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How Conversational AI Makes In-Car Infotainment Systems Safer




When we talk about the benefits of conversational AI, it’s normally about making life faster and easier for people to interact with technology. But last week, in conjunction with the UK’s Transport Research Laboratory (TRL), we looked at totally different benefit — safety.

With driver distraction estimated to be a factor in up to 30% of vehicle collisions across Europe, IAM Roadsmart, FIA Road Safety and Rees Jeffreys Road Fund commissioned the TRL to look at the impact of in-vehicle infotainment systems such as Android Auto and Apple CarPlay on driving performance.

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Evidence shows that driver distraction levels are much higher when using touch screen technologies compared to voice activated systems. Key findings in the report show that participants underestimated the time they thought they spent looking away from the road when engaging via touch control.

“Driver distraction is widely recognised as an important road safety issue; the more attention a driver diverts away from the main driving task, the more their driving behaviour will be negatively affected,” says Dr Neale Kinnear, Head of Behavioural Science at TRL. “The results of this study clearly show that touch control infotainment systems are highly distracting to drivers, far more so than voice activated systems. However, even current voice control systems increase drivers’ reaction times and remains a concern for road safety.”

Currently, voice control systems only understand a set of key commands. What’s required are speech enabled interfaces that operate at a complex level.

According to the TRL, by improving voice activated systems through the use of such solutions as conversational artificial intelligence, it’s possible to make infotainment systems less distracting for drivers. Because of the flexibility offered by conversational AI, this remit will widen to include all forms of dialogue to enable a versatile, natural interaction. Once this is achieved, in-car voice control systems will be revolutionised.

The TRL is recommending that that more work is put into developing a framework for testing and improving the human factors of such systems. This includes improving voice activation as a method of control, as this has the opportunity to greatly reduce the workload on a driver thanks to innovations being made in conversational AI.

TRL would like to see safety standards improved around infotainment systems, not just by their definition, but also through the harmonisation of standards across the entire transport sector.

I was fortunate enough to be a part of a recent online event with the TRL to talk about these issues. You can see my part of my presentation here, where I speak about how conversational AI is changing in-car systems and some of the challenges car manufacturers faced up until recently.

There is no doubt that conversational AI will be the defining technology of the next decade. As it becomes smarter, faster and more advanced, AI will be of immense benefit to in-vehicle systems by allowing more complex user demands to be understood and fulfilled, as well as enabling multi-directional interaction to occur between the system and the consumer.



Using a Credit Card to Get Ahead Financially




When managed correctly, a credit card can help you take charge of your finances so you can get ahead. You can avoid high-interest rates and fees when you select the right credit card for your circumstances. 

When you take out a new credit card it should be a decision made with a clear head. You can compare different products side by side to determine whether or not you are getting a good deal. 

If you are looking to make changes to your budget, here are a few ways your credit card can support your financial goals.

Rewards Points

Using your credit card for day-to-day purchases can be beneficial. Some companies offer a rewards program that gives consumers points when they spend money on dining out, shopping, gas and groceries. The point system may vary between cards, so it is important to choose the best solution for your shopping habits.

You can save up your points to receive discounted or free merchandise, flights, cruises, or charity donations. You don’t need to change your spending patterns, as these points will be awarded every time you shop.


For those who would like more money in their pocket, a cashback credit card is a great option. Depending on your eligibility and requirements, you could receive a percentage back when you use your card. 

You could receive between 1 and 5 percent back, and this might suit you better than a traditional rewards program. Some credit card companies such as USAA give added cashback benefits to military personnel.

Say Goodbye to Late Fees

Your household bills won’t all come at once, which can make budgeting difficult. If you feel as though you are living week to week, it can cause significant financial stress. You might be subjected to late fees from a utility company or service provider, and a credit card could be the solution. Remember, when you pay your bills on time you can take advantage of rewards or cashback.

Once your salary hits your bank account you can pay off your credit card. It is worth monitoring your spending as you may be able to avoid paying interest if your balance is paid by the due date.

Secure Transactions

Using a credit card has other benefits. Increased security and transparency will give you peace of mind that your money is safe. Credit card companies are experienced in dealing with identity theft and will often flag any questionable activity at the time it occurs.

You could be given extended warranties on any purchases you make, and you should not be liable for any unauthorized purchases. When you use a credit card from a reputable company, you can shop knowing you are protected. There won’t be any severe financial implications if something unexpected happens, so you can stay on track.

Using a Credit Card to Get Ahead Financially

When managed correctly, a credit card can help you get ahead. With rewards or cash back, you will benefit every time you shop. With added protection, your money will be secure, and you will not be responsible for any unauthorized purchases.

Paying your bills on time with a credit card could save you from late fees, and as long as you keep your balance low, your credit card can give you a helping hand.


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Netflix Data Science Interview Questions




Netflix is a streaming media company headquartered in Los Gatos, California that has permeated culture as the largest content media company disrupted by tech. Founded in 1997, Netflix started out as a DVD rental service, and then expanded to the streaming business. Now Netflix had over 150 million paid subscriptions worldwide, including its 60 million US users. With streaming supported on over a thousand devices and around 3 billion hours watched every month, data is collected on over 100 billion events per day.

Data science is in the DNA of Netflix and Netflix leverages data science in improving every aspect of the user experience. Netflix has over the years been leveraging data science for its content recommendation engine, to decide which movies and tv shows to produce and to improve user experience.

The Data Science Role at Netflix

The role of a data scientist at Netflix is heavily determined by the team. However, general data scientist roles at Netflix cut across business analytics, statistical modeling, machine learning, and deep learning implementation. Netflix is a large company that has data scientists working in over 30 different teams including personalization and algorithms, marketing analytics team, and the product research and tooling team, with skillsets ranging from basic analytics to heavy machine learning algorithms.

Required Skills

Netflix hires only qualified data scientists with at least five years of relevant experience. Their requirements are very specific and recruiters are keen to hire specifically for each job role. It helps to have industry experience specific to the role on the team.

Other relevant qualifications include:

  • Advanced degree (MS or PhD) in Statistics, Econometrics, Computer Science, Physics, or a related quantitative field.
  • 5+ years of relevant experience with a proven track record of leveraging massive amounts of data to drive product innovation.
  • Experience with distributed analytic processing technologies (Spark, SQL, Pig, Presto, or Hive) and strong programming skills in Python, R, Java, or Scala.
  • Experience in building real-world machine learning models with demonstrated impact.
  • Deep statistical skills utilized in A/B testing, analyzing observational data, and modeling.
  • Experience in creating data products and dashboards in Tableau, R Shiny, or D3.

What are the data science teams at Netflix?

The term data science at Netflix encompasses a wide scope of fields and titles related to data science. The title data scientist comprises of roles and functions that span from product analytics-focused data scientists to data engineering and machine learning functions.

  • Personalization Algorithms: Collaborate with product and engineering teams to evaluate the performance and optimize personalization algorithms used to suggest movies, TV shows, artwork, and trailers to Netflix members.
  • Member UI Data Science and Engineering: Leveraging custom machine learning models to optimize the user experience of the product for all subscribers.
  • Product Research and Tooling: Developing and implementing methods to advance experimentation at Netflix at scale. This involves developing data visualization frameworks, tools, and analytics applications that provide other teams with insights into member behavior and product performance.
  • Growth Data Science and Engineering: Focus on growing the subscriber base by building and designing highly scalable data pipelines and clean datasets around key business metrics.
  • Marketing Data Science Engineering: Creating reliable, distributed data pipelines and building intuitive data products that provide stakeholders with means of leveraging data across domains in a self-service manner for all non-technical teams.

The Interview Process

The data science interview process at Netflix is similar to other big tech companies. The interview process starts with an initial phone screen with a recruiter and then a short hiring manager screen before proceeding to a technical interview. After passing the technical screen, an onsite interview will be scheduled. This interview comprises of two parts with 6 or 7 people.

Initial Screen

The initial screen at Netflix is a 30 minute phone call with a recruiter. The recruiters at Netflix are highly specialized and very technical. Their job is to understand your resume and see if your past experience, projects, and skillset matches up to the role. The second point of this part of the interview is to test your general communication skills and explain the role and its background to you.

Next is the hiring manager interview. This one will focus more on past experience and dive into more of the technical portion of what you’ve done within data science and machine learning. While the recruiter gets a sense of your projects at a high level to fit with the team, the hiring manager will ask you more in-depth questions like why you used certain algorithms for a project or how you built different machine learning or analytics systems.

The hiring manager will also get to tell you more about the roles and responsibilities of the team. Note that Netflix is big on the culture and values, and you may be asked to pick a value and explain how best it suits you.

Technical Screen

After passing the initial screening, the technical screen is the next step in the interview. This interview is usually 45 minutes long, and it involves technical questions that span across SQL, experimentation and A/B testing, and machine learning technical questions.

Example Questions:

  • What do you know about A/B testing in the context of streaming?
  • What are the differences between L1 and L2 regularization, why don’t people use L0.5 regularization for instance?
  • What is the difference between online and batch gradient descent?
  • What is the best way to communicate ML results to stakeholders?

Onsite Interview

The onsite interview is the last stage in the interview process, and it comprises of two-part interviews with a lunch break in-between. If you’re from out of state, Netflix will fly you out to Los Altos or Los Angeles for the on-site and you’ll first meet with the recruiter to go over the interview.

It involves one-on-one interviews with 6 or 7 people including data scientist team members, team managers, and a product manager. The Netflix onsite interview is a combination of product, machine learning, and various analytical concepts. This interview will comprise of questions around product sense, statistics including A/B testing (hypothesis testing), SQL and Python coding, experimental and metric design, and culture fit. If the role is more focused on engineering, expect more machine learning and possibly deep learning interview questions.

Notes and Tips

  • Remember, the goal of the interview is to assess how you can apply analytical concepts and machine learning algorithms and models to predict value in users and content. Brush up on knowledge of statistics and probability, A/B testing and experimental design, and regression and classification modeling concepts.
  • Please, please, please remember to read the Netflix culture deck. Culture is everything at Netflix and they have created a unique and famous work culture that they have transcribed into a 100+ page slide deck online.
  • At its core, Netflix’s culture is about building a team of high performers and setting them up in an environment that enables them to excel. This is represented by a healthy amount of freedom & responsibility, strong context provided by managers with limited top-down control, and a compensation and promotion system that rewards A-players.
  • In offer negotiation, note that the compensation packages at Netflix are extremely high. Their average salaries for technical hires exceed $300,000 and many times is almost always in cash with an option to convert some into RSUs. This is why their interviews are difficult, and baseline to hire is super high.

Netflix Data Science Interview Questions

    • Write the equation for building a classifier using Logistic Regression.
    • Given a month’s worth of login data from Netflix such as account_id, device_id, and metadata concerning payments, how would you detect payment fraud?
    • How would you design an experiment for a new content recommendation model we’re thinking of rolling out? What metrics would matter?
    • Write SQL queries to find a time difference between two events.
    • How would you build and test a metric to compare two users’s ranked lists of movie/tv show preferences?
    • How would you select a representative sample of search queries from five million?
    • Why is Rectified Linear Unit a good activation function?
    • If Netflix is looking to expand its presence in Asia, what are some factors that you can use to evaluate the size of the Asia market, and what can Netflix do to capture this market?
    • How would we approach to attribution modeling to measure marketing effectiveness?
    • How would you determine if the price of a Netflix subscription is truly the deciding factor for a consumer?

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

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The 3D Printed Homes of the Future Are Giant Eggs on Mars




Last month, a 3D printed house that can float on a pontoon was unveiled in the Czech Republic. Last year, work started on a community of 3D printed houses for low-income families in Mexico. While building homes with 3D printers is becoming more scalable, it’s also still a fun way to play around with unique designs and futuristic concepts for our living spaces.

It doesn’t get much more futuristic than living on Mars—and guess what? There’s a 3D printed home for that, too. In fact, there are a few; last year saw the conclusion of a contest held by NASA called the 3D Printed Habitat Challenge.

The long-running competition, started in 2015, tasked participants with creating homes that would be viable to build on Mars. Teams had to consider not just the technology they’d use, but what type of material will be available on the Red Planet and what kind of features a Martian home will need to have for a human to survive (and ideally, to survive comfortably); the structures need to be strong enough to make it through a meteor collision, for example, and able to hold an atmosphere very different than the one just outside their walls.

Artist rendering of the second-level kitchen and office. Image Credit: AI Space Factory

The top prize ($500,000) went to AI Space Factory, a New York-based architecture and construction technologies company focused on building for space exploration. Their dual-shell, four-level design is called Marsha, and unlike Martian habitats we’ve seen on the big screen or read about in sci-fi novels, it’s neither a dome nor an underground bunker. In fact, it sits fully above ground and it looks like a cross between a hive and a giant egg.

The team chose the hive-egg shape very deliberately, saying that it’s not only optimized to handle the pressure and temperature demands of the Martian atmosphere, but building it with a 3D printer will be easier because the printer won’t have to move around as much as it would to build a structure with a larger footprint. That means less risk of errors and a faster building speed.

“It’s important to be structurally efficient as a shape, because that means you can use less material,” said David Malott, AI Space Factory’s founder and CEO. “If you think about an eggshell on Earth, [it’s] a very efficient shape. The eggshell can be very, very thin, and still it has the right amount of strength.”

Artist rendering of the home’s top floor rec area. Image Credit: AI Space Factory

The home’s layout is like a multi-level townhouse, except with some Mars-specific tweaks; the first floor is both a preparation area, where occupants can get suited up before heading outside, and a “wet lab” for research. There’s a rover docking port just outside the prep area, attached to the house.

On the second floor is what I’d consider the most important room—the kitchen—and the third floor has a garden, bathroom, and sleeping pods that take the place of bedrooms (sorry, no space for your antique dresser or Ikea desk here). The top floor is a rec area where you can recreate either by watching TV or exercising—or perhaps both simultaneously.

It took 30 hours to build a one-third scale model of the home, but this doesn’t mean it would take 90 hours to build the real thing; printing during the contest was done in 10-hour increments, and since the model contains all the same structural aspects of the full-size home, the 3D printer would just need to expand its reachable surface area and height to print the real thing.

If all goes as planned (which, really, there are no plans yet; just ideas), there will be plenty of material on hand to build the real thing in the real place (Mars, that is). AI Space Factory collaborated with a materials design company called Techmer PM to come up with a super-strong mix of basalt fiber—which would come from rocks on Mars—and a renewable bioplastic that could be made from plants grown on Mars. In NASA’s tests, the material was shown to be stronger and more durable than concrete and more resistant to repeated freezes and thaws.

The company was set to open an Earth version of Marsha, called Tera, in upstate New York this past March, and people leaped at the chance to pay $175-500 to sleep in the structure for a night; but the plans were derailed by the coronavirus pandemic, and the company hasn’t yet announced a re-opening of the Earthbound cabin.

Image Credit: AI Space Factory


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