One of CES' major trends over the last few years has been the connected car -- the concept of adding Internet connectivity and networking to our vehicles. Stealing the spotlight this year was Nvidia, which launched the Drive PX 2 -- an in-car artificial intelligence system. PX 2 is designed for automakers exploring autonomous driving and includes 360-degree situational awareness, deep learning and the processing power of 150 MacBook Pros. Deep learning -- an advanced type of artificial intelligence (AI) -- is driving significant change for autonomous vehicles and for the automotive and transportation industries in general, according to a new report from advisory firm KPMG. The study predicts that by 2030 a new mobility services segment linked to products and services related to autonomy, mobility, and connectivity will be worth more than $1 trillion worldwide.

This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Scala: Scala is a general purpose programming language - like Java or C . It's functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark. Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback.

In this article, we discuss various strategies used to generate exponential traffic growth, while preserving traffic quality, and user loyalty. Raw data science: getting the right data sets, leveraging them, Playing with various tools and API's: designing an automated machine-to-machine communication service between Hootsuite and Twitter / LinkedIn based on insights automatically distilled from the following data sources: (1) data obtained via the Google Analytics API (traffic statistics about 50,000 live DSC articles), and (2) data collected via a web crawler written in Python A blend of high-level (strategic) data science and low-level (tactical or operational) data science. In the end, relatively little coding is involved in the process. Domain expertise and smart innovation play a critical role. Optimizing parameters of the statistical process used to select articles, create tweets, and schedule them, using experimental design and A/B testing Artificial intelligence: detection and removal of articles that are time-sensitive, automated creation of relevant hash-tags for selected tweets, and creation of a taxonomy of all our articles using simple indexing classification scheme Smart analytic-driven advertising on Twitter, using a good list of data science thought leaders worth following, as our core data set for advertising purposes. The creation of this list is an interesting data science project in itself.

IBM Watson's AI is trying to find new cancer treatments IBM continued Watson's evolution on Thursday when the computing company announced it will use the artificial intelligence system to seek out new treatments for cancer patients. The plan also includes a partnership with drug-maker Pfizer, and for good reason: It will be one of the first companies to use Watson for drug research and the first to customize the A.I. for its own purposes. Pfizer will use Watson to "support the identification of new drug targets, combination therapies for study, and patient selection strategies in immuno-oncology." This collaboration is part of IBM's efforts to use Watson as a medical tool. The company said that the A.I. has "ingested" more than 1 million articles from medical journals, 4 million patents, and 25 million abstracts from Medline, a medical supplies manufacturer and distributor.

The tiny bird, named Obi, is a member of the second smallest parrot species The aim of the study was to gain insight into how birds generate enough lift to fly Obi was trained to fly through lasers illuminating tiny aerosol particles This enabled researchers to track the movement of the particles, and in turn, the air movement created by the bird's flapping wings This enabled researchers to track the movement of the particles, and in turn, the air movement created by the bird's flapping wings A brave bird named Obi was issued with a pair of special goggles before flying through lasers in a bizarre experiment. This created the clearest picture to date of the wake left by a flying animals, said the researchers. The REAL paleo diet: More than 9,000 plant remains reveal... So much for rise of the machines: Starship drone inventor is... Rare weasel species makes a comeback: Pacific fishers... Malaria caused'widespread death' in ancient Rome: DNA... The REAL paleo diet: More than 9,000 plant remains reveal...

In July of this year, we sent out a series of survey questions to past guests who have been featured on the TechEmergence podcast, including academic researchers, founders, and executives who are experts in the machine learning domain. We received 58 total responses from 30 researchers and executives (the survey structure allowed respondents to choose from one to four relevant response categories, with an average individual response rate of 1.93 chosen categories). On the whole, the trend in responses aligns with what we might have predicted, based on previous proprietary editorials/research and external published media on the topic. For example, the apparent optimistic bent towards healthcare & pharmaceuticals, followed by eCommerce, aligns with CB Insights' tracking of popular areas for artificial intelligence venture capital deals in 2016 (an exception is robotics, though this may be a domain "on the horizon"). At the end of this article, we expand on insights received from our survey, specifically taking a closer look at trends in the industries where experts expressed the most optimism and touching on why some of these industries are more likely to be disrupted than others.

The new platform uses artificial intelligence to help drive the transition from traditional marketing creative to digital-first, mobile video marketing for businesses of all sizes. Magisto for Marketers introduces Intelligent Marketing creative into Magisto's patented artificial intelligence engine – Emotional Sense Technology . This gives marketing professionals the ability to easily create marketing videos for a variety of disciplines including content marketing, product marketing, brand marketing and performance marketing. Magisto's new offering allows marketers to create multiple variations of optimized videos by use case, platform, audience, message, product and ROI performance. The AI technology incorporates easy-to-use editing styles designed for businesses, the ability to add custom branding to clone videos and to direct Magisto's AI to refine messaging and produce multiple A/B test creatives.

This post is the third in a series of posts that I'm writing about Monte Carlo (MC) simulation, especially as it applies to econometrics. If you've already seen the first two posts in the series ( and) then you'll know that my intention is to provide a very elementary introduction to this topic. There are lots of details that I've been avoiding, deliberately. In this post we're going to pick up from where the previous post about estimator properties based on the sampling distribution left off. Specifically, I'll be applying the ideas that were introduced in that post in the context of regression analysis. We'll take a look at the properties of the Least Squares estimator in three different situations. In doing so, I'll be able to illustrate, through simulation, some "text book" results that will know about already. If you haven't read the immediately preceding post in this series already, I urge you to do so before continuing.

It may have been a difficult year for the wider world, but 2016 did at least see a lot of excellent video games, from the glossy action movie thrills of Uncharted 4 to the agenda-setting multiplayer fun of Overwatch and the solemn dystopian vision of Inside. But we wanted to know what the industry itself thinks were the best games to come out in the past 12 months. To find out, we asked 50 of our favourite developers, including 30-year veterans, Bafta award winners and rising indie stars. We've split the list into categories, and at the end we have a list of the most popular titles of them all. Karla Zimonja (Fullbright) Known for: BioShock 2, Gone Home Working on: Tacoma My favorite game this year is Dishonored 2, which I am still in the midst of. I love stealth games, so I'm taking my time with this one! Emily's powers are super satisfying to combine in entertaining ways. Anne Lewis (Bethesda Softworks) Known for: Doom, Dishonored 2 Working on: Prey It might sound like I'm shilling for my company when I say this, but Dishonored 2 is my favorite game of 2016. Dishonored is one of the reasons I wanted to work for Bethesda.

Ride-hailing requires a lot of machine smarts to maintain a competitive edge, so it's not surprising to see Uber make a strategic acquisition in the artificial intelligence space. The company has acquired Geometric Intelligence, a startup co-founded by academic researchers with AI experience, and its team will provide the core for a new central AI lab being established at Uber's SF HQ. Uber's doing a lot with machine learning already through its research team in Pittsburgh, but they're focused specifically on solving issues related to autonomous driving. This new core team will be looking at applications for AI more broadly, with a focus on basic research that's likely to have impact across a range of potential uses, including things like route management. It's also yet another sign that Uber wants to be passed among tech bigs like Google, Apple and Microsoft whose interests range beyond a single domain.