[মিউজিক] বাস্তব আর স্বপ্ন কিছু সময় আগেও বা কিছু ঘন্টা আগেও আমার স্বপ্ন ছিল এটা হন আমি যদি কিছু বলতাম আমাকে যখন এইজন্য মনোনয়ন দেয়া হলো আমি চিন্তা করলাম যে আমি তো ভাতের ব্যবসা করি রাস্তার পাশে ভাত বিক্রি করি আমার আবার কি বলার আছে ওরা আমাকে বলল জন্য আমি কিছু বলতেই হবে তখন আমার একটা স্বপ্ন থেকে ফেল আমি কিছু বলবো আর এখন বাস্টার স্বপ্নটাই আমার বাসটা হল যে আমি বলতেছি আমার কুমিল্লা জেলার উপজেলায় এলাকায় আমার বাড়ি আমার নাম এত বুফুর রহমান আমরা পীর ভাই বাবা সরকারি চাকরি করতেন মা এখনো আমাদের সাথে ছায়ার মত আছে এই রেস্ ...

Ethics & Compliance - Alexandria Ocasio-Cortez violated House rules during Met Gala appearance [1] Legal & Regulatory - Ethics Committee investigated and made the determination of violation [1]

Alexandria Ocasio-Cortez Violated House Rules During Met Gala Appearance, Ethics Committee Says https://t.co/WHh51vUusA https://t.co/WHh51vUusA ...

Alexandria Ocasio-Cortez Violated House Rules During Met Gala Appearance, Ethics Committee Says https://t.co/p1wzx9fUyT https://t.co/p1wzx9fUyT ...

I want you to think about this question. Is it ever justifiable to break the law for a greater good. Hopefully, you've never had to think of an issue like this before.But if we explore these questions, they lead us in a number of interesting directions. We humans from the beginning of time have been a mixture of the good and the bad. Me want your cave. Me hit you with rock and take your cave.Now mom's cave. This is just how we are made. You can watch any 2-year-old with a new toy for proof of this.You have ...

Donald Trump wasn’t making much from foreign deals. Then he regained power and set aside the pretense that he cared about ethics. https://t.co/gmOelOWDwz ...

AI Bias & Fairness - AI systems mimic human intelligence, learning from data to make classifications and predictions [6][8] - Bias in AI arises from non-diverse or incomplete data, leading to unfair or discriminatory outcomes [3][4] - AI amplifies existing inequalities by reflecting and perpetuating biases present in the data it's trained on [18] - Assumptions about identity are not valid data, yet AI learns from these assumptions if they are included in the training data [3][4] Examples of AI Bias - Applicant tracking systems can penalize resumes based on gendered language due to biased training data [12][13] - Facial recognition systems misidentify African and Asian faces more frequently than white faces [14] - Voice assistants misunderstand non-white speakers nearly twice as often as white speakers, with error rates up to 35% [16] - Insurance algorithms may charge higher premiums to certain demographics based on biased risk models [16][17] Addressing AI Bias - It is crucial to question the data used to train AI, ensuring it represents the entire population [19] - Building diverse teams can help recognize and identify biases that others might miss [19] - Organizations should set ethical guidelines and audit AI systems for bias, making fairness a performance metric [19][20] - Transparency in AI systems is essential, with models providing citations and reasoning for their decisions [21]

former federal prosecutor and senior writer for political magazine on Kushkadori and Liz Oyer a former justice department pardon attorney. You could say this is all mega drama and it's happening over there and it's a sideshow except for the fact that the mega drama is happening inside the department of justice which means that it has implications for all Americans. What do you see as the stakes of what's going on right now.You know, I think all of this is bad enough within its four corners. you know, either ...

[Music] Right. Before I begin my presentation, quick show of hands. How many of you have followed an order in the past 24 hours. Hands up if you have followed any sort of order.Right. Hands down. Now take a moment to think what was the order.I have a shocking statistic for you all today that reveals some disturbing nature about humans. Did you know that 65% people were willing to shock a stranger. That came from Mgrim's 1963 experiment.It was considered controversial as he studied how ordinary people would ...

Innovation & Talent - The healthcare industry emphasizes the importance of talent and innovators in overcoming obstacles and meeting unmet needs [6] - The industry encourages curiosity and questioning of established facts to stay informed [7][8] - A learning journey is crucial, with no time frame, age, or limits, especially with the accessibility of information [9][10] Translational Impact & Collaboration - Healthcare innovation should focus on translating ideas into real-world impact, exemplified by simple solutions addressing complex needs [13][14] - Collaboration across borders, disciplines, and perspectives is essential for avoiding failures and advancing healthcare [18] - Decentralizing healthcare, including clinical trials, is a growing trend, requiring integration of telemedicine and other non-medical components [19][20][21] Ethics & Equity - Ethics should be a primary consideration in healthcare innovation, especially with advancements like at-home DNA tests [21][22] - The industry should work towards health equity, drawing inspiration from examples like the unpatented polio vaccine [24][25] - Innovators should be translators, communicating ideas plainly and considering societal needs for relevance [27] Leadership & Purpose - Ego should be avoided, and innovators should focus on the purpose of helping patients and communities [28][29] - Supporting and encouraging the next generation of scientists and innovators is crucial for the future of healthcare [30][31] - Femtech, innovations targeting women's health, is an example of addressing specific healthcare needs [30]