t boil the ocean,” Brendan advised. “Start with one moment in the journey that can be improved and work cross-functionally to get it right.” Earning Location Opt-ins the Right Way Location data is a powerful lever for marketing and operations, but only if opt-ins are earned with care. Nick shared a practical framework: “It comes down to three things: transparency, value, and timing. You can’t just ask up front with no context and expect users to say yes.” He pointed to Outback Steakhouse as a standout example: “They clearly explain the value, guide users through branded screens, and then request OS-level permissions. It’s thoughtful, and it works.” AI in Action: Real Business Impact Artificial intelligence was another hot topic. Brendan shared two areas where AI is delivering real value: smarter cross-sell/upsell and feedback intelligence. “Even simple segmentation by daypart or region can lift basket size. Some tools report a 10% increase just by turning it on,” he said. “It doesn’t need to be complex to be effective.” QSR Webinar Recap: Closing Thoughts Whether optimizing app experiences, trying to unify your tech stack, automating manual processes, or building stronger loyalty loops, the advice was clear: start small, stay focused, and partner with tools and teams that can scale with you. Watch the full webinar on demand to explore these examples further and learn how MoEngage, Radar, and Bottle Rocket can help your team accelerate QSR engagement.   The post Digital Meets Dine-In: 5 Expert QSR Engagement Strategies appeared first on MoEngage.

Credit: Mashable Photo Composite/Beats Deal pricing and availability subject to change after time of publication. Learn more about how we select deals. SAVE $30: As of June 5, the Beats Fit Pro earbuds are on sale for $169.95 at Amazon. That's a 15% savings on the list price. Opens in a new window Credit: Beats Beats Fit Pro $169.95 at Amazon $199.95 Save $30 The Beats Fit Pro are back on sale at Amazon, so if you're looking for a quality pair of earbuds for a great price, look no further.As of June 5, you can save 15% on these earbuds, reduced to $169.95. This deal is specific to the volt yellow and coral pink colors. You May Also Like As with any Beats product, these earbuds offer amazing sound quality. It's incredibly immersive, not to mention powerful, and some of the best I've experienced. The noise cancellation modes are stellar, with several options on how much you want to block out surrounding noises (full cancellation, transparency, etc). This is great for outdoor activities such as running when you want to hear your music but also stay aware of those around you.They also boast a seriously impressive battery life, with a single charge lasting days (often weeks). And for those who want to use these for sports, they're a great option. The silicon tip slides into your ear seamlessly, lodging in your ear and staying put for all types of activity at all intensities. I've tested these during many sports with varying degrees of intensity, and they always stay firmly in place. Mashable Deals Want more hand-picked deals from our shopping experts? Sign up for the Mashable Deals newsletter. By clicking Sign Me Up, you confirm you are 16+ and agree to our Terms of Use and Privacy Policy. Thanks for signing up! Grab these discounted earbuds from Amazon. Lois Mackenzie Lois Mackenzie is a freelance reporter at Mashable. Over the years she has written for many publications, covering everything from the local news to the best pair of running shoes. You can find bylines in publications including Fit&Well, Metro, and Coach magazine, usually covering deals on everything from earbuds to TVs, or guides on how to beat your half marathon time.Lois also holds a Master's degree in Digital Journalism from Strathclyde University and obtained a Master of Arts in English Literature at the University of Aberdeen.

Explore the real benefits of ditching the notebook The post The Journey from Jupyter to Programmer: A Quick-Start Guide appeared first on Towards Data Science.

CIOs and business leaders know they’re sitting on a goldmine of business data. And while traditional tools such as business intelligence platforms and statistical analysis software can effectively surface insights from the collated data resources, doing so quickly, in real-time and at scale remains an unsolved challenge.Enterprise AI, when deployed responsibly and at scale, can turn these bottlenecks into opportunities. Acting quickly on data, even ‘live’ (during a customer interaction, for example), is one of the technology’s abilities, as is scalability: AI can process large amounts of information from disparate sources almost as easily as it can summarize a one-page spreadsheet.But deploying an AI solution in the modern enterprise isn’t simple. It takes structure, trust and the right talent. Along with the practical implementation challenges, using AI brings its own challenges, such as data governance, the need to impose guardrails on AI responses and training data, and persistent staffing issues.We met with Rani Radhakrishnan, PwC Principal, Technology Managed Services – AI, Data Analytics and Insights, to talk candidly about what’s working — and what’s holding back CIOs in their AI journey. We spoke ahead of her speaking engagement at TechEx AI & Big Data Expo North America, June 4 and 5, at the Santa Clara Convention Center.Rani is especially attuned to some of the governance, data privacy and sovereignty issues that face enterprises, having spent many years in her career working with numerous clients in the health sector — an area where issues like privacy, data oversight and above all data accuracy are make-or-break aspects of technology deployments.“It’s not enough to just have a prompt engineer or a Python developer. … You still need the human in the loop to curate the right training data sets, review and address any bias in the outputs.” —Rani Radhakrishnan, PwCFrom support to strategy: shifting expectations for AIRani said that there’s a growing enthusiasm from PwC’s clients for AI-powered managed services that can provide both business insights in every sector, and for the technology to be used more proactively, in so-called agentic roles where agents can independently act on data and user input; where autonomous AI agents can take action based on interactions with humans, access to data resources and automation.For example, PwC’s agent OS is a modular AI platform that connects systems and scales intelligent agents into workflows, many times faster than traditional computing methods. It’s an example of how PwC responds to the demand for AI from its clients, many of whom see the potential of this new technology, but lack the in-house expertise and staff to act on their needs.Depending on the sector of the organization, the interest in AI can come from many different places in the business. Proactive monitoring of physical or digital systems; predictive maintenance in manufacturing or engineering; or cost efficiencies won by automation in complex, customer-facing environments, are just a few examples.But regardless of where AI can bring value, most companies don’t yet have in-house the range of skills and people necessary for effective AI deployment — or at least, deployments that achieve ROI and don’t come with significant risk.“It’s not enough to just have a prompt engineer or a Python developer,” Rani said. “You’ve got to put all of these together in a very structured manner, and you still need the human in the loop to curate the right training data sets, review and address any bias in the outputs.”Cleaning house: the data challenge behind AIRani says that effective AI implementations need a mix of technical skills — data engineering, data science, prompt engineering — in combination with an organization’s domain expertise. Internal domain expertise can define the right outcomes, and technical staff can cover the responsible AI practices, like data collation and governance, and confirm that AI systems work responsibly and within company guidelines.“In order to get the most value out of AI, an organization has to get the underlying data right,” she said. “I don’t know of a single company that says its data is in great shape … you’ve got to get it into the right structure and normalize it properly so you can query, analyze, and annotate it and identify emerging trends.”Part of the work enterprises have to put in for effective AI use is the observation for and correction of bias — in both output of AI systems and in the analysis of potential bias inherent in training and operational data.It’s important that as part of the underlying architecture of AI systems, teams apply stringent data sanitization, normalization, and data annotation processes. The latter requires “a lot of human effort,” Rani said, and the skilled personnel required are among the new breed of data professionals that are beginning to emerge.If data and personnel challenges can be overcome, then the feedback loop makes the possible outcomes from generative AI really valuable, Rani said. “Now you have an opportunity with AI prompts to go back and refine the answer that you get. And that’s what makes it so unique and so valuable because now you’re training the model to answer the questions the way you want them answered.”For CIOs, the shift isn’t just about tech enablement. It’s about integrating AI into enterprise architecture, aligning with business strategy, and managing the governance risks that come with scale. CIOs are becoming AI stewards — architecting not just systems, but trust and transformation.ConclusionIt’s only been a few years since AI emerged from its roots in academic computer science research, so it’s understandable that today’s enterprise organizations are, to a certain extent, feeling their way towards realizing AI’s potential.But a new playbook is emerging — one that helps CIOs access the value held in their data reserves, in business strategy, operational improvement, customer-facing experiences and a dozen more areas of the business.As a company that’s steeped in experience with clients large and small from all over the world, PwC is one of the leading choices that decision-makers turn to, to begin or rationalize and direct their existing AI journeys.Explore how PwC is helping CIOs embed AI into core operations, and see Rani’s latest insights at the June TechEx AI & Big Data Expo North America.(Image source: “Network Rack” by one individual is licensed under CC BY-SA 2.0.)

Martian "slope streaks" spotted by NASA's Mars Reconnaissance Orbiter in 2017. Scientists previously thought these large, discolored features may be signs of running water. (Image credit: NASA/JPL-Caltech/University of Arizona) Mysterious dark streaks flowing across Mars's surface may not be the result of running water after all, a new artificial intelligence (AI) analysis suggests.The streaks, first observed running along Mars's cliffsides and crater walls by NASA's Viking mission in 1976, were long thought by scientists to have formed as a result of the flow of ancient water across the now mostly desiccated planet's surface.But an AI algorithm trained on slope streak observations has revealed a different origin for the streaks — likely being formed from wind and dust, not water. The findings, published May 19 in the journal Nature Communications, could have important implications for where humans choose to explore Mars, and the places they search for evidence of possible ancient life. "That's the advantage of this big data approach," study co-author Adomas Valantinas, a planetary scientist at Brown University, said in a statement. "It helps us to rule out some hypotheses from orbit before we send spacecraft to explore."The sinewy lines are darker than the surrounding Martian ground and extend for hundreds of meters downhill. The shorter-lived of these features are called recurring slope lineae (RSL), and regularly spring up during Mars's warmer spells.This led some planetary scientists to suggest that seasonal temperature fluctuations could be causing ice or frozen aquifers to melt, or humid air to condense, sending streams of salty water trickling down the planet's craters. If this were true, it would make these regions of particular interest to future Mars missions.To investigate this, the scientists behind the study trained a machine learning algorithm on confirmed streak sightings before making it scan through 86,000 satellite images to create a map of 500,000 streak features.RELATED STORIES"Once we had this global map, we could compare it to databases and catalogs of other things like temperature, wind speed, hydration, rock slide activity and other factors." Bickel said. "Then we could look for correlations over hundreds of thousands of cases to better understand the conditions under which these features form."Using the map, the scientists found the streaks were most likely to form in places where wind speed and dust deposition was high, suggesting that they came from layers of fine dust sliding off steep slopes.Other studies have pointed to tantalizing evidence of water and even life on Mars. If the study findings hold up, they could serve as a guide to sift between the Red Planet's useful leads and its red herrings. Sign up for the Live Science daily newsletter nowGet the world’s most fascinating discoveries delivered straight to your inbox.

SSRN Exploring Grey Literature on SSRN There has been a growing discussion in the academic community surrounding the concept of grey literature, a broad term that encompasses documents, data, research, and materials created outside of the traditional pathways of academic publication, and often for non-academic audiences. This work contributes to the information ecosystem by providing sources of knowledge that are timely and broad, filling in gaps in research and offering original data and insights that extend beyond the typical channels for academic publishing. In practice, what does this look like? Grey literature includes various reports, conference proceedings, datasets, legal transcripts, working papers, dissertations, blog posts, policy documents, and a wide range of other work that expands the knowledge base and enriches modern scholarship. The Purpose of Grey Literature Traditional academic publishing involves peer review, a lengthy publication process, and such documents may not be widely accessible to those without academic library privileges. Grey literature may be released more quickly and is often directly accessible for all, allowing current research within a field to be shared in real-time. This provides the opportunity for dissemination of ongoing research, recent developments in policy and government, and relevant reports that help inform the academic discourse of the present and influence the development of research in the future. Grey literature provides other benefits beyond its timeliness. The structure of the work itself provides the opportunity to fill in research and knowledge gaps. This can be through the release of up-to-date data, case studies, and reports that don’t fall within the scope of academic publications, or it can present preliminary findings that serve as complementary to previously published works. Grey literature captures perspectives that have a wider scope and therefore rounds out the scholarly record. The accessibility and relevance of grey literature allows the work to have significance outside the world of academia. It helps inform policies, programs, and future academic research. Grey literature takes research and data and translates it into real-world impact. Joshua Tucker, professor and researcher at NYU, shared his grey literature on SSRN. He was pleased to see that his report  – which would not be included in traditional academic publications – had a presence on SSRN, generating additional attention and citations it wouldn’t have received otherwise. He shared with SSRN that, “This review of the literature was never intended to be an academic article. It was a report commissioned by the Hewlett Foundation, and the Hewlett Foundation put it on its website. I thought people in the policy community were going to see it on the Hewlett website, but I’d love for people to see it in the academic community. I thought that maybe we’d get a few citations out of it, and [decided] to throw it up on SSRN, on a whim. And now it’s been downloaded over 40,000 times and continues to be cited all the time. In that sense, [SSRN] filled this really nice niche: we had something that we didn’t write to be an academic publication [and] weren’t going to send to journals. It’s a nice home for things that don’t have a natural fit.“ Grey Literature’s Place on SSRN As a repository for early-stage research, SSRN provides a home for research in all stages of development. Work submitted to SSRN is made available quickly, creating an outlet for real-time research. SSRN is a platform where research of many mediums can thrive. We define research broadly: presentations, infographics, case studies, white papers, proceedings, working papers, datasets, conference proceedings, informational guides, reports and more. They exist side-by-side, all with the objective of sharing knowledge at a global level. Because of this, SSRN is a great place for grey literature of all kinds. Even research that doesn’t take a traditional academic pathway can thrive on SSRN. The Future of Research The world changes quickly – with technology, faster than ever – and SSRN allows the flow of research to keep up with the changing times. The relevance and impact of research matters, and grey literature is a big contributor to that. SSRN is where it starts; submit your research in real-time, bring work of any scale and any format, and contribute to the future of this evolving research and scholarship landscape. Want to share your grey literature or other early-stage research on SSRN? Click here to submit your research today.