AI chip startup Graphcore raises $50 million to battle Nvidia and Intel


With a focus on chips and artificial intelligence, U.K.-based Graphcore can now be considered one of Europe's hottest startups. Today, the company announced it has raised a $50 million round of funding led by Silicon Valley's Sequoia Capital, a firm not known for investing much in Europe. This follows the $60 million that Graphcore had already raised over the last 18 months. In a blog post, Graphcore cofounder Nigel Toon wrote that the company's partnership with Sequoia is an indication that it intends to remain independent as it seeks to compete in the surging AI chip market. "So over the last few weeks, Graphcore and Sequoia Capital have worked together on a scale-up business plan and on a funding plan which will allow us to grow more quickly and to support our prospective customers more deeply as we bring products to market," Toon wrote.

Efficient data acquisition in MATLAB: Streaming HD video in real-time


The acquisition and processing of a video stream can be very computationally expensive. In MATLAB we can get multi-threading by interfacing with other languages, but there is a significant cost associated with exchanging data across the resulting language barrier. In this blog post, we compare different approaches for getting data through MATLAB's Java interface, and we show how to acquire high-resolution video streams in real-time and with low overhead. For our booth at ICRA 2014, we put together a demo system in MATLAB that used stereo vision for tracking colored bean bags, and a robot arm to pick them up. We used two IP cameras that streamed H.264 video over RTSP.

Edge computing and AI: From theory to implementation - IoT Agenda


The huge coverage devoted to the topics of AI and edge computing sparked an idea when I recently visited JFK Airport. My journey coincided with a severe weather storm that disrupted travel along the East Coast. This situation illustrates how customer service agents assist passengers (at the edge) when dealing with uncertainty and changing circumstances (relying predictive analysis and intelligent decision-making under uncertainty). The IoT is imminent – and so are the security challenges it will inevitably bring. Get up to speed on IoT security basics and learn how to devise your own IoT security strategy in our new e-guide.

Singapore puts fintech in spotlight with AI investment, global partnerships


Singapore has announced a slew of initiatives aimed at driving the development and adoption of new technologies in the financial sector, including a S$27 million (US$19.85 million) investment in artificial intelligence (AI). The Monetary Authority of Singapore (MAS) said the monies would go towards a new AI and Data Analytics (AIDA) grant to facilitate the deployment of these technologies amongst financial institutions operating in the country. These organisations would be able to tap the grant to subsidise up to 50 percent of the cost of projects that used AI and data analytics to glean insights and and support their decision-making process. These could include techniques such as machine learning, natural language processing or text analytics, and neural networks. Applicants would need to demonstrate the impact of such initiatives on their workforce and develop relevant training programmes, which could include equipping their employees with new analytics skillsets.

This New AI Chip Could Help Google, Facebook 'See' Videos


Graphcore won't release images of its AI-focused chip, called an IPU, until its first shipments to customers in early 2018. Instead, it's offered images of the'computational graph' that runs on the chip, like the one above. I've just clicked on a tutorial video on YouTube about puppy-training, but there's nary an ad about dogs or even pet care. Instead, YouTube cues up a video ad for dishwashing tablets, before popping up a banner ad for a mobile game I'll probably never play. Google has struggled to make its video ads on YouTube relevant to what people are watching.

Google debuts TensorFlow Lite to enable machine learning on mobile devices - SiliconANGLE


Google Inc. is launching a lightweight version of its open-source TensorFlow machine learning library for mobile platforms. Announced at Google's I/O developer conference in May, TensorFlow Lite is now available for both Android and iOS developers in preview. TensorFlow is an open-source software library that was released in 2015 by Google to make it easier for developers to design, build and train deep learning models. TensorFlow can be thought of as a kind of artificial brain through which complex data structures or "tensors" flow. Google says this process is a central aspect of deep learning that can be used to enhance many technology products.

Cloud-based Data Mining Tools for Storage, Distributed Processing, and Machine Learning Systems for Scientific Data


This hands-on training is intended to familiarize researchers and data scientists with the services Azure offers to aid them in their research, especially with regard to high-performance computing, big-data analysis, and analyzing data streaming from Internet-of-Things (IoT) devices.

What is a decision tree and why should my chatbot use it?


The most effective way to discover the intent behind your customer's questions and provide the right answer is by using a decision tree. What are they and how do they work? When it comes to chatbots, businesses want to know one thing. The million dollar question for a market which will be worth billions within a few years is – can my virtual agent answer my customers' questions? Assuming your chatbot has robust natural language processing (NLP technology), the most effective way to do this is through decision trees.

IBM beefs up its AI credentials with Power9 systems and new software


IBM is doubling down on AI: releasing new software to help train machine-learning models and talking up the potential for its new Power9 systems to accelerate intelligent software. Today IBM unveiled new software that will make it easier to train machine-learning models to take decisions and extract insights from big data. The Deep Learning Impact software tools will help users develop AI models using popular open-source, deep-learning frameworks, such as TensorFlow and Caffe, and will be added to IBM's Spectrum Conductor software from December. Alongside the software reveal, IBM has been talking up new systems based around its new Power9 processor -- which are on display at this year's SC17 event. IBM says these systems are tailored towards AI workloads, due to their ability to rapidly shuttle data between between Power9 CPUs and hardware accelerators, such as GPUs and FPGAs, commonly used both in training and running machine-learning models.

China now has more supercomputers on the world’s top 500 list than the U.S.


China has reached a supercomputing milestone. The country now has more machines on a list of the world's 500 fastest supercomputers than the U.S. SEE ALSO: Nvidia's new supercomputer is designed to drive fully autonomous vehicles China has 202 systems on the Top500's supercomputer list, with the U.S. comparatively having only 143. The U.S. ranking is its lowest since the Top500 rankings began 25 years ago, though the country still manages to come in at second place. Japan comes in third with 35 supercomputers, and Germany fourth with 20. According to Top500, China's managed to turn things around pretty fast.