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How Stitch Fix used AI to personalize its online shopping experience

July 6, 2020 Big Data

Online retailers have long lured customers with the ability to browse vast selections of merchandise from home, quickly compare prices and offers, and have goods conveniently delivered to their doorstep. But much of the in-person shopping experience has been lost, not the least of which is trying on clothes to see how they fit, how the colors work with your complexion, and so on.

Companies like Stitch Fix, Wantable, and Trunk Club have attempted to address this problem by hiring professionals to choose clothes based on your custom parameters and ship them out to you. You can try things on, keep what you like, and send back what you don’t. Stitch Fix’s version of this service is called Fixes. Customers get a personalized Style Card with an outfit inspiration. It’s algorithmically driven and helps human style experts match a garment with a particular shopper. Each Fix included a Style Card that showed clothing options to complete outfits based on the various items in a customer’s Fix. Due to popular demand, last year the company began testing a way for shoppers to buy those related items directly from Stitch Fix through a program called Shop Your Looks.

AI is a natural fit for such services, and Stitch Fix has embraced the technology to accelerate and improve Shop Your Looks. On the tech front, this puts the company in direct competition with behemoths Facebook, Amazon, and Google, all of which are aggressively building out AI-powered clothes shopping experiences.

Stitch Fix told VentureBeat that during the Shop Your Looks beta period, “more than one-third of clients who purchased through Shop Your Looks engaged with the feature multiple times, and approximately 60% of clients who purchased through the offering bought two items or more.” It’s been successful enough that the company recently expanded to include an entire shoppable collection using the same underlying technology to personalize outfit and item recommendations as you shop.

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Stitch Fix data scientists Hilary Parker and Natalia Gardiol explained to VentureBeat in an email interview what drove the company to develop Shop Your Looks; how the team used AI to build it out; and the methods they used, like factorization machines.

In this case study:

Problem: How to expand the scope of its service that matches outfits to online customers using a mix of algorithms and human expertise.
The result is “Shop Your Looks.”
It grew out of an experiment by a small team of Stitch Fix data scientists, then expanded across other units within the company.
The biggest challenge was how to determine what is a “good” outfit, when taste is so subjective and context matters.
Stitch Fix used a combination of human-crafted rules to store, sort, and manipulate data, along with AI models called factorization machines

This interview has been edited for clarity and brevity.

VentureBeat: Did Stitch Fix kind of fall in love with an AI tool or technique, using that as inspiration to make a product using that tool or technique? Or did the company start with a problem or challenge and eventually settle on an AI-powered solution?

Stitch Fix: To create Shop Your Looks, we had to evolve our algorithm capabilities from matching a client with an individual item in a Fix to now matching an entire outfit based on a client’s past purchases and preferences. This is an incredibly complex challenge because it means not only understanding which items go together but also which of these outfits an individual client will actually like. For example, one person may like bold patterns mixed together and another person may prefer a bold top with a more muted bottom.

To help us solve this problem, we took advantage of our existing framework that provides Stylists with item recommendations for a Fix and determined what new information we needed to feed into that framework, and how we could collect it.

First, it’s important to understand how clients currently share information with us:

Style Profile: When a client signs up for Stitch Fix, we receive 90 different data points — from style to price point to size.
Feedback at checkout: 85% of our clients tell us why they are keeping or returning an item. This is incredibly rich data, including details on fit and style — no other retailer gets this level of feedback.
Style Shuffle: an interactive feature within our app and on our website where clients can “thumbs up” or “thumbs down” an image of an item or an outfit. They can do this at any time — so not just when they receive a Fix. So far, we’ve received an incredible 4 billion item ratings from clients.
Personalized request notes to Stylists: Clients give their Stylists specific requests, such as if they are looking for an outfit for an event, or if they’ve seen an item that they really like.

For Shop Your Looks, we supplement this with information about what items go together. The outfits in Style Cards, outfits our Creative Styling Team builds, and outfits we serve to clients in Style Shuffle give us valuable additional insight into a client’s outfit style preferences

VB: How did you go about starting this project? Did you need to hire new talent?

SF: Data science is core to what we do. We have more than 125 data scientists who work across our business, including in recommendation systems, human computation, resource management, inventory management, and apparel design.

Data-driven experimentation is an important part of the team’s culture, so like many initiatives at Stitch Fix, Shop Your Looks was born out of an experiment from a small team of data scientists. As the project grew beyond the initial data collecting phase and into beta testing, the data science team worked with other groups across the business. For example, our Creative Styling Team is tuned in to customer needs and able to recommend looks that are approachable, aspirational, and inspirational.

VB: What was the biggest or most interesting challenge you had to overcome in the process of creating Shop Your Looks?

SF: Creating outfits for clients is a really complex problem because what makes a good outfit is so subjective to each individual. What one person believes is a great outfit, another might not. The toughest part of solving this problem is that an outfit is not a fixed entity — it’s fundamentally contextual. Tackling this problem required gathering new insights, not just about specific items that clients like, but also about how clients reacted to items grouped together.

And because style is so subjective, we had to rethink how we qualified a “good” outfit for our algorithms, since there’s not simply one perfect outfit that exists. Clients have different style preferences, so we believe a “good” outfit is one that a certain set of our clients like, but not necessarily all.

We learn a lot about how clients react to items grouped together when we share outfits with clients and ask them to rate them via Style Shuffle.

VB: What AI tools and techniques does Stitch Fix employ — generally, and for Shop Your Looks?

SF: Shop Your Looks combines AI models and human-crafted rules to store, sort, and manipulate data.

The system is roughly based on a class of AI models called factorization machines and has a few distinct steps. Because generating outfits is complicated, we can’t just create an outfit and call it good. In the first step, we create a pairing model, which is able to predict pairs of items that go well together, such as a pair of shoes and a skirt or a pair of pants and a T-shirt.

We then move on to the next stage — outfit assembly. Here we select a set of items that all come together to form a cohesive outfit (based on the predictions from the pairing model). In this system, we use “outfit templates,” which provide a guideline of what an outfit consists of. For example, one template is tops, pants, shoes, and a bag, and another is a dress, necklace, and shoes.

In the final phase of recommending outfits for Shop Your Looks, there are several factors that come into play. We set an anchor item, which is an item the client kept from a past Fix, which we’d like to build outfits around. The algorithm also has to factor in what inventory is available at any given time. Once that is done, the algorithm develops personalized recommendations tailored to each client’s preferences. Clients can then browse and shop these looks directly from the Shop tab on mobile or desktop. The outfit recommendations refresh throughout the day, so clients can regularly check back for new outfit inspiration.

VB: What did you learn that’s applicable to future AI projects?

SF: We introduced Shop Your Looks to a small number of our clients in the U.S. last year, and throughout this initial beta period we learned a lot about how they interact with the product and how our algorithms performed.

A key tenet of our personalization model is that the more information clients share, the better we are able to personalize their recommendations. We are usually able to adapt the model based on feedback from our clients; however, rules-based systems aren’t generally adaptive. We need the system to learn from client feedback on the outfits it recommends. We’re receiving immensely helpful feedback, from how clients engage with the outfit recommendations and also from a custom-built internal QA system. The model is in its early days, and we are continually adding more information to show clients more highly personalized outfits. For example, while seasonal trends are important overall, recommendations should be customized to a client’s local climate so that clients who experience summer weather earlier than others will start to receive summer items before those in cooler climates.

As we serve more clients, we are receiving an additional data set that strengthens the feedback loop and continues to make our personalization capabilities stronger.

VB: What’s the next AI-related project for Stitch Fix (that you can talk about)?

SF: One of the most interesting aspects of data science at Stitch Fix is the unusual degree to which the algorithms team is engaged with virtually every aspect of the business — from marketing to managing inventory and operations, and of course in helping our Stylists choose items our clients will love.

We believe that when we look to the future, the data science team will still be focused on improving personalization. This could include anything from sizing to predicting your styling needs before you even know you need something.

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Big Data – VentureBeat

CCPA compliance lags as enforcement begins in earnest

July 5, 2020 Big Data

Enforcement of the California Consumer Privacy Act (CCPA) began on Wednesday July 1, despite the final proposed regulations having just been published on June 1 and pending review by the California Office of Administrative Law (OAL). The July 1 date has left companies, many of which were hoping for leniency during the pandemic, scrambling to prepare.

COVID-19 appears to be shifting the privacy compliance landscape in other parts of the world — both Brazil’s LGDP and India’s PDPB have seen delays that will impact when the laws will go into effect. Nonetheless, the California Attorney General (CAG) has not capitulated on the CCPA’s timeline, with the attorney general’s office stating: “CCPA has been in effect since January 1, 2020. We’re committed to enforcing the law starting July 1 … We encourage businesses to be particularly mindful of data security in this time of emergency.”

With the CCPA being one of the most demanding pieces of privacy legislation that some companies have ever faced, compliance has understandably lagged. In 2019, different estimates placed the percentage of organizations that would be ready for the CCPA by Jan 2020 somewhere between 12% and 34%. A recent poll by ArcTrust revealed that as of June 2020 just 14% of companies were completely done with CCPA compliance, while another 15% have a plan but haven’t started implementation. This leaves an additional 71% of companies whose plans for CCPA compliance are unaccounted for. These numbers, while large, might not be all that surprising as only 28% of firms were compliant with GDPR over a year after it went into effect, with companies greatly underestimating what it would take to be compliant.

What should companies expect next?

Although the CAG’s ability to take enforcement actions is now in effect, companies can be held liable for breaches of the law that occurred earlier in the year. Additionally, consumers have been able to take legal action against non-compliant companies since the beginning of the year, with at least 19 lawsuits having been filed since Jan 1, 2020. These lawsuits illustrate the circumstances under which enforcement can take place as well as the potential compliance blindspots companies might face. Companies also face the prospect of new California privacy legislation in the form of the The California Privacy Rights Act of 2020 (CalPRA or CPRA), colloquially referred to as CCPA 2.0. The initiative has collected over 900,000 signatures and is expected to be on the November 2020 ballot, with 88% of Californians supporting its passage. Although this bill is not expected to take effect until January 1, 2023, organizations lagging behind on CCPA compliance will likely struggle to meet their obligations under the CPRA as well.

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What should companies behind on CCPA compliance be doing?

Companies that are just now starting to implement their compliance programs should do their best to align themselves with the final regulations that have been sent to the OAL. While there’s no silver bullet to doing this, below are some considerations worth taking into account:

Operationalizing the CCPA at scale requires a serious commitment to security. The CCPA has formally made clear that the era of security as an afterthought is over. Although the legislation is fairly agnostic about the types of security frameworks and controls organizations will have to deploy to ensure CCPA compliance, it’s apparent that satisfying the functional requirements of the CCPA will require developing comprehensive data discovery and data security programs organization-wide. For example, the ability to provide accurate disclosure notices at collection or within privacy policies, as well as the ability to process consumer requests and reduce breach risk all implicitly require companies to understand the categories of data they ingest. Companies will also need to know how this data is used, where it’s stored, and who has access to it. This will often require building consistent security processes with the help of tools like privileged access management, securely configured firewalls, and application security controls like data loss prevention. While it’s true that strong security practices alone aren’t enough to operationalize CCPA compliance, companies who are already complying with one or more privacy regimes or who otherwise have mature information security programs will likely find compliance easier.

Continuous compliance requires clear ownership within your compliance program. While IT and security will form the bedrock of an organization’s ability to comply with the CCPA, it may not be the case that IT or security should own the entirety of your organization’s compliance initiative. Your organization’s structure and the business purpose served by consumer data collection should inform who the relevant stakeholders will be. Clearly delineating who’s responsible for which aspects of your organization’s compliance program will be critical to making sure your program makes sense and will scale well as the privacy landscape continues to evolve.

Make your compliance program future-proof. While no one in your organization likely has a crystal ball, you don’t exactly need one to see that privacy is the future and that investing in consumer privacy today is a smart decision. Despite stalled privacy legislation stateside and abroad, the GDPR, CCPA, and potentially the CPRA will continue to serve as bulwarks that future legislation will aspire to. This means that should your organization limit itself to simply satisfying CCPA requirements, you’ll likely be playing catch-up as you suddenly find the privacy landscape maturing. Aiming to have your security and compliance programs scale to ensure the same rights and protections across your entire customer base will ensure you stay ahead of the game.

Michael Osakwe is a tech writer and Content Marketing Manager at Nightfall AI.

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Big Data – VentureBeat

Advocacy groups raise concerns over Google’s $2.1 billion Fitbit bid

July 4, 2020 Big Data

(Reuters) — Twenty advocacy groups from the United States, Europe, Latin America, and elsewhere signed a statement Wednesday urging regulators to be wary of Google’s $ 2.1 billion bid for fitness tracker company Fitbit because of privacy and competition concerns.

The 20 organizations — which include the U.S.-based Public Citizen, Access Now from Europe and the Brazilian Institute of Consumer Defense — argued that the deal would expand Alphabet subsidiary Google’s already considerable clout in digital markets.

Acquiring Fitbit would give Google such intimate information about users as how many steps they take daily, the quality of their sleep, and their heart rates.

“Past experience shows that regulators must be very wary of any promises made by merging parties about restricting the use of the acquisition target’s data. Regulators must assume that Google will in practice utilize the entirety of Fitbit’s currently independent unique, highly sensitive data set in combination with its own,” the groups said.

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Australian and Canadian groups were among the signatories.

A Google spokesperson said the tech wearables space was crowded.

“This deal is about devices, not data,” she said. “We believe the combination of Google’s and Fitbit’s hardware efforts will increase competition in the sector.”

Google announced the deal in November to take on competitors in the crowded market for fitness trackers and smart watches. Fitbit’s market share has been threatened by deep-pocketed companies like Apple and Samsung.

Australia’s competition authority said this month that it may have concerns about the deal and would make a final decision in August.

EU antitrust regulators will decide by July 20 whether to clear the deal with or without concessions or open a longer investigation.

In Washington, Google is under antitrust investigation by the Justice Department, a congressional committee, and dozens of states for allegedly using its massive market power to harm smaller competitors.

(Reporting by Diane Bartz, editing by Lisa Shumaker.)

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Big Data – VentureBeat

COVID-KG uses AI to scan thousands of studies to answer doctors’ coronavirus questions

July 3, 2020 Big Data

The number of studies about COVID-19 has risen steeply from the start of the pandemic, from around 20,000 in early March to over 30,000 as of late June. In an effort to help clinicians digest the vast amount of biomedical knowledge in the literature, researchers affiliated with Columbia, Brandeis, DARPA, UCLA, and UIUC developed a framework — COVID-KG, for “knowledge graph” — that draws on papers to answer natural language questions about drug purposing and more.

The sheer volume of COVID-19 research makes it difficult to sort the wheat from the chaff. Some false information has been promoted on social media and in publication venues like journals. And many results about the virus from different labs and sources are redundant, complementary, or even conflicting.

COVID-KG aims to solve the challenge by reading papers to build multimedia knowledge graphs consisting of nodes and edges. The nodes represent entities and concepts extracted from papers’ text and images, while the edges represent relations involving these entities.

COVID-KG ingests entity types including genes, diseases, chemicals, and organisms; relations like mechanisms, therapeutics, and increased expressions; and events such as gene expression, transcription, and localization. It also draws on entities annotated from an open source data set tailored for COVID-19 studies, which includes entity types like coronaviruses, viral proteins, evolution, materials, and immune response).

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COVID-KG extracts visual information from figure images (e.g., microscopic images, dosage response curves, and relational diagrams) to enrich the knowledge graph. After detecting and isolating figures from each document with text in its caption or referring context, it then applies computer vision to spot and separate non-overlapping regions and recognize the molecular structures within each figure.

COVID-KG provides semantic visualizations like tag clouds and heat maps that allow researchers to get a view of selected relations from hundreds or thousands of papers at a single glance. This, in turn, allows for the identification of relationships that would typically be missed by keyword searches or simple word cloud or heatmap displays.

In a case study, the researchers posed a series of 11 questions typically answered in a drug repurposing report to COVID-KG, like “Was the drug identified by manual or computation screen?” and “Has the drug shown evidence of systemic toxicity?” With three drugs suggested by DARPA biologists (benazepril, losartan, and amodiaquine) as targets, they used COVID-KG to construct a knowledge base from 25,534 peer-reviewed papers.

Given the question “What is the drug class and what is it currently approved to treat?” for benazepril, COVID-KG responded with:

The team reports that in the opinion of clinicians and medical school students who reviewed the results, COVID-KG’s answers were “informative, valid, and sound.” In the future, the coauthors plan to extend the system to automate the creation of new hypotheses by predicting new links. They also hope to produce a common semantic space for literature and apply it to improve COVID-KG’s cross-media knowledge grounding, inference, and transfer.

“With COVID-KG, researchers and clinicians are able to obtain trustworthy and non-trivial answers from scientific literature, and thus focus on more important hypothesis testing, and prioritize the analysis efforts for candidate exploration directions,” the coauthors wrote. “In our ongoing work we have created a new ontology that includes 77 entity subtypes and 58 event subtypes, and we are re-building an end-to-end joint neural … system following this new ontology.”

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Big Data – VentureBeat

AI system learns to model how fabrics interact by watching videos

July 3, 2020 Big Data

In a paper published on the preprint server Arxiv.org, researchers at MIT CSAIL, Nvidia, the University of Washington, and the University of Toronto describe an AI system that learns the physical interactions affecting materials like fabric by watching videos. They claim the system can extrapolate to interactions it hasn’t seen before, like those involving multiple shirts and pants, enabling it to make long-term predictions.

Causal understanding is the basis of counterfactual reasoning, or the imagining of possible alternatives to events that have already happened. For example, in an image containing a pair of balls connected to each other by a spring, counterfactual reasoning would entail predicting the ways the spring affects the balls’ interactions.

The researchers’ system — a Visual Causal Discovery Network (V-CDN) — guesses at interactions with three modules: one for visual perception, one for structure inference, and one for dynamics prediction. The perception model is trained to extract certain keypoints (areas of interest) from videos, from which the interference module identifies the variables that govern interactions between pairs of keypoints. Meanwhile, the dynamics module learns to predict the future movements of the keypoints, drawing on a graph neural network created by the inference module.

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The researchers studied V-CDN in a simulated environment containing fabric of various shapes: shirts, pants, and towels of varying appearances and lengths. They applied forces on the contours of the fabrics to deform them and move them around, with the goal of producing a single model that could handle fabrics of different types and shapes.

The results show that V-CDN’s performance increased as it observed more video frames, according to the researchers, correlating with the intuition that more observations provide a better estimate of the variables governing the fabrics’ behaviors. “The model neither assumes access to the ground truth causal graph, nor … the dynamics that describes the effect of the physical interactions,” they wrote. “Instead, it learns to discover the dependency structures and model the causal mechanisms end-to-end from images in an unsupervised way, which we hope can facilitate future studies of more generalizable visual reasoning systems.”

The researchers are careful to note that V-CDN doesn’t solve the grand challenge of causal modeling. Rather, they see their work as an initial step toward the broader goal of building physically grounded “visual intelligence” capable of modeling dynamic systems. “We hope to draw people’s attention to this grand challenge and inspire future research on generalizable physically grounded reasoning from visual inputs without domain-specific feature engineering,” they wrote.

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Big Data – VentureBeat

MIT takes down 80 Million Tiny Images data set due to racist and offensive content

July 2, 2020 Big Data

Creators of the 80 Million Tiny Images data set from MIT and NYU took the collection offline this week, apologized, and asked other researchers to refrain from using the data set and delete any existing copies. The news was shared Monday in a letter by MIT professors Bill Freeman and Antonio Torralba and NYU professor Rob Fergus published on the MIT CSAIL website.

Introduced in 2006 and containing photos scraped from internet search engines, 80 Million Tiny Images was recently found to contain a range of racist, sexist, and otherwise offensive labels such as nearly 2,000 images labeled with the N-word, and labels like “rape suspect” and “child molester.” The data set also contained pornographic content like non-consensual photos taken up women’s skirts. Creators of the 79.3 million-image data set said it was too large and its 32 x 32 images too small, making visual inspection of the data set’s complete contents difficult. According to Google Scholar, 80 Million Tiny Images has been cited more 1,700 times.

MIT takes down 80 Million Tiny Images data set due to racist and offensive content

Above: Offensive labels found in the 80 Million Tiny Images data set

“Biases, offensive and prejudicial images, and derogatory terminology alienates an important part of our community — precisely those that we are making efforts to include,” the professors wrote in a joint letter. “It also contributes to harmful biases in AI systems trained on such data. Additionally, the presence of such prejudicial images hurts efforts to foster a culture of inclusivity in the computer vision community. This is extremely unfortunate and runs counter to the values that we strive to uphold.”

The trio of professors say the data set’s shortcoming were brought to their attention by an analysis and audit published late last month (PDF) by University of Dublin Ph.D. student Abeba Birhane and Carnegie Mellon University Ph.D. student Vinay Prabhu. The authors say their assessment is the first known critique of 80 Million Tiny Images.

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Both the paper authors and the 80 Million Tiny Images creators say part of the problem comes from automated data collection and nouns from the WordNet data set for semantic hierarchy. Before the data set was taken offline, the coauthors suggested the creators of 80 Million Tiny Images do like ImageNet creators did and assess labels used in the people category of the data set. The paper finds that large-scale image data sets erode privacy and can have a disproportionately negative impact on women, racial and ethnic minorities, and communities at the margin of society.

Birhane and Prabhu assert that the computer vision community must begin having more conversations about the ethical use of large-scale image data sets now in part due to the growing availability of image-scraping tools and reverse image search technology. Citing previous work like the Excavating AI analysis of ImageNet, the analysis of large-scale image data sets shows that it’s not just a matter of data, but a matter of a culture in academia and industry that finds it acceptable to create large-scale data sets without the consent of participants “under the guise of anonymization.”

“We posit that the deeper problems are rooted in the wider structural traditions, incentives, and discourse of a field that treats ethical issues as an afterthought. A field where in the wild is often a euphemism for without consent. We are up against a system that has veritably mastered ethics shopping, ethics bluewashing, ethics lobbying, ethics dumping, and ethics shirking,” the paper states.

To create more ethical large-scale image data sets, Birhane and Prabhu suggest:

Blur the faces of people in data sets
Do not use Creative Commons licensed material
Collect imagery with clear consent from data set participants
Include a data set audit card with large-scale image data sets, akin to the model cards Google AI uses and the datasheets for data sets Microsoft Research proposed

The work incorporates Birhane’s previous work on relational ethics, which suggests that the creators of machine learning systems should begin their work by speaking with the people most affected by machine learning systems, and that concepts of bias, fairness, and justice are moving targets.

ImageNet was introduced at CVPR in 2009 and is widely considered important to the advancement of computer vision and machine learning. Whereas previously some of the largest data sets could be counted in the tens of thousands, ImageNet contains more than 14 million images. The ImageNet Large Scale Visual Recognition Challenge ran from 2010 to 2017 and led to the launch of a variety of startups like Clarifai and MetaMind, a company Salesforce acquired in 2017. According to Google Scholar, ImageNet has been cited nearly 17,000 times.

As part of a series of changes detailed in December 2019, ImageNet creators including lead author Jia Deng and Dr. Fei-Fei Li found that 1,593 of the 2,832 people categories in the data set potentially contain offensive labels, which they said they plan to remove.

“We indeed celebrate ImageNet’s achievement and recognize the creators’ efforts to grapple with some ethical questions. Nonetheless, ImageNet as well as other large image datasets remain troublesome,” the Birhane and Prabhu paper reads.

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Big Data – VentureBeat

TuSimple plans autonomous truck network backed by UPS

July 1, 2020 Big Data

Ahead of the commercial rollout of its driverless trucks by 2024, TuSimple today outlined the launch of what it’s calling the world’s first autonomous freight network. In partnership with UPS, Penske, U.S. Xpress, and McLane Company, the company plans to establish an “ecosystem” of autonomous trucks complemented by digitally mapped routes, strategically placed terminals, and a monitoring system dubbed TuSimple Connect.

TuSimple says it believes the autonomous freight network is the “safest” and “most efficient” way to bring self-driving trucks to market. As the pandemic drives unprecedented growth in the logistics and ground transportation market, Aurora, Waymo, and other rivals are investing increased resources in fully autonomous solutions.

TuSimple’s autonomous freight network will roll out in three phases. During the first — spanning this year to 2021 — TuSimple will offer delivery service between the cities of Phoenix, Tucson, El Paso, Dallas, Houston, and San Antonio. The second phase, which will begin in 2022 and end in 2023, will see an expansion in shipping from Los Angeles to Jacksonville that will connect the East with the West Coast. The third phase, starting in 2023, will grow TuSimple’s driverless operations nationwide, adding routes in 48 states and allowing customers to use TuSimple-equipped trucks on the freight network (with priority given to existing customers and companies with large orders).

Similar deployments in Europe and Asia will follow if all goes well, building upon TuSimple’s delivery pilot in Shanghai. There, as here, businesses will pay TuSimple to haul goods on their behalf.

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TuSimple already operates autonomously on seven different routes between Phoenix, Tucson, El Paso, and Dallas, and it intends to open a shipping terminal in Dallas in alignment with its freight network plans. For high-volume customers, TuSimple tells VentureBeat it will continue to map routes and deliver to distribution centers. And it says it will give clients of all sizes the ability to track freight in real time using TuSimple Connect.

To accelerate the network’s growth, TuSimple completed a series of 22-hour self-driving truck runs along the I-10, I-20, and I-30 corridors through Arizona, New Mexico, and Texas. Leading up to today, the company was completing 50 commercial runs per week, and it now plans to conduct 93 commercial runs per week in the U.S., using 40 trucks.

Penske, which operates more than 750 service facilities throughout North America, has agreed to scale TuSimple’s fleet operations and provide maintenance and over-the-road service. U.S. Xpress and UPS will give TuSimple runs to haul for them, guiding which routes open next and enabling the company to further refine its autonomous technology.

TuSimple — which is reportedly seeking $ 250 million in new funding — is proposing an ambitious timeline, but it’s arguably better-positioned than most to deliver. The company recently expanded a freight-hauling pilot program with UPS to 20 trips per week, with additional routes between Arizona and Texas and El Paso and Dallas, and its trucks are performing revenue-generating trips for over a dozen customers, including Fortune 100 companies and “household names.” Moreover, TuSimple has R&D and testing centers globally in hotspots like Beijing, San Diego, Shanghai, and Tucson. It also has a war chest totaling over $ 298 million, with contributions from Sina, Nvidia, UPS, and Mando.

On the other hand, analysts like Boston Consulting Group’s Brian Collie now believe broad commercialization of autonomous cars won’t happen before 2025 or 2026, at least three years later than originally anticipated. Partly as a consequence of halted real-world vehicle testing, the coronavirus has delayed Lyft rival Waymo’s work by at least two months. Meanwhile, Ford pushed the launch of its driverless vehicle service from 2021 to 2022.

TuSimple’s Class 8 semi-trucks can carry loads exceeding 33,000 pounds and are level 4 under the guidelines penned by the Society of Automotive Engineers (SAE), meaning they’re capable of full autonomy in controlled (and often geofenced) highways and local streets. Unlike self-driving perception platforms from the likes of Uber and Waymo, TuSimple’s is decidedly camera-forward. Using an eight-camera array and other sensors, it can detect cars, pedestrians, and other obstacles up to 1,000 meters away.

TuSimple says its approach enables it to achieve three-centimeter precision for truck positioning, even in inclement weather and at night, and it allows for plenty of leeway in real-time decision-making. It also claims better efficiency than its competitors. TuSimple says its trucks are able to monitor traffic flow far ahead to maintain a given speed more consistently than human drivers and competing autonomous vehicle systems. The company claims this reduces fuel consumption by as much as 15% and average purchased transportation costs by 30%.

TuSimple has plenty in the way of competition, like Thor Trucks and Pronto.ai. There’s also Ike, a self-driving truck startup founded by former Apple, Google, and Uber Advanced Technologies Group engineers that has raised $ 52 million. And then there’s venture-backed Swedish driverless car company Einride. Meanwhile, Paz Eshel and former Uber and Otto engineer Don Burnette recently secured $ 40 million for their startup, Kodiak Robotics. That’s not to mention Embark, which integrates its self-driving systems into Peterbilt semis and launched a pilot with Amazon to haul cargo, or driverless truck solutions from incumbents like Daimler and Volvo.

But demand for driverless trucks is strong. The industry stands to save the logistics and shipping industry $ 70 billion annually while boosting productivity by 30%. According to a recent study from the Consumer Technology Association, a quarter (26%) of consumers now view autonomous delivery technologies more favorably than before the health crisis. Besides cost savings, the growth is driven in part by a shortage of human drivers. In 2018, the American Trucking Associates estimated that 50,000 more truckers were needed to close the gap in the U.S., even despite the sidelining of proposed U.S. Transportation Department screenings for sleep apnea.

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Big Data – VentureBeat

Waymo to expand autonomous truck testing in the American Southwest

June 30, 2020 Big Data

Today during a briefing with members of the media, Waymo head of commercialization for trucking Charlie Jatt outlined the company’s go-to-market plans for Waymo Via, its self-driving delivery division. In the future, Waymo will partner with OEMs and Tier 1 suppliers to equip cloud-based trucks manufactured and sold to the market with its autonomous systems. In addition, Waymo will work with fleets to provide its software services and offer support for things like mapping and remote fleet assistance.

As Waymo transitions to this model, Jatt said that Waymo intends to own and offer its own fleet of trucks — at least in the short term. One of the delivery solutions it’s exploring is a transfer-hub model where, rather than an automated truck covering an entire journey, it’ll be a mix of an automated portion and a portion involving manually driven, human-manned trucks. Automated vehicle transfer hubs close to highways would handle the switch-off and minimize surface street driving.

In a first step toward this vision, Waymo says it’ll soon expand testing on roads in New Mexico, Arizona, and Texas along the I-10 corridor between Phoenix and Tuscon, as previously announced. It this year mapped routes between Phoenix, El Paso, Dallas, and Houston and ramped up testing in California on freeways in Mountain View, but the focus in 2020 will be on the American Southwest.

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Tests will be primarily along Interstates 10, 20, and 45 and through metropolitan areas like El Paso, Dallas, and Houston. Chrysler Pacifica vans retrofitted with Waymo’s technology stack will map roads ahead of driverless Peterbilt trucks as part of a project known as Husky.

Waymo is also engaged with local delivery under the Waymo Via umbrella, the company reiterated. It currently has two partnerships in the Phoenix area — one with AutoNation and one with UPS. On the AutoNation side, Waymo is performing “hot shot” deliveries where Waymo vehicles travel to certain AutoNation locations and deliver car parts. And on the UPS side, the company is ferrying packages from stores to UPS sorting centers.

Waymo began piloting dedicated goods delivery with class A trucks — 18-wheelers — in 2017. After completing tests in 2018 with real loads from Google datacenters in Atlanta, Waymo began limited testing on roads in the San Francisco Bay Area, Michigan, Arizona, and Georgia, and on Metro Phoenix freeways.

Waymo’s autonomous trucks employ a combination of lidars, radars, and cameras to understand the world around them. They have roughly twice as many sensors as Waymo’s cars to handle the trucks’ unique shape and the occlusions they cause, and they place a greater emphasis on long-length perception (the perception range is somewhere beyond 300 meters). But they use the same compute platform found in the fifth-generation Waymo Driver.

As the pandemic drives unprecedented growth in the logistics and ground transportation market, Aurora, TuSimple, and other rivals are investing increased resources in fully autonomous solutions. They stand to save the logistics and shipping industry $ 70 billion annually while boosting productivity by 30%; according to a recent study from the Consumer Technology Association, a quarter (26%) of consumers now view autonomous delivery technologies more favorably than before the health crisis.

Besides cost savings, the growth in autonomous trucking has been driven in part by a shortage of human drivers. In 2018, the American Trucking Associates estimated that 50,000 more truckers were needed to close the gap in the U.S., even despite the sidelining of proposed U.S. Transportation Department screenings for sleep apnea.

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Amazon launches AI-powered code review service CodeGuru in general availability

June 30, 2020 Big Data

Amazon today announced the general availability of CodeGuru, an AI-powered developer tool that provides recommendations for improving code quality. It was first revealed during the company’s Amazon Web Services (AWS) re:Invent 2019 conference in Las Vegas, and starting today, it’s available with usage-based pricing.

Software teams perform code reviews to check the logic, syntax, and style before new code is added to an existing application codebase — it’s an industry-standard practice. But it’s often challenging finding enough developers to perform reviews and monitor the apps post-deployment. Plus, there’s no guarantee those developers won’t miss problems, resulting in bugs and performance issues.

CodeGuru ostensibly solves this with a component that integrates with existing integrated development environments (IDEs) and taps AI algorithms trained on over 10,000 of the most popular open source projects to evaluate code as it’s being written. Where there’s an issue, CodeGuru proffers a human-readable comment that explains what the issue is and suggests potential remediations. The tool also finds the most inefficient and unproductive lines of code by creating a profile that takes into account things like latency and processor utilization.

It’s a two-part system. CodeGuru Reviewer — which uses a combination of rule mining and supervised machine learning models — detects deviation from best practices for using AWS APIs and SDKs, flagging common issues that can lead to production issues such as detection of missing pagination, error handling with batch operations, and the use of classes that are not thread-safe. Developers commit their code as usual to the repository of their choice (e.g. GitHub, GitHub Enterprise, Bitbucket Cloud, and AWS CodeCommit) and add Reviewer as one of the code reviewers. Reviewer then analyzes existing code bases in the repository, identifies bugs and issues, and creates a baseline for successive code reviews by opening a pull request. The service also provides a dashboard that lists information for all code reviews, which reflects feedback solicited from developers.

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CodeGuru Profiler delivers specific recommendations on issues like extravagant recreation of objects, expensive deserialization, usage of inefficient libraries, and excessive logging. Users install an agent in their app that observes the app run time and profiles the app to detect code quality issues (along with details on latency and CPU usage). Profiler then uses machine learning to automatically identify code and anomalous behaviors that are most impacting latency and CPU usage. The information is brought together in a profile that shows the areas of code that are most inefficient. This profile includes recommendations on how developers can fix issues to improve performance and also estimates the cost of continuing to run inefficient code.

Amazon says that CodeGuru — which encodes AWS’ best practices — has been used internally to optimize 80,000 applications, leading to tens of millions of dollars in savings. In fact, Amazon claims that some teams were able to reduce processor utilization by 325% and lower costs by 39% in just a year.

CodeGuru is available now in US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), EU (London), EU (Frankfurt), EU (Stockholm), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo) with availability expanding to additional regions in the coming months. Early adopters include Atlassian, cloud tech consultancy EagleDream Technologies, enterprise software developer DevFactory, condominium review website operator Renga, and scheduling program startup YouCanBook.me.

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Enko Chem raises $45 million to develop crop protection solutions with AI

June 29, 2020 Big Data

Enko Chem, which harnesses machine learning to help farmers sustainably protect their crops from pests and disease, has raised $ 45 million. Founder and CEO Jacqueline Heard said the funds will be used to expand the Boston-based startup’s technology platform and ready its pipeline of crop protection solutions for field testing and regulatory trials. Enko also plans to continue pursuing studies and collaborations with industry partners as part of its broader mission.

Protecting crops from pests is no easy feat. A recent study showed that worldwide, over 600 species of insects and fungi have developed a level of pesticide resistance, threatening losses in yield of between 10.1% and 40.9% for crops like rice, corn, potatoes, and soybeans. In the U.S. alone, insecticides are used to protect crops on approximately 45 million acres, and it’s estimated yields would decrease by 40% to 50% without treatment.

Enko, which was founded in 2017 and incubated by Anterra Capital, a venture fund focused on startups addressing food supply chain challenges, aims to discover safe, economical solutions that promote sustainable staple crop production. The company produces large discovery data sets that are amenable to machine learning and AI models. In tandem with predictive analytics tools, these data sets ostensibly decrease design cycles and bolster success in finding safe, novel, and effective compounds more quickly and cost-effectively than rivals.

Enko uses information about protein targets and pests to inform its product development strategy, sourcing DNA-encoded libraries containing an array of 120 billion compounds. It claims an experimental approach allows it to get an early read on both safety and effectiveness and deliver machine learning- and AI-driven improvements in predictability. Enko says it’s able to cast a broad net and survey hundreds of billions of small molecules while narrowing the scope of the solutions it pursues.

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These predictive analytics tools enable Enko to focus on compounds with the highest likelihood of commercial success, explained a spokesperson. “We use a suite of predictive analytics tools across our pipeline and for every target. We will have billions of data points about target binding, target inhibition, bioactivity, and safety that can be used to train ensembles of machine learning models and heuristics,” the spokesperson told VentureBeat via email. “It would take a very large, concerted, and expensive effort for us to collect a similar training set for a single target using traditional high throughput screening methods. For example, we can use [this] data and models to broadly understand the features that separate compounds that are most likely to be potent, safe, and active in the whole organism versus not. Our database will also grow with time, increasing the power and value of our approach.”

Among other products, Enko is developing insecticides, fungicides, and herbicides for staple crop types in the U.S. and globally. Heard notes that the yields of most fruit and vegetable crops increase 50% to 90% with fungicides and that over 90% of U.S. cropland is now treated with herbicides, an uptick driven by the popularization of no-till farming. No-till farming decreases harmful soil erosion but often isn’t practical without protection against invasive species.

Enko’s goal is to develop alternatives to suites of chemicals like Roundup, which the International Agency for Research on Cancer classified as a “probably carcinogenic” in 2015. Beyond the health risks, overuse of any one chemical eventually allows more resistant pests to flourish, which results in crop damage and leads to food waste. That’s why in addition to its ongoing pilots, Enko hopes to offer some pesticides as a low- or no-cost option to small farms, potentially via a Gates Foundation partnership.

Enko’s recent round was led by the Bill & Melinda Gates Foundation, with participation from existing investors Anterra Capital and Rabo Food & Agri Innovation Fund. It included new investors Finistere Ventures, Novalis LifeSciences, Germin8 Ventures, and TO Ventures Food. The series B follows a $ 7.5 million venture round in July 2018 and brings the company’s total raised to $ 66 million.

As part of the raise, Navalis LifeSciences founder and chair Marijn Dekkers and Finistere Ventures cofounder and partner Spencer Maughan will join Enko’s board of directors. “Small molecule crop protection is a critical lever to sustaining agricultural yields and minimizing land use, but innovation hasn’t kept pace with farmers’ needs and consumers’ safety concerns,” Dekkers told VentureBeat. “By using the unique techniques that led to a paradigm shift in small molecule drug discovery, Enko is now emerging as a new leader in innovation for the next generation of small molecule crop protection products that deliver on the promise of safety and sustainability.”

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Big Data – VentureBeat

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