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Nvidia and Mercedes-Benz detail self-driving system with automated routing and parking




Nvidia today shed light on an expanded collaboration with Mercedes-Benz to roll out an in-vehicle computing system and AI infrastructure starting in 2024, which was first revealed last January. The two companies say the platform will launch across the fleet of next-generation Mercedes-Benz vehicles, imbuing those vehicles with upgradable automated driving functions.

The efforts build on a longstanding collaboration between Nvidia and Mercedes. At the 2018 Consumer Electronics Show, the companies showcased a concept cockpit dubbed the Mercedes-Benz User Experience, which infused AI into car infotainment systems. And in July 2018, Nvidia and Mercedes along with Bosch announced a partnership to operate a robo-taxi service in San Jose.

A headlining feature of the forthcoming Nvidia-designed system for Mercedes vehicles, which will be based on the former’s Drive product, is the ability to automate driving of regular routes from any address to address. In addition, the platform will allow customers to download in-car safety, convenience, entertainment, and subscription apps and services via an over-the-air in-car system akin to Tesla’s.

Nvidia Mercedes-Benz self-driving

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Nvidia’s Drive AGX Orin will power the new platform. It slots alongside Nvidia’s existing AGX Drive platforms — AGX Drive Xavier and AGX Drive Pegasus — and it’s architected to run a large number of apps and AI models while achieving safety standards such as ISO 26262 ASIL-D. At the heart of Orin is a system-on-chip comprising 17 billion transistors in total that integrates with Nvidia’s graphics chip architecture and Hercules cores, both of which are complemented by AI and machine learning accelerator cores that deliver 200 trillion operations per second (TOPS) compared with Pegasus’ 320 TOPS and Xavier’s 30 TOPS. Orin can handle over 200Gbps of data while consuming only 60 watts to 70 watts of power (at 200 TOPS), all told.

The Nvidia-Mercedes platform will also benefit from access to the models at the core of Drive. Nvidia plans to make available AI subsystems tailored to tasks like traffic light and sign recognition, object-spotting of vehicles and pedestrians, path perception, and gaze detection and gesture recognition. One model recently spotlighted on the company’s blog automatically generates control outputs for cars’ high beams using signals derived from road conditions.

Each Drive model can be customized and enhanced with Nvidia’s newly released suite of tools, which enable training using a range of machine learning development techniques. There’s active learning, for example, which improves accuracy and reduces data collection costs by automating data selection using AI; federated learning, which enables the use of data sets across countries and with other parties while maintaining data privacy; and transfer learning, which leverages pretraining and fine-tuning to develop models for specific apps and capabilities.

Nvidia Mercedes-Benz self-driving

Nvidia and Mercedes intend to jointly develop the AI and automated vehicle applications capable of level 2 and 3 self-driving, as well as automated parking functions up to level 4. According to the Society of Automotive Engineers, level 2 entails systems that take full control of vehicles but require drivers to be prepared to intervene at any time, while level 3 allows drivers to safely turn their attention away from driving tasks and level 4 requires no driver attention for safety.

The reveal of Nvidia’s and Mercedes’ self-driving platform comes after Ford unveiled an autonomous driving system to rival Tesla’s Autopilot. First available on the Mach-E followed by other models in Ford’s 2021 lineup, notably the all-new F-150, Active Drive Assist can control vehicle speed and steering through cameras and radar on pre-mapped roads. Meanwhile, GM recently pledged to expand its semi-autonomous highway assist system, Super Cruise, to 22 vehicles by 2023, including 10 by next year.



Microsoft Data Science Interview Questions




Microsoft has been a big player in the data science industry after Azure and it’s machine learning tools have been slowly dominating as the biggest service provider in the cloud-computing market. As a result, Microsoft has been building out its data science team slowly but surely over the past five years to become one of the biggest companies hiring for the role.

The Data Scientist Role

The role of a data scientist at Microsoft varies a lot and is dependent on whichever team you interview with. Each Microsoft data science job is different and spans from analytics-based roles to more machine learning heavy. As a huge multi-conglomerate corporation, Microsoft has different teams that work on speech and language, artificial intelligence, machine learning infrastructure on Azure, data science consulting for cloud computing, and much more.

Required Skills

Microsoft generally prefers to hire experienced candidates with about a minimum of 2+ years of experience working in data science for a mid-level role. General qualifications are:

  • Ph.D. in a quantitative field and previous experience in DNN, NLP, time series, reinforcement learning, network analysis, causal inference or any related areas.
  • Proficiency in any of the following numerical programming languages (Python/Numpy/Scipy, R, SQL, C#, or Spark).
  • Experience with cloud-based architectures such as AWS or Azure.

What are the types of data scientists?

Microsoft has a department under engineering that is called data and applied science. Employees in this department are often placed in teams and go by three main titles: data scientists, applied scientists, and machine learning engineers. Depending on the team their functions would include:

  • Writing codes to ship models to production.
  • Writing codes for machine learning algorithms to be used by other data scientists.
  • Working with customers directly or indirectly to resolve technical issues.
  • Working on metrics and experimentation.
  • Working on product features.

The ideal candidate for the Microsoft Data and Applied Scientist role is expected to be able to apply a breadth of machine learning tools and analytical techniques to answer a wide range of high-impact business questions and present the insights in a concise and effective manner.

The Microsoft Data Scientist Interview

Microsoft Data Science Interview

Azure ML

Initial Screen

After submitting your application for the job, the first phone interview may or may not be a recruiter depending on the seniority level of the role. Many times the hiring manager will conduct a 30 minute interview first to understand your past experience.

Expect this part of the phone interview to come in two parts. You will be asked about your background and projects as well as a few technical interview questions. The technical interview questions will be more theoretical along the lines of explaining how a machine learning concept works or a quick probability or statistical problem.


  • What’s the difference between lasso and ridge regression?
  • How would you explain how a deep learning model works to a business person?
  • How would you define a p-value to someone who’s non-technical?

The Technical Screen

After the hiring manager screen, the recruiter will schedule a second more technical screen with a Microsoft data scientist. Generally this screen is 45 minutes to an hour and designed to test pure technical skills and how well you can code and explain your thought-process.

The technical screen consists of around three different questions covering the topics of algorithms, SQL coding, and probability and statistics. Expect questions akin to data structures and algorithms in Python along with data processing type questions.


  • Given an array of words and a max width parameter, format the text such that each line has exactly X characters.
  • Write a query to randomly sample a row from a table with 100 million rows.
  • What’s the probability that you roll at least two 3s when rolling three die?

The Onsite Interview

The onsite interview consists of a full day event from 9 am to 4 pm. You will meet with five different data scientists and go on a lunch interview as well.

Here’s what the interview panel generally looks like:

  • Probability and statistics
  • Data structures and algorithms
  • Modeling and machine learning systems
  • Hiring manager and behavioral interview
  • Data manipulation
  • You’ll also spend 1:1 time with one or two data scientists during a lunch break to learn more about Microsoft and the team. This is usually a one hour lunch interview that they’ll let you take a break or talk through what they work on.

The onsite interview will be mostly a combination of all the different technical concepts. Remember to study different model assessment metrics in different circumstances, the bias/variance tradeoff of coefficients under collinearity, open-ended questions about sampling schemes, experimental and A/B testing design, explaining p-values to a 5 year old, different concepts of Bayes’ theorem, and teaching the interviewer a statistical learning technique of your choice.

Another big focus for Microsoft is on communication, since the data science team at Microsoft has partnerships throughout the organization to ensure the team is doing useful work.

You can find many of the data structures and algorithm questions on Interview Query or Leetcode. It’s also advisable to get a white-board to practice writing code on, given how different is coding on a whiteboard versus the computer.

Sample Microsoft Data Science Interview Questions

  • How would you select a representative sample of search queries from six million?
  • Find the maximum of sub sequence in an integer list?
  • Give an example of a scenario where you would use Naive Bayes over another classifier?
  • How would you explain what MapReduce does as concise as possible?
  • What is the ROC curve and the meaning of sensitivity, specificity, confusion matrix?
  • The autocomplete feature: How would you implement it and can you highlight the flaws in this tool today?
  • Describe efficient ways to merge a given k sorted arrays of size n each.

Check out Interview Query for more data scientist interview questions.

This article was originally published on Interview Query Blog and re-published to TOPBOTS with permission from the author.

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The Tour de France Is Going Virtual, and It Starts This Weekend




The coronavirus pandemic has changed the way we do things, big-time. The events, places, and activities we were used to enjoying have been canceled, closed, or in some cases, permanently shut down. Virtual versions of just about everything have sprung up: meetings, concerts, parties, classes, conventions. This week another event was added to the list of things gone virtual: the Tour de France.

First held in 1903, the Tour de France has gone on every year since, with the only exceptions being during the first and second World Wars. As of right now, the in-the-flesh tour is still scheduled to take place, though it’s been pushed back to an August 29 start date (it usually takes place in July).

With all the smaller cycling races that usually go on during the summer having been canceled, the virtual Tour will give cyclists some motivation to train, and a chance to see how they stack up against their competitors (whose training routines have no doubt been equally disrupted over the last few months). Participants will be on stationary bikes in their homes rather than real bikes on the road, and there are some other key differences between the virtual Tour and the real thing.

For starters, the Tour is normally broken down into 21 parts, or “stages,” each classified as flat, hilly, or mountain. Cyclists have 23 consecutive days to complete all the stages, with the total distance spanning a whopping 3,500 kilometers (2,200 miles), about the distance from San Francisco to Chicago.

The virtual Tour will look a little different (or, let’s be honest—a lot different. About as different as possible while still being called a bike race). Rather than consecutive days, the race will happen over three weekends in July, with six stages lasting one to two hours apiece. As in real life, each stage will tend toward being mostly hilly, mountain, or flat (meaning participants will need to be adjusting the resistance on their trainer bikes and sometimes standing or crouching to simulate climbing a hill; if you’ve ever done a spin class, you know how it works).

The race will be conducted on a virtual platform called Zwift. Zwift isn’t brand-new—it’s been around for a few years—and it markets itself as a training app for cyclists, runners, and triathletes. Athletes use a treadmill or stationary bike in combination with an array of sensors plus their laptop or smartphone. They can access customized training programs and join virtual races against other users all over the world.

Ideally, competitors in the virtual Tour will have a big screen in front of them simulating their ride through virtual environments, some of which Zwift created especially for this event. For the first two days of the race, riders will bike through Watopia, a virtual world created by Zwift. But the company also rushed to build new, custom worlds for the Tour, mainly mimicking the real-life locations where the race usually takes place, including the French countryside, a 6,263-foot peak in Provence called Mont Ventoux, and the finish line on the famous Champs-Elysées in Paris.

In one cycling coach’s opinion, riding on Zwift can actually feel more physically challenging than being out on a real bike, for three reasons: it’s harder for your body to cool off, the bike’s resistance works differently, and “your motivation dwindles due to not having the wind in your hair and the road moving underneath you.”

That last point is key. The pandemic has played out very differently than it would have just 10 years ago; technologies like Zoom and Slack allowed millions of people to work from home, our smartphones helped us stay ultra-connected even when physically apart, and quick access to information kept us informed of what was going on.

Of course, talking to our friends or watching musicians stream on a screen will never be a good-enough substitute for doing these things in person, just as riding a stationary bike through a virtual world will never give you that wind-in-your-hair, road-beneath-your-feet feeling.

But in a time when we have no choice but to appreciate the small things, it’s better than the alternative, which is… nothing. Alas, depending how the pandemic continues to play out, we may be in for a highly virtualized future, with events we never would’ve thought could go virtual finding a way to do just that.

23 men’s teams and 17 women’s teams have registered for the virtual bike race, including the last three winners of the real-life event. “Footage” will be broadcast in more than 130 countries.

Let’s just hope all the contestants have stable internet connections.

Image Credit: Zwift


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My Invisalign app uses machine learning and facial recognition to sell the benefits of dental work




Align Technology uses DevSecOps tactics to keep complex projects on track and align business and IT goals.

Woman Putting Transparent Aligner In Teeth

Image: AndreyPopov/Getty Images/iStockphoto

Align Technology’s Chief Digital Officer Sreelakshmi Kolli is using machine learning and DevOps tactics to power the company’s digital transformation.

Kolli led the cross-functional team that developed the latest version of the company’s My Invisalign app. The app combines several technologies into one product including virtual reality, facial recognition, and machine learning. Kolli said that using a DevOps approach helped to keep this complex work on track.

“The feasibility and proof of concept phase gives us an understanding of how the technology drives revenue and/or customer experience,” she said. “Modular architecture and microservices allows incremental feature delivery that reduces risk and allows for continuous delivery of innovation.”

SEE: Research: Microservices bring faster application delivery and greater flexibility to enterprises (TechRepublic Premium)

The customer-facing app accomplishes several goals at once, the company said:

  • Offers a preview of life after braces via SmileView
  • Sends weekly treatment reminders
  • Keeps patients in touch with their doctors during treatment 

More than 7.5 million people have used the clear plastic molds to straighten their teeth, the company said. Align Technology has used data from these patients to train a machine learning algorithm that powers the visualization feature in the mobile app. The SmileView feature uses machine learning to predict what a person’s smile will look like when the braces come off. 

Kolli started with Align Technology as a software engineer in 2003. Now she leads an integrated software engineering group focused on product technology strategy and development of global  consumer, customer and enterprise applications and infrastructure. This includes end user and cloud computing, voice and data networks and storage. She also led the company’s global business transformation initiative to deliver platforms to support customer experience and to simplify business processes.

Kolli used the development process of the My Invisalign app as an opportunity to move the dev team to DevSecOps practices. Kolli said that this shift represents a cultural change, and making the transition requires a common understanding among all teams on what the approach means to the engineering lifecycle. 

“Teams can make small incremental changes to get on the DevSecOps journey (instead of a large transformation initiative),” she said. “Investing in automation is also a must for continuous integration, continuous testing, continuous code analysis and vulnerability scans.”  
To build the machine learning expertise required to improve and support the My Invisalign app, she has hired team members with that skill set and built up expertise internally.

“We continue to integrate data science to all applications to deliver great visualization experiences and quality outcomes,” she said.

Align Technology uses AWS to run its workloads.

Aligning business and IT goals to power transformation

In addition to keeping patients connected with orthodontists, the My Invisalign app is a marketing tool to convince families to opt for the transparent but expensive alternative to metal braces. 

Kolli said that IT leaders should work closely with business leaders to make sure initiatives support business goals such as revenue growth, improved customer experience, or operational efficiencies, and modernize the IT operation as well. 

“Making the line of connection between the technology tasks and agility to go to market helps build shared accountability to keep technical debt in control,” she said. 

Align Technology released the revamped app in late 2019. In May of this year, the company released a digital version tool for doctors that combines a photo of the patient’s face with their 3D Invisalign treatment plan.

This ClinCheck “In-Face” Visualization is designed to help doctors manage patient treatment plans.

The visualization workflow combines three components of Align’s digital treatment platform: Invisalign Photo Uploader for patient photos, the iTero intraoral scanner to capture data needed for the 3D model of the patient’s teeth, and ClinCheck Pro 6.0. ClinCheck Pro 6.0 allows doctors to modify treatment plans through 3D controls.

These new product releases are the first in a series of innovations to reimagine the digital treatment planning process for doctors, Raj Pudipeddi, Align’s chief innovation, product, and marketing officer and senior vice president, said in a press release about the product. 

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