Here's Why Apple Isn't Building an Amazon Echo Rival


Taken together, these steps all mean that if you're a heavy Apple user, Siri is probably always at your beck and call. Echo owners can use the device for everything from setting a timer while cooking to controlling other web-connected smart home devices, like light fixtures. After talking with Apple executives, I've come away with the impression that they're more interested in turning Siri into an omnipresent AI assistant across devices, rather than designing a single device specifically to serve as a Siri machine. Apple is also very interested in positioning Siri as a control hub for smart home devices, as evidenced by a recent and impressive HomeKit demo I observed.

Machine Learning for Echocardiographic Imaging JACC: Journal of the American College of Cardiology


Computers are incredibly fast, accurate, and stupid. Human beings are incredibly slow, inaccurate, and brilliant. Together they are powerful beyond imagination.--Albert As I read the exciting work of Narula et al. (2) published in this issue of the Journal, I could not help but be transported back to the early days of my career at Mayo Clinic in Rochester, Minnesota. It is always enjoyable to take a journey down memory lane!

Amazon Echo Dot the home companion


It's a 360 degree speaker with voice recognition powered by Amazon Alexa voice service. Amazon Echo Dot has the avabilityes to play music, give news, weather, sport results, traffic and much more. It can even order pizza from Domino's Pizza.

AI's Factions Get Feisty. But Really, They're All on the Same Team


Vigoda claims his particular breed of probabilistic programming can produce AI that learns more quickly than neural networks, using much smaller amounts of data. Probabilistic programming lets researchers build machine learning algorithms more like coders build computer programs. In fact, Google researchers are building systems that combine the two. "Deep neural networks and probabilistic models are closely related," says David Blei, a Columbia University computer scientist and an advisor to Gamalon who has worked with Google research on these types of mixed models.

Object Tracking using OpenCV (C /Python)


We will learn how and when to use the 6 different trackers available in OpenCV 3.2 -- BOOSTING, MIL, KCF, TLD, MEDIANFLOW, and GOTURN. There are 6 different trackers available in OpenCV 3.2 -- BOOSTING, MIL, KCF, TLD, MEDIANFLOW, and GOTURN. Note: OpenCV 3.1 has implementations of these 5 trackers -- BOOSTING, MIL, KCF, TLD, MEDIANFLOW. Even if the current location of the tracked object is not accurate, when samples from the neighborhood of the current location are put in the positive bag, there is a good chance that this bag contains at least one image in which the object is nicely centered.

Time Series Analysis - Theory and Practice SkillsCast


Tetiana is a mathematician turned data scientist currently working with NanoTechGalaxy on developing machine learning algorithms for medical image processing. She is also working on AI risk research as part of the Pareto Fellowship awarded by the Centre of Effective Altruism.

Siri has some fresh thoughts on love just in time for Valentine's Day


If you're the kind of person who likes to talk love on Valentine's Day, you can now stop annoying your Valentine's Day-hating peers (we know they're out there). Apple has equipped Siri with some custom answers to Valentine's Day queries, from booking a romantic restaurant or choosing an appropriately saccharine playlist. Things take a turn for the awkward and strange when you ask Siri for its thoughts on love. So it seems Siri wins the ability to answer the most questions related to love and Valentine's Day.

Joint Attention and Brain Functional Connectivity in Infants and Toddlers Cerebral Cortex


Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. The neural substrates underlying IJA in early childhood are poorly understood (Barak and Feng 2016), due in part to significant methodological challenges in imaging localized brain function that supports social behaviors in children during the first 2 years of life.

Probabilistic Pentesting


Pentesting tools like Metasploit, Burp, ExploitPack, BeEF, etc. are used by security practitioners to identify possible vulnerability points and to assess compliance with security policies. Pentesting tools come with a library of known exploits that have to be configured or customized for your particular environment. This configuration typically takes the form of a DSL or a set of fairly complex UIs to configure individual attacks. There are two major shortcomings with this approach (1) scanning doesn't yield perfect knowledge (2) scanning generates significant network traffic and can run for a very long time on a large network (Sarraute). It is perhaps due to these shortcomings (and maybe 0day exploits) that "most testing tools, provide no guarantee of soundness.

An extensive list of European AI tech startups to watch in 2017


I wanted to show the great potential of European AI startups and eventually put together a list that I would like to share with all of you. I've been looking into the European AI and ML landscape for a while and I realized during my research that this list could be valuable to many of us, especially if you are: Many others have put together great overviews of AI or ML startups across the world like MMC Ventures' UK AI Landscape, the European Machine Intelligence Landscape from Project Juno, and The Current State of Machine Intelligence 3.0 from Bloomberg Beta's Shivon Zilis. Frank Chen from Andreessen Horowitz listed the subfields of AI as: 1) reasoning, 2) knowledge representation, 3) planning and navigation, 4) natural language processing, 5) perception, and 6) general intelligence (including emotional intelligence, creativity, moral reasoning, intuition, etc.). Typical application areas can be categorized by industry (finance, insurance, manufacturing, healthcare,…), function (marketing, sales, developer tools, HR, compliance,…), or technology focus (computer vision, natural language processing, VR/AR, autonomous systems,…).