Snapdragon Summit 2025: Performance Gains Meet Ecosystem Momentum
AI is fundamentally shifting the way users interact with their devices and data. From LLMs powering intelligent assistants to generative audio, video, and image applications, these experiences are computationally intensive in ways that traditional computing workloads never were. As a result, much of what determines user productivity and satisfaction, as well as what's possible for ISV applications and innovation, is gated behind compute performance.
Google Cloud Next ’25: Debts Paid, Stakes Raised
A Deep Dive into Google’s Evolving Agent Strategy and Platform Direction Another Google Cloud Next is in the books. There was a lot of news to digest from the event – 229 announcements according to the official count, spanning platform technologies, models, infrastructure, customer wins, and more. I'll cover the most important of these for AI leaders and builders in this post, but before diving into specifics, a bit of context is helpful.
ML Platforms for the Generative AI Era
Five years ago, I published the _Definitive Guide to Machine Learning Platforms_. Looking back, it was a pivotal time: enterprises were starting to scale up efforts to build predictive AI models, while the tooling and best practices needed to reliably move models from the lab into production were just beginning to emerge.
The Robots are Coming
My recent conversation with Sergey Levine—Associate Professor at UC Berkeley, co-founder of Physical Intelligence, and frequent guest on the podcast—discussed the robotics startup's recently open-sourced π0 model. This open-source foundation model for robotics combines a vision-language model with diffusion-based action expertise—a great example of the kind of integration driving progress across the field. If you're interested in robotic learning, the role of synthetic data, or how advanced tokenization can enables more efficient robots, I recommend giving this episode a listen.
Google Cloud Next '24 Reflections
I recently returned from the Google Cloud Next '24 conference, held in Las Vegas in April. Walking into the MGM conference center for the event, I couldn't shake the feeling that I had only just left last year's Next. Next '23 was, in fact, only nine months before.
Google Cloud Next ‘23 Recap
Google’s premier cloud computing AI conference, Google Cloud Next 2023, took place the last week of August at Moscone Center in San Francisco. I attended the event and had the opportunity to spend several days in a variety of keynotes, briefings, sessions, as well as explore the event’s expo floor. Of course, I shared some of my real-time observations via Twitter X, which you can check out here. Here, I’ll share a few of my key takeaways from the event.
Google Cloud Executive Forum: Highlights and Takeaways
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.
Join Our Weekly Study Groups!
Are you interested in exploring and experimenting with Generative AI and Large-Language Models, or brushing up on your deep learning fundamentals? If so, look no further than our weekly study groups!
Writers: Dismiss ChatGPT at your peril
A recent New Yorker article, "ChatGPT Is a Blurry JPEG of the Web,” has been making the rounds.
How Can I Help?
With the widespread news of tech company layoffs, it looks like the industry is entering the inevitable downward phase in the economic cycle.
TWIMLcon Platform is Now Open - Register While You Can!
Guess what? TWIMLcon: AI Platforms is just a few days away and our virtual conference platform opens TODAY! If you’ve been procrastinating with your registration, now is the time! We've got a great lineup of sessions, workshops, panels, and fun networking events in store for attendees. Last year at TWIMLcon we launched TWIMLconnect, our attendee networking program, which is back again this year. Attendees can win big by taking full advantage of all that TWIMLcon has to offer. I’ll be kicking things off on Tuesday with a live keynote interview with Niall Murphy and Todd Underwood, two of the co-authors of “Reliable Machine Learning: Applying SRE Principles to ML in Production,” a soon-to-be released O'Reilly book for which I’ve written the Foreword.
Top 10 Reasons to Attend TWIMLcon
TWIMLcon: AI Platforms starts NEXT WEEK 🎉🎊🙌! I’m obviously super excited for the conference, and I think you should be too! Here are the top 10 reasons why I think you should register for TWIMLcon today! 1\. Great Sessions: We’ve got an incredible session line-up for this year’s TWIMLcon. If you care about real-world ML or MLOps, you’ll definitely want to catch these great talks on topics like GitOps, Ray, real-time ML, model quality & testing, programmatic labeling, and much more.
Towards a Unified Batch and Streaming Platform at Intuit
You may know Intuit as the public company (INTU) behind Quickbooks and Turbotax but thanks to $20B of recent acquisitions, they are also the new owners of the Mailchimp marketing automation company and Credit Karma - a personal finance application. The company invests heavily in machine learning and AI as a way to deliver new features and capabilities in their products, to enhance the customer experience, and to improve operational efficiencies.
Demo: Model Quantization and Compression for Edge Devices with AIMET
Check out this demo of Qualcomm Technologies' AIMET, the AI Model Efficiency Toolkit.
Favorite Features of the New TWIML Website!
Earlier this year, we rolled out a major update to the TWIML website designed to make it easier for you to discover, use, and share podcast episodes and other TWIML content. Here are our top 10 favorite features of the new site:
The Evolution of Machine Learning Platforms at Facebook Webcast Recap
Back in the fall of 2018, we conducted a series of interviews with some of the people behind the large-scale ML platforms at organizations like Facebook, Airbnb, LinkedIn, OpenAI and more. That series of interviews turned into the first volume of our AI Platforms podcast series, led to the publication of the _The Definitive Guide to Machine Learning Platforms_ ebook, and ultimately to us launching the first TWIMLcon: AI Platforms conference in San Francisco the following fall.
Innovation at the Edge and in the Metaverse at Qualcomm
A conversation with Ziad Asghar Qualcomm Technologies provides connectivity and intelligence for a wide variety of devices, spanning mobile to vehicles to robotics and beyond. The company launched its first AI project in 2007 and has since launched 15 platforms and systems with AI on board. To support this innovation, the company invests in fundamental AI research in areas like neural compression, perception, reinforcement learning, and federated learning, which informs and powers their efforts to bring innovative new technologies to market across a variety of use cases.
2021 Guest Book Roundup
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).
Holiday Gift Guide 2021
Running out of gift ideas and need a little inspiration? The TWIML team has you covered! We put together a 2021 Holiday Gift Guide featuring some of our favorite new AI products. This is just a small sampling of some of the nifty gadgets and services that caught our attention this holiday season. This year, we wanted to include options for different budgets and different ages, for whoever needs a present in your life!
9 AI Nonprofits to Support for #GivingTuesdAI
A couple of days ago was #GivingTuesday, a “global generosity movement” that aims to harness some of the energy of Black Friday and Cyber Monday in support of charitable organizations. Hopefully you took advantage of the opportunity to donate to organizations you believe in. While you’re in the giving spirit, don’t forget those organizations working to make the field of AI more accessible, responsible and diverse.
House Hunters: How ML at Redfin is influencing the housing market with Akshat Kaul
MLOps at Redfin Redfin is a real estate brokerage founded by software developers that makes the process of buying and selling houses more efficient. They are the biggest real estate brokerage site on the web. When Akshat initially joined the company, the machine learning team at Redfin was a separate unit that focused on both the company’s early ML use cases as well as the infrastructure required to support them. However, this “siloed” structure became difficult to scale as Redfin continued to expand their operations. Now, the infrastructure team is working to standardize and democratize the ML platform, so data scientists within the company’s various product teams can focus on building the models they need while taking advantage of the centralized infrastructure Akshat’s team provides. While AI is built into many aspects of Redfin’s product, their two main ML-driven features are their estimation and recommendation algorithms. Redfin Estimate is a product that calculates the market value of any given home. The algorithm takes in a number of different variables about the market, the neighborhood, and aspects of the house itself, inputting over 500 data points for each calculation. Initially, Redfin Estimate was developed as a big user growth driver, as anyone could use it to estimate the value of their home. However, now that it’s being used to inform Redfin’s instant buying business, it has also become a key internal tool for the company’s agents. Redfin Recommendations is the company’s newer ML-driven product, ultimately responsible for 25% of the site’s traffic. Recommendations provide a list of recommended homes to customers on the website, and send personalized emails and push notifications to alert customers when a home they might be interested in arrives on the market. One interesting finding Akshat shared was that the recommendation algorithm is actually better at predicting homes people are interested in than their own self-identified saved searches. For example, when Akshat bought his own home in the Seattle area, the recommendation algorithm showed him a home in a different area than he had initially searched. Turned out that this home was within reasonable commuting distance for him, and a better fit for him than the others he was considering. Akshat ended up buying the home and moving in there!
Fighting Malware with Adversarial Machine Learning
Who is Edward Raff? Edward Raff works as a head scientist at the consulting firm Booz Allen Hamilton (BAH). As Edward describes it, their business model is “renting out people’s brains” to business and government organizations. Edward sees BAH research as both a way to establish expertise in their field and a way to train staff in how to solve interesting problems.
Dungeons, Dragons, and Deep Reinforcement Learning with Tim Rocktäschel
Who is Tim Rocktäschel? Tim Rocktäschel got his start in NLP research, earning a PhD from University College London. Initially, his research focused on knowledge representation and textual entailment, until he became disillusioned with the static nature of the data sets and tired of “chasing scores on a leaderboard.” Tim’s transition to the field of reinforcement learning was inspired by open-source work being done at DeepMind and OpenAI, and his postdoc at Oxford enriched his knowledge base.
Pondering Memory in Deep Neural Networks with Andrea Banino
Getting to know Andrea Banino Andrea’s background as a neuroscientist informed his work in deep learning. At DeepMind, Andrea’s research falls in the realm of Artificial General Intelligence, specifically memory, along with investigating ways to shape deep learning systems so they better mimic the human brain.
Advances at Microsoft Speech with Li Jiang
_“Speech is the most natural way to communicate, so using voice to interact with machines has always been one of the top scenarios associated with AI.”_
Building Communities at Stack Overflow with Prashanth Chandrasekar
A quick note: While they’re still early in their ML/AI journey, Stack Overflow is a company that is well-known and beloved in technical communities, and often one of the first places we look to solve problems big or small. For that reason I’m excited to share the conversation with you. That said, if you’re looking for an in-depth technical analysis, you might want to skip this one.
Rebuilding the Brain from AI “Legos” with Kanaka Rajan
Getting to Know Kanaka Rajan Kanaka Rajan had a circuitous route into computational neuroscience. Initially trained in biomedical engineering, she switched to experimental neuroscience in graduate school at Columbia University. While she enjoyed the challenge of the experiments, she felt impeded by experiment design, and ended up following some of her more mathematical training to computational neuroscience.
ML Mashups: Creativity in the Past, Present, and Future of Machine Learning
My recent conversation with Paperspace’s Dillon Erb got me thinking about creativity and machine learning in the context of the “mashup economy”--remember that term?
Celebrating 500 Episodes of the TWIML AI Podcast!
Hey everyone! I’m excited to share that show you’re listening to at this very moment in episode 500 of the TWIML AI Podcast! It’s been so amazing having the opportunity to explore, learn about, and chronicle the work of so many amazing people in machine learning and AI over the past five years, and it has been a true pleasure having so many of you along for the journey with me. The community that has formed around this show is a constant source of inspiration for me and the entire TWIML team.
Causal Modeling in Machine Learning Webinar - May 2021
Causality and causal modeling Causality and causal modeling is one of the hottest topics in machine learning. Earlier this year we launched a new causality course and study group with instructor Robert Osazuwa Ness, which received great feedback from students:
Using AI to Map the Human Immune System with Jabran Zahid - Transcript
Sam Charrington: \[00:00:00\] All right, everyone. I am here with Jabran Zahid, Senior Researcher with Microsoft Research. Jabran, welcome to the TWIML AI Podcast.
Accelerating Innovation with AI at Scale with David Carmona - Transcript
Sam Charrington: \[00:00:00\] Welcome to the TWIML AI podcast. I'm your host, Sam Charrington. Hey, what's up, everyone. Before we jump into today's interview, I'd like to give a huge thanks to our friends at Microsoft for their continued support of the podcast. Microsoft's mission is to empower every single person on the planet to achieve more, to inspire customers to reimagine their businesses and the world. Learn more at Microsoft.com/AI and Microsoft.com/innovation. And now, onto the show. All right, everyone. I am here with David Carmona. David is the general manager of artificial intelligence and innovation at Microsoft. David, welcome to the TWIML AI podcast.
From 1 to 100+ ML Models in Four Years
Solmaz Shahalizadeh In our recent TWIMLcon conversation with Solmaz Shahalizadeh, VP of Commerce Intelligence at Shopify, she shared her journey from deploying Shopify’s first machine learning model four years ago to now having over 100 models in production across all aspects of the business.
Architectural Patterns in ML
In our recent conversation with Nishan Subedi, VP of Algorithms at Overstock.com, he shared with us his approach to machine learning systems and organizational design.
Building Agility and Velocity In Machine Learning From The Ground Up
How do you build a machine learning platform for one of the world’s largest websites from the ground up? This is the question we posed to Chris Albon, Director of Machine Learning, at the Wikimedia Foundation.
TWIMLcon Day 5: Architecting ML Systems for Inevitable Change
What a start to week two of TWIMLcon 2021! Today’s sessions featured speakers from WikiMedia, Prosus Group, Palo Alto Networks, Clorox, Dataiku, Janssen Pharmaceutical Companies, iRobot, Algorithmia, and ClearML sharing their thoughts on building and running data science and ML platforms. We also got an overview of major themes and trends in machine learning for 2021. Without further ado, let’s review.
Key Factors When Building a Global Data Science Team
What are the most important factors to consider when building a global data science team? Ya Xu, Head of Data Science at LinkedIn, recently shared at TWIMLcon how she went from being an individual contributor to becoming a data science leader with over 350 global team members.
Innovating Neural Machine Translation with Arul Menezes - Transcript
Sam Charrington: \[00:00:00\] Welcome to The TWIML AI Podcast. I’m your host, Sam Charrington. Before we jump into the interview, I’d like to take a moment to thank Microsoft for their support of the show and their sponsorship of this series of episodes highlighting just a few of the fundamental innovations behind Azure Cognitive Services. Cognitive Services is a portfolio of domain-specific capabilities that brings AI within the reach of every developer without requiring machine learning expertise. All it takes is an API call to embed the ability to see, hear, speak, search, understand and accelerate decision making into your apps. Visit aka.ms/cognitive to learn how customers like Volkswagen, Uber and the BBC have used Azure Cognitive Services to embed services like realtime translation, facial recognition, and natural language understanding to create robust and intelligent user experiences in their apps. While you’re there, you can take advantage of the $200 credit to start building your own intelligent applications when you open and Azure free account. That link again is aka.ms/cognitive. And now, on to the show.
TWIMLcon Day 6: Moving Faster with Data Science and Machine Learning Platforms
Today was the sixth day of TWIMLcon and the final day of presentations before we head into a full day of workshops and then a wrap-up unconference. Today we were fortunate to speak to folks from LinkedIn, Intuit, Cloudera, Yelp, Rakuten, Microsoft, Salesforce, and Fiddler. We covered a variety of subjects including:
The Future of Autonomous Systems with Gurdeep Pall (Transcript)
Sam Charrington: Hey Everyone! Last week was the first week of our TWIMLcon: AI Platforms conference, and what a great first week it was! Following three days of informative sessions and workshops, we concluded the week with our inaugural TWIMLcon Executive Summit, a packed day featuring insightful and inspiring sessions with leaders from companies like BP, Walmart, Accenture, Qualcomm, Orangtheory Fitness, Cruise, and many more. If you’re not attending the conference and would like a sense of what’s been happening, check out twimlcon.com/blog for our daily recaps, and consider joining us for week two! Before we jump into today’s interview, I’d like to say thanks to our friends at Microsoft for their continued support of the podcast and their sponsorship of this series! Microsoft’s mission is to empower every single person on the planet to achieve more. We’re excited to partner with them on this series of shows, in which we share experiences at the intersection of AI and innovation to inspire customers to reimagine their businesses and the world. Learn more at Microsoft.com/ai and Microsoft.com/innovation
TWIMLcon Day 4: Executive Summit
Friday’s TWIMLcon Executive Summit closed out a full first week at the conference! Speakers from BP, Walmart, Accenture, Qualcomm, Orangetheory Fitness, and more shared their experiences and insights on key issues faced by AI/ML leaders and teams.
TWIMLcon Day 3: How to Get Models from Development to Production Consistently and Predictably
Day 3 of TWIMLcon 2012: AI Platforms was all about how to get models from development to production reliably and consistently by using modern MLOps tools and platforms.
TWIMLcon Day 2: The Secret Life of Production ML Systems
Day 2 (of 8!) of TWIMLcon: AI Platforms 2021 was a day of sharing hard-earned lessons.
TWIMLcon Day 1: If You’re Serious About Your Data, Invest in Your Platforms
We had a solid kick-off today at TWIMLcon 2021: AI Platforms. The conference started today and runs through January 29, 2021. It’s not too late to join in! Use discount code GREATCONTENT for 25% off registration.
Podcast Series to Binge!
We hope you’re taking some time to relax over the holidays and want to make sure you have plenty of content for when you’re in the mood for a technical binge!
2020 Reflections
One of the silver linings to an otherwise crazy year was that I spent a lot of time at a desk that would have otherwise been spent in planes, trains, and automobiles. As a result, Team TWIML got quite a lot done this year!
Causality & Probabilistic Modeling in Machine Learning Webinar
Causality and causal modeling is one of the hottest topics in machine learning. Earlier this year we launched a new causality course and study group with instructor Robert Osazuwa Ness, which received great feedback from students:
The Rise of the Model-Driven Enterprise and the Importance of ML/AI Platforms
In spite of all the hype, the reality is that the typical enterprise doesn’t need to do anything different to benefit from AI. They won’t need to hire data scientists, or collect any training data, or build any machine learning or deep learning models.
The TWIML AI Gift Guide!
Running out of gift ideas and need a little inspiration? The TWIML team has you covered! We put together a Holiday Gift Guide featuring some of our favorite AI-enabled products. It’s probably no surprise if you listen to the podcast, but AI has found its way into a bunch of different areas. This is just a small sampling of some of the nifty gadgets and services that caught our attention this holiday season. Surprise the AI enthusiast (or non-enthusiast) in your life with:
2021 Agenda Updates!
We have a TON of practical presentations coming your way from companies like Google, Spotify, and Intuit - and we’re so excited about these keynotes we just announced!
Webinar: Feature Stores for Accelerating AI Development
In this webinar on Feature Stores for Accelerating AI Development we're joined by leaders from Tecton, Gojek, and Preset to discuss how organizations can increase value and decrease time-to-market for machine learning using feature stores, MLOps, and open source.
Spatial Analysis for Real-Time Video Processing with Adina Trufinescu - Transcript
Sam Charrington: Hey, what’s up everyone!
How Deep Learning has Revolutionized OCR with Cha Zhang - Transcript
Sam Charrington: Hey, what’s up everyone! We are just a week away from kicking off TWIMLfest, and I’m super excited to share a rundown of what we’ve got in store for week 1. On deck are the Codenames Bot Competition kickoff, an Accessibility and Computer Vision panel, the first of our Wellness Wednesdays sessions featuring meditation and yoga, as well as the first block of our Unconference Sessions proposed and delivered by folks like you. The leaderboard currently includes sessions on Sampling vs Profiling for Data Logging, Deep Learning for Time Series in Industry, and Machine Learning for Sustainable Agriculture. You can check out and vote on the current proposals or submit your own by visiting https://twimlai.com/twimlfest/vote/. And of course, we’ll have a couple of amazing keynote interviews that we’ll be unveiling shortly! As if great content isn’t reason enough to get registered for TWIMLcon, by popular demand we are extending our TWIMLfest SWAG BAG giveaway by just a few more days! Everyone who registers for TWIMLfest between now and Wednesday October 7th, will be automatically entered into a drawing for one of five TWIMLfest SWAG BAGs, including a mug, t-shirt, and stickers. Registration and all the action takes place at twimlfest.com, so if you have not registered yet, be sure to jump over and do it now! We’ll wait here for you.
Causal Modeling in Machine Learning Webinar
Causality and causal modeling is one of the hottest topics in machine learning. Earlier this year we launched a new causality course and study group with instructor Robert Osazuwa Ness, which received great feedback from students:
Machine Learning Systems for Leaders & Innovators Webinar
We're collaborating with machine learning practitioner and leader Louis Dorard to bring his course and study group, ML Systems for Leaders & Innovators, to the TWIML Community. If you’re a machine learning manager or decision-maker or aspire to be one, please check out our recent webinar above to learn more about how Louis can help you take your career to the next level.
TWIML Watch Party/AMA with Dillon Erb of Paperspace
Today, we premiere my conversation with Dillon Erb, Co-founder and CEO of Paperspace on the latest TWIML Watch Party! Dillon and I will be in the chat answering all of your questions and sharing our reflections on the interview.
TWIML Watch Party/AMA - Machine Learning as a Software Engineering Discipline with Dillon Erb
Join us on Thursday, August 27th as we premiere my conversation with Dillon Erb, Co-founder and CEO of Paperspace on the latest TWIML Watch Party! Dillon and I will be in the chat answering all of your questions and sharing our reflections on the interview. In our conversation, Dillon and I explore the relationship (differences and overlap) between traditional software development principles and MLOps. Dillon and I will be in the chat answering all of your questions about:
Deploying ML Models with Amazon SageMaker Webinar
We're collaborating with machine learning practitioner and instructor Luigi Patruno to bring his course and study group, Building, Deploying, and Monitoring Machine Learning Models with Amazon SageMaker, to the TWIML Community. Please visit our Building, Deploying, and Monitoring Machine Learning Models with Amazon SageMaker course page to learn more about the course, peruse FAQs, and enroll.
2020 Model Explainability Forum
The use of machine learning in business, government, and other settings that require users to understand the model’s predictions has exploded in recent years. This growth, combined with the increased popularity of opaque ML models like deep learning, has led to the development of a thriving field of model explainability research and practice.
Live Viewing Party: Quantum Machine Learning: The Next Frontier?
Join us at 3pm ET / 12pm PT on Monday, August 3rd for another live TWIML watch party and AMA session featuring Iordanis Kerenidis, Research Director at CNRS Paris and Head of Quantum Algorithms at QC Ware. In our interview, we explored questions like:
The Great ML Language (Un)Debate
Use Python? We need your input! Take our survey to help us better understand the Python data science ecosystem. It only takes 3 minutes and as a token of our appreciation, we'll be selecting three lucky winners to receive a $25 Amazon gift card.
Building, Deploying, and Monitoring Machine Learning Models with Amazon SageMaker Webinar
We're collaborating with machine learning practitioner and instructor Luigi Patruno to bring his course and study group, Building, Deploying, and Monitoring Machine Learning Models with Amazon SageMaker, to the TWIML Community.
Bias in AI: Taking the Broad View
The issue of bias in AI was the subject of much discussion in the AI community last week. The publication of PULSE, a machine learning model by Duke University researchers, sparked a great deal of it. PULSE proposes a new approach to the image super-resolution problem, i.e. generating a faithful higher-resolution version of a low-resolution image.
Last Chance to Register: Causal Modeling in ML course
Reminder: Today is the last day to enroll in Causal Modeling in Machine Learning. Over the past few years, causality and causal modeling has become a very hot topic in machine learning.
Register Now: Causal Modeling in Machine Learning Webinar (June 25th at 10am PST / 1pm EST)
Causality and causal modeling is one of the hottest topics in machine learning. Earlier this year we launched a new causality course and study group with instructor Robert Osazuwa Ness, which received great feedback from students:
#AmplifyBlackSTEM - A TWIML Playlist
In solidarity with #AmplifyBlackSTEM, we’ve put together a playlist to do just that. Over the last four years, we’ve had the privilege of speaking with some of the best in both industry and academia. We’re excited today to highlight their immense talent and hard work.
ML Pulse 2020: ML Development, Deployment, and Operations Survey
Thank you for your interest in TWIML's ML Pulse 2020: ML Development, Deployment and Operations Survey. The data collection phase of the survey is now complete, and we are deep in the data analysis process. The resulting report will be available soon, and will cover:
Ethics, Bias, and Fairness in AI - A TWIML Playlist
In a message last week, I addressed the recent death of George Floyd, the protests, and the future we are working towards. While we all have a responsibility to engage in the fight against racism, the ML/AI community has a unique responsibility to ensure that the technologies we produce are fair and responsible and don’t reinforce racial and socioeconomic biases.
On George Floyd, Empathy, and the Road Ahead
What a week. Those of you who follow me on Twitter may have seen that, a week ago today, I was expressing my anger over the behavior of Amy Cooper, a white woman who, after being asked to leash her dog in an area of Central Park where this is required, proceeded to call the police on the Black man, Christian Cooper who simply asked her to obey the law. All of this was caught on video, and as a Black man it really made my blood boil because it was obvious to me, and many others in fact, white and black, that her 911 call was a threat of violence, a call to authorities who would likely hear her tone and his description and respond brashly and harshly.
Mitigating Discrimination and Bias with Ai Fairness-360 - Democast 5
The Democast is BACK! This month, we had the pleasure of chatting with Karthi Natesan Ramamurthy, a research staff member at the IBM TJ Watson Research Center, and one of the architects of today’s demo topic, IBM’s AI Fairness 360 Toolkit. We had the opportunity to get an early look at 360 leading up to, and during, TWIMLcon: AI Platforms last year, where Trisha Mahoney presented on the topic. You can find our conversation with Trisha here, and for her full presentation, you can purchase the TWIMLcon video pass here.
Secure and Private Deep Learning with Pysyft - Democast 4
Welcome back, friends! This month, we had the pleasure of sitting down with Andrew Trask, PhD Student at the University of Oxford, and leader of the OpenMined community, for our latest installment of TWIML Democast. Some of you might remember Andrew from the podcast, where he joined Sam in episode #241 to discuss Privacy-Preserving Decentralized Data Science, a great precursor for the video you’re about to watch. OpenMined is focused on “making the world more privacy-preserving by lowering the barrier-to-entry to private AI technologies.” Since our initial conversation with Andrew, the OpenMined community has exploded, with now over 7000 members on Slack, and a recently introduced research arm, OpenMined Research.
Epipolar Geometry to Improve Neural Rendering in Scene Representation
To hear about Josh Tobin’s various projects in robot learning, check out the full interview! ### The discussion on projects outside of the NeurIPS paper picks up at 15:58 in the podcast. Enjoy!
Dex-Net and the Third Wave of Robot Learning
_Ken Goldberg is involved in several projects in collaboration with multiple organizations at UC Berkeley, including some technology-based art projects. To hear about all of them, check out the recent TWIML AI Podcast interview, The Third Wave of Robotic Learning with Ken Goldberg._
ViLBERT: Bridging Visual and Linguistic Inputs to Improve Interaction between Humans and Machines
_Stefan Lee is involved in a number of projects around emergent communication._ _To hear about more of them, in addition to the ViLBERT model, check out the full interview! TWiML Talk #358 with Stefan Lee._
COVID-19 Update from Team TWIML
Unless you’re Jared Leto, or a contestant on Big Brother Germany, you’ve undoubtedly seen your world shift dramatically over the last few weeks. For most of us, this is uncharted territory and unfortunately, we’re not quite able to see the finish line just yet.
SLIDE: The Algorithmic Alternative that Outperforms GPUs
For more on the SLIDE algorithm check out the podcast that inspired this article—\[TWIML AI Podcast #355 with Beidi Chen\]. ### The conversation goes deeper into the team’s work on locality-sensitive hashing.
Stabilizing Off-Policy Reinforcement Learning
For more on the latest advancements in reinforcement learning, check out the podcast that inspired this article—\[TWIML AI Podcast #355 with Sergey Levine\]. In addition to discussing his team’s work on off-policy reinforcement learning, he also updates us on their efforts in model-based and causal RL.
Managing Real Life Data Science Projects with Metaflow
Last November, Sam had the pleasure of sitting down for a conversation with Ville Tuulos, Manager of Machine Learning Infrastructure at Netflix. They spoke just hours after he announced the open-sourcing of their Python library, Metaflow. At the time, we’d planned to launch a new video series called TWIML Democasts as a way to walk through concrete technologies in a visual way that the podcast format doesn’t allow.
AI Enterprise Workflow Study Group Recap – Course 1
Recently we kicked off a study group to work through the AI Enterprise Workflow sequence of courses created by IBM and on Coursera. Each of the courses in the six course series is two weeks long, and we just completed the second week of the first course. In this post we’ll recap some of the key concepts we discussed in the study group and share various resources including the videos and slides form the sessions.
TWIML on TR’s Breakthrough Technologies
Recently, the fine folks at Technology Review released their annual list of “Breakthrough Technologies,” highlighting 10 areas of tech they see having a real impact on the world in the near future.
Optimizing 5G at Qualcomm with Joseph Soriaga
Getting to Know Joseph Soriaga Before the deep learning revolution began, Joseph was studying computing information theory, working to use belief propagation to achieve Shannon capacity in wireless networks, which basically means he was trying to optimize the way information was transmitted and received.
Automated Machine Learning with Erez Barak (Transcript)
Sam Charrington: Hey, what's up everyone? This is Sam. A quick reminder that we've got a bunch of newly formed or forming study groups, including groups focused on Kaggle competitions and the fast.ai NLP and Deep Learning for Coders part one courses. It's not too late to join us, which you can do by visiting twimlai.com/community. Also, this week I'm at re:Invent and next week I'll be at NeurIPS. If you're at either event, please reach out. I'd love to connect.
Responsible AI in Practice with Sarah Bird (Transcript)
Sam Charrington: Hey, what's up everyone? This is Sam. A quick reminder that we've got a bunch of newly formed or forming study groups, including groups focused on Kaggle competitions and the fast.ai NLP and Deep Learning for Coders part one courses. It's not too late to join us, which you can do by visiting twimlai.com/community. Also, this week I'm at re:Invent and next week I'll be at NeurIPS. If you're at either event, please reach out. I'd love to connect.
Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards (Transcript)
Sam Charrington: Hey, what's up everyone? This is Sam. A quick reminder that we've got a bunch of newly formed or forming study groups, including groups focused on Kaggle competitions and the fast.ai NLP and Deep Learning for Coders part one courses. It's not too late to join us, which you can do by visiting twimlai.com/community. Also, this week I'm at re:Invent and next week I'll be at NeurIPS. If you're at either event, please reach out. I'd love to connect. All right. This week on the podcast, I'm excited to share a series of shows recorded in Orlando during the Microsoft Ignite conference. Before we jump in, I'd like to thank Microsoft for their support of the show and their sponsorship of this series. Thanks to decades of breakthrough research and technology, Microsoft is making AI real for businesses with Azure AI, a set of services that span vision, speech, language processing, custom machine learning, and more. Millions of developers and data scientists around the world are using Azure AI to build innovative applications and machine learning models for their organizations, including 85% of the Fortune 100. Microsoft customers like Spotify, Lexmark, and Airbus, choose Azure AI because of its proven enterprise grade capabilities and innovations, wide range of developer tools and services and trusted approach.
Live from #TWIMLcon 2019: Community Hall Shorts, Part 3
On the heels of TWIMLcon, we bring you the final edition of #TWIMLcon Shorts. During and after day 2 of TWIMLcon, we were joined by a few of the awesome sponsors, attendees, and even some former podcast guests, to chat about what they are working on, their favorite TWIML podcast episode, parting thoughts on #TWIMLcon and more!
Live from #TWIMLcon 2019: Community Hall Shorts, Part 2
Continuing the live interviews from #TWIMLcon! Last night we sat down with a few of the awesome #TWIMLcon speakers, sponsors, and attendees to chat about what they are working on, their favorite TWIML podcast episode, the best #TWIMLcon session so far and more!
Live from #TWIMLcon 2019: Community Hall Shorts
Day 1 #TWIMLcon! We sat down with a few of the awesome #TWIMLcon speakers, sponsors, and attendees to discuss their favorite TWIML podcast episode, the best #TWIMLcon moment so far and their thoughts on the evolution of machine learning and AI platforms - check'em out!
TWIMLcon in the News
Over the past couple weeks I got to sit on the other side of the (proverbial) interview table and take part in a few fantastic podcasts and video conversations about the state of machine learning in the enterprise. We also cover current trends in AI, and some of the exciting plans we have in store for TWIMLcon: AI Platforms. Each of these chats has its own unique flavor and I’m excited to share them with you.
#TWIMLcon Shorts: Rosie Pongracz & Trisha Mahoney, IBM
Another #TWIMLcon short with the wonderful Rosie Pongracz and Trisha Mahoney, from a Founding sponsor who you all know, IBM. Rosie is the World Wide Director of Technical Go-to-Market and Evangelism for Data Science and Trisha is a Senior Tech Evangelist. We chat about the latest IBM research, projects, and products, including AI Fairness 360, which will be the focus of Tricia’s session at TWIMLcon. The IBM booth also promises to bring the heat, with a variety of open source projects and resources for the data science community. See you there!
#TWIMLcon Shorts: Scott Clark, SigOpt
I had the chance to sit down with Scott Clark, Founder & CEO of SigOpt, a Founding sponsor of the upcoming TWIMLcon: AI Platforms! Scott discusses what SigOpt has been up to, the unique value he sees #TWIMLcon bringing to the ML/AI industry and what you can expect from the expert-driven SigOpt session and booth!
#TWIMLcon Shorts: Luke Marsden, Dotscience
Welcome to #TWIMLcon Shorts - a series where I sit down with some of our awesome Founding Sponsors and talk about their ML/AI journey, current work in the field and what we can expect from them at TWIMLcon: AI Platforms!
Ebook: The Definitive Guide to Machine Learning Platforms
Drum roll please...I’m excited to share our brand new ebook, The Definitive Guide to Machine Learning Platforms! This project has been a long time in the making and is the result of extensive research and interviews.
Scaling Machine Learning & AI in the Enterprise: More than meets the eye
Recently, with the publication of our ML Platforms ebook, and our conference on the topic quickly approaching, I've been talking a lot about scaling machine learning, deep learning and AI in the enterprise.
Beyond the POC. How to Make Machine Learning Real in the Enterprise
There's no doubt about it. Machine learning and AI are getting real in the enterprise. Many of the ML/AI leaders and practitioners I speak to report being in a similar place: With their initial POC projects maturing, and a few successes under their belts, their colleagues across the business finally get it. They're ready. They want in. Meaning, the various line-of-business leaders want to apply the power of machine learning to their own businesses.
TWIMLcon: AI Platforms 2019: Topics You Won’t Want To Miss!
We’ve got an amazing TWIMLcon agenda taking shape for you! We’ll be announcing speakers and more agenda details shortly, but in the meantime I wanted to share a preview of some of the keynote interviews we’ve got planned. I’m super pumped about them!
Spotlight on Innovation at Siemens
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.
Microsoft open sources Bing vector search and more from TWIML & AI
Bits & Bytes ### Microsoft open sources Bing vector search.
Happy 3rd Birthday TWIML!!!
Today is a very special day! We're celebrating the third (!!!) birthday of your favorite ML and AI podcast!
Facebook research creates controllable game characters from videos and more from TWIML & AI
Bits & Bytes ### Google releases MorphNet as open source.
Google scraps controversial AI ethics council days after it was announced and more from TWIML & AI
Bits & Bytes ### Google scraps controversial AI ethics council days after it was announced.
AWS offers Nvidia’s Tesla T4 chip and more from TWIML & AI
Bits & Bytes ### AWS offers Nvidia’s Tesla T4 chip for AI inference.
TensorFlow Edge Kit Giveaway!
In conjunction with the TensorFlow 2.0 alpha release, and our TensorFlow Dev Summit series, we invite you to enter our TensorFlow Edge Kit Giveaway. Winners will receive a gift box from Google that includes some fun toys including the new Coral Edge TPU device and the SparkFun Edge development board powered by TensorFlow.
Google announces TensorFlow 2.0 Alpha and more from TWIML & AI
Bits & Bytes ### Google announces TensorFlow 2.0 Alpha, TensorFlow Federated, TensorFlow Privacy.
Win a Free Ticket to the AI Conference in New York!
Enter to win a FREE ticket to O’Reilly’s AI Conference in New York April 17-18th! Participate in one of the most highly renowned AI conferences of the year, joined by a number of industry leaders and speakers such as Matt Zeiler, Christopher Ré, and Lise Geetor.
Human-Centered Design with Mira Lane - Transcript
Sam Charrington: Today we're excited to present the final episode in our AI for the Benefit of Society series, in which we're joined by Mira Lane, Partner Director for Ethics and Society at Microsoft. Mira and I focus our conversation on the role of culture and human-centered design in AI. We discuss how Mira defines human-centered design, its connections to culture and responsible innovation, and how these ideas can be scalability implemented across large engineering organization. Before diving in I'd like to thank Microsoft once again for their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with this intelligent technology to help solve previously intractable societal challenges spanning, sustainability, accessibility, and humanitarian action. Learn more about their plan at Microsoft.ai. Enjoy.
Fairness in Machine Learning with Hanna Wallach - Transcript
Sam Charrington: Today we're excited to continue the AI for the benefit of society series that we've partnered with Microsoft to bring to you. Today we're joined by Hanna Wallach principal researcher at Microsoft research. Hanna and I really dig into how bias and a lack of interpretability and transparency show up across machine learning. We discuss the role that human biases, even those that are inadvertent, play in tainting data, whether deployment of fair ML algorithms can actually be achieved in practice and much more. Along the way, Hannah points us to a ton of papers and resources to further explore the topic of fairness in ML. You'll definitely want to check out the show notes page for this episode, which you'll find at twimlai.com/talk/232. Before diving in I'd like to thank Microsoft for their support of the show and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with this intelligent technology to help solve previously intractable societal challenges, spanning sustainability, accessibility and humanitarian action. Learn more about their plan at Microsoft.ai. Enjoy.
IBM takes Watson AI to AWS, Google, Azure and more from TWIML & AI
Bits & Bytes ### IBM takes Watson AI to AWS, Google, Azure.
AI in Healthcare with Peter Lee - Transcript
Sam Charrington: Today we're excited to continue the AI for the Benefit of Society series that we've partnered with Microsoft to bring you. In this episode. We're joined by Peter Lee, Corporate Vice President at Microsoft Research responsible for the company's healthcare initiatives. Peter and I met a few months ago at the Microsoft ignite conference where he gave me some really interesting takes on AI development in China. We reference those in the conversation and you can find more on that topic in the show notes. This conversation centers on three impact areas that Peter sees for AI and healthcare, namely diagnostics and therapeutics, tools and the future of precision medicine. We dig into some examples in each area and Peter details the realities of applying machine learning and some of the impediments to rapid scale. Before diving in I'd like to thank Microsoft for their support of the show and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with this intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more about their plan at Microsoft.ai. Enjoy.
Studying Language Evolution With Reinforcement Learning and more from TWIML & AI
Bits & Bytes ### Facebook and Google researchers build RL framework to study language evolution.
AI for Accessibility with Wendy Chisholm - Transcript
Talk 227 – AI for Accessibility Interview
AI for Humanitarian Action with Justin Spelhaug - Transcript
Talk 226 – MSFT AI for Humanitarian Action Interview Transcript
Qualcomm shows off AI-equipped car at CES 2019 and more from TWIML & AI
Bits & Bytes ### Google introduces Feast: an open source feature store for ML.
Microsoft leads the AI Patent Race and more from TWIML & AI
Bits & Bytes ### Microsoft leads the AI patent race.
New Year! New Tech!
Happy New Year! I've spent the week at CES in Las Vegas this week, checking out a bunch of exciting new technology. (And a bunch of not-so-exciting technology as well.)
Happy Holidays & New Year! - The Final Bits & Bytes of 2018
Bits & Bytes ### IBM, Nvidia pair up on AI-optimized converged storage system.
It's a Wrap! The AWS re:Invent Recap
A couple of weeks ago I spent the week in Las Vegas at the Amazon Web Services (AWS) re:Invent conference, and we shared a few of my interviews from the event in our AWS re:Invent Series.
Thanks + Meet me at re:Invent, NeurIPS & Kubecon
On Friday we published episode number 200 of the TWIML Talk podcast. That achievement, along with the fact that tomorrow is Thanksgiving here in the U.S., seems like a worthy prompt for a bit of, well, giving thanks.
Building Platforms for ML & AI
"AI Platforms Month" continues on the podcast with our in-depth coverage of scaling machine learning in the enterprise. I'm super-excited about what we've done here, and I really hope you're enjoying it!
AI Platform Series and eBook Announcement
This week we kicked off our AI Platforms podcast series with interviews with Facebook's Aditya Kalro and Airbnb's Atul Kale. We also announced our upcoming eBooks on the same topic.
Announcing the TWIML AI Platforms podcast series and eBooks
One of the exciting aspects of my day-to-day work involves understanding the way large companies are adopting machine learning, deep learning, and AI. I do quite a bit of this via interviews, and I’m excited to be able to bring you along for the ride by publishing many of them as podcasts.
TWIML & AI x Fast.ai Deep Learning Study Group – v3 Session 1 – October 27, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
TWIML & AI x Fast.ai Machine Learning Study Group – Session 4 – October 28, 2018
This video is a recap of our Fast.ai x TWIML Online Machine Learning Study Group.
Learning Active Learning from Data – Meetup #13 – October 2018
This video is a recap of our October 2018 Americas TWIML Online Meetup.
TWIML & AI x Fast.ai Deep Learning Study Group – Session 3B – October 20, 2018
This video is a recap of our Fast.ai x TWIML Online Deep Learning Study Group.
TWIML & AI x Fast.ai Machine Learning Study Group – Session 3 – October 21, 2018
This video is a recap of our Fast.ai x TWIML Online Machine Learning Study Group.
TWIML & AI x Fast.ai Deep Learning Study Group – Session 3A – October 13, 2018
This video is a recap of our Fast.ai x TWIML Online Deep Learning Study Group.
TWIML & AI x Fast.ai Machine Learning Study Group – Session 1 – October 7, 2018
This video is a recap of our Fast.ai x TWIML Online Machine Learning Study Group.
TWIML & AI x Fast.ai Machine Learning Study Group – Session 2 – October 14, 2018
This video is a recap of our Fast.ai x TWIML Online Machine Learning Study Group.
Thoughts on PyTorch and the Growing PyTorch Ecosystem
Last week Facebook convened their inaugural PyTorch Developer Conference. Highlights of the conference included the release of PyTorch 1.0 beta and a host of ecosystem vendors announcing their support for the framework.
Relevancy and Redundancy of Features - EMEA Meetup #2 - October 2018
This video is a recap of our October 2018 EMEA TWIML Online Meetup.
TWIML & AI x Fast.ai Fall Study Group - Session 2b - October 6, 2018
This video is a recap of our Fall Fast.ai x TWIML Online Deep Learning Study Group.
TWIML & AI x Fast.ai Fall Study Group - Session 1a - September 15, 2018
In this recap of our first session of the fall Fast.ai Deep Learning course study group. This week we review lesson one of the course, Recognizing Cats and Dogs.
TWIML & AI x Fast.ai Fall Study Group - Session 2a - September 29, 2018
This video is a recap of our Fall Fast.ai x TWIML Online Deep Learning Study Group.
On AI in China, with Microsoft Research's Peter Lee
Last week I attended the Microsoft Ignite conference in Orlando.
New Transcription Insights
Earlier this summer I posted about my daughter Malaika's internship with TWIML, exploring the use of machine learning services to transcribe the podcast. She's off enjoying her freshman year at college now, but she posted an update on her project a few weeks ago that I thought I'd share here. If you're interested in the state of cognitive services like the Google Speech-to-Text API, you'll undoubtedly find some of her observations interesting.
DeepMimic: Example-Guided Deep Reinforcement Learning – Meetup #12 – September 2018
This video is a recap of our September 2018 Americas TWIML Online Meetup.
Exploring Superintelligence with Nick Bostrom
This week on the podcast we published my discussion with Oxford’s Nick Bostrom about his work in the growing field of AI safety and ethics. Bostrom heads up the Future of Humanity Institute, a multidisciplinary institute focused on answering big-picture questions for humanity with regards to the safe and ethical development and use of AI. He is, of course, also well-known as the author of the book Superintelligence: Paths, Dangers, Strategies.
Capsule Networks - EMEA Meetup #1 - September 2018
This video is a recap of our September 2018 EMEA TWIML Online Meetup.
TWIML & AI x Fast.ai Study Group – Session 5 Part 2 – July 21, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
TWIML & AI x Fast.ai Study Group – Session 6 Part 1 – July 28, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
TWIML & AI x Fast.ai Study Group - Session 6 Part 2 - August 4, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
TWIML & AI x Fast.ai Study Group - Session 7 Part 1 - August 11, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
TWIML & AI x Fast.ai Study Group - Session 7 Part 2 - August 18, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
Bits & Bytes - 8.16.18
Bits & Bytes ### IBM Research presents 'DeepLocker,' AI-powered malware.
TWIML Online Meetup Goes Global
I didn't realize it until sitting down to write this, but today marks the one-year anniversary of the TWIML Online Meetup!
Bits & Bytes - 8.3.18
Bits & Bytes Google announced a bunch of interesting ML/AI-related news at last week’s Next conference. Here are the highlights, along with a few other tidbits.
When Frontier Technologies Collide
A few weeks ago I had the opportunity to speak at the inaugural FWD:DFW conference sponsored by Capital One Finance, the bank’s Plano-based auto finance arm. The event, which attracted speakers from around the country and across Capital One and attendees from throughout the Dallas-Fort Worth region, was focused on data-driven innovation.
Bits & Bytes - 7.27.18
Bits & Bytes ### Elon Musk, DeepMind co-founders promise never to make killer robots.
Quantum Machine Learning – Meetup #10 – July 2018
This video is a recap of our July 2018 TWIML Online Meetup.
TWIML & AI x Fast.ai Study Group – Session 4 Part 2 – July 7, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
TWIML & AI x Fast.ai Study Group – Session 5 Part 1 – July 14, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
Special Guest Post: Comparing Machine Transcription Services
It's been an exciting summer on many fronts here at TWIML HQ. One of the things I'm most pumped up about is the work of our first intern, Malaika, a recent high school graduate (and my daughter ????). Malaika wrote a blog post introducing herself and her project, and just published her first project update. I'm including her latest here as a guest post.
TWIML & AI x Fast.ai Study Group – Session 3 – June 23, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
TWIML & AI x Fast.ai Study Group – Session 4 Part 1 – June 30, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
Nominate TWIML for the People's Choice Podcast Awards
Hey everybody! We’ve got some great news to share and also favor to ask! We’re in the running for this year’s People's Choice podcast awards, in both the People’s Choice and Technology categories and we’d really appreciate your support!
Bits & Bytes - 6.21.18
Bits & Bytes ### DeepMind AI needs just a few images to construct a 3D model.
Coming soon: Your next job in ML & AI
One of the pleasures of my frequent travel is the opportunity to meet with TWIML listeners at conferences and in their home cities. During one such informal meetup last year I sat at a sidewalk cafe in Toronto sipping a coffee and chatting with a listener about his search for his next data science job. As we talked it occurred to me that many of the listeners I’ve met with have mentioned either looking for their next gig or recently switching jobs. Apparently, this isn’t just the folks I hang out with; a recent survey showed that nearly a quarter of data scientists changed jobs in 2017!
Cardiologist-Level Arrhythmia Detection w/ Convolutional Neural Networks - Meetup #9 - June 2018
This video is a recap of our June 2018 TWIML Online Meetup.
TWIML & AI x Fast.ai Study Group – Session 2 – June 16, 2018
This video is a recap of our Fast.ai x TWIML Online Study Group.
TWIML Online Meetup - Fast.ai Session 1 - June 9, 2018
In this recap of our second session of the Fast.ai Deep Learning course study group. This week we review lesson one of the course, Recognizing Cats and Dogs.
3 Things we get wrong about AI
Much of my work involves acting as a babel fish) of sorts, translating between worlds unable to easily understand one another. This often manifests itself as helping business leaders understand technology and technologists, but it goes the other way as well, and there are a good number of other communities that I'm often called on to help connect.
Fast.ai Study Group – Session 0 – June 2, 2018
In this recap of our first session of the Fast.ai Deep Learning course study group, we review our timeline for the course, and have an extensive Q&A session.
UPDATED FOR 2019! Learn Machine Learning & Deep Learning with us: TWIML x Fast.ai Study Group
TWIML x Fast.ai Practical Deep Learning for Coders Study Groups ### NEW: June – November ’19: Stanford CS224n
Bits & Bytes - 5.24.18
Bits and Bytes This week news from Google I/O and Microsoft Build have dominated the news. Here are the highlights:
Happy 2nd Birthday!
Hi everyone! We're super excited to share the second anniversary of This Week in Machine Learning & AI with all of you. What a fun ride! With your help, we’ve hit a bunch of exciting milestones over the past year. With over 2 million plays and 1000s of likes and shares, we’re reaching and teaching more people than ever before about machine learning and artificial intelligence.
YOLO9000: Better, Stronger, Faster - TWIML Online Meetup #8 - May 2018
In this month's community segment we chatted about the recent demo of Google’s Duplex, a automated appointment making chatbot. We talk about the implications of bots mimicking humans, if this tech qualifies as worthy of a passing Turing test grade and more. Community member @_NicT_ also shares a medium post he wrote, inspired by our recent Differential Privacy series.
Bits & Bytes - 5.17.18
Bits and Bytes This week news from Google I/O and Microsoft Build have dominated the news. Here are the highlights:
Exploring Machine Learning Privacy
We recently ran a series of shows on differential privacy on the podcast. It’s an especially salient topic given the rollout of the EU’s General Data Protection Regulation (GDPR), which becomes effective this month, not to mention scandals like the Facebook/Cambridge Analytica breach and other attacks on private data.
Bits & Bytes - 5.11.18
Bits and Bytes Forgive the Facebook news bias here. There were a few interesting announcements from their F8 developer conference last week.
Bits & Bytes - 5.4.18
Bits & Bytes ### Gartner pins “global AI business value” at $1.2 billion in 2018.
Lessons Learned from the TWIML AI Summit
I’m still a bit high off of the energy from this week’s TWIML AI Summit at the Interop ITX conference. What a great event!
Trust in AI - TWIML Online Meetup #7 - April 2018
In this month's community segment we chatted about explainability, Carlos Guestrin’s LIME paper, Europe’s attempt to ban “untrustworthy” AI systems and finally, Community member Nicolas Teague shares a blog post he wrote entitled "A Sight for Obscured Eye, Adversary, Optics, and Illusions,” which explores the parallels between computer vision adversarial examples & human vision optical illusions.
Are Datasheets the answer to AI bias?
In my recent podcast with Facebook AI research scientist Moustapha Cissé, Cissé shared the insightful quote, “you are what you eat and right now we feed our models junk food.” Well, just like you can’t eat better if you don’t know what‘s in your food, you can’t train less biased models if you don’t know what’s in your training data. That’s why the recent paper, _Datasheets for Datasets_, by Timnit Gebru (see her TWIML podcast and meetup) and her co-authors from Microsoft Research and elsewhere is so interesting. In this paper, Timnit and company propose the equivalent of food nutrition labeling for datasets.
Bits & Bytes - 4.19.18
Bits & Bytes ### Google develops AI that can pick out voices in a crowd.
Top Updates from this year's Nvidia GTC Conference
!Sam at GTCMy travel comes in waves centered around the spring and fall conference seasons. A couple of weeks ago, in spite of there being no signs of a true springtime here in St. Louis, things shifted into high gear with me attending the Scaled ML conference at Stanford and Nvidia GTC over the course of a few days. Following me on Twitter is the best way to stay on top of the action as it happens, but for those who missed my live-tweeting, I thought I’d reflect a bit on Nvidia and GTC. (You’ll need to check out my #scaledmlconf tweets for my fleeting thoughts on that one.)
Bits & Bytes - 4.4.18
Bits and Bytes Last week I attended the GTC - GPU Technology Conference in San Jose. NVIDIA made quite a few announcements so you’ll see quite a few of those in this week’s news run down.
Reproducibility Crisis in Data Science
Last week on the podcast I interviewed Clare Gollnick, CTO of Terbium Labs, on the reproducibility crisis in science and its implications for data scientists. We also got into an interesting conversation about the philosophy of data, a topic I hadn’t previously thought much about. The interview seemed to really resonate with listeners, judging by the number of comments we’ve received via the show notes page and Twitter. I think there are several reasons for this.
Bits & Bytes - 3.28.18
Bits and Bytes ### Amazon text-to-speech service, Polly releases new Breath feature.
Leveling Up in Machine Learning and AI
I’ve mentioned my upcoming AI Summit event on the podcast recently. What I'm creating is an “executive AI boot camp” of sorts—a learning experience targeting IT, technology and (tech-savvy) business leaders who need to get smart on the broad spectrum of machine learning and AI opportunities in the enterprise. If this sounds like you, or someone you know or work with, I think you’ll find the event very interesting.
AI and the Future of Healthcare
Healthcare applications of machine learning and AI have been in the news a bit more than usual recently, concurrent with the recent HiMSS conference in Las Vegas. HiMSS is a 45,000+ attendee conference dedicated to healthcare IT. Surprising no one, AI was a major factor at this year’s event. There was a whole subconference focused on ML & AI, plus a ton of AI-focused sessions in the regular conference and a good number of announcements by industry leaders and startups alike.
Playing Atari with Deep Reinforcement Learning - TWIML Online Meetup #6 - March 2018
This video is a recap of our March 2018 TWIML Online Meetup.
I'm hosting an AI Summit & Boot Camp
If you’re like most line-of-business or IT leaders, you probably hear a lot about machine learning and AI. You know they’ll eventually have a huge impact on your company, your job, and even your personal life, but you’re not sure exactly what all the buzz is about.
Bits & Bytes - 3.15.18
Bits & Bytes ### Google’s AI is being used on US military drone footage.
What's Hot in AI: Deep Reinforcement Learning Edition
Deep reinforcement learning (DRL) is an exciting area of AI research, with potential applicability to a variety of problem areas. We’ve discussed DRL several times on the podcast to date and just this week took a deep dive into it during the TWIML Online Meetup. (_Shout out to everyone who attended!)_ Our presenter, Sean Devlin, did a great job explaining the major ideas underlying DRL. If this week’s newsletter inspires you to dig more deeply into how RL works, the meetup recording, which will be posted shortly, would be a good place to start.
Bits & Bytes - 3.9.18
Bits & Bytes ### Philips and Microsoft launch AI products for healthcare.
What Top Investors are asking about AI
I just got home last night from San Francisco, this time returning from the KeyBanc Capital Markets Emerging Technology Summit. As a participant in their MOSAIC Industry Leaders program, my role at the conference is to participate in one-on-one and group meetings with their institutional investor clients as a subject-matter expert on ML and AI.
Bits & Bytes - 2.21.18
Bits & Bytes ### Amazon to design its own AI chips for Alexa, Echo devices.
#MyAI, Your AI: Thoughts from our TWIML Community on Consumer AI
#MyAI, Your AI A few weeks back, following my visit to CES, I asked you to share your thoughts on AI in our personal lives. We've seen some insightful responses so far and, as the contest comes to an end, I thought I’d share some of them. As a reminder, we asked listeners to record short video responses to the questions: How has AI impacted your personal and home life? And how do you see it impacting you in the future?
Bits and Bytes - 2.8.18
Bits and Bytes ### Google used ML to help block 700K bad apps on the Play Store.
Thoughts on Home and Personal AI
I wrote about my key takeaways from CES in one of last month's newsletters. But something’s been gnawing at me ever since. Beyond the initial kid-in-a-candy-store feeling of exploring three giant buildings filled with new toys, I was left a bit disillusioned about the state of AI in our personal lives.
Google Cloud AutoML: What You Need to Know & Broader Implications
This week's main article is a bit longer than usual, but I hope you'll find it both interesting and thought provoking.
Using Deep Learning to Estimate Demographics - TWIML Online Meetup #5 - January 2017
This is a recap of the TWIML Online Meetup, held on Jan 16, 2018, we focus on the paper “Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States” by Microsoft Research post doctoral researcher Timnit Gebru. We recap some of the major ML/AI Resolutions for 2018, community predictions for 2018, our favorite TWIML podcast episodes of 2017 and more. Thanks again to our presenter Timnit Gebru! Make sure you Like this video, and Subscribe to our channel below!
Bits & Bytes - 1.18.18
Bits and Bytes ### Microsoft and Adaptive Biotechnologies want to decode the human immune system.
CES Lessons Learned
Last week I attended CES, the gigantic consumer electronics show in Vegas, for the first time. The conference attracts some 200,000 people and was, unsurprisingly, a bit of a mad house. But I was able to capture some solid interviews and see a lot of cool tech toys, so I left happy!
Bits & Bytes - 1.10.18
Bits & Bytes A few interesting ML and AI related tidbits from around the web over the past week or so:
Hot Topics in ML & AI in 2018!
Happy New Year! I hope you had a great one and that you’re as pumped about 2018 as I am. I’ve enjoyed the opportunity to relax a bit with family over the past few weeks, but it’s also been great to jump back into the podcast, my research and other projects!
Understanding Deep Learning Requires Rethinking Generalization - TWIML Online Meetup #4 - December 2017
In this recap of the TWIML Online Meetup, held on Dec 13, 2017, we focus on the paper "Understanding Deep Learning Requires Rethinking Generalization" by Google Brain researchers Chiyuan Zhang, Samy Bengio and others. We also recap some of the major ML and AI advancements of the year, and take a look ahead to some of the key trends we’ll be following in 2018, such as deep reinforcement learning, capsule and schema networks and more. Thanks again to our presenter Bruno Gonçalves! Make sure you Like this video, and Subscribe to our channel above!
1 Million Listens Giveaway!!
Recently we hit a very exciting milestone for the podcast: One Million Listens!!! What an amazing way to close out an amazing year for the podcast. We’d hate to miss an opportunity to show you some love, so we're holding another listener appreciation contest to celebrate the occasion. Tweet to us @twimlai using #TWIML1MIL to enter. Every entry gets a fly #TWIML1MIL sticker plus a chance to win one of 10 limited edition t-shirts commemorating the occasion. We’ll be giving away some other mystery prizes from the magic TWIML swag bag along the way, so you should definitely enter. If you’re not on twitter, or want more ways to enter, just look below for more chances to win!!!
Bits & Bytes - 11.22.17
Bits & Bytes ### Google’s new TensorFlow Lite targets mobile and embedded.
Learning Long-Term Dependencies with Gradient Descent is Difficult - TWIML Online Meetup #2 - September 2017
This is a recording of the TWIML Online Meetup group. This month we discuss the paper "Learning Long-Term Dependencies with Gradient Descent is Difficult" by Yoshua Bengio & company, one of the classic papers on Recurrent Neural Networks. Huge thank you to listener Nikola Kučerová for presenting. Make sure you Like this video, and subscribe to our channel!
September Meetup: Learning Long-Term Dependencies with Gradient Descent is Difficult
After a successful first run, the TWIML paper reading group is back! I'm excited to share the details of the upcoming TWIML & AI meetup! The focus of the meetup will be discussing academic papers and other texts in the machine learning and AI space, though I hope we get to see some implementation demos from time to time as well.
Learning From Simulated & Unsupervised Images through Adversarial Training - TWIML Online Meetup #1 - August 2017
This is a recap of the first monthly TWIML Online Meetup, held on Aug 16 2017. The focus of the meetup was the CVPR best-paper-award-winner "Learning From Simulated and and Unsupervised Images through Adversarial Training" by researchers from Apple (Link Below).
Bits & Bytes - 8.25.17
Bits & Bytes - Researchers from the University of Pennsylvania’s Institute for Biomedical Informatics benchmark 13 state of the art ML algorithms on 165 publicly available classification problems, and present the results in Data-Driven Advice for Applying Machine Learning to Bioinformatics Problems. In the end they learn what every Kaggler knows… Gradient Boosted Decision Trees work really well. - AI gets really interesting when we can write software that creates better models faster for us, which is why Neural Optimizer Search with Reinforcement Learning \[PDF\], a Google Brain paper presented at the recent ICML conference, is so interesting. The team used reinforcement learning to train an RNN to search for better ways to train small convolutional networks. - There’s been a bunch of chatter in the press of late about who’s winning the “AI war.” See America Can't Afford to Lose the Artificial Intelligence War, How Baidu Will Win China’s Ai Race—And, Maybe, The World’s, and China’s Plan for World Domination in AI Isn’t So Crazy After All for more. As I’ve said before, proficiency in AI will bode well for individuals and companies; the same holds true for nations. - Sony’s getting in on the deep learning framework action with its Neural Network Console, a tool for training, evaluating and designing neural nets. I’ve not played with it, but it’s got an interesting GUI for designing deep neural networks. - A follow-on to the Facebook AI language-creating-bots kerfuffle: Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog. - Do yourself a favor and follow @KaggleDatasets on Twitter. You don’t want to miss out on goodies like this 360k favicon dataset. - A new post on Apple’s ML research blog talks about how they’ve used Deep Learning to give Siri a more natural, smoother voice in iOS 10 and 11: Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis. - I’ve mentioned Fast.ai’s Deep Learning for Coders online course before, and they also offer an in-person version. Last week they announced the availability of a diversity scholarship to help address the diversity crisis in AI.
Bits & Bytes - 8.17.17
Bits & Bytes - AI luminary Andrew Ng launched a successor to his famous Stanford online machine learning course. The new course—actually a sequence of courses—is focused on deep learning and hosted over on Coursera. I’m planning to work through it. Let me know if you are too! - According to an SEC filing, Ng is also planning to launch a $150 million VC fund called AI Fund, L.P. It’ll be interesting to see the deals he funds via this fund. - An OpenAI bot trained using reinforcement learning beats professional Dota 2 video game players in 1v1 matches. There’s some discussion in the community as to whether the bot’s use of hardcoded rules and the Dota bot API take away from the victory. Also good discussion on the significance of the feat, .e.g. as compared to AlphaGo. - Google presented a paper at the recent KDD conference on Vizier, a system for hyperparameter optimization, i.e. model tuning. - A nice piece in the NY Times highlights the work of researchers from OpenAI, Berkeley and elsewhere on AI safety. To dig deeper into the topic, consider the materials cited in 80,000 hours’ AI safety syllabus. - MIT CSAIL researchers developed Pensieve, a system that uses reinforcement learning to optimize streaming video delivery and reduce buffering. Pensieve will be presented at next week’s SIGCOMM conference.
Bits & Bytes - 8.10.17
Bits & Bytes - Microsoft made headlines last week when it formally added AI to its corporate vision statement, dropping references to “mobile-first” "Our strategic vision is to compete and grow by building best-in-class platforms and productivity services for an intelligent cloud and an intelligent edge infused with AI." - BuzzFeed trains a random forest ML algorithm to identify instances of spy planes in flight tracking data in “BuzzFeed News Trained A Computer To Search For Hidden Spy Planes. This Is What We Found.” Very interesting read, despite the characteristically clickbaity title. - The FastText team at Facebook AI Research release a new set of pre-trained word vectors trained on Wikipedia, news and web crawl data. - Facebook also announced that they were transitioning entirely to neural networks for language translation, from a phrase-based statistical system. The new system uses sequence-to-sequence LSTMs with attention. Stay tuned for a deep dive into LSTMs coming later this month! - OpenAI released RL-Teacher, an open-source interface for training reinforcement learning based AIs via occasional human feedback rather than mathematically expressed reward functions. - As a New Yorker, I consider sarcasm to be somewhat of an art form. A group of researchers from MIT Media Lab and elsewhere published a paper on DeepMoji \[PDF\], a deep learning model that can detect sarcasm, sentiment, and emotion from emoji.
Bits & Bytes - 8.2.17
Bits & Bytes - A Facebook research program into multi-agent negotiations resulted in bots creating their own streamlined ways to communicate. The truth behind the ballyhoo about those language-inventing Facebook bots. - Meanwhile, Google DeepMind researchers are working on developing agents that imagine the future and plan their tasks in two new papers focused on “imagination-based planning.” - If all this freaks you out, researchers from Cornell, Univ. of Montreal and Univ. of Louisville published “Guidelines for Artificial Intelligence Containment,” in which they propose guidelines for helping AI researchers develop reliable sandboxing software for intelligent programs. - In January of this year, Google Cloud hired Fei-Fei Li, director of the Stanford AI & Vision labs, as its chief scientist for AI & ML. Dr. Li is perhaps best known for her role in creating the ImageNet dataset upon which much of the recent progress in object recognition has been based. A couple of months later, in March, at their Next conference, Google Cloud announced its acquisition of Kaggle. With this context, it’s not too surprising that Kaggle just announced that it’ll be hosting all 3 ImageNet challenges for the first time ever. - Fast.ai announced the availability of Part 2 of their highly regarded (and FREE) online course, Deep Learning for Coders. Topics include TensorFlow and style transfer, generative models and GANs, memory networks, attentional models and more. - Things continue to heat up in the AI startup ecosystem: Google announced Launchpad Studio, a new accelerator program for AI & ML startups, while autonomous vehicle startup Momenta closed a $46M series B financing, and AI-for-robots company Vicarious closed a $50M series C.
August Meetup: Learning from Simulated and Unsupervised Images through Adversarial Training
A number of you have expressed interest in participating in a TWIML paper reading group and I'm excited to share the details of the inaugural TWIML & AI meetup! The focus of the meetup will be discussing academic papers and other texts in the machine learning and AI space, though I hope we get to see some implementation demos from time to time as well.
Bits & Bytes - 7.27.17
Bits & Bytes - Apple launched their Machine Learning Journal site to publish their research. The first article is on a technique for improving the realism of fake images using a technique similar to GANs. - Google updated their 9 million large Open Images dataset to add some 2 million bounding boxes and several million more labels. - OpenAI published research on a new approach to reinforcement learning called Proximal Policy Optimization (PPO). PPO aims to outperform the current state-of-the-art methods while being simpler to implement. - In a paper published in Neuron30509-3?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627317305093%3Fshowall%3Dtrue), Google DeepMind founder Demis Hassabis and co-authors argue that understanding human intelligence is the key to creating artificial intelligence. - An interesting discussion of some ways technical debt is accumulatedin machine learning projects. - Harvard Business Review features a nice profile of Facebook’s Applied Machine Learning group. - The IEEE Computer Vision and Pattern Recognition (CVPR) conference just ended. Best Paper winners were Densely Connected Convolutional Networks and Learning from Simulated and Unsupervised Images through Adversarial Training. Perhaps someone reading this would like to present one of these papers at an upcoming meetup?
Bits & Bytes - 7.19.17
Bits & Bytes - More on Intel Xeon Scalable for AI by Intel’s Chief Data Scientist Bob Rogers. - What are some new and exciting areas in adversarial machine learning research? Google Brain research scientist and GAN pioneer Ian Goodfellow answered this question in a recent post on Quora. - Elon Musk is really freaking people out about AI. I’ve been asked about this several times this week. I personally think we’re far away from needing to worry about being subjugated by omnipotent AIs, and I’m not too worried about being just a bit in a simulation either. What do you think? - Google open sources Facets, a new visualization tool for ML training datasets. - Neat tutorial on how to “How to Visualize Your Recurrent Neural Network with Attention in Keras.”
Bits & Bytes - 7.12.17
Bits & Bytes - Interesting article in Science about explainability approaches for deep neural networks. Marco Ribeiro and Carlos Guestrin’s LIME is discussed—check out my interview with Carlos for more detail on that project. This recent article on interpreting neurons in an LSTM network is related, and also very interesting. - Impressive work by the EFF compiling a bunch of metrics of progress in ML/AI. Nice touch presenting it as a Jupyter notebook! - Researchers from Stanford and iRythm collaborate to develop 34-layer CNN that can detect arrhythmias in single-lead ECG better than a cardiologist. - Three new papers from Google DeepMind explore teaching complex movement to simulated humanoid forms. - Baidu releases Apollo—a comprehensive open source platform for self-driving cars. Is this the future Android OS for the autonomous, connected vehicle? They announced with a huge partner ecosystem, so it will be interesting to see where this goes.
O'Really? - O'Reilly AI Series and more TWIML News
As you may have heard on the podcast, I’m trying the newsletter thing again. I’m not sure what it’ll evolve into, but my goals are to make it personal, informative and brief/skimmable. I hope you’ll come along for the ride. As always, please let me know what you think!
TWIML Meetup Planning
\\\ UPDATE: Visit https://twimlai.com/meetup for information on the current meetup! \\\ Thanks for your interest in the paper reading group. I'm excited that so many folks have expressed interest. I'd like to use this page to share my thoughts on how this will be organized, the input I need from the community, and how you can help. Please submit your thoughts via the comments.
AI Networking Happy Hour
AI Networking Happy Hour Join us for a night of networking during the happiest hours of Thursday, 6/29/17 at The Ainsworth Midtown after the O'Reilly AI Conference.
Happy Birthday TWIML!!!
May 20th was the one year anniversary of This Week in Machine learning & AI. I started this podcast last year as a way to share what I was learning about the field, and to be quite honest, I really had no idea what I was getting into. But what an amazing ride it’s been. The amount of interest in the podcast has been incredible, and I could not be more appreciative of, or impressed with, the TWIML listener community. Thank you so much!!! We’re working really hard to be worthy of your attention by continuing to bring you amazing content you can’t find anyplace else.
The Future of Data Summit
In 1996, Bill Gates popularized the saying "content is king." Twenty years later it's data that's king, and those able to harness it for better insights, predictions and experiences are the new kingmakers. To help give you a view into the next twenty years of data, and how to take advantage of it today, I've partnered with the team at Interop ITX to create the _Future of Data Summit_.
Podcast Update: Focus on Interviews
If you’ve listened to the podcast before, you know that what I’ve tried to do each week is to collect and discuss the most interesting and important machine learning and AI news and research. I’ve really come to love the news style format, but it takes a ton of time to create. While that’s been manageable for a while, some things have shifted both at home and with work that have made that unworkable for the time being.
Intel Buys Nervana Systems to Break NVIDIA's Hold on Deep Learning Hardware
On the heels of last week’s $200 million acquisition by Apple of Turi, Intel announced on Tuesday yet another acquisition in the machine learning and AI space, this time with the $400 million acquisition of deep learning cloud startup Nervana Systems. This is another exciting acquisition; let’s take a minute to unpack it.
Another Huge ML Acquisition, AI in the Olympics + Win a Free Ticket to the O’Reilly AI Conference—TWIML 2016/08/12
This week we discuss Intel’s latest deep learning acquisition, AI in the Olympics, image completion with deep learning in TensorFlow, and how you can win a free ticket to the O’Reilly AI Conference in New York City, plus a bunch more.
Self-Driving Car Startup Comma.ai Releases Video and Sensor Dataset
Autonomous driving startup Comma.ai released a small dataset that lets you try your hand at building your own models for controlling a self-driving vehicle. The dataset consists 10 video clips recorded at 20 Hz from a camera mounted on the windshield of a 2016 Acura ILX.
Machine Learning Identifies Autism Genes, Weaponized AI for Phishing, and a Twitter Sarcasm Detector
We’ve talked fairly extensively about the use of Deep Learning in medicine in previous shows. Breast cancer and eye disease were a couple of the use cases we discussed, with both of these sharing the common feature that they’re based on image analysis. Well this week a team of researchers from Princeton University published a paper outlining their work applying machine learning to the challenge of identifying genetic causes of autism.
New TITAN X Benchmark, Plus What to Do When You Need More GPUs
I recently reported on the launch of the new NVIDIA TITAN X. At the time it wasn’t in the hands of any users so any thoughts on relative performance were either vendor provided or speculative. Well, a couple of researchers on the MXNet team were among the lucky folks that have their hands on the GPU at this point and they published an initial benchmark this week following the deepmark deep learning benchmarking protocol.
ML/AI Companies CognitiveScale, People.ai, Artisto on the Move
Startup CognitiveScale, which bills itself as “The Cognitive Cloud Company”, announce the close of $21.8M in series b financing from Norwest Ventures and Intel Capital. Intel’s been pretty busy investor in the ML space—you’ll recall we touched on their Itseez acquisition and Lumiata investment back in June.
Apple Acquires Machine Learning Startup Turi
News broke late last week of Apple’s acquisition of Seattle-based machine learning startup Turi, for a reported $200 million. Actually, I haven’t seen any definitive confirmation of the acquisition at the time of my initial research, but neither have there been any denials. You’ll recall we spoke about Turi just a few weeks ago, in the context of the Data Science Summit the company hosted in San Francisco, shortly after changing its name from Dato due to a legal dispute.
Cybersecurity, AI, and the Autonomous Hacker Bots Competing in DARPA's Grand Cyber Challenge
Each year, computer security conferences host a high tech version of the kids game “capture the flag,” so that teams of hackers and security researchers can demonstrate their hacking prowess. The game requires teams to secure a computer system by identifying intentional and unintentional vulnerabilities in various software modules while launching and defending against threats from competitive teams.
TWIML Newsletter #1
First off, thanks everyone for your interest in the podcast. If you haven’t listened to the latest show, it’s a bit different than the previous ones. It’s the first in a series of interviews with folks doing interesting things in the machine learning and AI arena. I hope you find it interesting!
New Layer Normalization Technique Speeds RNN Training
I want to talk about a paper published this week by some folks in Geoffrey Hinton’s group at the University of Toronto. You’ll recall I mentioned Hinton last time when discussing some of the seminal papers in deep learning for image recognition. The paper, by Jimmy Lei Ba, Jamie Ryan Kiros and Hinton, introduces a new technique for training deep networks called Layer Normalization.
Questionable Enterprise AI Adoption Data
A potentially interesting survey crossed the wires this week, and I while I’m bringing it up here, I do so with caveats, because the numbers seem a bit wonky. The survey, titled “Outlook on Artificial Intelligence in the Enterprise 2016” was published by Narrative Science, a “data storytelling” company that uses natural language generation to turn data into narratives. Narrative Science had help from the National Business Research Institute, a survey company that did the data collection for them.
NVIDIA’s New “Crazy, Reckless” GPU For Deep Learning
Last week, at a Machine Learning meetup at Stanford University, NVIDIA CEO Jen-Hsun Huang unveiled the company’s new flagship GPU, the NVIDIA TITAN X, and gifted the first device off of the assembly line to famed ML Researcher Andrew Ng. The new TITAN X, which holds the same name as the previous version of the device, is based on the company’s new Pascal graphics architecture, which was unveiled back in May.
Google Cloud Platform Releases Two New Cloud Machine Learning Products
The Google Cloud Platform team made news this week, with the announcement of the public beta of two new Cloud Machine Learning products: the Cloud Natural Language and Cloud Speech APIs.
AI to Keep Stray Cats Off Your Lawn, Controlling a Toy Robot With TensorFlow
What do you do if you’re an NVIDIA employee and you’re tired of your neighbor’s cats hanging out on your front lawn? Well, if you’re Bob Bond you build a deep learning based controller for your sprinkler system and train it to recognize cats!
Algorithmia's Deep-Learning Marketplace & New CNN Benchmarks
I’ve mentioned in the past my excitement about cloud based machine learning, particularly the cognitive services offered by the likes of Google, Microsoft and IBM. Well, add Seattle-based Algorithmia to the fray. The company, which launched in 2013 and has been offering an online marketplace for algorithmic web services announced this week support for the Caffe, TensorFlow and Theano deep learning frameworks. With this new support in place, 3rd party developers can upload trained models to Algorithmia where they can be used by Algorithmia customers on a public or pay-per-call basis. The company seeded the market with 16 of their own open source implementations of popular research papers, including Google’s InceptionNet and Parsey McParseface based on Tensorflow, a real estate and illustration tagger based on Caffe, and an image stylizer based on the Theano-based ArtsyNetworks project for folks who want to build the next Prisma.
Google Uses Machine Learning to Cut Datacenter Power Usage
In a blog post on Wednesday, Rich Evans, a Research Engineer at Google DeepMind and Jim Gao, a Data Center Engineer at Google, described work the company has done to manage the power consumption of one of their data centers using machine learning, the result of which has been to lower the amount of energy they’ve used for cooling by as much as 40%. This is a particularly impressive number because it is on top of a number of investments the company has already made in reducing DC power consumption, such as developing extremely efficient servers, highly efficient cooling strategies and renewable energy sources.
Google's Wide & Deep Learning Models
One of the papers I’ve been meaning to look into is the Wide and Deep Learning paper published by Google Research a couple of weeks ago. It turns out that the paper is both short and very much on the applied side of the spectrum, so it’s relatively easy reading. There’s also a lot of supporting material, between the Google Research blog, the TensorFlow docs and the video they created, though I found that reading the paper helped me understand the video, as opposed to the other way around!
Making art on your mobile phone with deep learning, and more ML/AI business news
This week Prisma Labs released their new app, Prisma, that seeks to bring generative AI to the masses. This is an app that allows you to apply filters to your pictures that renders them in the style of famous artists like Van Gogh and Picasso. If this sounds familiar that’s because it should… We discussed the paper that this is based on which is called “A Neural Algorithm of Artistic Style” back in the beginning of June, in the same show we discussed Google’s Magenta project and some other developments in the field of generative artistic AI.
A Conversation About Public Datasets for AI Research
In this post I want to revisit some comments that I made last week while discussing the news that Google DeepMind was granted access to a collection of 1,000,000 eye scan images by the British National Health System. If you’ll recall, I asked whether this data, which was collected by a government-funded public health organization should instead of being exclusively handed over to a single research organization, should rather be made publicly available to all researchers.
Pokémon Go and AI
I feel obligated to talk a bit about Pokemon Go and artificial intelligence. But really, there’s not much out there to speak of, or at least I couldn’t find anything.
A BS Meter for AI, Predator Robots, and a Free O’Reilly eBook
This week’s show covers the White House’s AI Now workshop, tuning your AI BS meter, research on predatory robots, an AI that writes Python code, plus acquisitions, financing, technology updates and a bunch more.
Fatal AI Autopilot Crash, EU May Prohibit Machine Learning & More—TWIML 2016/07/01
This week’s show covers the first fatal Tesla autopilot crash, a new EU law that could prohibit machine learning, the AI that shot down a human fighter pilot, the 2016 CVPR conference, 10 hot AI startups, the business implications of machine learning, cool chatbot projects and, if you can believe it, even more.
Dueling Neural Networks at ICML, Plus Training a Robotic Housekeeper—TWIML 2016/06/24
This week’s show covers the International Conference on Machine Learning (ICML 2016), “dueling architectures” for reinforcement learning, AI safety goals for robots, plus top AI business deals, tech announcement, projects and more.
ML & AI at Apple, IBM’s Deep Thunder & Exciting New Deep Learning Research—TWIML 2016/06/17
This week’s podcast digs into Apple’s ML and AI announcements at WWDC, looks at the new Deep Thunder offering by IBM and The Weather Company, and discusses exciting new deep learning research from MIT, OpenAI and Google Discover More Here.
Self-Motivated AI, Plus A Kill-Switch for Rogue Bots—TWIML 2016/06/10
This week’s podcast looks at new research on intrinsic motivation for AI systems, a kill-switch for intelligent agents, “knu” chips for machine learning, a screenplay made by a neural net, and more.
Facebook’s DeepText, ML & Art, Artificial Assistants—TWIML 2016/06/03
This week’s show looks at Facebooks’ new DeepText engine, creating art with deep learning and Google Magenta, how to build artificial assistants and bots, and applying economics to machine learning models.
The White House on AI & Aggressive Self-Driving Cars—TWIML 2016/05/27
This week’s episode explores the White House workshops on AI, human bias in AI and machine learning models, a company working on machine learning for small datasets, plus the latest AI & ML news and a self-driving car that learned how to drive aggressively.
Google IO, Deep Learning Hardware & an AI to Save You From Conference Call Hell — TWIML 2016/05/20
Every week I end the week with close to 100 tabs filled with stories—some good, some not so good—spanning all corners of the cloud computing, big data, machine learning and AI web. I thought it would be useful to bring you the best of these stories in a weekly podcast. I have no idea whether this will be sustainable or not—this first episode took a lot of work—but let’s run with it and see what happens.