Kolibree, a smart oral care company, has unveiled what it claims is the first toothbrush embedded with AI at CES 2017. The Ara is clearly no ordinary mouth scrubber. Just read how Kolibree founder and CEO Thomas Serval describes Ara's tech: "Patented deep learning algorithms are embedded directly inside the toothbrush on a low-power processor," he said in a release. "Raw data from the sensors runs through the processor, enabling the system to learn your habits and refine accuracy the more it's used." Ara picks up precisely where you're brushing, and will log information, such as frequency, duration and location, in either on or offline mode.

WHAT: TechCode, through a strategic partnership with the new AI Frontiers Conference, will host a demo day on Thursday, January 12, featuring startups with a niche focus on artificial intelligence. The TechCode AI Accelerator Demo Day will include demos from fifteen startups and a Q&A session from a panel of Venture Capitalists. In its first year, the AI Frontiers Conference brings together leaders in applied artificial intelligence from companies such as Google, Facebook, Microsoft and Amazon to share insights into the latest deep learning advancements and products. The conference will cover six major areas including speech-enabled assistants, Internet of Things, natural language processing, computer vision, deep-learning frameworks and autonomous driving. All the startups featured in the demo day will be available for in-person interviews at the event.

The Internet of Things (IoT) is touching every technology sector around the world, and it's having a significant impact on how enterprises and consumers interact with machines and devices. TechRepublic talked to IoT experts in a range of disciplines to find out what they think the biggest trends will be in 2017. Participants were Kevin Curran, IEEE senior member and senior lecturer in computer science at Ulster University; Francesco Cetraro, head of registrations, .cloud; Artificial intelligence, augmented reality, virtual reality, healthcare IoT, industrial IoT, and wearables are some of the topics of conversation about where the Internet of Things is headed in 2017. Kevin Curran: "AI, and machine learning in particular, is the process of building a scientific model after discovering knowledge from a data set. It is the complex computation process of automatic pattern recognition and intelligent decision making based on training sample data. AI techniques can replicate some specific elements of intellectual ability. Computers can already solve problems in limited realms. The basic idea of AI is simple but its execution is complicated. First, the AI algorithm gathers facts about a situation through sensors or human input. The computer compares this information to stored data and decides what the information signifies. The computer runs through various possible actions and predicts which action will be most successful based on the collected information. The IT giants such as Google, Microsoft, Facebook, and others have all been using AI techniques in various research projects. Google acquired DeepMind technologies who use neural networks and deep learning methods that deploy low-level transistor networks to produce high-level effects."

As we kick off what will surely be another very exciting year of progress in artificial intelligence, machine learning and data science, we start with a quick recap of our "Top 10" most popular posts (based on aggregate readership) from the year just concluded. We announced a major set of updates to the Data Science VM (DSVM) in September last year. DSVM gives you a comprehensive set of tools for data movement, storage, exploration/visualization, modeling with ML/AI algorithms, and operationalization – and using multiple languages in either Linux or Windows environments. Just last month, we announced our latest and most powerful version of Microsoft R Server. Supporting popular operating systems and a variety of data sources, MRS 9.0 helps you create and deploy sophisticated analytics models for real world problems, efficiently and at scale.

I wanted to try and detect glioblastoma in diseased brain MRI from healthy ones, but I changed it to tiles because I guess for medical imaging it was a nightmare trying to scour the web and collect images to train on.

Before the likes of Oral-B started selling Bluetooth-enabled, app-connected toothbrushes, there was Kolibree. The startup developed one of the first "smart" toothbrushes that incentivized regular brushing and documented oral hygiene habits. We caught the first Kolibree brush at CES several years ago ahead of its successful Kickstarter campaign, and this year the company is back at the tech show with a new model: the Ara. "Patented deep learning algorithms are embedded directly inside the toothbrush on a low-power processor," Kolibree's press release reads. One of the benefits of this new chip is you don't need the companion app open on your mobile for brushing data to be recorded.

We uncovered the following insights and trends: • Semi-autonomous vehicles are the stepping stone to fully autonomous vehicles. Most car manufacturers and technology companies have taken Tesla's lead and are offering features like self- parking, adaptive cruise control, emergency braking and semi-hands off driving in highway/interstate conditions. Semi-autonomous features help consumers become comfortable with the idea of robots taking the wheel.

Caffe* is a deep learning framework that is useful for convolutional and fully connected networks, and recently recurrent neural networks were added. There are various forks of Caffe branches that cover a variety of tasks. Optimized for Intel Architecture offers all the goodness of main Caffe with the addition of CPU optimized functionality and multi-node distributor training. This video tutorial shows you how to install Caffe* Optimized for Intel Architecture. Training and Deploying Deep Learning Networks with Caffe* Optimized for Intel Architecture This tutorial article provides detailed instructions on how to build Caffe optimized for Intel architecture, train deep network models using one or more compute nodes, and deploy networks.

This was inspired by a bright high school student that emailed me for advice about his interest in deep learning. I've been trying to find good resources for deep learning, but the field does seem rather cryptic and a bit technically prohibitive for me at this point. If you wouldn't mind, I had a couple of questions I'd love to ask you about learning deep learning: A: Your assessment that most deep learning resources are either too brief or too mathematical is spot-on! My partner Jeremy Howard and I feel the same way, and we are working to create more practical resources. We will soon be producing a MOOC based on the in-person course we taught this autumn in collaboration with the Data Institute at USF.

BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters. Modeled after Torch, BigDL provides comprehensive support for deep learning, including numeric computing (via Tensor) and high level neural networks; in addition, users can load pre-trained Caffe or Torch models into Spark programs using BigDL. To achieve high performance, BigDL uses Intel MKL and multi-threaded programming in each Spark task. Consequently, it is orders of magnitude faster than out-of-box open source Caffe, Torch or TensorFlow on a single-node Xeon. BigDL can efficiently scale out to perform data analytics at "Big Data scale", by leveraging Apache Spark (a lightning fast distributed data processing framework), as well as efficient implementations of synchronous SGD and all-reduce communications on Spark. You want to analyze a large amount of data on the same Big Data (Hadoop/Spark) cluster where the data are stored (in, say, HDFS, HBase, Hive, etc.).