Imagine your job is to personalize search results on an e-commerce site for returning customers, classify the presence or absence of pedestrians in street photos, or develop an app that translates languages. In all of these cases, a basic ingredient is a dataset of annotations provided by a human. For any company seeking to personalize experience for its customers, combining human computation with algorithmic computation is essential. This is also true for Stitch Fix. At Stitch Fix, we recently launched Stitch Fix Freestyle, our direct-shopping experience, where our algorithmic recommendations are now directly shared with clients in their own personal shopping feed – a different approach from our original Fix experience, where a team of expert stylists determined what should go in the client's Fix.

As artificial intelligence (AI) continues its march toward becoming the backbone of digital transformation, it's creating a new class of entrepreneurs. This new class is being empowered by the ability to use AI to generate ideas for and launch new businesses, and it's quickly changing the landscape of business innovation. According to a new report from Boston Consulting Group (BCG), AI is unleashing a new wave of entrepreneurs who are using it to generate new business ideas, create and launch new products and services, and build new businesses. So, I didn't write the above paragraph--the Generative Pre-trained Transformer 3 (GPT-3) model did. The AI so advanced that it even cited a credible source to support its argument.

In August, first prize in the digital-art category of the Colorado State Fair's fine-art competition went to a man who used artificial intelligence (AI) to generate his submission, "Théâtre d'Opéra Spatial." He supplied the AI, a program called Midjourney, with only a "prompt"--a textual description of what he wanted. Systems like Midjourney and the similar DALL-E 2 have led to a new role in our AI age: "prompt engineer." Such people can even sell their textual wares in an online market called PromptBase. Midjourney and DALL-E 2 emerged too late to be included in "Working With AI: Real Stories of Human-Machine Collaboration," by Thomas Davenport and Steven Miller, information-systems professors at Babson College and Singapore Management University, respectively.

It's been just a few weeks since OpenAI began allowing customers to commercially use images created by DALL-E 2, its remarkably powerful AI text-to-image system. But in spite of the current technical limitations and lack of volume licensing, not to mention API, some pioneers say they're already testing the system for various business use cases -- awaiting the day when DALL-E 2 becomes stable enough to deploy into production. Stitch Fix, the online service that uses recommendation algorithms to personalize apparel, says it has experimented with DALL-2 to visualize its products based on specific characteristics like color, fabric and style. For example, if a Stitch Fix customer asked for a "high-rise, red, stretchy, skinny jean" during the pilot, DALL-E 2 was tapped to generate images of that item, which a stylist could use to match with a similar product in Stitch Fix's inventory. "DALL-E 2 helps us surface the most informative characteristics of a product in a visual way, ultimately helping stylists find the perfect item that matches what a client has requested in their written feedback," a spokesperson told TechCrunch via email.

Olivia Liao is Senior Director of Data Science at Stitch Fix, a company that uses data science and expert stylists to deliver personalization at scale. We discuss how they blend data science and domain expertise, how they tune recommendations in light of logistics and supply chain constraints, and how they incorporate new developments in large language models, multimodal models and Responsible AI.

Welcome to the age of blended workforces, where intelligent machines and humans combine to accelerate business success. In short, now that we have increasingly capable robots and artificial intelligence (AI) systems – capable of taking on tasks that were previously the sole domain of humans – it's easier than ever for organizations to leverage intelligent machines. But this leaves employers with some major questions to answer: how do we find the right balance between intelligent machines and human intelligence? What roles should be given over to machines? And which roles are best suited to humans?

A personalized customer experience has become the basis for competitive advantage. However, providing personalization requires more than just a technological fix. Businesses must design intelligent experience engines, which assemble high-quality, end-to-end customer experiences using AI powered by customer data. Brinks is a 163-year-old business well-known for its fleet of armored trucks. The company also licenses its brand to a lesser-known, independently operated sister company, Brinks Home. The Dallas-based smart-home-technology business has struggled to gain brand recognition commensurate with the Brinks name. It competes against better-known systems from ADT, Google Nest, and Ring, and although it has earned stellar reviews from industry analysts and customers, its market share is only 2%.

Estimated to be worth $3T by the end of the decade, per CB Insights' Industry Analyst Consensus, the fashion industry is growing at a fast pace, led by cutting-edge technologies. From robots that sew and cut fabric to AI algorithms that predict style trends, VR mirrors in dressing rooms, shopping off the runway and a number of other innovations show how technology is automating and evolving the industry. In 2016, Google collaborated with online fashion platform Zalando and production company Stinkdigital to launch predictive design engine, Project Muze. The algorithm consisted of a set of aesthetic parameter and trained a neural network to comprehend colours, textures and styles derived from Google Fashion Trends Report and data sourced by Zalando -- to create designs in sync with with style preferences identified by the network. Amazon is taking an algorithmic approach to fashion as well.

Thank you to all the 2020 summer interns that worked with the Stitch Fix Algorithms team. For the first time, the internship program was fully remote, but that hasn't stopped them from working on impactful projects. This post summarizes some of the projects they worked on. We appreciate all your contributions and insights! This summer, I worked on the Merchandise Algorithms team.

Recently, I came across a Reddit thread on the different roles in data science and machine learning: data scientist, decision scientist, product data scientist, data engineer, machine learning engineer, machine learning tooling engineer, AI architect, etc. It's difficult to be effective when the data science process (problem framing, data engineering, ML, deployment/maintenance) is split across different people. It leads to coordination overhead, diffusion of responsibility, and lack of a big picture view. IMHO, I believe data scientists can be more effective by being end-to-end. Here, I'll discuss the benefits and counter-arguments, how to become end-to-end, and the experiences of Stitch Fix and Netflix. I find these definitions to be more prescriptive than I prefer. Instead, I have a simple (and pragmatic) definition: An end-to-end data scientist can identify and solve problems with data to deliver value.