AI Trends and Innovations

AI systems built without women's voices miss half the world and actively distort reality for everyone. On International Women's Day - and every day - this truth demands our attention. After more than two decades working at the intersection of technological innovation and human rights, I've observed a consistent pattern: systems designed without inclusive input inevitably encode the inequalities of the world we have today, incorporating biases in data, algorithms, and even policy. Building technology that works requires our shared participation as the foundation of effective innovation. The data is sobering: women represent only 30% of the AI workforce and a mere 12% of AI research and development positions according to UNESCO's Gender and AI Outlook. This absence shapes the technology itself. And a UNESCO study on Large Language Models (LLMs) found persistent gender biases - where female names were disproportionately linked to domestic roles, while male names were associated with leadership and executive careers. UNESCO's @women4EthicalAI initiative, led by the visionary and inspiring Gabriela Ramos and Dr. Alessandra Sala, is fighting this pattern by developing frameworks for non-discriminatory AI and pushing for gender equity in technology leadership. Their work extends the UNESCO Recommendation on the Ethics of AI, a powerful global standard centering human rights in AI governance. Today's decision is whether AI will transform our world into one that replicates today's inequities or helps us build something better. Examine your AI teams and processes today. Where are the gaps in representation affecting your outcomes? Document these blind spots, set measurable inclusion targets, and build accountability systems that outlast good intentions. The technology we create reflects who creates it - and gives us a path to a better world. #InternationalWomensDay #AI #GenderBias #EthicalAI #WomenInAI #UNESCO #ArtificialIntelligence The Patrick J. McGovern Foundation Mariagrazia Squicciarini Miriam Vogel Vivian Schiller Karen Gill Mary Rodriguez, MBA Erika Quada Mathilde Barge Gwen Hotaling Yolanda Botti-Lodovico

Holiday 2025 will be the most AI-driven season in marketing history and it will separate winners from losers. The winners won’t just use AI to write email copy. They’ll use AI to make their entire marketing smarter. Here are 5 AI plays brands should use this holiday season 👇 1. Predictive Offers, Not Blanket Discounts AI models can forecast which customers respond to free shipping, which convert on BOGO, and which only buy when bundles are suggested. Stop blasting 50% off to everyone. Start sending the right incentive to the right inbox. 2. Dynamic Content at Scale Every customer sees a different holiday email. AI swaps banners, CTAs, and product blocks in real time: so a parent browsing kids’ toys gets gift guides for toddlers, while a frequent traveler sees deals on travel accessories. 3. Creative Variations, On Brand AI generates dozens of subject lines, headers, and layouts but within your brand guardrails. Run 10 experiments in the time it once took to draft 1. Let data, not guesswork, pick the winners. 4. Cross-Channel Orchestration Email isn’t in isolation. AI coordinates timing across SMS, push, and ads: so the customer who just got a gift reminder in their inbox isn’t spammed with the same offer everywhere else. 5. Fatigue Detection & Smart Suppression Your best customers are also on 20 other mailing lists. AI tracks engagement signals and you should monitor send frequency before they hit unsubscribe: critical in December when inboxes are chaos. The takeaway: Holiday inboxes in 2025 will be noisier than ever. You won’t win by shouting louder. You will win by making every email smarter, faster, and more personal and relevant for users. Add value to users life and make it easy for them to make a decision. At Mailmodo, we’re watching this shift unfold daily. The brands leaning into AI this season aren’t just boosting CTRs: they’re creating holiday experiences customers actually look forward to. What would you add to this list? Drop your own AI holiday hacks in the comments 👇

I often hear people say that "AI will not replace humans; but those that use AI will." While that might seem comforting in some ways, deep down, we must ask: Who are those humans that can use AI, or have access to the technology then? As it turns out, opportunities are not quite evenly distributed after all. Consider this: While AI advances accelerate, women remain significantly underrepresented in the talent pool that's driving the transformation, comprising less than one-third of the STEM workforce and merely 12.2% of the STEM C-suite roles. Imagine a future world where AI augmentation benefits equally across genders. How might our innovation ecosystem flourish with truly diverse talent pools that operate at their fullest potential? The question isn't whether we can afford to prioritize gender parity in AI — it's whether we can afford not to. #AI #FutureOfWork #BankingOnAI #GenderEquity

When I scroll through AI leadership profiles, I see a pattern that is so predictable that it's almost algorithmic itself - men, men, and more men. In a field shaping humanity's future, only 12% of AI researchers globally are women. This isn't just a diversity issue; it's a design flaw in our digital future. The stakes couldn't be higher. With just 11 women among 136 founding team members of major AI companies valued over $500M, we're allowing critical technologies to develop with massive blind spots. Meanwhile, India's 10M+ tech workforce faces transformation, with AI potentially replacing up to 50% of voice-based roles and 30% of IT service positions. This disruption can either entrench existing inequalities or create new pathways for inclusion. The choice isn't automatic - it depends entirely on who designs these systems and for whom they're designed. At Kalaari Capital, we view this imbalance as both a moral concern and an untapped opportunity. Our investment thesis increasingly focuses on founders building inclusive AI applications that bridge India's stark digital divides rather than widening them. India stands at a pivotal moment, ranked 4th globally for AI preparedness. Our homegrown innovations like Sarvam 1 demonstrate our technical capabilities. Now we must demonstrate our commitment to ensuring women help architect our AI future, not just adapt to it. If you're a woman building in AI, I would love to hear from you. If you're an investor or tech leader there is a need to build AI that is ethical and inclusive for all of humanity. #AI #Technology #Leadership

Google DeepMind created a Gen AI model to predict extreme heat, and cyclones -- and it's faster and more accurate than traditional prediction models. It's going to be a huge deal as the climate crisis keeps getting worse. The model's called GenCast, and it uses a diffusion model, similar to those in image generation, adapted for Earth's spherical geometry. The model was trained on four decades of weather data from ECMWF's ERA5 archive. It generates 50+ possible weather scenarios, giving probabilistic ensemble forecasts. These forecasts predict daily weather and extreme events like cyclones with high accuracy. GenCast operates faster and more efficiently than traditional systems, needing just 8 minutes per forecast using TPUs. GenCast outperformed ECMWF’s ENS on 97.2% of forecasting targets, especially for extreme heat, wind, and cyclones. Its speed and precision help safeguard lives, improve renewable energy reliability, and support climate resilience. #GenAI #AI

"This report developed by UNESCO and in collaboration with the Women for Ethical AI (W4EAI) platform, is based on and inspired by the gender chapter of UNESCO’s Recommendation on the Ethics of Artificial Intelligence. This concrete commitment, adopted by 194 Member States, is the first and only recommendation to incorporate provisions to advance gender equality within the AI ecosystem. The primary motivation for this study lies in the realization that, despite progress in technology and AI, women remain significantly underrepresented in its development and leadership, particularly in the field of AI. For instance, currently, women reportedly make up only 29% of researchers in the field of science and development (R&D),1 while this drops to 12% in specific AI research positions.2 Additionally, only 16% of the faculty in universities conducting AI research are women, reflecting a significant lack of diversity in academic and research spaces.3 Moreover, only 30% of professionals in the AI sector are women,4 and the gender gap increases further in leadership roles, with only 18% of in C-Suite positions at AI startups being held by women.5 Another crucial finding of the study is the lack of inclusion of gender perspectives in regulatory frameworks and AI-related policies. Of the 138 countries assessed by the Global Index for Responsible AI, only 24 have frameworks that mention gender aspects, and of these, only 18 make any significant reference to gender issues in relation to AI. Even in these cases, mentions of gender equality are often superficial and do not include concrete plans or resources to address existing inequalities. The study also reveals a concerning lack of genderdisaggregated data in the fields of technology and AI, which hinders accurate measurement of progress and persistent inequalities. It highlights that in many countries, statistics on female participation are based on general STEM or ICT data, which may mask broader disparities in specific fields like AI. For example, there is a reported 44% gender gap in software development roles,6 in contrast to a 15% gap in general ICT professions.7 Furthermore, the report identifies significant risks for women due to bias in, and misuse of, AI systems. Recruitment algorithms, for instance, have shown a tendency to favor male candidates. Additionally, voice and facial recognition systems perform poorly when dealing with female voices and faces, increasing the risk of exclusion and discrimination in accessing services and technologies. Women are also disproportionately likely to be the victims of AI-enabled online harassment. The document also highlights the intersectionality of these issues, pointing out that women with additional marginalized identities (such as race, sexual orientation, socioeconomic status, or disability) face even greater barriers to accessing and participating in the AI field."

🌦️ GenAI in Weather Forecasting: Decoding Unseen Patterns 🌦️ Imagine a world where weather predictions are so accurate, they can anticipate even the most subtle changes in the atmosphere. This is not science fiction—it's the power of Generative AI (GenAI) in weather forecasting. Why GenAI? 1. Decoding Satellite Images: Traditional weather forecasting relies heavily on interpreting satellite images. GenAI can process these images with unparalleled precision, identifying patterns and anomalies that human eyes might miss. 2. Unseen Patterns: The true strength of GenAI lies in its ability to detect unseen patterns in vast datasets. By analyzing historical and real-time data, it can predict weather events with greater accuracy. How Does It Work? - Data Processing: GenAI processes massive amounts of data from satellites, sensors, and historical records. - Pattern Recognition: It uses advanced algorithms to recognize patterns that indicate specific weather conditions. - Predictive Modeling: The AI generates predictive models that can forecast weather events with higher precision than ever before. The Impact 🌪️ Disaster Preparedness: More accurate predictions mean better preparation for natural disasters, potentially saving lives and reducing economic losses. 🚜 Agricultural Benefits: Farmers can make more informed decisions about planting and harvesting, leading to better yields and more sustainable practices. ✈️ Aviation Safety: Improved forecasts can enhance flight safety and efficiency, reducing delays and optimizing routes. The Future The integration of GenAI in weather forecasting is just the beginning. As technology evolves, we can expect even more refined and accurate predictions, leading to a safer and more efficient world. 🔍 Curious about the future of weather forecasting with GenAI? Let's explore it together! P.S. Have you experienced the benefits of advanced weather forecasting in your field? Share your story below! 🌍

The world of paying 22-25 year-olds $70k/year to hit GO in a sales engagement platform are over. The biggest threat to Outreach isn’t the overinflated $5B valuation they won't grow into, it’s that within 12 months SDRs won’t be sending the cold emails. Here's why: BACKGROUND The more you think about it… It’s pretty wild we had a crazy enough bull run in SaaS during the ZIRP era where teams built armies of SDRs who literally just hit “send” on sequences written by leaders. And it worked. That ship has sailed, yet there are a lot of GTM leaders still holding on, convinced that better messaging, a new SDR manager, tighter 1-1s, and better enablement is going to change that. Rob B. Anderson, CRO at TitanX, and ex BDR leader of 75+ person teams at companies like Docebo and Gong told me his team doesn’t use sales engagement at all anymore. Guess what? We haven’t either in the last 9 months… HERE IS WHAT’S HAPPENING Email will become centralized by a single person (or tiny team) who manages ALL outbound email for the company. They may be separate across marketing and sales in the short-term but over time, they will blend. These people are AI experts with strong copywriting chops. They not only be great at email infrastructure/domain warm up/email rotation, but also elite at creating offers, testing CTAs, and driving people to events, webinars, and to engage with content. They will use AI to ingest thousands of data points for their target account and broader market — and have a series of both always-on trigger based emails and a small set of experimental emails running at all time. The goal will be mainly opens and awareness. Replies will get routed to a smaller team SDRs (or AEs) inboxes directly. This is already happening for many companies. Makes you think.... What processes and strategies do you know no longer pencil but you’re still asking your team to do? What do you know you should cut but haven’t? PS: Had Rob on my podcast — he shared other “hot takes” and I agree with them all…listen down there 👇

Thrilled to unveil our latest work: multi-modal machine learning to forecast localized weather! We construct a graph neural network to learn dynamics at point locations, where typical gridded forecasts miss significant variation. Paper: https://lnkd.in/eBmfsJin Weather dataset: https://lnkd.in/ejCG8bKs Code: https://lnkd.in/eQg-JzQJ AI weather models have made huge strides, but most still emulate products like ERA5, which struggle to capture near-surface wind dynamics. The correlation between ERA5 and ground weather station data is low due to topography, buildings, vegetation, and other local factors. In this work, we forecast near-surface wind at localized off-grid locations using a message-passing graph neural network ("MPNN"). The graph is heterogeneous, integrating both global forecasts (ERA5) and historical local weather station data as different nodes. What do we find? First off, ERA5 interpolation performs poorly, failing to capture local wind variations, especially in coastal and inland regions with complex conditions. An MLP trained on historical data at a location performs better than ERA5 interpolation, as it learns from the station's past observations. However, it struggles with longer lead times and lacks the spatial context necessary to capture weather patterns. Meanwhile, our MPNN dramatically improves performance, reducing the error by over 50% compared to the MLP. This is because the MPNN incorporates spatial information through message passing, allowing it to learn local weather dynamics from both station data and global forecasts. Interestingly, adding ERA5 data to the MLP does not improve its performance significantly. The MLP struggles to integrate spatial information from global forecasts, while the MPNN excels, highlighting the importance of combining global and local data. Large improvements in forecast accuracy occur at both coastal and inland locations. Our model shows a 92% reduction in MSE relative to ERA5 interpolation overall. This work showcases the strength of machine learning in combining multi-modal data. By using a graph to integrate global and local weather data, we were able to generate much more accurate localized weather forecasts! Congrats to Qidong Yang and Jonathan Giezendanner for the great work, and thanks to Campbell Watson, Daniel Salles Chevitarese, Johannes Jakubik, Eric Schmitt, Anirban C., Jeremy Vila, Detlef Hohl, and Chris Hill for a wonderful collaboration. Thanks also to our partners at Amazon Web Services (AWS) for providing cloud computing and technical support!

AI is revolutionizing email marketing, opening doors to highly specific, niche solutions. Here’s where it’s set to shine: Micro-Segmentation: Use AI to group audiences by subtle behaviors, enabling hyper-personalized campaigns and behavioral triggers. Mood Detection: Leverage sentiment analysis to tailor email tone and timing based on real-time audience feelings. Proactive Retention: Predict churn and send automated win-back campaigns to re-engage at-risk customers. Accessibility: AI ensures inclusivity with automated alt text and screen reader optimizations. Intent Prediction: Deliver pre-sales content aligned with user actions, improving conversions. Zero-Party Data: AI-enhanced surveys encourage users to share data willingly for precise personalization. Compliance Automation: Manage GDPR and CAN-SPAM requirements with AI-driven consent tracking and privacy alerts. Real-Time Personalization: Update email content dynamically post-send, like weather-based recommendations. Feedback Integration: Use real-time customer feedback to refine email strategies and drive sentiment-based retargeting. Journey Builder: Map adaptive email journeys that change based on user behavior, ensuring hyper-relevance. 2025 is set to redefine email marketing with these AI-driven solutions. Want to stay ahead? Contact Prospect Engine to unlock your email marketing potential with cutting-edge strategies. #EmailMarketing #AIMarketing #DigitalStrategy