Latest news with #experimentation
Times
20 hours ago
- Science
- Times
How to make chemistry fun for kids
Why is breakfast cereal magnetic? How does sand help you to see? Why did that German guy try to turn wee into gold and what did he find instead? Elements of the Day doesn't just blast off soon-to-be-forgotten facts about the periodic table. It tells the story of chemical elements in our everyday lives by fusing them with the daily, domestic moments that will be familiar to every child. It's a clever formula — and an effective one. From wake up to bedtime, we are introduced to many of the scientific miracles that are happening all around us. 'Elements — and all the stuff you can build from them — make every part of your day possible in unexpected and fascinating ways, though most people don't give them much thought. However, once you open your eyes, you'll start seeing lots of these elements in your daily life,' the author Samantha Lewis says as she urges young readers to experiment with their breakfast.
Entrepreneur
28-05-2025
- Business
- Entrepreneur
How to Build an AI-Driven Company Culture
A practical guide for business leaders on how to build a company culture that embraces AI through curiosity, experimentation and hands-on learning. Opinions expressed by Entrepreneur contributors are their own. In the early 1900s, as the automotive revolution reshaped industries, blacksmiths and carriage-makers struggled to adapt. More than a century later, we face a similar inflection point with AI. Just as horse-drawn carriages gave way to automobiles, entire industries are being redefined by algorithms today. The question isn't whether your company will adopt AI, but how. And the answer hinges on one critical factor: culture. Related: How to Create a Workplace Culture That Supports Digital Transformation (and Why It's Important) What does an "AI culture" look like? Building an AI-driven culture isn't always about buying tools or hiring machine learning scientists. It's about fostering a mindset where experimentation, learning and human-AI collaboration are core to your company's DNA. Here's how to start: Model curiosity to dispel fear: Leadership must champion AI, but grassroots innovation is what embeds it into real workflows. At CodeSignal, our engineering team doesn't just use AI — they build with it. From leveraging GitHub Copilot for complex refactoring to fine-tuning custom LLM agents for internal tools, AI is part of their daily toolkit. And it's not just engineering. Our marketers, for instance, prototype campaign ideas in Claude and validate messaging variations with Gemini. The key? Leaders must model curiosity. Share your own AI experiments — and failures — with your team. CodeSignal has a Slack channel dedicated to experimentation with LLMs, where team members share how they've been using AI and what they're learning ("productivity hacks" are a team favorite). I have been studying AI technology and building AI-native products for over a decade, but this doesn't stop me from continuing to learn. I regularly share my learnings, from using the latest LLM models for everything from code writing to email writing to image generation, and debate with my colleagues on how different models perform on complex math challenges. The point of me doing this is to set the example that incorporating AI into your daily workflow doesn't have to be intimidating, and in fact it can be quite enjoyable. It also reinforces that we're all learning this new technology and figuring out how best to use it to do our work together. Provide access to the right AI tools: Today, tools like ChatGPT and Midjourney are free, yet many companies still gatekeep access. That's a big mistake. We give every team member a ChatGPT Teams subscription, with the expectation that they'll play around with it and even create their own GPTs to augment their workflow. In the past year, our employees have created over 50 custom GPTs that help them draft sales emails, gather market insights, extract data, answer HR questions and more. Make AI literacy a core expectation — then build on it: Giving people access to AI tools is necessary, but it's just the first step. To create a meaningful impact, leaders must pair access to tools with training. CodeSignal does this by asking every team member to complete AI literacy training, where they build skills in using and interacting with LLMs with hands-on practice. Our team recently finished a "spring training" in generative AI literacy, where everyone at the company (even me!) completed a series of experiential learning courses online and shared our learnings, questions and ah-ha moments in a Slack channel. We boosted motivation for completing the training by setting up a goal of 95% participation — rewarded by cool new swag when we met the goal. Next, we're building on this foundation of AI literacy by running an AI hackathon at our next in-person meetup. Here, team members will break into teams based on how they use AI and their depth of knowledge. Some teams will explore using LLMs to draft creative campaigns and set project timelines, for example, while others will be building custom GPTs to automate actual parts of their job. The machine learning experts on our team, meanwhile, will be working on building innovative new AI applications from the ground up. The goal here is to set the expectation that everyone uses AI, yes — but more than that, to give team members ownership of what they do with it and the freedom to choose which parts of their job can best be complemented by AI. Related: AI is the Coworker of the Future — 3 Ways Employers Can Get Ready The stakes have never been higher For some organizations and teams, adopting AI will be uncomfortable at first. AI tools raise a range of new technical, regulatory and ethical questions. Many employees fear that AI will displace them from their jobs. That discomfort is real — and it deserves our attention. As leaders, our responsibility is to guide our teams through uncertainty with integrity and transparency by showing how embracing AI can help them become even more impactful in their jobs. I do this by modeling AI use in my everyday work and openly sharing my learnings with my team. This gives team members permission to experiment on their own and helps move them from a mindset of fear to curiosity about how AI can be a partner to them in their jobs. To return to the analogy of the automotive revolution: We're teaching our carriage-makers how to build self-driving cars. If you're a business leader, ask yourself: Am I modeling what it looks like to learn and take risks? Am I giving my team the tools and training they need to build AI literacy? Am I fostering a culture of exploration and experimentation on my team? The AI revolution is already here, and the future isn't going to wait for companies to catch up. Neither should we.
Forbes
23-05-2025
- Business
- Forbes
The Edge Gap: Why Experimentation Is Important For Brands In 2025
Liam Wade - Performance Director at Performance Marketing Agency, Impression. In 2025, the biggest risk in marketing isn't making a bold move—it's playing by the book. AI-driven tools have made marketing faster, smarter and more scalable. But they've also created a paradox: When everyone is using the same software, platforms, targeting models and campaign techniques, competitive advantage collapses. Best practice has quietly become common practice, and that's where the danger lies. I'm in conversation with at least 10 new brands per week. Nine out of 10 of them say the same thing: What's "tried and tested" isn't working anymore. Some blame the algorithm. Others realize they've become too cautious. This is the "edge gap"—the space between brands optimizing for efficiency and those exploring for effectiveness. The only way to close it without wasting budget? Treat experimentation not as a tactic, but a mindset—a strategic advantage in an overly risk-averse landscape. Marketing in 2025 is marked by an uncomfortable truth: Everyone is using the same tools to chase the same outcomes. Meta's Advantage+ campaigns hit a $20 billion annual run rate in Q4 2024—a 70% year-over-year increase. Meanwhile, 95% of retail advertisers using shopping ads have adopted Google's Performance Max, according to Tinuiti's Digital Ads Benchmark Report Q4 2024. These aren't just trends; they're signs of a marketing ecosystem that's been optimized into sameness. This is the edge gap in practice: the growing divide between brands that stick with automated, AI-driven campaign systems and then focus on efficiency, versus those actively exploring new paths to stand out. The edge gap is the widening space between marketing strategies that sharpen a brand's competitive edge and those dulled by algorithmic automation, uniform targeting and the rinse-and-repeat logic of platform and industry best practice. It's not a theory; from the clients I speak to, there are signals across the industry that marketers chasing a competitive edge are starting to walk away from "black box" campaign types in search of something more original. Algorithms don't just optimize—they homogenize. And we're all using the same targeting technology. So when every brand plays by the same rules, creative solutions become the only way to get a competitive advantage within your advertising. Many brands think they're innovating, but they're only tweaking. Adding more data, cutting "wasted spend" or rotating similar creatives may boost ROI, but these are optimizations, not exploration, and rarely drive real revenue growth. Exploration means going off script and testing bold ideas outside the industry playbook. It's trying unfamiliar channels, formats or creative styles, and even breaking tools to use them differently. The trap is that iteration feels safe. It offers progress without disruption and wins boardroom approval with fast, measurable results. But over-optimizing what already exists limits exploration and weakens long-term performance. Real progress needs a system, one built on experimentation. Experimentation is the antidote to risk-averse marketing. It gives brands a way to explore bold ideas without betting the entire budget. It's not guesswork, and it's not chaos—it's a system for learning. It's a way to try before you buy, measure before you scale and push boundaries with purpose. High risk can equal high reward. But experimentation works as the arbitrator, minimizing those risks by testing parts of the strategy before deciding where to invest fully. It gives teams the confidence to try something genuinely different, without getting shut down at the first sign of uncertainty. The upside is well documented. Bain has highlighted countless examples where marketing experimentation has driven measurable ROI growth. And yet, McKinsey reports that only 25% of C-level marketers say they've embedded a test-and-learn culture into their teams. The gap isn't one of knowledge—it's one of commitment. Too often, data and analytics teams are focused on proving value, not growing it. They're looking backward at what worked, not forward at what could. Experimentation flips that model. It uses data as a launchpad for future media effectiveness, not just past efficiency. Saying you value experimentation is easy, but embedding it into how your team thinks and works is far harder. The best brands share three traits: They reward original thinking and protect teams who take risks. Failure is essential for discovery. At Impression, some of our best-performing ads started as long shots. True creative progress requires leadership to back bold testing, financially and culturally. Not every idea needs testing, and not all tests deliver instant results. Smart teams prioritize high-impact hypotheses, bets that, if right, unlock real growth. Too often, testing is used only to justify spend, leading to cautious, shallow efforts that are cut too soon. Real learning requires time and commitment. Without structure, testing loses credibility. Vague or biased experiments create confusion, not clarity. That's why rigorous data science is nonnegotiable. It means setting clear hypotheses, managing variables, using control groups and ensuring significance before declaring wins. Strong teams treat experimentation as a continuous system for learning, not one-off projects. In a marketing world increasingly shaped by black-box automation and AI, we've been guilty of adjusting our businesses' goals to fit the platform's best practices. But when everyone is optimizing using the same tools, best practice becomes common practice. Standing still is falling behind. Experimentation isn't just a tactical add-on. It's a cultural capability, a mindset that allows your brand to move fast without falling into the sameness trap. It's how you test the unconventional and find your next breakthrough before your competitors do. Because as we rocket through this decade, the edge gap will only widen. It will separate the brands that play it safe from the brands that grow. Perhaps you're lucky right now … if your competitors are still following standard practice, you're probably doing fine. But that won't last. Eventually, someone in your category will decide to explore. One type of brand will focus on iteration. The other will explore. And only one will survive. Forbes Communications Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?
SBS Australia
09-05-2025
- Business
- SBS Australia
German International School Sydney opens Australia's most modern school laboratory
Principal Michael Cordes is delighted about the collaboration with the German company Waldner Education, which developed the technology for the new school laboratory. Credit: privat The new school laboratory is definitely something for those who like to experiment. Credit: privat
