“Stanford AI4ALL aims to increase diversity in the field of Artificial Intelligence. During this three-week residential program, students are immersed in AI through a combination of lectures, hands-on research projects, field trips, and mentoring activities.”
“Everything you wanted to know about artificial intelligence (AI) but were afraid to ask! AI, inspired by our understanding of how the human brain learns and processes information, has given rise to powerful techniques known as neural networks and deep learning. This workshop will provide a high-level overview of these and other artificial intelligence techniques. Through pre-built hands-on exercises, we will discuss how current AI platforms compare with how the brain works, how systems actually “learn,” and how to build and apply neural networks. We will also discuss the societal and ethical issues surrounding the real-world applications of neural networks. By the end of the course, students will understand how AI techniques work so they can: (1) converse with neural network practitioners and companies; (2) critically evaluate AI news stories and technologies; and (3) consider what the future of AI can hold and what barriers need to be overcome with current neural network models. This workshop is ideal for product managers who interact with data scientists, software engineers who wish for more AI exposure, and anyone in the general public who wants to know how current AI works.
Ronjon Nag, Interdisciplinary Fellow, Stanford Distinguished Careers Institute; Fellow, Stanford Center for the Study of Language and Information; Founder and Managing Partner, R42 Group
Ronjon Nag has invented and deployed artificial intelligence systems for over three decades. He received a PhD in engineering from Cambridge, an MS from MIT, and the IET Mountbatten Medal, and he was a Harkness Fellow at Stanford. Companies he has co-founded or advised have been sold to Motorola, BlackBerry, and Apple.”
“The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intelligence.
Its mission is to provide unbiased, rigorous, and comprehensive data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI.
The 2019 edition tracks three times as many data sets as the 2018 edition. To help navigate the data, we’ve produced two tools. The Global AI Vibrancy Tool compares 28 countries’ global activities across 34 indicators, including both a cross-country perspective, as well as a country-specific drill down. The AI Index arXiv Monitor helps people conduct their own research into current technological progress in AI.
The AI Index Report is comprised of nine chapters:
Research and Development
Examines bibliometrics data, including volume of journal, conference and patent publications and their citation impacts by world regions. We also present Github Stars for key AI software libraries, and gender diversity of AI researchers based on arXiv.
Outlines data from a variety of sources on AI conferences. Specifically, we dive into event attendance, summaries of conference topics, and policy milestones achieved.
Tracks technical progress in tasks across Computer Vision (Images, Videos, and Image+Language), Natural Language, potential limitations (Omniglot Challenge), and trends in computational capabilities.
Covers three specific topics: jobs, investment, and corporate activity. We present both global and US-specific data relating to AI jobs, hiring, and skill levels. We also analyze startup investment trends for the world, by country, and by sector. The final section includes data on adoption of AI capabilities in industry and presents global trends in robot installations across countries.
Investigates trends in education and AI. This includes analyzing global data in machine learning (ML) and AI training and digging into trends in gender and international diversity for AI PhD’s. We also examine efforts to integrate ethics into computer science curricula and look at global trends in undergraduate enrollment in introductory ML and AI courses.
Analyzes data around autonomous vehicles (AV’s) and autonomous weapons (AW’s). We highlight countries and cities testing AV’s and present known types of autonomous weapon deployments.
Covers public perception of central banks, global governments, and the corporate world. We analyze data on how central banks communicate around AI, investigate AI mentions within the US Congress and Canadian and UK Parliaments, and examine AI-related terms mentioned on US earnings calls. We also dive into US web search data for AI-relevant phrases.
Examines ethical challenges, global news on AI ethics, and AI applications for sustainable development. We present ethical challenge data by looking across ethical AI guidelines and also examine news coverage around AI’s ethical use. This section also maps AI use cases to the UN’s Sustainable Development Goals.
National Strategies and Global AI Vibrancy
The National Strategies metrics looks at official strategy documents issued by countries. The Global AI Vibrancy Tool, as mentioned above, covers 28 countries across 34 metrics grouped into three high-level dimensions of AI starting in 2015: research and development, economy, and inclusion.”
“This workshop will teach the business implications of artificial intelligence (AI) and how product managers and business managers can pivot their careers to use it. Artificial intelligence combines insights resulting from newly emerging data sources and algorithms possessing powerful analytic techniques. This two-day workshop will introduce students to the variety, volume, veracity, and velocity of business data in many industries and how they can use it to create new products and business innovations.
Day one will explore various artificial intelligence applications such as facial recognition and machine learning arising from the convergence of Internet of Things (IoT) technologies and Big Data algorithms, all of which create new business models and efficiencies in various industries like advertising, manufacturing, energy, healthcare, fintech, and transportation. Specific case studies will build an understanding of the far-reaching impact of these evolutions across functional areas and industries.
Day two will teach students a business framework detailing how to build new products and transform existing businesses using AI. Guest speakers will add to class lectures by sharing industry best practices and investors’ perspectives of the AI business ecosystem. Students will leave the course with new insights into innovation opportunities in AI and with a framework to apply AI to their existing or entrepreneurial enterprises.”
- “Stanford University recently called on the U.S. government to make a $120 billion investment in the nation’s AI ecosystem over the course of the next 10 years,
- Yet Stanford professor David Cheriton recently said that AI has been a promising technology since he first encountered it 35 years ago, and it’s still promising but “suffers from being overpromising.”
- Strong AI, also known as Artificial General Intelligence (AGI), does not yet exist. An AGI machine could perform any task that a human can. Surveys suggest it will be until 2060 before AGI exists,”