Ello’s Post

If interviewing were an Olympic sport, what would the event be called? "𝐓𝐡𝐞 𝐂𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞 𝐃𝐞𝐜𝐚𝐭𝐡𝐥𝐨𝐧" 🎖️ 10 rounds of intense question volley 🎖️ Agility in decision-making, and  🎖️ A finish line where you’ve found the perfect match But what if, in this race, you had a co-pilot? An AI-powered assistant that doesn’t just keep score but helps you: ✔️ Hurdle bias  ✔️ Vault over blind spots, and  ✔️ Sprint through data with the finesse of a pro athlete. BarRaiser’s AI Interview Co-Pilot is ready to help you break records in efficiency and accuracy, all while keeping the spirit of fair play. So next time you're in the interviewer’s chair, remember: even the best Olympians have coaches. Why not have one for interviewing? Book a Demo - https://lnkd.in/dSS45RwZ

The future is here

#AI #predictions vs. #Betting odds At the beginning of yesterday’s The Championships, Wimbledon Men’s final, the #AI based predictions were spot on predicting that #Alcaraz had almost a 50% better chance of winning than #Djokovic Compare this with the bookies betting odds (with millions at stake), that only marginally favored Alcaraz (by 15%). What could explain the big difference between the two? My guess is that the AI model is likely placing more emphasis on recent performance data and statistical analysis, while the betting system must be balancing this with historical performance and other intangible and human sentiment factors. Let’s dig a little deeper into the potential reasons why AI might have predicted a higher chance of winning for Alcaraz: 1. Recent performance and form – Alcaraz’s superior record in 2024 (33-6) vs. Djokovic (22-7). 2. 2024 titles – Alcaraz 3 vs Djokovic 0. 3. 2024 Grass court performance – Alcaraz has a slightly better record on grass (8-1 with one title) vs. Djokovic (6-1 with no titles on grass). 4. Return game – Alcaraz has a significantly better return game on grass (35.7% overall, 32.5% on grass); Djokovic (30.8% overall, 23.7% on grass). 5. Age factor – 16 yr gap – The AI may be favoring the younger player in a potentially long and grueling match (Their past few head to head matches have been long and hard fought). 6. Injury and recent surgery - Novak had a knee surgery just 4 weeks ago, which could have been a factor in the AI algo. On the other hand, the bookies’ betting odds only marginally favored Alcaraz. Let’s examine why: 1. Djokovic's historical performance – As an all time great and multiple-time Wimbledon champion, bettors were likely giving more weight to his experience and past success. 2. Service game strength – Djokovic's service game winning percentage on grass (89.0%) is higher than Alcaraz's (84.0%), which could have been be a significant factor not fully accounted for by the AI. 3. Human factors – Betting odds typically incorporate factors like public perception and historical betting patterns, which the AI system likely did not consider. This could explain the discrepancy between the AI prediction and the betting odds. #wimbledon #sport #analytics #datascience #tennis Cache AI

🚀 Exciting Update: Enhancing Predictive Models with Polynomial Features and Standardization! 🚀 With the Olympics now concluded, I’ve had the opportunity to refine my medal prediction model with some advanced techniques to better understand the results and improve accuracy for future events. Here’s a quick rundown of the enhancements: 🔍 Polynomial Features: I’ve introduced polynomial features of degree 2 to capture non-linear relationships between predictors (such as the number of athletes and previous medals) and the target variable (the number of medals). While the direct relationship between predictors and medals might be linear, polynomial features help the model uncover more subtle and complex patterns and interactions in the data. This allows for a more nuanced understanding of how multiple factors influence outcomes. 🔧 Standardization: To ensure consistent feature contributions, I’ve standardized the polynomial features. This scales the data to have a mean of 0 and a standard deviation of 1, stabilizing the training process and improving the model’s performance. 🌟 Results: These updates have led to more accurate predictions and a deeper understanding of how various factors influence medal counts. By capturing quadratic relationships and normalizing features, the model now provides more reliable insights into Olympic performances. I’m excited about these improvements and eager to see how they’ll enhance future predictions. https://lnkd.in/e6AjjtQM #DataScience #MachineLearning #PolynomialFeatures #Standardization #PredictiveModeling #Olympics

Ever watched Olympians compete and felt inadequate? Me too. I put this together so that now we can visualise that feeling of inadequacy too! The backstory: I watched a couple of clips on the BBC comparing us ‘averages’ with olympians:  "How long can you keep up with Keely Hodgkinson’s 800m pace?" "Could you lift as much as Team GB's Emily Campbell?" I thought it would be fun to visualise some events compared to my capabilities, for a bit of gentle self-deprecation! Usually I wouldn’t have a chance of creating something like this - the visuals sure, the code, no way! But the AI tool Claude has got a really interesting feature called ‘Artifacts’ that builds code for you based on conversational instructions. This took me about 8 rounds of tweaks, but with so little effort on my part. There are some things I would change and ‘fix’ if I was to go deeper with it, but for now it’s a bit of fun. If you want to try it yourself it's here: https://lnkd.in/eFZGXU_N Lots of interesting thoughts about what handing everyone the ability to ‘build’ their ideas could look like. Exciting times! (fun fact:  I had to remove the audio of a lawn mower I accidentally captured during my screen recording!)

.81 seconds improvement in 100 Years.. Is the performance justified wrt to time? With technological advancements where we see KPI's for sports cars bragging about 0 to 100 in xx seconds this seems insignificant. But this is the an improvement of human time in a 100 mts race over 100 Years for two Olympic champions one in 1924 and the other in recently concluded 2024 Olympics. Now it seems impressive.. Our lives have also been greatly impacted with the disruptions in markets during these 100 years and striving to be better and better in what ever we are doing personally and professionally. The video talks about the advancements in this sport in 100 years. I think next in line should be a race between a human and a Leopard. https://lnkd.in/g7jTGSJK

FUTURE FOCUS “The amateur focuses on getting hits and expects short-term results. The professional plays the long game and prioritizes inputs, perfecting them for years with no immediate payoff. “This is why the tennis player Maria Sharapova describes focus on outcomes as the worst mistake that beginning tennis players make. Watch the ball as long as you can, Sharapova cautions, and zero in on the inputs. By taking the pressure off the outcome, you get better at your craft. Success becomes a consequence, not the goal.” https://lnkd.in/gTn_NQ4W #future #goals #success

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This is a great demo, with 3DEXPERIENCE Delmia its quick to create these advanced robotics simulations using the pre-installed robot library to easily assess which robot will best meet your needs and generate offline programming data which can be connected to the live robots. When you combine this with a host of smart tools allowing you to hugely reduce the timescales for programming, true collaborative working to reduce silos in the business & extensive virtual commissioning solutions DELMIA is the smart choice for automated production.