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Artificial Intelligence

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Are AI Chatbots Intelligent ?

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Welcome to the world of intelligent chatbots, your companion and conversation agents which would make your life smarter. A leading research paper even said by 2020, the average person will have more conversations with bots than with their spouse. So be ready to embrace this new life in a year from now.

Ok hold on, have you ever tried telling Siri or Google to “Find restaurants which doesn’t serve pizza”. At least they are both consistent in some way, they gave the same answer – suggesting restaurants which serve pizza.

Ok how about Sofia, the first citizen humanoid robot, which is making its way to every media event and giving interviews and boost of human like conversations. Well the truth is far from reality, it is providing an illusion of understanding conversation, but as you start asking intelligent questions you would realize it can answer fixed set of questions.

Well by now, you should be able to clear out the noise from reality. So, should I invest in chatbots with all these limitations? Yes, any technology would have its limitations, but you need to be aware of what you can build now, what to avoid and how to work around the limitations. I have seen many companies trying to build sophisticated chatbots using products from leading chatbot vendors and cloud offerings, spending million on dollars and hitting a roadblock.

If you go by what is being projected and start building it out, you would soon realize these limitations one way or the other. The problem is that most of the vendors claim it’s very easy to build a chatbot, but in reality, all of these techniques fall short when it comes to building a true conversational agent.

With current implementations of chatbot, we are probably at the first generation of AI chatbots which are more or less scripted and giving answers to pointed questions. What I mean by scripted is that it is trained to understand general vocabulary, entities, the metaphor, synonyms etc. The chatbot uses fixed set of flows to understand the context. For domain specific use cases, additional training is required, and you need to train on specific domain terminology and relationship between the words. While, there are research going on using deep neural nets, we are still quite far away from building a true conversational chatbot which understands the nitty-gritty of language and domain.

For instance, if you are building a shopping advisor chatbot, the term “black and white dress” implies “black and white” as color and dress as category. You might expect the color “black and white” is fairly generic and should be easily identified by the AI system, but that’s not really the case.

Based on my experience on building a sophisticated shopping personalized advisor, none of the AI NLP implementation fitted the requirements. A simple scenario is these 3 sets of sentences – “black and white dress”, “and black dress” and “blue jeans and white shirt”. In all these 3 examples, the use of word “and” means different meaning. In the first case, its represents a combined color “black and white”, in second instance “and” represent a brand and in third instance two queries joined by a conjunction (i.e. and). Even with required training, a generalizing model was not possible with any of the available solutions. These are just one of the many examples I am highlighting.Imagine the complexity when dealing with medical literature.

In order to get a realistic view of what an AI chatbots can achieve in today’s environment, current limitations and workaround and what to expect in the future, you can refer to my book REAL AI for more details.

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ArticlesArtificial IntelligenceBooksFeatured

Real AI A Definitive Handbook

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PROMOTION – FOR 6TH AND 7TH APRIL, GET THE BOOK FOR FREE FROM AMAZON – https://amzn.to/2WqLa7h. DEAL STARTS ON 6TH APRIL 00:00 PST.

Since time immemorial, the human race has been fascinated with what machines could or couldn’t do. The quest for creating intelligence that can surpass human intelligence continues to grow and its coined in a term called “Artificial Intelligence (AI)”.

The term AI though was coined by Mr John McCarthy in 1955, the computer visionary who I am sure would have been glad that his Shakespearean display caught the eye of the researchers around the world as it ranked 4th in the Scopus scholarly database. It’s even fascinating to know that it didn’t appear in the top 10 searches the year before. A spectacular performance. One could relate it to a New blockbuster film opening weekend where it overtakes fading glories. But in the case of AI, it would be fair to say it’s a sequel story. The sequel story is only behind cancer for obvious reasons. (As most researchers/scientist belong to the medical discipline and the cure to cancer remains the last undiscovered El dorado in the field). The other 2 words in the list are two rather newly coined terms Blockchain and Big Data. No surprises here if you are following the technology trends, as all the 3 are linked to AI in some way or another, with a potential to create intelligent and trusted systems in future.

AI is the latest marketing buzzword that is made to find its place in every possible use case – from driverless cars to intelligent chatbots, from robots like Sophia to solving cancer problems, from winning games to providing human like intelligence. But is this current hype real or we have just started scratching the surface of intelligence.

In order to make the distinction on hype v/s reality, let’s go in some basic technical details of AI technology. 

AI stands for artificial intelligence. Its an intelligence system put together artificially to learn and provide an output. Learning can be done by providing data to the AI system. Data can be big data, customer data, unstructured text, audio, visuals, environment surrounding details etc. Based on the data provided, an AI system would learn and identify hidden patterns and provide an output.

For instance, if an AI is recommending what food to order, it must know your food preferences, what you had ordered before, where do you usually order from, what days you usually order specific cuisine and lot of other details to recommend the right cuisine for you. The output can be a list of Top 5 food orders for today.

Similarly if an AI is assisting a doctor for providing options for cancer treatment, the system must have the complete patient medical history, must understand the the complete cancer domain (or the respective specialisation) and also periodically learn any new treatments or findings from medical  journals. Understanding the complete cancer domain is a every complex process, where one needs to train the system to understand the medical terminology and the vast ever growing cancer literature, identify patterns and correlations from existing patients, their suggestive treatments and outcome and finally suggest options for treatment. There can be many more data points and this is a continuous process where system would be trained from the feedback and their outcome. While we keep hearing AI is helping solve cancer cases, this is far from reality and systems have just started to touch the surface.

To make life simpler, just remember the following distinction 

AI can learn, but can’t think“.

Thinking would always be left to Human on how to use the output of an AI system. AI systems and their knowledge would always be boxed to what it has learned, but can never be generalised (like humans) to think outside the domain it has been trained on. Understanding this distinction is very important. Human intelligence with only few set of observations can learn, think and apply their learnings on different domains quite easily.  A simple example would be of a doctor treating cancer patients can give you advice for common cold, but an AI system trained specifically on cancer data, may not even understand what common cold means, leave aside the treatment options. Building a generalised AI system may or may not happen in future. The current focus should be building domain specific intelligence and get it right.

AI can never be a replacement for Human Intelligence. While simple to medium outputs of  AI can be automated to skip an Human expert, the majority of the decision making and critical intelligence would always needs Human intelligence.

While AI is been projected as the next big technology that can transform our world, we are far from away in releasing this vision. You may hear many successful AI marketing strategies, but AI is yet to deliver its true value. AI alone will not lead to transformation, but a combinatorial power of various technologies and advancements in computing power would bring it closer to its true potential.   

Through this book, I plan to provide a realistic view on what AI system can achieve in today’s environment and what to expect in future. I plan to draw the reality and bring you closer to Real-AI. Hence, the book is titled – “Real AI”. 

After going through several iterations on the format of the book, I started writing this book in a Question and Answer format as it provides direct answers to some of the questions the readers would want to know. The book is being written and freely available on my website – http://navveenbalani.com/index.php/books/download-real-ai-a-definitive-handbook/. The book will cover the following topics –

  • AI Introduction –  Real facts minus the hype.
  • AI chatbots – The not so intelligent chatbots.
  • Recommenders – The race towards personalised recommendations
  • Predictions – The illusion of AI surpassing human intelligence 
  • Computational Creativity – An AI that can paint, sing or dance
  • What’s in future    More buzzwords to keep you busy – The Ethical AI, Explainable AI, Auditable AI and the list would go on..

One chapter of the book  (AI chatbots – The not so intelligent chatbots.) is available now.

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.

If you have any questions and comments on the book, please write to me at me@navveenbalani.com.  You can purchase the book from Amazon at – https://amzn.to/2WqLa7h  (all royalty will go to charity) OR  Download the free copy of the book via the real-ai-book

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Artificial IntelligenceBooksFeatured

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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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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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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