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How to Assess Clustering Tendency: Unsupervised Machine Learning

How To Assess Clustering Tendency: Unsupervised Machine Learning

The meaning of clustering algorithms include partitioning methods (PAM, K-means, FANNY, CLARA etc) along with hierarchical clustering which are used to split the dataset into two groups or clusters of similar objects.

A natural question that comes, before applying any clustering method on the dataset is:

Does the dataset comprise of any inherent clusters?

A big problem associated to this, in case of unsupervised machine learning is that clustering methods often return clusters even though the data does not include any clusters. Put in other words, if one blindly applies a clustering analysis on a dataset, it will divide the data into several clusters because that is precisely what they are supposed to do. Continue reading “How to Assess Clustering Tendency: Unsupervised Machine Learning”

Here Are Four Predictions For AI This 2017!

Last year was the year, which saw artificial intelligence, went mainstream.

 

Here Are Four Predictions For AI This 2017!

 

By that, we do not mean just getting filtered raunchy photos on Twitter or getting the fake news suggestions on Facebook.

Here is what to look for in Artificial Intelligence for this New Year:

  • Driven by unprecedented financial support (along with a growing open source ecosystem), founders have been delivering artificial intelligence start-ups at a record high rate.
  • GE, Google, Intel, Microsoft, Facebook, Apple, Salesforce and Samsung, and several other name brands made rigorous AI investments last year.
  • There are now five million homes, which, are talking about their music and shopping choices with the help of Alexa from Amazon.
  • There is a whole new department of U.S. Department of Transportation Committee for self-driving cars. Even a few years ago, there were people talking about 2025 or so for the accessibility of self-driving cars (of level 5 autonomy), but this is a reality now, much before we could reach 2020. It is also amazing to think that self-driving cars may whittle down the 1.2 million annual deaths from automobiles.
  • Also in other interesting news, two AI unicorns just grew their horns, the Cylance in Silicon Valley and iCarbonX in China.
  • Also more than one-fifth of the MIT 50 smartest companies list, include AI as a core approach these days.

Continue reading “Here Are Four Predictions For AI This 2017!”

5 Major Problems in AI (Artificial Intelligence)

5 Major Problems in AI (Artificial Intelligence)

Before we get started with the topic, let us first get an idea about its background. Have you ever given a thought as to how many cats does it take to identify one cat?

In this article we will cover the five types of problems that people face with Artificial Intelligence (AI) i.e. we will address the all important question of – in which situation must one make use of AI (artificial intelligence)?

To have a better understanding of such concepts you can take up a Machine Learning course in Delhi.

Here is some background:

Just some time ago, we conducted a strategy workshop for a bunch of senior executives who are running a large multinational company. In that workshop, someone asked this question – “How many cats will it need to identify a cat?”

In this post, we will discuss the problems which can be uniquely resolved through Artificial Intelligence. While this may not be the exact taxonomy, but it still is pretty comprehensive. The main reason we have added extra emphasis on Enterprise AI issues, because we believe that this subject will have a deep impact on many mainstream applications, but despite that a lot of media attention focuses at the more esoteric avenues. Further, information about these concepts are available in our Machine Learning training course.

But before we delve into AI application types, we must discuss the main distinguishing characters between AI / Deep Learning / Machine Learning.

The term Artificial Intelligence by definition implies that machines can reason with the help of this feature. However, here is a better more complete list of AI characteristics:

  1. AI is capable of reasoning: they can solve complex problems through logical deductions on their own
  2. AI has knowledge: the capability to represent knowledge about the world or our understanding of it, that there are numerous events, entities, and varied situations that occur in the world and such elements have properties, which can be categorised.
  3. AI can plan: they have the ability to set and achieve targets. A specific state of the planet, which we desire along with a sequence of actions that can be undertaken which will help us, progress towards it.
  4. AI can communicate: they have the capability to comprehend well-written and spoken language.
  5. AI has its own perception: they have the ability to deduce things about their surrounding world through the visual images, sounds and other external sensory inputs just like us humans!

With developments in Deep Learning algorithms, AI is driven forward. The various deep learning algorithms can detect numerous patterns without having any prior definition of these features. And in a broader sense, Machine Learning means the application of any algorithm which can be applied against a set of data to discover a pattern within the same. Such algorithms have features like supervised, unsupervised, classification, segmentation, or regression. Moreover, while they are very popular, there are many reasons why Deep Learning algorithms may not make other Machine Learning algorithms.

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The 5 major types of problems with AI:

Now that we have some background knowledge, we can now discuss the five major types of problems with AI:

Domain expertise: troubles involving reasoning based on a complex body of knowledge

This consists of tasks that are based on learning several knowledge bodies like financial, legal, and more, and then formulating a process where the machine will be able to simulate as an expert in the given field.

Domain extension: problems surrounding extension of a complex body of knowledge

In this case, the machine learns a complex body of knowledge like information regarding the existing medication and much more, and then suggests new ideas to the domain itself, like for instance new drugs for curing diseases.

Complex planning: projects that require complicated planning

There are many logistics and scheduling projects, which can be done by current (non AI) algorithms. But as optimization keeps developing and gets more complex AI would slowly grow.

Proficient communicator: tasks that involve developing existing communications

AI and deep learning can offer benefits to many communication modes such as intelligent agents, automatic and much more.

Fresh perception: projects that involve a unique perception

Deep learning and AI can be capable of producing newer forms of perception which enables new services like autonomous automotives and more.

Take up a Machine Learning Certification in order to make a change with AI.

 

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Why Businesses Must Adapt to AI To Thrive in The Market?

Why Businesses Must Adapt to AI To Thrive in The Market?

It is a fact that Artificial Intelligence is no longer just a sci-fi hype anymore, but is in fact a major reality. The approached based on Artificial Intelligence like Natural Language Processing (NLP), Machine Learning (ML) and Deep Learning are slowly emerging to be highly realistic technologies within the industry.

Today we have a very efficient NLP engine system, which is as powerful as ML and deep learning algorithms available. In a recent article, published on WIRED we read about the perpetual death of code (i.e. programs and programming) and how we will soon be training in systems, just as the way we train our pets!

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Machine Learning is the same as learning from examples and experiences just like in real life. It is all about digesting huge volumes of data. We see great new developments within the industry such as IBM and Memorial Sloan Kettering are training Watson in things like Oncology by making use of massive amounts of patient medical records throughout the world. Watson learns from knowing how doctors are treating patients with cancer around the globe, just as how a medical student learns but only on a much larger scale.

Another great example of machine learning is from Japan. The farmers here are cultivating crispy fresh cucumbers with several prickles on them. The straight and thick cucumbers with a vibrant colour and lots of prickles are known to be of premium grade quality. Each cucumber has a different colour, quality, shape and freshness. They are sorted into nine different classes based on their size, shape, texture, colour, the amount of small scratches and whether or not they are crooked, along with the most important part of the amount of prickles on them. However, there is not well-defined instruction set for the classification of cucumbers in Japan.

AI Trends

Image Source: magisteradvisors.com

A farmer and agricultural scientist Makoto Koike has been studying this problem for several years now, and has been helping his farmer parents sort out cucumbers. But now with the use of Google’s TesorFlow based machine learning algorithm, he has been able to develop a system that learns from the precise way his parents have been sorting cucumbers in their farm. For achieving this, he had trained his system by using 7000 images of cucumbers that have been sorted by his mother, and at present the system classifies cucumbers with a much better rate of success and that too at a rapid speed.

Companies like Capgemeni have been making use of the technology of IBM’s Watson to improve efficiency and effectiveness in the resource supply chain.

Image Source: vceestartups.com

Image Source: vceestartups.com

It is predicted that the AI wave will definitely take the industry by storm and have a profound impact on almost all business and transform the present technology climate.

Moreover, we need to quickly turn our businesses into an AI-based approach along with implementation of Machine Learning, which will be supported by NLP and OCR (optical character recognition), speech recognition, and image recognition.

There are three trends in favour of the present technology service providers and their team of workers:

  1. The global expense on technology is increasing. So, technology enterprises will increase their size and market share by adapting to these new ways of working.
  2. The present availability of AI technology across the world is less than the amount that the world needs. So, the companies and individuals must pick up the pace to quickly expand their AI capabilities, and only then they will shine in the market. As for those interested in AI this is the best time to advance in their skills to become market leaders.
  3. The industry transformation has resulted in the marginalization of the CIO role in business and the expense into technology services by business buyers. This gives an edge to the business-oriented teams in play.

Image Source: cbi-blog.s3.amazonaws.com

Image Source: cbi-blog.s3.amazonaws.com

But reacting to this new demand for technology also needs AI and will bring newer challenges on board. The first being, change can only happen when the stakeholders of the company believe in the same. But sadly, many employees and managers do not believe in the capabilities of AI until they experience it on their own. It is for those who believe and develop their required skills and embrace the impending digital evolution that is destined to flourish.

However, secondly companies must address the problem of how to deal with the possible cannibalization of the existing revenues in order to adopt these new technologies. And finally, the lack of skill in the world of technology will make it even harder to build and expand AI capabilities.

Nevertheless, due to an industry boom, over the past 20 years a large percentage of the existing staff has skills that are almost obsolete and will not have new ones. Thus, this will bring an interesting future journey for the tech industry.

 

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Will AI Replace The Intelligentsia? Google’s AI writes mournful poetry

Will AI Replace The Intelligentsia? Google’s AI writes mournful poetry

It is no new news that Artificial Intelligence can now control self driving cars; they can beat the best humans at highly challenging board games like chess, and even fight cancer. But still one thing it cannot do perfectly is communicate.

So, to help solve this problem Google has been feeding its Artificial Intelligence with more than 11,000 unpublished books, which include more than 3000 steamy romantic titles. And in response the AI has penned down its own version of mournful poems.

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The poems read something like this:

I went o the store to buy some groceries.

I store to buy some groceries.

I were to buy any groceries.

Horses are to buy any groceries.

Horses are to buy any animal.

Horses the favourite any animal.

Horses the favourite favourite animal.

Horses are my favourite animal.

And here is another one from Google’s AI:

he said.

“no,” he said.

“no,” i said.

“i know,” she said.

“thank you,” she said.

“come with me,” she said.

“talk to me,” she said.

“don’t worry about it,” she said.

 

The way this happened was, Google’s team fed their AI with unpublished works into a neural network and gave the system two sentences from the book; it was then up to this ingenious artificial intelligence to build its own poetry based on available information.

In example above, the team of researchers gave their AI two sentences one about buying some groceries and the other one about horses being a favourite animal (these are the first and the last lines of the above mentioned passages). The team then directed the artificial intelligence to morph between the two sentences.

In the research paper the team further went on to explain the AI system was able to “create coherent and diverse sentences through purely continuous sampling”.

With the use of an autoanecdoter, which is a type of AI network that makes use of data sets to reproduce a result, in this case that was writing sentences, using much fewer steps the team was able to produce these sentences.

The main principle behind this research is to create an Artificial Intelligence which will be proficient in communicating via “natural language sentences”.

This research holds the possibilities of developing a system that is capable of communicating in a more human-like manner. Such a breakthrough is essential in the creation of more useful and responsive chat bots and Artificial Intelligence powered personal assistants like that of Siri and Google Now.

In a similar project, the researchers at Google have been teaching an AI how to understand language by replicating and predicting the work of bygone authors and poets under their project Gutenberg.

This standalone team at Google fed the AI with an input sentence and then asked it to predict what should come next. And by analysing the text, the AI was capable of identifying what author was likely to have written the sentence and was able to emulate his style.

In another incident, on June, 2015 another team of talented researchers at Google were able to create a chatbot that even threatened its creators. The AI learned the art of conversation by analysis of a million movie scripts thereby allowing it to realize and muse on the meaning of life, the colour of blood, and even on deeper subjects like mortality; Ss, much so that the bot could even get angry on its human inquisitor. When the bot was asked with a puzzling philosophical question about what is the meaning of life, it replied by saying – “to live forever”.

In other such similar works, Facebook has also been teaching its artificial intelligence with the use of children’s books. As per the New Scientist which is a social network, it has been using novels such as The Jungle Book, Alice in Wonderland and Peter Pan.

If you are yearning for some more of AI’s written word, then here are the rest of Google AI’s poems.

You’re right.

“All right.

You’re right.

Okay, fine.

“Okay, fine.

Yes, right here.

No, not right now.

“No, not right now.

“Talk to me right now.

Please talk to me right now.

I’ll talk to you right now.

“I’ll talk to you right now.

“You need to talk to me now. —

 

Amazing, isn’t it?

So, what is it?

It hurts, isn’t it?

Why would you do that?

“You can do it.

“I can do it.

I can’t do it.

“I can do it.

“Don’t do it.

“I can do it.

I couldn’t do it. —

 

There is no one else in the world.

There is no one else in sight.

They were the only ones who mattered.

They were the only ones left.

He had to be with me.

She had to be with him.

I had to do this.

I wanted to kill him.

I started to cry.

I turned to him. —

 

I don’t like it, he said.

I waited for what had happened.

It was almost thirty years ago.

It was over thirty years ago.

That was six years ago.

He had died two years ago.

Ten, thirty years ago. — “it’s all right here.

“Everything is all right here.

“It’s all right here.

It’s all right here.

We are all right here.

Come here in five minutes.

“But you need to talk to me now.

 

To feed in adequate information on Machine Learning Using Python, reach us at DexLab Analytics. Our Machine Learning Certification is garnering a lot of attention owing to its program-centric course module.

 

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Python Vs R- Which You Want To Learn First

Python Vs R- Which You Want To Learn First

If Big Data interests you as a career choice and you are pretty much aware of the skills you need in order to be proficient in this field, in all likelihood you must be aware that R and Python are two leading languages used for analyzing data. And in case you are not really sure as to learn which of the mentioned articles first, this post will help you in making that decision.

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In the field of analysis of data, R and Python both are free solutions that are easy to install and get started with. And it is normal for the layman to wonder which to learn first. But you may thank the heavens as both are excellent choices.

Let’s Make Visualizations Better In Python with Matplotlib – @Dexlabanalytics.

A recent poll on the most widely used programming languages for analytics and data science reveal the following:

Python Vs R- Which You Want To Learn First

 

Reasons to Choose R

R has an illustrious history that stretches for a considerable period of time. In addition you receive support from an active, dedicated and thriving community. That translates to the fact that you are more likely to be helped in case you are in need of some assistance or have any queries to resolve. In addition another factor that works in the favor of R is the abundance of packages that contribute greatly to increasing its functionality and make it more accessible which put R as one of the front runners to being the data science tool of choice. R works well with computer languages like Java, C and C++.

How to Parse Data with Python – @Dexlabanalytics.

In situations that call for heavy tasks in statistical analysis as well as creating graphics R programming is the tool that you want to turn to. In R, you are able to perform convoluted mathematical operations with surprising ease like matrix multiplication. And the array-centered syntax of the language make the process of translating the math into lines of code far easier which especially true of persons with little or no coding knowledge and experience.

Reasons to Opt for Python

In contrast to the specialized nature of R, Python is a programming language that serves general purposes and is able to perform a variety of tasks like munging data, engineering and wrangling data, building web applications and scraping websites amongst others. It is also the easier one to master among the two especially if you have learned an OOP or object-oriented programming language previously. In addition the Code written in Python is scalable and may be maintained with more robust code than it is possible in case of R.

The Choice Between SAS Vs. R Vs. Python: Which to Learn First? – @Dexlabanalytics.

Though the data packages available are not as large and comprehensive as R, Python when used in conjunction with tools like Numpy, Pandas, Scikit etc it comes pretty close to the comprehensive functionality of R. Python is also being adopted for tasks like statistical work of intermediate and basic complexity as well as machine learning.

 

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3 Exceptional Free E-Books On Machine Learning

books on e-learning

According to the experts at Wikipedia Machine Learning happens to be computer science sub-field that has its origins in the detailed examination of recognition of patterns as well as the “computational learning theory” as put into practice in the world of A.I. or artificial intelligence.The subject investigates the study as well as the construction of algorithms which have the ability to pick up skills from and make predictions on the basis of the data that is available.

In this blog post we list some of the key texts that help out students and researchers in this particular field of study.

The Math Behind Machine Learning: How it Works – @Dexlabanalytics.

1. Machine Learning, Neural and Statistical Classification

Edited By: D.J. Spiegelhalter, D. Michie and C.C. Taylor

This book has for its base the ESPRIT or EC project Statlog which compared and made evaluations about a broad range of techniques on classification while at the same time assessing their merits and demerits in addition to applications across the range. The volume listed here is the integrated one which conducts a brief examination of a particular method along with their commercial application to real world scenarios. It encourages cross-disciplinarystudy of the fields of machine learning, neural networks as well as statistics.

Uber: Pioneering Machine Learning into Everything it Does – @Dexlabanalytics.

2. Bayesian Reasoning and Machine Learning

Written By: David Barber

The methods of machine learning have the ability to mine out the values out of data sets that are nothing short of being vast without taxing the computational abilities of the computer. They have established themselves as essential tools in industrial applications of a wide range like analysis of stock markets, search engines as well as sequencing of DNA and locomotion of robots. The field is a promising one and this book helps the students of computer science grasp the tough subject even if their mathematical backgrounds are decent at best.

Pandora: Blending Music with Machine Learning – @Dexlabanalytics.

3. Gaussian Processes for Machine Learning

Authors: Christopher Williams and Carl Rasmussen

Gaussian Processes or more known simply as GPs serve as a practical, principled and probabilistic approach to the learning as conducted in kernel machines. The Machine Learning community has been providing increased attention towards GPs throughout the better part of the last decade and the book serves the important function of sufficing as a unified and systematic treatment of the role of practical as well as theoretical aspect of GPs as present in machine learning. There was a long felt need for such a book and it does not disappoint with its self-contained and comprehensive treatment. This book is highly useful for students as well as researchers in the fields of applied statistics and machine learning.

If your appetite for knowledge on machine learning is far from being satiated, contact DexLab Analytics. It is a pioneering Data Science training institute catering for hundreds of aspiring students. Their analytics courses in Delhi are widely popular.

 

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