FedEx robot sent packing by NYC

FedEx’s autonomous delivery bot got a cold reception from New York City officials.

After the company’s SameDay Bots — named Roxo — popped up on New York City streets last week, Mayor Bill de Blasio and transportation officials delivered a sharp response: Get out.

FedEx told TechCrunch that the bots were there for a preview party for its Small Business Saturday event and are not testing in New York. Even this promotional event was too much for city officials concerned with congestion and bots taking jobs from humans.

After reports of the bot sightings, the mayor tweeted that FedEx didn’t receive permission to deploy the robots; he also criticized the company for using a bot to perform a task that a New Yorker could do. The New York Department of Transportation has sent FedEx a cease-and-desist order to stop operations the bots,  which TechCrunch has viewed.

The letter informs FedEx that its bots violate several vehicle and traffic laws, including that motor vehicles are prohibited on sidewalks. Vehicles that receive approval to operate on sidewalks must receive a special exemption and be registered. 

First of all, @FedEx, never get a robot to do a New Yorker’s job. We have the finest workers in the world.

Second of all, we didn’t grant permission for these to clog up our streets. If we see ANY of these bots we’ll send them packing. https://t.co/XxJIrIW9vr

— Mayor Bill de Blasio (@NYCMayor) November 23, 2019

FedEx has been experimenting with autonomous delivery bots. Postmates and Amazon also have been testing autonomous delivery robots.

FedEx first unveiled its SameDay Bot in February 2019. The company said at the time it planned to work with AutoZone, Lowe’s, Pizza Hut,  Target, Walgreens and Walmart to figure out how autonomous robots might fit into its delivery business. The idea was for FedEx to provide a way for retailers to accept orders from nearby customers and deliver them by bot directly to customers’ homes or businesses the same day.

FedEx said its initials test would involve deliveries between selected FedEx Office locations. Ultimately, the FedEx bot will complement the FedEx SameDay City service, which operates in 32 markets and 1,900 cities.

The company has tested the bots in Memphis, Tennessee as well as Plano and Frisco, Texas and Manchester, New Hampshire, according to a spokesperson.

Wall-E out here flexing all over FedEx delivery drivers #whatisnewyork pic.twitter.com/TOevthTUJQ

— WhatIsNewYork (@whatisny) November 21, 2019

The underlying roots of the SameDay Bot is the iBot. The FedEx bot was developed in collaboration with DEKA Development & Research Corp. and its founder Dean Kamen who invented the Segway  and iBot wheelchair.

DEKA built upon the power base of the iBot, an FDA-approved mobility device for the disabled population, to develop FedEx’s product.

The FedEx bot is equipped with sensing technology such as LiDAR and multiple cameras, which when combined with machine learning algorithms should allow the device to detect and avoid obstacles and plot a safe path, all while following the rules of the road (or sidewalk).

from Amazon – TechCrunch https://techcrunch.com/2019/11/26/fedex-robots-sent-packing-by-nyc/

New Amazon capabilities put machine learning in reach of more developers

Today, Amazon announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas.

While the company offers plenty of tools for data scientists to build machine learning models and process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL.

By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood.

“This announcement is all about is making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases,” Wood told TechCrunch.

For starters, Wood says developers can take advantage of Aurora, the company’s SQL (and Postgres) compatible database to build a simple SQL query into an application, which will automatically pull the data into the application and run whatever machine learning model the developer associates with it.

The second piece involves Athena, the company’s serverless query service. As with Aurora, developers can write a SQL query — in this case, against any data store — and based on a machine learning model they choose, return a set of data for use in an application.

The final piece is QuickSight, which is Amazon’s data visualization tool. Using one of the other tools to return some set of data, developers can use that data to create visualizations based on it inside whatever application they are creating.

“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we’re announcing today help to make ML more usable and accessible to database developers and business analysts. Now anyone who can write SQL can make — and importantly use — predictions in their applications without any custom code,” Amazon’s Matt Assay wrote in a blog post announcing these new capabilities.

Assay added that this approach is far easier than what developers had to do in the past to achieve this. “There is often a large amount of fiddly, manual work required to take these predictions and make them part of a broader application, process or analytics dashboard,” he wrote.

As an example, Wood offers a lead-scoring model you might use to pick the most likely sales targets to convert. “Today, in order to do lead scoring you have to go off and wire up all these pieces together in order to be able to get the predictions into the application,” he said. With this new capability, you can get there much faster.

“Now, as a developer I can just say that I have this lead scoring model which is deployed in SageMaker, and all I have to do is write literally one SQL statement that I do all day long into Aurora, and I can start getting back that lead scoring information. And then I just display it in my application and away I go,” Wood explained.

As for the machine learning models, these can come pre-built from Amazon, be developed by an in-house data science team or purchased in a machine learning model marketplace on Amazon, says Wood.

Today’s announcements from Amazon are designed to simplify machine learning and data access, and reduce the amount of coding to get from query to answer faster.

AWS releases SageMaker to make it easier to build and deploy machine learning models

from Amazon – TechCrunch https://techcrunch.com/2019/11/26/new-amazon-capabilities-put-machine-learning-in-reach-of-more-developers/

Amazon launches medication management features for Alexa

As Amazon moves further into the healthcare market, the company today is rolling out a medication management feature for Alexa owners. The feature will allow customers to set up their own medication reminders and request voice refills using their prescription information. At launch, these capabilities are only available to customers of Giant Eagle Pharmacy, a regional retailer in the Midwest and East Coast.

That being said, there are obvious ties to Amazon’s larger plans with regard to prescription management and healthcare. Amazon has now acquired two health startups, first with online pharmacy PillPack in 2018 for slightly less than $1 billion. This was followed by last month’s acquisition of Health Navigator, which will become a part of Amazon’s pilot healthcare service program for its employees, the recently launched Amazon Care.

The new Alexa features seem to be custom designed for integrations with both Amazon Care and PillPack prescription ordering, even though neither of the two services are referenced today as part of Amazon’s current or future plans with the Alexa features.

Asked about this, an Amazon spokesperson said only that the company would not “comment or speculate on the future.”

Instead, Amazon says it has teamed up with medication management solution and adherence tool provider Ominicell to enable the new features, which were inspired by how people were already using Alexa’s reminders system and other feedback.

For example, some customers said they would like to set time frames for reminders like “twice a day.”

To use the new Alexa medication management, customers will first need to enable the Giant Eagle Pharmacy skill and link their accounts. They’ll also need to create an Alexa voice profile, which helps Alexa to verify the person who is speaking, and they’ll need to create a personal passcode for an extra layer of security. Amazon notes that it had already rolled out a way for developers to build HIPAA-compliant skills using its platform, which not only includes the added authentication steps, but also redacts users’ interactions with the skill from the Alexa app for further privacy.

In addition, Amazon had also recently added a way for customers to view and delete recordings at any time, including from the Privacy Settings page, in the Alexa app, or by voice.

Once their account is set up, the customer can then say “Alexa, manage my medication” to get started setting up their reminders. Alexa will help the customer to review their current prescriptions and set up reminders based on when they prefer to take each medication.

When the reminders go off, customers can ask “Alexa, what medication am I supposed to take right now?”

When it’s time, customers can also use Alexa to request refills from the pharmacy by saying “Alexa, refill my prescription.”

The features, though limited to one regional pharmacy for the time being, offer a view into how Amazon envisions voice-ordering for prescriptions will work for its customer base, and how such a system could be integrated with its own health care program at some later date, perhaps.

“Voice has proven to be beneficial for a variety of use cases because it removes barriers, and simplifies daily tasks. We believe this new Alexa feature will help simplify the way people manage their medication by removing the need to continuously think about what medications they’ve taken that day or what they need to take,” noted Rachel Jiang, Head of Alexa Health & Wellness, in an announcement about the new features.

“We want to make it easy for people to get the information they need and to manage their healthcare needs at home while maintaining the privacy and security of their information, and hope this feature is a step toward that vision,” she added.

 

 

from Amazon – TechCrunch https://techcrunch.com/2019/11/26/amazon-launches-medication-management-features-for-alexa/

A chance for better

Perfect is the enemy of good.

Of course it is.

But that simple sentence becomes more urgent when we realize that nothing (and no one) is perfect. How could it be?

And so, if your hero, your cause, your holiday, your background, your relationship… if it’s not perfect, does that mean you should hide it? Be ashamed of it? Be afraid of it?

We’re surrounded by injustice, and yesterday was even worse. It’s so easy to find things that are imperfect and criticize them or worse, shame them.

Better, I think, to find glimmers of good and seek to amplify them. Mistakes can be seen, errors can be improved upon, progress can be made. But only if we embrace the chance for good.

The imperfect is an opportunity for better.

       

from Seth Godin's Blog on marketing, tribes and respect https://feeds.feedblitz.com/~/610226170/0/sethsblog~A-chance-for-better/

New Amazon tool helps machine learning models identify unique objects

Amazon announced a new capability today called Amazon Rekognition Custom Labels to help customers train machine learning models to understand a set of objects when there is a limited set of information.

Typically, machine learning models have to work on large data sets to learn something like what’s a picture of a dog, as opposed to some other animals. Amazon Rekognition Custom Labels can work with a limited data set to teach the algorithm a group of objects specific to a given use case.

“Instead of having to train a model from scratch, which requires specialized machine learning expertise and millions of high-quality labeled images, customers can now use Amazon Rekognition Custom Labels to achieve state-of-the-art performance for their unique image analysis needs,” the company wrote in a blog post announcing the new feature.

For example, you may want to teach the model to identify a set of engine parts, a limited set of information, which has a lot of meaning to a specific use case. Less information like this actually poses a problem for most machine learning models, but this feature has been designed specifically to learn from a smaller amount of data. Instead of hundreds or thousands of images, Amazon Rekognition Custom Labels can work with as few as ten images to learn to identify the object.

Amazon has gotten flack from the ACLU and shareholders in the past for selling Amazon Rekognition to law enforcement to help identify faces. This feature offers a more benign use of similar technology.

The new feature goes live next week on December 3rd, right in time for AWS re:Invent, the company’s customer conference taking place in Las Vegas.

Amazon facial recognition software raises privacy concerns with the ACLU

from Amazon – TechCrunch https://techcrunch.com/2019/11/25/new-amazon-tool-helps-machine-learning-models-identify-unique-objects/

Amazon-backed Shuttl raises $18M to expand its app-based bus aggregator in India

Shuttl, a startup that runs an app-based bus aggregator service in India, said on Monday that it has raised $18 million in a new financing round as it looks to scale its business in the country.

Toyota Tsusho Corporate and SPARX Group, through its Mirai Creation Fund II, funded Shuttl’s Series C financing round, the four-year-old startup said. Shuttl, which is based in Gurgaon and counts Amazon as one of its investors, has raised about $66.5 million to date.

Shuttl operates about 1,800 buses that clock over 100,000 rides each day in six cities in India. Customers book their rides through the app and on-board the bus through specified bus stations.

The buses on the platform are equipped with a range of safety features such as an emergency button that automatically slows down the bus until completely stopping at the nearest bus stop. It also offers a live feed that any passenger could share with their loved ones.

Another mandatory feature requires drivers to identify themselves before starting the journey and take an instant alcohol test. Passengers, who are required to book a ticket in advance of riding the bus — different from how traditional buses operate in India — are also authenticated before they can get on with their rides.

These safety features have made the service especially popular among women, Amit Singh, cofounder and chief executive of Shuttl, told TechCrunch in a recent interview. More than 40% of Shuttl’s passengers are female, who find the rides on its buses a safer commute option. This is a promising feat, as women only make up for about 20% of India’s workforce, according to industry estimates.

Singh said that Shuttl, which recently added new routes in New Delhi, Chennai, and Kolkata, will use the fresh capital to grow within and beyond its circle of six cities.

Unlike Ola and Uber, Shuttl has been slow with its expansion. Singh explained that Shuttl’s business is different from any other app-based transportation service provider. “If they sign up a large number of drivers, they can service a city rather quickly. For buses, it is different. For one, a bus is not going to show up to a customer’s door. So you have to first figure out different routes. You have to identify a route, establish pick-up points, train drivers, and persuade customers to follow these routes,” he added.

But he is optimistic that Shuttl is inching closer to reaching “escape velocity” — which when it has hit, it would be able to scale at a faster pace.

In a statement, Shigeru Harada, chief operating officer for automotive division of Toyota Tsusho Corporation, said, “Shuttl has taken the lead in solving for traffic congestion and air pollution through a technology-enabled mass transport solution. We look forward to work together with them to disseminate relatively energy efficient MaaS solutions, such as Shuttl’s app-based mass transportation service, through our global network.”

Earlier this year, Shuttl started to provide meals on its buses. Singh said he continues to explore what all value-added services the startup could offer to customers and also to make best use of its logistics network.

As for numbers, Shuttl doubled its revenue in the fiscal year that ended in March. Its revenue crossed 100 crore Indian rupees, or $14 million.

from Amazon – TechCrunch https://techcrunch.com/2019/11/25/shuttl-india-bus-aggregator/

If every day were Thanksgiving

It’s my favorite holiday for a good reason: It doesn’t matter what country, what culture or what background you come from…

Gratitude works.

Gratitude scales.

Gratitude creates a positive cycle of more gratitude.

When in doubt, default to gratitude.

[And, for the fourth year in a row, we’re offering the free Thanksgiving Reader. You can print it out at home and have it ready for the holiday, wherever and whenever you choose to celebrate. It’s a modern tradition.]

       

from Seth Godin's Blog on marketing, tribes and respect https://feeds.feedblitz.com/~/610104986/0/sethsblog~If-every-day-were-Thanksgiving/