Mixity_EN

Why #Diversity in #AI Makes Better AI for All: The Case for Inclusivity and Innovation Innovation, Competitiveness, and Building Trust in AI 👇 shrm.org/mena/topics-tools/f… @SHRMMEA by @Nichol_Bradford Only through intentional, inclusive actions can we build a future where AI benefits everyone, regardless of their race, gender, or background. #ResponsibleAI #Inclusion -- Cc @DominiqueCrochu @AkwyZ @jblefevre60 @kalydeoo @Ym78200 @Nicochan33 @AnthonyRochand @CathCervoni @mallys_ @Nad_Alves @enilev @RLDI_Lamy @Khulood_Almani @LaurentAlaus

#AI Research 👇 Towards Time Series Reasoning with Large Language Models (Time series analysis in #DataScience involves forecasting data collected over time) 👇 buff.ly/4int5lm #GenerativeAI MyPoV: This is a valuable contribution to understanding the capabilities and limitations of language models for time series reasoning tasks. The authors acknowledge that LLMs can still be improved over specialized time series models. This highlights the need for further research into LLMs in order to work better with sequential data. One potential limitation of the study is the focus on relatively simple time series forecasting tasks. Real-world time-series data often present more complex patterns and dynamics that can pose greater challenges to LLMs. As LLMs continue to evolve and find new applications, the ability to deal effectively with time series data will become increasingly valuable in a wide range of fields, from #Fintech and #HealthTech to environmental monitoring, #ClimateAction, and beyond. It is therefore a pity that this research does not address the interpretability and explicability of LLM-based time series models. As these models become more widely deployed, an understanding of their decision-making processes will be essential to build trust and ensure responsible use. #ResponsibleAI -- Cc @DeepLearn007 @BroadenView @AkwyZ @Ym78200 @kalydeoo @ahier @Nicochan33 @jblefevre60 @IanLJones98 @BetaMoroney @enilev @EvanKirstel @mvollmer1 @PawlowskiMario @mikeflache @gvalan @Khulood_Almani @YuHelenYu @HaroldSinnott @RLDI_Lamy @DominiqueCrochu @CathCervoni @SpirosMargaris @aure79lien @OlgenBurcu @roxananasoi

#AIEthics With regard to artificial intelligence models, today I'd like to highlight two important concepts that are often used interchangeably, that are similar yet different, and whose difference I think is important to understand 👇 #AI Interpretability vs. Explainability 👇 Focused on transparency, interpretability delves into the inner workings of a model and how each part contributes to the final output. Engineers and ethicists often utilize this to assess compliance with ethical standards. Explainability for its part focuses on the broad rationale behind how a model processes input to generate an output. It does not require insight into the model's internal workings like interpretability does. This approach relies on external observations to explain the model's behavior to users. more 👇 algolia.com/blog/ai/interpre… @algolia -- In a nutshell, incorporating more AI into our lives requires a focus on transparency and clarity to ensure its positive impact for all. Striving for interpretability & explainability of #MachineLearning entities will lead in the future to the development of more #ResponsibleAI systems and trusted effective AI tools. -- Cc @DeepLearn007 @BroadenView @AkwyZ @Ym78200 @kalydeoo @tewoz @ahier @Nicochan33 @jblefevre60 @IanLJones98 @BetaMoroney @enilev @EvanKirstel @mvollmer1 @PawlowskiMario @mikeflache @gvalan @Khulood_Almani @YuHelenYu @HaroldSinnott @RLDI_Lamy @DominiqueCrochu @CathCervoni @dcallahan2

#AI #GenerativeAI Transformer Architecture in Large Language Models -- Transformers: 👇 Core Concept Architecture Key Components Practical Applications + Challenges & Opportunities 👇 truefoundry.com/blog/transfo… v/ @truefoundry And one of the important, not to say paramount, aspects as we look to the future with models such as GPT-4 is that we face essential challenges and opportunities in terms of scalability, interpretability and ethics. Increasing the size and complexity of these powerful models requires a great deal of computing power and energy, raising questions of cost and environmental impact. At the same time, it's important that we can understand how these models make decisions, especially when used in important areas such as healthcare ,finance, hiring... We need so to consider the ethical side of things, including how to prevent the amplification of biases (gender, ethnic, political, ...), hallucinations or the spread of misinformation, and also to understand the effects of AI replacing jobs. Addressing these issues requires efforts from everyone involved in AI and also all the players in human society, to ensure that the growth of Transformer models, and of AI entities in general, is responsible and beneficial to society. #ResponsibleAI -- Cc @DeepLearn007 @AkwyZ @ahier @jblefevre60 @gvalan @LaurentAlaus @CurieuxExplorer @PawlowskiMario @mvollmer1 @Analytics_699 @Khulood_Almani @tewoz @Nicochan33 @Ym78200 @IsabellePiel29 @mallys @aure79lien @jeancayeux @DanielleLargier @SpirosMargaris @HaroldSinnott @FrRonconi @Fabriziobustama @chidambara09 @CHRISTINESOTO12 @DominiqueCrochu @BroadenView @kalydeoo @IanLJones98