IBM Research and ETH AI Center collaborate on AI and Quantum Computing fellowships

Does higher education evolve as quickly as technology? Jad Tarifi, an early AI pioneer who founded Google's first generative AI team, wouldn’t recommend getting a Ph.D. in artificial intelligence anymore. Tarifi earned a doctorate in AI at the University of Florida in 2012. He says in the five to seven years it takes to graduate with a Ph.D., AI's problems will already be solved. He recommended staying away from the Ph.D. route altogether or choosing to specialize in a subfield of AI that is still in its early stages, like AI for biology. Read more at the link below: https://lnkd.in/eRn_PeUv

The launch of UNESCO’s new Booklet on Generative AI in Education and Research marks an important step in empowering educators and researchers to harness AI responsibly, ethically, and effectively in their work. This pivotal guide offers practical strategies and thoughtful insights, helping the education community navigate the future of learning with confidence.

🌟OLCF INTERN SPOTLIGHT🌟 OLCF Intern Weizhi Gao from North Carolina State University has spent the summer working with Oak Ridge National Laboratory computational scientist Junqi Yin their project: Toward Trustworthy AI: Accelerating Hallucination Detection in LLMs✨ Their project focuses on detecting hallucinations in large language models (LLMs), tackling one of the biggest challenges in making AI reliable for critical fields like scientific research. By accelerating self-consistency methods, they’re paving the way for faster and more dependable applications.💡 #AI #LLMs #BigScience

Ilya Sutskever's convocation address at the University of Toronto on June 6, 2025 🎓 Open AI pioneer Ilya Sutskever’s Convocation Address at the University of Toronto Ilya Sutskever, co-founder of OpenAI and a leading figure in artificial intelligence, delivered a poignant convocation speech at his alma mater, the University of Toronto, upon receiving an honorary Doctor of Science degree. Reflecting on his decade-long journey at the university, Sutskever expressed deep gratitude for the mentorship of Geoffrey Hinton and the institution's pivotal role in AI research. 🧠 The Unprecedented Challenge of AI Sutskever emphasized that humanity is entering an era where artificial intelligence is poised to transform every facet of life. While acknowledging AI's current limitations, he projected that it will eventually match and surpass human capabilities. Drawing a parallel between the human brain and digital computers, he stated, > "The brain is a biological computer, so why can't a digital computer, a digital brain, do the same things?" He cautioned that as AI continues to evolve, its trajectory may become increasingly unpredictable, presenting challenges that are difficult to foresee. Advice to Graduates Offering counsel to the graduates, Sutskever urged them to adopt a mindset of acceptance and forward-thinking. He advised, > "Accept reality as it is, try not to regret the past, and try to improve the situation." He acknowledged the difficulty of this approach but highlighted its importance in navigating the complexities of life and the rapidly changing technological landscape. The Greatest Challenge and Reward Concluding his address, Sutskever framed the advent of AI as both the greatest challenge and the greatest reward humanity will face. He emphasized the necessity for vigilance and proactive engagement with AI's development to ensure its alignment with human values and safety. RWG

Looking forward to attending Gurobi Days Digital Academic 2025—an event dedicated to the academic optimization community. With the theme “AI & Generative AI: Impact and Applications in Optimization and Academia”, this year’s event will highlight how AI is transforming research, modeling, and education. As someone working on optimization in biological systems, I’m excited to explore how these advancements can enhance metabolic modeling and complex biological network analysis and learn tools that could open new possibilities for data-driven research. #GurobiDays #AI #GenerativeAI #Optimization #BiologicalSystems #MetabolicModeling #AcademicResearch #PhDLife #AIinAcademia

AI literacy is the foundation to strong AI Governance. I spent the day working with PhD students Western Sydney University at the HDR Conference. We used Generative AI tools to hack into the UN Sustainability Goals and develop innovative solutions. In the process the students learned a process of how to use GenAI as a powerful creative assistant and also saw first hand the challenges of Automation Bias, hallucinations and the Black Box phenomena. Important skills were developed in assisting them to become a wise guide for AI rather than a replacement for their expertise. The practical application. Used well, Gen AI can unlock ways to commercialize their research in directions they may not have thought of. Amazing results and a great mix of really good students across a range of disciplines building future ready skills. #aispeaker #aiworkshops #aigovernance #ailiteracy #conferencespeaker #keynotespeaker

🚀 Call for Papers: Special Issue on "Artificial Intelligence Algorithms and Generative AI in Education (2nd Edition)" 🧠 For this Special Issue, we invite contributions that explore the intersection of AI algorithms and generative AI with educational practices, examining their impact on learning outcomes, pedagogical strategies, and the overall educational ecosystem. We are particularly interested in research that investigates the potential of these technologies to create more engaging, efficient, and equitable learning environments. 📚 Guest Editors: Dr. Antonio Sarasa Cabezuelo and Dr. María Estefanía Avilés Mariño 🔍 Explore the full details and submission guidelines here: https://lnkd.in/ebr7enAB MDPI Universidad Complutense de Madrid Universidad Politécnica de Madrid #education #artificialintelligence #generativeAI #educationaltechnology #personalizedlearning #AIpoweredassessment #educationaldatamining #naturallanguageprocessing #machinelearning #CallForPapers

Shaping AI's Future: PhD or Industry? 💡 A highly qualified AI researcher faces a pivotal career choice: pursuing a PhD at a top university or joining an industry lab/startup. This decision highlights critical considerations for advancing the frontier of intelligence, especially in LLMs. Where will the next AI breakthroughs emerge? * Academia fosters foundational research without immediate product pressures. * Industry offers rapid iteration & direct application in real-world products. * Large-scale compute access is vital for LLM advancements in either path. Find out more about this news in the full article: https://lnkd.in/eMqq-7tR Which path do you believe offers the most impactful contributions to AI's frontier? #AI #LLMs #AIResearch #FutureOfAI #TechCareers

🚀 Mathematics Before AI Today we see a trend in research where AI is often applied just to “see if it can solve the problem.” While this curiosity is valuable, it does not always address the real scientific requirement of the problem itself. Too often, AI is used without first verifying whether the problem truly demands it. This has caused the number of AI-based publications to skyrocket—yet in many cases, the same problems could be solved more efficiently, robustly, and transparently with pure mathematics. A great example comes from the work of Enzo Ferandier and Martin CURIEN at the Laboratoire de recherche LASSENA of the École de technologie supérieure University, who tackled two highly complex classification and identification challenges in LEO PNT, under my and Prof. René Jr Landry’s supervision. Instead of relying on black-box AI methods, they solved them entirely through rigorous mathematics—demonstrating that strong analytical thinking can outperform over-applied AI. It is reasonable to use AI when problems are too complex, dynamic, or data-intensive for closed-form mathematical solutions alone. AI is best applied to optimize, approximate, or generalize where exact models are impractical, such as in pattern recognition, prediction under uncertainty, or real-time decision-making with large datasets. 👉 Mathematics remains the real baseline of scientific solutions. AI should be the tool that optimizes, not replaces, the foundation. At the end : I would like to sincerely thank all my colleagues in the Laboratoire de recherche LASSENA and NeSIVA teams, specially Jean-Marc Gagne, for their dedication and scientific spirit. Your contributions continue to push the boundaries of innovation, and I look forward to seeing even greater achievements from you in the future. #Mathematics #AI #Research #Innovation #LEO #PNT #SignalsOfOpportunity #Science #Engineering #Navigation #Satellite #Aerospace #DataScience #Optimization #LASSENA

🔬 Honored to be mentioned in this reflection on the role of mathematics vs. AI in scientific research. Together with Ahmad Esmaeilkhah at the Laboratoire de recherche LASSENA (École de technologie supérieure (ÉTS)), we faced a complex challenge in LEO PNT — and instead of defaulting to AI-based methods, we chose a path grounded in rigorous mathematical modeling. 📐 We demonstrated that in some cases, mathematics alone can offer more transparent, robust, and efficient solutions than black-box AI. 💭 This experience has been a strong reminder: while AI is a powerful tool, it should complement — not replace — deep analytical thinking. Scientific rigor starts with understanding the core of the problem, and sometimes the best solution is not the trendiest one...