Podcasts about Microsoft Research Asia

In this episode, Kai-Fu and Peter discuss 01.AI's growth, Chinese entrepreneurship, and how open-source AI can impact the world.   Recorded on Oct 19th, 2024 Views are my own thoughts; not Financial, Medical, or Legal Advice. Kai-Fu Lee is the Chairman and CEO of Sinovation Ventures, a venture capital firm he founded in 2009 that manages over $2 billion in assets and focuses on fostering the next generation of Chinese high-tech companies. In 2023, Lee launched 01.AI, a startup that built AI applications tailored for China, including Wanzhi, a productivity assistant similar to Microsoft Office 365 Copilot. As a leading figure in artificial intelligence, Lee continues to shape the tech landscape in China, where he recently noted that Chinese AI models are only 6 to 9 months behind their U.S. counterparts. He has authored influential books such as AI Superpowers (2018) and AI 2041 (2021) and was named one of Time Magazine's 100 most influential people in 2013. Earlier in his career, Lee held prominent positions in tech, including Vice President at Google, President of Google China (2005-2009), and Corporate Vice President at Microsoft (2000- 2005). He also founded and led Microsoft Research Asia from 1998 to 2000. Lee remains a highly respected thought leader in AI and continues to drive innovation in the field. Beago: https://www.beago.ai/  01.AI: https://www.01.ai/  Kai-Fu's X: https://x.com/kaifulee  Kai-Fu's LinkedIn: https://www.linkedin.com/in/kaifulee/  Pre-Order my Longevity Guidebook here: https://longevityguidebook.com/  ____________ I only endorse products and services I personally use. To see what they are, please support this podcast by checking out our sponsors:  Get started with Fountain Life and become the CEO of your health: https://fountainlife.com/peter/ AI-powered precision diagnosis you NEED for a healthy gut: https://www.viome.com/peter  Get 15% off OneSkin with the code PETER at  https://www.oneskin.co/ #oneskinpod Get real-time feedback on how diet impacts your health with https://join.levelshealth.com/peter/ _____________ I send weekly emails with the latest insights and trends on today's and tomorrow's exponential technologies. Stay ahead of the curve, and sign up now:  Blog _____________ Connect With Peter: Twitter Instagram Youtube Moonshots

Kai-Fu Lee is the co-founder of Sinovation Ventures, a venture capital firm funding Chinese start-ups. Before this, he was the president of Google China, and founder of Microsoft Research Asia. He's also the inventor of a speaker-independent, continuous speech recognition system. In our conversation we discuss: [00:00] - Introductions[01:50] - Kai Fu's background[05:44] - What is the first AI[11:56] - AI's future[15:01] - China's strength in AI technology[20:09] - China's Super App[24:38] - 996 and 997[28:10] - Engineering background[30:30] - Opportunities from AI[38:30] - AI as a double-edged sword[44:03] - Contribution to society[46:08] - Four Quadrants[49:39] - Relevant skillsets[52:59] - Humans falling in love with technology[55:54] - Working smart vs working hard[58:09] - Taking risks[59:20] - Longevity Watch full episodes on: https://www.youtube.com/@seankim?sub_confirmation=1 Connect on IG: https://instagram.com/heyseankim

Two of the world's top software engineers will share their expertise with researchers at Lero, the Science Foundation Ireland Research Centre for Software, as part of the David Lorge Parnas Fellowship scheme. Professor Daniela Damian, Professor of Software Engineering and the ECS-CAPI Chair in Inclusive Science, Technology and Engineering at the University of Victoria, Canada, and Prof. Hongyu Zhang of the School of Big Data and Software Engineering, Chongqing University, China, are the latest recipients of the prestigious David Lorge Parnas Fellowship. Lero Director Prof. Lionel Briand said Lero research teams are delighted that Prof. Zhang and Prof. Damian will pay extended visits to Lero, enabling collaboration and knowledge sharing while acknowledging Lero's global standing as a software research centre. "These fellowships, established to honour Professor David Parnas, have brought world-class software researchers and developers here to interact with our research teams all over Ireland. Daniela and Hongyu will each deliver much anticipated distinguished lectures while at Lero. The fellowship programme has enabled Lero to attract some of the best software researchers in the world to Ireland to share their experience and expertise with Lero researchers at our 12 partner higher education institutions nationwide," commented Prof. Briand. Fellowship recipient Prof. Zhang has also worked at University of Newcastle, Microsoft Research Asia, and Tsinghua University. He received his PhD degree from the National University of Singapore in 2003. His research is in software engineering, in particular, intelligent software engineering, software analytics, and software maintenance. "Ireland's reputation as a global technology hub and Lero's world-class software research makes this a really attractive fellowship. I am honoured to receive it and am really looking forward to sharing knowledge with my fellow researchers," said Prof. Zhang. The central theme of Prof. Zhang's research is to improve software quality and developer productivity by mining software data. He has published more than 250 research papers in reputable international journals and conferences and received eight ACM Distinguished Paper and Best Paper awards. Prof. Zhang is a general co-chair of APSEC 2024 and ICSME 2020 and an associate editor of ACM Computing Surveys, Journal of Systems and Software, and Automated Software Engineering. He is a Distinguished Member of ACM, a Distinguished Member of CCF, and a Fellow of Engineers Australia (FIEAust). Parnas fellowship recipient Prof. Daniela Damian researches human and collaborative aspects of software engineering, global software engineering, requirements engineering, diversity and inclusion in software teams, and software education. In 2022 Prof. Damian founded INSPIRE:STEM for Social Impact, a program supported by a consortium of industry and community partners, which engages students from the University of Victoria as well as Asia in community-driven, sustainability-focused experiential projects to develop skills, connections, and a supporting network for the underrepresented talent in Science, Technology and Engineering. Prof. Damian received the 2019 Royal Society New Zealand Catalyst: International Leader Award and the 2020 Provost's REACH Award for Excellence in Experiential Teaching at the University of Victoria. She served as program chair at several ACM/IEEE conferences, including the International Conference on Software Engineering (2022), and as a member of the editorial and advisory boards of the top-ranked journals in software engineering. "I'm delighted to receive this honour. Lero's research is world renowned and the centre's record of education and public engagement as well as its work in the area of diversity, equity and inclusion is really outstanding. I look forward to building strong connections with Lero researchers as part of the Parnas Fellowship programme," she said. See more stories here.

This week, Microsoft Research Asia unveiled VASA-1, an AI model that can create a synchronized animated video of a person talking or singing from a single photo and an existing audio track. We find out more on the App Update with John Biehler.

This week, we can find the Cherry Blossoms blooming at Toronto's High Park. Gardener Susan Kerney tells us more about this popular flower. Plus, she highlights Pet Grass, something she started growing as a treat for her dogs. This week, Microsoft Research Asia unveiled VASA-1, an AI model that can create a synchronized animated video of a person talking or singing from a single photo and an existing audio track. We find out more on the App Update with John Biehler. Disney has announced changes to its longstanding Disability Access Service, and is facing much backlash by disability communities. We get the scoop on The Buzz, with Nisreen Abdel-Majid. A confusing situation between New York Yankees manager, Aaron Boone and a Homeplate umpire earlier this week lead to much media coverage. Brock Richardson fills us in on our Sports Update. Let's get to conversation recaps and comment on segments from the past week, on Cut for Time.

In this episode, we explore the Chinese AI ecosystem with 'L-squared,' an anonymous tech worker based in Beijing. We discuss major players, model quality, public engagement, regulation, and the US 'chip ban.' Discover the similarities and differences between US and Chinese AI landscapes, and gain a nuanced perspective on the current state of AI in China. USEFUL RESOURCES: Testing Chinese models: Yi-34B-Chat (made by Kai-Fu Lee's team 01.AI) can be tried out via Replicate (https://replicate.com/01-ai/yi-34b-chat) or Hugging Face. You can also use the ChatGLM playground (https://open.bigmodel.cn/trialcenter) and Baidu's ERNIE (https://yiyan.baidu.com/) without a Chinese SIM card. Benchmarking models: SuperCLUE is one of the most prominent benchmarks - the latest results are on GitHub (https://github.com/CLUEbenchmark/SuperCLUE) and the paper explaining the methodology is here (https://arxiv.org/abs/2307.15020). Regulation: Explainer (https://carnegieendowment.org/2023/07/10/china-s-ai-regulations-and-how-they-get-made-pub-90117) from Matt Sheehan; piece (https://www.chinatalk.media/p/how-tight-ai-regs-hurt-chinese-firms) on how genAI regs are affecting Chinese companies. US-China competition: Jeff Ding's work (https://www.tandfonline.com/doi/full/10.1080/09692290.2023.2173633) on the diffusion deficit in S&T; Bloomberg piece (https://www.bloomberg.com/graphics/2023-china-huawei-semiconductor/) on Huawei's semiconductor development efforts. Staying up to date: Sign up to alerts from CSET's Scout tool (https://scout.eto.tech/); subscribe to Concordia AI's AI safety (https://aisafetychina.substack.com/) in China newsletter (disclaimer: I used to work at Concordia!) A 2016 profile (https://chinai.substack.com/p/chinai-37-happy-20th-anniversary) on Microsoft Research Asia by Wang Jingjing, covered in Jeff Ding's ChinAI newsletter SPONSORS: Oracle Cloud Infrastructure (OCI) is a single platform for your infrastructure, database, application development, and AI needs. OCI has four to eight times the bandwidth of other clouds; offers one consistent price, instead of...does data better than Oracle. If you want to do more and spend less, take a free test drive of OCI at https://oracle.com/cognitive Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off http://www.omneky.com/ The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR Head to Squad to access global engineering without the headache and at a fraction of the cost: head to choosesquad.com and mention “Turpentine” to skip the waitlist. Plumb is a no-code AI app builder designed for product teams who care about quality and speed. What is taking you weeks to hand-code today can be done confidently in hours. Check out https://bit.ly/PlumbTCR for early access. TIMESTAMPS: (00:00) Introduction (07:24) China's AI Ecosystem (13:40) Public AI Engagement (17:33) Sponsors : OCI / Omneky (18:50) AI Tools Comparison (35:37) Sponsors : Brave / Squad / Plumb (39:14) AI Regulatory Maze (51:02) AI Performance, Censorship (55:28) Chinese AI Regulations (01:04:37) Tech, Research Role (01:12:11) Global AI Ecosystem (01:23:22) Cultural AI Perspectives (01:29:14) AI Safety, Cooperation

Members of the research community at Microsoft work continuously to advance their respective fields. Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements.In this episode, Xing Xie, a Senior Principal Research Manager of Microsoft Research Asia, joins host Dr. Gretchen Huizinga to discuss “Evaluating General-Purpose AI with Psychometrics.” As AI capabilities move from task specific to more general purpose, the paper explores psychometrics, a subfield of psychology, as an alternative to traditional methods for evaluating model performance and for supporting consistent and reliable systems.Read the paper: Evaluating General-Purpose AI with Psychometrics

Dr. Siwei Lyu, SUNY Empire Innovation Professor at the University at Buffalo Dr. Siwei Lyu received his B.S. degree (Information Science) in 1997 and his M.S. degree (Computer Science) in 2000, both from Peking University, China. He received his Ph.D. degree in Computer Science from Dartmouth College in 2005. From 1998 to 2000, he worked at the Founder Research and Development Center (Beijing, China) as a Software Engineer. From 2000 to 2001, he worked at Microsoft Research Asia (then Microsoft Research China) as an Assistant Researcher. From 2005 to 2008, he was a Post-Doctoral Research Associate at the Howard Hughes Medical Institute and the Center for Neural Science of New York University. Starting in 2008, he is Assistant Professor at the Computer Science Department of University at Albany, State University of New York. Dr. Lyu is the recipient of the Alumni Thesis Award of Dartmouth College in 2005, IEEE Signal Processing Society Best Paper Award in 2010, and the NSF CAREER Award in 2010. He has authored one book, and held two U.S. and one E.U. patents. He has published more than 50 conference and journal papers in the research fields of natural image statistics, digital image forensics, machine learning and computer vision. For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/govpodcast/e260

Rapid Rundown - Series 7 Episode 3 All the key news since our episode on 6th November - including new research on AI in education, and a big tech news week! It's okay to write research papers with Generative AI - but not to review them! The publishing arm of American Association for Advancement of Science (they publish 6 science journals, including the "Science" journal) says authors can use “AI-assisted technologies as components of their research study or as aids in the writing or presentation of the manuscript” as long as their use is noted.  But they've banned AI-generated images and other multimedia" without explicit permission from the editors”. And they won't allow the use of AI by reviewers because this “could breach the confidentiality of the manuscript”. A number of other publishers have made announcements recently, including the International Committee of Medical Journal Editors , the World Association of Medical Editors and the  Council of Science Editors. https://www.science.org/content/blog-post/change-policy-use-generative-ai-and-large-language-models   Learning From Mistakes Makes LLM Better Reasoner https://arxiv.org/abs/2310.20689 News Article: https://venturebeat.com/ai/microsoft-unveils-lema-a-revolutionary-ai-learning-method-mirroring-human-problem-solving Researchers from Microsoft Research Asia, Peking University, and Xi'an Jiaotong University have developed a new technique to improve large language models' (LLMs) ability to solve math problems by having them learn from their mistakes, akin to how humans learn. The researchers have revealed a pioneering strategy, Learning from Mistakes (LeMa), which trains AI to correct its own mistakes, leading to enhanced reasoning abilities, according to a research paper published this week. The researchers first had models like LLaMA-2 generate flawed reasoning paths for math word problems. GPT-4 then identified errors in the reasoning, explained them and provided corrected reasoning paths. The researchers used the corrected data to further train the original models.     Role of AI chatbots in education: systematic literature review International Journal of Educational Technology in Higher Education https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-023-00426-1#Sec8 Looks at chatbots from the perspective of students and educators, and the benefits and concerns raised in the 67 research papers they studied We found that students primarily gain from AI-powered chatbots in three key areas: homework and study assistance, a personalized learning experience, and the development of various skills. For educators, the main advantages are the time-saving assistance and improved pedagogy. However, our research also emphasizes significant challenges and critical factors that educators need to handle diligently. These include concerns related to AI applications such as reliability, accuracy, and ethical considerations." Also, a fantastic list of references for papers discussing chatbots in education, many from this year     More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problems https://arxiv.org/abs/2311.04926 https://arxiv.org/pdf/2311.04926.pdf Parsons problems are a type of programming puzzle where learners are given jumbled code snippets and must arrange them in the correct logical sequence rather than producing the code from scratch "While some scholars have advocated for the integration of visual problems as a safeguard against the capabilities of language models, new multimodal language models now have vision and language capabilities that may allow them to analyze and solve visual problems. … Our results show that GPT-4V solved 96.7% of these visual problems" The research's findings have significant implications for computing education. The high success rate of GPT-4V in solving visually diverse Parsons Problems suggests that relying solely on visual complexity in coding assignments might not effectively challenge students or assess their true understanding in the era of advanced AI tools. This raises questions about the effectiveness of traditional assessment methods in programming education and the need for innovative approaches that can more accurately evaluate a student's coding skills and understanding. Interesting to note some research earlier in the year found that LLMs could only solve half the problems - so things have moved very fast!     The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 https://arxiv.org/pdf/2311.07361.pdf By Microsoft Research and Microsoft Azure Quantum researchers "Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks" The study explores the impact of GPT-4 in advancing scientific discovery across various domains. It investigates its use in drug discovery, biology, computational chemistry, materials design, and solving Partial Differential Equations (PDEs). The study primarily uses qualitative assessments and some quantitative measures to evaluate GPT-4's understanding of complex scientific concepts and problem-solving abilities. While GPT-4 shows remarkable potential and understanding in these areas, particularly in drug discovery and biology, it faces limitations in precise calculations and processing complex data formats. The research underscores GPT-4's strengths in integrating knowledge, predicting properties, and aiding interdisciplinary research.     An Interdisciplinary Outlook on Large Language Models for Scientific Research https://arxiv.org/abs/2311.04929 Overall, the paper presents LLMs as powerful tools that can significantly enhance scientific research. They offer the promise of faster, more efficient research processes, but this comes with the responsibility to use them well and critically, ensuring the integrity and ethical standards of scientific inquiry. It discusses how they are being used effectively in eight areas of science, and deals with issues like hallucinations - but, as it points out, even in Engineering where there's low tolerance for mistakes, GPT-4 can pass critical exams. This research is a good source of focus for researchers thinking about how it may help or change their research areas, and help with scientific communication and collaboration.     With ChatGPT, do we have to rewrite our learning objectives -- CASE study in Cybersecurity https://arxiv.org/abs/2311.06261 This paper examines how AI tools like ChatGPT can change the way cybersecurity is taught in universities. It uses a method called "Understanding by Design" to look at learning objectives in cybersecurity courses. The study suggests that ChatGPT can help students achieve these objectives more quickly and understand complex concepts better. However, it also raises questions about how much students should rely on AI tools. The paper argues that while AI can assist in learning, it's crucial for students to understand fundamental concepts from the ground up. The study provides examples of how ChatGPT could be integrated into a cybersecurity curriculum, proposing a balance between traditional learning and AI-assisted education.   "We hypothesize that ChatGPT will allow us to accelerate some of our existing LOs, given the tool's capabilities… From this exercise, we have learned two things in particular that we believe we will need to be further examined by all educators. First, our experiences with ChatGPT suggest that the tool can provide a powerful means to allow learners to generate pieces of their work quickly…. Second, we will need to consider how to teach concepts that need to be experienced from “first-principle” learning approaches and learn how to motivate students to perform some rudimentary exercises that “the tool” can easily do for me."     A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models https://arxiv.org/abs/2311.07491 What this means is that AI is continuing to get better, and people are finding ways to make it even better, at passing exams and multi-choice questions     Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study https://arxiv.org/abs/2311.07387 Good news for me though - I still have a skill that can't be replaced by a robot. It seems that AI might be great at playing Go, and Chess, and seemingly everything else. BUT it turns out it can't play Minesweeper as well as a person. So my leisure time is safe!     DEMASQ: Unmasking the ChatGPT Wordsmith https://arxiv.org/abs/2311.05019 Finally, I'll mention this research, where the researchers have proposed a new method of ChatGPT detection, where they're assessing the 'energy' of the writing. It might be a step forward, but tbh it took me a while to find the thing I'm always looking for with detectors, which is the False Positive rate - ie how many students in a class of 100 will it accuse of writing something with ChatGPT when they actually wrote it themself.  And the answer is it has a 4% false positive rate on research abstracts published on ArXiv - but apparently it's 100% accurate on Reddit. Not sure that's really good enough for education use, where students are more likely to be using academic style than Reddit style! I'll leave you to read the research if you want to know more, and learn about the battle between AI writers and AI detectors     Harvard's AI Pedagogy Project And outside of research, it's worth taking a look at work from the metaLAB at Harvard called "Creative and critical engagement with AI in education" It's a collection of assignments and materials inspired by the humanities, for educators curious about how AI affects their students and their syllabi. It includes an AI starter, an LLM tutorial, lots of resources, and a set of assignments https://aipedagogy.org/     Microsoft Ignite Book of News There's way too much to fit into the shownotes, so just head straight to the Book of News for all the huge AI announcements from Microsoft's big developer conference Link: Microsoft Ignite 2023 Book of News  

Hong Z. Tan is a renowned haptics scientist at Google, known for her research on perception-based haptic interfaces. With a background in Biomedical Engineering and Electrical Engineering from Shanghai Jiao Tong University and MIT, respectively, Hong has also had extensive industry experience, including a stint at Microsoft Research Asia. She is a Fellow of IEEE for her contributions to wearable haptics and holds concurrent appointments at Purdue University in the fields of Electrical and Computer Engineering, Mechanical Engineering, and Psychological Sciences. Learn more about Haptics Club Check out our website https://thehapticsclub.com Follow us on Twitter https://twitter.com/hapticsclub Listen on Spotify https://open.spotify.com/show/7er4WCd... Listen on Apple Podcast https://podcasts.apple.com/fr/podcast... Listen on Google Podcast https://podcasts.google.com/feed/aHR0...

This week we catch up with Dr. Siwei Lyu, a SUNY Empire Innovation Professor and founding Co-Director of Center for Information Integrity (CII) at the University at Buffalo, State University of New York. Siwei breaks down the deepfake experience, both the good and the misleading aspects of the technology. He shares insights on techniques researchers are developing to detect deepfakes, including GAN (Generative Adversarial Network) detected artifacts that produce tell-tale deepfake signs – if you know where to look. He also delves into the area of audio deepfakes and the sophistication of the human auditory system that makes this pathway a tough one to win. And, fun fact, to learn more about Siwei's research contributions be sure to Google “DeepFake-o-meter”!    Dr. Siwei Lyu, SUNY Empire Innovation Professor at the University at Buffalo Dr. Siwei Lyu received his B.S. degree (Information Science) in 1997 and his M.S. degree (Computer Science) in 2000, both from Peking University, China. He received his Ph.D. degree in Computer Science from Dartmouth College in 2005. From 1998 to 2000, he worked at the Founder Research and Development Center (Beijing, China) as a Software Engineer. From 2000 to 2001, he worked at Microsoft Research Asia (then Microsoft Research China) as an Assistant Researcher. From 2005 to 2008, he was a Post-Doctoral Research Associate at the Howard Hughes Medical Institute and the Center for Neural Science of New York University. Starting in 2008, he is Assistant Professor at the Computer Science Department of University at Albany, State University of New York. Dr. Lyu is the recipient of the Alumni Thesis Award of Dartmouth College in 2005, IEEE Signal Processing Society Best Paper Award in 2010, and the NSF CAREER Award in 2010. He has authored one book, and held two U.S. and one E.U. patents. He has published more than 50 conference and journal papers in the research fields of natural image statistics, digital image forensics, machine learning and computer vision. For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/govpodcast/e180

Welcome to Episode 30 of the Asian Hustle Network Podcast! We are very excited to have Poseidon Ho on this week's episode. We interview Asian entrepreneurs around the world to amplify their voices and empower Asians to pursue their dreams and goals. We believe that each person has a message and a unique story from their entrepreneurial journey that they can share with all of us. Check us out on Anchor, iTunes, Stitcher, Google Play Music, TuneIn, Spotify, and more. If you enjoyed this episode, please subscribe and leave us a positive 5-star review. This is our opportunity to use the voices of the Asian community and share these incredible stories with the world. We release a new episode every Wednesday, so stay tuned! Poseidon Ho is a Taiwanese Founder & General Partner of Outliers Fund, a research-driven venture fund/accelerator betting on the outliers who turn science fiction into scientific facts. Poseidon's early career was all about curiosity-driven innovation research such as studying Ant-Inspired Collective Intelligence at MIT Media Lab, building Pokemon GO-like Augmented Reality Gamifications for the 100th anniversary of San Diego Zoo Global, and launching Microsoft HoloLens in China for Microsoft Research Asia. Poseidon is known for building large-scale LEGO cities for ant colonies and raising $2M+ in a week as a decentralized VC when he was a student at MIT. In 2020, 2 of Poseidon's invested/accelerated startups are filing IPO and he is raising a $100M Outliers Venture Fund. This podcast is sponsored by The Funding Note (thefundingnote.com) is where you can easily search and track all the funding programs, grants, loans, tax credit programs in the United States that will help your business get access to capital. It's free to use and is updated on a daily basis and is currently tracking thousands of programs across the nation. Please check out our Patreon at @asianhustlenetwork. We want AHN to continue to be meaningful and give back to the Asian community. If you enjoy our podcast and would like to contribute to our future, we hope you’ll consider becoming a patron. --- Support this podcast: https://anchor.fm/asianhustlenetwork/support

This week on CUTalks, we are speaking to Dr. Kai-fu Lee who is the Chairman and CEO of Sinovation Ventures, a leading Chinese technology venture capital firm. He was also the founder of Microsoft Research Asia and the president of Google China as well as authoring the bestseller ‘AI Superpowers’. Kai-fu shared his journey into AI and gave an overview of the entrepreneurial landscape in China. He then went on to give insights into how governments can support AI, how humans can adapt as AI adopts certain tasks and how AI can revolutionise education. This podcast was produced by Carl Homer, Cambridge TV.

Kai-Fu Lee is the Chairman and CEO of Sinovation Ventures that manages a 2 billion dollar dual currency investment fund with a focus on developing the next generation of Chinese high-tech companies. He is the former President of Google China and the founder of what is now called Microsoft Research Asia, an institute that trained many of the AI leaders in China, including CTOs or AI execs at Baidu, Tencent, Alibaba, Lenovo, and Huawei. He was named one of the 100 most influential people in the world by TIME Magazine. He is the author of seven best-selling books in Chinese,

If you’re like me, you’re no longer amazed by how all your technologies can work for you. Rather, you’ve begun to take for granted that they simply should work for you. Instantly. All together. All the time. The fact that you’re not amazed is a testimony to the work that people like Dr. Lidong Zhou, Assistant Managing Director of Microsoft Research Asia, do every day. He oversees some of the cutting-edge systems and networking research that goes on behind the scenes to make sure you’re not amazed when your technologies work together seamlessly but rather, can continue to take it for granted that they will! Today, Dr. Zhou talks about systems and networking research in an era of unprecedented systems complexity and what happens when old assumptions don’t apply to new systems, explains how projects like CloudBrain are taking aim at real-time troubleshooting to address cloud-scale, network-related problems like “gray failure,” and tells us why he believes now is the most exciting time to be a systems and networking researcher.

The media tends to hyperbolize and boosterize technologists and the work that they do, creating all kinds of absurdly over-the-top titles for them. But when CBS’s 60 Minutes dubbed Kai-Fu Lee “the oracle of A.I.” earlier this year, it was actually a spot-on assessment. Lee has indeed been at the forefront of the field for more than three decades and is without question an artificial intelligence visionary. There are few people in the world who understand A.I. so astutely, especially within so many social and cultural contexts. His accolades speak volumes: In 2013, Lee was named to that year’s Time 100 list of the world’s most influential people, and this January, he was named co-chair of the World Economic Forum’s A.I. Council. His new book, A.I. Superpowers: China, Silicon Valley, and the New World Order, quickly rose to become a New York Times bestseller. Lee’s got one extraordinary résumé: After receiving a B.S.in computer science from Columbia University in 1983, he went on to get his Ph.D. in 1988 from Carnegie Mellon, where developed Sphinx, the first-ever speaker-independent continuous speech recognition system. In 1990, he joined Apple as a research scientist, heading up multiple R&D groups there for several years. From 1998 to 2005, he worked at Microsoft, where he established what would become Microsoft Research Asia, and later, upon returning to the U.S., he was named a vice president at the company. In 2005, he decamped to Google, resulting in a widely publicized five-month legal battle with Microsoft. Once settled, Lee helped bring Google to China, overseeing its growth and operations there for four years. Lee now runs Sinovation Ventures, a venture capital firm that invests in start-ups in China, many of them in the A.I. space. As of a year ago, according to Bloomberg, Sinovation had $2 billion under asset management with more than 300 companies in its portfolio. On this episode of Time Sensitive, Lee shares with Andrew Zuckerman his fascinating story of emigrating from China to Oak Ridge, Tennessee, at age 11; why he remains rationally optimistic about A.I. (and its increasingly potent presence in our lives); and how a recent bout with cancer drastically altered his outlook on life and work.

Dr. Kai-Fu Lee is the Chairman and CEO of Sinovation Ventures and President of Sinovation Venture's Artificial Intelligence Institute. Sinovation Ventures, manages $2 billion in investment funds, is a leading venture capital firm focusing on developing the next generation of Chinese high-tech companies. Prior to founding Sinovation in 2009, Dr. Lee was the President of Google China and held executive positions at Microsoft, SGI, and Apple. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Times 100 in 2013, WIRED 25 Icons , Asian Business Leader 2018 by Asia House, and followed by over 50 million audience on social media. In the field of artificial intelligence, Dr. Lee built one of the first game playing programs to defeat a world champion (1988, Othello), as well as the world's first large-vocabulary, speaker-independent continuous speech recognition system. Dr. Lee founded Microsoft Research China, which was named as the hottest research lab by MIT Technology Review. Later renamed Microsoft Research Asia, this institute trained the great majority of AI leaders in China, including CTOs or AI heads at Baidu, Tencent, Alibaba, Lenovo, Huawei, and Haier. While with Apple, Dr. Lee led AI projects in speech and natural language, which have been featured on Good Morning America on ABC Television and the front page of Wall Street Journal. He has authored 10 U.S. patents, and more than 100 journal and conference papers. Altogether, Dr. Lee has been in artificial intelligence research, development, and investment for more than 30 years. His New York Time and Wall Street Journal bestselling book AI Superpowers discusses US-China co-leadership in the age of AI as well as the greater societal impacts brought upon by the AI technology revolution.   Connect Linkedin – https://www.linkedin.com/in/kaifulee/ Twitter – https://twitter.com/kaifulee Wikipedia – https://en.wikipedia.org/wiki/Kai-Fu_Lee Instagram – https://www.instagram.com/kaifu_lee/ Facebook – https://www.facebook.com/drkaifulee/ Google+ – https://plus.google.com/101657038397569061811 Website – http://www.sinovationventures.com   People Mentioned Confucius Elon Musk – https://twitter.com/elonmusk   Resources VIP Kid – https://www.vipkidteachers.com/    Books AI Superpowers by Kai-Fu Lee: https://amzn.to/2QmNV6y

The influence of artificial intelligence on our world is only growing, as smart home products, algorithm-based streaming platforms, and even autonomous vehicles become a part of our daily lives. Since the 1990s, American tech companies in Silicon Valley have dominated the development and application of AI-driven technologies. However, AI pioneer Dr. Kai-Fu Lee explains that China has rapidly caught up with the United States, accelerating AI innovation and implementation in our daily lives. Lee argues that the future of AI will be even larger than the industrial revolution, posing unprecedented challenges and responsibilities for both AI superpowers. Are the United States and China going to cooperate or compete? Dr. Kai-Fu Lee is the chairman and CEO of Sinovation Ventures and president of Sinovation Venture’s Artificial Intelligence Institute. Sinovation Ventures, is a leading technology investment firm focusing on developing the next generation of Chinese high-tech companies. Prior to founding Sinovation in 2009, Dr. Lee was the president of Google China. Previously, he held executive positions at Microsoft, SGI, and Apple. Dr. Lee received his bachelor’s degree from Columbia University, Ph.D. from Carnegie Mellon University both in computer science, as well as honorary doctorate degrees from both Carnegie Mellon and the City University of Hong Kong. He is also a fellow of the Institute of Electrical and Electronics Engineers (IEEE). In the field of artificial intelligence, Dr. Lee founded Microsoft Research China, which was named as the hottest research lab by MIT Technology Review in 2004. Later renamed Microsoft Research Asia, this institute trained the great majority of AI leaders in China, including CTOs or AI heads at Baidu, Tencent, Alibaba, Lenovo, Huawei, and Haier. While with Apple, Dr. Lee led AI projects in speech and natural language, which have been featured on Good Morning America and in the Wall Street Journal. He has received 10 U.S. patents, published more than 100 journal and conference papers, and written seven top selling books in Chinese. He has over 50 million followers on social media.    

KAI-FU LEE, the founder of the Beijing-based Sinovation Ventures, is ranked #1 in technology in China by Forbes. Educated as a computer scientist at Columbia and Carnegie Mellon, his distinguished career includes working as a research scientist at Apple; Vice President of the Web Products Division at Silicon Graphics; Corporate Vice President at Microsoft and founder of Microsoft Research Asia in Beijing, one of the world’s top research labs; and then Google Corporate President and President of Google Greater China. As an Internet celebrity, he has fifty million+ followers on the Chinese micro-blogging website Weibo. As an author, among his seven bestsellers in the Chinese language, two have sold more than one million copies each. His first book in English is AI Superpowers: China, Silicon Valley, and the New World Order. The Conversation: https://www.edge.org/conversation/kaifulee-we-are-here-to-create

Lin Bin, the co-founder, president, and head of mobile for Xiaomi, reveals the secret sauce of one of the most valuable private companies in the world, which is reportedly going public later this year. Xiaomi had an unprecedented “comeback year” in 2017. In January 2018, Xiaomi was the third-largest smartphone seller in the world by shipment, the No. 1 smartphone seller in India, and one of the top five sellers in 12 other countries. Lin Bin, an engineer by training, recounts the founding story of Xiaomi, the uniqueness of the “Xiaomi ecosystem,” the phenomenon called “Mi Fans,” how he learned to fall in love with offline retail, and his takeaways from Xiaomi's meteoric rise in 2017 following a sluggish 2016. Prior to Xiaomi, Bin worked at Microsoft for 11 years and Google for 4 years. He served as the engineering director of Microsoft Research Asia, the vice president of the Google China Institute of Engineering, and the engineering director of Google Global. Hans Tung, one of the earliest investors and a former board member of Xiaomi, discusses how he first met the team and why he believed in a company whose success was considered “almost impossible.” Join our listeners' community via WeChat/Slack at 996.ggvc.com/community. GGV Capital also produces a biweekly email newsletter in English, also called "996," which has a roundup of the week's most important happenings in tech in China. Subscribe at 996.ggvc.com. The 996 Podcast is brought to you by GGV Capital, a multi-stage venture capital firm based in Silicon Valley, Shanghai, and Beijing. We have been partnering with leading technology entrepreneurs for the past 18 years from seed to pre-IPO. With $3.8 billion in capital under management across eight funds, GGV invests in globally minded entrepreneurs in consumer internet, e-commerce, frontier tech, and enterprise. GGV has invested in over 280 companies, with 30 companies valued at over $1 billion. Portfolio companies include Airbnb, Alibaba, Bytedance (Toutiao), Ctrip, Didi Chuxing, DOMO, Hashicorp, Hellobike, Houzz, Keep, Musical.ly, Slack, Square, Wish, Xiaohongshu, YY, and others. Find out more at ggvc.com.

Lin Bin, the co-founder, president, and head of mobile for Xiaomi, reveals the secret sauce of one of the most valuable private companies in the world, which is reportedly going public later this year. Xiaomi had an unprecedented “comeback year” in 2017. In January 2018, Xiaomi was the third-largest smartphone seller in the world by shipment, the No. 1 smartphone seller in India, and one of the top five sellers in 12 other countries. Lin Bin, an engineer by training, recounts the founding story of Xiaomi, the uniqueness of the “Xiaomi ecosystem,” the phenomenon called “Mi Fans,” how he learned to fall in love with offline retail, and his takeaways from Xiaomi’s meteoric rise in 2017 following a sluggish 2016. Prior to Xiaomi, Bin worked at Microsoft for 11 years and Google for 4 years. He served as the engineering director of Microsoft Research Asia, the vice president of the Google China Institute of Engineering, and the engineering director of Google Global. Hans Tung, one of the earliest investors and a former board member of Xiaomi, discusses how he first met the team and why he believed in a company whose success was considered “almost impossible.” Join our listeners' community via WeChat/Slack at 996.ggvc.com/community. GGV Capital also produces a biweekly email newsletter in English, also called "996," which has a roundup of the week's most important happenings in tech in China. Subscribe at 996.ggvc.com. The 996 Podcast is brought to you by GGV Capital, a multi-stage venture capital firm based in Silicon Valley, Shanghai, and Beijing. We have been partnering with leading technology entrepreneurs for the past 18 years from seed to pre-IPO. With $3.8 billion in capital under management across eight funds, GGV invests in globally minded entrepreneurs in consumer internet, e-commerce, frontier tech, and enterprise. GGV has invested in over 280 companies, with 30 companies valued at over $1 billion. Portfolio companies include Airbnb, Alibaba, Bytedance (Toutiao), Ctrip, Didi Chuxing, DOMO, Hashicorp, Hellobike, Houzz, Keep, Musical.ly, Slack, Square, Wish, Xiaohongshu, YY, and others. Find out more at ggvc.com.

GGV Capital's Hans Tung and Zara Zhang interview Kai-Fu Lee, the founder and CEO of Sinovation Ventures (an early-stage VC fund in China) and a renowned computer scientist known for his work in artificial intelligence. He was the founding president of Google China and played a key role in establishing Microsoft Research Asia as well. In this episode, Kai-Fu recounts the journey that took him from Taiwan to Tennessee and then to prominence in the tech sector. At first a computer science student working on speech recognition, he became one of the world's experts on artificial intelligence. He has helped U.S. giants like Google and Microsoft expand into China and assisted Chinese entrepreneurs as an investor, mentor, and thought leader. Join our listeners' community via WeChat/Slack at 996.ggvc.com/community. GGV Capital also produces a biweekly email newsletter in English, also called "996," which has a roundup of the week's most important happenings in tech in China. Subscribe at 996.ggvc.com. The 996 Podcast is brought to you by GGV Capital, a multi-stage venture capital firm based in Silicon Valley, Shanghai, and Beijing. We have been partnering with leading technology entrepreneurs for the past 18 years from seed to pre-IPO. With $3.8 billion in capital under management across eight funds, GGV invests in globally minded entrepreneurs in consumer internet, e-commerce, frontier tech, and enterprise. GGV has invested in over 280 companies, with 30 companies valued at over $1 billion. Portfolio companies include Airbnb, Alibaba, Bytedance (Toutiao), Ctrip, Didi Chuxing, DOMO, Hashicorp, Hellobike, Houzz, Keep, Musical.ly, Slack, Square, Wish, Xiaohongshu, YY, and others. Find out more at ggvc.com.

GGV Capital’s Hans Tung and Zara Zhang interview Kai-Fu Lee, the founder and CEO of Sinovation Ventures (an early-stage VC fund in China) and a renowned computer scientist known for his work in artificial intelligence. He was the founding president of Google China and played a key role in establishing Microsoft Research Asia as well. In this episode, Kai-Fu recounts the journey that took him from Taiwan to Tennessee and then to prominence in the tech sector. At first a computer science student working on speech recognition, he became one of the world’s experts on artificial intelligence. He has helped U.S. giants like Google and Microsoft expand into China and assisted Chinese entrepreneurs as an investor, mentor, and thought leader. Join our listeners' community via WeChat/Slack at 996.ggvc.com/community. GGV Capital also produces a biweekly email newsletter in English, also called "996," which has a roundup of the week's most important happenings in tech in China. Subscribe at 996.ggvc.com. The 996 Podcast is brought to you by GGV Capital, a multi-stage venture capital firm based in Silicon Valley, Shanghai, and Beijing. We have been partnering with leading technology entrepreneurs for the past 18 years from seed to pre-IPO. With $3.8 billion in capital under management across eight funds, GGV invests in globally minded entrepreneurs in consumer internet, e-commerce, frontier tech, and enterprise. GGV has invested in over 280 companies, with 30 companies valued at over $1 billion. Portfolio companies include Airbnb, Alibaba, Bytedance (Toutiao), Ctrip, Didi Chuxing, DOMO, Hashicorp, Hellobike, Houzz, Keep, Musical.ly, Slack, Square, Wish, Xiaohongshu, YY, and others. Find out more at ggvc.com.

Nicholas McConnell, PhD candidate in Astrophysics at UCB summer 2012, and Jeff Silverman, PhD of Astrophysics from UCB in 2011, part one of three, talk about their work with supernovae and black holes. To help analyze astronomy data go to www.galaxyzoo.org or www.planethunters.orgTranscriptSpeaker 1: Spectrum's next Speaker 2: [inaudible].Speaker 1: Welcome to spectrum the science and technology [00:00:30] show on k a l x Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events and news. Speaker 3: Good afternoon. My name is Brad Swift. I'm joined today by a spectrum of contributors, Rick Karnofsky and Lisa Katovich. Our interview is with Jeff Silverman, a recent phd in astrophysics from UC Berkeley and Nicholas McConnell, a phd [00:01:00] candidate unscheduled to be awarded a phd in astrophysics by UC Berkeley this summer. Jeff and Nicholas have generously agreed to help spectrum present a three part astronomy survey explaining the big ideas, recent experiments, collaborations and improvements in observation technology that are transforming astronomy. This is part two of three and in it we discussed Super Novi and black holes. Jeff, would you please start part two explaining Super Novi [inaudible] Speaker 4: observations [00:01:30] of exploding stars. These supernovae have been going on for thousands of years. Whether or not we knew what we were looking at for most of that time, we now know that those were exploding stars. Something that I did my phd thesis work on as well. I want to talk about a two exploding stars in particular that were found in 2011. The first one I'll talk about was found in late May, early June last year. It was founded by a handful of amateur astronomers, which is they find maybe hundred supernova per year. This has been going on for about a decade [00:02:00] or so. Uh, this one in particular, however, was so young and knew that somebody had emailed somebody who had emailed somebody who had actually tweeted about this new supernova. And so I got forwarded a tweet that said there's a new supernova in this very nearby galaxy and I happen to be using the Keck telescope, one of the biggest optical telescopes in the world, controlling it from UC Berkeley. Speaker 4: Saw this in my inbox. And we pointed at this supernova. We were the first ones to classify what kind of exploding start was confirmed that it was indeed [00:02:30] an exploding star and not some other, uh, asteroid that was just along the line of sight in the way or something else. Uh, and so that was as far as I know, the first time that a supernova was ever classified based on a tweet. The other Supernova, I want to talk about sort of the opposite end of having amateurs looking at a handful of galaxies. I'm part of a large international collaboration known as the Palomar transient factory PTF. And this collaboration uses a telescope down in San Diego to automatically monitor a bunch of these galaxies, [00:03:00] run these big computer programs to try and find if there is a new supernova, new bright spot in any of the images. Speaker 4: And this has been running for about two years now and we've been tweaking the algorithms to get faster and faster detections of these new spots. And so in August of last year there was some images taken in San Diego. Dr Peter Nugent, a professor in the astronomy department, was going through some of the newest candidates of what the computer program spit out and saw what looked like a very good supernova candidate and another very nearby galaxy, [00:03:30] a different one, but about the same distance, 20 or so million light years. We had an image from the night before that was very good and there was absolutely nothing at that position. So this clearly looked like a brand new spot. It couldn't be that old. So he immediately gets on the email list for this international collaboration. This was sort of the afternoon in California, but it was already nighttime in the eastern hemisphere. And we have collaborators who use telescopes in the Canary Islands. Speaker 4: So they point to it. They got not a great observation, but an observation that confirmed there was something there. And it was probably one of these [00:04:00] exploding stars by the time that they had worked on their data and emailed us. It was already nighttime in California and Hawaii. So we had the lick observatory telescopes out in San Jose as well as the Kecks in Hawaii pointing at this and absolutely confirming that it, it was a supernova. And within a few weeks we had already written a bunch of papers looking at the data very carefully. And we had actually found this supernova 11 hours after it exploded. So one of the earliest detections of an exploding star ever. People had speculated what you might [00:04:30] see that early and we actually got to throw out a lot of people's models saying we didn't see these things that you predicted possibly confirming some other predictions at this early time. Speaker 4: And this thing is still bright at its brightest. You could see it in a small backyard telescope are good binoculars from the Oakland hills. Uh, I saw it with my own eyes through a telescope, which was awesome. I think just an amazing, amazing proof of concept or success story of this huge collaboration without the the algorithms to, to run this quickly, we wouldn't have realized it was there until [00:05:00] days later without an international collaboration of friends expanding the globe. We wouldn't have been able to track it and confirm that it was the supernovas so quickly and so early and easily. So if I can ask, what's the biggest mystery about the way stars explode that you help solve by knowing about a supernova? Just a few hours after an explosion is actually happened. We'll solve as a strong word in science, but we can at least help get towards the truth. Speaker 4: As my advisor likes to say, this one that was discovered by the Palomar transient [00:05:30] factory in August is a specific kind of supernova that should have very consistent amount of energy. Sort of, you can think of it as a a hundred watt light bulb. It has the same amount of energy output always basically. So if you see it's very, very faint, it must be very, very far away. If you see it's very, very bright, it must be very, very close because it's sort of each of these objects has the same amount of light coming out of it and so we can measure very accurately how bright they are. We can compare to what we know they should be, how bright they should be, and we get a very accurate distance measurement to [00:06:00] all of these different supernova and figure out very accurate distances. How that distance has changed with time, and this is in fact how the accelerating expansion of the universe was discovered in the late nineties using these types of supernovae, which I will plug did win the Nobel Prize last year for physics and we're all very proud of that. Speaker 4: Saul Perlmutter up at the Berkeley lab was one of the winners and many of our group here at Berkeley and other places have collaborated on those projects over the years. So one thing that we aren't quite sure of, even though these are very, very consistent [00:06:30] explosions, we've observed them for a long time. We don't actually know the details of what stars are involved in the original explosion. We have some idea that a very dense star called a white dwarf made of mostly carbon and oxygen is blowing up. What exactly is around that star that's helping it blow up by actually feeding it some extra material and then pushing it over a limit to explode? We're a little bit unclear and so since this star that is feeding the mass to the white dwarf should be very close by. [00:07:00] They should be right near each other. One of the best ways you're going to observe it is right after the explosion, the explosion goes off. Speaker 4: The light and energy from that explosion could interact with the donor star that's right next door and then very quickly the explosion has expanded much further beyond that neighboring star and then it's sort of just hidden until either much, much later or perhaps never. And so by observing this supernova back in August 11 hours after the explosion and then taking subsequent observations sort of for the following few days, [00:07:30] we could rule out certain ideas of what that other star could be. There are very strong predictions. You should see some extra light in certain ways. If you had a certain type of star sitting there and we didn't see that, so it must be a very small star. Maybe something like the sun, maybe something like two times the mass of the sun. Speaker 2: Nope. This is spectrum k l x Berkeley. And you boys have been talking with Jeff Silverman [00:08:00] and Nicholas McConnell about supernova and black holes. So the Supernova is an issue. Speaker 4: Delusion of carbon and oxygen. You were saying that's great. What's the relationship of those explosions? Supernova to the black holes that were now discovered to be at the heart of every galaxy. So black holes come in a few different flavors, a certain kinds of supernovae uh, not the, the white [00:08:30] door of carbon oxygen ones. I was talking about a different flavor of Supernova that come from very massive stars that have 10 times the mass of the center bigger. They do explode as the different kinds of supernova collapse on themselves and can create black holes. The black holes end up weighing something like a few times the mass of the sun, maybe up to 20, 30 times the mass of the sun at the most. But those are sort of just kind of peppered throughout galaxies. What we've found over the past few decades and did a lot of work on lately is the supermassive black holes that can get up to hundreds of [00:09:00] millions or billions of times as massive as the sun. And those are found in the cores of galaxies as opposed to kind of peppered throughout them. And so there probably is a different formation mechanism that's still a very open question, how you make these giant black holes. But there are many, many orders of magnitude bigger than the ones that come from supernovae. Uh, and, and I'd actually say this is possibly a good segue that some interesting observation, right? Speaker 5: Progress is being made on which the most likely mechanisms are for forming these so-called seed [00:09:30] black holes that eventually grew into the monsters that we now observe at the senators of most galaxies in our own universe, in our current universe. Speaker 4: So was that a big shift then the, the idea of these supermassive black holes, Speaker 5: there's possibly a, a complicated relationship between the black hole at the center of the Galaxy and the galaxy itself, the black holes. Gravity is not sufficient to hold the entire galaxy together even though it is an extremely massive object and very near [00:10:00] to it. There's extremely powerful gravitational forces. Galaxies are so large and so extended that out in the the normal regions of the galaxy out near where the sun orbits in the Milky Way Galaxy. The fact that our Milky Way has a central black hole doesn't have any direct impact on our lives as the sun orbiting in the galaxy. On the other hand, if you consider the life cycle of a black hole starting from when it is formed from some seed object or birth process relatively early in the universe and evolving all the way toward [00:10:30] our present day universe over more than 10 billion years, black holes have very interesting variations in what they're doing over the course of their lifetimes. Speaker 5: In particular, when a black hole comes into proximity with a lot of gas, the gas spirals down and is funnel basically into the black hole and whereas some of the gas goes into the black hole and has never heard from again and increases the mass of the black hole. A lot of the guests on its way down heats up and releases tremendous amounts of light [00:11:00] because it takes time for light to travel. The distance between the object of meeting the light and us some of the furthest and therefore youngest things that we see of corresponding to very early times in the universe are in fact black holes that are swallowing tremendous amounts of gas. And some interesting discoveries that have happened recently is astronomers have been using different observational techniques to push further and further back into the universe's past, finding more and more distant black holes, swallowing [00:11:30] gas and learning about the universe at earlier and earlier times based on these observations. Speaker 5: And I think the current record holder now is a black hole that lived about 800 million years after the big bang, which translates to almost 13 billion years, 13,000 million years before our present day now. So looking that far back in time, we can no, first of all that these tremendous black holes exist that early in the universe. And we [00:12:00] can actually using techniques that follow up on the initial discovery and try to get more detailed analysis of them, we can make estimates of how massive they are. And in the case of the one that occurred when the universe was only 800 million years old, we learned that that black hole is far more massive than the black hole at the center of the Milky Way Galaxy bowed as massive as some of the most massive black holes that we've observed today. Um, so at least in some cases, black holes appear to have been seated by things that were relatively small, bigger than the tens of solar [00:12:30] masses that Jeff mentioned, but maybe a few thousand solar masses. And yet in the very earliest stage of the universe, they were able to grow tremendously fast and actually gain a ton of mass early in the universe. And then may have lived more peacefully throughout most of the duration of the universe. Speaker 2: You're listening to spectrum on k a l x, Berkeley, 90.7 FM. Today we're talking with Jeff Silverman and [00:13:00] Nicholas McConnell, both astrophysicists. We're discussing supernova. I am black homes. This is part two of a series three. Speaker 5: Another interesting outcome of looking at supermassive black holes early in the universe is it's often easier to see them far away than it is nearby because when they're far away and we see them, that's because they're swallowing a lot of gas. Many of the galaxies in today's universe [00:13:30] don't have gas near their black holes of the black holes are quiet. Uh, and in fact, you have to make very, very precise measurements of stars orbiting in their gravitational field to even know that a black hole is there. So one of the mysteries that had been going around for awhile is if you believe the masses of black holes very early in the universe, and you see these tremendously early things, but you want to know where are they now? They've had 13 billion years to evolve. What kind of galaxy is do these black holes live in today? Speaker 5: [00:14:00] Then you need to look in the nearby universe and try to find their quiet, ancient remnants. And recently, along with a couple other researchers at UC Berkeley, some other researchers around the country, my team discovered the two most massive black holes that we know about in today's universe. Black holes more than 10 billion times the mass of our sun, more than 2000 times the mass of the black hole at the center of the Milky Way. And because these are the most massive black holes that we know about in today's universe, [00:14:30] and they're roughly correspond to the estimated masses of the most massive black holes that we observe very, very early in the universe. We think we're beginning to answer the question of what kind of environment do these very young black holes actually end up in after the entire history of the universe between them. If I could ask a question, do you other properties of the galaxies that are now hosting these most massive black holes that are different than other nearby galaxies [00:15:00] that may have less massive black holes, something like the Milky Way size. Speaker 5: One interesting thing about the galaxies that we looked at is that they're also anchoring large galaxy clusters. And so specifically we found the most massive black holes at the centers of galaxy clusters. Now that's not a perfectly robust result because to be perfectly honest, we started by looking in the centers of galaxy clusters. And so we haven't done a wide sample of other galaxies and other environments, but it's possible that there is an environmental effect [00:15:30] based on not only the galaxy that the black hole resides in, but the overall neighborhood of how many galaxies are around that central object that may have something to do with the final massive its black hole. And where do you go with this research now, Nicholas, are there specific experiments? Are you relying on certain data? Where are you drawing this information from? And so we use data from a few different telescopes because these galaxies are distant and we're trying to look at stars in a very [00:16:00] small region of space. Speaker 5: We rely on very large telescopes to give us good light collecting power and good spatial resolution. So we use the Keck telescopes in Hawaii. We also use the Gemini telescopes in Hawaii and Sheila and there is a telescope in Texas that we've done some work with and we are trying to use these telescopes to find black holes in as many galaxies as the telescope committees will allow us to look at. Uh, so each semester with the generosity of, of getting, observing time, we're able to look at [00:16:30] two or three more galaxies and hopefully over a few years we'll have a good dozen or so objects that we can search directly for the most massive black holes in addition to a few dozen that have been discovered by other teams throughout the world over the last 10 years or so. And that really is one of the big limiting factors, isn't it? Speaker 5: The access to the equipment because there's so much going on in astronomy. Everybody's in the queue. Yeah, that's right. A, just like most scientists apply for amounts of funding from [00:17:00] various organizations, astronomers do that. In addition to applying for telescope time, the oversubscription rates for many of the biggest telescopes, the Hubble space telescope, the Keck telescopes is something like eight to one 10 to one. So the total number of requested hours is something like eight or 10 times the number of nighttime hours. There are in a semester or in a year, so it's, it's very much like a funding situation and there is so many nighttime hours and there's so many telescopes in the world. It's very competitive and we're very lucky when we do get access to [00:17:30] these huge telescopes with amazing instruments and computing power. How does that allocated time work when you want to make observations within a couple of hours of something that you've just heard about? So that's a great question. There's been something that has been used by astronomers over sort of the last decade but really a lot in the last five years called target of observations Speaker 4: too is as we call them and it's sort of in addition to or separate from your standard classically scheduled nights where you will use the telescope on this night. You can [00:18:00] also apply if you have a good science case, which many of us do, especially for these kinds of exploding stars that go off and we want to look at it very quickly, you can apply for time that is allocated through this t o program. And basically what it is is the telescope committees have said, okay, you get so many times to interrupt any observer and say you have to go look at this. And as an observer at that observatory, you know that that's part of the program and that at any point somebody could call you and say, drop what you're doing and go move over to [00:18:30] this. And many times people want to do the best science and are very happy to help out. And oftentimes there'll be offered co-authorship or at least acknowledged to, you know, thanking them for their help. Uh, certainly for these two Supernova I spoke about earlier, we definitely used our target of opportunity and they did turn out to be these very interesting supernovae Speaker 6: [inaudible]. That concludes part two of our astronomy series. Be sure to join us in two weeks [00:19:00] when we discuss dark energy or dark matter. Part three a regular feature of spectrum is to mention a few of the science and technology events happening in the bay area over the next few weeks. Rick Karnofsky and Lisa Kovich join me for the calendar. Speaker 7: The fix-it clinic will be held on Sunday, March 25th at the Lawrence Hall of science in Berkeley from one to 4:00 PM bring your broken non-functioning [00:19:30] things, electronics, appliances, computers, toys, and so on. For assessment, disassembly and possible repair. We'll provide workspace specialty tools and guidance to help you take apart and troubleshoot your item. Whether we fix it or not, you'll learn more about how it was manufactured and how it worked. This is a family friendly event. Children are hardly invited. This event is included in admission to the Lawrence Hall of Science. Speaker 3: The Mount Diablo Astronomical Society [00:20:00] holds its general monthly meetings the fourth Tuesday of each month, except for November and December. At the March 27th meeting, UC Berkeley Professor Jeff Marcy will speak about the future directions in extra solar planet investigations. The meeting begins at 7:15 PM and lasts until 9:30 PM the event will be held at the Concord Police Association facility. Five zero six zero Avi Law road in Concord. The society website is m [00:20:30] d a s. Dot. N. E. T. The computer history museums Speaker for March 28th will be New York Times magazine writer John Gardner who will talk about his book, the idea factory bell labs and the great age of American innovation to cake. Speaker 8: You edis Dave Iverson Bell labs was the most innovative production and research institution from the 1920s to the 1980s at its peak, bell labs employed nearly 15,000 people. [00:21:00] 1200 had PhDs. 13 would go on to win Nobel prizes. These ingenious, often eccentric men would become revolutionaries and sometimes legends, whether for inventing radio astronomy in their spare time and on the company's dime, riding unicycles through the corridors or pioneering the principles that propelled today's technology. Bell labs combined the best aspects of academic and corporate worlds, hiring the brightest and usually the youngest minds creating a culture and even architecture that [00:21:30] forced employees in different fields to work together in virtually complete intellectual freedom with little pressure to create moneymaking innovations in Gartner's portrait. We come to understand why both researchers and business leaders look to bell labs as a model and long to incorporate its magic into their own work. The talk starts at seven at the Computer History Museum, 14 Zero One north shoreline boulevard and mountain view. Visit www.computer history.org to register Speaker 7: [00:22:00] Thursday April 4th from three to 4:00 PM Andy Grove, Co founder and former CEO of Intel will speak on the UC Berkeley campus. His talk is titled of microchips and Men Tales from the translational medicine front. Andy Grove had a major influence on the ascent of micro electronics. Can a similar technological advance be achieved in medicine? He will discuss how we might open the pipeline to get life changing technologies to market without increasing the cost of care. [00:22:30] This event will be at the Sibley auditorium in the Bechdel engineering center. On the UC Berkeley campus. Speaker 8: The Marine Science seminar brings local engineers, physicians, computer programmers, and research scientists to speak to high school students and other interested people. It happens six Wednesdays per semester, seven 30 to 8:30 PM at the Terra Linda High School in San Rafael in the physiology lab. Two zero seven the guests for April 4th to meeting is the lead [00:23:00] of Pixars research and future spectrum guest, Tony rose. He will present on math in the movies. Film making is undergoing a digital revolution brought on by advances in areas such as computer technology, computational physics, geometry, and approximation theory. Using numerous examples drawn from Pixars feature films. This talk will provide a behind the scenes look at the role that math plays in the revolution. Visit www.marinescienceseminar.com [00:23:30] now news with Rick, Lisa and myself last September, the opera experiment located under the Grand Sazo Mountain in central Italy reported measuring neutrinos moving at faster than the speed of light from cern in Switzerland. Speaker 8: The Icarus experiment located in meters away from opera has published a preprint on the archive on March 15th showing that neutrinos move at speeds close to the speed of light, but that there is no evidence that they exceeded [00:24:00] opera is measurement was conducted with 10 microsecond pulses while Icarus was conducted with pulses that were only four nanoseconds, 2,500 times shorter. This led to far more accurate timing measurements. Opera head claim neutrinos arrived 60 nanoseconds before it would be predicted, but scientists had remained skeptical in part due to issues with timing [inaudible], Icarus, LVD, and opera. We'll all be making new measurements with pulse beams from cern in May to give us the final verdict Speaker 7: [00:24:30] according to technology review.com and the I a. E. A website. The disaster at Japan's Fukushima Daiichi plant a year ago prompted nations that generate atomic power to reexamine the safety of their reactors and even reevaluate their nuclear ambitions. Several countries have completely changed course. Japan has taken offline 52 of its 54 reactors and the future of nuclear power there is extremely uncertain. Germany shutdown seven reactors, [00:25:00] also elected not to restart another that had been down for maintenance and plans to decommission its remaining nine reactors by 2022 Italy, Switzerland and Mexico have each retreated from plans to build new nuclear plants and Belgium's government which took over in 2011 wants to make the country nuclear free by 2025 several other economically developed countries including the u s the United Kingdom and France are still generating roughly the same amount as they were before the Fukushima disaster and maintain [00:25:30] modest plans for future construction of additional reactors. But the future of nuclear power in the developing world is a different story. According to the International Atomic Energy Agency or I, a 45 countries are now considering embarking on nuclear power programs as Vietnam, Bangladesh, United Arab Emirates, Turkey and Belarus are likely to start building this year and Jordan and Saudi Arabia following in 2013 as of this week, the I a report [00:26:00] 63 new reactors under construction in 15 countries. The top constructors are China with 26 Russia with 10 India with seven and South Korea with three. The remaining 11 countries are building one or two reactors. Speaker 3: Technology review.com reports that researchers at Microsoft have made software that can learn the sound of your voice and then use it to speak a language that you don't. The system could be used to make language tutoring software more [00:26:30] personal or to make tools for travelers. In a demonstration at Microsoft's Redmond, Washington campus in early March, Microsoft research scientist Frank soon showed how his software could read out text in Spanish using the voice of his boss, Rick Rashid, who leads Microsoft's research efforts in a second demonstration soon used his software to grant Craig Mundie, Microsoft's chief research and strategy officer, the ability to speak Mandarin. [00:27:00] Frank soon created the system with his colleagues at Microsoft Research Asia, the company's research lab in Beijing, China. The system needs around an hour of training to develop a model, able to read out any text in a person's own voice. That model is converted into one able to read out text in another language by comparing it with a stock text to speech model for the target language. Individual sounds used by the first model to build up words using a [00:27:30] person's voice and his or her own language are carefully tweaked to give the new texts to speech model, a full ability to sound out phrases. In the second language, someone says that this approach can convert between any pair of 26 languages including Mandarin Chinese, Spanish and Italian Speaker 8: nature. News reports that researchers from the University of California, San Francisco and the Howard Hughes Medical Institutions Janelia Farm Research Center [00:28:00] near Ashburn, Virginia. I found that male fruit players are more likely to choose to consume alcohol if they have been sexually rejected by females. The key seems to be in Neuropeptide F, which is generated as a reward for either sex or alcohol consumption. When fly's denied of sex are given neuropeptide f they avoid alcohol and mammals. No transmitter y might act similarly for more information. You can see their article in the March 15th issue of Science Speaker 6: [00:28:30] [inaudible] [inaudible] spectrum shirts are gradually being made available online at iTunes university. Go to itunes.berkeley.edu and click through to Berkeley on iTunes. Then search for Calex 99.7 FM to finer the spectrum podcasts. [inaudible] [00:29:00] music heard during the show is from a low stone at David's album titled the Folk in Houston made available by creative Commons license 3.0 attribution. [inaudible]. Thank you for listening to spectrum. If you have comments about the show, please send them to us via email. Our email address is spectrum [00:29:30] dot k a l s@yahoo.com join us in two weeks at this same time. See acast.com/privacy for privacy and opt-out information.

Nicholas McConnell, PhD candidate in Astrophysics at UCB summer 2012, and Jeff Silverman, PhD of Astrophysics from UCB in 2011, part one of three, talk about their work with supernovae and black holes. To help analyze astronomy data go to www.galaxyzoo.org or www.planethunters.orgTranscriptSpeaker 1: Spectrum's next Speaker 2: [inaudible].Speaker 1: Welcome to spectrum the science and technology [00:00:30] show on k a l x Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events and news. Speaker 3: Good afternoon. My name is Brad Swift. I'm joined today by a spectrum of contributors, Rick Karnofsky and Lisa Katovich. Our interview is with Jeff Silverman, a recent phd in astrophysics from UC Berkeley and Nicholas McConnell, a phd [00:01:00] candidate unscheduled to be awarded a phd in astrophysics by UC Berkeley this summer. Jeff and Nicholas have generously agreed to help spectrum present a three part astronomy survey explaining the big ideas, recent experiments, collaborations and improvements in observation technology that are transforming astronomy. This is part two of three and in it we discussed Super Novi and black holes. Jeff, would you please start part two explaining Super Novi [inaudible] Speaker 4: observations [00:01:30] of exploding stars. These supernovae have been going on for thousands of years. Whether or not we knew what we were looking at for most of that time, we now know that those were exploding stars. Something that I did my phd thesis work on as well. I want to talk about a two exploding stars in particular that were found in 2011. The first one I'll talk about was found in late May, early June last year. It was founded by a handful of amateur astronomers, which is they find maybe hundred supernova per year. This has been going on for about a decade [00:02:00] or so. Uh, this one in particular, however, was so young and knew that somebody had emailed somebody who had emailed somebody who had actually tweeted about this new supernova. And so I got forwarded a tweet that said there's a new supernova in this very nearby galaxy and I happen to be using the Keck telescope, one of the biggest optical telescopes in the world, controlling it from UC Berkeley. Speaker 4: Saw this in my inbox. And we pointed at this supernova. We were the first ones to classify what kind of exploding start was confirmed that it was indeed [00:02:30] an exploding star and not some other, uh, asteroid that was just along the line of sight in the way or something else. Uh, and so that was as far as I know, the first time that a supernova was ever classified based on a tweet. The other Supernova, I want to talk about sort of the opposite end of having amateurs looking at a handful of galaxies. I'm part of a large international collaboration known as the Palomar transient factory PTF. And this collaboration uses a telescope down in San Diego to automatically monitor a bunch of these galaxies, [00:03:00] run these big computer programs to try and find if there is a new supernova, new bright spot in any of the images. Speaker 4: And this has been running for about two years now and we've been tweaking the algorithms to get faster and faster detections of these new spots. And so in August of last year there was some images taken in San Diego. Dr Peter Nugent, a professor in the astronomy department, was going through some of the newest candidates of what the computer program spit out and saw what looked like a very good supernova candidate and another very nearby galaxy, [00:03:30] a different one, but about the same distance, 20 or so million light years. We had an image from the night before that was very good and there was absolutely nothing at that position. So this clearly looked like a brand new spot. It couldn't be that old. So he immediately gets on the email list for this international collaboration. This was sort of the afternoon in California, but it was already nighttime in the eastern hemisphere. And we have collaborators who use telescopes in the Canary Islands. Speaker 4: So they point to it. They got not a great observation, but an observation that confirmed there was something there. And it was probably one of these [00:04:00] exploding stars by the time that they had worked on their data and emailed us. It was already nighttime in California and Hawaii. So we had the lick observatory telescopes out in San Jose as well as the Kecks in Hawaii pointing at this and absolutely confirming that it, it was a supernova. And within a few weeks we had already written a bunch of papers looking at the data very carefully. And we had actually found this supernova 11 hours after it exploded. So one of the earliest detections of an exploding star ever. People had speculated what you might [00:04:30] see that early and we actually got to throw out a lot of people's models saying we didn't see these things that you predicted possibly confirming some other predictions at this early time. Speaker 4: And this thing is still bright at its brightest. You could see it in a small backyard telescope are good binoculars from the Oakland hills. Uh, I saw it with my own eyes through a telescope, which was awesome. I think just an amazing, amazing proof of concept or success story of this huge collaboration without the the algorithms to, to run this quickly, we wouldn't have realized it was there until [00:05:00] days later without an international collaboration of friends expanding the globe. We wouldn't have been able to track it and confirm that it was the supernovas so quickly and so early and easily. So if I can ask, what's the biggest mystery about the way stars explode that you help solve by knowing about a supernova? Just a few hours after an explosion is actually happened. We'll solve as a strong word in science, but we can at least help get towards the truth. Speaker 4: As my advisor likes to say, this one that was discovered by the Palomar transient [00:05:30] factory in August is a specific kind of supernova that should have very consistent amount of energy. Sort of, you can think of it as a a hundred watt light bulb. It has the same amount of energy output always basically. So if you see it's very, very faint, it must be very, very far away. If you see it's very, very bright, it must be very, very close because it's sort of each of these objects has the same amount of light coming out of it and so we can measure very accurately how bright they are. We can compare to what we know they should be, how bright they should be, and we get a very accurate distance measurement to [00:06:00] all of these different supernova and figure out very accurate distances. How that distance has changed with time, and this is in fact how the accelerating expansion of the universe was discovered in the late nineties using these types of supernovae, which I will plug did win the Nobel Prize last year for physics and we're all very proud of that. Speaker 4: Saul Perlmutter up at the Berkeley lab was one of the winners and many of our group here at Berkeley and other places have collaborated on those projects over the years. So one thing that we aren't quite sure of, even though these are very, very consistent [00:06:30] explosions, we've observed them for a long time. We don't actually know the details of what stars are involved in the original explosion. We have some idea that a very dense star called a white dwarf made of mostly carbon and oxygen is blowing up. What exactly is around that star that's helping it blow up by actually feeding it some extra material and then pushing it over a limit to explode? We're a little bit unclear and so since this star that is feeding the mass to the white dwarf should be very close by. [00:07:00] They should be right near each other. One of the best ways you're going to observe it is right after the explosion, the explosion goes off. Speaker 4: The light and energy from that explosion could interact with the donor star that's right next door and then very quickly the explosion has expanded much further beyond that neighboring star and then it's sort of just hidden until either much, much later or perhaps never. And so by observing this supernova back in August 11 hours after the explosion and then taking subsequent observations sort of for the following few days, [00:07:30] we could rule out certain ideas of what that other star could be. There are very strong predictions. You should see some extra light in certain ways. If you had a certain type of star sitting there and we didn't see that, so it must be a very small star. Maybe something like the sun, maybe something like two times the mass of the sun. Speaker 2: Nope. This is spectrum k l x Berkeley. And you boys have been talking with Jeff Silverman [00:08:00] and Nicholas McConnell about supernova and black holes. So the Supernova is an issue. Speaker 4: Delusion of carbon and oxygen. You were saying that's great. What's the relationship of those explosions? Supernova to the black holes that were now discovered to be at the heart of every galaxy. So black holes come in a few different flavors, a certain kinds of supernovae uh, not the, the white [00:08:30] door of carbon oxygen ones. I was talking about a different flavor of Supernova that come from very massive stars that have 10 times the mass of the center bigger. They do explode as the different kinds of supernova collapse on themselves and can create black holes. The black holes end up weighing something like a few times the mass of the sun, maybe up to 20, 30 times the mass of the sun at the most. But those are sort of just kind of peppered throughout galaxies. What we've found over the past few decades and did a lot of work on lately is the supermassive black holes that can get up to hundreds of [00:09:00] millions or billions of times as massive as the sun. And those are found in the cores of galaxies as opposed to kind of peppered throughout them. And so there probably is a different formation mechanism that's still a very open question, how you make these giant black holes. But there are many, many orders of magnitude bigger than the ones that come from supernovae. Uh, and, and I'd actually say this is possibly a good segue that some interesting observation, right? Speaker 5: Progress is being made on which the most likely mechanisms are for forming these so-called seed [00:09:30] black holes that eventually grew into the monsters that we now observe at the senators of most galaxies in our own universe, in our current universe. Speaker 4: So was that a big shift then the, the idea of these supermassive black holes, Speaker 5: there's possibly a, a complicated relationship between the black hole at the center of the Galaxy and the galaxy itself, the black holes. Gravity is not sufficient to hold the entire galaxy together even though it is an extremely massive object and very near [00:10:00] to it. There's extremely powerful gravitational forces. Galaxies are so large and so extended that out in the the normal regions of the galaxy out near where the sun orbits in the Milky Way Galaxy. The fact that our Milky Way has a central black hole doesn't have any direct impact on our lives as the sun orbiting in the galaxy. On the other hand, if you consider the life cycle of a black hole starting from when it is formed from some seed object or birth process relatively early in the universe and evolving all the way toward [00:10:30] our present day universe over more than 10 billion years, black holes have very interesting variations in what they're doing over the course of their lifetimes. Speaker 5: In particular, when a black hole comes into proximity with a lot of gas, the gas spirals down and is funnel basically into the black hole and whereas some of the gas goes into the black hole and has never heard from again and increases the mass of the black hole. A lot of the guests on its way down heats up and releases tremendous amounts of light [00:11:00] because it takes time for light to travel. The distance between the object of meeting the light and us some of the furthest and therefore youngest things that we see of corresponding to very early times in the universe are in fact black holes that are swallowing tremendous amounts of gas. And some interesting discoveries that have happened recently is astronomers have been using different observational techniques to push further and further back into the universe's past, finding more and more distant black holes, swallowing [00:11:30] gas and learning about the universe at earlier and earlier times based on these observations. Speaker 5: And I think the current record holder now is a black hole that lived about 800 million years after the big bang, which translates to almost 13 billion years, 13,000 million years before our present day now. So looking that far back in time, we can no, first of all that these tremendous black holes exist that early in the universe. And we [00:12:00] can actually using techniques that follow up on the initial discovery and try to get more detailed analysis of them, we can make estimates of how massive they are. And in the case of the one that occurred when the universe was only 800 million years old, we learned that that black hole is far more massive than the black hole at the center of the Milky Way Galaxy bowed as massive as some of the most massive black holes that we've observed today. Um, so at least in some cases, black holes appear to have been seated by things that were relatively small, bigger than the tens of solar [00:12:30] masses that Jeff mentioned, but maybe a few thousand solar masses. And yet in the very earliest stage of the universe, they were able to grow tremendously fast and actually gain a ton of mass early in the universe. And then may have lived more peacefully throughout most of the duration of the universe. Speaker 2: You're listening to spectrum on k a l x, Berkeley, 90.7 FM. Today we're talking with Jeff Silverman and [00:13:00] Nicholas McConnell, both astrophysicists. We're discussing supernova. I am black homes. This is part two of a series three. Speaker 5: Another interesting outcome of looking at supermassive black holes early in the universe is it's often easier to see them far away than it is nearby because when they're far away and we see them, that's because they're swallowing a lot of gas. Many of the galaxies in today's universe [00:13:30] don't have gas near their black holes of the black holes are quiet. Uh, and in fact, you have to make very, very precise measurements of stars orbiting in their gravitational field to even know that a black hole is there. So one of the mysteries that had been going around for awhile is if you believe the masses of black holes very early in the universe, and you see these tremendously early things, but you want to know where are they now? They've had 13 billion years to evolve. What kind of galaxy is do these black holes live in today? Speaker 5: [00:14:00] Then you need to look in the nearby universe and try to find their quiet, ancient remnants. And recently, along with a couple other researchers at UC Berkeley, some other researchers around the country, my team discovered the two most massive black holes that we know about in today's universe. Black holes more than 10 billion times the mass of our sun, more than 2000 times the mass of the black hole at the center of the Milky Way. And because these are the most massive black holes that we know about in today's universe, [00:14:30] and they're roughly correspond to the estimated masses of the most massive black holes that we observe very, very early in the universe. We think we're beginning to answer the question of what kind of environment do these very young black holes actually end up in after the entire history of the universe between them. If I could ask a question, do you other properties of the galaxies that are now hosting these most massive black holes that are different than other nearby galaxies [00:15:00] that may have less massive black holes, something like the Milky Way size. Speaker 5: One interesting thing about the galaxies that we looked at is that they're also anchoring large galaxy clusters. And so specifically we found the most massive black holes at the centers of galaxy clusters. Now that's not a perfectly robust result because to be perfectly honest, we started by looking in the centers of galaxy clusters. And so we haven't done a wide sample of other galaxies and other environments, but it's possible that there is an environmental effect [00:15:30] based on not only the galaxy that the black hole resides in, but the overall neighborhood of how many galaxies are around that central object that may have something to do with the final massive its black hole. And where do you go with this research now, Nicholas, are there specific experiments? Are you relying on certain data? Where are you drawing this information from? And so we use data from a few different telescopes because these galaxies are distant and we're trying to look at stars in a very [00:16:00] small region of space. Speaker 5: We rely on very large telescopes to give us good light collecting power and good spatial resolution. So we use the Keck telescopes in Hawaii. We also use the Gemini telescopes in Hawaii and Sheila and there is a telescope in Texas that we've done some work with and we are trying to use these telescopes to find black holes in as many galaxies as the telescope committees will allow us to look at. Uh, so each semester with the generosity of, of getting, observing time, we're able to look at [00:16:30] two or three more galaxies and hopefully over a few years we'll have a good dozen or so objects that we can search directly for the most massive black holes in addition to a few dozen that have been discovered by other teams throughout the world over the last 10 years or so. And that really is one of the big limiting factors, isn't it? Speaker 5: The access to the equipment because there's so much going on in astronomy. Everybody's in the queue. Yeah, that's right. A, just like most scientists apply for amounts of funding from [00:17:00] various organizations, astronomers do that. In addition to applying for telescope time, the oversubscription rates for many of the biggest telescopes, the Hubble space telescope, the Keck telescopes is something like eight to one 10 to one. So the total number of requested hours is something like eight or 10 times the number of nighttime hours. There are in a semester or in a year, so it's, it's very much like a funding situation and there is so many nighttime hours and there's so many telescopes in the world. It's very competitive and we're very lucky when we do get access to [00:17:30] these huge telescopes with amazing instruments and computing power. How does that allocated time work when you want to make observations within a couple of hours of something that you've just heard about? So that's a great question. There's been something that has been used by astronomers over sort of the last decade but really a lot in the last five years called target of observations Speaker 4: too is as we call them and it's sort of in addition to or separate from your standard classically scheduled nights where you will use the telescope on this night. You can [00:18:00] also apply if you have a good science case, which many of us do, especially for these kinds of exploding stars that go off and we want to look at it very quickly, you can apply for time that is allocated through this t o program. And basically what it is is the telescope committees have said, okay, you get so many times to interrupt any observer and say you have to go look at this. And as an observer at that observatory, you know that that's part of the program and that at any point somebody could call you and say, drop what you're doing and go move over to [00:18:30] this. And many times people want to do the best science and are very happy to help out. And oftentimes there'll be offered co-authorship or at least acknowledged to, you know, thanking them for their help. Uh, certainly for these two Supernova I spoke about earlier, we definitely used our target of opportunity and they did turn out to be these very interesting supernovae Speaker 6: [inaudible]. That concludes part two of our astronomy series. Be sure to join us in two weeks [00:19:00] when we discuss dark energy or dark matter. Part three a regular feature of spectrum is to mention a few of the science and technology events happening in the bay area over the next few weeks. Rick Karnofsky and Lisa Kovich join me for the calendar. Speaker 7: The fix-it clinic will be held on Sunday, March 25th at the Lawrence Hall of science in Berkeley from one to 4:00 PM bring your broken non-functioning [00:19:30] things, electronics, appliances, computers, toys, and so on. For assessment, disassembly and possible repair. We'll provide workspace specialty tools and guidance to help you take apart and troubleshoot your item. Whether we fix it or not, you'll learn more about how it was manufactured and how it worked. This is a family friendly event. Children are hardly invited. This event is included in admission to the Lawrence Hall of Science. Speaker 3: The Mount Diablo Astronomical Society [00:20:00] holds its general monthly meetings the fourth Tuesday of each month, except for November and December. At the March 27th meeting, UC Berkeley Professor Jeff Marcy will speak about the future directions in extra solar planet investigations. The meeting begins at 7:15 PM and lasts until 9:30 PM the event will be held at the Concord Police Association facility. Five zero six zero Avi Law road in Concord. The society website is m [00:20:30] d a s. Dot. N. E. T. The computer history museums Speaker for March 28th will be New York Times magazine writer John Gardner who will talk about his book, the idea factory bell labs and the great age of American innovation to cake. Speaker 8: You edis Dave Iverson Bell labs was the most innovative production and research institution from the 1920s to the 1980s at its peak, bell labs employed nearly 15,000 people. [00:21:00] 1200 had PhDs. 13 would go on to win Nobel prizes. These ingenious, often eccentric men would become revolutionaries and sometimes legends, whether for inventing radio astronomy in their spare time and on the company's dime, riding unicycles through the corridors or pioneering the principles that propelled today's technology. Bell labs combined the best aspects of academic and corporate worlds, hiring the brightest and usually the youngest minds creating a culture and even architecture that [00:21:30] forced employees in different fields to work together in virtually complete intellectual freedom with little pressure to create moneymaking innovations in Gartner's portrait. We come to understand why both researchers and business leaders look to bell labs as a model and long to incorporate its magic into their own work. The talk starts at seven at the Computer History Museum, 14 Zero One north shoreline boulevard and mountain view. Visit www.computer history.org to register Speaker 7: [00:22:00] Thursday April 4th from three to 4:00 PM Andy Grove, Co founder and former CEO of Intel will speak on the UC Berkeley campus. His talk is titled of microchips and Men Tales from the translational medicine front. Andy Grove had a major influence on the ascent of micro electronics. Can a similar technological advance be achieved in medicine? He will discuss how we might open the pipeline to get life changing technologies to market without increasing the cost of care. [00:22:30] This event will be at the Sibley auditorium in the Bechdel engineering center. On the UC Berkeley campus. Speaker 8: The Marine Science seminar brings local engineers, physicians, computer programmers, and research scientists to speak to high school students and other interested people. It happens six Wednesdays per semester, seven 30 to 8:30 PM at the Terra Linda High School in San Rafael in the physiology lab. Two zero seven the guests for April 4th to meeting is the lead [00:23:00] of Pixars research and future spectrum guest, Tony rose. He will present on math in the movies. Film making is undergoing a digital revolution brought on by advances in areas such as computer technology, computational physics, geometry, and approximation theory. Using numerous examples drawn from Pixars feature films. This talk will provide a behind the scenes look at the role that math plays in the revolution. Visit www.marinescienceseminar.com [00:23:30] now news with Rick, Lisa and myself last September, the opera experiment located under the Grand Sazo Mountain in central Italy reported measuring neutrinos moving at faster than the speed of light from cern in Switzerland. Speaker 8: The Icarus experiment located in meters away from opera has published a preprint on the archive on March 15th showing that neutrinos move at speeds close to the speed of light, but that there is no evidence that they exceeded [00:24:00] opera is measurement was conducted with 10 microsecond pulses while Icarus was conducted with pulses that were only four nanoseconds, 2,500 times shorter. This led to far more accurate timing measurements. Opera head claim neutrinos arrived 60 nanoseconds before it would be predicted, but scientists had remained skeptical in part due to issues with timing [inaudible], Icarus, LVD, and opera. We'll all be making new measurements with pulse beams from cern in May to give us the final verdict Speaker 7: [00:24:30] according to technology review.com and the I a. E. A website. The disaster at Japan's Fukushima Daiichi plant a year ago prompted nations that generate atomic power to reexamine the safety of their reactors and even reevaluate their nuclear ambitions. Several countries have completely changed course. Japan has taken offline 52 of its 54 reactors and the future of nuclear power there is extremely uncertain. Germany shutdown seven reactors, [00:25:00] also elected not to restart another that had been down for maintenance and plans to decommission its remaining nine reactors by 2022 Italy, Switzerland and Mexico have each retreated from plans to build new nuclear plants and Belgium's government which took over in 2011 wants to make the country nuclear free by 2025 several other economically developed countries including the u s the United Kingdom and France are still generating roughly the same amount as they were before the Fukushima disaster and maintain [00:25:30] modest plans for future construction of additional reactors. But the future of nuclear power in the developing world is a different story. According to the International Atomic Energy Agency or I, a 45 countries are now considering embarking on nuclear power programs as Vietnam, Bangladesh, United Arab Emirates, Turkey and Belarus are likely to start building this year and Jordan and Saudi Arabia following in 2013 as of this week, the I a report [00:26:00] 63 new reactors under construction in 15 countries. The top constructors are China with 26 Russia with 10 India with seven and South Korea with three. The remaining 11 countries are building one or two reactors. Speaker 3: Technology review.com reports that researchers at Microsoft have made software that can learn the sound of your voice and then use it to speak a language that you don't. The system could be used to make language tutoring software more [00:26:30] personal or to make tools for travelers. In a demonstration at Microsoft's Redmond, Washington campus in early March, Microsoft research scientist Frank soon showed how his software could read out text in Spanish using the voice of his boss, Rick Rashid, who leads Microsoft's research efforts in a second demonstration soon used his software to grant Craig Mundie, Microsoft's chief research and strategy officer, the ability to speak Mandarin. [00:27:00] Frank soon created the system with his colleagues at Microsoft Research Asia, the company's research lab in Beijing, China. The system needs around an hour of training to develop a model, able to read out any text in a person's own voice. That model is converted into one able to read out text in another language by comparing it with a stock text to speech model for the target language. Individual sounds used by the first model to build up words using a [00:27:30] person's voice and his or her own language are carefully tweaked to give the new texts to speech model, a full ability to sound out phrases. In the second language, someone says that this approach can convert between any pair of 26 languages including Mandarin Chinese, Spanish and Italian Speaker 8: nature. News reports that researchers from the University of California, San Francisco and the Howard Hughes Medical Institutions Janelia Farm Research Center [00:28:00] near Ashburn, Virginia. I found that male fruit players are more likely to choose to consume alcohol if they have been sexually rejected by females. The key seems to be in Neuropeptide F, which is generated as a reward for either sex or alcohol consumption. When fly's denied of sex are given neuropeptide f they avoid alcohol and mammals. No transmitter y might act similarly for more information. You can see their article in the March 15th issue of Science Speaker 6: [00:28:30] [inaudible] [inaudible] spectrum shirts are gradually being made available online at iTunes university. Go to itunes.berkeley.edu and click through to Berkeley on iTunes. Then search for Calex 99.7 FM to finer the spectrum podcasts. [inaudible] [00:29:00] music heard during the show is from a low stone at David's album titled the Folk in Houston made available by creative Commons license 3.0 attribution. [inaudible]. Thank you for listening to spectrum. If you have comments about the show, please send them to us via email. Our email address is spectrum [00:29:30] dot k a l s@yahoo.com join us in two weeks at this same time. Hosted on Acast. See acast.com/privacy for more information.

Scott talks via Skype to Haixun Wang at Microsoft Research Asia about Trinity: a distributed graph database and computing platform. What is a GraphDB? How is it different from a traditional Relational DB, a Document DB or even just a naive in-memory distributed data structure? Will your next database be a graph database?