Multitudes

AI is increasingly shaping how we see and represent ourselves, so understanding how these systems interpret gender is crucial. Gender is deeply personal and much more than just anatomy, but AI systems often reduce this complexity to simple binary classifications. Is it time we started challenging the binary frameworks that dominate machine learning? Join us on Wed 29 Jan/30 Jan for a #TechLeaderChat with Dr J. Rosenbaum Melbourne-based AI artist, researcher and lecturer, on “How to overcome AI’s biased perceptions of gender.” We'll cover:  💡 How and why AI perceives gender 💡 Why gender is fundamentally misunderstood by machine learning systems 💡 The implications for visual representation present in generative people images 💡 How de-biasing data affects generative images RSVP 👉 https://lnkd.in/gjunidru #AI #Creativity #TechLeadership #Innovation #HumanCenteredAI

Day 1 of Multitudes's first 2025 team week – done and dusted! ✨ Today our team came together in person to connect and align on what's in store for the year ahead. We explored Multitudes' journey so far, our plans to keep delivering value to customers in 2025, and dove into what drives us individually and as a team. A huge thank you to Lightspeed Commerce for sharing their board room (and amazing professional coffee machine ☕️😍) with us, and to Miléna Le Mancq for leading such an engaging day. You might also spot some new faces in the photos below! We're thrilled to welcome three new team members: ➡️ Barend Bootha as our new Engineering Lead ➡️ Kevin Chan as our Full Stack Developer ➡️ Laura Kerrison as our Product & Design Lead Stay tuned for proper introductions on our page later this week 👀 #TeamWeek #2025 #Collaboration #ProductRoadmap #Multitudes

What are the traits of a high performing, or "strong" engineer? 💭 Sean Goedecke, Staff Software Engineer at GitHub, recently shared what he believed are the concrete skills or traits that make up a "strong" engineer's ability to do a wide range of tasks, to a high standard. He believes it boils down to four things (in order of importance): self-belief, pragmatism, speed, and technical ability. 💡 Self-belief – In Sean’s words: “Software problems are all unfamiliar problems, therefore engineers work in the dark.” So, for each new piece of work, it’s impossible to say how tricky it’ll be until it’s completed (this is why estimating work is so challenging!). Strong engineers have the confidence to know they can figure it out, whatever it is. A feedback loop is also created – engineers with a lot of confidence tend to work on hard problems, which builds up their confidence, pushing them to harder problems, and so on. 💡 Pragmatism – They get things done and have a bias towards working solutions. Strong engineers are ruthless pragmatists: every design decision is judged by how well it will work, not how clean or elegant it is. They’re typically happy to make compromises in the interest of shipping. 💡 Speed – They work fast. Sean believes there are two reasons for this – they don’t put off hard work, and they tackle hard tasks which means they accumulate experience rapidly. 💡 Technical ability – Deeply technical people will see simple solutions that others miss, and be able to solve whole categories of problems that others can’t. Sean also notes that he doesn’t think strong engineers need to be particularly hard workers in the sense of working long hours. His experience suggests that good work looks like short intense bursts of productivity separated by long periods of relative inactivity. Do you agree, or disagree? What do you think are the traits of high-performing team members (or teams)? Tell us in the comments! 👇 Read more in the full blog, below. https://lnkd.in/gtjNa3bQ #HighPerformingTeams #EngineeringTeams #Engineering #Teams

Why do AI systems oversimplify gender as a binary classification based on physical traits? 💭 Join Dr J. Rosenbaum, a Melbourne-based AI artist, researcher and lecturer, for a thought-provoking #TechLeaderChat on “How to overcome AI’s biased perceptions of gender.” J. will share their unique insights on:  🤖 How and why AI perceives gender 🤖 Why gender is fundamentally misunderstood by machine learning systems  🤖 The implications for visual representation present in generative people images   🤖 How de-biasing data affects generative images As tech leaders, we need to be mindful of how we consume and shape AI’s developments, ensuring we limit the influence of biases where possible. Bring your questions and experiences - let's have an honest conversation about our role in reducing bias in AI systems! ✨ RSVP via the link in the comments 👇 #AI #Creativity #TechLeadership #Innovation #HumanCenteredAI

As a manager, how hard should you work to retain employees who hint at leaving? As an employee, what is reasonable to expect from a manager? In her latest blog, Charity Majors, Co-Founder & CTO at honeycomb.io, shares her perspective on the employer-employee relationship, with some honest truths along the way. Our standout takeaways from the blog were: 💭 If the wants and needs of the company and employee are not aligned, you’re not doing them any favors by dragging it out or keeping the employee around in a prolonged state of purgatory. 💭 As a manager, it’s your role to: > Bring clarity – expectations of the role, what success looks like, what support the employee needs to get there > Make your employee’s current career opportunity as great as possible > Be honest if the employee could find better opportunities elsewhere (based on their career goals) 💭 Making sure the right people are on the team is the #1 job of a manager. If this requires managing people out, then figuring this out swiftly (months, not years) is critical. 💭 You should rarely try to change someone’s mind about leaving. But always try to understand why they’re leaving. Charity has seen success in turning a departure around not by throwing more money at the employee, but by including some form of development – identify the underlying source of discontent, and meet them with action. Be sure to read Charity's blog for more great tips and insights! Link below. https://lnkd.in/g4TvyVNj

Are you looking to deepen team connections and improve retros? Nic Pegg, Staff Engineer at Thinkific, explains how he approaches fostering meaningful team interactions in his blog below. The TLDR: try these three exercises during your next retro: 💡 Help me understand – ask your team about the challenges faced, how these affected them emotionally, what kind of support would have helped, and how they overcame any challenges. 💡A skill swap – each team member shares one skill they felt confident in and one they struggled with. Look at opportunities to help others grow. 💡 An empathy map – each team member shares what they need, can offer, what helps them when they're stuck, and how they like to ask for help. #TeamBuilding #TeamPerformance #Retros #EngineeringTeams

Our community for Tech Leader Chats has hit 800 members! 🎉 We’re thrilled to hit this milestone, as our group continues to grow and be a place where engineering, product, and data leaders come to learn, grow, and connect. If you’re looking to be part of a community of human-centric product, data, and engineering leaders, join us! Find a link to our meetup group and Slack channel in the comments below. 👇 #Community #TechLeaderChats #EngineeringLeaders #ProductLeaders #DataLeaders #TechLeaders

How do you turn data ethics principles into an ongoing practice? It’s cheap to store data, but why might we want to set limits on what we save (and measure) from a data ethics standpoint? These are just some of the questions discussed at DataEngBytes’ recent Data Ethics Panel, featuring Multitudes Founder & CEO, Lauren Peate. Tune in below for the full panel discussion, and to hear Lauren’s perspectives on data ethics. https://lnkd.in/gv_uy6uF #DataEthics #DataEngBytes #DataEthicsPrinciples

Are you wondering about how increased AI usage will impact code development? During Kristine Howard’s talk about creativity in the age of AI, we asked our #TechLeaderChats community questions about AI and code development. While it was a small sample size (16 webinar attendees), the data showed interesting insights: In terms of HOW engineering teams are using AI tools, they’re primarily using them in: 1️⃣ Code generation (56%)  2️⃣ Documentation generation (20%) 3️⃣ Test case generation (13%) By far, the biggest concern about AI usage in coding was ethical considerations, with 56% of respondents selecting this. During the #TechLeaderChat, some teams shared that the use of AI is banned at their workplace due in part to these concerns. Do you agree/disagree with this sentiment? Do you share these concerns? Tell us in the comments! Note that it was a single-choice selection, due to the limitations of Zoom’s surveying tool. #AI #development #engineering #aiandengineering #engineeringteams #aitools

Another great #TechLeaderChat last week! We got to hear Kristine Howard’s perspective on the role of creativity in the age of AI (and no, ChatGPT did not write the talk!). We learned about the "growing sea of 'delvish'" and considered whether the goal is saving time or creating something great. A punchy, insightful talk, it’s one not to miss! Find the recording and slides via the comments. 👇 #creativity #ai #creativityandai #thoughtleadership #technology