We live in the era of Big Data. Its algorithms pervade our lives-shaping our purchases, our finances, our health care, our education, our communities, our public policy. Armed with phones, computers, and countless other devices, society has produced more data in the past two years – a zettabyte – than
Probability is the backbone of science, but how well do you understand it? Odds are, not as well as you think; it is a surprisingly subtle concept that is often misunderstood, sometimes even by professionals who use it to guide crucial and far-reaching decisions. In this program, experts from technology,
Demis Hassabis, (Co-Founder & CEO, DeepMind) will discuss the capabilities and power of self-learning systems. He will illustrate this with reference to some of DeepMind’s recent breakthroughs, including the AlphaZero, AlphaStar and AlphaFold systems, and talk about the implications of cutting-edge AI research for scientific and philosophical discovery.
All over the globe, digital transformation is opening new paths for women. In Indonesia, more and more are taking the opportunities for independence it offers to open a business. But how do young founders cope with the challenges posed by the role?
One way advertisers must shift their strategy is to partner with creators whose influence can help promote their brand. Brands are really just beginning to figure this out, and the whole business of influencer marketing is just taking form. This chapter of our docu-series explores how brands can and should work with influencers. We take a close look at the challenges in doing so. Check it out!
Demis Hassabis will discuss the capabilities and power of self-learning systems. He will illustrate this with reference to some of DeepMind’s recent breakthroughs, including the AlphaZero, AlphaStar and AlphaFold systems, and talk about the implications of cutting-edge AI research for scientific and philosophical discovery.
Deep Learning (DL) has enabled significant progress in computer perception, natural language understanding, and control. Almost all these successes rely on supervised learning, where the machine is required to predict human-provided annotations, or model-free reinforcement learning, where the machine learns policies that maximize rewards. Supervised learning paradigms have been extremely successful for an increasingly large number of practical applications such as medical image analysis, autonomous driving, virtual assistants, information filtering, ranking, search and retrieval, language translation, and many more.
Companies are aggressively turning to artificial intelligence and machine learning (AI/ML) to gain a competitive advantage. But for that strategy to succeed, companies must develop algorithms that rely on AI/ML technology to run their business.