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How to use AI Machine Learning in B2B Marketplace – Becoming Human

#artificialintelligence

Machine Learning has certainly come a long way. The confluence of factors such as the evolution of data growth, advancements in computational algorithm and faster machine processing helped create an ideal environment for Deep Neural network and AI to finally gain adoption in the main stream. Now we have a mother load of data thanks to increasing amount of social sharing and rise of digital devices, Internet of Things (IOT) etc etc. Just look at the data we have accumulated over the last decade since the creation of Online streaming, Social Media, Mobile and Internet of Things (IOTs). We are creating about 1.7MB of new information per human being on the planet.


5 reasons businesses are struggling with large-scale AI integration

#artificialintelligence

Artificial intelligence is an important vehicle for companies looking to automate processes, reduce the cost of operation, or fuel innovation. Despite the positive influence AI-supported activities have on business, a successful implementation won't happen overnight. First you need a complete understanding of your business goals, technology needs, and the impact AI will have on customers and employees. The majority of employees face challenges or concerns relating to AI adoption, and that needs addressing. The implication of successful AI adoption is far reaching for businesses undertaking full-cycle digital transformation, which places equal emphasis on automation, innovation, and learning.


Top 10 Videos on Deep Learning in Python

@machinelearnbot

This'Top 10' list has been created on the basis of best content, and not exactly the number of views. To help you choose an appropriate framework, we first start with a video that compares few of the popular Python DL libraries. I have included the highlights and my views on the pros and cons of each of these 10 items, so you can choose one that best suits your needs. I have saved the best for last- the most comprehensive yet free YouTube course on DL . Before I actually list the best DL in Python videos, it is important that one understands the differences between the 5 most popular deep learning frameworks -SciKit Learn, TensorFlow, Theano, Keras, and Caffe.


Google launches TensorFlow Lite for machine learning on mobile devices

@machinelearnbot

TensorFlow Lite for machine learning on mobile devices was first announced by Dave Burke, VP of engineering of Android at the Google I/O 2017. TensorFlow Lite is a lightweight version of Google's TensorFlow open source library that is mainly used for machine learning application by researchers and developers. Now, the search giant has launched the developer preview of a new machine learning toolkit designed specifically for smartphones and embedded devices and will be available for both Android and iOS app developers. This platform will allow developers to deploy AI on mobile devices. It enables on-device machine learning inference with low latency and a small binary size.


This Is What Facebook and Intel Are Collaborating On

#artificialintelligence

Intel is ready to ship its long awaited computer chip used to power artificial intelligence projects by the end of the year. Intel CEO Brian Krzanich explained the chip-maker's foray into the red-hot field of artificial intelligence Tuesday and said that Facebook (fb) has assisted the company in prelude to its new chip's debut. "We are thrilled to have Facebook in close collaboration sharing its technical insights as we bring this new generation of AI hardware to market," Krzanich wrote. An Intel spokesperson wrote to Fortune in an email that while the two companies are collaborating, they do not have a formal partnership. The genesis of the Intel Nervana Neural Network Processor comes from Intel's acquisition of the chip startup Nervana Systems in 2016.


Capsule Networks Are Shaking up AI – Here's How to Use Them

@machinelearnbot

If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today. Geoffrey Hinton is known as the father of "deep learning." Back in the 50s the idea of deep neural networks began to surface and, in theory, could solve a vast amount of problems. However, nobody was able to figure out how to train them and people started to give up.


The Outlook for Technology M&A

Wall Street Journal

MS. KIM: What is the outlook for big acquisitions? NASON: I am bullish about 2018 for activity. Money is still virtually free. The equity market has been very responsive to M&A. Prices are still high, so we're seeing very high valuations.


A primer on universal function approximation with deep learning (in Torch and R)

@machinelearnbot

Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.


SK Telecom, Hyundai to create $45m AI fund

ZDNet

SK Telecom, Hyundai Motor Company, Hanwha Asset Management, and Element AI will create a joint fund to invest in startups with innovative technology, the companies said. Called the AI Alliance Fund, South Korea's largest mobile carrier and the country's biggest car maker, along with Hanwha, will put in $45 million to invest in startups working in AI, smart mobility, and fintech in Europe, Israel, and the US. Element AI, an AI solutions provider founded by AI authority professor Yoshua Bengio of Montreal University, will be the fund's AI advisor. It will leverage the research group's expertise and global network to find promising startups. SK Telecom said it hoped the startups will ultimately improve business for the companies involved.


Australian 4G coverage in global top 10: OpenSignal

ZDNet

Telstra has the highest average 4G speeds while Optus has the best 4G latency and Vodafone Australia the highest 4G availability, according to telecommunications coverage mapping company OpenSignal's latest report. The overall average download speed for each telco was 30.88Mbps for Telstra, 29.44Mbps for Vodafone, and 24.85Mbps for Optus. On speeds, OpenSignal pointed towards Telstra aggregating five 4G channels, Optus aggregating between two and four, and Vodafone doing the same though over less spectrum. Telstra and Optus also use 4x4 Multiple-Input Multiple-Output (4x4 MIMO) and 256 Quadrature Amplitude Moderation (256 QAM), the report said, meaning speeds will climb even higher across Australia once more consumers begin using compatible devices. On the percentage of time that each telco's customers had 4G available to them, Vodafone ranked highest, at 85.88 percent, followed by Optus at 85.43 percent and Telstra at 85.07 percent -- although it should be noted that Telstra has a higher number of regional and rural customers where 4G may not be available.