Podcasts about landing ai

Fala galera, nesse episódio no podcast eu converso com o Hugo Honda, Engenheiro de Machine Learning na Landing AI, que também trabalhou como ML Eng na Upwork e em outras empresas brasileiras!No episódio falamos sobre os desafios tecnicos, modelos e frameworks que ele usou na sua trajetória profissional. O Hugo é um cara muito gente boa e excelente tecnicamente, espero que curtam o episódio!Aqui está o link para a página de vendas para saber mais sobre mim e sobre o curso: https://filipe0lauar.hotmart.host/curso-deep-learningAqui está o link para se inscrever: https://pay.hotmart.com/W98240617ULink do grupo do wpp:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://chat.whatsapp.com/GNLhf8aCurbHQc9ayX5oCP⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠F⁠⁠⁠Instagram do podcast: https://www.instagram.com/podcast.lifewithaiMeu Linkedin: https://www.linkedin.com/in/filipe-lauar/Linkedin do Hugo: https://www.linkedin.com/in/hugohonda/

This podcast episode discusses the transformative impact of Vision AI, a type of artificial intelligence, on food and agriculture. Vision AI agents analyse visual data from various sources to improve crop yields, enhance food safety, and optimise supply chains. The episode highlights numerous companies utilising this technology, showcasing applications in precision farming, automated harvesting, food sorting, livestock monitoring, and quality control. Future applications, such as autonomous harvesting and AI-driven supply chain optimisation, are also explored, along with the potential of platforms like Landing AI to simplify Vision AI development. The episode concludes with a call to action for listeners in the agricultural and food industries to explore the benefits of this technology.

Dr. Andrew Ng is a globally recognized AI leader, founder of DeepLearning.AI and Landing AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and Adjunct Professor at Stanford University. Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Pocket Casts • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes - with links to many references - can be found on The Data Exchange web site.

Jens Wonneberger is the CISO of automotive supplier Mann+Hummel. He explains to us what rules the company has set itself when it comes to GenAI and LLMs. Jens Wonneberger is a big AI advocate, but we've never had a CISO on the Industrial AI Podcast. But the Mann+Hummel rules can certainly help other companies too. In the news section, we talk about BCW24 and one of the highlights was the announcement that Landing AI is a new partner of the crtlX ecosystem. But we also talk about Harting's Industrial Ethernet Week and ask ourselves: Are models ultimately a commodity?

Join us this week with AI-pioneer, Andrew Ng (Founder of DeepLearning.AI, Landing AI, Coursera, General Partner at the AI Found, adjunct professor at Stanford University) as we discuss the likelihood of AI's existential threat, the merits of regulation, the transformative power of generative AI, and the need for greater AI literacy.―Resources mentioned in this episode:Written Statement of Andrew Ng Before the U.S. Senate Insight Forum

Welcome to episode #131 of the Eye on AI podcast with Andrew Ng. Get ready to challenge your perspectives as we sit down with Andrew Ng. We navigate the widely disputed topic of AI as a potential existential threat, with Andrew assuring us that, with time and global cooperation, safety measures can be built to prevent disaster.  He offers insight into the debates surrounding the harm AI might cause, including the notions of AI as a bio-weapon and the notorious ‘paper clip argument'. Listen as Andrew debunks these theories, delivering an interesting argument for why he believes the associated risks are minimal.Onwards, we venture into the intriguing realm of AI's capability to understand the world, setting the stage for a conversation on how we can objectively assess their comprehension. We explore the safety measures of AI, drawing parallels with the rigour of the aviation industry, and contemplate on the consensus within the research community regarding the danger posed by AI. (00:00) Preview (01:08) Introduction (02:15) Existential risk of artificial intelligence (05:50) Aviation analogy with artificial intelligence (10:00) The threat of AI & deep learning   (13:15) Lack of consensus in AI dangers  (18:00) How AI can solve climate change (24:00) Landing AI and Andrew Ng (27:30) Visual prompting for images Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI Our sponsor for this episode is Masterworks, an art investing platform. They buy the art outright, from contemporary masters like Picasso and Banksy, then qualify it with the SEC, and offer it as an investment. Net proceeds from its sale are distributed to its investors. Since their inception, they have sold over $45 million dollars worth of artwork And so far, each of Masterworks' exits have returned positive net returns to their investors. Masterworks has over 750,000 users, and their art offerings usually sell out in hours, which is why they've had to make a waitlist. But Eye on AI viewers can skip the line and get priority access right now by clicking this link: https://www.masterworks.art/eyeonai Purchase shares in great masterpieces from artists like Pablo Picasso, Banksy, Andy Warhol, and more. See important Masterworks disclosures: https://www.masterworks.com/cd “Net Return" refers to the annualized internal rate of return net of all fees and costs, calculated from the offering closing date to the date the sale is consummated. IRR may not be indicative of Masterworks paintings not yet sold and past performance is not indicative of future results. Returns shown are 4 examples of midrange returns selected to demonstrate Masterworks performance history. Returns may be higher or lower. Investing involves risk, including loss of principal.  

In this Ask Me Anything session during this year's Abundance360 summit, Andrew, Emad, Alexandr, and Peter discuss how the world will change post-A.I explosion,  including how to reinvent your business, your skills, and more.  You will learn about: 03:39 | How Do We Educate On New Technologies In Our Changing World? 15:22 | Is There Any Industry That AI Will Never Disrupt? 28:27 | Will The First Trillionaire Be Born From The Power Of AI? Emad Mostaque is the Founder and CEO of Stability AI, the company behind Stable Diffusion. Alexandr Wang is the world's youngest self-made billionaire at 24 and is the Founder and CEO of Scale AI. Andrew Ng is the Founder of DeepLearning.AI and the Founder & CEO of Landing AI.  > Try out Stable Diffusion > Visit Scale AI > Learn AI with DeepLearning.AI _____________ I only endorse products and services I personally use. To see what they are,  please support this podcast by checking out our sponsor:  Use my code MOONSHOTS for 25% off your first month's supply of Seed's DS-01® Daily Synbiotic: seed.com/moonshots Levels: Real-time feedback on how diet impacts your health. levels.link/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:  Tech Blog Join me on a 5-Star Platinum Longevity Trip at Abundance Platinum _____________ Connect With Peter: Twitter Instagram Youtube Moonshots and Mindsets Learn more about your ad choices. Visit megaphone.fm/adchoices

The Boys expose the truth about the moon landing, discuss the terrifying potential of Ai and deep fakes and have an eye-opening conversation about ChatGPT and what that might mean for business and society moving forward. WATCH ON SPOTIFY: ⁠⁠⁠Click here⁠⁠ WATCH ON YOUTUBE: ⁠⁠Click here⁠ .

自从OpenAI 发布的chatGPT掀起席卷世界的AI热潮，不到3个月就积累了超过1亿月活用户，超过1300万日活用户，展现了AI让人惊叹的能力，让人感叹，下一个科技革命终于要到来。于是，这次硬核讨论就来了！ Hello World, who is Onboard!? 这次的嘉宾都是绝对的一线。有 Google Brain 的研究员，Xuezhi, 她是 Google 大语言模型 PaLM (Pathways language model) 的作者之一。还有来自 Stability AI 的技术产品经理，和来自某硅谷科技大厂的AI产品经理，曾任前吴恩达教授的Landing AI 机器学习产品负责人。还邀请到了一位一直关注AI的投资人朋友，Bill，作为主持嘉宾。 这次没有做太多chatGPT 天马行空的畅想，而是针对LLM和生成式AI，相当务实的讨论。技术角度，现在的技术可能的天花板和大变量会在哪儿？产品角度，AI和大模型产品化落地的难点？商业角度，整个生态可能随着大模型出现有什么演变？最后，还有投资人视角的回顾、总结和畅想。 这里还有一个小update, 在本集发布的时候，Google 也对爆发增长的ChatGPT 做出了回应，正在测试一个基于LaMDA 模型的聊天机器人 Apprentice Bard。正式发布后会有什么惊喜，我们拭目以待。 AI无疑是未来几年最令人兴奋的变量之一。我们未来会邀请到更多一线从业者，从不同角度讨论技术演进、商业的可能，甚至未来对于我们每个人和社会意味着什么。不论你是做创业、研究、产品还是投资，或许都能为你提供一些思考。 这次的讨论有些技术硬核，需要各位对生成式AI、大模型都有一些基础了解。讨论中涉及到的论文和重要概念，也总结在 show notes 中，供大家复习参考。其中几位嘉宾在北美工作生活多年，夹杂英文在所难免，也请大家体谅。 欢迎来到未来，Enjoy! 嘉宾介绍 Xuezhi Wang, Google Brain 研究员，Google 语言大模型 PaLM 作者之一。 Yizhou Zheng, Stability AI 产品和工程经理 Yiwen Li, 某硅谷科技大厂机器学习产品经理，前 Landing AI 产品负责人，天使投资人 我们都聊了什么 02:52 嘉宾自我介绍，fun fact: 你们感兴趣的一个生成式AI项目 大语言模型（LLM）的硬核讨论 12:07 Google PaML 是什么？跟GPT3有什么区别？ 15:47 GPT3比起GPT2 的巨大提升是怎么实现的？In-context learning 的机制是什么？ 24:02 大模型不断增加参数的过程有什么挑战？还有什么影响模型效果的重要因素？ 27:35 模型参数已经到瓶颈了吗？增加模型参数对落地应用有什么影响？ 31:13 高质量的训练数据规模会成为模型扩展的瓶颈吗？ 37:24 大模型基础上，具体场景的模型训练对数据要求有什么变化？ Stability AI 和开源商业模式 42:59 什么是Stable Diffusion? 跟其他图片生成模型的区别是什么？ 55:27 Stability AI 开源商业模式是什么？ 59:40 大模型背景下的AI商业模式会有什么变化？ 01:08:26 训练成本会如何影响AI产品和基于开源的商业模式？ 01:13:13 底层模型提供商会形成类似iOS，安卓这样的生态吗？AI PaaS 生态会如何演变？ 实现下一步的可能挑战 01:22:12 What's next: 现在的LLM还有什么瓶颈？基于现有模型的天花板是什么？ 01:31:27 大语言模型如何提升能力：多语言的挑战，多模态数据的作用 01:36:32 图片生成模型目前的瓶颈和未来提升的方向 01:39:18 多模态为什么对于模型提升很重要 01:42:37 多模态的难点是什么？ 大模型的落地和产品化 01:44:46 What's next: 基于大模型需要不一样的AI开发工具链吗？基础模型提供商会不会提供配套工具？ 01:58:48 产品经理如何思考AI的应用场景？ 02:03:25 Closing: 嘉宾未来关注的AI发展领域 Monica & Bill 投资人讨论 02:07:05 今天讨论印象深刻的点 02:09:39 这次AI浪潮跟上一波有什么不同？投资人如何评估生成式AI创业公司？ 02:14:44 现在是好的投资时点吗？ 02:17:03 随着训练成本降低，做模型的企业护城河是什么？ 02:20:00 如何看待中国的生成式AI创业机会？ 02:25:36 未来让我们兴奋的AI创业机会还有哪些？ 参考文章 Introducing Pathways: A next-generation AI architecture More Efficient In-Context Learning with GLaM Flamingo: a Visual Language Model for Few-Shot Learning Language Models Perform Reasoning via Chain of Thought Stable Diffusion with Core ML on Apple Silicon 欢迎关注M小姐的微信公众号，了解更多中美企业服务的干货内容！ M小姐研习录 (ID: MissMStudy) 大家的点赞、评论、转发是对我们最好的鼓励！希望你分享给对这个话题感兴趣的朋友哦~如果你有希望我们聊的话题，希望我们邀请的访谈嘉宾，都欢迎在留言中告诉我们。

Retail is big business. But like many other sectors it's undergoing a transformation, largely affected by the shift of consumer behavior from physical to digital. Many retailers are looking to analytics and AI to help them cope with the challenges. Andrew Ng, among the most prominent figures in AI, is now turning his sights to doing precisely that with his new venture Netail Founded in 2022 as part of Landing AI, Netail, a technology that enables retailers to auto-identify competitors across the internet and track their assortments, availability and optimize prices in real-time, today announced the closing of $5M in seed funding. We connected with retail veteran Mark Chrystal who is Netail's CEO to discuss the changing landscape in retail and Netail's offering.

Kelly Seelig, career marketer and current Head of Marketing and Communications at Landing AI, joins us to talk about why startups give great experiences for marketers, why building strong teams and partnerships are huge, and why centering around the customer is foundational to good business.

In der Mittagsfolge sprechen wir mit Frederic Krahforst, CEO und Co-Founder von TradeLink über die erfolgreiche Finanzierungsrunde in Höhe von 12 Millionen Euro. TradeLink hat eine Plattform entwickelt, mit der sich Mitarbeitende der Lagerlogistik untereinander vernetzen können. Zudem kann die Kommunikation um die Lieferanten, Spediteure und Kunden erweitert werden. Durch die Plattform wird die Zusammenarbeit in der Lieferkette zwischen allen beteiligten Partnern verstärkt und somit die Kommunikation der Logistik vereinfacht. Das LogTech wurde im Jahr 2020 von Frederic Krahforst und Tobias Nendel in München gegründet. Das Startup beschäftigt 60 Mitarbeitende aus 7 Ländern. Mittlerweile nutzen bereits über 1.000 Unternehmen die Lösung der Logistik-Plattform. TradeLink hat nun in einer Finanzierungsrunde 12 Millionen Euro eingesammelt. Neben den Bestandsinvestoren Point Nine und Fly Ventures hat sich auch der Risikokapitalgeber Insight Partners aus New York an der Runde beteiligt. Zu seinem Portfolio gehören u.a. Trivago (Exit), Delivery Hero, Tumblr (Exit), Shopify (Exit), HelloFresh, Twitter (Exit), Shutterstock, Udemy, Docker, Salesloft, OneTrust, Calm, Lightricks, JFrog, Armis, Bullhorn, Diligent, Avo, Canditech, CompanyCam, Leapsome, Landing AI, Metabase, Monolith, MotorQ, Rattle, Rewind, Rudderstack, Slice, Shelf, Taxbit, Kira, CloudBolt und AnyDesk. Außerdem haben Business Angels, wie u.a. der ehemalige Finanzvorstand der Deutschen Bahn Alexander Doll, der CEO von Sennder,David Nothacker und der ehemalige CFO von TeamViewer Stephan Kniewasser in das Münchener Startup investiert.

Andrew Ng, founder of Google Brain, Coursera and Landing AI, talks about his vision of data-centric AI, MLOps and the future of supervised versus unsupervised learning. This episode is from the Eye on AI podcast series. To check out more episodes, click here.

Andrew Ng, founder of Google Brain, Coursera and Landing AI, talks about his vision of data-centric AI, MLOps and the future of supervised vs unsupervised learning. The Eye on AI podcast is sponsored by ClearML.

Color Science and Digital ImagingI don't think many people paid too much attention to the sort of miracle that produces color technology in a digital device, because we were so used to using color film, which itself is a technological masterpiece to balance the various photo-sensitive pigments that would result in images that we perceive with our human visual system to look the same as the scene we took a picture of. This week’s conversation is with Jeff Carmichael. An expert on imaging systems particularly around the use of filter technology for looking at light from different parts of the spectrum.Color sensors don’t see color, they detect light and have either a red, blue or green filter over each pixel (a Bayer filter array) which blows my mind in terms of manufacturing, not to mention integrating all the data it captures across millions of pixels. Then all that data (voltages counting photons) is turned into numbers that can be delivered to a screen that somehow reverses the whole process and presents us with an image that looks very much like the original scene. Jeff described for me how light of different wavelengths were associated with our actual perception of those colors based on a couple of experiments by the Commission Internationale de l'éclairage (CIE) involving 17 people in the early 20th century. You may have seen one of these chromaticity diagrams in relation to calibrating a computer screen.Now imagine your camera collecting light in three different buckets, just red, blue and green, each of those somewhat arbitrary and imprecise, and still making an image you would hang on your wall, watch on Netflix or further manipulate before you post on Instagram. It’s all just math, right?Microscopes with digital cameras are an obvious application. But what else can we do inside the imaging world in life science?Remote SensingHow about remote sensing of environmental features where by looking at specific parts of the spectrum through various filters, you can make some determinations as to what is going on? Think about things like assessing the growth or health of crops, or the biomass of plants in the rainforest. Pour yourself a glass of wine, look up at the night sky and appreciate the fact that the satellite you see whizzing by may have helped determine the maturation of grapes in your glass. The Normalized Differential Vegetation Index (NDVI) is a measure of vegetation based on comparisons between the red and near infrared light reflected from them (NIR - R) / (NIR + R) = (math again). I asked if that was a form of machine vision. Machine VisionIt turns out machine vision is a little different from remote sensing. While remote sensing is at the mercy of ambient light for the most part (we talked about LIDAR as an exception) machine vision is more like studio photography -highly dependent on consistent light sources. It’s used primarily for repetitive analyses like looking for defects or sorting pistachios. In life science, machine vision may currently be limited to routine applications ensuring that sample tubes have adequate sample, caps are on, the right tubes in place, etc. for high volume automated analyses.What Jeff is interested in is following a developmental or disease model e.g. an embryology experiment where one might be watching for a long time without interacting with the subject. Now, perhaps someone out there is hearing us and thinking, Jeff, you idiot. We do this all the time and maybe someone does. But it's not widespread like it is in industrial automation. If that someone is you, leave a comment. I’d love to chat with you. As would Jeff, I’m sure. Two separate pathsOne interesting thing that Jeff has observed is that the machine vision space and the life science space seem to be on parallel tracks seemingly invisible to each other.For example, both sectors have companies developing similar light sources, LED light engines he calls them, that can be controlled by software to get the lighting you want. But he’s not aware of any interaction between companies in different sectors.LEDs have become, they have been for a long time now the dominant type of light source used for lighting and machine vision applications. They don't get hot, they use little energy. They can pump out a lot of light in a very small wavelength range. You can, overdrive them so that they cycle really fast and can do very fast imaging.The same seems to be true in artificial intelligence (we had to get there, right?)For example, Landing AI and Path AI are both in the life science space, focused on machine vision and pathology respectively. Jeff is curious whether each segment (ML and LS) has something to be learned from the other. Because usually things mix and sorta come out in the wash in the end. So one resolution, I don't know, it could be that they borrow from each other on their particular AI approaches, right? And machine vision, they might be using a certain way of approaching AI that they hadn't thought of in the life science and vice versa. That could be the way that they merge. Like in music, you borrow from each other.We’ll be listening to see what happens. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cclifescience.substack.com

Did you ever feel you've had enough of your current line of work, and wanted to shift gears? If you have, you're definitely not alone. Besides the Great Resignation, however, there are also less radical approaches, like the one Andrew Ng is taking. Ng is among the most prominent figures in AI. Founder of deeplearning.ai, Co-Chairman and Co-Founder of Coursera, and Adjunct Professor at Stanford University. He was also Chief Scientist at Baidu Inc., and Founder & Lead for the Google Brain Project. Yet, his current priority has shifted -- from bits to things, as he puts it. Andrew Ng is also the Founder & CEO of Landing AI, a startup working on facilitating the adoption of AI in manufacturing since 2017. This effort has apparently contributed in shaping Ng's perception of what it takes to get AI to work beyond Big Tech, in what he calls the data-centric approach. We connected with Ng to discuss the data-centric approach to AI, and how it relates to his work with Landing AI and the big picture of AI today.

* Cristián habla personalmente y no representando a Quansight de ninguna manera. Cristián García es el mayor referente de Jax, un framework de machine learning de reciente creación, en Latinoamérica y uno de los principales en open source para machine learning! Trabaja como Machine Learning Engineer en Quansigh y antes **en Landing AI, empresa fundada por Andrew Ng. **Es el creador y desarrollador principal de Treex, librería para Jax. Es organizador del meetup Machine Learning Colombia y del Jax Global Meetup con Weights and Biases. Twitter: @cgarciae88 Treex: github.com/cgarciae/treex Jax Global Meetup: www.meetup.com/es/jax-global-meetup/ Quansight es una de las empresas más relevantes en deep learning. Fue fundada por el creador de numpy y scipy; dos de las librerías más importantes del ecosistema de data science y machine learning. Quansight está diseñada para apoyar el ecosistema open source. Sobre Jax y Treex: Jax entra como un tercer framework para machine learning. Es una propuesta nueva. Tiene muchas ventajas como que aprendió todo lo que hizo TensorFlow y Pytorch. Busca hacer las cosas mejor desde 0. Jax opera muy similar a numpy y a diferencia de Pytorch pide solo funciones puras. Es un requerimiento duro pero a cambio de ese sacrificio te regresa funciones que hacen cosas que en otros frameworks son difíciles. Una de sus ventajas es la programación distribuida hecha de manera sencilla. Treex es una oportunidad para hacer deep learning en Jax a través de pytrees. Una abstracción muy poderosa que trajo Jax y que incluso Pytorch ya está integrando. Treex trata de ser lo más parecido a Pytorch en términos de simplicidad y se ha propuesto ser un complemento específicamente de Flax, más que un competidor directo. El público de Jax es de muy alto nivel. Es algo emocionante y a la vez intimidante del ecosistema. Se mueven personas con un calibre muy alto y quieren aprender de forma acelerada. Sin embargo, falta comunicación, tutoriales y modelos para que las personas que están iniciando puedan motivarse a formar parte de la comunidad. Hoy en día es más fácil entrar al ecosistema Jax en comparación con hace un año. Aunque aún hay retos. Flax, por ejemplo, no tiene una API muy sencilla. Son cosas que aún le faltan al ecosistema de Jax.Algo que a Cristián le genera curiosidad es qué va a pasar con TensorFlow dada la fuerza que está ganando Jax.

Timestamps(02:00) Aarti shared her upbringing growing up in India and going to New York for undergraduate.(04:47) Aarti recalled her academic experience getting dual degrees in Computer Science and Computer Engineering at New York University.(07:17) Aarti shared details about her involvement with the ACM chapter and the Women in Computing club at NYU.(10:46) Aarti shared valuable lessons from her research internships.(14:16) Aarti discussed her decision to pursue an MS degree in Computer Science at Stanford University.(20:27) Aarti reflected on her learnings being the Head Teaching Assistant for CS 230, one of Stanford's most popular Deep Learning courses.(23:59) Aarti shared her thoughts on ML applications in both clinical and administrative healthcare settings.(26:47) Aarti unpacked the motivation and empirical work behind CheXNet, an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.(29:39) Aarti went over the implications of MURA, a large dataset of musculoskeletal radiographs containing over 40,000 images from close to 15,000 studies, for ML applications in radiology.(32:50) Aarti went over her experience working briefly as an ML engineer at Andrew Ng's startup Landing AI and applying ML to visual inspection tasks in manufacturing.(36:56) Aarti talked about her participation in external entrepreneurial initiatives such as Threshold Venture Fellowship and Greylock X Fellowship.(43:41) Aarti reminisced her time in a hybrid ML engineer/product manager/VC associate role at AI Fund, which works intensively with entrepreneurs during their startups' most critical and risky phase from 0 to 1.(48:43) Aarti shared advice that AI fund companies tended to receive regarding product-market fit and go-to-market fit strategy.(54:04) Aarti walked through her decision to onboard Snorkel AI, the startup behind the popular Snorkel open-source project capable of quickly generating training data with weak supervision.(56:36) Aarti reflected on the difference between being an ML researcher and an ML engineer.(01:00:18) Closing segment.Aarti's Contact InfoLinkedInTwitterGoogle ScholarPeopleAndrew NgJohn LangfordDavid SontagBooks and Papers“The Art of Doing Science & Engineering” (by Richard Hamming)“Deep Medicine: How AI Can Make Healthcare Human Again” (by Eric Topol)“CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning” (Dec 2017)“MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs” (May 2018)About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

This week's guest is Gregory Diamos, Machine Learning Systems Engineering at Landing AI. Run by one of the most prominent names in the business AI world, Andrew Ng, Landing AI has raised over $50M to date. Before Andrew founded Landing AI, he and Gregory worked together in the Baidu Silicon Valley Research Lab. In today's episode, Gregory discusses a broad set of AI use-cases in agriculture that he thinks are going to make the most significant impact in the years ahead. If you're interested in unlocking our AI best practice guides, frameworks for AI ROI, and specific resources for AI consultants, visit emerj.com/p1.

In this conversation with Dr. Andrew Ng, Stanford University Professor, co-founder of Coursera, founding lead of Google Brain, founder of deeplearning.ai, former chief scientist at Baidu, Founder and CEO of Landing AI, we look at the current and future state of AI, explore the concept of data centric AI as well as the latest developments at Landing AI.

Our 77th episode with a summary and discussion of last week's big AI news! Subscribe: RSS | iTunes | Spotify | YouTube Check our text version of this news roundup over at lastweekin.ai. This week: (01:06) Landing AI brings in $57M for its machine learning operations tools (05:13) As the Arctic Warms, AI Forecasts Scope Out Shifting Sea Ice (09:07) Why Facebook (Or Meta) Is Making Tactile Sensors for Robots  (14:12) Google AI Introduces ‘GoEmotions': An NLP Dataset for Fine-Grained Emotion Classification (18:28) Facebook, Citing Societal Concerns, Plans to Shut Down Facial Recognition System (21:40) Google Wants to Work With the Pentagon Again, Despite Employee Concerns (25:50) Australia Ordered Clearview AI to Destroy its Database, As Its Violating Privacy Laws  (27:54) AIBA uses AI technology to vet judges and refs at Belgrade worlds (30:40) Miso Introduces Second Generation Restaurant Kitchen Robot, the Flippy 2 Music: Deliberate Thought, Inspired by Kevin MacLeod (incompetech.com)

Why and where do companies fail at productionizing ML models? Watch the full podcast with Aarti here: https://youtu.be/VWJXiszQpTUAarti is a machine learning engineer at Snorkel AI. Prior to that, she worked closely with Andrew Ng in various capacities. She graduated with a master's in CS from Stanford, and bachelor's in CS and Computer Engineering from @New York University, and at @Microsoft  Research as a research intern for John Langford, where she contributed to Vowpal Wabbit, an open-source project. About the Host:Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.Jay Shah: https://www.linkedin.com/in/shahjay22/You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Show Notes(02:07) Emeli shared her educational background getting degrees in Applied Mathematics and Informatics from the Peoples' Friendship University of Russia in the early 2010s.(04:24) Emeli went over her experience getting a Master's Degree at Yandex School of Data Analysis.(07:06) Emeli reflected on lessons learned from her first job out of university working as a Software Developer at Rambler, one of the biggest Russian web portals.(09:33) Emeli walked over her first year as a Data Scientist developing e-commerce recommendation systems at Yandex.(13:38) Emeli discussed core projects accomplished as the Chief Data Scientist at Yandex Data Factory, Yandex's end-to-end data platform.(17:52) Emeli shared her learnings transitioning from an IC to a manager role.(19:21) Emeli mentioned key components of success for industrial AI, given her time as the co-founder and Chief Data Scientist at Mechanica AI.(22:40) Emeli dissected the makings of her Coursera specializations — “Machine Learning and Data Analysis” and “Big Data Essentials.”(26:14) Emeli discussed her teaching activities at Moscow Institute of Physics and Technology, Yandex School of Data Analysis, Harbour.Space, and Graduate School of Management — St. Petersburg State University.(30:12) Emeli shared the story behind the founding of Evidently AI, which is building a human interface to machine learning, so that companies can trust, monitor, and improve the performance of their AI solutions.(32:32) Emeli explained the concept of model monitoring and exposed the monitoring gap in the enterprise (read Part 1 and Part 2 of the Monitoring series).(34:13) Emeli looked at possible data quality and integrity issues while proposing how to track them (read Part 3, Part 4, and Part 5 of the Monitoring series).(36:47) Emeli revealed the pros and cons of building an open-source product.(39:13) Emeli talked about prioritizing product roadmap for Evidently AI.(41:24) Emeli described the data community in Moscow.(42:03) Closing segment.Emeli's Contact InfoLinkedInTwitterCourseraGitHubMediumEvidently AI's ResourcesWebsiteTwitterLinkedInGitHubDocumentationMentioned ContentBlog PostsML Monitoring, Part 1: What Is It and How It Differs? (Aug 2020)ML Monitoring, Part 2: Who Should Care and What We Are Missing? (Aug 2020)ML Monitoring, Part 3: What Can Go Wrong With Your Data? (Sep 2020)ML Monitoring, Part 4: How To Track Data Quality and Data Integrity? (Oct 2020)ML Monitoring, Part 5: Why Should You Care About Data And Concept Drift? (Nov 2020)ML Monitoring, Part 6: Can You Build a Machine Learning Model to Monitor Another Model? (April 2021)Courses“Machine Learning and Data Analysis”“Big Data Essentials”PeopleYann LeCun (Professor at NYU, Chief AI Scientist at Facebook)Tomas Mikolov (the creator of Word2Vec, ex-scientist at Google and Facebook)Andrew Ng (Professor at Stanford, Co-Founder of Google Brain, Coursera, and Landing AI, Ex-Chief Scientist at Baidu)Book“The Elements of Statistical Learning” (by Trevor Hastie, Robert Tibshirani, and Jerome Friedman)New UpdatesSince the podcast was recorded, a lot has happened at Evidently! You can use this open-source tool (https://github.com/evidentlyai/evidently) to generate a variety of interactive reports on the ML model performance and integrate it into your pipelines using JSON profiles.This monitoring tutorial is a great showcase of what can go wrong with your models in production and how to keep an eye on them: https://evidentlyai.com/blog/tutorial-1-model-analytics-in-production.About The ShowDatacast features long-form conversations with practitioners and researchers in the data community to walk through their professional journey and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths - from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

Show Notes(02:07) Emeli shared her educational background getting degrees in Applied Mathematics and Informatics from the Peoples' Friendship University of Russia in the early 2010s.(04:24) Emeli went over her experience getting a Master's Degree at Yandex School of Data Analysis.(07:06) Emeli reflected on lessons learned from her first job out of university working as a Software Developer at Rambler, one of the biggest Russian web portals.(09:33) Emeli walked over her first year as a Data Scientist developing e-commerce recommendation systems at Yandex.(13:38) Emeli discussed core projects accomplished as the Chief Data Scientist at Yandex Data Factory, Yandex's end-to-end data platform.(17:52) Emeli shared her learnings transitioning from an IC to a manager role.(19:21) Emeli mentioned key components of success for industrial AI, given her time as the co-founder and Chief Data Scientist at Mechanica AI.(22:40) Emeli dissected the makings of her Coursera specializations — “Machine Learning and Data Analysis” and “Big Data Essentials.”(26:14) Emeli discussed her teaching activities at Moscow Institute of Physics and Technology, Yandex School of Data Analysis, Harbour.Space, and Graduate School of Management — St. Petersburg State University.(30:12) Emeli shared the story behind the founding of Evidently AI, which is building a human interface to machine learning, so that companies can trust, monitor, and improve the performance of their AI solutions.(32:32) Emeli explained the concept of model monitoring and exposed the monitoring gap in the enterprise (read Part 1 and Part 2 of the Monitoring series).(34:13) Emeli looked at possible data quality and integrity issues while proposing how to track them (read Part 3, Part 4, and Part 5 of the Monitoring series).(36:47) Emeli revealed the pros and cons of building an open-source product.(39:13) Emeli talked about prioritizing product roadmap for Evidently AI.(41:24) Emeli described the data community in Moscow.(42:03) Closing segment.Emeli's Contact InfoLinkedInTwitterCourseraGitHubMediumEvidently AI's ResourcesWebsiteTwitterLinkedInGitHubDocumentationMentioned ContentBlog PostsML Monitoring, Part 1: What Is It and How It Differs? (Aug 2020)ML Monitoring, Part 2: Who Should Care and What We Are Missing? (Aug 2020)ML Monitoring, Part 3: What Can Go Wrong With Your Data? (Sep 2020)ML Monitoring, Part 4: How To Track Data Quality and Data Integrity? (Oct 2020)ML Monitoring, Part 5: Why Should You Care About Data And Concept Drift? (Nov 2020)ML Monitoring, Part 6: Can You Build a Machine Learning Model to Monitor Another Model? (April 2021)Courses“Machine Learning and Data Analysis”“Big Data Essentials”PeopleYann LeCun (Professor at NYU, Chief AI Scientist at Facebook)Tomas Mikolov (the creator of Word2Vec, ex-scientist at Google and Facebook)Andrew Ng (Professor at Stanford, Co-Founder of Google Brain, Coursera, and Landing AI, Ex-Chief Scientist at Baidu)Book“The Elements of Statistical Learning” (by Trevor Hastie, Robert Tibshirani, and Jerome Friedman)New UpdatesSince the podcast was recorded, a lot has happened at Evidently! You can use this open-source tool (https://github.com/evidentlyai/evidently) to generate a variety of interactive reports on the ML model performance and integrate it into your pipelines using JSON profiles.This monitoring tutorial is a great showcase of what can go wrong with your models in production and how to keep an eye on them: https://evidentlyai.com/blog/tutorial-1-model-analytics-in-production.About The ShowDatacast features long-form conversations with practitioners and researchers in the data community to walk through their professional journey and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths - from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

F5 NEWS

Learn how AI technology is able to utilize small data sets to help companies quickly capture a wide variety of product anomalies, and dynamically update defect books for real-time training. Today's AI solutions are maximizing efficiency, minimizing product defects, and eliminating wasted inventory for companies worldwide. Speaker info: Dr. Dongyan Wang, VP AI Transformation at Landing AI, a company founded by Andrew Ng to empower companies with artificial intelligence. Prior to Landing AI, Dr. Wang served as senior executive for multiple Fortune Global 500 enterprises such as Midea Group, NetApp, and Cisco, leveraging artificial intelligence, data science and blockchain technologies to lead strategic enterprise-wide transformation initiatives. He has also served as the COO of Grand Intelligence, a technology consulting company, and Chief AI Officer of a Blockchain + AI startup. Dr. Wang holds a PhD in Electrical Engineering and Computer Science. He holds more than 10 granted US granted US patents, 10+ international patents, and 30+ pending patents.

Learn more: Pivotal Deeplearning.ai Landing AI AI Fund Follow everyone on Twitter: Intersect Andrew Ng Derrick Harris Deeplearning.ai Landing AI AI Fund Pivotal

Learn more: Pivotal Deeplearning.ai Landing AI AI Fund Follow everyone on Twitter: Intersect Andrew Ng Derrick Harris Deeplearning.ai Landing AI AI Fund Pivotal