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Forget Ride Hailing. Uber Wants To Be Your One-Stop Shop For Everything

NPR Technology

Uber acquired Drizly, an alcohol e-commerce platform, for $1.1 billion in cash and stock last week. It's just the latest brand Uber has added to its portfolio as the company seeks to satisfy consumer appetites. Uber is no longer synonymous with ride-hailing. These days, it has more to offer. Uber acquired a majority stake in Cornershop, its answer to Instacart, in 2019.


We saw the future in 2020 and the future sucks

Mashable

Flying cars are starting to look like a crock of shit. I contend we're living in the future, and -- spoiler ahead -- flying cars aren't the future we got. Listen, I hate this gut feeling as much you probably do, but I can't quite shake it: 2020 looks a whole hell of a lot like the future. We lived through screens -- at least, you did if you were fortunate and caring -- and limited our human interaction to a bare minimum. Hours upon hours poured into television or immersive video game worlds. It all reminds me of a piece my friend Mike Murphy wrote for Quartz in 2016 titled, "The future is a place where we won't have to talk to or hear from anyone we don't want to."


Algorithms Are Making Economic Inequality Worse

#artificialintelligence

The risks of algorithmic discrimination and bias have received much attention and scrutiny, and rightly so. Yet there is another more insidious side-effect of our increasingly AI-powered society -- the systematic inequality created by the changing nature of work itself. We fear a future where robots take our jobs, but what happens when a significant portion of the workforce ends up in algorithmically managed jobs with little future and few possibilities for advancement? One of the classic tropes of self-made success is the leader who comes from humble beginnings, working their way up from the mailroom, the cash register, or the factory floor. And while doing that is considerably tougher than Hollywood might suggest, bottom-up mobility was at least possible in traditional organizations.


Basket Recommendation with Multi-Intent Translation Graph Neural Network

arXiv.org Artificial Intelligence

The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket. Existing methods solve this problem by assuming the items within the same basket are correlated by one semantic relation, thus optimizing the item embeddings. However, this assumption breaks when there exist multiple intents within a basket. For example, assuming a basket contains \{\textit{bread, cereal, yogurt, soap, detergent}\} where \{\textit{bread, cereal, yogurt}\} are correlated through the "breakfast" intent, while \{\textit{soap, detergent}\} are of "cleaning" intent, ignoring multiple relations among the items spoils the ability of the model to learn the embeddings. To resolve this issue, it is required to discover the intents within the basket. However, retrieving a multi-intent pattern is rather challenging, as intents are latent within the basket. Additionally, intents within the basket may also be correlated. Moreover, discovering a multi-intent pattern requires modeling high-order interactions, as the intents across different baskets are also correlated. To this end, we propose a new framework named as \textbf{M}ulti-\textbf{I}ntent \textbf{T}ranslation \textbf{G}raph \textbf{N}eural \textbf{N}etwork~({\textbf{MITGNN}}). MITGNN models $T$ intents as tail entities translated from one corresponding basket embedding via $T$ relation vectors. The relation vectors are learned through multi-head aggregators to handle user and item information. Additionally, MITGNN propagates multiple intents across our defined basket graph to learn the embeddings of users and items by aggregating neighbors. Extensive experiments on two real-world datasets prove the effectiveness of our proposed model on both transductive and inductive BR. The code is available online at https://github.com/JimLiu96/MITGNN.


How to build a deep learning model in 15 minutes

#artificialintelligence

As Instacart has grown, we've learned a few things the hard way. We're open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers. To address these issues we're standardizing our machine learning in Lore. At Instacart, three of our teams are using Lore for all new machine learning development, and we are currently running a dozen Lore models in production. Skip to the Outline if you want the full tour.


Data science and deep learning in retail

#artificialintelligence

Jeremy Stanley is giving a talk, "How Instacart is Using AI to Create the Most Efficient Shoppers Ever," at the O'Reilly Artificial Intelligence Conference in San Francisco, September 17-20, 2017. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Jeremy Stanley, VP of data science at Instacart, a popular grocery delivery service that is expanding rapidly. As Stanley describes it, Instacart operates a four-sided marketplace comprised of retail stores, products within the stores, shoppers assigned to the stores, and customers who order from Instacart.


Predicting real-time availability of 200 million grocery items in North American stores

#artificialintelligence

Ever wished there was a way to know if your favorite Ben and Jerry's ice cream flavor is currently available in a grocery store near you? Instacart's machine learning team has built tools to figure that out! Our marketplace's scale lets us build sophisticated prediction models. Our community of over 70,000 personal shoppers scans millions of items per day across 15,000 physical stores and delivers them to the customers. These stores belong to our grocery retail partners like Aldi, Costco, Krogers, Safeway, and Wegmans.


Walmart to offer online grocery shopping with Google Assistant

Engadget

Walmart customers once again will be able to voice-order their groceries with Google Assistant, another bid by the brick-and-mortar store to compete with Amazon. The retailer announced the partnership on Tuesday, and said it would gradually roll out the feature in the next few weeks. The development comes after Walmart unceremoniously left Google Express, Google's online shopping tool, back in January, reportedly to develop its own Google Assistant shopping feature. Walmart shoppers will soon be able to add items to their shopping carts by saying "Hey Google, talk to Walmart." The feature will be cross-platform, meaning customers can shop from any device that has the Google Assistant feature, ranging from smart speakers and displays to their Android watch or iPhone.


Kroger ends its unmanned-vehicle grocery delivery pilot program in Arizona

USATODAY - Tech Top Stories

Nuro has partnered with Fry's Food Stores to utilize its autonomous vehicles to deliver groceries in Scottsdale. Supermarket giant Kroger said it soon will end a pilot program in which more than 2,000 grocery deliveries were made in self-driving vehicles from a store in Scottsdale, Arizona. The program, launched last August, featured deliveries in autonomous vehicles from robotics company Nuro from the Kroger-owned Fry's store at 7770 E. McDowell Road for customers in ZIP code 85257. The companies described it as the nation's first program featuring deliveries to the general public from fully unmanned vehicles. Wednesday will mark the final day of deliveries.


My secret sauce to be in top 2% of a Kaggle competition

#artificialintelligence

Competing in kaggle competitions is fun and addictive! And over the last couple of years, I developed some standard ways to explore features and build better machine learning models. These simple, but powerful techniques helped me get a top 2% rank in Instacart Market Basket Analysis competition and I use them outside of kaggle as well. So, let's get right into it! One of the most important aspects of building any supervised learning model on numeric data is to understand the features well.