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I recently had the opportunity to attend the Google Cloud Executive Forum, held at Google’s impressive new Bay View campus, in Mountain View, California. The Forum was an invitation-only event that brought together CIOs and CTOs of leading companies to discuss Generative AI and showcase Google Cloud’s latest advancements in the domain. I shared my real-time reactions to the event content via Twitter, some of which you can find here. (Some weren't hash-tagged, but you can find most by navigating the threads.) In this post I’ll add a few key takeaways and observations from the day I spent at the event. Key Takeaways Continued product velocity Google Cloud has executed impressively against the Generative AI opportunity, with a wide variety of product offerings announced at the Google Data Cloud & AI Summit in March and at Google I/O in May. These include new tools like Generative AI Studio and Generative AI App Builder; models like PaLM for Text and Chat, Chirp, Imagen, and Codey; Embeddings APIs for Text and Images; Duet AI for Google Workspace and Google Cloud; new hardware offerings; and more. The company took the opportunity of the Forum to announce the general availability of Generative AI Studio and Model Garden, both part of the Vertex AI platform, as well as the pre-order availability of Duet AI for Google Workspace. Nenshad Bardoliwalla, product director for Vertex AI, delivered an impressive demo showing one-click fine tuning and deployment of foundation models on the platform. /*! elementor - v3.12.2 - 23-04-2023 */.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=".svg"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block} Considering that the post-ChatGPT Generative AI wave is only six months old, Google’s ability to quickly get Gen AI products out the door and into customer hands quickly has been noteworthy. Customer and partner traction Speaking of customers, this was another area where Google Cloud’s performance has been impressive. The company announced several new Generative AI customer case studies at the Forum, including Mayo Clinic, GA Telesis, Priceline, and PhotoRoom. Executives from Wendy’s, Wayfair, Priceline and Mayo participated in an engaging customer panel that was part of the opening keynote session. Several other customers were mentioned during various keynotes and sessions, as well as in private meetings I had with Google Cloud execs. See my Twitter thread for highlights and perspectives from the customer panel, which shared interesting insights about how those orgs are thinking about generative AI. Strong positioning While Models Aren’t Everything™, in a generative AI competitive landscape in which Microsoft’s strategy is strongly oriented around a single opaque model (ChatGPT via its OpenAI investment) and AWS’ strategy is strongly oriented around models from partners and open source communities, Google Cloud is promoting itself as a one-stop shop with strong first party models from Google AI, support for open source models via its Model Garden, as well as partnerships with external research labs like AI21, Anthropic and Cohere. The company also demonstrates a strong understanding of enterprise customer requirements around generative AI, with particular emphasis on data and model privacy, security and governance. The company’s strategy will continue to evolve and unfold in the upcoming months and much more will be discussed at Google Cloud Next in August, but I liked what I heard from product leaders at the event about the direction they’re heading. One hint: they have some strong ideas about how to address hallucination, which is one of the biggest drawbacks to enterprise use of large language models (LLMs). I don’t believe that hallucinations by LLMs can ever be completely eliminated, but in the context of a complete system with access to a comprehensive map of the world’s knowledge, there’s a good chance that the issue can be sufficiently mitigated to make LLMs useful in a wide variety of customer-facing enterprise use cases. Complex communication environment and need to educate In his opening keynote to an audience of executives, TK introduced concepts like reinforcement learning from human feedback, low-rank adaptation, synthetic data generation, and more. While impressive, and to some degree an issue of TK’s personal style, it’s also a bit indicative of where we are in this market that we’re talking to CIOs about LoRA and not ROI. This will certainly evolve as customers get more sophisticated and use cases get more stabilized, but it’s indicative of the complex communication challenges Google faces in evangelizing highly technical products in a brand new space to a rapidly growing audience. This also highlights the need for strong customer and market education efforts, to help bring all the new entrants up to speed. To this end, Google Cloud announced new consulting offerings, learning journeys, and reference architectures at the Forum to help customers get up to speed. (To add to the training courses announced at I/O). I also got to chat 1:1 with one of their “black belt ambassadors,” part of a team they’ve put in place to help support the broader engineering, sales and other internal teams at the company. Overall, I think the company’s success will be in large part dependent on their effectiveness at helping to bring these external and internal communities up to speed on Generative AI. Broad range of attitudes A broad range of attitudes about Generative AI was present at the event. On the one hand there was what I took as a very healthy “moderated enthusiasm” on the part of some. Wayfair CTO Fiona Tan exemplified this perspective both in her comments on the customer panel and in our lunch discussion. She talked about the need to manage “digital legacy” and the importance of platform investments, and was clear in noting that many of the company’s early investments in generative AI were experiments (e.g. a stable-diffusion based room designer they’re working on). On the other hand, there were comments clearly indicative of “inflated expectations,” like those of another panelist who speculated that using code generation would allow enterprises to reduce the time it takes to build applications from six weeks to two days or those of a fellow analyst who proclaimed that generative AI was the solution to healthcare in America. The quicker we get everyone past this stage the better. For its part, Google Cloud did a good job navigating this communication challenge by staying grounded on what real companies were doing with its products. I’m grateful to the Google Cloud Analyst Relations team for bringing me out to attend the event. Disclosure: Google is a client.
There are few things I love more than cuddling up with an exciting new book. There are always more things I want to learn than time I have in the day, and I think books are such a fun, long-form way of engaging (one where I won’t be tempted to check Twitter partway through). This book roundup is a selection from the last few years of TWIML guests, counting only the ones related to ML/AI published in the past 10 years. We hope that some of their insights are useful to you! If you liked their book or want to hear more about them before taking the leap into longform writing, check out the accompanying podcast episode (linked on the guest’s name). (Note: These links are affiliate links, which means that ordering through them helps support our show!) Adversarial ML Generative Adversarial Learning: Architectures and Applications (2022), Jürgen Schmidhuber AI Ethics Sex, Race, and Robots: How to Be Human in the Age of AI (2019), Ayanna Howard Ethics and Data Science (2018), Hilary Mason AI Sci-Fi AI 2041: Ten Visions for Our Future (2021), Kai-Fu Lee AI Analysis AI Superpowers: China, Silicon Valley, And The New World Order (2018), Kai-Fu Lee Rebooting AI: Building Artificial Intelligence We Can Trust (2019), Gary Marcus Artificial Unintelligence: How Computers Misunderstand the World (The MIT Press) (2019), Meredith Broussard Complexity: A Guided Tour (2011), Melanie Mitchell Artificial Intelligence: A Guide for Thinking Humans (2019), Melanie Mitchell Career Insights My Journey into AI (2018), Kai-Fu Lee Build a Career in Data Science (2020), Jacqueline Nolis Computational Neuroscience The Computational Brain (2016), Terrence Sejnowski Computer Vision Large-Scale Visual Geo-Localization (Advances in Computer Vision and Pattern Recognition) (2016), Amir Zamir Image Understanding using Sparse Representations (2014), Pavan Turaga Visual Attributes (Advances in Computer Vision and Pattern Recognition) (2017), Devi Parikh Crowdsourcing in Computer Vision (Foundations and Trends(r) in Computer Graphics and Vision) (2016), Adriana Kovashka Riemannian Computing in Computer Vision (2015), Pavan Turaga Databases Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases (2021), Xin Luna Dong Big Data Integration (Synthesis Lectures on Data Management) (2015), Xin Luna Dong Deep Learning The Deep Learning Revolution (2016), Terrence Sejnowski Dive into Deep Learning (2021), Zachary Lipton Introduction to Machine Learning A Course in Machine Learning (2020), Hal Daume III Approaching (Almost) Any Machine Learning Problem (2020), Abhishek Thakur Building Machine Learning Powered Applications: Going from Idea to Product (2020), Emmanuel Ameisen ML Organization Data Driven (2015), Hilary Mason The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI (2019), David Carmona MLOps Effective Data Science Infrastructure: How to make data scientists productive (2022), Ville Tuulos Model Specifics An Introduction to Variational Autoencoders (Foundations and Trends(r) in Machine Learning) (2019), Max Welling NLP Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics (2013), Emily M. Bender Robotics What to Expect When You’re Expecting Robots (2021), Julie Shah The New Breed: What Our History with Animals Reveals about Our Future with Robots (2021), Kate Darling Software How To Kernel-based Approximation Methods Using Matlab (2015), Michael McCourt
A few weeks ago I had the opportunity to visit Siemens’ Spotlight on Innovation event in Orlando, Florida. The event aimed to bring together industry leaders, technologists, local government leaders, and other innovators for a real-world look at the way technologies like AI, cybersecurity, IoT, digital twin, and smart infrastructure are helping businesses and cities unlock their potential. Siemens put together a nice pre-event program the day before the formal conference which included a tour of their Gamesa Wind Turbine Training Center. We got a peek into the way these machines are assembled, repaired, and managed. As expected, wind turbines are increasingly being fitted with sensors that, when coupled with machine learning algorithms, allow the company to optimize their performance and do predictive maintenance. AI figured prominently into the discussions at the main conference and the highlight for me was Norbert Gaus, head of R&D at Siemens, presenting an overview of the four main AI use cases that the company is interested in: Generative product design Automated product planning Adaptable autonomous machines Real-time simulation and digital twin He covered, though not in much detail, examples in each of these areas. (My Industrial AI ebook is a good reference for more on the opportunities, challenges, and use cases in this space.) Gaus also provided an interesting overview of the systems and software tools the company was building for internal and customer/partner use. These spanned AI-enabled hardware, industry-specific algorithms and services, AI development tools and workflows, pretrained AI models and software libraries, and industrial knowledge graphs. I was able to capture a couple of really interesting conversations with Siemens applied AI research engineers about some of the things the company is up to. Over on Twitter you can check out a short video I made with Siemens engineer Ines Ugalde where she demonstrates a computer vision powered robot arm that she worked on that uses the deep learning based YOLO algorithm for object detection and the Dex-Net grasp quality prediction algorithm designed in conjunction with Ken Goldberg’s AUTOLAB at UC Berkeley, with all inference running in real time on an Intel Movidius VPU. I also had an opportunity to interview Batu Arisoy for Episode 281 of the podcast. Batu is a research manager with the Vision Technologies & Solutions team at Siemens Corporate Technology. Batu’s research focus is solving limited-data computer vision problems. We cover a lot of ground in our conversation, including an interesting use case where simulation and synthetic data are used to recognize spare parts in place, in cases where the part cannot be isolated. “The first way we use simulation is actually to generate synthetic data and one great example use case that we have developed in the past is about spare part recognition. This is a problem if you have a mechanical object that you deploy in the field and you need to perform maintenance and service operations on this mechanical functional object over time. In order to solve this problem what we are working on is using simulation to synthetically generate a training data set for object recognition for large amount of entities. In other words, we synthetically generate images as if these images are collected in real world from an expert and they’re annotated from an expert and this actually comes for free using the simulation. […]We deployed this for the maintenance applications of trains and the main goal is a service engineer goes to the field, he takes his tablet, he takes a picture, then he draws a rectangle box and the system automatically identifies what is the object of interest that the service engineer would like to replace and in order to make the system reliable we have to take into consideration different lighting conditions, texture, colors, or whatever these parts can look like in a real world environment.” There’s a ton of great detail in this conversation. In particular, we dive into quite a few of the details behind how this works, including a couple of methods that they apply which were published in his group’s recent CVPR papers, including Tell Me Where to Look, which introduced the Guided Attention Inference Network, and Learning Without Memorizing. Definitely check out the full interview! Thanks once again to Siemens for hosting this event and for sponsoring my visit, this post, and my conversation with Batu.