Category: UNCATEGORIZED

Six months ago, Ford laid out an ambitious plan to expand its GoRide Health transportation service with an aim at delivering thousands of rides every day to hospitals, doctor offices and other health care facilities by the end of the year. In a few weeks, the service, which provided transportation for non-emergency care, will no longer exist — at least in its current form.

GoRide Health is pivoting. The automaker will shut down GoRide Health services in five cities in which it currently operates, including Detroit as well as Toledo, Dayton, Cleveland and Cincinnati, Ohio. The company will now concentrate its efforts on Miami, where it has yet to launch, with a focus on autonomous vehicles.

Ford wouldn’t provide details on what the service in Miami might look like, except that it’s a pilot project that will research how transporting people for non-emergency care like doctor’s appointments matches up with its go-to-market strategy for autonomous vehicles.

Ford has advised its customers and suppliers that the mobility services delivered by GoRide Health over the past two years will move to the next phase by aligning operations with its AV launch cities, a Ford spokesperson said in a email.

The Ford spokesperson said the planned Miami pilot will help the automaker better understand the role AVs can play in this “important transport sector.”

“Our learnings from GoRide Health have led to a shift in strategy,” Ford said in an emailed response to TechCrunch. “We are moving to test the potential for AV technology to help improve access to transportation for those with limited mobility.”

Ford is pursuing two parallel tracks in its development of autonomous vehicles, which will eventually combine ahead of a commercial launch. The automaker is testing and homing in on what its AV business model might look like, while separately developing autonomous vehicle technology.

Argo AI, the Pittsburgh-based company into which Ford invested $1 billion in 2017, is developing the virtual driver system and high-definition maps designed for Ford’s self-driving vehicles. Meanwhile, Ford is testing its go-to-market strategy through pilot programs with partners like Walmart, Domino’s and Postmates, and even some local businesses.

The decision to close GoRide Health lies in stark contrast to Ford’s plans just a few months ago.

In May, Ford announced a multi-year expansion plan that would see the business expand in 2019 from Detroit and Toledo to several other Ohio cities, as well as Miami, Fla. Ford said that the expansion would continue in 2020 with GoRide offering services in North Carolina, Louisiana, Texas and California.

Ford touted its approach at the time, noting that the service had “spent more than a year perfecting its operations and customer experience in Southeast Michigan” before deciding to expand. The company also pointed to GoRide Health’s reputation for its 95% on-time rate through the first quarter of the year, a statistic that attracted the attention of large managed-care organizations.

The planned expansion wasn’t just geographic. GoRide Health was going to assist city transit agencies, beginning with the Greater Dayton Regional Transit Authority (RTA) in Ohio. The aim was to give residents access to transportation via the RTA’s Connect Paratransit and on-demand programs.

The decision to end the services and change its strategy was made recently and timed with supplier contract extensions. It’s being shuttered despite Ford’s contention that the service was well-received and achieved high on-time rates thanks to its dynamic routing technology that automatically dispatches and pools rides.

AWS today announced CodeGuru, a new machine learning-based service that automates code reviews based on the data the company has gathered from doing code reviews internally.

Developers write the code and simply add CodeGuru to the pull requests. It supports GitHub and CodeCommit, for the time being. The CodeGuru uses its knowledge of reviews from Amazon and about 10,000 open source projects to find issues and then comments on the pull request as needed. It will obviously identify the issues, but it will also suggest remediations and offer links to the relevant documentation.

Encoded in CodeGuru are AWS’s own best practices. Among other things, it also finds concurrency issues, incorrect handling of resources, and issues with input validation.

AWS and Amazon’s consumer side have used the profiler part of CodeGuru for the last few years to find the ‘most expensive line of code.’ Over the last few years, even as some of the company’s applications grew, some teams were able to increase their CPU utilization by over 325 percent at 36 percent lower cost.

Today at AWS re:Invent in Las Vegas, the company announced AutoPilot, a tool that gives you greater visibility in automated machine learning model creation. This is available as part of the new SageMaker Studio also announced today.

As AWS CEO Andy Jassy pointed out on stage today, one of the problems with AutoML is that it’s basically a black box. “First they build this OK, simple model initially, but that is a total black box. If you want to improve a mediocre model or just evolve it for your business, you you have no idea how it was built.” he explained.

The idea behind AutoPilot is to give you the ease of model creation you get from and AutoML-generated model, but also giving you much deeper insight into how the system built the model. “AutoMl in a way to create a model automatically but give full visibility and control,” Jassy said.

You can look at the model’s parameters, and see 50 automated model, then the it provides you with a leader board of what models were the best. You can look at the notebook, and also see what trade-offs were made to generate that best model. It may be the most accurate, but sacrifices speed to get that.

Your company may have its own set of unique requirements and you can choose the best model based on whatever parameters you consider to be most important for your company, even though this was generated in an automated fashion.

With this insight and visibility into your automatically generated model, you can pick the best one based on you requirements. Once you have the model, you like best, you can go into SageMaker Studio, select it and launch it with a single click.

Today at Amazon re:Invent, Amazon announced a new ability to manage Cassandra databases on AWS.

Already used by companies as diverse as Grubhub.com, Netflix, Ooyala, Openwave, Reddit, and Uber, to manage distributed NoSQL databases, that handle large amounts of data across commodity servers,  the new Amazon Managed Apache Cassandra Service, is AWS’ attempt to offer Cassandra directly instead of through third party vendors.

The Amazon MCS is serverless so users pay for the resources they use and it can automatically scale up and down in response to application traffic, the company says. It enables the development of applications that can serve thousands of requests per second with unlimited throughput and storage (something that can be incredibly attractive for IOT customers).

With the new services, developers can run Cassandra workloads on AWS using the same application code and developer tools they’re already using. Application updates just require changing the endpoint to the one in the Amazon MCS service table, the company said.

The data is also encrypted at rest by default using encryption keys that are stored in AWS Key Management Service (KMS). And Amazon MCS is also integrated with AWS Identity and Access Management (IAM) to help developers manage and access table data, the company said.

Amazon also said it would work with the Cassandra API libraries and contribute to bug fixes to the open source Apache Cassandra project. AWS is charging for on-demand capacity during the preview and at general availability the company will make provisioned throughput available for more predictable workloads.

For now, the product is a part of Amazon’s free tier and for the first three months companies can receive a free tier of 30 million write request units, 30 million read request units and one gig of storage.

Amazon says Cyber Monday 2019 has now become the retailer’s biggest shopping day of all-time, based on the number of items sold worldwide. However, in the U.S., a different trend took shape over the big sales holiday weekend kicked off by Black Friday. This year, Walmart became the No. 1 shopping app in the U.S. on Black Friday for the first time ever, according to App Annie and Sensor Tower’s analysis.

Walmart’s app reached No. 1 among all shopping the U.S. after peaking on Thanksgiving as No. 6 among all apps (not just shopping), noted App Annie, based on both iOS and Android downloads.

Sensor Tower confirmed the same, noting that Walmart was the most-installed shopping app on the U.S. App Store on Black Friday, with 113,000 new downloads, representing a year-over-year increase of 23%.

Remarkably, this made 2019 the first year that Amazon’s app didn’t top the App Store’s list of most-downloaded shopping apps, the firm says.

Amazon’s app was a close second with 102,000 first-time installs, but this figure represented a 10% decrease from Black Friday 2018.

The further top 10 list included: Target, Best Buy, GOAT, Nike, Kohl’s, Wish, Macy’s, and Adidas. Combined, these top 10 most-download apps grew 11% year-over-year, reaching a total 527,000 installs. That’s 28.8% of all downloads for the Shopping category — the largest percentage since the Shopping category’s creation in 2015.

Overall, the number of first-time downloads in the U.S. App Store’s Shopping category on Black Friday increased 8% year-over-year, and totaled 1.8 million downloads.

Though Walmart was No 1 in the U.S., Amazon was No. 1 when apps were ranked globally instead of U.S.-only and it was No. 1 among all U.S. online-only apps.

While Sensor Tower’s analysis is based on Black Friday installs, not Cyber Monday data, Amazon’s worldwide dominance on this day does seem to back up the retailer’s own claims of its record-breaking sales on a global basis on Monday.

Amazon said customers bought “hundreds of millions” of products between Thanksgiving and Cyber Monday. And it said “millions more” customers bought Amazon devices, like Echo Dot and Fire TV Stick, compared with the same period last year. (That may be true, but Amazon devices are also more broadly available to customers worldwide compared with 2018 — so perhaps this isn’t the fairest comparison.)

Walmart, meanwhile, didn’t tout its Cyber Monday event’s numbers, but said “millions” of customers joined for Black Friday sales online and in stores.

One thing that could have boosted Walmart’s app downloads this year was its focus on the in-store customer.

The app now includes an updated Store Map that makes it easier to navigate Walmart’s aisles, and it features “Check out with Me” — a way for shoppers to avoid checkout lines and instead check out with store personnel in the aisles.

At its re:Invent conference, AWS CEO Andy Jassy today announced the launch of SageMaker Studio, a web-based IDO for building and training machine learning workflows. It includes everything a data scientist would need to get started with, including ways to organize notebooks, data sets, code and models, for example. It essentially wants to be a one-stop-shop for all the machine learning tools and results you need to get started.

At the core of Studio is also the ability to share projects and folders with others who are working on the same project, including the ability to discuss notebooks and results.

Since you need to train those models, too, the service is obviously integrated with AWS’s SageMaker machine learning service, which can automatically scale based on your needs.

In addition to Studio, AWS also today announced a number of other updates to SageMaker that are integrated into Studio. Most of these run under the hood of Studio, but you can also use them as stand-alone tools. These include a debugger, a monitoring tool, and Autopilot, which automatically creates the best models for you based on your data, with full visibility into how it decides to build your models.

Related to this AWS also launched SageMaker Notebooks today, which is also integrated into Studio. These are, in essence, notebooks as a managed service. Data scientists won’t have to provision instances for this as the service will automatically provision them as necessary.

Ideally, Studio will make building models significantly more accessible to a wider range of developers. AWS calls this the middle-layer of the stack, which is meant for machine learning practitioners who don’t want to delve into all the details but still have a lot of hands-on control.

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1. Cyber Monday totalled $9.2B in US online sales, smartphones accounted for a record $3B

Cyber Monday — the final day of the extended Thanksgiving weekend that traditionally kicks off holiday season spending — broke another e-commerce record: U.S. shoppers racked up a total of $9.2 billion in online sales, according to figures from Adobe.

That said, there is an undercurrent of sluggishness. Following the pattern set during Thanksgiving and Black Friday, Adobe had predicted that spending would reach $9.4 billion — so the actual total fell a bit short.

2. DHS wants to expand airport face recognition scans to include US citizens

In a filing, the department has proposed that all travelers — not just foreign nationals or visitors — will have to complete a facial recognition check before they are allowed to enter the U.S., and also to leave the country.

3. Facebook expands its efforts against ad discrimination

Under the terms of a settlement with the ACLU and other civil rights groups earlier this year, Facebook has been taking steps to prevent discriminatory ad targeting. Today, it’s expanding the enforcement of these rules beyond Facebook Ad Manager to encompass every other place where someone might buy ads on Facebook.

4. AWS launches Braket, its quantum computing service

Amazon isn’t building its own quantum computer. Instead, it’s partnering with D-Wave, IonQ and Rigetti and making their systems available through its cloud.

5. Twitter launches a Privacy Center to centralize its data protection efforts

The Twitter Privacy Center will host information about Twitter’s initiatives, announcements and new privacy products, as well as other communication about security incidents. The company says it wanted to create a centralized resource so it would be easier to find all the information about Twitter’s work in this area.

6. Why Notion is staying small as its valuation gets bigger

We interview Akshay Kothari, COO of work tools startup Notion, in which he discusses the company’s philosophy of staying small — as well as challenges to this strategy as competitors raise massive sums. (Extra Crunch membership required.)

7. Marvel’s new ‘Black Widow’ trailer teases the spy thriller Natasha Romanoff deserves

It’s kind of crazy that we’re only getting a “Black Widow” movie now, but at least the cast — David Harbour! Florence Pugh! Rachel Weisz!! — looks amazing.

AWS announced a new tier for Amazon Elasticsearch Service. And it could potentially lead to some cost savings. It is now available in preview. There are now two storage tiers for Amazon Elasticsearch Service — hot and UltraWarm.

Hot is still the most effective tier when you care about performance. You can use the hot tier for indexing and anything that requires fast access to data.

With UltraWarm, you can store up to 900TB of storage and get up to 90% in cost reduction compared to existing options. With UltraWarm, Amazon Elasticsearch Service looks at blocks of data and determines if they’re frequently accessed or not frequently accessed. Not frequently accessed data is moved to S3, which helps you save money.

Interestingly, you can query and visualize Elasticsearch data across both hot and UltraWarm data. Data stored on the UltraWarm tier uses the same APIs, so you can use the same tools you already use. You can also enable encryption at rest and in flight, integrated alerting, SQL querying just like you were already doing.

The UltraWarm tier leverages he AWS Nitro System to cache and query data. Customers can access the preview in the US East (N. Virginia) and US West (Oregon) regions.

At its re:Invent conference, AWS CEO Andy Jassy today announced the launch of AQUA (the Advanced Query Accelerator) for Amazon Redshift, the company’s data warehousing service. As Jassy noted in his keynote, it’s hard to scale data warehouses when you want to do analytics over that data. At some point, as your data warehouse or lake grows, the data starts overwhelming your network or available compute, even with today’s highspeed networks and chips. So to handle this, AQUA is essentially a hardware-accelerated cache and promises up to 10x better query performance than competing cloud-based data warehouses.

“Think about how much data you have to move over the network to get to your compute,” Jassy said. And if that’s not a problem for a company today, he added, it will likely become one soon, given how much data most enterprises now generate.

With this, Jassy explained, you’re bringing the compute power you need directly to the storage layer. The cache sits on top of Amazon’s standard S3 service and can hence scale out as needed across as many nodes as needed.

AWS designed its own analytics processors to power this service and accelerate the data compression and encryption on the fly.

Unsurprisingly, the service is also 100% compatible with the current version of Redshift.

In addition, AWS also today announced next-generation compute instances for Redshift, the RA3 instances, with 48 vCPUs and 384GiB of memory and up to 64 TB of storage. You can build clusters of these with up to 128 instances.

Today at AWS re:Invent in Las Vegas, the company announced that Elastic Kubernetes Service is available on Fargate.

EKS is Amazon’s flavor of Kubernetes. Fargate is a service announced in 2017 that enables you to launch containerized applications without worrying about the underlying infrastructure.

“Starting today, you can start using Amazon Elastic Kubernetes Service to run Kubernetes pods on AWS Fargate. Amazon EKS and Fargate make it straightforward to run Kubernetes-based applications on AWS by removing the need to provision and manage infrastructure for pods,” the company wrote in a blog post announcing the new feature.

Pods are simply a group of containers you launch on the same Kubernetes cluster. If you think about the fact that Kubernetes enables you to launch these pods in an automated fashion, it makes sense to also provision the underlying infrastructure required to run those pods in an automated fashion.

“With AWS Fargate, you pay only for the amount of vCPU and memory resources that your pod needs to run. This includes the resources the pod requests in addition to a small amount of memory needed to run Kubernetes components alongside the pod. Pods running on Fargate follow the existing pricing model,” the company wrote in the blog.

That means developers won’t have to worry about over provisioning because Fargate should run the exact number of resources needed to run that pod at any given moment and no more.

This feature is available starting today in US East (N. Virginia), US East (Ohio), Europe (Ireland), and Asia Pacific (Tokyo).