Microsoft Bets Big on General AI

Here are five important AI stories from the week.

Microsoft Bets Big on Artificial General Intelligence (The New York Times)

Microsoft invests $1 billion in the A.I. research lab founded by Elon Musk and headed now by the former head of Y Combinator Sam Altman. OpenAI’s successes to date include releasing a very impressive language model called GPT-2; OpenAI made headlines earlier this year when it chose NOT to release the code because it feared releasing the code would lead bad actors to disseminate false information using the model. OpenAI also designed an AI to beat the world’s best players at a complex strategy-based video game called Dota 2.

According to Sam Altman, OpenAI will focus on building a quantum computer next. What does Microsoft capture from the deal? A portion of profits. Microsoft also will eventually become the sole provider of cloud infrastructure for OpenAI. Fortune also did a great job covering this transaction.

SoftBank Launches Massive $108 Billion Fund to Invest in A.I. (CNBC)

A few years after launching its initial $100 billion Vision Fund, SoftBank is at it again, expecting to raise $108 billion for this second Vision Fund. Other likely investors include Apple, Microsoft, Foxconn, and several major financial giants. Its mission this time: to “facilitate the continued acceleration of the AI (artificial intelligence) revolution through investment in market-leading, tech-enabled growth companies.”

Autonomous Driving Proves Harder Than Expected (The New York Times)

Autonomous vehicles perform very well when they do not encounter abnormal situations such as cars or cyclists or pedestrians running lights or inclement weather. Although autonomous vehicles perform well in most driving conditions, they struggle with the edge cases. And since driving poorly has potentially fatal consequences, autonomous vehicles cannot yet drive on open roads. In other words, self-driving cars are almost here but not quite yet.

Panic over Data Privacy After Russian App Goes Viral (The Washington Post)

Two weeks ago, a photo-transforming app went viral; users were able to upload photos of their face and see “aged” versions of themselves, courtesy of synthetic image generation powered by machine learning. The app almost magically transformed faces, adding in wrinkles and graying hair. But, users of the app eventually realized the app had been developed by a mysterious Russian firm, creating some paranoia about what would happen to the data they had made available to the Russian firm. Concerns over data privacy are on the rise as users become more aware of how their data is being used.

How StitchFix Uses BERT for AI in Retail (StitchFix)

Google released BERT late last year, and many firms such as StitchFix are rapidly adopting the model for use in their core business. At StitchFix, stylists use notes provided by consumers to find the most suitable clothes. Instead of relying on just humans, StitchFix has an array of machine learning solutions to narrow down the search for stylists. With BERT, StitchFix is able to extract information from text and automatically map text to clothes that the consumers will like with considerably less human involvement. Here is just how it all works.

More Stories Worth Reading and Watching…

Videos from the SpaCy IRL Conference (YouTube)

For More on the SpaCy IRL Conference (SpaCy)

Turn Selfies into Renaissance Art (MIT Technology Review)

Google’s DeepMind Reaches an Important Milestone in Healthcare (Bloomberg)

How AI Could Help with Climate Change (National Geographic)

How NYC Might Use Data and AI to Surveil Cars (The Intercept)

Code Autocompletion With Deep Learning (TabNine)

Transformers Keep Setting New NLP Records (Hacking Semantics)

A Long, Comprehensive Guide to Labeling Data (LightTag)

The Many Forms of AI in Video Games (Hewlett Packard Enterprise)

Ankur Patel
The Future of AI Is Unsupervised

Here are five important AI stories from the week.

The Future of AI Is Unsupervised (MIT Technology Review)

Today’s machine learning applications need a lot of labeled data to have good performance, but most of the world’s data is not labeled. For machine learning to advance, algorithms will need to learn from unlabeled data and make sense of the world from pure observation, much like how children learn to operate in the real world after birth without too much guidance.

According to Yann LeCun, one of the fathers of machine learning and currently the chief AI scientist at Facebook, the future of machine learning will be driven by unsupervised or self-supervised learning systems. For more, please turn to this article in the MIT Technology Review or my book on unsupervised learning.

The Value Chain in Machine Learning (Medium)

Compared to a few years ago, solutions for a lot of the common machine learning tasks have been commoditized; companies have built robust solutions to help developers set up cloud infrastructure for machine learning, acquire data to train their models, clean and prepare the data, apply machine learning algorithms and perform hyper-parameter tuning, and deploy their trained models. The best way for startups to provide value in machine learning is not by trying to reinvent what has already been commoditized but rather by developing domain-specific solutions to high value business problems. In other words, let’s focus obsessively on solving the business problem not just on the latest and greatest tech.

An Overview of Machine Learning Applications Today (Analytics Vidhya)

Most people consume machine learning applications throughout the day without ever realizing it. Machine learning is not some high tech that will come in the future; it’s already here. This articles explores machine learning applications in smartphones, transportation, web services, sales and marketing, security, and finance.

Amazon To Spend $700 Million to Retrain Its People (The Wall Street Journal)

Amazon is one of the leaders in machine learning today, and it recognizes just how disruptive the technology will be to the existing labor force. In preparation, Amazon has set aside $700 million to retrain a third of its U.S. workforce — nearly 100,000 people. Non-corporate workers will be transitioned to IT support roles and non-technical corporate workers will retrain as software engineers.

Companies Are Hungry For Your Face Data (The New York Times)

Companies that build computer vision applications need lots and lots of photos to power their facial recognition technology. Over the past several years, these companies have crawled photos online to build these massive datasets and, in some cases, installed cameras in public spaces to capture this data. This article does a great job exploring just how that data gets collected and used, often without consent from the users.

More Stories Worth Reading and Watching…

Google Releases New Text-Processing Library (InfoQ)

Transfer Learning for NLP (Cloudera Fast Forward Labs)

Unsupervised and Semantic Learning in NLP (Science)

Review of the Hundred-Page Machine Learning Book (Medium)

Ankur Patel
AI Conquers Poker

Here are five important AI stories from the week.

AI Conquers Multi-Player Poker (Facebook)

Facebook and Carnegie Mellon have built the first AI bot that is capable of winning multi-player poker (no-limit Hold’em), building upon the single-player success that Libratus demonstrated last year. Unlike chess and Go, poker is a game with hidden information - the AI cannot see the cards that are held by its opponents. This makes poker incredibly complex and challenging for an AI to win. Read the article for details on how the AI bot Pluribus conquered the game.

The Geopolitics of AI (Scientific American)

Whoever moves fastest in AI will be able to export its AI to the rest of the world. In other words, the race to AI is not a simply a technological race - it has significant geopolitical consequences. For example, will China’s AI or the U.S.’s AI be exported to Russia and India? To help establish proper rules of engagement, this article argues for an AI Trade Organization (AITO), modeled after the World Trade Organization.

ROI of AI in Enterprise (Fortune)

Compared to a year ago, business executives are less optimistic on how quickly AI will deliver a significant return on investment. Over 51% of those surveyed say it will take three to five years; a year ago, only 28% thought it would take that long. AI is harder to adopt than many believe but executives still firmly think it is worth investing in.

The Self-Driving Car Movement Cools (Fortune)

After laying off 190 autonomous vehicle employees earlier this year, Apple acquires engineers and intellectual property from struggling autonomous vehicle startup Drive.ai for less than $77 million. Drive.ai was once valued at $200 million. Both Apple’s moves and the declining fortunes of Drive.ai demonstrate just how sobering the progress in autonomous vehicles has been lately after years of optimism and hype.

XLNet Explained (ML Explained)

In the past 18 months, there have been many watershed moments in NLP. In December 2018, Google released BERT, trouncing previous records in many NLP tasks. A few weeks ago, researchers at Carnegie Mellon University and Google Brain released XLNet, which beat BERT and is now state of the art. This article does a beautiful job explaining just how XLNet achieved its superior performance.

More Stories Worth Reading and Watching…

XLNet, the Latest State of the Art in NLP (Borealis AI)

Major Misconceptions of China and AI (Fortune)

Ankur Patel
Europe Needs to Shape Up

Here are five important AI stories from the week.

Europe Needs to Shape Up (European Council on Foreign Relations)

The U.S. and China are leading the AI race. Other players, such as Russia and India, are tier-two contenders. To compete with these AI powers, Europe has a lot of work to do. First, it needs to retain AI talent, which it so often loses to the U.S. Europe does not pay competitive enough salaries. Second, very stringent data privacy regulations are hampering access to good training data. Third, Europe is reliant on US chipmakers for its AI needs. Without progress on these fronts, Europe will be a weak contender in the global AI race.

Movers and Shakers in AI (VentureBeat)

This read explores what’s front and center for the various thought leaders in AI: Google, Amazon, Cloudera, OpenAI, and Microsoft.

Deep Learning at Nvidia (DeepLearning.AI)

Great read on the day-to-day life of the VP of Applied Deep Learning Research Bryan Catanzaro at Nvidia. His advice for people that are trying to break into AI: “Just get started and iterate quickly. You’ll find your way as long as you keep moving and keep adjusting.” This advice is similar to how a deep learning algorithm trains on data to find an optimal solution.

Deepfakes to Improve Cancer Diagnosis (MIT Technology Review)

Generative adversarial networks (GANs) are capable of generating near-realistic synthetic data called deepfakes. While the news coverage on such deepfakes has been negative (e.g., raising alarms of fake news), the technology could also be applied positively. In medicine, GANs could generate synthetic medical images to help supplement the training set of real medical images. As the set of training data increases, the models for cancer diagnosis will become better, providing a very valuable boost to AI-enabled cancer diagnosis.

AI in Fashion (Vogue Business)

The CEO of Yoox Net-a-Porter discusses just how much data is available to data-first fashion companies and how that data is being used to deliver more personalized fashion and improve logistics.

More Stories Worth Reading and Watching…

A Code-First Course on NLP (fast.ai)

Airplanes Perform Autonomous Landing for the First Time (TechCrunch)

AI to Count Number of Protesters in China (The New York Times)

Ankur Patel