Free Learning

Build sophisticated algorithms that are fundamental to #deeplearning and #AI with #Java

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  • Time is running out to claim this free ebook

    Deep learning is being used across a broad range of industries so it’s a great area to expand your Java skill set.

  • Today’s free eBook will help you move beyond the realm of data science and into the future of predictive insights and machine intelligence.
  • Get familiar with popular deep learning frameworks, Deep Belief Nets and Stacked Denoising Autoencoders algorithms, Dropout and Convolutional Neural Networks, the DL4J library, and more!

A new free programming tutorial book every day! Develop new tech skills and knowledge with Packt Publishing’s daily free learning giveaway.
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7 AI trends to watch in 2017

Seven Artificial Intelligence trends to watch in 2017 - #AI

  • A recent Forrester survey of business and technology professionals found that 58% of them are researching AI, but only 12% are using AI systems.
  • Concerns about AI stealing jobs are nothing new but we anticipate deeper, more nuanced conversations on what AI will mean economically.
  • Expect to hear (a little) less about malevolent AI taking over the world and more about the economic impacts of AI.
  • Most AI systems are black boxes -and immensely complex.
  • Watch highlights covering artificial intelligence, machine learning, intelligence engineering, and more.

From tools, to research, to ethics, Ben Lorica looks at what’s in store for artificial intelligence in 2017.
Continue reading “7 AI trends to watch in 2017”

Marc Andreessen on the atomization of AI

Marc Andreessen on the atomization of #AI

  • Myki rolls out a password manager that locks all your info away on your phone
  • Posted 15 hours ago by Connie Loizos ( @cookie )
  • Uber starts self-driving car pickups in Pittsburgh
  • Apply now to the Startup Battlefield at Disrupt London
  • It seemed particularly daunting to investors given the data that big companies are able to amass and slice and dice and use to train AI. “The theory went that if you were a startup, even if you could get people, you couldn’t get the data,” he said.

Earlier this year, Andreessen Horowitz investor Chris Dixon noted that how challenging it’s become for investors to help groom promising AI startups, given..
Continue reading “Marc Andreessen on the atomization of AI”

Artificial Intelligence and the Role of the Salesperson

Good Read

Artificial Intelligence and the Role of the Salesperson 

 #fintech @lexology #AI

  • It is not accurate enough to provide a consistent level of service, unlike a human being.
  • Recognising the fact, Fast Company believes that businesses can capitalise on it to retain sales staff and provide a level of service that AI cannot.
  • For now, AI can take on the burden of simple sales and service questions, but there is a long way to go before it replaces salespeople.
  • The example of DigitalGenius demonstrates nicely how useful AI can be to retail and customer service, but there are still plenty of barriers to replacing human salespeople.
  • The interactions we have with AI are still rather different to those we have with human beings.

Read the full article, click here.


@SpirosMargaris: “Good Read

Artificial Intelligence and the Role of the Salesperson

#fintech @lexology #AI”


There was a bit of a to-do back in 2013 as the press speculated on the potential for robotics, powered by artificial intelligence ("AI"), to replace…


Artificial Intelligence and the Role of the Salesperson

AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

#AI, #Deep Learning, and #MachineLearning: A Primer – Andreessen Horowitz

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@MikeTamir: “#AI, #Deep Learning, and #MachineLearning: A Primer – Andreessen Horowitz”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

#AI, #DeepLearning, and #MachineLearning: a 45-minute video primer at  #BigData #DataScience

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@KirkDBorne: “#AI, #DeepLearning, and #MachineLearning: a 45-minute video primer at #BigData #DataScience”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz