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Time of India
02-07-2025
- Science
- Time of India
How to Become an AI Genius: Lessons students can learn from Meta's $100 million hires
If you want to become an AI genius – the kind that Mark Zuckerberg offers $50–$100 million to join his quest for artificial general intelligence (AGI) – here's the blueprint, decoded from Meta's elite hires. 1. Build a rock-solid maths foundation Almost every AI superstar Meta poached – from Lucas Beyer to Trapit Bansal – started with hardcore mathematics or computer science degrees. Linear algebra, calculus, probability, and optimisation aren't optional. They are your bread and butter. Why? Because AI models are just giant stacks of matrix multiplications optimised over billions of parameters. If you can't handle eigenvectors or gradient descent, you'll be stuck fine-tuning open-source models instead of inventing the next GPT-5. 2. Specialise in deep learning Next comes deep learning mastery. Study neural networks, convolutional networks for vision, transformers for language, and recurrent models for sequence data. The Vision Transformer (ViT) co-created by Lucas Beyer and Alexander Kolesnikov redefined computer vision precisely because they understood both transformer architectures and vision systems deeply. Recommended learning path: Undergraduate/early coursework : Machine learning, statistics, data structures, algorithms. Graduate-level depth : Neural network architectures, representation learning, reinforcement learning. 3. Research, research, research The real differentiator isn't coding ability alone. It's original research. Look at Meta's dream team: Jack Rae did a PhD in neural memory and reasoning. Xiaohua Zhai published groundbreaking papers on large-scale vision transformers. Trapit Bansal earned his PhD in meta-learning and reinforcement learning at UMass Amherst before co-creating OpenAI's o-series reasoning models. Top AI labs hire researchers who push knowledge forward, not just engineers who implement existing algorithms. This means: Reading papers daily (Arxiv sanity or Twitter AI circles help). Writing papers for conferences like NeurIPS, ICML, CVPR, ACL. 4. Dive into multimodal and reasoning systems If you want to be at the AGI frontier, focus on multimodal AI (vision + language + speech) and reasoning/planning systems. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Glicemia acima de 100? Insira essa fruta na sua dieta Saúde Nacional Undo Why? Because AGI isn't just about language models completing your sentences. It's about: Understanding images, videos, and speech seamlessly Performing logical reasoning and planning over long contexts For example, Hongyu Ren's work combines knowledge graphs with LLMs to improve question answering. Jack Rae focuses on LLM memory and chain-of-thought reasoning. This is the cutting edge. 5. Optimise your engineering skills Finally, remember that AI breakthroughs don't live in papers alone. They need to run efficiently at scale. Pei Sun and Joel Pobar are prime examples: engineering leaders who ensure giant models run on hardware without melting the data centre. Learn: Distributed training frameworks (PyTorch, TensorFlow) Systems optimisation (CUDA, GPUs, AI accelerators) Software engineering best practices for scalable deployment The bottom line Becoming an AI genius isn't about quick YouTube tutorials. It's about mastering mathematics, deep learning architectures, original research, multimodal reasoning, and scalable engineering. Do this, and maybe one day, Mark Zuckerberg will knock on your door offering you a $50 million signing bonus to build his artificial god. Until then, back to those linear algebra problem sets. The future belongs to those who understand tensors. Is your child ready for the careers of tomorrow? Enroll now and take advantage of our early bird offer! Spaces are limited.
Yahoo
28-05-2025
- Business
- Yahoo
Quantiphi Unveils Phi Labs Research and Development Hub, Accelerating Breakthroughs in Generative AI, Life Sciences, Digital Twins
MARLBOROUGH, Mass., May 28, 2025 /PRNewswire/ -- Quantiphi, an AI-first digital engineering company, today unveiled Phi Labs by Quantiphi, the formal identity of Quantiphi's long-standing Research and Development (R&D) hub, addressing real-world business challenges through breakthrough AI solutions. Quantiphi's Research and Development has been at the forefront of innovation in Generative AI, Life Sciences and Digital Twins since 2018, with a patent portfolio that underscores its commitment to future-proofing its technology. Now known as Phi Labs by Quantiphi, the R&D hub's pioneering work has been featured in leading forums like Neural Information Processing Systems (NeurIPS) and the International Conference on Machine Learning (ICML). "Phi Labs embodies Quantiphi's vision to redefine AI's business impact," Quantiphi Global R&D Head Dagnachew (Dan) Birru said. "By integrating cutting-edge research with practical applications, we enable our clients to navigate the evolving AI landscape with confidence." Phi Labs' key achievements include: Technology and IP Transfers: More than 15 transfers leading to accelerators and product launches like baioniq, Dociphi and Codeaira Innovative Milestones: Projects ranging from Speech-to-Text for Finance, 3D-GANs for seismic imaging and advanced healthcare diagnostics, achieving up to a 90 percent reduction in processing time Recent Developments: From 2021 to 2024, Quantiphi filed 50-plus patents—including 30-plus by Phi Labs, secured multiple technology transfers and earned accolades through academic collaborations and publications, including being featured in the Financial Times. Phi Labs by Quantiphi focuses on: Generative AI: Advanced reasoning, agentic workflows, efficient models and intelligent document processing Life Sciences: Small molecule synthesis, genomics, protein engineering and pharmacovigilance Digital Twins: Operations optimization, digital twin agents, engineering simulation and climate risk modeling Quantum Computing: Quantum machine learning, hybrid quantum-classical systems "Unlike labs driven by theory, our approach embeds innovation into enterprise environments directly, while upholding Responsible AI standards," Birru said. "Our engineering-driven team pairs deep research with industry expertise, ensuring every solution delivers tangible business impact." About Quantiphi:Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud and data engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results. Learn more at and follow us on LinkedIn, X, formerly Twitter, and Instagram and YouTube. Media Contact:H. Newsroom View original content to download multimedia: SOURCE Quantiphi Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
17-12-2024
- Business
- Yahoo
Runway Shows There's Money to Be Made in AI Video Too; ‘Scaling Laws' and Reasoning Dominate NeurIPS
A young startup gives hopeful signs for the AI video market. Questions around the end of pre-training dominate NeurIPS. Sign in to access your portfolio
