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Real Artificial Intelligence: A Definitive handbook : Preorder available

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I am happy to share, our third definitive handbook series book – Real Artificial Intelligence is available for pre order on Amazon.

 

Our aim in this book is to provide a real view of AI, where it exists today and what we can achieve in future.

Through this book, we plan to answer the following questions in a vendor neutral way –

• If we have all the data, can AI help to build any possible solution?

• Can AI platforms developed for solving quiz, solve cancer problems?

• Does an AI platform learn on its own?

• Can we build Enterprise AI solutions by just using AI, ML and Cognitive services from cloud vendor?

• Is today’s generation of chatbots a glorified version of if–then-else-entity based rules?

• Does a true Investment AI assistant exist today?

• Is general intelligence a reality or myth?

• Is there a recipe to build a successful AI project?

• Will AI ever take away jobs ?

The Real AI book is part of our “The Definitive handbook” series. Our vision in the – “The Definitive handbook” series is to
enable our readers to understand the technology in simple terms and provide a practical go-to reference
and a recipe for building any real-world application using the latest technology.

Real AI would be a series of book. Volume 1 of Real AI, planned to be released in August 2018, would focus on applying AI for computer vision technology using a real-world use case.

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Artificial IntelligenceBooks

REAL AI Book

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While AI means artificial intelligence, we need real implementation rather than marketing intelligence”.

Every one is talking about AI, implementing AI, but where are the tangible results? Barring few players, rest every other news is marketing and the real value of AI is yet to be delivered. There is a clear mismatch between what we can achieve today and what lays ahead in future.

real-aiOur aim in this book is to provide a real view of AI, where it exists today and what we can achieve in future. Through this book, we plan to answer these following questions in a vendor neutral way –

  • If we have all the data, can AI help to build any possible solution?
  • Can AI platform developed for solving quiz, solve cancer problems?
  • Does an AI platform learn on its own?
  • Can we build Enterprise AI solutions by just using AI, ML and Cognitive services from cloud vendors?
  • Is today’s generation of chatbots a glorified version of if–then-else-entity based rules?
  • Does a true Investment AI assistant exist today?
  • Is general intelligence a reality or myth?
  • Is there a recipe to build a successful AI project?

 

The Real AI book is part of our “The Definitive handbook” series. Our vision in the – “The Definitive handbook” series is to enable our readers to understand the technology in simple terms and provide a go-to reference and a recipe for building any real-world application using the latest technology. The book is planned to be released early next year. If you like to provide topic and suggestions for the book, kindly write to me at me@naveenbalani.com

This is our third – “Definitive handbook” series work, the first being – “Enterprise IoT” which got acknowledged as one of the Top Computing book for 2016 by computingreview.com (http://computingreviews.com/recommend/bestof/notableitems.cfm?bestYear=2016) and the latest being – “Enterprise Blockchain“.

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ArticlesConferencesDeep LearningFeaturedMachine Learning

Using Generative Adversarial Network for Image Generation – Eclipse Conference

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Generative Adversarial Network (GAN) is class of deep learning algorithm, comprising of 2 networks – a generator and discriminator, both competing against each other to solve a goal. For instance, for image generation, the generator goal is to generate real like images which discriminator can’t classify as a fake or unreal image. The discriminator goal is to classify real images from fake ones. Initially the generator network would start off from blank images and keep on generating better images after each iteration, up to a point it start generating real like images. The discriminator network would take an input of real images and the images provided by the generator network and classifies the image as real or fake, up to a point where generator start generating real like images which is hard for the discriminator to discriminate.  The same algorithm is being applied in other domains also. However, based on my experiments, lot of optimization need to happen for large image sizes. I had to create a custom generator/discriminator network to work against input size of 128*128 and 256*256 image pixels and lot of iterations to generate real-like images. The training data used was of Indian Bird.

Here is a snippet of my talk on GAN at the Eclipse Summit Conference, which demonstrates the experiment.

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ArticlesArtificial IntelligenceConferencesDeep LearningIOTMachine LearningViews & Opinions

Building Intelligent Connected Products using Artificial Intelligence, Cognitive and Blockchain

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Building Intelligent Connected Products using Artificial Intelligence, Cognitive and Blockchain

My technical talk at IoTNext, where I talked about applying intelligence at the edge gateway and cloud. Topics covered – Deep Learning, Computer Vision at the Edge Gateway for security and surveillance, Cognitive IoT – Cognitive Cricket and Connected Car and Security and Trust compliance using Blockchain as a service.

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Artificial IntelligenceDeep LearningMachine Learning

Convolutional Neural Network with Internet of Birds

iob-cnn

I am happy to share we are live with Internet of Birds platform.

Internet of Birds (IoB) is the first citizen science platform to identify birds from the Indian subcontinent through the power of Artificial Intelligence, Deep Learning and Image Recognition. IoB is a citizen science platform by Accenture Labs in collaboration with BNHS.

Internet of Birds is trained on Indian birds using a Convolutional Neural Network. I will share my findings on building the Internet of Birds platform in a later blog, where i would describe the challenges in building up a generic Image recognition service. The same learnings can be applied to any use cases. The end solution can be accessed as a service over the cloud or at the edge network (for more details listen to my talk on – building connected products – edge gateway and CNN) for real-time decision making using Images as one of the context parameters.

The internet of birds website can be accessed at – http://internetofbirds.com. Here is the youtube video on IoB –

More news @ http://punemirror.indiatimes.com/pune/civic/now-new-tech-comes-to-birdwatchers-rescue/articleshow/56260620.cms

 

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Architecture PatternsArticlesArtificial IntelligenceCognitive ComputingDeep LearningIOTMachine Learning

Cognitive IoT in Sports

cogx

Cognitive Internet of Things is about enabling current IoT technologies with human-like intelligence. The end goal is to provide expert advice based on the domains being targeted.  Cognitive IoT can be applied on the edge gateway or in the cloud as part of the solution.

Let’s see how we can apply Cognitive IoT technologies for Sports domain. In  Sports domain, there are actually 3 primarily use case –

  • Learning from an expert/coach (or visually) and improving one’s game
  • Personalization – where all information is personalized to improve a player’s game
  • Continuous learning to keep a player improving his game based on how is he is playing from current and past records.

I will talk about an example of cricket. (I call this as connected cricket -) ).The real value that we want to derive is to enable batsman understand their game better, help them master various batting strokes like cover drive, pull shot etc., analyse their performance continuously to be an expert batsman, for instance what should I do to bat like Sachin Tendulkar.

With respect to a baller, the baller would like to understand how well he is bowling, his speed, his run-up, the way he delivers the ball, spin variations, all these insights can improve his game continuously (so there is a feedback loop) and how similar he is bowling to an expert baller, may be like Ashwin.

So let’s talk about how do you go about realizing it.

  1. Embedded device on cricket ball (without increasing form factor)
  2. Embedded device on cricket bat, pads, gloves
  3. A Connected Stadium.

cogxFor an architecture stack perspective, you have the low powered embedded device  installed inside the ball or embedded as part of the design and manufacturing process, its provides at least 6 Axis combo sensor for accelerometer and gyroscopes reading to identify any movement in 3d space. A Motion SDK is installed on top of the device to identify any movements in general and communicate the reading to the cloud. In cloud, we have the learning model or the training data. Basically, we would ask an expert batsman to bat and play various expert strokes like cover drive etc. and record their movements from sensors (bats/pads etc) as well as visuals (postures etc), this would be used as the training / test data and comparison would be made against it. As we are comparing 3D models, machine learning approaches like dimension reduction can be employed ( and many new innovation approaches) to compare two motion and predict the similarity. Similar training data is captured from an expert baller, along with other conceptual information like hand movements, pitch angles etc.

The feedback is continuously captured and the system provides guidance for improving a player’s game. The player tracks all this information on his mobile and can now look at these insights and suggestions on how he can be an expert in his game. For instance, a player can ask a system “what is takes to master a cover drive like Sachin” and the system analyses the motion information from batting strokes (sensors on bats, pads etc.), visual information (postures etc.), compares it with an expert model and provide an accuracy score and suggestions to improve a players’ game. The key here is that the cognitive system understands the domain and its trained on the domain to provide an expert advice or suggestion.

The same technique and concept can be applied in any game to get cognitive insights.  In future, technology would be a key enabler in Sports.

The following is part of my presentation that I delivered at IoTNext. I will update the article with the youtube video once available.

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