Andrew Ng with his grandfather
Jan 01, 2020

Hopes for AI in 2020: Yann LeCun, Kai-Fu Lee, Anima Anandkumar, Richard Socher

Happy New Year! Every winter holiday, I pursue a learning goal around a new topic. In between visits with family, I end up reading a lot. About a decade ago, my holiday topic was pedagogy — I still remember lugging a heavy suitcase of books through the airport — and this...
Illustration of Christmas gifts
Dec 24, 2019

The Batch: Biggest AI Stories of 2019: Driverless Cars Stall, Deepfakes Go Mainstream, Face Recognition Gets Banned

We here at wish you a wonderful holiday season. As you consider your New Year’s resolutions and set goals for 2020, consider not just what you want to do, but what you want to learn: What courses do you want to take this year?
Andrew Ng and other speakers at the NeurIPS 2019 conference
Dec 18, 2019

The Batch: Companies Slipping on AI Goals, Self Training for Better Vision, Muppets and Models, China Vs US?, Only the Best

I’ve been reflecting on the NeurIPS 2019 conference, which ended on Saturday. It’s always a wonderful event, but this year I found it a bittersweet experience. Bitter because the conference has grown so much that we no longer focus on a handful of ideas.
Pie & AI event on AI ethics
Dec 11, 2019

The Batch: Amazon's Surveillance Network, AI That Gets the Facts Right, Deepfakes Get Regulated, Predicting Volcanic Eruptions

I’ve been thinking about AI and ethics. With the techlash and an erosion of trust in technology as a positive force, it’s more important than ever that we make sure the AI community acts ethically.
Andrew Ng holding a sweatshirt that says "Trust the robot"
Dec 04, 2019

The Batch: Google AI Explains Itself, Neural Net Fights Bias, AI Demoralizes Champions, Solar Power Heats Up

Recently I wrote about major reasons why AI projects fail, such as small data, robustness, and change management. Given that some AI systems don't work, users and customers sometimes rightly wonder whether they should trust an AI system.
Illustration of a robotic pigeon and a "happy thanksgiving" message from DeepLearning.AI
Nov 27, 2019

The Batch: Sony Goes AI, Intel's GPU Killers, Transformer Networks In Disguise, Malicious Models Fool Bias Detection

I’ll be spending Thanksgiving with Nova and watching her taste turkey for the first time. To those of you who celebrate Thanksgiving, I hope you spend time with loved ones, reflect on what you are thankful for, and discuss some very important topics around the dinner table...
Road sign with the text "new way"
Nov 20, 2019

The Batch: Artificial Noses, Surveillance on Wheels, Unwelcome Researchers, Privacy Problems, Beyond Bounding Boxes

My last two letters explored robustness and small data as common reasons why AI projects fail. In the final letter of this three-part series, I’d like to discuss change management. Change management isn’t an issue specific to AI, but given the technology’s disruptive nature...
Charts with data explaining how ML works with data distribution
Nov 13, 2019

The Batch: Self-Driving Cars That Can't See Pedestrians?! Evolutionary Algorithms, Fish Recognition, Fighting Fraud

In this series exploring why machine learning projects fail, let’s examine the challenge of “small data.” Given 1 million labeled images, many teams can build a good classifier using open source.
Diagram showing what's needed to build a machine learning product
Nov 06, 2019

The Batch: DeepMind Masters StarCraft 2, AI Attacks on Amazon, A Career in Robot Management, Banks Embrace Bots

Building AI systems is hard. Despite all the hype, AI engineers struggle with difficult problems every day. For the next few weeks, I’ll explore some of the major challenges. Today’s topic: The challenge of building AI systems that are robust to real-world conditions.
Illustration of a ghost
Oct 30, 2019

The Batch: Daemon Spawn, AGI Takeover, Deepfake Deluge, Bias Crisis - How Scared Should You Be?

Welcome to the Halloween edition of The Batch! I promised last week to share some common reasons for AI project failures. But first, let’s start with some of the least common reasons.
Pie & AI and IASI AI cupcakes
Oct 23, 2019

The Batch: Robot Hand Works Rubik’s Cube, Self-Driving Tanks Roll Toward Battle, Face Rec Dataset Sparks Lawsuit, Bayes Finds

I’ve heard this conversation in multiple companies: Machine learning engineer: Look how well I did on the test set! Business owner: But your ML system doesn’t work. This sucks! Machine learning engineer: But look how well I did on the test set!
Drone race
Oct 16, 2019

The Batch: TensorFlow Versus PyTorch, Autonomous Drone Races, State-of-the-Art with Less Compute, NLP for Rare Languages

I just replaced my two-year-old phone with a new one and figured out how to take long-exposure photos of Nova even while she’s asleep and the lights are very low. This piece of technology brought me a surprising amount of joy!
DeepScale's automated vehicle technology
Oct 09, 2019

The Batch: Tesla Acquires DeepScale, France Backs Face Recognition, Robots Learn in Virtual Reality, Acquirers Snag AI Startups

Last week, I saw a lot of social media discussion about a paper using deep learning to generate artificial comments on news articles. I’m not sure why anyone thinks this is a good idea. At best, it adds noise to the media environment.
"No silver bullet" book cover
Oct 02, 2019

The Batch: Google Achieves Quantum Supremacy, Amazon Aims To Sway Lawmakers, AI Predicts Basketball Plays, Face Detector

Thinking about the future of machine learning programming frameworks, I recently reread computer scientist Fred Brooks’ classic essay, “No Silver Bullet: Essence and Accidents of Software Engineering.” Three decades after...
Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom
Sep 25, 2019

The Batch: Global Surveillance Survey, AI’s Crisis of Reproducibility, Construction Drones, Bots Cheat at Hide-and-Seek

I read an interesting paper comparing the results of traditional passive learning (sitting in a lecture) versus active methods like the flipped classroom, where students watch videos at home and work on exercises in class.

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