Good evening everyone. Uh actually I thought really badly how I can start this speech and I am preparing a really hard question to you. I know that most probably you generally avoid that kind of question, you know, during a night with at dinner maybe with your friends or maybe your first date with somebody or maybe a family gatherings.Absolutely. Do you believe in God. Do you believe in numbers. Do you believe in justice. Do you believe in people.You are questioning them don't you. Because actually certaint ...

Core Insights - The importance of foundational computer science skills is increasing in the technology industry as AI tools become more prevalent in software development workflows [1][10] - Companies are prioritizing engineers who can reason about algorithms, design scalable systems, and evaluate complex trade-offs that automated tools cannot assess [1][2] Industry Trends - AI is lowering the barrier for basic coding tasks, leading to differentiation among engineers based on higher-level thinking rather than syntax knowledge [2][10] - The software development process now involves defining problems, using AI tools for potential implementations, and validating outputs against requirements, emphasizing the need for core computer science fundamentals [4][10] Skills Demand - Technology companies emphasize foundational skills because they are critical for major engineering decisions, especially for systems that serve millions of users or process large data volumes [5][6] - Engineers with strong fundamentals are better positioned to adapt to evolving technologies compared to those focused narrowly on specific frameworks [6][12] Educational Response - Many professionals are revisiting foundational topics like data structures, algorithms, and system design as part of their career strategy, moving away from chasing technology trends [7][12] - Interview Kickstart's programs focus on data structures and algorithms, aiming to develop reasoning and architectural judgment rather than just teaching syntax [8][9] Program Structure - The curriculum includes structured coursework and mock interviews that reflect real hiring processes, focusing on trade-offs, complexity evaluation, and design decisions [9][10] - Instruction is provided by experienced engineers and hiring managers, offering insights into how technical decisions are evaluated in practice [9][10] Future Outlook - The renewed emphasis on fundamentals is expected to persist as AI systems take on more routine tasks, requiring engineers to oversee system behavior and validate outcomes [10][11] - Engineers investing in foundational knowledge are likely to remain relevant as technologies evolve and automation accelerates [12]

Historical Context & Societal Impact - Technological revolutions, from fire to AI, have historically created divides, empowering some while leaving others behind [3][4][5][6][7][8][19] - Agriculture, while powering civilization, led to inequality, hierarchy, slavery, and war [5] - The industrial revolution, despite increasing output and innovation, resulted in child labor and pollution [8] AI's Current Divides - Algorithmic divide: A few companies control the algorithms that dictate the rules [14] - Compute and energy divide: Africa, with 14 亿 (1.4 billion) people, has access to less than 1% of global compute [14] - Data divide: 95% of large language models (LLMs) are trained in English, despite it being only the fifth language by native speakers [16] - Capital divide: Developing LLMs requires hundreds of billions of dollars, creating a barrier to market entry [17][18] Digital Inclusion & The Internet - Only 67% of the global population has access to the internet, leaving 26 亿 (2.6 billion) individuals cut off from the digital economy [10] Call to Action - Learn how to use AI and integrate it into daily life [20] - Share AI knowledge with peers, network, family, and friends to ensure no one is left behind [20][21] - Demand responsibility from organizations regarding data usage [21]

🚨 VITALIK: “He's clearly actively tweaking algorithms to boost some things and deboost other things.”On Musk's role in amplifying coordinated hate sessions on X. https://t.co/1Z8Hecctpz ...

AI Bias and its Impact - AI systems learn and repeat biases and inequalities present in the world due to being trained on biased data [5] - AI bias can lead to exclusion, such as in hiring processes where AI tools favor certain demographics over others [7][8][10][11] - Lack of representation in AI training data can result in systems that do not accurately recognize or serve all users [15][20] The Importance of Awareness and Critical Thinking - Awareness and AI literacy are crucial for challenging biased AI systems and promoting change [6][23] - Questioning AI responses and outcomes can help break the cycle of reinforcing existing biases [6][18] - Individuals have the power to shape the future of AI by being informed, curious, and unafraid to challenge the systems around them [24] Examples of AI Bias and Exclusion - An AI hiring tool favored men due to being trained on 10 years of resumes primarily from men [7][9] - Facial recognition systems have difficulty accurately detecting darker-skinned and female faces [20] - Voice recognition systems may not be trained to recognize higher-pitched voices, excluding women [15] Call to Action - Individuals should understand the biases rooted within technology to question and challenge outcomes [18] - Promoting ethical and equitable AI systems requires collective effort and awareness [22] - The goal is to lead AI and the world towards a future that is fair, inclusive, and human-centered [25]

Algorithms are hard to visualize and for me it's literally impossible. I have affantasia which means that I can't form mental images at all. But today I'm going to walk you through how I use codeex openi's agentic coding tool to build my very own algorithms visualizer website so I can finally see what my brain is unable to generate by itself.Step one, tell what to build. I asked Codeex to come up with a list of common algorithms and create a website with their visualizations. That's it.No need to specify la ...

[applause] If Africa does not shape AI, then AI will shape Africa. A couple years ago, I was having a conversation with a good friend of mine named Nala. Nala was telling me about her upbringing in an impoverished rural town back home in Ghana. She was telling me that the educational system back home had broken down.There could be 50 students in one classroom sharing textbooks, pens, whatever they could just to get by. Nala wasn't even exposed to computers. despite there being a small internet cafe within t ...

Digital Transformation in Healthcare - Healthcare is undergoing digital transformation with paperless hospitals and automated processes like blood analysis [2] - Accumulated data facilitates predictive analytics, forecasting patient needs weeks or months in advance [4] Future Medical Systems Requirements - Future medical systems need to be adaptive, sustainable, and equitable, integrating technology for everyone's benefit [5][6] - Systems should anticipate individual patient demands upon arrival, personalizing treatment and tests [9] - Addressing patient flow peaks, such as during respiratory virus seasons, is crucial [7] The Role of Algorithms - Algorithms are already improving diagnostics through image enhancement in ultrasounds and CT scans [4] - Algorithms alone cannot design future-ready medical systems; empathy is essential [8][20] - Hybrid models using AI chatbots for preliminary assessments can prepare clinicians and augment empathy [18][19] Ethical Considerations - Respect for patient autonomy is necessary, allowing individuals to opt out of data collection and analysis [12] - Balancing efficiency with human experience is critical, as current systems prioritize efficiency [16] - Empathy should be a measurable design metric, tested for its ability to detect and respond to patient feelings [22] Co-creation and the Future - Co-creation with clinicians, patients, researchers, and engineers is vital for capturing diverse perspectives [23] - The goal is to use data to restore the human touch in medicine, enabling algorithms to design future-ready systems [23]

comparteix una anècdota rural amb una àvia i la seva neta per il·lustrar com els algoritmes de xarxes socials dissolen la nostra identitat (com sal en aigua clara) i creen bombolles polaritzades. Critica com aquests sistemes fomenten l’individualisme i la desinformació, mentre l’IA avança ràpidament (superant exàmens humans del 5% al 27% en 15 dies), oferint oportunitats (cures mèdiques, predicció climàtica) però riscos (tensions geopolítiques, robots autònoms). Crida a fomentar la intel·ligència col·lectiv ...

The Age of Automation is upon us.Financial markets are dominated by algorithms. Artificial intelligence is pushing profit margins higher. Humanoid robots are poised to infiltrate every corner of society.Any work that can be automated, will be automated. ...